Inside an Earthquake Swarm: Objective Identification and Analysis of Spatiotemporal Subclusters of the Mogul 2008 Earthquake Swarm in Reno, NV
Friday, 18 December 2015
Poster Hall (Moscone South)
Recorded by numerous local and temporary stations and containing over 5000 earthquakes shallower than 5-km depth, the 2008 Mogul Earthquake sequence (MES) in west Reno, NV provides an excellent dataset for investigating earthquake interaction and physical properties of a long-duration earthquake swarm. The MES was energetic for a 6-month period beginning in late Feb 2008 with 8 weeks of foreshock activity leading to the Mw 5.0 mainshock on Apr 26, 2008. We develop a high-precision catalog with relative hypocentral errors less than 100 meters using the double-difference algorithm HypoDD (Waldhauser and Ellsworth, 2000). The data is objectively decomposed into spatiotemporal subclusters by applying the statistical declustering methods of Zaliapin et al. (2008) based on nearest-neighbor space-time-magnitude distances. We choose a cluster threshold using a Gaussian mixture model. This approach results in 57 distinct subclusters, with a minimum of 5 earthquakes each, that together comprise 66% of the entire MES relocated catalog. Ten of the 57 subclusters have mainshocks that occur prior to the Mw 5.0 mainshock. All single events or those clustered with less than 4 other events are ignored in further analysis. We compare the spatial and temporal patterns, including aftershock decay and/or diffusion rates, of each subcluster as well as Poisson’s ratio, focal mechanisms, and source parameters for each. Over 1000 focal mechanisms developed using the HASH algorithm of Hardebeck and Shearer (2002) reveal distinct right-lateral, left-lateral, normal, and normal-oblique structures, which the declustering method identifies successfully. Stress drops for 90 events greater than or equal to Ml 2.5 are estimated from multiple EGF spectral ratios of P- and S-waves, objectively chosen based on signal-to-noise ratio, cross-correlation, bandwidth, and corner-frequency variance. We also investigate how the subclusters interact with and overlap each other in time and space to understand the interior dynamics of an earthquake swarm.