Elsevier Editorial System(tm) for Tectonophysics Manuscript Draft Manuscript Number: TECTO11694R2 Title: Dynamical characterization of the 1982-2015 seismicity of Aswan Region (Egypt) Article Type: Research Paper Keywords: Aswan, induced seismicity, b-value, coefficient of variation, clustering Corresponding Author: Dr. Luciano Telesca, Corresponding Author's Institution: IMAA First Author: Luciano Telesca Order of Authors: Luciano Telesca; Raafat Fat Elbary; Tony A. Stabile; Mohamed Haggag; Mohamed Elgabry Abstract: In this study, the seismicity occurred in Aswan region from 1982 to 2015 is deeply investigated using robust statistical methodologies. The completeness magnitude, estimated by using two different methods (MAXC and GFT) is 2.5 for the whole catalogue with classes of events were identified with a threshold at about 12 km. The events deeper and shallower than the threshold could be likely generated by different mechanisms; the deep events are mainly due to tectonic processes of stress accumulation and release, while the loading/unloading operation of the Lake Nasser reservoir could significantly influence the time dynamics of the shallow ones. In fact, the analysis of the timeclustering properties of the shallow earthquakes reveals the presence of annual modulation that is absent in the time dynamics of the deep earthquakes. Furthermore, the shallow events are featured by the Allan Factor scaling exponent (measuring the strength of the time-clustering in an earthquake sequence) lower than that of the deep events, indicating a tendency of the time dynamics of the shallow earthquakes to behave more regularly than the deep ones. The detrended fluctuation analysis of the magnitude series suggests that the earthquake series are weakly persistent, characterized by the tendency of events of similar value of magnitude to follow each other. Suggested Reviewers: P. Varotsos
[email protected] Qinghua Huang
[email protected] Leticia Flores-Marquez
[email protected] Ashutosh Chamoli
[email protected]
Cover Letter
Dear Editor We have re-revised our paper addressing all the issues of referee and Editor. We are confident that our paper, now, thanks to the remarks and suggestions, could meet your approval. Regards The authors
*Abstract Click here to download Abstract: abstract_re_rev.doc
Dynamical characterization of the 1982-2015 seismicity of Aswan Region (Egypt)
Luciano Telesca1*, Raafat Fat Elbary2, Tony A. Stabile1, Mohamed Haggag2, Mohamed Elgabry3 1
National Research Council, Institute of Methodologies for Environmental Analysis, C.da S. Loja,
85050 Tito (PZ) Italy 2
Aswan Regional Earthquake Center, Aswan, Egypt
3
National Research Institute of Astronomy and Geophysics, 11421 Helwan, Cairo, Egypt
*Corresponding author: tel. +39-0971-427277, fax +39-0971-427277, email:
[email protected]
Abstract In this study, the seismicit y that occurred in Aswan region from 1982 to 2015 is investigated using robust statistical methodologies. The completeness magnitude, estimated by using two different methods (MAXC and GFT) is 2.5 for the whole catalogue with b 1.07. By using the expectation maximization algorithm, two depth classes of events were identified with a threshold at about 12 km. The events deeper and shallower than the thresho ld could be likel y generated by the same mechanism:
the loading/unloading operation of the Lake Nasser reservoir. We
suggest that the shallow seismicit y occurs on shallow small fractures in correspondence of the intersection of N -S faults with E -W faults, which may form a minor pull -apart basin. The deep events mainl y occur along the right -lateral, strike-slip, E–W Kalabsha fault and the seismicit y is characterized by mainshock -
aftershocks sequences that mask the annual periodicit y if not properl y aftersho ckdepleted. Indeed, before appl ying the declustering on the seismic catalogue, the anal ysis of the time-clustering properties of the shallow earthquakes reveals already the presence of annual modulation that is not evident in the time dynamics of the deep earthquakes. Furthermore, the shallow events are featured by the Allan Factor scaling exponent (measuring the strength of the time -clustering in an earthquake sequence) lower than that of the deep events, indicating a tendency of the time dynamics of the shallow earthquakes to behave more regularl y than the deep ones. The detrended fluctuation analysis of the magnitude series suggests that the earthquake series are weakl y persistent, characterized by the tendency of events of similar value of magnitude to follow each other.
Keywords: Aswan, induced seismicity, b-value, coefficient of variation, clustering
*Revision Notes Click here to download Revision Notes: reply.doc
Editor I asked the more critical one of the two original reviewers to read your revised manuscript. The reviewer is quite happy with the revision but has suggested some further minor changes. However, I am rather disappointed with your effort to improve the English writing. For example, the error in the use of the word "occur" was corrected in parts of the abstract but still keeps occurring in the rest of the paper. I request that you ask a native English speaker to proofread and correct the English before submitting the next version. To avoid yet another round of minor revision just for language correction, please take this request seriously. We have revised the English and correct properly. Reviewer #2 1. Fig. 5: In order to keep consistency with other figures (e.g., Figs.7, 10, 11), it would be better to exchange the order of Fig. 5a and Fig. 5b (i.e., show the result of shallow earthquakes in Fig. 5a). Done 2. The format of references is inconsistent with the journal format, e.g., the references in Lines 697, 779, 781, 784 and 822 do not follow the required order; the correct complete information in Line 739 should be "111(B4), B04301, doi:10.1029/2005JB003982". The references in lines 697, 779, 781, 784 do not present page numbers but article number; this is the format used by the journals where these papers were published. We corrected the reference at line 822 and at line 739.
*Highlights
1. Aswan 1982-2015 seismicit y is complete for M 2.5 with b1.07 2. A depth threshold is identified at a bout 12 km 3. Lake Nasser level variations seem to influence shallow as well as deep seismicit y 4. Time-clustering of shallow seismicit y is lower than that of the d eep one 5. The magnitudes are weakl y persistent
*Revised manuscript with changes marked Click here to view linked References
1 2
Dynamical characterization of the 1982-2015 seismicity of Aswan Region (Egypt)
3 4
5
Luciano Telesca1*, Raafat Fat Elbary2, Tony A. Stabile1, Mohamed Haggag2, Mohamed Elgabry3
6
1
7
85050 Tito (PZ) Italy
8
2
Aswan Regional Earthquake Center, Aswan, Egypt
9
3
National Research Institute of Astronomy and Geophysics, 11421 Helwan, Cairo, Egypt
National Research Council, Institute of Methodologies for Environmental Analysis, C.da S. Loja,
10 11
*Corresponding author: tel. +39-0971-427277, fax +39-0971-427277, email:
12
[email protected]
13 14
Abstract
15
In this study, the seismicit y that occurred in Aswan region from 198 2 to 2015 is
16
investigated using robust statistical methodologies. The completeness magnitude,
17
estimated by using two different methods (MAXC and GFT) is 2.5 for the whole
18
catalogue with b 1.07. By using the expectation maximization algorithm, two depth
19
classes of events were identified with a threshold at about 12 km. The events deeper
20
and shallower than the thresho ld could be likel y generated by the same mechanism:
21
the loading/unloading operation of the Lake Nasser reservoir. We suggest that the
22
shallow seismicit y occurs on shallow small fractures in correspondence of the
23
intersection of N-S faults with E-W faults, which may form a minor pull -apart basin.
2
24
The deep events mainl y occur along the right -lateral, strike-slip, E–W Kalabsha fault
25
and the seismicit y is characterized by mainshock -aftershocks sequences that mask the
26
annual periodicit y if not properl y aftersho ck-depleted. Indeed, before appl ying the
27
declustering on the seismic catalogue, the anal ysis of the time -clustering properties
28
of the shallow earthquakes reveals already the presence of annual modulation that is
29
not evident in the time dynamics of the deep earthquakes. Furthermore, the shallow
30
events are featured by the Allan Factor scaling exponent (measuring the strength of
31
the time-clustering in an earthquake sequence) lower than that of the deep events,
32
indicating a tendency of the time dynamics of the shallow earthquakes to behave
33
more regularl y than the deep ones. The detrended fluctuation anal ysis of the
34
magnitude
35
characterized by the tendency of events of similar value of magnitude to follow each
36
other.
37
Keywords: Aswan, induced seismicity, b-value, coefficient of variation, clustering
series
suggests
that
the
earthquake
series
are
weakl y
persistent,
38 39
1. Introduction
40
River Nile, the longest river in the world, is a common basin between 11 countries ,
41
traveling over 2700 km through Sahara Desert withou t any significant perennial
42
tributary inputs (Woodward et. al, 2007). Throughout time the river has gone through
43
natural and anthropological changes , being a structurall y controlled stream since its
44
earl y stages. Several studies (i.e. Adamson and Williams, 1980; Said, 1981, 1993)
45
have proposed that the river Nile is in continuous evolving process by major tectonic
2
3
46
phenomena and climatic changes including the Rifting of East African that could be
47
the shaping factor for the location of sedimentary basin and t he drainage pattern.
48
The river has gone through many water resources management mega projects ; the
49
largest till now is the Aswan High Dam constructed between 1960 -1971, which is 111
50
m high, a crest length of 3830 m , and impounds the second largest reservoir in the
51
world, the Lake Nasser, that has a gross capacit y of 169 billio n cubic meters . On
52
November 14, 1981, an Ms 5.3 earthquake took place south of the dam. This
53
earthquake has raised the concerns about the dam stabilit y from one side and its
54
relation to seismicity from the other side. A network of 13 station s was established
55
(Simpson et al., 1987) to monitor seismicit y aro und the lake since 1982 , and intense
56
seismic activit y has been recorded since then (Simpson et al., 1 990; Gahalaut et al.,
57
2016, and references therein ).
58
Several researchers explored the relationship between the Aswan reservoir water
59
level and the obser ved seismicit y in the region ( e.g., Kebeasy et al., 1981; Simpson et
60
al., 1990; Hassoup 1994; Selim et al., 2002; Mekkawi et al., 2004; Haggag et al.,
61
2008; Telesca et al., 2012 ; Gahalaut et al., 2016 ) and, even if in some periods there
62
were found no or we aker reservoir influence ( Hassoup 1994; Selim et al., 2002;
63
Mekkawi et al., 2004 ; Telesca et al., 2012 ), it has been commonl y accepted t hat the
64
Aswan seismicit y is a case of continuous reservoir triggered seismicit y (RTS).
65
Therefore, Aswan area belongs to the reservoir sites that exhibit triggered seismicit y
66
every year or after a gap of a few years , such as Lake Mead in the USA (Carder,
67
1945), Koyna-Warna reservoir
68
2010), Nurek Dam in Tajikistan (Simpson and Negmatullaev, 1981),
69
Brazil (El Hariri et al., 2010), and Pertusillo reservoir in Italy (Stabile et al., 2014 ;
-
,
3
4
70
Telesca et al., 2015). In all the cases mentioned above delayed seismic response to
71
water
72
mechanism of the observed continued RTS is the diffusion of pore fluid pressures .
73
Particularl y for the As wan area, Gahalaut and Hassoup (2012) demonstrated from an
74
anal ytical simulation that although the stress due to the reservoir load stabilizes
75
seimogenic faults of the area, the effect of pore fluid pressure leads the faults to go
76
beyond the critical stres s for failure.
77
Due to the presence of the High Dam and the seismic risk that it could raise, a deep
78
dynamical
79
challenging and, at same time, crucial to best understand the mechanisms related t o
80
the generation of local earthquakes.
81
In fact, it is widely recognized that investigating with detail the structure of
82
magnitude, space and time distribution of earthquakes is fundamental for earthquake
83
hazard assessment as well as for the comprehension o f properties of seismic
84
processes (see e.g. Goltz, 1997; Kagan, 1997; Matcharashvili et al., 2009). In
85
particular, the investigation of the characteristics of time distribution of earthquake
86
occurrences on various temporal scales has been the focus of very intense research.
87
Several studies based on different conceptual frameworks approached the anal ysis of
88
earthquake time patterns by means of both field and laboratory data as well as
89
numerical
90
Matcharashvili et al., 2000; Telesca et al., 2004). Most of such studies highlight that
91
seismic processes are characterized by intermittent time behavior with phases of
92
intense seismic activit y interspersed with those of low seismicit y (Ben -Zion and
93
Lyakhovsky, 2002; Ki yashchenko et al., 2004; Pliakis et al., 2012; Vallianatos et al.,
fluctuations
of
reservoirs
characterization
simulations
of
( e.g.
was
the
Issac
observed
seismic
et
al.,
suggesting
process
2004;
that
governin g
Lyakhovsky
the
dominant
Aswan
et
al.,
area
is
2001;
4
5
94
2012); thus, evidencing the presence of nonrandom components in earthquake
95
generation in energy, space and time domains ( Lliopoulos and Pavlos, 2010).
96
Within this context, i n this study, we aim at investigating the dynamical properties of
97
the most updated seismic catalogue of Aswan appl ying several robust statistical
98
methodologies to the magnitude and time distribution of seismicit y that occurred
99
nearby Lake Nasser from 1982 to 2015 in order to better characterize its time
100
dynamics.
101 102 103
2. Seismo-tectonic settings and data description
104
Aswan (or S yene as its Greek name, which is named after the t ype localit y for the
105
igneous rock syenite) is located in the southern part of Egypt at the interface between
106
the stable Archean craton of the Nubian Shield and the less stable Pan -African
107
orogeny of the southern most of the Egyptian Eastern Desert. Aswan exhibits a
108
complex geological situation with a number of different rock t ypes , ranging from
109
quaternary deposits to cretaceous sedimentary rocks of Nubian sandstone , to igneous
110
and metamorphic rocks of the deep -lying basement complex, which have been
111
uplifted and exposed to surface. In many areas in and around Aswan the river has
112
eroded the ov erl ying Nubian sandstone and carved deep channels into the igneous
113
rocks (Greiling, et, al. 1994).
114
From earl y satellite imagery, El Shazl y et al. (1973) has described the structural
115
trends in Aswan area to be mainl y NNW -SSE fractures making notable horizon tal
116
separation and NW -SE fractures showing horizontal and vertical separation along
117
geological and relief boundaries. Furthermore, the NNE -SSW fault west to Kurkur 5
6
118
shows horizontal left -lateral trend. In addition, two major fracture trends are present
119
in NE-SW and the ENE -WSW. According to El Shazl y et al. (1973) the NE -SW
120
fractures do not show separation, and they may be major tension fractures
121
perpendicular to the principal force creating the previousl y mentioned two major
122
fault trends. Fractures seem to represent tension zones along the hinges of major
123
folds, which may have been faulted along the same zones.
124
These trends been reactivated as both strike -slip E-W dextral and N-S sinistral faults
125
(WCC, 1985; Abdeen et al., 2000) , and dip-slip faults (Issawi, 1978) and propagated
126
up through the sedimentary cover. The seismic activit y is concentrated along N -S and
127
E-W fault intersections. The N -S faults have less activit y than that of E -W faults.
128
Recent stress regime deduced from Earthquak e focal mechanism (Hus sein et al.,
129
2013) is in good agreement with that of geological studies and borehole breako ut data
130
(Bosworth and Strecker, 1997) with Shmax E-W and Shmin NNE-SSW. Under this
131
stress regime, the province is found to be consistent with both right lateral faul ts (E–
132
W striking) and left lateral faults (N –S striking).
133
The largest of the Aswan earthquakes was of magnitude Ms 5.3, and occurred on 14
134
November 1981. From the intensit y data, the well-determined locations of numerous
135
aftershocks recorded using portable stations and a telemetered network, the event
136
appears located on the Kalabsha fault beneath Gebel Marawa (Kebeasy et al. 1987).
137
The depths of the aftershocks and the special study of teleseismic records of the
138
mainshock (WCC 1985) indicate that the mainshock was at a depth from 18 to 20 km.
139
Prior November 14, 1981, no earthquakes had been reported in the Aswan area in the
140
c
141
1920. Because of the lack of continuous and reliab le data during the earl y stages of
g
f h
S
g c
C
SC)
c
h
SC’
c
6
7
142
the filling of the Aswan reservoir, it is not possible to determine exactl y when low -
143
magnitude activit y may have started. The first seismographs installed in the Aswan
144
area that were capable of recording small local earth quakes were Soviet short -period
145
(SMK) instruments installed at Aswan and Abu Simble in 1975. Although the
146
operation of the stations was irregular prior to 1981, 20 events of magnitude greater
147
than 2.5 have been identified by Helwan Institute located in the Wadi Kalabsha area.
