Determining SAFOD area microearthquake locations solely with the Pilot Hole seismic array data

Transcription

1 GEOPHYSICAL RESEARCH LETTERS, VOL. 31, L12S10, doi: /2003gl019403, 2004 Determining SAFOD area microearthquake locations solely with the Pilot Hole seismic array data Volker Oye NORSAR, Kjeller, Norway J. Andres Chavarria and Peter E. Malin Division of Earth and Ocean Sciences, Duke University, Durham, North Carolina, USA Received 31 December 2003; revised 20 February 2004; accepted 23 February 2004; published 19 May [1] In August 2002, an array of 32 three-component geophones was installed in the San Andreas Fault Observatory at Depth (SAFOD) Pilot Hole (PH) at Parkfield, CA. As an independent test of surfaceobservation-based microearthquake locations, we have located such events using only data recorded on the PH array. We then compared these locations with locations from a combined set of PH and Parkfield High Resolution Seismic Network (HRSN) observations. We determined the uncertainties in the locations as they relate to errors in the travel time picks and the velocity model by the bootstrap method. Based on the PH and combined locations, we find that the C2 cluster to the northeast of the PH has the smallest location uncertainties. Events in this cluster also have the most similar waveforms and largest magnitudes. This confirms earlier suggestions that the C2 cluster is a promising target for the SAFOD Main Hole. INDEX TERMS: 7205 Seismology: Continental crust (1242); 7215 Seismology: Earthquake parameters; 7203 Seismology: Body wave propagation; 7230 Seismology: Seismicity and seismotectonics; 8124 Tectonophysics: Earth s interior composition and state (1212). Citation: Oye, V., J. A. Chavarria, and P. E. Malin (2004), Determining SAFOD area microearthquake locations solely with the Pilot Hole seismic array data, Geophys. Res. Lett., 31, L12S10, doi: / 2003GL Introduction [2] The SAFOD segment of the San Andreas Fault (SAF) at Parkfield separates the heterogeneous Franciscan mélange of deformed trench rocks to the northeast from the more homogeneous granitic and sedimentary Salinian block to the southwest [Page, 1981]. The Salinian block rocks typically have higher seismic wave velocities than those in the Franciscan block [Lees and Malin, 1990; Michelini and McEvilly, 1991; Eberhart-Phillips and Michael, 1993; Thurber et al., 2003]. The SAFOD PH is located on the Salinian block, about 1.8 km southwest of the SAF surface trace, close to Parkfield [ earthscope.org]. [3] The 32 levels of 3-component SAFOD PH geophones are evenly spaced from 200 to 1400 m below sea level, which is 660 m below the wellhead. Abercrombie [1995], for example, has shown that microearthquake data from Copyright 2004 by the American Geophysical Union /04/2003GL such borehole geophones are generally less attenuated and show larger signal-to-noise ratios (SNR) than surface or shallower installations. Hence the PH data should give highly useful information on the locations of target earthquakes for the SAFOD Main Hole drilling program The Data [4] From August 2002 to January 2003, PH earthquake data were recorded at 1 khz. The sampling rate was then increased to 2 khz. Small, nearby events were observed to have useful frequency information up to as high as 900 Hz. In this study we analysed 42 events with S-P wave travel time differences less than 500 ms, corresponding to offsets of less than 3 km (e.g., Figure 1). All these events have signal-to-noise ratios larger than 3 on at least half of the array s sensors. These events lie primarily in two clusters, designated as C1 and C2 (Figure 2). To compare the sizes of the events in our data set, many of which were not detected for cataloguing by the USGS surface network, we determined an empirical duration magnitude scale [Eaton, 1992]. Our estimated relation for the Parkfield area is Md = log d D where d is time from the P-wave to a point where the coda has decayed to within 3-times the background noise and D is the travel path length. The events in C1 ranged from 0.1 < Md < 1.6 while those in C2 ranged from 0 < Md < 2.1. The earthquakes being targeted by SAFOD are located within the C2 cluster and last occurred on Oct. 20 and 21, Source characteristics for these target earthquakes are discussed by Nadeau et al. [2004] and Imanishi et al. [2004]. Events outside these clusters ranged from 0 < Md < 0.6. [5] The cross correlation between the waveforms of Parkfield microearthquakes is a measure of their similarity in rupture mechanisms and locations [Nadeau et al., 1994]. As their superimposed waveforms show, the two cluster C2 events plotted in Figure 1c are nearly identical, with a correlation coefficient of 0.98, suggesting focuses only meters apart. The cross correlation coefficients between the events within C2 are all greater than 0.80, dropping to less than 0.40 between C2 events and those outside of this cluster. Events within the C1 cluster have coefficients greater than All other event parings have negligible correlations. As we will show, our location methods give results consistent with these measures of event proximity. [6] We processed our data with software that determines both (1) P- and S-wave arrival times by prediction-error filtering with an auto-regressive model of the waveforms and (2) estimates of the P-wave polarization [Oye and Roth, L12S10 1of5

