J Radioanal Nucl Chem DOI 10.1007/s10967-014-3020-6
Design and investigation of a continuous radon monitoring network for earthquake precursory process in Great Tehran A. Negarestani • M. Namvaran • M. Shahpasandzadeh S. J. Fatemi • S. A. Alavi • S. M. Hashemi • M. Mokhtari
•
Received: 25 August 2013 Ó Akade´miai Kiado´, Budapest, Hungary 2014
Abstract Earthquakes usually occur after some preliminary anomalies in the physical and chemical characteristics of environment and earth interior. Construction of the models which can explain these anomalies, prompt scientists to monitor geophysical and geochemical characteristics in the seismic areas for earthquake prediction. A review of studies has been done so far, denoted that radon gas shows more sensitivity than other geo-gas as a precursor. Based on previous researches, radon is a short-term precursor of earthquake from time point of view. There are equal experimental equations about the relation between earthquake magnitude and its effective distance on radon concentration variations. In this work, an algorithm based on Dobrovolsky equation ðD ¼ 100:43M Þ with defining the Expectation and Investigation circles for great Tehran has been used. Radon concentration was measured with RAD7 detector in the more than 40 springs. Concentration of radon in spring, spring discharge, water temperature and the closeness of spring location to active faults, have been A. Negarestani M. Shahpasandzadeh S. J. Fatemi Earthquake Research Center of Shahid Bahonar University, Kerman, Iran A. Negarestani (&) M. Namvaran M. Shahpasandzadeh S. M. Hashemi Graduate University of Advanced Technology, End of Haft Bagh Highway, Mahan Knowledge Paradise, P. O. Box: 76315-115, Kerman, Iran e-mail:
[email protected] A. Negarestani S. A. Alavi Disaster Management Center of Kerman Municipality, Kerman, Iran M. Mokhtari International Institute of Earthquake Engineering & Seismology (IIEES), Tehran, Iran
considered as the significant factors to select the best spring for a radon continuous monitoring site implementation. According to these factors, thirteen springs have been selected as follow: Bayjan, Mahallat-Hotel, Avaj, Aala, Larijan, Delir, Lavij, Ramsar, Semnan, Lavieh, Legahi, Kooteh-Koomeh and Sarein. Keywords Radon Spring Tehran Fault Dobrovolsky equation Earthquake
Introduction Earthquakes occur in some parts of the outer shell of the solid Earth, called the lithosphere [1]. In seismically active regions a significant part of tectonic development is realized through the earthquakes. Generally, earthquakes are classified by the depth of occurrence in two groups: deep and shallow [2]. Deep earthquakes, which compose the minority of seismic events,occur at depth from about 300 to 680 km, while shallow events occur at depths down to 300 km [1, 3]. Based on geotechnical studies, shallow earthqaukes produce more damage to buildings and installation [4]. In addition, the mechanism that produce shallow events is almost known, while for deep earthquakes is not fully understood [2, 3, 5]. Measuring the variations of gas levels in soil and groundwater is one of useful techniques for tracing the changes in stresses due to seismotectonic activities; which are well documented and are used regularly in seismotectonic studies, including fault tracing and seismic surveillance as a precursor [6, 7]. Geochemical behavior of geogases usually depends on their chemical properties. Noble geo-gases rarely combine with other materials during their migration to the surface. Therefore, these gases that are
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J Radioanal Nucl Chem Table 1 Historical seismicity around great Tehran before 1964
Date/ Time
Longitude
Magnitude
Ref.
300 BC
35.5
51.8
7.6
Ambraseys and Melville (1982)
743-xx-xx
35.3
52.2
7.2
Ambraseys and Melville (1982)
855-xx-xx
35.6
51.5
7.1
Ambraseys and Melville (1982)
864
35.7
51.0
5.3
Ambraseys and Melville (1982)
958-02-23
36.0
51.1
7.7
Ambraseys and Melville (1982)
1175-05-xx
35.7
50.7
7.2
Ambraseys and Melville (1982)
1383
Rey fault
VIII
Berberian et al. (1993)
1665
35.7
6.5
Berberian et al. (1993)
(1786?)
