An automatic method for microseismic events ...

An automatic method for microseismic events detection based on earthquake phase pickers Juan I. Sabbione∗ and Danilo R. Velis, Facultad de Ciencias Astron´omicas y Geof´ısicas, Universidad Nacional de La Plata and CONICET, Argentina

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SUMMARY In this work we present a simple and robust strategy for the automatic detection and picking of microseismic events. The method is a two-step process based on three pickers borrowed from earthquake seismology. The first step provides accurate single-trace picks which may or may not correspond to an actual microseismic event. In the second step, a multichannel strategy is used to associate the previous picks with an actual event by taking into account their expected alignment in all the available channels, thus preventing the declaration of false events. As a result, the proposed technique provides the number of declared microseismic events, a confidence indicator associated with each of them, and the corresponding traveltime picks. Results using two field noisy data records demonstrate that the automatic detection and picking of microseismic events can be carried out with a relatively high confidence level and accuracy.

INTRODUCTION It is well known the importance of fluid injection to ease the development of oil reservoirs, specially for secondary recovery. The pressure changes generated by this process usually lead to hydraulic micro-fractures that need to be monitored on real time both to control the injection process itself and to map the reservoir dynamics (Maxwell, 2005). The monitoring is carried out by analyzing the induced faulting and microseismicity, which requires the knowledge of the spatio-temporal distribution of the hypocenters of such events. In recent years, theses techniques have been applied to a number of engineering and oil industry studies (Maxwell, 2011), showing the importance of the correct processing and interpretation of microseismic data. Microseismic data are collected by placing an array of triaxial seismic sensors within one or more nearby wells for a certain period of time. Then data are analyzed in order to detect the advent of microseismic events. For this purpose, precise P and S-wave arrivals together with an appropriate subsurface model are needed to estimate the location of the hypocenters. Nowadays applications require efficient and fast techniques to automatically pick traveltimes, so that time-lapse images used to map the spatio-temporal distribution of the faulting (Maxwell, 2009) are readily available while the injection process is taking place. One distinct feature of microseismic events is that their magnitude are low and thus the signal-to-noise ratio of the collected data is very poor (Shemeta and Anderson, 2010). Further, depending on the source focal mechanism, one or more signal components may be partially or completely masked by the background noise. Though diverse denoising strategies

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may be used to alleviate this difficulties (Han et al., 2009; Vera Rodriguez et al., 2011), the detection and precise picking of microseismic events represent a challenge for any automated technique. In earthquake seismology, seismic events are usually detected by means of the so-called “short-term average” and “longterm average” (STA/LTA) methods. Essentially, these techniques rely on the observed changes in the ratio between some attribute or “characteristic function” (CF) which is averaged within a short and a long moving time-windows along the seismogram. An event is declared when this ratio exceeds a given threshold. Recently, various authors have proposed the use of this kind of techniques for processing microseismic data (Munro, 2004; Chen and Stewart, 2006; Wong et al., 2009; Han et al., 2009; Vera Rodriguez, 2011). However, the results of these studies show that the automatic microseismic event detection and picking problem remains unresolved when the signal-to-noise ratio is low, being still an issue of increasing interest within the seismic exploration community. In this work we first apply a trace-by-trace process which is based on three of the most commonly used methods in earthquake seismology. These include the methods of Earle and Shearer (1994), Allen (1978), and Baer and Kradolfer (1987). In a previous work, we tested the good performance of these methods when applied to seismological data (Sabbione et al., 2011). Secondly, we present a multichannel strategy that not only detects the microseismic events automatically, but also helps to prevent the declaration of false events by taking into account the expected alignment of the arrivals throughout the different channels. Finally, we provide an indicator to measure the detection confidence. We illustrate the proposed method using two eight-channels three-component field data records that exhibit moderate to high noise levels.

SINGLE TRACE METHODS The methods we use for the single-trace analysis depend on certain parameters whose values are summarized in Table 1. These include the STA and LTA window lengths, a Hanning filter length, and the threshold T HR, which is used to control the sensitivity of the single-trace pickers in declaring an event. Parameter TSTA (ms) TLTA (ms) THan (ms) T HR

ESM 5 50 10 2.5

MAM 5 50 10 6.0

MBKM 10 5.0

Table 1: Parameters used in the trace-by-trace process. ESM: Earle and Shearer Method; MAM: Modified Allen Method; MBKM: Modified Baer and Kradolfer Method.

