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Wang, W, Shearer PM.  2015.  No clear evidence for localized tidal periodicities in earthquakes in the central Japan region. Journal of Geophysical Research-Solid Earth. 120:6317-6328.   10.1002/2015jb011937   AbstractWebsite

We search for possible localized tidal triggering in earthquake occurrence near Japan by testing for tidal periodicities in seismicity within a variety of space/time bins. We examine 74,610 earthquakes of M3 in the Japan Meteorological Agency catalog from January 2000 to April 2013. Because we use many earthquakes for which accurate focal mechanisms are not available, we do not compute tidal stresses on individual fault planes but instead assume that the mechanisms are likely to be similar enough among nearby events that tidal triggering will promote earthquake occurrence at specific tidal phases. After dividing the data into cells at a range of spatial (0.2 degrees, 0.5 degrees, and 1.0 degrees) and temporal dimensions (100, 200, and 400days), we apply Schuster's test for nonrandom event occurrence with respect to both the semidiurnal and semimonthly tidal phases. Because the resulting p values will be biased by temporal clustering caused by aftershocks, we apply a declustering method that retains only one event per tidal cycle per phase increment. Our results show a wide range of p values for the localized earthquake bins, but the number of bins with very small p values (e.g., p < 0.05) is no more than might be expected due to random chance, and there is no correlation of low p value bins with the time of the 2010 M 9.0 Tohoku-Oki earthquake.

Shearer, PM, Orcutt JA, Jordan TH, Whitmarsh RB, Kim II, Adair RG, Burnett MS.  1987.  The Ngendei Seismic Refraction Experiment at Deep-Sea Drilling Project Hole 595B - Ocean Bottom Seismometer Data and Evidence for Crustal and Upper Mantle Anisotropy. Initial Reports of the Deep Sea Drilling Project. 88:385-435.Website
Denolle, MA, Shearer PM.  2016.  New perspectives on self-similarity for shallow thrust earthquakes. Journal of Geophysical Research-Solid Earth. 121:6533-6565.   10.1002/2016jb013105   AbstractWebsite

Scaling of dynamic rupture processes from small to large earthquakes is critical to seismic hazard assessment. Large subduction earthquakes are typically remote, and we mostly rely on teleseismic body waves to extract information on their slip rate functions. We estimate the P wave source spectra of 942 thrust earthquakes of magnitude M-w 5.5 and above by carefully removing wave propagation effects (geometrical spreading, attenuation, and free surface effects). The conventional spectral model of a single-corner frequency and high-frequency falloff rate does not explain our data, and we instead introduce a double-corner-frequency model, modified from the Haskell propagating source model, with an intermediate falloff of f(-1). The first corner frequency f(1) relates closely to the source duration T-1, its scaling follows M0T13for M-w<7.5, and changes to M0T12 for larger earthquakes. An elliptical rupture geometry better explains the observed scaling than circular crack models. The second time scale T-2 varies more weakly with moment, M0T25, varies weakly with depth, and can be interpreted either as expressions of starting and stopping phases, as a pulse-like rupture, or a dynamic weakening process. Estimated stress drops and scaled energy (ratio of radiated energy over seismic moment) are both invariant with seismic moment. However, the observed earthquakes are not self-similar because their source geometry and spectral shapes vary with earthquake size. We find and map global variations of these source parameters.

Zhang, Q, Shearer PM.  2016.  A new method to identify earthquake swarms applied to seismicity near the San Jacinto Fault, California. Geophysical Journal International. 205:995-1005.   10.1093/gji/ggw073   AbstractWebsite

Understanding earthquake clustering in space and time is important but also challenging because of complexities in earthquake patterns and the large and diverse nature of earthquake catalogues. Swarms are of particular interest because they likely result from physical changes in the crust, such as slow slip or fluid flow. Both swarms and clusters resulting from aftershock sequences can span a wide range of spatial and temporal scales. Here we test and implement a new method to identify seismicity clusters of varying sizes and discriminate them from randomly occurring background seismicity. Our method searches for the closest neighbouring earthquakes in space and time and compares the number of neighbours to the background events in larger space/time windows. Applying our method to California's San Jacinto Fault Zone (SJFZ), we find a total of 89 swarm-like groups. These groups range in size from 0.14 to 7.23 km and last from 15 min to 22 d. The most striking spatial pattern is the larger fraction of swarms at the northern and southern ends of the SJFZ than its central segment, which may be related to more normal-faulting events at the two ends. In order to explore possible driving mechanisms, we study the spatial migration of events in swarms containing at least 20 events by fitting with both linear and diffusion migration models. Our results suggest that SJFZ swarms are better explained by fluid flow because their estimated linear migration velocities are far smaller than those of typical creep events while large values of best-fitting hydraulic diffusivity are found.

Hardebeck, JL, Shearer PM.  2002.  A new method for determining first-motion focal mechanisms. Bulletin of the Seismological Society of America. 92:2264-2276.   10.1785/0120010200   AbstractWebsite

We introduce a new method for determining earthquake focal mechanisms from P-wave first-motion polarities. Our technique differs from previous methods in that it accounts for possible errors in the assumed earthquake location and seismic-velocity model, as well as in the polarity observations. The set of acceptable focal mechanisms, allowing for the expected errors in polarities and takeoff angles, is found for each event. Multiple trials are performed with different source locations and velocity models, and mechanisms with up to a specified fraction of misfit polarities are included in the set of acceptable mechanisms. The average of the set is returned as the preferred mechanism, and the uncertainty is represented by the distribution of acceptable mechanisms. The solution is considered adequately stable only if the set of acceptable mechanisms is tightly clustered around the preferred mechanism. We validate the method by demonstrating that the well-constrained mechanisms found for clusters of closely spaced events with similar waveforrns are indeed very similar. Tests on noisy synthetic data, which mimic the event and station coverage of real data, show that the method accurately recovers the mechanisms and that the uncertainty estimates are reasonable. We also investigate the sensitivity of focal mechanisms to changes in polarities, event depth, and seismic-velocity model, and we find that mechanisms are most sensitive to changes in the vertical velocity gradient.

Bulow, RC, Johnson CL, Shearer PM.  2005.  New events discovered in the Apollo lunar seismic data. Journal of Geophysical Research-Planets. 110   10.1029/2005je002414   AbstractWebsite

We use modern seismological data processing tools to revisit the Apollo lunar seismic data set with the goal of extending and further characterizing the existing catalog of deep moonquakes. Our studies focus on the long-period data and include filtering and despiking noisy data, event classification, cluster identification, and robust methods for amplitude estimation. We perform cross-correlation analyses for known groups of deep events, confirming earlier visual classifications. By combining the cross-correlation approach with a robust median despiking algorithm, we produce improved differential times and amplitudes, enabling us to construct cleaner stacks. Each event group, represented by a single waveform stack of its constituent members, is cross correlated with the continuous time series. We focus on the A1 cluster because it has more cataloged events than any other cluster and is generally well characterized. Using this approach, we identify additional events that can be associated with previously defined deep clusters. For the deep event group A1 we have found 123 new events, which show phase behavior similar to the 323 previously cataloged events. Our new event search allows us to create optimized event stacks with improved signal to noise from which revised travel time picks (and thus location estimates) can be made. Application of our methods to other deep clusters should form a more complete event catalog and improve our understanding of the spatial and temporal distribution of deep lunar events.