Rey fault
Berberian et al. (1993)
1802
Mosha fault
Berberian et al. (1993)
1811-06-20 1815-06
Mosha fault 35.9
52.2
7.1
Berberian et al. (1993) Ambraseys and Melville (1982)
1830-03-27
35.7
52.5
VIII
Berberian et al. (1993)
1895-12-24
Tehran fault
1930-10-02
35.76
52.00
5.2
Berberian et al. (1993)
1945-05-11
35.30
52.41
4.7
Berberian et al. (1993)
1955-11-24
35.75
52.05
4.0
Berberian et al. (1993)
released from the depths of Earth can provide useful information about movements in infrastructures of upper mantle and crust [8–10]. Radon-222 is a radioactive gas with a half-life of approximately 3.8 days. It forms naturally from the decay of radioactive elements, such as 238U, which are found at different levels in soil and rock can move into the air and into groundwater and surface water [8]. Radon is the main source of radioactivity in earth, especially in atmosphere. It can be used to forecast the forthcoming earthquake and also to locate uranium deposits and oil [9, 11]. This is because radon is a radioactive element and breaks down into solid radioactive elements called radon progeny, which this process gives off alpha particles, a form of high-energy radiation that can detects and traces by using alpha sensitive soil-state nuclear track detectors or some other suitable detectors [11]. In other hands, the reason for capability of radon in precursory process is that the concentration of radon in groundwater does not change much under normal conditions and transport process of radon in different geological setting can be described on the basis of physical processes [12–14]. An earthquake prediction states the probability of occurrence, time span, region, and magnitude range of the earthquake [8]. Prediction of time span is divided into ‘long-term’ referring to a time window of some decades of years; ‘medium-term’ referring to a time window of a few years and ‘short-term’ referring a time window of the order of up to a couple of weeks to couple of months. In addition, sometimes the term ‘immediate’ is used when the time window is in the order of a few days [8, 15]. Radon, as one
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Latitude
52.1
Berberian et al. (1993)
Fig. 1 Seismicity map of central Alborz. This figure reports historical seismicity [28] with a circle proportional to the seismic moment to show the seismic energy release. It also reports relocated instrumental seismicity for magnitude Ms [ 4.5 [27] and the CMT solution [23]
of accepted earthquake precursors by IASPEI1 has been classified in short term and immediate term [15]. This radioactive gas has more sensitivity to changes in earth’s crust strain during seismicity than other gases based precursory measures and is considered as the most efficient
1
International Association of Seismology and Physics of the Earth’s Interior (www.iaspei.org).
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Fig. 2 Seismicity map of central Alborz from 2006 to 2013 for M [ 3.0. The circle of each event is proportional to the released seismic energy
Table 2 Description of springs to design a continuous radon monitoring network Dischargea (Liter/sec)
Name of springs
Location (longitude–latitude)
Average radon concentration (kBq/m3)
Temperature (°C)
1
Aala
Long: 52.03 Lat: 35.44
6.7
28
5
60.6
–
2
Larijan
Long: 52.90 Lat: 35.52
6.1
22
11
71.6
4.61 \ M \ 4.64
3 4
Delir Bayjan
Long: 51.50 Lat: 36.19 Long: 52.16 Lat: 35.58
210.2 [1,000
26 20
16 4
74.3 85.8
4.64 \ M \ 4.75 4.75 \ M \ 4.83
5
Lavij
Long: 52.20 Lat: 36.22
182.6
30
7
94.5
4.83 \ M \ 5.20
6
Ramsar
Long: 50.39 Lat: 36.54
19.5
42
10
148.4
5.20 \ M \ 5.28
7
Semnan
Long: 53.11 Lat: 35.39
145.5
35
11
162.6
5.28 \ M \ 5.48
8
Mahallat-Hotel
Long: 50.33 Lat: 34.00
180.8
30
3
202.9
5.48 \ M \ 5.54
9
Lavieh
Long: 49.26 Lat: 36.50
14.7
33
9
216.3
5.54 \ M \ 5.59
10
Avaj
Long: 48.51 Lat: 35.47
9.6
32
12
229.3
5.59 \ M \ 5.83
11
Legahi
Long: 48.24 Lat: 36.51
97.4
29
13
296.9
5.83 \ M \ 6.03
12
Kooteh-Koomeh
Long: 48.50 Lat: 38.18
100.3
28
16
367.4
6.03 \ M \ 6.11
13
Sarein
Long: 48.40 Lat: 38.90
45.0
45
140
401.1
6.11 \ M
Distance from Tehran (R0 ) (km)
Estimated magnitudeb (Richter)
a
Discharge rate of water is approximately Notice: If this spring is the farthest spring which is showing anomaly, the estimated magnitude, considering all the springs, will be obtained: MMin \ M \ MMax
b