DOI http://dx.doi.org/10.1190/segam2012-1430.1 Page 1

Microseismic events detection a) 1.0

Seismic trace

Seismic trace

a)

0.0 −1.0

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40 30 20 10 0

LTA

BK

40 30 20 10 0

20 15 10 5 0

Smoothed BK

STA/LTA c)

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Smoothed STA/LTA


b)

1.0

20 15 10 5 0

STA

c)

THR

0.200

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0.300 Time [s]

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Figure 1: MAM: (a) Normalized seismic trace and final pick (vertical line). (b) STA/LTA ratio and windows scheme. (c) Smoothed STA/LTA ratio and final pick at the maximum above T HR. Earle and Shearer’s method (ESM) In the method proposed by Earle and Shearer (1994), the CF is given by the envelope of the signal, which is computed via ESi =

q

s2i + s˜i 2 ,

(1)

where si is the i-th sample of the signal and s˜i the Hilbert transform. Then, ESi is averaged within two consecutive moving windows of lengths TSTA and TLTA , respectively, with TLTA > TSTA . Thus, the STA/LTA ratio is obtained by means of STAi = LTAi

1 NSTA 1 NLTA

Pi+NSTA −1 j=i

ES j

Pi−1

j=i−NLTA

ES j

,

THR

(2)

where NSTA and NLTA are the corresponding lengths in samples. To avoid rapid fluctuations that may lead to wrong picks, a low-pass Hanning filter is used to smooth the results (Earle and Shearer, 1994). Finally, the events are declared when the smoothed STA/LTA ratio exceeds the threshold T HR, and the arrival time is picked at the inflection point that immediately precedes the maximum of the STA/LTA ratio.

0.250

0.300 Time [s]

0.350

0.400

Figure 2: MBKM: (a) Normalized seismic trace and final pick (vertical line). (b) Characteristic function BK. (c) Smoothed BK and final pick at the first sample exceeding T HR.

MAM uses the same windows scheme as in the ESM. Thus, the STA/LTA ratio is computed replacing ES j with CF j into equation 2 and assigned to the first sample of the window ahead in time, which corresponds to STA. The windows scheme together with the STA/LTA ratio are illustrated in Figure 1b. We then smooth the ratio using a Hanning filter and declare an event when the smoothed ratio exceeds a given threshold T HR. Finally, the arrival times are picked at the corresponding local maxima, as shown in Figure 1c. Modified Baer and Kradolfer’s method (MBKM) The method proposed by Baer and Kradolfer (1987) relies on a function Ei which is similar to the CF used by Allen: Ei 2 = s2i + Pi

Pi

2 j=1 s j

2 j=1 (s j − s j−1 )

(si − si−1 )2 .

(4)

Then, instead of using a STA/LTA scheme, they calculate the following “normalized” statistical variable for each sample along the seismic trace (shown in Figure 2b): BKi =

Ei4 − Ei4

σ (Ei4 )

.

(5)

Modified Allen’s method (MAM) I this STA/LTA method we first calculate the same CF used by Allen in his original work (Allen, 1978), which is: CFi = s2i +Ci (si − si−1 )2 ,

Pi |s j | Ci = Pi j=1 , j=1

|s j −s j−1 |

(3)

As in the MAM, and in order to avoid rapid fluctuations of BK, in the MBKM we use a low-pass Hanning filter, which was not used in the original algorithm. The events are declared if the smoothed BK exceeds the selected threshold T HR. Finally, the arrival times are picked at the first sample for which T HR is exceeded, as shown in Figure 2c.

where Ci is a weighting factor that balances the two terms: the first one related to the signal energy, and the later to the signal frequency.

MULTICHANNEL STRATEGY

Instead of calculating the averages STA and LTA by means of the two constants C3 and C4 defined in the work of Allen, the

Due to the sources-receivers geometry arising in a typical microseismic survey, the seismic arrivals in a microseismic re-



cord tend to align into hyperbolas. Besides, depending on the radiation pattern, which is in general unknown, a given seismic event might not be observed in one or more receiver’s components. Clearly, the single-trace pickers do not take into account these considerations. Next, we describe a multichannel strategy that takes into account the information provided by the expected alignment of an event, and a criterion to declare a microseismic even when it is not recorded in all the channels.

