Earthquake detection through computationally efficient similarity search

C E. Yoon, O OReilly, K J. Bergen, & G C. Beroza

Published December 4, 2015, SCEC Contribution #6016

Seismology is experiencing rapid growth in the quantity of data, which has outpaced the development of processing algorithms. Earthquake detection - identification of seismic events in continuous data - is a fundamental operation for observational seismology. We developed an efficient method to detect earthquakes using waveform similarity, which overcomes disadvantages of existing detection methods. Our method, called Fingerprint And Similarity Thresholding (FAST), can analyze a week of continuous seismic waveform data in less than two hours, or 140 times faster than autocorrelation. FAST adapts a data mining algorithm, originally designed to identify similar audio clips within large databases; it first creates compact “fingerprints” of waveforms by extracting key discriminative features, then groups similar fingerprints together within a database to facilitate fast, scalable search for similar fingerprint pairs, and finally generates a list of earthquake detections. FAST detected most (21/24) cataloged earthquakes and 68 uncataloged earthquakes in one week of continuous data from a station located near the Calaveras Fault in central California, achieving comparable detection performance to autocorrelation, with some additional false detections. FAST is expected to realize its full potential when applied to extremely long duration data sets over a distributed network of seismic stations. The widespread application of FAST has the potential to aid in discovery of unexpected seismic signals, improve seismic monitoring, and promote a greater understanding of a variety of earthquake processes.

Yoon, C. E., OReilly, O., Bergen, K. J., & Beroza, G. C. (2015). Earthquake detection through computationally efficient similarity search. Science Advances, 1(11), e1501057-e1501057. doi: 10.1126/sciadv.1501057.

Related Projects & Working Groups
Seismology, Computational Science