– According to the proposed algorithm for the first site, there is uncertainly to forecast magnitude of coming earthquakes
geo-gas form precursors in earthquake forecasting studies [15]. According to the analytical algorithm proposed by Hashemi et al. [16] to predict the location and magnitude of
future earthquake, the consequences based on single station studies are not reliable. So, it is necessary to interpret several monitoring stations data to reach more accurate results. Generally, in this case, Hashemi et al. [16] presents
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J Radioanal Nucl Chem Fig. 3 Measured radon concentration in four samples of springs and average value (black dash-line). 1 Aala = 6.7 kBq/m3, 2 Larijan = 6.1 kBq/m3, 3 Delir = 210.2 kBq/m3, 4 Bayjan = 737 kBq/m3 (After a half-life), 5 Lavij = 182.6 kBq/ m3, 6 Ramsar = 19.5 kBq/m3
the best definition of the location of stations and distance between them and target zone. Continuous radon monitoring stations for groundwater radon monitoring was set up on several springs in Tehran province and adjacent provinces to design a comprehensive pre-earthquake geo-radon observatory network to use in earthquake prediction process. This study is the first comprehensive radon monitoring for earthquake precursory in south part of Alborz Mountains.
Geological setting of case study Iran is one of the most seismically active countries in the world, as more than 90 % of the country falls within an active seismic zone, the Alpine-Himalayan belt [17, 18].
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This earthquake-prone country has experienced more than 130 earthquakes with a magnitude of 6.0 or higher in the last seven decades and in the 20th century alone, more than 126,000 people have died in earthquakes [19]. Tehran has developed on recent sediment and quaternary [20]. Geological maps confirm that quaternary and Pliocene alluviums and moraine deposit have developed in Tehran desert [20]. The bedrock of Tehran is the Tertiary formations, mostly Eocene lava, located in the mountainous areas in the north of the city [21]. The younger sediments have formed on this bedrock. The bedrock of the eastern heights in Sehpayeh and Bibi-Shahrbanoo mountains has been formed from the dolomite limestone from Triassic and Cretaceous ages [22]. Hezardarreh is the name of the formation, literally ‘‘bad land morphology’’ has been inspired by the geomorphologic properties of its surface and the existence of the
J Radioanal Nucl Chem Fig. 4 Measured radon concentration in four samples of springs and average value (black dash-line). 7 Semnan = 145.5 kBq/m3, 8 Mahallat-Hotel = 180.8 kBq/ m3, 9 Lavieh = 14.7 kBq/m3, 10 Avaj = 9.6 kBq/m3, 11 Legahi = 97.4 kBq/m3, 12 Kooteh-Koomeh = 100.3 kBq/m3
multitude of its erosional valleys of great density [20]. This formation widens and rises and increases in bulk at Ghoochak defile in the northeast of the city. Its alluvial substances mainly consist of alluvial sediment and alluvial talus and volcanic rocks from Eocene age [23]. One of the main active faults in Tehran and the region is Mosha-Fesham Fault. This Thrust fault is one of the basic faults of the central Alborz situated in the north of Tehran crossing the middle part of Jajrood basin [24]. It is sloped toward the north all along, varying between 35° and 70° [25]. This fault may be considered up lifting certain parts and as a thrust in certain others [25]. North Tehran fault is the most salient tectonic factor in the vicinity of the city of Tehran. It begins in the slope of Alborz mountains and
Toochal heights (35 km) and extends from Kan in the west to Lashgarak in the east [26]. South and north Rey faults are the most salient faults in the southern plain of Tehran. They have dispersed on both sides of Rey subsidence [24]. Based on the existing seismic data (historical and instrumental) (Table 1), Tehran has so far been hit by over 1,000 small and big earthquakes within the 200 km of the around of Tehran (Fig. 1) [23, 27]. Based on this data, micro-seismic activities have basically happened in the following areas: Southeast of Tehran, south of Tehran, near south and north Rey faults, east end of Tehran, along Mosha-Fesham fault [28]. Fortunately, Tehran has not been hit by a severe earthquake in the last 150 years. According to the catalog