(a)

For this purpose we define a fixed-length time-window that moves along the seismic record sample by sample searching for picked times and considering all channels simultaneously. The aim of this moving window is to count the number of traces with at least one event within the window at every position. Then, for each component separately, we declare a microseism if at least half of the channels contain an event.

(b)

Traces 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Time (s)

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0.4 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Time (s)

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0.4 Traces 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 0.0

0.1

Figure 4: (a) First and last contiguous windows that declare the microseism. (b) Large (green) and final (red) windows. Data correspond to Record 1. The picks were obtained with the MAM.

0.2 Time (s)


Microseismic events detection

0.3

0.4

0.5

0.6

Figure 3: Moving window used to count and detect events. Data belongs to Record 1. The picks were obtained with the MAM. The strategy is illustrated using two eight-channels field data records with moderate noise level (Record 1) and high noise level (Record 2). Figure 3 shows the moving window in action for part of Record 1. In the following examples, the window length was fixed in 50 ms for the two records. As it can be seen in the figure, in general there will be a certain number of contiguous windows declaring the same event. The first and the last of these windows are depicted in Figure 4a. Thus, the actual multichannel detection strategy makes use of two additional windows: (1) A large window which is the union of all the contiguous windows that declare a microseism, as depicted in Figure 4b (green). Note that all the intermediate windows will also declare the microseism. (2) A final window which is the large window after being shortened as much as possible without leaving events outside, as depicted in Figure 4b (red). The algorithm also considers the cases in which more than one microseismic event exists within the large window. However,


the description of this situation is a little bit cumbersome and we will omit it. Thus, as a result of this strategy, we obtain the automatic detection of the microseisms, their final windows, and the corresponding picked times for those traces where the event was detected. Confidence indicator We propose a simple confidence indicator measure P defined via: Neve P[%] = × 100%, (6) Ntot where Neve is the number of traces with one or more events inside the final window, and Ntot is the number of available channels. Clearly, P = 100% whenever an event is detected in every trace within the final window. Conversely, P = 16.67% whenever the microseism is declared by the minimum condition (i.e., detected events in only half of the channels in one of the three components). It is worth noting that when the radiation pattern is such that the signal arrives polarized in one component only, even in the case of a very high signal-to-noise ratio where the single-trace pickers do not fail, the confidence indicator P will be equal to 33.3%. In other words, high values of P indicate high detection confidence, but relatively low values of P do not necessarily indicate low detection confidence.

RESULTS Table 2 shows the confidence indicator P for the two considered data records. Since the microseismic event in Record 1 was detected in most of the channels for the three methods (see Figure 5), P attains relatively high values. On the other hand, in the case of low-quality data Record 2, the confidence levels are much lower, despite the fact that the event was also detected in all three cases, as shown in Figure 6.


Microseismic events detection (a)

(a)

Traces

Traces

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

0.2 Tiempo (s)

Time (s)

0.2

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

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Time (s)

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

(c)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

0.2 Tiempo (s)

0.2 Time (s)


0.4 (b)

0.3

0.3

0.4

Figure 5: Record 1: final picks and window: (a) ESM, (b) MAM and (c) MBKM.

Record 1 Record 2

ESM 70, 8% 33, 3%

MAM 66, 6% 20, 8%

MBKM 70, 8% 29, 2%

Table 2: Confidence indicator of the detected microseismic events.

A detailed inspection of Figure 5 (Record 1) reveals that the high values of P can be explained by the fact that within the final window the microseismic event becomes relatively apparent in most of the traces. As a consequence, the three singletrace pickers succeeded in signaling the arrival in about 16-17 out of 24 channels. Note that, however, some of the channels present a very low signal-to-noise ratio (e.g. traces 9 to 11). In other cases the event is not visible at all, either because of the low signal-to-noise ratio or because the corresponding seismic sensor presents some problem (e.g. trace 5). These problems are remarkably more prominent in the case of Record 2 (Figure 6). Not only there are some bad traces and/or channels with very low signal-to-noise ratio, but also, due to the nature of the microseismic event at hand or the extremely high noise level, the event does not show at all in the zcomponent (traces 17 to 24). As a consequence, the event can be picked in just a few traces and the confidence values shown in Table 2 are relatively low. Nevertheless, this fact does not prevent the proposed strategy being successful in detecting and declaring the microseismic event automatically.

