Export 3 results:
Sort by: Author Title Type [ Year  (Desc)]
Wang, W, Shearer PM.  2019.  An improved method to determine coda-Q, earthquake magnitude, and site amplification: Theory and application to Southern California. Journal of Geophysical Research-Solid Earth. 124:578-598.   10.1029/2018jb015961   AbstractWebsite

Seismic coda waves can be used to constrain attenuation, estimate earthquake magnitudes, and determine site amplification factors. We have developed a new multistation and multievent method to determine these three important seismic parameters simultaneously. We analyze 642 representative local (100 km) and shallow (20 km) earthquakes with catalog magnitudes between 1.8 and 5.4 in southern California at multiple frequency bands centered at 1.5, 3, 6, and 12 Hz. We find that the length of the moving average time window can affect the measurement of coda attenuation Q(C), but our tests indicate that the optimal window length is about 15 times the dominant data period. We use linear regression to fit each coda section and use only those portions that agree with the model decay rate with a correlation coefficient larger than 0.9. For a frequency-dependent coda-Q(C) model (Q(C) = Q(0)f(n)) at 1-Hz reference frequency, our results yield estimates for Q(0) and n of 107-288 and 0.42-1.14, respectively. Our coda magnitude estimates are linearly correlated with catalog magnitudes, and our observed lateral variations in coda-Q(C) and our site amplification factors are in general agreement with previous results, although there are notable differences at some locations. This approach provides a unified, accurate, and stable method to measure coda-Q(C), earthquake magnitude, and site amplification using coda waves of locally recorded earthquakes.

Fan, WY, Shearer PM.  2018.  Coherent Seismic Arrivals in the P Wave Coda of the 2012 M(w)7.2 Sumatra Earthquake: Water Reverberations or an Early Aftershock? Journal of Geophysical Research-Solid Earth. 123:3147-3159.   10.1002/2018jb015573   AbstractWebsite

Teleseismic records of the 2012M(w)7.2 Sumatra earthquake contain prominent phases in the P wave train, arriving about 50 to 100s after the direct P arrival. Azimuthal variations in these arrivals, together with back-projection analysis, led Fan and Shearer (, ) to conclude that they originated from early aftershock(s), located approximate to 150 km northeast of the mainshock and landward of the trench. However, recently, Yue et al. (, ) argued that the anomalous arrivals are more likely water reverberations from the mainshock, based mostly on empirical Green's function analysis of a M6 earthquake near the mainshock and a water phase synthetic test. Here we present detailed back-projection and waveform analyses of three M6 earthquakes within 100km of the M(w)7.2 earthquake, including the empirical Green's function event analyzed in Yue et al. (, ). In addition, we examine the waveforms of three M5.5 reverse-faulting earthquakes close to the inferred early aftershock location in Fan and Shearer (, ). These results suggest that the reverberatory character of the anomalous arrivals in the mainshock coda is consistent with water reverberations, but the origin of this energy is more likely an early aftershock rather than delayed and displaced water reverberations from the mainshock.

Trugman, DT, Shearer PM.  2018.  Strong correlation between stress drop and peak ground acceleration for recent m 1-4 earthquakes in the San Francisco Bay Area. Bulletin of the Seismological Society of America. 108:929-945.   10.1785/0120170245   AbstractWebsite

Theoretical and observational studies suggest that between-event variability in the median ground motions of larger (M >= 5) earthquakes is controlled primarily by the dynamic properties of the earthquake source, such as Brune-type stress drop. Analogous results remain equivocal for smaller events due to the lack of comprehensive and overlapping ground-motion and source-parameter datasets in this regime. Here, we investigate the relationship between peak ground acceleration (PGA) and dynamic stress drop for a new dataset of 5297 earthquakes that occurred in the San Francisco Bay area from 2002 through 2016. For each event, we measure PGA on horizontal-component channels of stations within 100 km and estimate stress drop from P-wave spectra recorded on vertical-component channels of the same stations. We then develop a nonparametric ground-motion prediction equation (GMPE) applicable for the moderate (M 1-4) earthquakes in our study region, using a mixed-effects generalization of the Random Forest algorithm. We use the Random Forest GMPE to model the joint influence of magnitude, distance, and near-site effects on observed PGA. We observe a strong correlation between dynamic stress drop and the residual PGA of each event, with the events with higher-than-expected PGA associated with higher values of stress drop. The strength of this correlation increases as a function of magnitude but remains significant even for smaller magnitude events with corner frequencies that approach the observable bandwidth of the acceleration records. Mainshock events are characterized by systematically higher stress drop and PGA than aftershocks of equivalent magnitude. Coherent local variations in the distribution of dynamic stress drop provide observational constraints to support the future development of nonergodic GMPEs that account for variations in median stress drop at different source locations. Electronic Supplement: Figures showing the relation between M-w and M-L, comparison of the ground-motion measurements from this study with the cross-listed records in the Next Generation Attenuation ground-motion database, the validation curve used to select the optimal tree depth for the Random Forest ground-motion prediction equation (GMPE) used in this study, the between-event ground-motion residual is plotted versus: (a) stress drop, (b) magnitude-adjusted stress drop, (c) depth, and (d) depth-adjusted stress drop, a table containing the ground-motion and stressdrop measurements associated with this study, and an example Python notebook.