1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Application of Phase Matched Filtering on Surface Waves for Regional Moment Tensor Analysis Andrea Chiang a and G. Eli Baker b a Berkeley Seismological Laboratory, Berkeley, California 94720 b Air Force Research Laboratory, Albuquerque, New Mexico 87117 Abstract For small magnitude events (<M4) the signal-to-noise levels (SNR) decreases rapidly with increasing epicentral distance in the intermediate- to long-period ranges. Therefore to increase nuclear explosion monitoring capabilities using regional moment tensor analysis, we need to increase the SNR for regional distance stations, especially in sparse monitoring situations. In this study we investigate the use of phase matched filtering to increase the SNR of surface waves by separating out individual modes from background noise. We applied the technique to well-recorded naturally occurring and possibly induced earthquakes at the Geysers Enhanced Geothermal Field in Northern California. We obtained similar moment tensor solutions using phase matched filtered data compare to local solutions by Guilhem at al., (2013), and solutions from the Berkeley Seismological Laboratory (BSL) moment tensor catalog. Based on our preliminary analysis, we found that phase matched filtering is a promising technique to enhance SNR for small magnitude and sparse monitoring situations. Introduction Seismic source discrimination using intermediate- to long-period, complete waveform at regional distances has been well demonstrated for earthquakes, underground explosions and mine collapses in the western United States, North Korea, eastern Kazakhstan and northwestern China (Dreger et al., 2008; Ford et al., 2008; Ford et al., 2009a; Ford et al., 2009b; Ford et al., 2010; Chiang et al., 2013). The regional distance moment tensor inversion, coupled with Network Sensitivity Solutions (NSS) analysis, and the characterization of sensitivities and uncertainties due to random errors and systematic velocity model errors enables the discrimination of source-type in conditions of relatively sparse regional distance monitoring. However, previous studies have focused
32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 on moderate-sized events (~M4 or greater). For smaller events the signal-to-noise levels (SNR) decreases rapidly with increasing distance in the intermediate- to long-period ranges. Stations with low SNR cannot be included in the moment tensor analysis because the noise in the data maps into the waveform inversion, resulting in incorrect source mechanism with very low goodness of fit between the actual data and synthetics. Therefore to increase monitoring capabilities for smaller events, specifically for this study ~M3 events recorded approximately 200-400 km away, we need to increase the SNR for regional distance stations. Phase matched filtering has been used in seismology to minimize multipathing effect and identify primary surface wave arrivals for M s :m b discrimination (Herrin and Goforth, 1977; Stevens and McLaughlin, 2001). Previous studies have observed improvement in SNR after phase matched filtering is applied. In this study, we investigate the use of phase matched filtering on regional surface waves to enhance SNR in the Geysers Enhanced Geothermal Field in Northern California (Fig. 1). We have 13 earthquakes well recorded by the local and regional networks between 2009-2011, some possibly induced by water injections in the Geysers based on local and regional moment tensor solutions (Guilhem et al., 2013). Our goal of this study is to apply the phase matched filtering technique to regional waveform data, invert the phase matched filtered data for moment tensors, compare and validate our findings to local network moment tensor solutions.