Figure 6: Record 2: final picks and window: (a) ESM, (b) MAM and (c) MBKM.

CONCLUSIONS The three single-trace picking methods borrowed from earthquake seismology show, after some modifications, a reasonable performance when dealing with microseismic data. The results demonstrate that the proposed automatic techniques are robust with moderate to poor-quality data. There are two key features to remark: (1) the trace-by-trace picking process is very accurate and (2) the multichannel strategy together with the proposed confidence indicator contribute to prevent the declaration of false events. The selection of the parameters involved in the tuning of the three single-trace algorithms is very simple and rely on a visual inspection of the signal. In the case of the detection of a microseismic event, the sensibility of the proposed strategy depends on two key parameters: (1) the threshold that controls the trace-by-trace detection, and (2) the window size that controls the number of picks that are associated with a given event in the multichannel stage. Clearly, the lower the threshold and the larger the time-window, the smaller the risk of missing actual events, but the larger the risk of declaring false events. Results using two field data records suggest that the proposed strategy is very accurate and robust, and, because of its simplicity, it can be used with a certain confidence to process microseismic data on the fly, a key issue for nowadays applications.

In regards to the accuracy of the picks, note that in most cases the methods gave the correct arrival times, which approximately coincide with the central lobe of the detected event. Finally, note that for the considered datasets, none of the picks within the final window points to a wrong arrival.




http://dx.doi.org/10.1190/segam2012-1430.1 EDITED REFERENCES Note: This reference list is a copy-edited version of the reference list submitted by the author. Reference lists for the 2012 SEG Technical Program Expanded Abstracts have been copy edited so that references provided with the online metadata for each paper will achieve a high degree of linking to cited sources that appear on the Web. REFERENCES

Allen, R., 1978, Automatic earthquake recognition and timing from single traces: Bulletin of the Seismological Society of America, 68, 1521-1532. Baer, M., and U. Kradolfer, 1987, An automatic phase picker for local and teleseismic events: Bulletin of the Seismological Society of America, 77, 1437-1445. Chen, Z., and R. Stewart, 2006, A multi-window algorithm for real-time automatic detection and picking of p-phases of seismic events: CREWES Research Report, 18, 15.1-15.9. Earle, P., and P. Shearer, 1994, Characterization of global seismograms using an automatic -picking algorithm: Bulletin of the Seismological Society of America, 84, 366-376. Han, L., J. Wong, and J. Bancroft, 2009, Time picking and random noise reduction on microseismic data: CREWES Research Report, 21, 30.1-30.13. Maxwell, S., 2005, A brief guide to passive seismic monitoring: CSEG national convention abstracts, 177-178. Maxwell, S., 2009, Confidence and accuracy of microseismic images: CSPG CSEG CWLS convention abstracts, 480-483. Maxwell, S., 2011, What does microseismic tell us about hydraulic fractures?: 81st Annual International Meeting, SEG, Expanded Abstracts, 1565-1569. Munro, K., 2004, Automatic event detection and picking of p-wave arrivals: CREWES Research Report, 16, 12.1-12.10. Sabbione, J., M. Rosa, D. Velis, and N. Sabbione, 2011, Análisis comparativo de diferentes métodos de picado automático de fases de terremotos registrados en la Estación Sismológica de La Plata (LPA): Geoacta, 36, 189-209. Shemeta, J., and P. Anderson, 2010, It’s a matter of size: Magnitude and moment estimates for microseismic data: The Leading Edge, 29, 296-302. Vera Rodriguez, I., 2011, Automatic time-picking of microseismic data combining STA/LTA and the stationary discrete wavelet transform: CSPG CSEG CWLS convention abstracts. Vera Rodriguez, I., D. Bonar, and M. Sacchi, 2011, Microseismic record de-noising using a sparse timefrequency transform: 81st Annual International Meeting, SEG Expanded Abstracts, 1693-1698. Wong, J., L. Han, J. Bancroft, and R. Stewart, 2009, Automatic time-picking of first arrivals on noisy microseismic data: CSEG conference abstracts.

