Unsupervised Large‐Scale Search for Similar Earthquake Signals

Abstract

Abstract Seismology has continuously recorded ground‐motion spanning up to decades. Blind, uninformed search for similar‐signal waveforms within this continuous data can detect small earthquakes missing from earthquake catalogs, yet doing so with naive approaches is computationally infeasible. We present results from an improved version of the Fingerprint And Similarity Thresholding (FAST) algorithm, an unsupervised data‐mining approach to earthquake detection, now available as open‐source software. We use FAST to search for small earthquakes in 6–11 yr of continuous data from 27 channels over an 11‐station local seismic network near the Diablo Canyon nuclear power plant in central California. FAST detected 4554 earthquakes in this data set, with a 7.5% false detection rate: 4134 of the detected events were previously cataloged earthquakes located across California, and 420 were new local earthquake detections with magnitudes −0.3≤ML≤2.4, of which 224 events were located near the seismic network. Although seismicity rates are low, this study confirms that nearby faults are active. This example shows how seismology can leverage recent advances in data‐mining algorithms, along with improved computing power, to extract useful additional earthquake information from long‐duration continuous data sets.