Detecting Earthquakes over a Seismic Network using Single-Station Similarity Measures

Karianne Bergen, & Gregory C. Beroza

In Preparation October 18, 2017, SCEC Contribution #7955

New blind waveform-similarity-based detection methods, such as Fingerprint and Similarity Thresholding (FAST), have shown promise for detecting weak signals in long-duration, continuous waveform data. While blind detectors are capable of identifying similar or repeating waveforms without templates, they can also be susceptible to false detections due to local correlated noise. In this work, we present a new method for extending single-station similarity-based detection over a seismic network. The core technique, called pairwise pseudo-association, leverages the pairwise structure of event detections in its network detection model, which allows it to identify events observed at multiple stations in the network without modeling the expected move-out. Pairwise pseudo-association and supporting techniques, event-pair extraction and event resolution, complete a post-processing pipeline that combines single-station similarity measures from each station in a network into a list of candidate events Though our approach is general, we apply it to extend FAST over a sparse seismic network. We demonstrate that our method for network detection with FAST is both sensitive and maintains a low false detections rate. As a test case, we apply our approach to two weeks of continuous waveform data from five stations during the foreshock sequence prior to the 2014 Mw 8.2 Iquique earthquake. Our method identifies nearly five times as many events as the local seismicity catalog (including 95% of the catalog events), and less than 1% of these candidate events are false detections.

Key Words
earthquake detection, similarity search, data mining

Citation
Bergen, K., & Beroza, G. C. (2017). Detecting Earthquakes over a Seismic Network using Single-Station Similarity Measures. Seismological Research Letters, (in preparation).