148
Thirteen of these took place during approximatel y 200 days of station operation
149
between August 1980 and August 1981. A long sequence of aftershocks followed the
150
14 November 1981 earthquake , including the immediate aftershocks and the
151
continuation of Aswan activit y until the present both in the area of the mainshock and
152
around the northern part of the Aswan reservoir.
153
Following the mainshock, portable microearthquake recorders were installed in the
154
northern part of reservoir area by Eg yptian Geological survey from December to June
155
1982. In late June 1982 , the portable seismic field stations were replaced by a
156
telemetry network erected by Helwan Observatory and Lamont -Dohert y Geological
157
Observatory (USA). The purpose of the telemetry net work is to monitor the
158
induced/triggered seismicit y along the Kalabsha fault (Fig. 1), which continues to
159
occur in the area of the November 14, 1981 earthquake (Kebeasy et al 1987 ; Fat-
160
Helbary et al., 2002 ). Data were transmitted to a data center to record the output
161
signals coming from the field stations. Five monitors with pen recorder were used for
162
the visual record and all the data were recorded in the F M magnetic tape as analog
163
data and in the 9 -track tape as digital data. A playback unit and computer facilities
164
had been installed at the center to allow earthquakes to be quickl y anal yzed and
165
located. Since 2009, the Aswan Seismic Network has been updated and replaced by 7
8
166
new digital broad band network. The transmission system is changed from telemetry
167
to satellite and in addition some stations in field were moved to near better sites. As
168
well as the data from field stations is sent to the main center for the necessar y
169
anal ysis using the recent software programs such as Atlas and Earl ybird.
170
171 172
Fig. 1. Aswan Seismic Network .
173 174
The output data include latitude, longitude, focal depth, origin time, epicenter
175
distance and azimuth for each station. Various measures of location accuracy are also
8
9
176
given. The output data are used for constructing the seismici ty map of the Kalabsha
177
area (Fig. 2).
178 179
a)
9
10
6
5
Mi
4
3
2
1
0 0
2000
4000
6000
8000
i
180 181
b)
182
Fig. 2. (a) Seismicity map of Aswan region ( a: Gebel Marawa zone, b: East-1 of
183
Gebel Marawa zone, c: East -2 of Gebel Marawa zone, d: khore El -Ramla zone, e:
184
Abu Derwa zone, and f: Old stream zone). (b) Magnitude plot of the earthquakes in
185
the investigated region during 1982 –2015. Horizontal axis indicates the ith event and
186
vertical axis indicates its magnitude.
187 188
3. Methods and results
189
In this study we investigate the seismicit y that occurred from January, 1, 1982 to
190
December 31, 2015 in the area of Aswan (Fig. 2). We employed several independent
191
statistical methods to obtain the most complete picture of the dynamical properties of
192
the seismic process in the area.
193 10
11
194
3.1. The frequency -magnitude distribution
195
The Gutenberg-Richter (GR) law (Gutenberg and Richter, 1944) relates the threshold
196
magnitude M t h and the number of earthquakes with magnitude M> M t h in a power-law
197
manner usuall y represented in semi -log scales as log 1 0 (N)=a-bM t h , where N is the
198
number of earthquakes with magnitude M> M t h , a is the earthquake productivit y, and
199
b is a value that indicates the proportion of small events respect to the large ones.
200
The GR law is generall y used to fit t he frequency magnitude distribution (FMD). The
201
b-value is a critical parameter that can inform on the stress crustal conditions ;
202
therefore, estimating with a good accuracy and reliabilit y the b-value is important to
203
characteriz e different stages of the evolution of seismicit y, linked with dynamical
204
changes of the seismic process, and, as a consequence, is crucial for reliable s eismic
205
hazard assessments (Scholz, 1968; W yss, 1973) .
206
In our study, the estimation of the b-value was performed by using the maximum
207
likelihood method (MLE) (Aki, 1965),
208 209
b
log10 (e) , (1) M bin M Mc 2
210 211
where is the average magnitude of the sub-set of earthquakes whose magnitude
212
is larger or equal to the completeness magnitude M c and M b i n represents the binning
213
width of the catalogue (Utsu, 1999).
214
The standard deviation of the estimate of b
215
(1982) formula,
c c
by
g h
Sh
B
’
216 11
12
N
217
b 2.3b 2
M i 1
i
M
N N 1
2
. (2)
218 219
As it is deduced from formula (1), the b-value depends on the estimat ion of the
220
completeness magnitude M c that is the lower magnitude above which the seismic
221
catalogue can be considered complete.
222
It is obvious that to get reliable results of any anal ysis performed on the seismic
223
catalogue, onl y the events with magnitude MM c have to be selected . The maximum
224
curvature method (MAXC) (Wiemer and W yss, 2000) allows a fas t estimation of M c
225
that corresponds to the largest bin in the noncumulative FMD. Another method is the
226
goodness-of-fit (GFT) (Wiemer and W yss, 2000) between the observed and synthetic
227
cumulative FMDs, the last calculated employing the a- and b-values of th e GR-law of
228
the observed seismic set for magnitude larger or equal to a n increasing threshold
229
magnitude that estimates M c when the 90% of the observed data are well modeled by
230
a straight line.
231
As already shown above, s ince 1980 the Aswan seismic network un derwent several
232
upgrades that have certainl y influenced the earthquake detection sensitivit y and, thus,
233
the completeness magnitude through time. Therefore, it is necessary to investigate
234
preliminaril y the time variation of the completeness magnitude. A visual impression
235
about how the magnitude of completeness varies with time and about possible artifact
236
in the catalog can be got by using the M i -i plot with horizontal axis indicating the ith
237
earthquake and vertical axis indicating its magnitude M i (Huang, 2006). Fig. 2b
238
shows a slight decrease of the magnitude with time and this indicates that the 12
13
239
completeness magnitude changes with time. Fig. 3 shows the time variation of M c for
240
the seismicit y at Aswan calculated by means of the MAXC method (red) and the GFT
241
method (blue).
242
entire catalog with a shift of 1 0 events. In each window, the completeness magnitude
243
M c (associated with the time of the last event in the window) was computed onl y in
244
case the nu mber of events wa s larger or equal to 50 (Woessner and Wiemer, 2005).
245
We can see that MAXC and GFT methods give very similar results in all the three
246
examined cases ( W N =200, 500 and 1000 events), although the MAXC method
247
estimates a slightl y lower value o f the completeness magnitude. The range of
248
variation of the completeness magnitude depends on the length of the moving
249
window, being larger for W N =200 and smaller for W N =1000.
We considered a fixed event number window (W N ) sweeping the
250 251 MAXC GFT WN=200
3.5
MAXC GFT WN=500
3.0
3.0 2.5
2.5 2.0
MC
MC
2.0
1.5
1.0
1.0
0.5
0.5
0
2000
4000
6000
8000
10000
12000
14000
0
time (days since January 1, 1982)
252
1.5
2000
4000
6000
8000
10000
12000
14000
time (days since January 1, 1982)
a)
b)
13
14
MAXC GFT WN=1000
3.0
2.5
MC
2.0
1.5
1.0
0.5 0
2000
4000
6000
8000
10000
12000
14000
time (days since January 1, 1982)
253
c)
254
Fig. 3. Time variation of the completeness magnitude in the Aswan area (red: MAXC method; blue:
255
GFT method, see text for details): (a) WN=200 events; (b) WN=500 events; (c) WN=1000 events.
256 257
A conservative choice of the completeness magnitude would require taking it as the
258
largest value of the time evolution of M c . But, this choice would strongl y decrease
259
the amount of available data, and consequentl y would inc rease the uncertaint y of the
260
statistical estimates due to smaller sample sizes. For instance, considering the time
261
variation of M c for W N =1000 events, the highest completeness magnitude on the base
262
of the GFT method is 3.0 that would reduce the amount of availa ble data to 375 over
263
9592 that is the size of the whole catalog . So, it is impo rtant to find a sort of
264
compromise between the size of the dataset, large enough to perform the statistical
265
anal ysis with good accuracy, and the value of M c that should guarantee a good
266
completeness of the catalog. On the base of the results shown in Fig. 3, by visual
267
inspection we believe that a reasonable choice of M c is 2.5; in fact, it is larger than
268
any completeness magnitude calculated by the MAXC method and lower than the
269
completeness magnitude calculated in just few windows by using the GFT method; 14
15
270
furthermore, 2.5 is quite robust with respect the size of the moving window . In this
271
case the number of earthquakes with magnitude larger or equal to 2.5 is 1327 over
272
9592; and t his size is large enough to perform significantl y the statistical anal ysis on
273
the catalog. Therefore, with M c =2.5 the b=1.070.02.
274 275
3.2. The depth distribution
276
Considering the catalog of seismic events with magnitude larger or equal to 2.5, two
277
distinct depth classes characterize t he earthquakes that occurred in the Aswan area.
278
Fig. 4 shows the depth histogram (black line) of the seismic events. It is clear that
279
the distribution is bimodal, characterized by two depth ranges, one involving the
280
shallow events and the other the deep events. Furthermore, the depth distribution
281
seems to be a mixture of two Gaussian distributions.
282
a)
15
16
283 284
b)
16
17
285
c) 17
18
286 287
Fig. 4. a) Depth distribution of the seismicity at Aswan. The red and blue curves represent the best
288
Gaussian fit of the deep and shallow events. D0 represents the estimated depth threshold separating the
289
two classes of depths; b) Depth-longitude diagram of seismicity; c) Seismicity of Aswan area for
290
magnitude larger or equal to 2.5.
291 292
In order to better discriminate the two depth ranges, we applied the expectation
293
maximization (EM) algorithm (Borman, 2004) that is a statistical method to calculate
294
the maximum likelihood estimates of the parameters of two Gaussian distributions,
295
whose mixture seems to model the bimodal depth distribution of Fig. 4. We found
296
that one Gaussian distribution (blue curve) with G =4.8 and G 2 =8.0 models the
297
distribution of shallow events and the other (red curve) with G =19.0 and G 2 =9.7
298
models that of the deep ones. The intersection between the two curves D 0 12 km can
299
be considered as the threshold depth discriminating betwe en shallow and deep events.
300
In the forthcoming anal yses we will investigate the two earthquake sub-catalogues:
301
shallow (depth < 12 km) and deep (depth 12 km). The number of the shallow events
302
is 744 and that of the deep ones is 583. For the deep sub -catalog b=0.990.03, while
303
for the shallow one b=1.140.04.
304
Fig. 4b shows the vertical cross/section depth -longitude diagram of seismicit y. The
305
events appearing at shallower depths (less than 12 km) in this cross -section are from
306
East Gebel Marawa (the composite fault plane solution indicates strike -slip faulting
307
with a normal fault component) , Khore El-Ramla (the composite fault plane solution
308
indicates a strike -slip fault and the fault plane strikes 155° and dips 65°) , Abu Dirwa 18
19
309
(the focal mechanism of this zone includes strike-slip and normal components, and
310
the fault plane strikes 177° from the north and is dipping 61°) and old stream zones
311
(the fault plane solution indicates strike slip faulting and the fault plane strikes 161°
312
and dips 77°).
313
On the other hand, the seismicit y at Gebel Marawa zone is almost all between depth
314
of 12 and 26 km and the events align along the Kalabsha fault where the mainshock
315
of November 14, 1981 occurred deeper than 12 km (Fat -Helbary et al., 2002). The
316
composite fault plane so lution of the earthquakes indicates strike -slip faulting with a
317
normal-fault component. The fault plane strikes 78° and dips 70° (Fat -Helbary,
318
1989).
319 320
3.3. The coefficients of variation
321
The coefficient of variation C v is a simple measure employed to study the time-
322
clustering properties of an earthquake process . It is defined as
323 324
Cv
, (3)
325 326
where and are the standard deviation and the average of the interevent times ,
327
respectivel y. Fig. 5 shows the series of the interevent times of th e deep (Fig. 5a) and
328
shallow (Fig. 5b) sub -catalogues.
19
20
interevent time (day)
300
Shallow
200
100
0
interevent time (day)
-100 600
0
100
200
300
400
500
600
700
800
Deep
400 200 0 0
100
200
300
400
500
600
n
329 330
Fig. 5. Interevent time series of the shallow (a) and deep (b) sub-catalogues.
331 332
Depending on the numerical value of C v , the earthquake sequence is regular (or
333
periodic) if it is lower than 1 , purel y random (or Poissonian) if it is 1 or clustered if
334
it is larger than 1 (Kagan and Jackson, 1991). Recentl y, Telesca et al. (2016)
335
introduced the local coefficient of variation L v , defined by Shinomoto et al. (2005),
336
to investigate the local time-clustering properties of the volcano -related seismicity at
337
El Hierro, Canary Islands (Spain):
338
1 N 1 Ti Ti 1 Lv 3 N 1 i 1 Ti Ti 1 2 (4) 2
339
340
20
21
341
For Poissonian seismic processes, C v and L v are both 1, and for periodic processes
342
they are both 0. However, if C v can measure global variabilit y of a whole interevent
343
sequence and could be affected by event rate fluctuation s, L v measures local stepwise
344
variabilit y of interevent times , because it is rather independent of slow variation in
345
average rate. Just as an example, if one joins two peri odic point processes like those
346
in Fig. 6, C v >>1 because globall y the process appears highl y clustered, but L v 0,
347
because at local scales the process is periodic.
0
10
20
30
40
time
348 349
Fig. 6. Superposition of two periodic point processes.
350 351
We calculated both the coefficient s of variation for the deep and shallow seismicity
352
in Aswan for events with m agnitude M2.5, and obtained C v 3.37 and L v 1.16 for
353
deep seismicit y and C v 1.78 and L v 1.35 for the shallow one. To calculate the
354
significance of these results, we calculated bot h the quantities for 1,000 Poisson
355
processes randoml y generated with the same size and mean interevent time as the
356
original (deep and shallow) seismic interevent time series. The 95% confidence 21
22
357
interval, which is delimited by the 2.5 t h and 97.5 t h percentil es of the distribution of
358
C v and L v of the Poissonian surrogates , are [0.94, 1.07] for C v and [0.92, 1.08] for L v
359
in case of deep seismicit y, and are [0.93, 1.0 8] for C v and [0.92, 1.07] for L v in case
360
of shallow seismicity. The obtained values of the coef ficients of variation indicate
361
that both globall y and locall y the distribution s of the shallow and deep seismicity at
362
Aswan are clusterized at 95% confidence.
363 364
3.4. The Allan Factor
365
The coefficients of variation described in section 3.2 furnish information about the
366
time-clustering of a point process, but reveal none about the timescales where the
367
process is clusterized. To this aim, the Allan Factor (AF) is a suited method for
368
discriminating between the timescales where the process is clusterized from thos e
369
where it is not. Dividing the time axis into equall y spaced contiguous counting
370
windows of duration , that is timescale, a sequence of earthquake counts { N k ( )} is
371
obtained, with N k ( ) being the number of earthquakes falling in the k-th window
372
(Thurner et al., 1997). The AF, then, is defined as
373
374
( N k 1 ( ) N k ( )) 2 AF ( ) 2 N k ( ) , (5)
375 376 377
and it is related to the variabilit y of successive counts (Thurner et al., 1997); the y b
c
h
g
.
h
F h
b
g y
g
378
the time dynamics of point processes of different t ypes ( Telesca et al., 2001; Telesca
379
et al., 2005 ). 22
23
380
If the earthquake process is Poissonian that means memoryless and formed by
381
independent events, then the AF is rather flat at all timescales and assumes value
382
around 1 (except for very large timescales due to finite -size effects (Telesca et al.,
383
2012); but if the earthquake process is clusterized the AF changes with the timescale
384
. In particular, if the earthquake process is fractal (self-similar) in time, the AF
385
behaves as a power-law (scaling behavior) :
386
387
AF( ) 1 1 , (6)
388 389
where the exponent quantifies the strength of clusterization; 1 is the so-called
390
fractal onset time and marks the lower limit for significant scaling behavio r in the AF
391
(Thurner et al., 1997). Therefore, if 0 the earthquake process is Poissonian , while
392
if >0 it is clusterized.