2 Figure 1. A and B: Seismograms of the M = 2.1 SAFOD drilling target earthquake as recorded on the 32-level, 3-component PH seismograph array. C: Superimposed PH seismograms (with correlation coefficients) at the shallowest 3 levels for 2 Md = 0.25 events in the same cluster as the target event (black = #2883 and red = #3317 in C2 in Figure 2). The seismograms have been rotated into longitudinal (L in A and C) and transverse-horizontal (T-Sh in B and C) components. P-wave time picks are shown in red, the S-wave time picks in green, and the predicted S-P time after event location in blue. The S-P times are plotted using the P as reference. The 2 events in C, which have a correlation coefficient of 0.98, show how nearly identical the mechanisms and locations of these microearthquakes were. 2003]. In order to achieve accurate picks, we rotated the seismograms into longitudinal, transverse-horizontal and transverse-vertical components. Since many of the seismograms contain strong secondary phases between the P- and S- waves [Chavarria et al., 2003] we also visually checked and re-picked arrival times as needed. [7] Corresponding data with relatively clear P- and S-waves from the five 250 m deep HRSN stations near the PH were processed in the same way. (For station locations see Figure 2; taken from: berkeley.edu/hrsn/map.html). In our data set of 42 events, 6 were not registered on the HRSN and only half of the remaining events were recorded at all five stations. This loss of data could be due to the HRSN s lower sampling rate of 250 Hz in combination with the high frequency content of the events. 2. The Location Method [8] The location scheme we used is a type of directed grid-search inversion. This inversion iteratively applies the generalized matrix inversion location method so as to minimize the residuals [Aki and Richards, 2002]. We obtain the residuals by comparing the observed data at all levels in the array with travel times and arrival angles calculated with 3D ray tracing. The ray tracing was done with the wave front construction method [Vinje et al., 1996] from which it is easy to find both P- and S-wave travel times and the ray directions in terms of azimuth and incidence angles. [9] Various velocity models of the Parkfield area have been determined in previous studies. Our location scheme uses the one developed by Thurber et al. [2003]. This model has been constrained by large deployments (65) of surface seismographs in the SAFOD area and scores of local events. It has also been calibrated with surface shots, including some in areas where the lack of seismicity had resulted in poor ray coverage. The model spans a volume of km around the SAFOD site. It includes cells 1 2 km in dimension in its centre and 2 3 km near its edges. In our study we use an interpolation of this model to one with a node spacing of 200 m. [10] Using the principal of reciprocity, we placed sources for the wave front construction on the PH geophone 2of5

3 Figure 2. Lower right: a map showing the PH location as a large red triangle and HRSN stations as smaller red triangles. The strip outlined in blue contains the events we studied. The other diagrams show top and cross section views of earthquake locations using only PH data in green dots, and locations with combined PH and HRSN data in open triangles. The earthquakes closest to the PH have the smallest standard deviations: m along the SAF, 5 10 m normal to it, and depths within ±20 m. Note the proximity of events #2283 and #3317, consistent with their high correlation coefficient of locations and calculated P and S wave travel times at some 240,000 distributed grid points in the volume surrounding the events in our data set. The azimuth and incidence angles at the PH receivers are also calculated. [11] Only the S-P travel time differences and azimuth angles were input into the location process, and only the event location was inverted for. Based on our experience, we can pick the S-P wave travel times to within no more than 5 samples, which is equivalent to 2.5 ms for the PH 2 KHz data and 20 ms for the 250 Hz HRSN data. Also, given our previous experience in orienting the 3-component geophones with earthquake and explosion data, it seems the errors in azimuth are between During the location process, the variance of the model parameters was updated with the trace of the weighted covariance matrix of the generalised inverse [Press et al., 1999]. The model param- Figure 3. Bootstrap error estimations. Accurate travel times and angles were first calculated from the red dots to the PH. Random perturbations representing velocity and picking errors and biases were then added to these times and angles and the resulting locations were found. This procedure was repeated 500 times, resulting in the cloud of event locations surrounding the model event. Locations for the 42 study events using the 3-D velocity model are shown in green; those for the 2D/2-block model are shown in grey. 3of5