52 53 54 55 56 57 58 59 60 61 62 63 64 65 Figure 1. Map of the Berkeley Digital Seismic Network (BDSN). Black squares are stations, black lines are faults, and red outlines the Geysers geothermal field. Inset shows seismicity in the Geysers between 2009-2010 (gray dots) and the earthquakes analyzed in this study (black stars). Data and Methods Regional waveform data were downloaded from the Northern California Earthquake Data Center (NCEDC). The dataset consists of small (~M3) naturally occurring and possibly induced earthquakes from the Geysers. These earthquakes are recorded by both the Berkeley Digital Seismic Network (BDSN) operated by the Berkeley Seismological Laboratory (BSL) and partially supported by the U.S. Geological Survey (USGS), and a local network comprise of short-period instruments maintained by the Lawrence Berkeley National Laboratory (LBNL). Larger magnitude events of M4 or
66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 and greater are recorded by the BDSN but not by the LBNL local network. The local network s close proximity caused the short period instrument to clip for these larger magnitude events. Prior to phase matched filtering and moment tensor inversion, the broadband waveform data was instrument corrected, integrated to displacement, and rotated to radial and tangential components. Phase matched filters are linear filters in which the Fourier phase of the filter is the same as the Fourier phase of the signal, and can be used to improve SNR by compressing the dispersed signal (Herrin and Goforth, 1977). Using Computer Programs in Seismology 3.30 developed at Saint Louis University Earthquake Center (www.eas.slu.edu/eqc/eqccps.html), we derive the phase matched filter from Love and Rayleigh wave group velocity dispersions for each station and component (Fig. 2). We applied phase matched filter on regional surface waves to extract the fundamental surface wave, minimizing noise, higher modes and any possible multipathing effect. We then used the phase matched filtered waveform data to invert for the seismic moment tensor. 81 82 83 84 85 86 Figure 2. Vertical component Rayleigh wave group velocity curve at PACP for the 2010 earthquake. The figure is generated from Computers Programs in Seismology 3.30. The seismic moment tensor consists of nine force couples that represent the equivalent body forces for seismic sources of different geometries (Jost and Herrmann, 1989), that due to conservation of angular momentum reduce to six independent couples
87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 and dipoles. The data is represented by the convolution of Green s functions for a given Earth model, source terms and the moment tensor elements. The individual moment tensor elements are obtained using a generalized least square inversion and the goodness of fit between the data and synthetics is measured by the variance reduction (VR). The Green s functions used for the phase matched filter data are computed using modal summation and we used the regional Earth model GIL7 for Northern California (Dreger and Romanowicz, 1994), and Earth models derived directly by inverting the surface wave group velocities for each station and component. Preliminary Analysis The LBNL local deviatoric and full moment tensor solutions for the January 30 th, 2010 Geysers earthquake are predominantly double-couple (DC) with small contributions from compensated linear vector dipole (CLVD) and isotropic mechanisms (Guilhem et al., 2013). The earthquake is ~M w 3.5 from the local moment tensor solution, and we have data from a total of 31 BDSN broadband stations in which 12 of them have been phase matched filtered. The 12 processed stations are distributed between 190-420 km from the epicenter and 5 of the 12 stations are located at very similar azimuths relative to the source. We applied phase matched filter to noisy stations where we observe dispersion and have confidence in our fundamental surface wave group velocity measurements. We often have less difficulty picking group velocities for the vertical and tangential components but not the radial component. Radial components are often nosier than the other two components, and have little long period energy in the frequency band we are interested for moment tensor inversion, which is between 10-50 seconds. We aimed for the longer period surface waves to minimize errors from incorrect Earth model since longer period waves are less sensitive to the details of the velocity model. For stations with very noisy waveforms on the radial component that inhibits us to pick out the Rayleigh wave group velocity, we used the phase matched filtered defined from the vertical Rayleigh wave group velocity measurements. As will be discussed later, the ability to pick group velocities can be used as a quality control metric. The phase matched filter does a good job enhancing the SNR of noisy stations. Figure 3 compares
117 118 non-phase matched data versus phase matched data, we see good improvements in all three components. 119 120 121 122 123 124 125 126 127 128 129 130 131 132 Figure 3. Waveform comparison of non-phase matched filtered data (black) and phase matched filtered data (green, brown). We see improvement in SNR for all stations and components. Using 2-component (tangential and vertical), phase matched filtered data from three stations > 200 km with good azimuthal coverage, we obtained a deviatoric solution that is predominantly a normal mechanism (Fig. 4) and similar to the local solution but a slightly larger M w of 3.7 (Fig. 5). The full moment tensor solution has a greater CLVD component and M w of 3.9 compare to the local solution (Fig. 5). The improvement in VR between the full and deviatoric moment tensor solutions is ~1%, which is statistically insignificant, suggesting the non-dc components may not be real. Including the radial component gave us incorrect source mechanisms for both the deviatoric and full moment tensor inversions. We were not able to pick a clean Rayleigh wave group velocity on the
133 134 135 136 137 138 139 140 radial component for the three stations used in the inversion, instead we used the phase matched filter derived from the vertical component to compress the signal on the radial. Since including the radial component resulted in incorrect source mechanism, the phase matched filtered signal may still contain noise and other higher modes. Therefore we propose the ability to pick group velocity dispersion as a quality control metric to identify potential phase matched filtered signals that may still be contaminated by noise, higher modes and multipathing. 141 142 143 144 145 146 147 Figure 4. Deviatoric moment tensor for the 2010 earthquake using two-component, phase matched filtered waveforms. Variance reduction (VR) measures the goodness of fit between data (solid black lines) and synthetics (dashed red lines). Note Mw shown here is calculated using Dziewonski and Woodhouse (1983) s method, which is lower than the values computed using Bowers and Hudson (1999) s method as shown in Fig 4.