393 394
23
24
Shallow seismicity 1.2
2
log10(AF())
1.0
=0.40+0.02
0.8
0.6
1y
1
0.4
0.0
0.5
1.0
1.5
2.0
2.5
3.0
log10() (day)
395
a)
2.5
Deep seismicity
2
log10(AF())
2.0
1.5
=1.85+0.06 1.0
0.5
1
0.0
0.0
0.5
1.0
1.5
2.0
2.5
3.0
log10() (day)
396 397
b) Fig. 7. AF for the shallow (a) and deep (b) Aswan sub -catalogues.
398 399
Fig. 7 shows the AF (open circles) for the shallow (Fig. 7a) and deep (Fig. 7b)
400
seismicit y at Aswan, for events with magnitude M 2.5. Smoothing with adjacent 5 -
401
sample averaging (red curve) evidences more clearl y the scaling behavior in both
402
shallow and deep seismicit y between 1 and 2 that are between about 12 days and 24
25
403
about 5 months for shallow seism icit y, and between about 40 days and 9 months for
404
deep seismicit y. The scaling exponents are very different, being 0.40 for the shallow
405
and 1.85 for the deep seismicit y, indicating a higher clustering degree for deep
406
seismicit y than shallow . In order to verify the significance of the exponents, we
407
calculated the distribution of the exponents computed between 1 and 2 of 1,000
408
Poissonian seismic sequences generated with the same number of events and rate as
409
the shallow and deep original ones (Fig. 8). We can see that the obtained value of
410
for the shallow and the deep seismicity are well above the largest value of the
411
distribution of obtained for Poissonian surrogates; therefore, the exponents
412
calculated for the real seismicit y are signifi cant.
413
Another interesting feature is the drop at around 1 year in the smoothed curve (red)
414
obtained for the shallow seismicit y. The drop at a certain timescale in the AF curve
415
indicates the presence of periodicit y at that timescale ( Gebber et al., 2006 ); thus, the
416
drop found in the AF of the shallow seismicit y corresponds to the annual periodicit y
417
that is consistent with the loading/unloading operations of the Aswan Dam and
418
strengthens the existence of annual water modulation (Gahalaut et al., 2016) of the
419
shallow seismicit y.
420
25
26
Deep Shallow 80
N
60
40
20
0
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
421 422
Fig. 8. Distribution of for 1,000 Poissonian sequences with same rate and same
423
number of events as the original deep (blue) and shallow (red) sequences.
424 425
3.5. The detrended fluctuation analysis of the magnitude time series
426
The Detrended Fluctuation Anal ysis (DFA) (Peng et al., 199 5) was used to stud y
427
long-range correlations of non-stationary series of different t ypes (Telesca and
428
Lovallo, 2009; Telesca and Lovallo, 2010; Telesca and Lovallo, 2011; Telesca et al.,
429
2012; Telesca et al., 2016 ; Rong et al., 2012; Varotsos et al., 2008; Varotsos et al.,
430
2011). In the context of studies devoted to seismicit y, several authors focused on the
431
long-range correlations in the magnitude time series, since magnitude rep resent one
432
of the crucial parameter in the framework of seismic hazard assessment. Telesca et al.
433
(2016) observed that the scaling exponent of the magnitude time series calculated by
434
the DFA (see later in the text) had an increasing behavior when the volcanic activit y
435
at El Hierro, Canary Islands (Spain) reactivated in the 2011-2014. Varotsos et al. 26
27
436
(2014), performing the DFA on the magnitude series of several seismic areas,
437
evidenced a certain variabilit y of the scaling exponent with relationship with
438
earthquake prediction. Lennartz et al. (2008) us ed the DFA to investigate the long-
439
range correlations of the magnitude se ries of Northern and Southern California
440
seismicit y revealing the presence of long-term memory. Varotsos et al. (2012)
441
observed a rather sh arp variation from uncorrelated to correlated behavior in the
442
magnitudes of California seismic it y before the occurrence of large shocks .
443
Within this scientific context , we can understand how crucial is the anal ysis of the
444
temporal properties of magnitude time series to better understand the dynamics of a
445
seismic process.
446
The DFA method works as follows:
447
i) the magnitude series M i , where i=1,…,N, and N is the total number of e vents is
448
integrated:
449 450 451
yk i 1 M i M , (4) k
452 453
where indicates the mean magnitude;
454
ii) the integrated series y k is divided into non-overlapping boxes of same length n;
455
iii) in each n-size box, y k is fit by the least squares by the line y n , k and detrended;
456
iv) the fluctuation, F n , is obtained by
457 458
Fn
2 1 N yk yn,k ; (5) N k 1
27
28
459 460
v) the steps i)-iv) are repeated for all the available box sizes n; if the relationship
461
between F n n is a power-law, the magnitudes are long-range correlated:
462 463
F n n ; (6)
464 465
vi) from the numerical value of the so-called scaling exponent we can derive
466
information about the t ype of correlations: if the magnitude are uncorrelated, then
467
=0.5; if the magnitudes are persistently correlated (meaning that a large (small)
468
magnitude (compared to the mean) has larger probabilit y to be followed by a large
469
(small) magnitude), then >0.5; if the magnitudes
470
(meaning that a large (small) magnitude (compared to the mean) has larger
471
probabilit y to be followed by a small (large) magnitude), then 2.5
interevent time (day)
200
150
100
50
0
0
100
200
300
400
500
n
527
a)
31
32
800
Aftershock-depleted deep sub-catalog M>2.5
700
interevent time (day)
600 500 400 300 200 100 0
0
100
200
n
528
b)
529
Fig. 10. Interevent time series of the aftershock -depleted shallow (a) and deep (b)
530
sub-catalogues.
531 532
For the aftershock -depleted deep sub -catalog b=0.940.05, while for the shallow one
533
b=1.110.05 We also calculated for both the aftershock -depleted sub -catalogs the
534
global and local coeff icient of variations and obtained the following results (the 95%
535
confidence interval in parentheses): C v =1.31 [0.92,1.10] and L v =1.08 [0.88, 1.10] for
536
the shallow set and C v =1.93 [0.88, 1.14] and L v =1.01 [0.85, 1.16] for the deep set.
537
The onl y difference with un-declustered sub -catalogs is that after removing the
538
aftershocks both the sub -catalogs become locall y Poissonian; and this is reasonable,
539
because the aftershocks introduce a high level of clustering even for short time
540
scales.
32
33
1.0
Shallow seismicity 0.8
log10(AF(t))
0.6
0.4
a=0.38+0.02 0.2
1y 0.0
0.0
0.5
1.0
1.5
2.0
2.5
3.0
log10(t) (day)
541
a)
1.0
Deep seismicity
0.8
log10(AF(t))
0.6
a=1.02+0.05
0.4
0.2
410 d
0.0
-0.2 0.0
0.5
1.0
1.5
2.0
2.5
3.0
log10(t) (day)
542 543
b) Fig. 11. Allan Factor of the declustered shallow (a) and deep (b) sub -catalogs.
544 545
We applied the AF method to both the declustered sub -catalogs and the results are
546
shown in Fig. 11. We can observe that the scaling expo nent decreases, but more
547
slightl y for the shallow seismicit y; this is consistent with the removal of aftershocks
548
that generall y weakens the time -clustering of the seismicit y; however, the deep sub 33
34
549
catalog is still more clusterized than the shallow one. The adjacent -5-sample-
550
averaging smoothed curve (red) on the AF for the shallow seismicit y still shows the
551
periodicit y at about 1 year, as in the original sub -catalog. However, a very interesting
552
feature is now revealed in the AF of the deep sub -catalog: the presence of a
553
periodicit y at about 410 days, which seems compatible with the annual periodicit y
554
found in the AF of the shallow sub -catalog and also consistent with the annual cycle
555
of loading/unloading of the water reservoir. Such periodicit y was not reve aled in the
556
original deep sub -catalog because it was very likel y masked by the high number of
557
aftershocks following the November 14, 1981 event.
558 559
4. Discussion and conclusions
560
A detailed anal ysis of the seismicit y that occurred in Aswan region from 1980 to
561
2015 has been performed by utilizing the most robust statistical methodologies to
562
deepl y investigate its properties in time, magnitude and depth domains.
563
The anal ysis of the completeness of the catalogue was carried out by using two well
564
assessed methodologies (MAXC and GFT). The MAXC method generall y furnishes
565
values of the completeness magnitude slightl y lower than those obtained by the GFT
566
method, by using a sliding window with different sizes sweeping the entire catalog.
567
The completeness magnitude is not constant through time but varies, decreasing
568
around the end of the investigation period; this indicates that the catalog is not
569
homogeneous in time, and this is consistent with successive upgrades of the seismic
570
network that were performed through time . The non-homogeneit y on time of the
571
completeness magnitude and the comparison between the results obtained by the
572
MAXC and GFT methods with different sliding window sizes has led us to choose the 34
35
573
value of 2.5 for the completeness magnitude; this value not onl y is reasonabl y
574
consistent with the results shown in Fig. 3, but is also low enough to guarantee a
575
sufficientl y large size of events (with magnitude larger or equal to 2.5) to appl y the
576
statistical methodologies. The obtained value of the Gutenberg -Richter b is 1.07;
577
this value lies within the range of the b-values calculated for the global seismicit y
578
(Mogi, 1962). The obtained values of b and M c differ largely from the estimates for
579
approximatel y the same area calculated by Ali (2016), who found a b-value of the
580
0.554 and a completeness magnitude of 0.9. Ali (2016) applied onl y the MAXC
581
method and obtained the estimate of b from the whole catalog, which, however,
582
begins in 1997; in our case we considered a longer dataset, beginning in 1980,
583
compared th e results obtained by the MAXC and GFT methods and calculated the
584
value of b after computing the time variation of the completeness magnitude with
585
different size of sliding window.
586
The anal ysis of the distribution of hypocentral depths has revealed a very clear
587
discrimination of two depth classes, separated by the threshold of 12 km, which
588
separates earthquakes as shallower from deeper than the threshold.
589
Table 1 summarizes the results obtained from the different robust statistical
590
methodologies used in thi s study for the dynamical characterization of the 1982 -2015
591
seismicit y in the area of Aswan.
592 593 Parameter
Shallow seismicity
Deep seismicity
Result
b-value
1.14 (1.11)
0.99 (0.94)
b-value of shallow seismicit y
35
36
higher than b -value of deep seismicit y Cv
1.78 (1.31)
3.37 (1.93)
Globall y clusterized
Lv
1.35 (1.08)
1.16 (1.01)
Locall y
clusterized ;
aftershock-depleted
the sub-
catalogs are locall y Poissonian AF
0.40 (0.38)
1.85 (1.02)
Deep
seismicity
high
clusterized DFA
0.555
0.553
Magnitudes
persistentl y
correlated 594
Table 1: Statistical parameters obtained for shallow and deep seismicit y (in
595
parentheses those for the aftershock -depleted catalog) in the area of Aswan from
596
January, 1, 1982 to December 31, 2015.
597 598
The anal ysis of depth distribution was necess ary to discriminate between possibl y
599
different earthquake generation mechanisms and/or different types of time -dependent
600
earthquake occurrence.
601
The results of our anal ysis show that the loading/unloading operation of the Lake
602
Nasser reservoir could signifi cantl y influence the time dynamics of both the shallow
603
and deep events. Indeed, the AF curve of both the shallow and deep declustered sub-
604
catalogs evidences the existence of annual periodicit y ; such periodicit y could very
605
probabl y be linked to the annual p eriodicit y of the water level fluctuations and could
606
strengthen the reservoir -triggered nature of the seismicit y down to 30 km depth. It is
607
worth noting that the annual periodicit y is not present in the AF curve of the deep un36
37
608
declustered sub-catalogue, therefore it is mostl y characterized by mainshocks -
609
aftershocks sequences whereas this time -dependent earthquake occurrence character
610
is not evident in the shallow seismicit y (AF curve shows the annual periodicit y also
611
for the un -declustered shallow sub -catalog).
612
The deep and shallow declustered seismicit y are characterized by very close values of
613
the local coefficient of variation L v and similar values of the global coefficient of
614
variation C v , although the C v for the deep seismicit y is slightl y larger than t hat of
615
shallow seismicit y. This is also revealed by the scaling exponent obtained from the
616
AF that measure quantitativel y the strength of the time -clustering in an earthquake
617
sequence; the deep declustered sub-catalogue is characterized by 1.02, whereas the
618
shallow one by 0.38.
619
The DFA performed on the magnitude series of the shallow and deep events reveals
620
that both series are weakl y persistentl y correlated, indicating that there is a tendency
621
of similar magnitudes to follow each other. This finding support the consideration
622
that the physical driving mechanism is the diffusion of pore fluid pressure because
623
the events are responding to the same underl ying physical process that periodicall y
624
increases the seismicit y rate (Shearer, 2012) with many events of about the same
625
magnitude.
626
The higher b-value (1.11) of shallow seismicit y with respect to the b-value (0.94) of
627
the deep seismicit y indicates that the increase of pore pressure can enable shallow
628
small fractures to overcome the critical stress point fo r failure even with a low level
629
of accumulated stress. Indeed, it is well known (Scholz, 1968; W yss, 1973) that low
630
stresses cause earthquake series with high b-values.
37
38
631
The shallow seismicit y is mostl y located in the East of Gebel Marawa zone, Khore
632
El-Ramla zone, Abu Derwa zone, and Old stream zone (Fig. 4b and 4c) in
633
correspondence of the intersection of the left -lateral, strike-slip, N–S faults (El-
634
Barqa fault, Kurkur fault, and Abu -Dirwa fault) and the right -lateral, strike -slip, E–W
635
faults (Kalabsha fault and Seyal fault); therefore, w e suggest that the zones where the
636
shallow seismicit y occurs constitute a minor pull-apart basin where the seismic
637
deformation is accommodated on shallow small fractures . This hypothesis is also
638
supported by the observed oblique strike-slip motions of shallow earthquakes (focal
639
mechanisms indicate strike -slip faulting with a normal -fault component). Another
640
similar case of reservoir -triggered seismicit y in pull-apart basins generated by the
641
relative motion of strike -slip faults is the Koyna-Warna area in India (Catchings et
642
al., 2015).
643
The deep seismicit y is mostl y located in the Gebel Marawa zone along the Kalabsha
644
fault which is favorabl y oriented with the maximum principal stress direction of the
645
present tectonic stress regime and where the 14 November 1981, Ms 5.3 earthquake
646
occurred. The intersection of the eastern tip of the Kalabsha fault with the N–S faults
647
may promote fluid flow across the fault system and then along the Kalabsha fault
648
down to 30 km depth. Anyway, i t is not surprising that seismicit y can be induced by
649
the diffusion of pore fluid pressures at distance and at significant depths since it has
650
been largel y demonstrated in the literature (e.g., Rubinstein and Mehani, 2015 , and
651
references therein ).
652
Concludi ng, in the present study we have performed a detailed statistical anal ysis of
653
the
654
methodologies able to reveal dynamical properties of earthquakes in time, depth and
seismicit y
occurred
at
Aswan
from
1980
to
2015
by
utilizing
robust
38
39
655
magnitude domains. The obtained results depict the dynamics of the Aswan seismicit y
656
more deepl y evidencing the influence of the water level change in the lake on the
657
dynamics of both the shallow and the deep seismicit y which show a clear annual
658
periodicit y well correlated with t he annual loading of the lake. Moreover, the deep
659
seismicit y is mainly characterized by mainshock -aftershocks sequences mostl y
660
triggered by the water level fluctuations of the Nasser lake, and they mask the annual
661
periodicit y of the deep seismicit y if not properl y aftershock -depleted. However, this
662
study is restrained to the investigation of the seismicit y of the whole Aswan area,
663
while a more detailed statistical anal ysis of the spatial seismicit y separating different
664
source zones would lead to a better un derstanding of the seismic process.
665 666 667
References
668
Abdeen, M.M., Abdelsalam, M.G., Nielsen, K.C., Yehia, M.A., Cherif, O.H. , 2000.
669
Active dextral wrenching in southern Egypt. In: 38th Annunal Meeting of the
670
Geological Societ y of Egypt, Cairo.
