IMPROVING CORRELATION ALGORITHMS TO BETTER CHARACTERIZE AND INTERPRET INDUCED SEISMICITY

Cross correlation has proven useful for identifying repetitive microearthquake sequences in vast passive seismic datasets. Multi-station waveform template matching has been used to characterize induced seismicity from hydraulic fracturing (HF) and wastewater disposal (WD). A swarm of events with similar waveforms driven by localized fluid injection can help discern induced seismicity from naturally occurrences. Swarm detection with template matching using a regional network can occur in real-time without a local seismic deployments or industry data (e.g., injection volumes/pressures or stimulation reports), but this information can further support an induced or natural origin. Cross correlations from this technique are also ideal for advanced seismic source location and magnitude estimation. In Ohio, template matching of cataloged earthquakes since 2010 has identified 10 swarms correlated temporally and spatially with either HF or WD, while nearly 30 less repetitive earthquakes were not and appear to be natural.

We developed an agglomerative clustering algorithm to enhance detection of repetitive seismicity. This does not require a pre-existing cataloged template, which helps identify smaller M<2 sequences that typically precede larger M>2 induced seismicity. We identified 14 more sequences correlated with HF in Ohio using this approach. 70% have small magnitudes and shallower depths consistent with being caused by operational fracturing of the target interval, while 30% have larger magnitudes and deeper depths consistent with reactivation of basement faults.

Evidence for no induced seismicity in other areas of the Appalachian, Illinois, and Williston Basins despite an order of magnitude more wells suggests geology plays a key role in induced seismicity. We identify the proximity of basement to the target interval (either HF or WD) as a key factor, suggesting basement faults are likely needed to generate M>2 seismicity.