148 149 150 151 152 153 154 155 156 157 Discussion To understand what additional constraints is needed to obtain a stable moment tensor solution close to the LBNL solution, we used a combination of 2-component, phase matched filtered data and two additional stations at 30 and 77 km from the source that already have good SNR without phase matched filtering, and obtain deviatoric and full moment tensor solutions more similar to the local solutions in Guilhem et al. (2013). Compare to the three-station solution using only phase matched filtered data, the fivestation moment tensor inversion gave a result with high percent DC and low percent CLVD for both deviatoric and full moment tensor solutions (Fig. 5). 158 159 160 161 162 163 164 165 166 167 168 169 170 Figure 5. Deviatoric and full moment tensor solutions for the January 30 th, 2010 Geysers earthquake. LBNL local solutions from Guilhem et al. (2013), BSL cataloged solutions using stations < 100 km from the source, and BSL solutions from this study using phase matched filtered data (PMF) and a combination of non-phase matched filtered and phase matched filtered data (COMB). Our preliminary examination suggests phase matched filtering does have a potential in increasing our monitoring capabilities at regional distances for events < M4, but further analysis is needed to fully document the effect of phase matched filtered signal on the moment tensor inversion using different station combination and geometry, the limitations of phase matched filtering on improving the SNR, and more quality control on the phase matched filtered waveforms. From our preliminary analysis we
171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 found filtering long period drifts in the data prior to computing the dispersion increases our ability to observe and pick group velocity dispersion. Often times the group velocity curve is masked by the strong signal from the long period drift because the drift has much higher amplitude compare to the fundamental surface waves we are interested. Future studies on the application of phase matched filtering will include events of different source mechanisms in the Geysers. Examining the application of phase matched filtering on various source types is crucial in developing methods to improve regional moment tensor monitoring capabilities for small magnitude events. References Bowers, D. and J. A. Hudson (1999). Defining the scalar moment of a seismic source with a general moment tensor, Bull. Seismol. Soc. Am. 89, 1390-1394. Chiang, A., D. S. Dreger, S. R. Ford, and W. R. Walter (2013). Source characterization of underground explosions from combined regional moment tensor and first motion analysis, in preparation. Dreger, D. S., S. R. Ford, and W. R. Walter (2008). Source analysis of the Crandall Canyon, Utah, mine collapse, Science 321, 217. Dreger, D. S and B. Romanowicz (1994). Source characteristics of events in the San Francisco Bay Region, USGS Open-file report, 94-176, 301-309. Dziewonski, A. M. and J. H. Woodhouse (1983). An experiment in the systematic study of global seismicity: centroid-moment solutions for 201 moderate and large earthquakes, J. Geophys. Res. 88, 3247-3271. Ford, S. R., D. S. Dreger, and W. R. Walter (2008). Source characterization of the 6 August 2007 Crandall Canyon mine seismic event in Central Utah, Seismol. Res. Lett. 79, 637-644. Ford, S. R., D. S. Dreger, and W. R. Walter (2009). Source analysis of the Memorial Day explosion, Kimchaek, North Korea, Geophys. Res. Lett. 36, L21304, doi:10.1029/2009gl040003. Ford, S. R., D. S. Dreger, and W. R. Walter (2009). Identifying isotropic events using a regional moment tensor inversion, J. Geophys. Res. 114, B01306. Ford, S. R., D. S. Dreger, and W. R. Walter (2010). Network sensitivity solutions for
202 203 204 205 206 207 208 209 210 211 regional moment-tensor inversions, Bull. Seismol. Soc. Am. 100, 1962-1970. Ford, S. R., W. R. Walter, and D. S. Dreger (2012). Event discrimination using regional moment tensors, Bull. Seismol. Soc. Am. 102, 867-872. Guilhem, A, L. Hutchings, D. S. Dreger, and L. R. Johnson (2013). Moment tensor inversions of ~M3 earthquakes in the Geysers Geothermal Fields, California, under review. Herrin, E. and T. Goforth (1977). Phase-matched filters: applications to the study of Rayleigh waves, Bull. Seismol. Soc. Am. 67, 1259-1275. Stevens, J. and K. L. McLaughlin (2001). Optimization of surface wave identification and measurement, Pure Appl. Geophys. 158, 1547-1582.