671
Adamson, D.A. , Williams, F., 1980. Structural geology, tectonics and the control
672
of drainage in the Nile basin. In: The Sahara and The Nile: Quaternary
673
Environments and Prehistoric Occupation in Northern Africa (M.A.J. Williams and
674
H.Faure, Eds.).
675
Aki, K., 1965. Maximum likelihood estimate of b in the formula log(N) = a -bM
676
and its confidence limits. Bull. Earthq. Res. Inst., Univ. Tokyo 43, 237 –239.
677
Ali, S. M., 2016. Statistical anal ysis of seismicit y in Egypt and its surroundings .
678
Arabian J. Geosciences, 9, 52 . doi:10.1007/s12517 -015-2079-x. 39
40
679
Ben-Zion, Y., Lyakhovsky, V., 2002. Accelerated seismic release and related
680
aspects of seismicit y patterns on earthquake faults. Pure and Applied Geophysics
681
159, 2385–2412.
682
Borman, S., 2004. The Expectation Maximization Algorithm A s hort tutorial.
683
http://www.seanborman.com/publications/
684
Bosworth, W., Strecker, M.R., 1997. Stress field changes in the Afro -Arabian rift
685
s ystem during the Miocene to Recent period. Tectonophysics 278, 47 -62.
686
Carder, D. S., 1945. Seismic investigations in t he Boulder Dam area, 1940 –1944,
687
and the influence of reservoir loading on earthquake activity . Bull. Seismol. Soc.
688
Am. 35, 175–192.
689
Catchings, R.D., Dixit, M.M., Goldman, M.R., Kumar, S., 2015. Structure of the
690
Koyna-Warna Seismic Zone, Maharashtra, India: A possible model for large
691
induced earthquakes elsewhere. J. Geophys. Res. Solid Earth 120, 3479 –3506.
692
doi:10.1002/2014JB011695.
693
-
, I., and P. Talwani , P., 2010. Hydromechanics of the Koyna -Warna
694
region, India. Pure Appl. Geophys. 167, 183 –213. doi:10.1007/s00024 -009-0012-5.
695
Lippiello, E., de Arcangelis, L., Godano, C., 2008. Influence of Time and Space
696
Correlations on Ea rthquake Magnitude . Phys. Rev. Lett. 100, 038501
697
El Hariri, M., Abercrombie, R.A., Rowe, C.A., do Nascimento, A.F., 2010. The
698
role of fluids in triggering earthquakes: Observations from reservoir induced
699
seismicit y in Brazil . Geophys. J. Int. 181 (3), 1566–1574. doi:10.1111/j.1365 -
700
246X.2010.04554.x.
701
El Shazl y, E.M., Abdel-Hady, M.A., El Ghawaby, M.A., El Kassas, A., 1973.
702
Geologic interpretation of ERTS -1 satellite images for west Aswan area, Egypt. 40
41
703
Egyptian Academ y of Scientific Research and Technology, Re mote sensing
704
research project.
705
Fat-Helbary,
706
Determination Recorded by Aswan Seismic Network . Master Thesis, Sohag
707
Facult y, Assiute Universit y, Egypt
708
Fat-Helbary, R.E., Tealb A.A. , 2002. A study of seism icit y and earthquake hazard
709
at the proposed Kalabsha Dam Site, Aswan, Egypt . Natural Hazards 25, 117 -133.
710
Gahalaut, K., Hassoup, A. , 2012. Role of fluids in the earthquake occurrence
711
around Aswan reservoir, Egypt. J . Geophys. Res. 117, B02303. doi:10.1029/
712
2011J B008796.
713
Gahalaut, K., Hassoup, A., Hamed, H., Kundu, B., Gahalaut , V., 2016. Pure Appl.
714
Geophys., 174(1), 133–150. doi:10.1007/s00024-016-1397-6
715
Gebber, G.L., Orer, H.S., Barman, S.M., 2006. Fractal Noises and Motions in
716
Time
717
Neurophysiol. 95, 1176 –1184
718
Goltz, C., 1997. Fractal and Chaotic Properties of Earthquakes. Lecture Notes in
719
Earth Sciences, Berlin Springer.
720
Greiling, R.O., Abdeen, M.M., Dardir, A.A., El -Akhal, H., El-Raml y, M.F., Kamal
721
El-Din, G.M., Osman, A.F., Rashwan, A.A., Rice, A.H.N., Sadek, M.F. , 1994. A
722
structural
723
Geologische Rundshau 83, 484 –501.
724
Gupta, H.K., 2002. A review of recent studies of triggered earthqua kes by
725
artificial water reservoirs with special emphasis on earthquakes in Koyna, India .
726
Earth Sci. Rev. 58 (3/4), 279–310. doi:10.1016/S0012-8252(02)00063 -6.
Series
R.E.,
of
synthesis
1989.
A
Study
Presympatheti c
of
the
of
and
Proterozoic
the
Local
Sympathetic
Earthquake
Neural
Arabian –Nubian
Magnitude
Activities .
Shield
in
J.
Egypt.
41
42
727
Gutenberg, R., Richter, C.F., 1944. Frequency of earthquakes in California. Bull.
728
Seismol. Soc. Am. 34, 185–188.
729
Haggag, H.M., Gaber, H.H., Sayed, A.D., Ezzat, M.E. , 2008. A review of the
730
recent seismic activit y in the southern part of Egypt (upper Egypt). Acta
731
Geodynamica et Geomaterialia 5, 19 –29.
732
Hassoup, A., 1994. Investigation of the tectonic sett ing, seismic activit y and
733
crustal deformation in Aswan seismic region, Egypt . D.Sc. Thesis. Tokyo: Tokyo
734
Universit y.
735
Huang, Q., 2006. Search for reliable precursors: A case study of the seismic
736
quiescence of the 2000 western Tottori prefecture earthquake . J. Geophys. Res.
737
111(B4), B04301, doi:10.1029/2005JB003982 .
738
Hussein, H.M., Abou Elenean , K.M., Marzouk, I.A., Korrat, I.M., Abu El -Nader,
739
I.F., Ghazala, H.H., ElGabry, M.N., 2013. Present-day tectonic stress regime in
740
Egypt and surrounding area based on in version of earthquake focal mechanisms.
741
Journal of African Earth Sciences 81, 1 -15.
742
Iliopoulos, A.C., Pavlos G.P. , 2010. Global low dimensional seismic chaos in the
743
Hellenic region. International Journal of Bifurcation and Chaos 20(7), 2071 –2095.
744
Issac, M., Renuka, G., Venugopal, C., 2004. Wavelet analysis of long period
745
oscillations
746
Atmospheric and Solar -Terrestrial Physics 66, 919 –925.
747
Issawi, B., 1978. Geology of Nubia West area, Western Desert, Egypt. Annals of
748
the Geological Survey of Egypt 8, 237 -253.
749
Kagan, Y.Y., 1997. Are earthquakes predictable? . Geophys. J. Int. 131(3), 505–
750
525.
in
geomagnetic
field
over
the
magnetic
equator.
Journal
of
42
43
751
Kebeas y,
R.M.,
Maamoun,
M.,
Ibrahim,
E.,
1981.
Aswan
lake
induced
752
earthquakes. Bull. Int. Inst. Seismol. Earthq. Eng. 19, 155 –160.
753
Kebeas y, R.M., Maamoun, M., Ibrahim, E., Megahed, A., Simpson, D.W., Leith,
754
W.S., 1987. Earthquake studies at Aswan Reservoir, J. Geodynamics 7, 173 -193.
755
Ki yashchenko, D., Smirnova , N., Troyan, V., Saenger, E., Vallianatos, F., 2004.
756
Seismic hazard precursory evolution: fractal and multifractal aspects. Physics and
757
Chemistry of the Earth 29, 367 –378
758
Lennartz, S., Livina, V.N., Bunde, A., Havlin, S., 2008. Long -term memory in
759
earthquakes and the distribution of interoccurrence times. Europ hys. Lett. 81,
760
69001.
761
Lyakhovsky, V., Ben -zion, Y., Agnon, A., 2001. Earthquake cycle, fault zones,
762
and seismicit y patterns in a rheologically layered lithosphere. J . Geophys. Res.
763
106, 4103–4120.
764
Matcharashvili, T., Chelidze, T., Javakhishvili, Z., 2000. Nonlinear anal ysis of
765
magnitude and interevent time interval sequences for earthquakes of Caucasian
766
region. Nonlinear Processes in Geophysics 7, 9 –19.
767
Matcharashvili, T., Chelidze, T., Javakhishvili, Z., 2009. Dynamics, predictability
768
and risk assessment o f natural hazards. In: Fra Paleo, Urbano (Ed.), Building Safer
769
Communities. Risk Governance, Spatial Planning and Responses to Natural
770
Hazards. IOS Press, Amsterdam, pp. 148 –161.
771
Mekkawi, M., Grasso, J.R., Schneggm, P.A. , 2004. A long-lasting relaxation of
772
seismicit y at Aswan reservoir, Egypt, 1982 –2001. Bull. Seismol. Soc. Am. 94,
773
479–492.
43
44
774
Mogi,
K.,
1962.
Magnitude -frequency
relationship
for
elastic
shocks
775
accompanying fractures of various materials and some related problems in
776
earthquakes. Bull. Earthquak e Res. Inst. Univ. Tokyo 40 , 831-883.
777
Sarlis, N.V., Skordas, E.S., Varotsos, P.A., 2009. Multiplicative cascades and
778
seismicit y in natural time . Physical Review E 80, 022102.
779
Varotsos, P.A., Sarlis, N.V., Skordas, E.S., Lazaridou, M.S., 2008. Fluctuations,
780
under time reversal, of the natural time and the entropy distinguish similar looking
781
electric signals of different dynamics . J. Appl. Phys. 103, 014906.
782
Varotsos, P.A., Sarlis, N.V., Skordas, E.S., Uyeda, S., Kamogawa, M., 2011.
783
Natural time anal ysis of c ritical phenomena. Proc. Natl. Acad. Sci. USA 108,
784
11361-11364.
785
Peng, C.-K., Havlin, S., Stanley, H.E., Goldberger, A.L., 1995, Quantification of
786
scaling exponents and crossover phenomena in nonstationary heartbeat time series .
787
CHAOS 5, 82–87.
788
Pliakis, D., Papakostas, T., Vallianatos, F., 2012. A first principles approach to
789
understand the physics of precursory accelerating seismicit y. Ann . Geophys .
790
55(1). doi:10.4401/ag-5363.
791
Reasenberg, P.A., 1985. Second-order moment of central California seismicity,
792
1969-82. J. Geophys. Res. 90, 5479 –5495.
793
Rong, Y.M., Wang, Q., Ding, X., Huang, Q.H. , 2012. Non-uniform scaling
794
behaviour in Ultra-Low-Frequency (ULF) geomagnetic signals possibl y associated
795
with the 2011 M9.0 Tohoku earthquake . Chinese Journal Geophysics 55, 3709–
796
3717.
44
45
797
Rubinstein, J.L., Mehani, A.B., 2015. Myths and Facts on Wastewater Injection,
798
Hydraulic Fracturing, Enhanced Oil Recovery, and Induced Seismicit y. Seismol.
799
Res. Lett. 86(4), 1060 –1067. doi: 10.1785/0220150067.
800
Said, R., 1993. The River Nile: Geology, Hydrology and Utilization. Pergamon
801
Press, Oxford., 320 pp.
802
Said, R., 1981. The Geological Evolution of the River Nile. Springer -Verlag, New
803
York, 151 pp.
804
Scholz, C.H., 1968. The frequency-magnitude relation of microfracturing in rock
805
and its relation to earthquakes. Bull. Seismol. Soc. Am. 58 , 399-415
806
Selim, M.M., Imoto, M, Hurukawa, N. , 2002. Statistical investigation of reservoir -
807
induced seismicit y in Aswan area, Egypt. Earth Planets Space 54, 349 –356.
808
Shi, Y., Bolt, B., 1982. The standard error of the magnitude -frequency b
809
value. Bull. Seismol. Soc. Am. 72(5), 1677–1687
810
Shinomoto, S., Miura, K., Koyama, S., 2005. A measure of local variation of inter -
811
spike intervals. Biosystems 79, 67 –72.
812
Simpson, D.W., Negmatullaev , S.K., 1981. Induced seismicit y at Nurek Reservoir,
813
Tadjikistan, USSR. Bull. Seismol. Soc. Am. 71 (5), 1561–1586.
814
Simpson, D.W., Kebeasy, R.M., Nicholson, C., Maamoun, M., Albert, R.N.,
815
Ibrahim, E.M., Megahed, A., Gharib, A., Hussain, A., 1987. Aswan Telemetered
816
seismograph Network. J. Geophys. 7, 195 –203.
817
Simpson, D.W., Gharib, A.A., Kebeasy, R.M., 1990. Induced seismicit y and
818
changes in water level at Aswan reservoir, Egypt . Gerlands. Beitr. Geophys .
819
Leipzig 99, 191 –204.
45
46
820
Shearer, P. M. (2012), Space -time clustering of seismicit y in Cal ifornia and the
821
distance dependence of earthquake triggering, J. Geophys. Res. 117, B10306, doi:
822
10.1029/2012JB009471.
823
Stabile, T.A., Giocoli, A., Lapenna, V., Perrone, A., Piscitelli, S., Telesca, L.,
824
2014.
825
associated with the Pertusillo artificial lake (southern Ital y) . Bull. Seismol. Soc.
826
Am. 104(4). doi:10.1785/0120130333.
827
Telesca, L., Amatulli, G., Lasaponara, R., Lovallo, M., Santulli, A. , 2005. Time-
828
scaling properties in forest -fire sequences observed in Gargano area (southern
829
Ital y). Ecol. Model. 185, 531 -544.
830
Telesca, L., Cuomo, V., Lapenna, V., Macchiato, M., 2001. Statistical anal ysis of
831
fractal properties of point processes modelling seismic sequences, Phys. Earth
832
Planet. Int. 125, 65-83.
833
Telesca, L., ElShafey Fat ElBary, R., Amin Mohamed, A. E. -E., ElGabry, M. ,
834
2012. Anal ysis of the cross -correlation between seismicit y and water level in the
835
Aswan area (Egypt) from 1982 to 2010. Natural Hazards and Earth S ystem
836
Sciences 12, 2203–2207.
837
Telesca, L., Giocoli, A., Lapenna, V., Stabile, T.A., 2015. Robust identification of
838
periodic behavior in the time dynamics of short seismic series: the case of
839
seismicit y induced by Pertusillo Lake, southern Ital y. Stochastic Environmental
840
Research and Risk Assessment 29, 1437 –1446. doi:10.1007/s00477 -014-0980-6.
841
Telesca,
842
investigation of scaling properties in temporal patterns of seismic sequences.
843
Chaos, Solitons & Fractals 19(1), 1 –5.
Evidences
L.,
of
Lapenna,
low -magnitude
V.,
continued
Macchiato,
M.,
reservoir -induced
2004.
Mono -
and
seismicit y
multi-fractal
46
47
844
Telesca, L., Lovallo, Lopez, M.C., Molist, J.M. , 2016. Multiparametric statistical
845
investigation of seismicit y occurred at El Hierro (Canary Islands) from 2011 to
846
2014. Tectonophysics 672 -673, 121-128. doi: 10.1016/j.tecto.2016.01.045.
847
Telesca, L., Lovallo, M ., 2009. Non-uniform scaling features in Central Italy
848
seismicit y: a non -linear approach in investigating seismic patterns and detection
849
of possible earthquake precursors. Geophys. Res. Lett. 36, L01308.
850
Telesca, L., Lovallo, M., 2010. Long -range dependenc e in tree-ring width time
851
series of Austrocedrus chilensis revealed by means of the detrended fluctuation
852
anal ysis. Physica A 389, 4096 –4104.
853
Telesca, L., Lovallo, M., 2011. Anal ysis of time dynamics in wind records by
854
means
855
information plane . J. Stat. Mech. P07001.
856
Telesca, L., Mohamed, A. E. -E. A., ElGabry, M., El -hady, S., Abou Elenean, K.
857
M., 2012. Time dynamics in the point process modelling of seismicit y of Aswan
858
area (Egypt), Chaos Sol itons & Fractals 45, 47 -55.