4 Figure 4. Locations, magnitudes and origin times displayed in a 3D view with the P-wave velocity model and the PH array. Origin times are colour coded from blue to red for Nov 2002 to Oct eter variance of the final location is our estimate for the location error. 3. Results [12] For the 42 earthquakes near the PH that we studied, we obtain locations that lie on a vertical plain whose surface trace is parallel to that of the SAF, only offset 300 m to the southwest (Figures 2, 3, and 4). If this result is accurate, then these events most likely occurred on one of the secondary branches of the SAF zone system. The events have depths of about 2 km below sea level. Further, the events cluster into two areas of higher activity, one located to the north of the PH (C1) and the other to the northeast of the PH (C2). These two clusters are separated in depth by 500 m. [13] The location residuals of the earthquakes reveal a systematic bias in the S-P wave travel time differences for the uppermost receivers. This bias is such that, for these receivers, the final locations have 5 10 ms longer differential travel times than the observed travel times (compare the S and S-P times in Figure 1). A 2% underestimate of the P- and S-wave velocities in this depth range can account for this bias. This degree of uncertainty is consistent with the 2 3% v p and 1% v p /v s bounds quoted by Thurber et al. [2003] for their velocity model. [14] To test for other location and velocity model biases we also calculated locations using S-P times from both the PH array and the HRSN (Figures 2 and 3). In the C2 area, the locations agree within the estimated errors. In the C1 area, however, adding in HRSN S-P times shifts the events 400 m downward and 300 m to the west. Evidently, either the velocity model in this area or the picking of arrival times from events originating there is inaccurate (or both). [15] To better quantify the uncertainties in our locations, we applied the bootstrap method to our inversion procedure [e.g., Press et al., 1999] (Figure 3). We first calculated travel times and polarizations to the PH for hypothetical events in the SAFOD area using ray tracing. We then simulated local velocity and travel time picking errors by adding to each arrival random values between ±10 ms in S-P time and between ±20 in azimuth. To account for potential gross biases in the velocity model we then offset all the S-P times by a single random value between ±25 ms and all the azimuths by a single value between ±10. The location inversion was then run on 500 realizations of these errors. The results show that, even with this degree of error, the PH observations can clearly separate the C1 and C2 clusters, and further, separate events within the C2 cluster itself. The horizontal bias in the location errors reflects the average layered nature of the velocity model. [16] The 3D velocity model we used is probably the best that is currently available. In order to assess further the uncertainty in its location of the SAFOD target events, we also studied the effects of assuming a simpler model. This model consists of two laterally uniform blocks, one NE and the other SW of the SAF. The velocities in each block were allowed to increase with depth at rates equal to the lateral averages of the equivalent sides of the 3D velocity model. We then relocated the 42 earthquakes using only the PH data (Figure 3). The results were that the events of C1 relocate about 800 m west of those in the 3D model while the new locations for C2 remained within the boot-strap estimated uncertainties. 4. Conclusions [17] In this study we have used borehole recordings in the SAFOD PH to better determine the locations of target microearthquakes for the SAFOD drilling project. We have used 3D ray tracing on multiple levels at the PH and a directed grid search location method on a set of 42 microearthquakes to determine their location within the SAF. This includes a cluster of events (C2) with high correlation coefficients and a large range of magnitudes. Our final set of locations is shown in Figure 4. [18] The bootstrap method has allowed us to determine the uncertainties in the event locations that could arise from errors in the velocity model and our travel time picks. The differences in location of C2 events between using the 3-D and an alternative 2-D/2 block model agree with those found using the bootstrap method. The most active part of the C2 cluster spans an area of 600 m along and 200 m normal to the SAF. The centre of this cluster is located 680 m east and 1260 m north of the PH at a depth of 1690 m below sea level. [19] Thurber et al. [2004] compared locations of an earlier target event (March 2001) using different tomography solutions. Their average result is 300 m further to the east and about 450 m deeper than our estimated centre of the target. Using their most recent velocity model they achieve similar depths also for the two target events that occurred in Oct [Thurber, pers. comm.]