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A new strategy for earthquake focal mechanisms using waveform‐correlation‐derived relative polarities and cluster analysis: Application to the 2014 Long Valley Caldera earthquake swarm

In microseismicity analyses, reliable focal mechanisms can typically be obtained for only a small subset of located events. We address this limitation here, presenting a framework for determining robust focal mechanisms for entire populations of very small events. To achieve this, we resolve relative P and S wave polarities between pairs of waveforms by using their signed correlation coefficients—a by‐product of previously performed precise earthquake relocation. We then use cluster analysis to group events with similar patterns of polarities across the network. Finally, we apply a standard mechanism inversion to the grouped data, using either catalog or correlation‐derived P wave polarity data sets. This approach has great potential for enhancing analyses of spatially concentrated microseismicity such as earthquake swarms, mainshock‐aftershock sequences, and industrial reservoir stimulation or injection‐induced seismic sequences. To demonstrate its utility, we apply this technique to the 2014 Long Valley Caldera earthquake swarm. In our analysis, 85% of the events (7212 out of 8494 located by Shelly et al. [ ]) fall within five well‐constrained mechanism clusters, more than 12 times the number with network‐determined mechanisms. Of the earthquakes we characterize, 3023 (42%) have magnitudes smaller than 0.0. We find that mechanism variations are strongly associated with corresponding hypocentral structure, yet mechanism heterogeneity also occurs where it cannot be resolved by hypocentral patterns, often confined to small‐magnitude events. Small (5–20°) rotations between mechanism orientations and earthquake location trends persist when we apply 3‐D velocity models and might reflect a geometry of en echelon, interlinked shear, and dilational faulting.