859
Telesca, L., Pierini, J.O., Scian, B., 2012. Investigating the temporal variation of
860
the scaling behavior in rainfall data measured in central Argentina by means of the
861
detrended fluctuation anal ysis. Physica A 391, 1553 –1562.
862
Thurner, S., Lowen, S.B., Feurstein, M.C., Heneghan, C., Feichtinger, H.G.,
863
Teich, M.C., 1997 . Anal ysis, synthesis, and estimation of fractal -rate stochastic
864
point processes: Fractals 5, 565 -596.
865
Utsu, T., 1999. Representation and anal ysis of the earthquake size distribution: a
866
historical review and some new approaches. Pageoph 155, 509 –535.
of
multifractal
detrended
fluct uation
anal ysis
and
Fisher -Shannon
47
48
867
Vallianatos, F., Benson, P., Meredith, P., Sammonds, P., 2012. Experimental
868
evidence of a non -extensive statistical physics behavior of fracture in triaxially
869
deformed Etna basalt using acoustic emissions. Europhys . Lett. 97, 58002.
870
Varotsos, P.A., Sarlis, N.V., Skordas, E.S., 2012. Scale-specific order parameter
871
fluctuations
872
fluctuation anal ysis . Europhys. Lett. 99, 59001.
873
Varotsos, P.A., Sarlis, N.V., Skordas, E.S., 2014. Study of the temporal
874
correlations in the magnitude time series before major earthquakes in Japan. J.
875
Geophys. Res. Space Phys. 119, 9192 –9206.
876
WCC (Woodward-Clyde Consultants) , 1985.
877
and estimation of magnitudes and recurrence intervals . Internal Report, High and
878
Aswan Dams Authority, Egypt.
879
Wiemer, S., W yss, M., 2000. Minimum magnitude of completeness in earthquake
880
catalogs: examples from Alaska, t he Western United States, and Japan. Bull.
881
Seismol. Soc. Am. 90, 859 –869.
882
Woessner, J., Wiemer S., 2005. Assessing the qualit y of earthquake catalogues:
883
Estimating the magnitude of completeness and its uncertaint y . Bull. Seismol. Soc.
884
Am. 95(2), 684–698. doi: 10.1785/ 0120040007.
885
Woodward, J.C., Macklin, M.G., Krom , M.D., Williams, M.A.J., 2007. The Nile:
886
Evolution, Quaternary River Environments and Material Fluxes. In: Large Rivers:
887
Geomorphology and Management (Gupta, Eds).
888
Wyss, M., 1973. Towards a physi cal understanding of the earthquake frequency
889
distribution. Geophys. J. R. Astron. Soc. 31, 341 –359.
of
seismicit y before
mainshocks:
Natural
fic
time
f
and
hq
detrended
k
c
890 48
49
891
49
*Revised manuscript with no changes marked Click here to view linked References
1 2
Dynamical characterization of the 1982-2015 seismicity of Aswan Region (Egypt)
3 4
5
Luciano Telesca1*, Raafat Fat Elbary2, Tony A. Stabile1, Mohamed Haggag2, Mohamed Elgabry3
6
1
7
85050 Tito (PZ) Italy
8
2
Aswan Regional Earthquake Center, Aswan, Egypt
9
3
National Research Institute of Astronomy and Geophysics, 11421 Helwan, Cairo, Egypt
National Research Council, Institute of Methodologies for Environmental Analysis, C.da S. Loja,
10 11
*Corresponding author: tel. +39-0971-427277, fax +39-0971-427277, email:
12
[email protected]
13 14
Abstract
15
In this study, the seismicity that occurred in Aswan region from 198 2 to 2015 is
16
investigated using robust statistical methodologies. The completeness magnitude,
17
estimated by using two different methods (MAXC and GFT) is 2.5 for the whole
18
catalogue with b 1.07. By using the expectation maximization algorithm, two depth
19
classes of events were identified with a threshold at about 12 km. The events deeper
20
and shallower than the thresho ld could be likely generated by the same mechanism:
21
the loading/unloading operation of the Lake Nasser reservoir. We suggest that the
22
shallow seismicity occurs on shallow small fractures in correspondence of the
23
intersection of N-S faults with E-W faults, which may form a minor pull -apart basin.
2
24
The deep events mainly occur along the right -lateral, strike-slip, E–W Kalabsha fault
25
and the seismicity is characterized by mainshock -aftershocks sequences that mask the
26
annual periodicity if not properly aftersho ck-depleted. Indeed, before applying the
27
declustering on the seismic catalogue, the analysis of the time -clustering properties
28
of the shallow earthquakes reveals already the presence of annual modulation that is
29
not evident in the time dynamics of the deep earthquakes. Furthermore, the shallow
30
events are featured by the Allan Factor scaling exponent (measuring the strength of
31
the time-clustering in an earthquake sequence) lower than that of the deep events,
32
indicating a tendency of the time dynamics of the shallow earthquakes to behave
33
more regularly than the deep ones. The detrended fluctuation analysis of the
34
magnitude
35
characterized by the tendency of events of similar value of magnitude to follow each
36
other.
37
Keywords: Aswan, induced seismicity, b-value, coefficient of variation, clustering
series
suggests
that
the
earthquake
series
are
weakly
persistent,
38 39
1. Introduction
40
River Nile, the longest river in the world, is a common basin between 11 countries ,
41
traveling over 2700 km through Sahara Desert withou t any significant perennial
42
tributary inputs (Woodward et. al, 2007). Throughout time the river has gone through
43
natural and anthropological changes , being a structurally controlled stream since its
44
early stages. Several studies (i.e. Adamson and Williams, 1980; Said, 1981, 1993)
45
have proposed that the river Nile is in continuous evolving process by major tectonic
2
3
46
phenomena and climatic changes including the Rifting of East African that could be
47
the shaping factor for the location of sedimentary basin and t he drainage pattern.
48
The river has gone through many water resources management mega projects ; the
49
largest till now is the Aswan High Dam constructed between 1960 -1971, which is 111
50
m high, a crest length of 3830 m , and impounds the second largest reservoir in the
51
world, the Lake Nasser, that has a gross capacity of 169 billio n cubic meters . On
52
November 14, 1981, an Ms 5.3 earthquake took place south of the dam. This
53
earthquake has raised the concerns about the dam stability from one side and its
54
relation to seismicity from the other side . A network of 13 station s was established
55
(Simpson et al., 1987) to monitor seismicity aro und the lake since 1982 , and intense
56
seismic activity has been recorded since then (Simpson et al., 1 990; Gahalaut et al.,
57
2016, and references therein ).
58
Several researchers explored the relationship between the Aswan reservoir water
59
level and the obser ved seismicity in the region ( e.g., Kebeasy et al., 1981; Simpson et
60
al., 1990; Hassoup 1994; Selim et al., 2002; Mekkawi et al., 2004; Haggag et al.,
61
2008; Telesca et al., 2012 ; Gahalaut et al., 2016 ) and, even if in some periods there
62
were found no or we aker reservoir influence ( Hassoup 1994; Selim et al., 2002;
63
Mekkawi et al., 2004 ; Telesca et al., 2012), it has been commonly accepted t hat the
64
Aswan seismicity is a case of continuous reservoir triggered seismicity (RTS).
65
Therefore, Aswan area belongs to the reservoir sites that exhibit triggered seismicity
66
every year or after a gap of a few years , such as Lake Mead in the USA (Carder,
67
1945), Koyna-Warna reservoir
68
2010), Nurek Dam in Tajikistan (Simpson and Negmatullaev, 1981),
69
Brazil (El Hariri et al., 2010), and Pertusillo reservoir in Italy (Stabile et al., 2014 ;
-
,
3
Formatted: Not Highlight
4
70
Telesca et al., 2015). In all the cases mentioned above delayed seismic response to
71
water
72
mechanism of the observed continued RTS is the diffusion of pore fluid pressures .
73
Particularly for the As wan area, Gahalaut and Hassoup (2012) demonstrated from an
74
analytical simulation that although the stress due to the reservoir load stabilizes
75
seimogenic faults of the area, the effect of pore fluid pressure leads the faults to go
76
beyond the critical stres s for failure.
77
Due to the presence of the High Dam and the seismic risk that it could raise, a deep
78
dynamical
79
challenging and, at same time, crucial to best understand the mechanisms related t o
80
the generation of local earthquakes.
81
In fact, it is widely recognized that investigating with detail the structure of
82
magnitude, space and time distribution of earthquakes is fundamental for earthquake
83
hazard assessment as well as for the comprehension o f properties of seismic
84
processes (see e.g. Goltz, 1997; Kagan, 1997; Matcharashvili et al., 2009). In
85
particular, the investigation of the characteristics of time distribution of earthquake
86
occurrences on various temporal scales has been the focus of very intense research.
87
Several studies based on different conceptual frameworks approached the analysis of
88
earthquake time patterns by means of both field and laboratory data as well as
89
numerical
90
Matcharashvili et al., 2000; Telesca et al., 2004). Most of such studies highlight that
91
seismic processes are characterized by intermittent time behavior with phases of
92
intense seismic activity interspersed with those of low seismicity (Ben-Zion and
93
Lyakhovsky, 2002; Kiyashchenko et al., 2004; Pliakis et al., 2012; Vallianatos et al.,
fluctuations
of
reservoirs
characterization
simulations
of
(e.g.
was
the
Issac
observed
seismic
et
al.,
suggesting
process
2004;
that
governin g
Lyakhovsky
the
dominant
Aswan
et
al.,
area
is
2001;
4
5
94
2012); thus, evidencing the presence o f nonrandom components in earthquake
95
generation in energy, space and time domains ( Lliopoulos and Pavlos, 2010).
96
Within this context, i n this study, we aim at investigating the dynamical properties of
97
the most updated seismic catalogue of Aswan applying several robust statistical
98
methodologies to the magnitude and time distribution of seismicity that occurred
99
nearby Lake Nasser from 1982 to 2015 in order to better characterize its time
100
Formatted: Not Highlight
dynamics.
101 102 103
2. Seismo-tectonic settings and data description
104
Aswan (or Syene as its Greek name, which is named after the type locality for the
105
igneous rock syenite) is located in the southern part of Egypt at the interface between
106
the stable Archean craton of the Nubian Shield and the less stable Pan -African
107
orogeny of the southern most of the Egyptian Eastern Desert. Aswan exhibits a
108
complex geological situation with a number of different rock types , ranging from
109
quaternary deposits to cretaceous sedimentary rocks of Nubian sandstone , to igneous
110
and metamorphic rocks of the deep -lying basement complex, which have been
111
uplifted and exposed to surface. In many areas in and around Aswan the river has
112
eroded the overlying Nubian sandstone and carved deep channels into the igneous
113
rocks (Greiling, et, al. 1994).
114
From early satellite imagery, El Shazly et al. (1973) has described the structural
115
trends in Aswan area to be mainly NNW -SSE fractures making notable horizon tal
116
separation and NW -SE fractures showing horizontal and vertical separation along
117
geological and relief boundaries. Furthermore, the NNE -SSW fault west to Kurkur 5
Formatted: Not Highlight
6
118
shows horizontal left -lateral trend. In addition, two major fracture trends are present
Formatted: Not Highlight
119
in NE-SW and the ENE -WSW. According to El Shazly et al. (1973) the NE -SW
Formatted: Not Highlight
120
fractures do not show separation, and they may be major tension fractures
121
perpendicular to the principal force creating the previously mentioned two major
122
fault trends. Fractures seem to represent tension zones along the hinges of major
123
folds, which may have been faulted along the same zones.
124
These trends been reactivated as both strike -slip E-W dextral and N-S sinistral faults
125
(WCC, 1985; Abdeen et al., 2000) , and dip-slip faults (Issawi, 1978) and propagated
126
up through the sedimentary cover. The seismic activity is concentrated along N -S and
127
E-W fault intersections. The N -S faults have less activity than that of E -W faults.
128
Recent stress regime deduced from Earthquak e focal mechanism (Hus sein et al.,
129
2013) is in good agreement with that of geological studies and borehole breako ut data
130
(Bosworth and Strecker, 1997) with Shmax E-W and Shmin NNE-SSW. Under this
131
stress regime, the province is found to be consistent with both right lateral faul ts (E–
132
W striking) and left lateral faults (N –S striking).
133
The largest of the Aswan earthquakes was of magnitude Ms 5.3, and occurred on 14
134
November 1981. From the intensity data, the well-determined locations of numerous
135
aftershocks recorded using portable stations and a telemetered network, the event
136
appears located on the Kalabsha fault beneath Gebel Marawa (Kebeasy et al. 1987).
137
The depths of the aftershocks and the special study of teleseismic records of the
138
mainshock (WCC 1985) indicate that the mainshock was at a depth from 18 to 20 km.
139
Prior November 14, 1981, no earthquakes had been reported in the Aswan area in the
140
c
141
1920. Because of the lack of continuous and reliab le data during the early stages of
Formatted: Not Highlight
Formatted: Not Highlight
Formatted: Not Highlight Formatted: Not Highlight
g
f h
S
g c
C
SC)
c
h
SC’
c
6
7
142
the filling of the Aswan reservoir, it is not possible to determine exactly when low -
143
magnitude activity may have started. The first seismographs installed in the Aswan
144
area that were capable of recording small local earth quakes were Soviet short -period
145
(SMK) instruments installed at Aswan and Abu Simble in 1975. Although the
146
operation of the stations was irregular prior to 1981, 20 events of magnitude greater
147
than 2.5 have been identified by Helwan Institute located in the Wadi Kalabsha area.
148
Thirteen of these took place during approximately 200 days of station operation
149
between August 1980 and August 1981. A long sequence of aftershocks followed the
150
14 November 1981 earthquake , including the immediate aftershocks and the
151
continuation of Aswan activity until the present both in the area of the mainshock and
152
around the northern part of the Aswan reservoir.
153
Following the mainshock, portable microearthquake recorders were installed in the
154
northern part of reservoir area by Eg yptian Geological survey from December to June
155
1982. In late June 1982 , the portable seismic field stations were replaced by a
156
telemetry network erected by Helwan Observatory and Lamont -Doherty Geological
157
Observatory (USA). The purpose of the telemetry net work is to monitor the
158
induced/triggered seismicity along the Kalabsha fault (Fig. 1), which continues to
159
occur in the area of the November 14, 1981 earthquake (Kebeasy et al 1987 ; Fat-
160
Helbary et al., 2002 ). Data were transmitted to a data center to record the output
161
signals coming from the field stations. Five monitors with pen recorder were used for
162
the visual record and all the data were recorded in the FM magnetic tape as analog
163
data and in the 9 -track tape as digital data. A playback unit and computer facilities
164
had been installed at the center to allow earthquakes to be quickly analyzed and
165
located. Since 2009, the Aswan Seismic Network has been updated and replaced by 7
Formatted: Not Highlight
Formatted: Not Highlight
Formatted: Not Highlight
8
166
new digital broad band network. The transmission system is changed from telemetry
167
to satellite and in addition some stations in field were moved to near better sites. As
168
well as the data from field stations is sent to the main center for the necessary
169
analysis using the recent software programs such as Atlas and Earlybird.
170
171 172
Fig. 1. Aswan Seismic Network.
173 174
The output data include latitude, longitude, focal depth, origin time, epicenter
175
distance and azimuth for each station. Various measures of location accuracy are also
8
Formatted: Not Highlight
9
176
given. The output data are used for constructing the seismici ty map of the Kalabsha
177
area (Fig. 2).
178 179
a)
9
10
6
5
Mi
4
3
2
1
0 0
2000
4000
6000
8000
i
180 181
b) Formatted: Not Highlight
182
Fig. 2. (a) Seismicity map of Aswan region ( a: Gebel Marawa zone, b: East-1 of
183
Gebel Marawa zone, c: East -2 of Gebel Marawa zone, d: khore El -Ramla zone, e:
184
Abu Derwa zone, and f: Old stream zone). (b) Magnitude plot of the earthquakes in
185
the investigated region during 1982 –2015. Horizontal axis indicates the ith event and
186
vertical axis indicates its magnitude.