. The difference in target earthquake locations between their models and ours is larger than our estimated uncertainty, and we explain this through the use of different velocity models and different data sets. The velocity model that we used [Thurber et al., 2003] was only constructed from near surface recordings, while the velocity model of Thurber et al. [2004] and Roecker et al. [2004] was recently developed, based also on data from the PH that improved the ray coverage especially in the vicinity of the PH. The difference in locations is therefore related to the combination of different data and different models. 4of5

5 [20] Acknowledgments. We thank C. Thurber and S. Roecker for providing the velocity model and E. Shalev and NORSAR staff for discussions. We also thank A. Lomax and an anonymous reviewer for their constructive comments. References Abercrombie, R. E. (1995), Earthquake locations using single-station deep borehole recordings: Implications for microseismicity on the San Andreas fault in southern California, J. Geophys. Res., 100, 24,003 24,014. Aki, K., and P. G. Richards (2002), Quantitative Seismology, 2nd ed., 700 pp., Univ. Sci. Books, Sausalito, Calif. Chavarria, J. A., P. Malin, R. D. Catchings, and E. Shalev (2003), A look inside the San Andreas fault at Parkfield through vertical seismic profiling, Science, 302, Eaton, J. P. (1992), Determination of amplitude and duration magnitudes and site residuals from short-period seismographs in northern California, Bull. Seismol. Soc. Am., 82, Eberhart-Phillips, D., and A. J. Michael (1993), Three-dimensional velocity structure, seismicity and fault structure in the Parkfield region, central California, J. Geophys. Res., 98, 15,737 15,758. Imanishi, K., W. Ellsworth, and S. Prejean (2004), Earthquake source parameters determined by the SAFOD Pilot Hole vertical seismic array, Geophys. Res. Lett., 31, L12S09, doi: /2004gl Lees, J. M., and P. Malin (1990), Tomographic images of P-wave velocity variation at Parkfield, California, J. Geophys. Res., 95, 21,793 21,804. Michelini, A., and T. V. McEvilly (1991), Seismological studies at Parkfield, I: Simultaneous inversion for velocity structure and hypocenters using cubic b-splines parameterization, Bull. Seismol. Soc. Am., 81, Nadeau, R., A. Michelini, R. Uhrhammer, D. Dolenc, and T. McEvilly (2004), Fault structure, microearthquake recurrence and deep fault slip surrounding the SAFOD target, Geophys. Res. Lett., L12S08, doi: /2003gl Nadeau, R., M. Antolik, P. A. Johnson, W. Foxall, and T. V. McEvilly (1994), Seismological studies at Parkfield III: Microearthquake clusters in the study of fault-zone dynamics, Bull. Seismol. Soc. Am., 84, Oye, V., and M. Roth (2003), Automated seismic event location for hydrocarbon reservoirs, Comput. Geosci., 29, Page, B. M. (1981), The southern Coast Ranges, in The Geotectonic Development of California, edited by W. G. Ernst, pp , Prentice-Hall, Old Tappan, N. J. Press, W. H., S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery (1999), Numerical Recipes in Fortran 77: The Art of Scientific Computing, vol. 1, 2nd ed., 992 pp., Cambridge Univ. Press, New York. Roecker, S., C. Thurber, and D. McPhee (2004), Joint inversion of gravity and arrival time data from Parkfield: New constraints on structure and hypocenter locations near the SAFOD drill site, Geophys. Res. Lett., L12S04, doi: /2003gl Thurber, C., S. Roecker, K. Roberts, M. Gold, L. Powell, and K. Rittger (2003), Earthquake locations and three-dimensional fault zone structure along the creeping sections of the San Andreas fault near Parkfield, CA: Preparing for SAFOD, Geophys. Res. Lett., 30(3), 1112, doi: / 2002GL Thurber, C., S. Roecker, H. Zhang, S. Baher, and W. Ellsworth (2004), Fine-scale structure of the San Andreas fault zone and location of the SAFOD target earthquakes, Geophys. Res. Lett., L12S02, doi: / 2003GL Vinje, V., E. Iversen, K. Åstebøl, and H. Gjøystdal (1996), Estimation of multivalued arrivals in 3D models using wavefront construction-part I, Geophys. Prospect., 44, J. A. Chavarria and P. E. Malin, Division of Earth and Ocean Sciences, 103 Old Chemistry Bldg., B , Duke University, Durham, NC 27708, USA. V. Oye, NORSAR, Instituttveien 25, N-2007 Kjeller, Norway. (volker@ norsar.no) 5of5