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of historical earthquakes and the existing documents, many earthquakes have hit the regions around Tehran, which might have affected Tehran [23]. The biggest earthquake of all hit the region (50 km off Tehran) in 958. It measured *7.7 on the Richter scale. The scientists believed that earthquake was due to the activity of the western part of Mosha fault [26]. The fault of the north of Tehran is situated between the western part of Mosha fault and the city of Tehran [26]. If the north fault becomes active, the damages from the earthquakes will be more severe than those from the earthquake in 958 [22]. The north fault is situated between the urban areas and the northern mountain. It is more than 90 km long, but its northwest part is far from Tehran [25]. Thus, its eastern part can potentially be the center of a future earthquake. The fault in the south of Rey, too, can be dangerous [24]. This fault extends along the southern side of Rey subsidence and almost parallel to the fault in the north of Rey and along the northern side of the subsidence. The distance between these two faults is only 3–5 km [24]. It seems that both faults are branches of the same fault. It is possible that there are hidden faults under the sediment layers of Tehran. If that is the case, it is very difficult to determine their exact positions, and any part of the city will be equally probability to earthquake. What is certain is that Tehran is liable to earthquakes. This is evidenced by the history of the city and the mechanism of the faults of the region. But the urban management has time to adopt appropriate policies to lessen the possible damages. Generally, The recent seismicity of Central Alborz and great Tehran has been illustrated as Fig. 2.
Site selection In this research, 44 springs were studied and 13 of them were selected based on four properties (as below) which located in 7 different provinces2 (Table 2). Concentration of soluble radon gas in water In all stations, measuring has been done with a professional electronic radon detector called RAD7 (Durridge Company Inc.). For radon monitoring in groundwater, water samples were collected seasonal from each spring in a 250-ml sample bottle. The samples connected to RAD7 detector and level of soluble radon measured using bubbling procedure. These measured values are so difference (Figs. 3, 4, 5). Between selected springs, in maximum value Bayjan has more than 1,000 kBq/m3 (more than detector capability range) and in minimum value Aala has 6.7 kBq/m3. After a 2
Tehran, Mazandaran, Ardebil, Markazi, Zanjan, Semnan & Gilan.
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Fig. 5 Measured radon concentration in four samples of spring and average value (black dash-line). 13 Sarein = 45.0 kBq/m3
Fig. 6 Radon water solubility versus temperature (k = radon’s water solubility). This figure indicates that increasing the water’s temperature cause decrease the radon’s solubility in water [35]
half-life of Bayjan spring samples (about 91 h after sampling), the detector indicates 737 kBq/m3 and by assuming no radon leakage in bottles, the main concentration of radon has been derived about 1,474 kBq/m3 (737 9 2 = 1,474). In this stage of study, springs which have concentration more than 5 kBq/m3 in median radon concentration were separated. Temporal variation in radon concentration has many influencing parameters where soil moisture conditions, atmospheric temperature, wind speed, atmospheric pressure, rainfall level and seismic activity of study region have been reported to be important factors affecting its concentration in the soil gas [29–31]. In underground environments, radon concentration is often characterized by large temporal variation, mainly caused by seasonal changes in natural ventilation [32, 33].
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Fig. 7 Location of Target Zone (Tehran city) in Tehran province—central Alborz—and distribution of main faults and monitoring stations (springs) around Target Zone [25]
Temperature and discharge of spring’s water During the study, temperature and discharge rate of water in each spring were measured (Table 1—fifth and sixth columns). These factors caused to remove few springs from the main list. According to Ostwald solubility coefficient, increasing water’s temperature cause decrease soluble radon in water (Fig. 6) [34, 35]. So, springs which have higher temperature than the others have been removed from the list [36].