Formatted: Not Highlight
Formatted: Not Highlight
187 188
3. M ethods and results
189
In this study we investigate the seismicity that occurred from January, 1, 1982 to
190
December 31, 2015 in the area of Aswan (Fig. 2). We employed several independent
191
statistical methods to obtain the most complete picture of the dynamical properties of
192
the seismic process in the area.
193 10
Formatted: Not Highlight
11
194
3.1. The frequency -magnitude distribution
195
The Gutenberg-Richter (GR) law (Gutenberg and Richter, 1944) relates the threshold
196
magnitude M t h and the number of earthquakes with magnitude M> M t h in a power-law
197
manner usually represented in semi -log scales as log 1 0 (N)=a-bM t h , where N is the
198
number of ea rthquakes with magnitude M> M t h , a is the earthquake productivity, and
199
b is a value that indicates the proportion of small events respect to the large ones.
200
The GR law is generally used to fit t he frequency magnitude distribution (FMD). The
201
b-value is a critical parameter that can inform on the stress crustal conditions ;
202
therefore, estimating with a good accuracy and reliability the b-value is important to
203
characterize different stages of the evolution of seismicity, linked with dynamical
204
changes of the seismic process, and, as a consequence, is crucial for reliable s eismic
205
hazard assessments (Scholz, 1968; Wyss, 1973) .
206
In our study, the estimation of the b-value was performed by using the maximum
207
likelihood method (MLE) (Aki, 1965),
208 209
b
Field Code Changed
log10 (e) , (1) M bin M M c 2
210 211
where is the average magnitude of the sub-set of earthquakes whose magnitude
212
is larger or equal to the completeness magnitude M c and M b i n represents the binning
213
width of the catalogue (Utsu, 1999).
214
The standard deviation of the estimate of b
215
(1982) formula,
c c
by
g h
Sh
B
’
216 11
12
N
217
b 2.3b 2
M i 1
i
M
N N 1
Field Code Changed
2
. (2)
218 219
As it is deduced from formula (1), the b-value depends on the estimat ion of the
220
completeness magnitude M c that is the lower magnitude above which the seismi c
221
catalogue can be considered complete.
222
It is obvious that to get reliable results of any analysis performed on the seismi c
223
catalogue, only the events with magnitude MM c have to be selected . The maximum
224
curvature method (MAXC) (Wiemer and Wyss, 2000) allows a fas t estimation of M c
225
that corresponds to the largest bin in the noncumulative FMD. Another method is the
226
goodness -of-fit (GFT) (Wiemer and Wyss, 2000) between the observed and synthetic
227
cumulative FMDs, the last calculated employing the a- and b-values of th e GR-law of
228
the observed seismic set for magnitude larger or equal to a n increasing threshold
229
magnitude that estimates M c when the 90% of the observed data are well modeled by
230
a straight line.
231
As already shown above, s ince 1980 the Aswan seismic network un derwent several
232
upgrades that have certainly influenced the earthquake detection sensitivity and, thus,
233
the completeness magnitude through time. Therefore, it is necessary to investigate
234
preliminarily the time variation of the completeness magnitude. A visual impression
235
about how the magnitude of completeness varies with time and about possible artifact
236
in the catalog can be got by using the M i -i plot with horizontal axis indicating the ith
237
earthquake and vertical axis indicating its magnitude M i (Huang, 2006). Fig. 2b
238
shows a slight decrease of the magnitude with time and this indicates that the 12
13
239
completeness magnitude changes with time. Fig. 3 shows the time variation of M c for
240
the seismicity at Aswan calculated by means of the MAXC method (red) and the GFT
241
method (blue ).
242
entire catalog with a shift of 1 0 events. In each window, the completeness magnitude
243
M c (associated with the time of the last event in the window) was computed only in
244
case the number of events wa s larger or equal to 50 (Woessner and Wiemer, 2005).
245
We can see that MAXC and GFT methods give very similar results in all the three
246
examined cases ( W N =200, 500 and 1000 events), although the MAXC method
247
estimates a slightly lower value o f the completeness magnitude. The range of
248
variation of the completeness magnitude depends on the length of the moving
249
window, being larger for W N =200 and smaller for W N =1000.
We considered a fixed event number window (W N ) sweeping the
250 251 Field Code Changed MAXC GFT WN=200
3.5
MAXC GFT WN=500
3.0
Field Code Changed
3.0 2.5
2.5 2.0
MC
MC
2.0
1.5
1.0
1.0
0.5
0.5
0
2000
4000
6000
8000
10000
12000
14000
0
time (days since January 1, 1982)
252
1.5
2000
4000
6000
8000
10000
12000
14000
time (days since January 1, 1982)
a)
b)
13
14
Field Code Changed MAXC GFT WN=1000
3.0
2.5
MC
2.0
1.5
1.0
0.5 0
2000
4000
6000
8000
10000
12000
14000
time (days since January 1, 1982)
253
c)
254
Fig. 3. Time variation of the completeness magnitude in the Aswan area (red: MAXC method; blue:
255
GFT method, see text for details): (a) WN=200 events; (b) WN=500 events; (c) WN=1000 events.
256 257
A conservative choice of the completeness magnitude would require taking it as the
258
largest value of the time evolution of M c . But, this choice would strongly decrease
259
the amount of available data, and consequently would inc rease the uncertaint y of the
260
statistical estimates due to smaller sample sizes. For instance, considering the time
261
variation of M c for W N =1000 events, the highest completeness magnitude on the base
262
of the GFT method is 3.0 that would reduce the amount of availa ble data to 375 over
263
9592 that is the size of the whole catalog . So, it is impo rtant to find a sort of
264
compromise between the size of the dataset, large enough to perform the statistical
265
analysis with good accuracy, and the value of M c that should guarantee a good
266
completeness of the catalog. On the base of the results shown in Fig. 3, by visual
267
inspection we believe that a reasonable choice of M c is 2.5; in fact, it is larger than
268
any completeness magnitude calculated by the MAXC method and lower than the
269
completeness magnitude calculated in just few windows by using the GFT method; 14
Formatted: Not Highlight
15
270
furthermore, 2.5 is quite robust with respect the size of the moving window . In this
271
case the number of earthquakes with magnitude larger or equal to 2.5 is 1327 over
272
9592; and t his size is large enough to perform significantly the statistical analysis on
273
the catalog. Therefore, with M c =2.5 the b=1.070.02.
274 275
3.2. The depth distribution
276
Considering the catalog of seismic events with magnitude larger or equal to 2.5, two
277
distinct depth classes characterize t he earthquakes that occurred in the Aswan area.
278
Fig. 4 shows the depth histogram (black line) of the seismic events. It is clear that
279
the distribution is bimodal, characterized by two depth ranges, one involving the
280
shallow events and the other the deep events. Furthermore, the depth distribution
281
seems to be a mixture of two Gaussian distributions.
282
a)
15
Formatted: Not Highlight
16
283 284
b)
16
17
285
c) 17
18
286 287
Fig. 4. a) Depth distribution of the seismicity at Aswan. The red and blue curves represent the best
288
Gaussian fit of the deep and shallow events. D0 represents the estimated depth threshold separating the
289
two classes of depths; b) Depth-longitude diagram of seismicity; c) Seismicity of Aswan area for
290
magnitude larger or equal to 2.5.
291 292
In order to better discriminate the two depth ranges, we applied the expectation
293
maximization (EM) algorithm (Borman, 2004) that is a statistical method to calculate
294
the maximum likelihood estimates of the parameters of two Gaussian distributions,
295
whose mixture seems to model the bimodal depth distribution of Fig. 4. We found
296
that one Gaussian distribution (blue curve) with G =4.8 and G 2 =8.0 models the
297
distribution of shallow events and the other (red curve) with G =19.0 and G 2 =9.7
298
models that of the deep ones. The intersection between the two curves D 0 12 km can
299
be considered as the threshold depth discriminating betwe en shallow and deep events.
300
In the forthcoming analyses we will investigate the two earthquake sub-catalogues:
301
shallow (depth < 12 km) and deep (depth 12 km). The number of the shallow events
302
is 744 and that of the deep ones is 583. For the deep sub -catalog b=0.990.03, while
303
for the shallow one b=1.140.04.
304
Fig. 4b shows the vertical cross/section depth -longitude diagram of seismicity . The
305
events appearing at shallower depths (less than 12 km) in this cross -section are from
306
East Gebel Marawa (the composite fault plane solution indicates strike -slip faulting
307
with a normal fault component) , Khore El-Ramla (the composite fault plane solution
308
indicates a strike-slip fault and the fault plane strikes 155° and dips 65°) , Abu Dirwa 18
19
309
(the focal mechanism of this zone includes strike-slip and normal components, and
310
the fault plane strikes 177° from the north and is dipping 61°) and old stream zones
311
(the fault plane solution indicates strike slip faulting and the fault plane strikes 161°
312
and dips 77°).
313
On the other hand, the seismicity at Gebel Marawa zone is almost all between depth
314
of 12 and 26 km and the events align along the Kalabsha fault where the mainshock
315
of November 14, 1981 occurred deeper than 12 km (Fat -Helbary et al., 2002). The
316
composite fault plane so lution of the earthquakes indicates strike -slip faulting with a
317
normal-fault component. The fault plane strikes 78° and dips 70° (Fat -Helbary,
318
1989).
319 320
3.3. The coefficients of variation
321
The coefficient of variation C v is a simple measure employed to study the time-
322
clustering properties of an earthquake process . It is defined as
323 324
Cv
, (3)
Field Code Changed
325 326
where and are the standard deviation and the average of the interevent times ,
327
respectively. Fig. 5 shows the series of the interevent times of th e deep (Fig. 5a) and
328
shallow (Fig. 5b) sub-catalogues.
19
20
interevent time (day)
300
Shallow
200
100
0
interevent time (day)
-100 600
0
100
200
300
400
500
600
700
800
Deep
400 200 0 0
100
200
300
400
500
600
n
329 330
Formatted: Not Highlight
Fig. 5. Interevent time series of the shallow (a) and deep (b) sub-catalogues.
Formatted: Not Highlight
331 332
Depending on the numerical value of C v , the earthquake sequence is regular (or
333
periodic) if it is lower than 1, purely random (or Poissonian) if it is 1 or clustered if
334
it is larger than 1 (Kagan and Jackson, 1991). Recently, Telesca et al. (2016)
335
introduced the local coefficient of variation L v , defined by Shinomoto et al. (2005),
336
to investigate the local time-clustering properties of the volcano -related seismicity at
337
El Hierro, Canary Islands (Spain):
338
1 N 1 Ti Ti 1 3 N 1 i 1 Ti Ti 1 2 (4)
Field Code Changed
2
339
Lv
340
20
21
341
For Poissonian seismic processes, C v and L v are both 1, and for periodic processes
342
they are both 0. However, if C v can measure global variability of a whole interevent
343
sequence and could be affected by event rate fluctuation s, L v measures local stepwise
344
variability of interevent times , because it is rather independent of slow variation in
345
average rate. Just as an example, if one joins two peri odic point processes like those
346
in Fig. 6, C v >>1 because globally the process appears highly clustered, but L v 0,
347
because at local scales the process is periodic.
0
10
20
30
40
time
348 349
Fig. 6. Superposition of two periodic point processes.
350 351
We calculated both the coefficient s of variation for the deep and shallow seismicity
352
in Aswan for events with m agnitude M2.5, and obtained C v 3.37 and L v 1.16 for
353
deep seismicity and C v 1.78 and L v 1.35 for the shallow one. To calculate the
354
significance of these results, we calculated bot h the quantities for 1,000 Poisson
355
processes randomly generated with the same size and mean interevent time as the
356
original (deep and shallow) seismic interevent time series. The 95% confidence 21
22
357
interval, which is delimited by the 2.5 t h and 97.5 t h percentil es of the distribution of
358
C v and L v of the Poissonian surrogates , are [0.94, 1.07] for C v and [0.92, 1.08] for L v
359
in case of deep seismicity, and are [0.93, 1.0 8] for C v and [0.92, 1.07] for L v in case
360
of shallow seismicity. The obtained values of the coef ficients of variation indicate
361
that both global ly and locally the distribution s of the shallow and deep seismicity at
362
Aswan are clusterized at 95% confidence.
363 364
3.4. The Allan Factor
365
The coefficients of variation described in section 3.2 furnish information about the
366
time-clustering of a point process, but reveal none about the timescales where the
367
process is clusterized. To this aim, the Allan Factor (AF) is a suited method for
368
discriminating between the timescales where the process is clusterized from thos e
369
where it is not. Dividing the time axis into equally spaced contiguous counting
370
windows of duration , that is timescale, a sequence of earthquake counts { N k ( )} is
371
obtained, with N k ( ) being the number of earthquakes falling in the k-th window
372
(Thurner et al., 1997). The AF, then, is defined as
373
AF ( ) 374
( N k 1 ( ) N k ( )) 2 2 N k ( ) , (5)
Field Code Changed
375 376 377
and it is related to the variability of successive counts (Thurner et al., 1997); the y b
c
h
g
.
h
F h
b
g y
g
378
the time dynamics of point processes of different types ( Telesca et al., 2001; Telesca
379
et al., 2005). 22
23
380
If the earthquake process is Poissonian that means memoryless and formed by
381
independent events, then the AF is rather flat at all timescales and assumes value
382
around 1 (except for very large timescales due to finite -size effects (Telesca et al.,
383
2012); but if the earthquake process is clusterized the AF changes with the timescale
384
. In particular, if the earthquake process is fractal (self-similar) in time, the AF
385
behaves as a power-law (scaling behavior) :
386
387
AF( ) 1 1 , (6)
Field Code Changed
388 389
where the exponent quantifies the strength of clusterization; 1 is the so-called
390
fractal onset time and marks the lower limit for significant scaling behavio r in the AF
391
(Thurner et al., 1997). Therefore, if 0 the earthquake process is Poissonian, while
392
if >0 it is clusterized.
393 394
23
24
Shallow seismicity 1.2
2
log10(AF())
1.0
=0.40+0.02
0.8
0.6
1y
1
0.4
0.0
0.5
1.0
1.5
2.0
2.5
3.0
log10() (day)
395
a)
2.5
Deep seismicity
2
log10(AF())
2.0
1.5
=1.85+0.06 1.0
0.5
1
0.0
0.0
0.5
1.0
1.5
2.0
2.5
3.0
log10() (day)
396 397
b) Fig. 7. AF for the shallow (a) and deep (b) Aswan sub -catalogues.
398 399
Fig. 7 shows the AF (open circles) for the shallow (Fig. 7a) and deep (Fig. 7b)
400
seismicity at Aswan, for events with magnitude M 2.5. Smoothing with adjacent 5 -
401
sample averaging (red curve) evidences more clearly the scaling behavior in both
402
shallow and deep seismicity between 1 and 2 that are between about 12 days and 24
25
403
about 5 months for shallow seismicity, and between about 40 days and 9 months for
404
deep seismicity. The scaling exponents are very different, being 0.40 for the shallow
405
and 1.85 for the deep seismicity, indicating a higher clustering degree for deep
406
seismicity than shallow. In order to verify the significance of the exponents, we
407
calculated the distribution of the exponents computed between 1 and 2 of 1, 000
408
Poissonian seismic sequences generated with the same number of events and rate as
409
the shallow and deep original ones (Fig. 8). We can see that the obtained value of
410
for the shallow and the deep seismicity are well above the largest value of the
411
distribution of obtained for Poissonian surrogates; therefore, the exponents
412
calculated for the real seismicit y are signifi cant.
413
Another interesting feature is the drop at around 1 year in the smoothed curve (red)
414
obtained for the shallow seismicity. The drop at a certain timescale in the AF curve
415
indicates the presence of periodicity at that timescale ( Gebber et al., 2006 ); thus, the
416
drop found in the AF of the shallow seismicity corresponds to the annual periodicity
417
that is consistent with the loading/unloading operations of the Aswan Dam and
418
strengthens the existence of annual water modulation (Gahalaut et al., 2016) of the
419
shallow seismicity.
420
25
26
Field Code Changed Deep Shallow 80
N
60
40
20
0
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
421 422
Fig. 8. Distribution of for 1,000 Poissonian sequences with same rate and same
423
number of events as the original deep (blue) and shallow (red) sequences.
