Characterizing Earthquake Clusters in Oklahoma Using Subspace Detectors

Friday, 19 December 2014
Nicole D McMahon1, Harley Benz2, Richard C Aster3, Daniel E McNamara2 and Emma K Myers4, (1)Colorado State University, Fort Collins, CO, United States, (2)USGS National Earthquake Information Center Golden, Golden, CO, United States, (3)Colorado State University, Geosciences Department, Fort Collins, CO, United States, (4)Western Washington University, Geology Department, Bellingham, WA, United States
Subspace detection is a powerful and adaptive tool for continuously detecting low signal to noise seismic signals. Subspace detectors improve upon simple cross-correlation/matched filtering techniques by moving beyond the use of a single waveform template to the use of multiple orthogonal waveform templates that effectively span the signals from all previously identified events within a data set. Subspace detectors are particularly useful in event scenarios where a spatially limited source distribution produces earthquakes with highly similar waveforms. In this context, the methodology has been successfully deployed to identify low-frequency earthquakes within non-volcanic tremor, to characterize earthquakes swarms above magma bodies, and for detailed characterization of aftershock sequences. Here we apply a subspace detection methodology to characterize recent earthquakes clusters in Oklahoma. Since 2009, the state has experienced an unprecedented increase in seismicity, which has been attributed by others to recent expansion in deep wastewater injection well activity. Within the last few years, 99% of increased Oklahoma earthquake activity has occurred within 15 km of a Class II injection well. We analyze areas of dense seismic activity in central Oklahoma and construct more complete catalogues for analysis. For a typical cluster, we are able to achieve catalog completeness to near or below magnitude 1 and to continuously document seismic activity for periods of 6 months or more. Our catalog can more completely characterize these clusters in time and space with event numbers, magnitudes, b-values, energy, locations, etc. This detailed examination of swarm events should lead to a better understanding of time varying earthquake processes and hazards in the state of Oklahoma.