– – – – – –
Mahallat-Hotel: On the west part of Abyek fault Aala: On the Mosha fault and at east part of Target Zone Delir: On the north part of Kandevan fault Lavij: On the south part of N-Alborz-1 fault and near the Kandevan fault Ramsar: On the north part of Kashachal fault Semnan: On the north part of Pishva and Garmsar faults
Location of these springs Proximity to an active fault Another factor which helps us to select more effective springs is proximity of spring’s location to the fault area. In great Tehran, there are a lot of active faults which are shown in Fig. 7. Now we investigate the location of few selected springs than the faults range3: – 3
Bayjan: On the south part of N-Alborz-1 fault and east part of Kandevan fault
The study region is full of different type faults (Dip slip, strike slip and oblique slip faults/Normal and Reverse faults/High angle and low angle faults/and any other classification of faults). But the above descriptions of faults are only based on seismicity rate of each one during the time and other factors of faults were not studied.
In this study, location of each spring (longitude and latitude) and distance between them to approximate point in the center of great Tehran have been measured and listed in Table 1; third and seventh columns, respectively. Few springs have an acceptable radon concentration and have high water discharge and normal water temperature; but they have been removed. Because they are so far from cities and access to them is very excruciating. Also no power lines reach to spring’s range yet. In some cases, there were more than one spring-eye with almost same radon concentration (like Mahallat-Hotel) and so, the spring-eye with better physical properties (vicinity to power station and physical security assessment) have been considered.
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J Radioanal Nucl Chem Fig. 8 The location of 13 monitoring station, the Expectation circle (the tick red circle), investigation circles (the thin black circles) and estimated area for earthquake location (violet interface region), while supposed anomalies in Aala, Larijan, Delir and Bayjan stations have been observed
Results and discussion In this part, we will try to show ability of this network with hypothetical example. According to previous section, we assumed that the anomalies in radon concentration have been monitored in only 13 springs. Location of these springs has been shown in Fig. 8. As Hashemi et.al [16] explained in their paper, to describe a monitoring network for earthquake precursory, several parameters should be introduced as below: R0 : the distance between the farthest monitoring station and the center of target zone dr: according to the vastness of target zone, a value must be added to R0 as ‘Uncertainly Parameter’ R ¼ R0 þ dr In.Cs: a circle of radius R which has been drawn around the disturbed stations
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Ex.C: each drawn circles which its center being within the target zone Ex.Ss: the monitoring stations which located within the Ex.C Mmin : the lower-limit in magnitude of coming earthquake which obtained by solving Dobrovolsky equation ðMmin ¼ ðlog RÞ=0:43Þ L0 : distance between center of target zone and the nearest station which NOT shows anomaly and considering that is lies outside of Ex.C L ¼ L0 þ dr Mmax : by considering the L value and solving Dobrovolsky equation (Mmax ¼ ðlog LÞ=0:43) NA : the number of springs which show anomaly NE : the number of Ex.Ss g ¼ NA =NE (For more details refer to Hashemi et.al paper [16])
J Radioanal Nucl Chem Fig. 9 Vastness of Tehran City. In this study, radius of drawn circle around Tehran is 25 km as Uncertainly Parameter. This parameter can vary in each case study
In the first step, we assume that between these springs, 3 of them indicate anomaly in radon concentration (for example Larijan, Delir and Bayjan). In the second step, according to Hashemi et.al proposed algorithm, distance between Bayjan and Tehran city—which introduced as target zone—is about 85.8 km (R0 ) which is more than the other distances. So, we nominate the Bayjan as the farthest station which anomaly has been observed. In third step, by considering the vastness of Tehran city, it is inferable that dr = 25 km (Fig. 9) should be added to R0 . So we have: 0 R ¼ R þ dr ¼ 85:8 þ 25 ¼ 110:8 km. In the next step, we draw a circle of radius 110.8 km to the center of Tehran city as Ex.C (Fig. 8). Between the all selected springs, four of them located within this circle: Aala, Larijan, Delir and Bayjan. Then we draw four circles of radius R in the center of these springs as In.Cs. The area of interface between these four In.Cs illustrate the location of coming earthquake. Also, between the undisturbed springs, Lavij is the nearest station to Tehran city which lies outside of Ex.C. Therefore, by considering dr = 25 km for Tehran—as described before—we have: L ¼ L0 þ dr ¼ 94:5 þ 25 ¼ 119:5 km. In the final step, according to above parameter descriptions and also using the calculated values concluded:
Mmax ¼ ðlog LÞ=0:43 ¼ ðlog 119:5Þ=0:43 ¼ 4:83
ð2Þ
ð3Þ g ¼ NA =NE ¼ 3=4 ¼ 0:75 Table 1 arranged based on distance between springs to Tehran city. In this stage, we assumed that continuous radon monitoring has been done in all stations. Hence, if anomaly observed only in nearest spring to Tehran (Aala), we would have no uncertainly to determine magnitude of a coming earthquakes (Mmin and Mmax). Because, based on proposed algorithm, anomalies should be observed at least in two stations. So, if anomalies observed in Aala and Larijan springs as two nearest springs to Tehran, we could estimate magnitude range of a coming earthquake as well [16]. By continuing this trend, we can calculate magnitude range for springs which show anomaly. In Table 1 it’s clear that Mmax in each spring will be equal to Mmin in next one; except Sarein spring which there isn’t farther station than it to Tehran. All of these values were observed in Table 1— eighth column. By checking earthquake catalogue of Tehran province, we could find an event in 2003, M = 4.5, which occurred almost closeness to Tehran city (Earthquake catalogue of IRSC and IIEES4). Considering that if before this earthquake we executed a continuous radon monitoring network in this province and neighbor provinces, anomalies exactly 4
Mmin ¼ ðlog RÞ=0:43 ¼ ðlog 110:8Þ=0:43 ¼ 4:75
ð1Þ
International Institute of Earthquake Engineering & Seismology (www.iiees.ac.ir).
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might observe in Aala spring; because according to Dobrovolsky equation we can calculate the radius dispersion range of radon anomaly for this earthquake [37]. Therefore we have; R ¼ 100:43M ¼ 100:434:5 ¼ 86:09 km
ð4Þ
Now, according to proposed algorithm in this paper, if we draw a circle of radius 86.09 km when anomalies have been observed in several springs e.g. Aala, Larijan, Delir and Bayjan located in this circle we could calculate the magnitude range of this earthquake that explained above (4.49 \ 4.6 \ 4.75). Thus, the proposed algorithm can estimate the approximate location of earthquake’s epicenter. This real-life example can prove the integrity and the accuracy of the proposed algorithm as well. Generally, as points of discussion, it is better that the geochemical measured data has been compared with other models before and after the analyze. For example, the emitted radon—as one of geochemical precursors of earthquake—can be modeled by a hybrid electrical circuit (proposed by S. M. Musavi Nasab et.al. [38]) to reach a better control on radon soil—air migration and then investigate the correlation between levels of geo–gas variation with earthquake parameters to analyze the results significantly.
monitoring stations can improve the results of this study. Also, we propose that for multi-eye hot springs, a separate detector installed for each one. When all the detectors detect anomaly simultaneously, an anomaly for that hot spring will be considered. Also, we proposed that, for single-eye hot springs, two different devices have been installed (for example RAD7 and AlphaGUARD) to prevent the device systematic errors.
Acknowledgment The authors are extremely grateful to Mr. Morteza Haj Ebrahimi for his technical advices and Mr. Seyyed Fazel Shahcheraghi and Seyed Hadi Hosseini for their guides in drawing figures. Also the authors are so thankful to International Institute of Earthquake Engineering & Seismology (IIEES) and Disaster Management Center of Kerman Municipality for all their collaborations.
References
Conclusion According this continuous radon monitoring network and previous studies, optimum locations and magnitudes of earthquakes can be predictable for great Tehran as a target zone with high accuracy. Also, through the site selection and discussion, the following conclusions can be drawn: –
–
–
–
Tehran is the most populated region in Iran (with about 8 million populations) which also has seismicity active history. So, more earthquake precursory studies should be done in this mega-city. Hashemi et al.’s proposed algorithm establishes an innovative idea to forecast the magnitude and location of forthcoming earthquake. To select good places for radon monitoring, we considered four properties of springs; concentration of radon, temperature and discharge of spring’s water, proximity to a fault range and finally location of springs. However these factors are comprehensive, but new atmospheric or geologic parameters can be added to them. This method can be tested for any other geochemical earthquake precursors and can implementation in all kind of geological structures in soil and groundwater. Also it is clear that increasing the number of radon
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