424 425
3.5. The detrended fluctuation analysis of the magnitude time series
426
The Detrended Fluctuation Analysis (DFA) (Peng et al., 199 5) was used to study
427
long-range correlations of non-stationary series of different types (Telesca and
428
Lovallo, 2009; Telesca and Lovallo, 2010; Telesca and Lovallo, 2011; Telesca et al.,
429
2012; Telesca et al., 2016 ; Rong et al., 2012; Varotsos et al., 2008; Varotsos et al.,
430
2011). In the context of studies devoted to seismicity, several authors focused on the
431
long-range correlations in the magnitude time series, since magnitude rep resent one
432
of the crucial parameter in the framework of seismic hazard assessment. Telesca et al.
433
(2016) observed that the scaling exponent of the magnitude time series calculated by
434
the DFA (see later in the text) had an increasing behavior when the volcanic activity
435
at El Hierro, Canary Islands (Spain) reactivated in the 2011-2014. Varotsos et al. 26
27
436
(2014), performing the DFA on the magnitude series of several seismic areas,
437
evidenced a certain variability of the scaling exponent with relationship with
438
earthquake prediction . Lennartz et al. (2008) us ed the DFA to investigate the long-
439
range correlations of the magnitude se ries of Northern and Southern California
440
seismicity revealing the presence of long-term memory. Varotsos et al. (2012)
441
observed a rather sh arp variation from uncorrelated to correlated behavior in the
442
magnitudes of California seismicity before the occurrence of large shocks .
443
Within this scientific context , we can understand how crucial is the analysis of the
444
temporal properties of magnitude time series to better understand the dynamics of a
445
seismic process.
446
The DFA method works as follows:
447
i) the magnitude series M i , where i=1,…,N, and N is the total number of e vents is
448
integrated:
449 450 451
yk i 1 M i M , (4)
Field Code Changed
k
452 453
where indicates the mean magnitude;
454
ii) the integrated series y k is divided into non-overlapping boxes of same length n;
455
iii) in each n-size box, y k is fit by the least squares by the line y n , k and detrended;
456
iv) the fluctuation, F n , is obtained by
457 458
Fn
Field Code Changed
2 1 N yk yn,k ; (5) N k 1
27
28
459 460
v) the steps i)-iv) are repeated for all the available box sizes n; if the relationship
461
between F n n is a power-law, the magnitudes are long-range correlated:
462 463
F n n ; (6)
464 465
vi) from the numerical value of the so-called scaling exponent we can derive
466
information about the type of correlations: if the magnitude are uncorrelated, then
467
=0.5; if the magnitudes are persistently correlated (meaning that a large (small)
468
magnitude (compared to the mean) has larger probability to be followed by a large
469
(small) magnitude), then >0.5; if the magnitudes
470
(meaning that a large (small) magnitude (compared to the mean) has larger
471
probability to be followed by a small (large) magnitude), then 2.5
interevent time (day)
200
150
100
50
0
0
100
200
300
400
500
n
527
a)
31
32
Field Code Changed 800
Aftershock-depleted deep sub-catalog M>2.5
700
interevent time (day)
600 500 400 300 200 100 0
0
100
200
n
528
b)
529
Fig. 10. Interevent time series of the aftershock-depleted shallow (a) and deep (b)
530
sub-catalogues.
531 532
For the aftershock -depleted deep sub -catalog b=0.940.05, while for the shallow one
533
b=1.110.05 We also calculated for both the aftershock -depleted sub -catalogs the
534
global and local coeff icient of variations and obtained the following results (the 95%
535
confidence interval in parentheses): C v =1.31 [0.92,1.10] and L v =1.08 [0.88, 1.10] for
536
the shallow set and C v =1.93 [0.88, 1.14] and L v =1.01 [0.85, 1.16] for the deep set.
537
The only difference with un-declustered sub -catalogs is that after removing the
538
aftershocks both the sub -catalogs become locally Poissonian; and this is reasonable,
539
because the aftershocks introduce a high level of clustering even for short time
540
scales.
32
33
Field Code Changed 1.0
Shallow seismicity 0.8
log10(AF(t))
0.6
0.4
a=0.38+0.02 0.2
1y 0.0
0.0
0.5
1.0
1.5
2.0
2.5
3.0
log10(t) (day)
541
a) Field Code Changed 1.0
Deep seismicity
0.8
log10(AF(t))
0.6
a=1.02+0.05
0.4
0.2
410 d
0.0
-0.2 0.0
0.5
1.0
1.5
2.0
2.5
3.0
log10(t) (day)
542 543
b) Fig. 11. Allan Factor of the declustered shallow (a) and deep (b) sub -catalogs.
544 545
We applied the AF method to both the declustered sub -catalogs and the results are
546
shown in Fig. 11. We can observe that the scaling expo nent decreases, but more
547
slightly for the shallow seismicity; this is consistent with the removal of aftershocks
548
that generally weakens the time -clustering of the seismicity; however, the deep sub 33
34
549
catalog is still more clusterized than the shallow one. The adjacent -5-sample-
550
averaging smoothed curve (red) on the AF for the shallow seismicity still shows the
551
periodicity at about 1 year, as in the original sub -catalog. However, a very interesting
552
feature is now revealed in the AF of the deep sub -catalog: the presence of a
553
periodicity at about 410 days, which seems compatible with the annual periodicit y
554
found in the AF of the shallow sub -catalog and also consistent with the annual cycle
555
of loading/unloading of the water reservoir. Such periodicity was not reve aled in the
556
original deep sub -catalog because it was very likely masked by the high number of
557
aftershocks following the November 14, 1981 event.
558 559
4. Discussion and conclusions
560
A detailed analysis of the seismicity that occurred in Aswan region from 1980 to
561
2015 has been performed by utilizing the most robust statistical methodologies to
562
deeply investigate its properties in time, magnitude and depth domains.
563
The analysis of the completeness of the catalogue was carried out by using two well
564
assessed methodologies (MAXC and GFT). The MAXC method generally furnishes
565
values of the completeness magnitude slightly lower than those obtained by the GFT
566
method, by using a sliding window with different sizes sweeping the entire catalog.
567
The completeness magnitude is not constant through time but varies, decreasing
568
around the end of the investigation period; this indicates that the catalog is not
569
homogeneous in time, and this is consistent with successive upgrades of the seismic
570
network that were performed through time . The non-homogeneity on time of the
571
completeness magnitude and the comparison between the results obtained by the
572
MAXC and GFT methods with different sliding window sizes has led us to choose the 34
Formatted: Not Highlight
35
573
value of 2.5 for the completeness magnitude; this value not only is reasonably
574
consistent with the results shown in Fig. 3, but is also low enough to guarantee a
575
sufficiently large size of events (with magnitude larger or equal to 2.5) to apply the
576
statistical methodologies. The obtained value of the Gutenberg -Richter b is 1.07;
577
this value lies within the range of the b-values calculated for the global seismicity
578
(Mogi, 1962). The obtained values of b and M c differ largely from the estimates for
579
approximately the same area calculated by Ali (2016), who found a b-value of the
580
0.554 and a completeness magnitude of 0.9. Ali (2016) applied only the MAXC
581
method and obtained the estimate of b from the whole catalog, which, however,
582
begins in 1997; in our case we considered a longer dataset, beginning in 1980,
583
compared th e results obtained by the MAXC and GFT methods and calculated the
584
value of b after computing the time variation of the completeness magnitude with
585
different size of sliding window.
586
The analysis of the distribution of hypocentral depths has revealed a very clear
587
discrimination of two depth classes, separated by the threshold of 12 km, which
588
separates earthquakes as shallower from deeper than the threshold.
589
Table 1 summarizes the results obtained from the different robust statistical
590
methodologies used in thi s study for the dynamical characterization of the 1982 -2015
591
seismicity in the area of Aswan.
592 593 Parameter
Shallow seismicity
Deep seismicity
Result
b-value
1.14 (1.11)
0.99 (0.94)
b-value of shallow seismicity
35
36
higher than b -value of deep seismicity Cv
1.78 (1.31)
3.37 (1.93)
Globally clusterized
Lv
1.35 (1.08)
1.16 (1.01)
Locally
clusterized;
aftershock-depleted
the sub-
catalogs are locally Poissonian AF
0.40 (0.38)
1.85 (1.02)
Deep
seismicity
high
clusterized DFA
0.555
0.553
Magnitudes
persistently
correlated 594
Table 1: Statistical parameters obtained for shallow and deep seismicity (in
595
parentheses those for the aftershock -depleted catalog) in the area of Aswan from
596
January, 1, 1982 to December 31, 2015.
597 598
The analysis of depth distribution was necess ary to discriminate between possibly
599
different earthquake generation mechanisms and/or different types of time -dependent
600
earthquake occurrence.
601
The results of our analysis show that the loading/unloading operation of the Lake
602
Nasser reservoir could signifi cantly influence the time dynamics of both the shallow
603
and deep events. Indeed, the AF curve of both the shallow and deep declustered sub-
604
catalogs evidences the existence of annual periodicity ; such periodicity could very
605
probably be linked to the annual p eriodicity of the water level fluctuations and could
606
strengthen the reservoir -triggered nature of the seismicity down to 30 km depth. It is
607
worth noting that the annual periodicity is not present in the AF curve of the deep un36
37
608
declustered sub-catalogue, therefore it is mostly characterized by mainshocks -
609
aftershocks sequences whereas this time-dependent earthquake occurrence character
610
is not evident in the shallow seismicity (AF curve shows the annual periodicity also
611
for the un -declustered shallow sub -catalog).
612
The deep and shallow declustered seismicity are characterized by very close values of
613
the local coefficient of variation L v and similar values of the global coefficient of
614
variation C v , although the C v for the deep seismicity is slightly larger than t hat of
615
shallow seismicity. This is also revealed by the scaling exponent obtained from the
616
AF that measure quantitatively the strength of the time -clustering in an earthquake
617
sequence; the deep declustered sub-catalogue is characterized by 1.02, whereas the
618
shallow one by 0.38.
619
The DFA performed on the magnitude series of the shallow and deep events reveals
620
that both series are weakly persistently correlated, indicating that there is a tendency
621
of similar magnitudes to follow each other. This finding support the consideration
622
that the physical driving mechanism is the diffusion of pore fluid pressure because
623
the events are responding to the same underlying physical process that periodically
624
increases the seismicity rate (Shearer, 2012) with many events of about the same
625
magnitude.
626
The higher b-value (1.11) of shallow seismicity with respect to the b-value (0.94) of
627
the deep seismicity indicates that the increase of pore pressure can enable shallow
628
small fractures to overcome the critical stress point fo r failure even with a low level
629
of accumulated stress. Indeed, it is well known (Scholz, 1968; Wyss, 1973) that low
630
stresses cause earthquake series with high b-values.
37
38
631
The shallow seismicity is mostly located in the East of Gebel Marawa zone, Khore
632
El-Ramla zone, Abu Derwa zone, and Old stream zone (Fig. 4b and 4c) in
633
correspondence of the intersection of the left -lateral, strike-slip, N–S faults (El-
634
Barqa fault, Kurkur fault, and Abu -Dirwa fault) and the right -lateral, strike-slip, E–W
635
faults (Kalabsha fault and Seyal fault); therefore, w e suggest that the zones where the
636
shallow seismicity occurs constitute a minor pull-apart basin where the seismi c
637
deformation is accommodated on shallow small fractures . This hypothesis is also
638
supported by the observed oblique strike-slip motions of shallow earthquakes (focal
639
mechanisms indicate strike -slip faulting with a normal -fault component). Another
640
similar case of reservoir -triggered seismicity in pull-apart basins generated by the
641
relative motion of strike -slip faults is the Koyna-Warna area in India (Catchings et
642
al., 2015).
643
The deep seismicity is mostly located in the Gebel Marawa zone along the Kalabsha
644
fault which is favorably oriented with the maximum principal stress direction of the
645
present tectonic stress regime and where the 14 November 1981, Ms 5.3 earthquake
646
occurred. The intersection of the eastern tip of the Kalabsha fault with the N–S faults
647
may promote fluid flow across the fault system and then along the Kalabsha fault
648
down to 30 km depth. Anyway, i t is not surprising that seismicity can be induced by
649
the diffusion of pore fluid pressures at distance and at significant depths since it has
650
been largely demonstrated in the literature (e.g., Rubinstein and Mehani, 2015 , and
651
references therein ).
652
Concludi ng, in the present study we have performed a detailed statistical analysis of
653
the
654
methodologies able to reveal dynamical properties of earthquakes in time, depth and
seismicity
occurred
at
Aswan
from
1980
to
2015
by
utilizing
robust
38
39
655
magnitude domains. The obtained results depict the dynamics of the Aswan seismicity
656
more deeply evidencing the influence of the water level change in the lake on the
657
dynamics of both the shallow and the deep seismicity which show a clear annual
658
periodicity well correlated with t he annual loading of the lake. Moreover, the deep
659
seismicity is mainly characterized by mainshock -aftershocks sequences mostly
660
triggered by the water level fluctuations of the Nasser lake, and they mask the annual
661
periodicity of the deep seismicity if not properly aftershock -depleted. However, this
662
study is restrained to the investigation of the seismicity of the whole Aswan area,
663
while a more detailed statistical analysis of the spatial seismicity separating different
664
source zones would lead to a better un derstanding of the seismic process.
665 666 667
References
668
Abdeen, M.M., Abdelsalam, M.G., Nielsen, K.C., Yehia, M.A., Cherif, O.H. , 2000.
669
Active dextral wrenching in southern Egypt. In: 38th Annunal Meeting of the
670
Geological Society of Egypt, Cairo.
671
Adamson, D.A. , Williams, F., 1980. Structural geology, tectonics and the control
672
of drainage in the Nile basin. In: The Sahara and The Nile: Quaternary
673
Environments and Prehistoric Occupation in Northern Africa (M.A.J. Williams and
674
H.Faure, Eds.).
675
Aki, K., 1965. Maximum likelihood estimate of b in the formula log(N) = a -bM
676
and its confidence limits. Bull. Earthq. Res. Inst., Univ. Tokyo 43, 237 –239.
677
Ali, S. M., 2016. Statistical analysis of seismicity in Egypt and its surroundings .
678
Arabian J. Geosciences, 9, 52. doi:10.1007/s12517 -015-2079-x. 39
40
679
Ben-Zion, Y., Lyakhovsky, V., 2002. Accelerated seismic release and related
680
aspects of seismicity patterns on earthquake faults. Pure and Applied Geophysics
681
159, 2385–2412.
682
Borman, S., 2004. The Expectation Maximization Algorithm A s hort tutorial.
683
http://www.seanborman.com/publications/
684
Bosworth, W., Strecker, M.R., 1997. Stress field changes in the Afro -Arabian rift
685
system during the Miocene to Recent period. Tectonophysics 278, 47 -62.
686
Carder, D. S., 1945. Seismic investigations in t he Boulder Dam area, 1940 –1944,
687
and the influence of reservoir loading on earthquake activity . Bull. Seismol. Soc.
688
Am. 35, 175–192.
689
Catchings, R.D., Dixit, M.M., Goldman, M.R., Kumar, S., 2015. Structure of the
690
Koyna-Warna Seismic Zone, Maharashtra, India: A possible model for large
691
induced earthquakes elsewhere. J. Geophys. Res. Solid Earth 120, 3479 –3506.
692
doi:10.1002/2014JB011695.
693
-
, I., and P. Talwani, P., 2010. Hydromechanics of the Koyna -Warna
694
region, India. Pure Appl. Geophys. 167, 183 –213. doi:10.1007/s00024 -009-0012-5.
695
Lippiello, E., de Arcangelis, L., Godano, C., 2008. Influence of Time and Space
696
Correlations on Ea rthquake Magnitude . Phys. Rev. Lett. 100, 038501
697
El Hariri, M., Abercrombie, R.A., Rowe, C.A., do Nascimento, A.F., 2010. The
698
role of fluids in triggering earthquakes: Observations from reservoir induced
699
seismicity in Brazil . Geophys. J. Int. 181 (3), 1566–1574. doi:10.1111/j.1365 -
700
246X.2010.04554.x.
701
El Shazly, E.M., Abdel-Hady, M.A., El Ghawaby, M.A., El Kassas, A., 1973.
702
Geologic interpretation of ERTS -1 satellite images for west Aswan area, Egypt. 40
41
703
Egyptian Academy of Scientific Research and Technology, Re mote sensing
704
research project.
705
Fat-Helbary,
706
Determination Recorded by Aswan Seismic Network . Master Thesis, Sohag
707
Faculty, Assiute University, Egypt
708
Fat-Helbary, R.E., Tealb A.A. , 2002. A study of seismicity and earthquake hazard
709
at the proposed Kalabsha Dam Site, Aswan, Egypt . Natural Hazards 25, 117 -133.
710
Gahalaut, K., Hassoup, A. , 2012. Role of fluids in the earthquake occurrence
711
around Aswan reservoir, Egypt. J . Geophys. Res. 117, B02303. doi:10.1029/
712
2011JB008796.
713
Gahalaut, K., Hassoup, A., Hamed, H., Kundu, B., Gahalaut , V., 2016. Pure Appl.
714
Geophys. , 174(1), 133–150. doi:10.1007/s00024 -016-1397-6
715
Gebber, G.L., Orer, H.S., Barman, S.M., 2006. Fractal Noises and Motions in
716
Time
717
Neurophysiol. 95, 1176 –1184
718
Goltz, C., 1997. Fractal and Chaotic Properties of Earthquakes. Lecture Notes in
719
Earth Sciences, Berlin Springer.
720
Greiling, R.O., Abdeen, M.M., Dardir, A.A., El -Akhal, H., El-Ramly, M.F., Kamal
721
El-Din, G.M., Osman, A.F., Rashwan, A.A., Rice, A.H.N., Sadek, M.F. , 1994. A
722
structural
723
Geologische Rundshau 83, 484 –501.
724
Gupta, H.K., 2002. A review of recent studies of triggered earthqua kes by
725
artificial water reservoirs with special emphasis on earthquakes in Koyna, India .
726
Earth Sci. Rev. 58(3/4), 279–310. doi:10.1016/S0012 -8252(02)00063 -6.
Series
R.E.,
of
synthesis
1989.
A
Study
Presympatheti c
of
the
of
and
Proterozoic
the
Local
Sympathetic
Earthquake
Neural
Arabian –Nubian
Magnitude
Activities .
Shield
in
J.
Egypt.
41
42
727
Gutenberg, R., Richter, C.F., 1944. Frequency of earthquakes in California. Bull.
728
Seismol. Soc. Am. 34, 185–188.
729
Haggag, H.M., Gaber, H.H., Sayed, A.D., Ezzat, M.E. , 2008. A review of the
730
recent seismic activity in the southern part of Egypt (upper Egypt). Acta
731
Geodynamica et Geomaterialia 5, 19 –29.
732
Hassoup, A. , 1994. Investigation of the tectonic sett ing, seismic activity and
733
crustal deformation in Aswan seismic region, Egypt . D.Sc. Thesis. Tokyo: Tokyo
734
University.
735
Huang, Q., 2006. Search for reliable precursors: A case study of the seismic
736
quiescence of the 2000 western Tottori prefecture earthquake . J. Geophys. Res.
737
111(B4), B04301, doi:10.1029/2005JB003982 .
738
Hussein, H.M., Abou Elenean , K.M., Marzouk, I.A., Korrat, I.M., Abu El-Nader,
739
I.F., Ghazala, H.H., ElGabry, M.N., 2013. Present-day tectonic stress regime in
740
Egypt and surrounding area based on in version of earthquake focal mechanisms.
741
Journal of African Earth Sciences 81, 1 -15.
742
Iliopoulos, A.C., Pavlos G.P. , 2010. Global low dimensional seismic chaos in the
743
Hellenic region. International Journal of Bifurcation and Chaos 20(7), 2071 –2095.
744
Issac, M., Renuka, G., Venugopal, C., 2004. Wavelet analysis of long period
745
oscillations
746
Atmospheric and Solar -Terrestrial Physics 66, 919 –925.
747
Issawi, B., 1978. Geology of Nubia West area, Western Desert, Egypt. Annals of
748
the Geological Survey of Egypt 8, 237 -253.
749
Kagan, Y.Y., 1997. Are earthquakes predictable? . Geophys. J. Int. 131(3), 505–
750
525.
in
geomagnetic
field
over
the
Formatted: Not Highlight
magnetic
equator.
Journal
of
42
43
751
Kebeasy,
R.M.,
Maamoun,
M.,
Ibrahim,
E.,
1981.
Aswan
lake
induced
752
earthquakes. Bull. Int. Inst. Seismol. Earthq. Eng. 19, 155–160.
753
Kebeasy, R.M., Maamoun, M., Ibrahim, E., Megahed, A., Simpson, D.W., Leith,
754
W.S., 1987. Earthquake studies at Aswan Reservoir, J. Geodynamics 7, 173 -193.
755
Kiyashchenko, D., Smirnova , N., Troyan, V., Saenger, E., Vallianatos, F., 2004.
756
Seismic hazard precursory evolution: fractal and multifractal aspects. Physics and
757
Chemistry of the Earth 29, 367 –378
758
Lennartz, S., Livina, V.N., Bunde, A., Havlin, S., 2008. Long -term memory in
759
earthquakes and the distribution of interoccurrence times. Europ hys. Lett. 81,
760
69001.
761
Lyakhovsky, V., Ben -zion, Y., Agnon, A., 2001. Earthquake cycle, fault zones,
762
and seismicity patterns in a rheologically layered lithosphere. J . Geophys. Res.
763
106, 4103–4120.
764
Matcharashvili, T., Chelidze, T., Javakhishvili, Z., 2000. Nonlinear analysis of
765
magnitude and interevent time interval sequences for earthquakes of Caucasian
766
region. Nonlinear Processes in Geophysics 7, 9 –19.
767
Matcharashvili, T., Chelidze, T., Javakhishvili, Z., 2009. Dynamics, predictability
768
and risk assessment of natural hazards. In: Fra Paleo, Urbano (Ed.), Building Safer
769
Communities. Risk Governance, Spatial Planning and Responses to Natural
770
Hazards. IOS Press, Amsterdam, pp. 148 –161.
771
Mekkawi, M., Grasso, J.R., Schneggm, P.A. , 2004. A long-lasting relaxation of
772
seismicity at Aswan reservoir, Egypt, 1982 –2001. Bull. Seismol. Soc. Am. 94,
773
479–492.
43
44
774
Mogi,
K.,
1962.
Magnitude -frequency
relationship
for
elastic
shocks
775
accompanying fractures of various materials and some related problems in
776
earthquakes. Bull. Earthquak e Res. Inst. Univ. Tokyo 40 , 831-883.
777
Sarlis, N.V., Skordas, E.S., Varotsos, P.A., 2009. Multiplicative cascades and
778
seismicity in natural time. Physical Review E 80, 022102.
779
Varotsos, P.A., Sarlis, N.V., Skordas, E.S., Lazaridou, M.S., 2008. Fluctuations,
780
under time reversal, of the natural time and the entropy distinguish similar looking
781
electric signals of different dynamics . J. Appl. Phys. 103, 014906.
782
Varotsos, P.A., Sarlis, N.V., Skordas, E.S., Uyeda, S., Kamogawa, M., 2011.
783
Natural time analysis of c ritical phenomena. Proc. Natl. Acad. Sci. USA 108,
784
11361-11364.
785
Peng, C.-K., Havlin, S., Stanley, H.E., Goldberger, A.L., 1995, Quantification of
786
scaling exponents and crossover phenomena in nonstationary heartbeat time series .
787
CHAOS 5, 82–87.
788
Pliakis, D., Papakostas, T., Vallianatos, F., 2012. A first principles approach to
789
understand the physics of precursory accelerating seismicity. Ann . Geophys .
790
55(1). doi:10.4401/ag-5363.
791
Reasenberg, P.A. , 1985. Second-order moment of central California seismicity,
792
1969-82. J. Geophys. Res. 90, 5479–5495.
793
Rong, Y.M., Wang, Q., Ding, X., Huang, Q.H. , 2012. Non-uniform scaling
794
behaviour in Ultra -Low-Frequency (ULF) geomagnetic signals possibly associated
795
with the 2011 M9.0 Tohoku earthquake . Chinese Journal Geophysics 55, 3709–
796
3717.
44
45
797
Rubinstein, J.L., Mehani, A.B., 2015. Myths and Facts on Wastewater Injection,
798
Hydraulic Fracturing, Enhanced Oil Recovery, and Induced Seismicity. Seismol.
799
Res. Lett. 86(4), 1060–1067. doi: 10.1785/0220150067.
800
Said, R., 1993. The River Nile: Geology, Hydrology and Utilization. Pergamon
801
Press, Oxford. , 320 pp.
802
Said, R., 1981. The Geological Evolution of the River Nile. Springer -Verlag, New
803
York, 151 pp.
804
Scholz, C.H., 1968. The frequency -magnitude relation of microfracturing in rock
805
and its relation to earthquakes. Bull. Seismol. Soc. Am. 58 , 399-415
806
Selim, M.M., Imoto, M, Hurukawa, N. , 2002. Statistical investigation of reservoir -
807
induced seismicity in Aswan area, Egypt. Earth Planets Space 54, 349 –356.
808
Shi, Y., Bolt, B., 1982. The standard error of the magnitude -frequency b
809
value. Bull. Seismol. Soc. Am. 72(5), 1677–1687
810
Shinomoto, S., Miura, K., Koyama, S., 2005. A measure of local variation of inter -
811
spike intervals. Biosystems 79, 67 –72.
812
Simpson, D.W., Negmatullaev, S.K., 1981. Induced seismicit y at Nurek Reservoir,
813
Tadjikistan, USSR. Bull. Seismol. Soc. Am. 71(5), 1561–1586.
814
Simpson, D.W., Kebeasy, R.M., Nicholson, C., Maamoun, M., Albert, R.N.,
815
Ibrahim, E.M., Megahed, A., Gharib, A., Hussain, A., 1987. Aswan Telemetered
816
seismograph Network. J. Geophys. 7, 195 –203.
817
Simpson, D.W., Gharib, A.A., Kebeasy, R.M., 1990. Induced seismicity and
818
changes in water level at Aswan reservoir, Egypt . Gerlands. Beitr. Geophys.
819
Leipzig 99, 191–204.
45
46
820
Shearer, P. M. (2012), Space -time clustering of seismicity in Cal ifornia and the
821
distance dependence of earthquake triggering, J. Geophys. Res. 117, B10306, doi:
822
10.1029/2012JB009471.
823
Stabile, T.A., Giocoli, A., Lapenna, V., Perrone, A., Piscitelli, S., Telesca, L.,
824
2014.
825
associated with the Pertusillo artificial lake (southern Italy) . Bull. Seismol. Soc.
826
Am. 104(4). doi:10.1785/0120130333.
827
Telesca, L., Amatulli, G., Lasaponara, R., Lovallo, M., Santulli, A. , 2005. Time-
828
scaling properties in forest -fire sequences observed in Gargano area (southern
829
Italy). Ecol. Model. 185, 531 -544.
830
Telesca, L., Cuomo, V., Lapenna, V., Macchiato, M., 2001. Statistical analysis of
831
fractal properties of point processes modelling seismic sequences, Phys. Earth
832
Planet. Int. 125, 65-83.
833
Telesca, L., ElShafey Fat ElBary, R., Amin Mohamed, A. E. -E., ElGabry, M. ,
834
2012. Analysis of the cross -correlation between seismicity and water level in the
835
Aswan area (Egypt) from 1982 to 2010. Natural Hazards and Earth System
836
Sciences 12, 2203–2207.
837
Telesca, L., Giocoli, A., Lapenna, V., Stabile, T.A., 2015. Robust identification of
838
periodic behavior in the time dynamics of short seismic series: the case of
839
seismicity induced by Pertusillo Lake, southern Italy. Stochastic Environmental
840
Research and Risk Assessment 29, 1437–1446. doi:10.1007/s00477 -014-0980-6.
841
Telesca,
842
investigation of scaling properties in temporal patterns of seismic sequences.
843
Chaos, Solitons & Fractals 19(1), 1 –5.
Evidences
L.,
of
Lapenna,
low-magnitude
V.,
continued
Macchiato,
M.,
reservoir -induced
2004.
Mono -
and
seismicit y
multi -fractal
46
Formatted: Not Highlight
47
844
Telesca, L., Lovallo, Lopez, M.C., Molist, J.M. , 2016. Multiparametric statistical
845
investigation of seismicity occurred at El Hierro (Canary Islands) from 2011 to
846
2014. Tectonophysics 672 -673, 121 -128. doi: 10.1016/j.tecto.2016.01.045.
847
Telesca, L., Lovallo, M ., 2009. Non-uniform scaling features in Central Italy
848
seismicity: a non -linear approach in investigating seismic patterns and detection
849
of possible earthquake precursors. Geophys. Res. Lett. 36, L01308.
850
Telesca, L., Lovallo, M., 2010. Long -range dependenc e in tree-ring width time
851
series of Austrocedrus chilensis revealed by means of the detrended fluctuation
852
analysis. Physica A 389, 4096 –4104.
853
Telesca, L., Lovallo, M., 2011. Analysis of time dynamics in wind records by
854
means
855
information plane. J. Stat. Mech. P07001.
856
Telesca, L., Mohamed, A. E. -E. A., ElGabry, M., El -hady, S., Abou Elenean, K.
857
M., 2012. Time dynamics in the point process modelling of seismicity of Aswan
858
area (Egypt), Chaos Sol itons & Fractals 45, 47-55.
859
Telesca, L., Pierini, J.O., Scian, B., 2012. Investigating the temporal variation of
860
the scaling behavior in rainfall data measured in central Argentina by means of the
861
detrended fluctuation analysis. Physica A 391, 1553 –1562.
862
Thurner, S., Lowen, S.B., Feurstein, M.C., Heneghan, C., Feichtinger, H.G.,
863
Teich, M.C., 1997. Analysis, synthesis, and estimation of fractal -rate stochastic
864
point processes: Fractals 5, 565 -596.
865
Utsu, T., 1999. Representation and analysis of the earthquake size distribution: a
866
historical review and some new approaches. Pageoph 155, 509 –535.
of
multifractal
detrended
fluct uation
analysis
and
Fisher -Shannon
47
48
867
Vallianatos, F., Benson, P., Meredith, P., Sammonds, P., 2012. Experimental
868
evidence of a non -extensive statistical physics behavior of fracture in triaxially
869
deformed Etna basalt using acoustic emissions. Europhys . Lett. 97, 58002.
870
Varotsos, P.A., Sarlis, N.V., Skordas, E.S., 2012. Scale-specific order parameter
871
fluctuations
872
fluctuation analysis . Europhys. Lett. 99, 59001.
873
Varotsos, P.A., Sarlis, N.V., Skordas, E.S., 2014. Study of the temporal
874
correlations in the magnitude time series before major earthquakes in Japan. J.
875
Geophys. Res. Space Phys. 119, 9192 –9206.
876
WCC (Woodward-Clyde Consultants), 1985.
877
and estimation of magnitudes and recurrence intervals . Internal Report, High and
878
Aswan Dams Authority, Egypt.
879
Wiemer, S., Wyss, M., 2000. Minimum magnitude of completeness in earthquake
880
catalogs: examples from Alaska, t he Western United States, and Japan. Bull.
881
Seismol. Soc. Am. 90, 859–869.
882
Woessner, J., Wiemer S., 2005. Assessing the quality of earthquake catalogues:
883
Estimating the magnitude of completeness and its uncertainty . Bull. Seismol. Soc.
884
Am. 95(2), 684–698. doi: 10.1785/ 0120040007.
885
Woodward, J.C., Macklin, M.G., Krom, M.D., Williams, M.A.J., 2007. The Nile:
886
Evolution, Quaternary River Environments and Material Fluxes. In: Large Rivers:
887
Geomorphology and Management (Gupta, Eds).
888
Wyss, M., 1973. Towards a physi cal understanding of the earthquake frequency
889
distribution. Geophys. J. R. Astron. Soc. 31, 341 –359.
of
seismicity
before
mainshocks:
Natural
fic
time
f
and
hq
detrended
k
c
890 48
49
891
49