Earthquake Fingerprints: Extracting Waveform Features for Similarity-Based Earthquake Detection

Seismologists are increasingly adopting data mining and machine learning techniques to detect weak earthquake signals in large seismic data sets. The detection performance of these new methods, especially their sensitivity and false detection rate, depends on the choice of feature representation for waveform data. We have previously introduced Fingerprint and Similarity Thresholding (FAST), a new method for waveform-similarity-based earthquake detection that uses a pattern mining approach to detect earthquake signals without template waveforms. FAST has two key steps: fingerprint extraction and efficient indexing for similarity search. In this work, we focus on FAST fingerprint extraction: the method used to map short-duration waveforms to a set of features, called waveform fingerprints, used for detection. We describe the FAST fingerprint extraction method, a data-adaptive variation on the Waveprint audio fingerprinting method tailored for use in continuous seismic data. We compare the performance of the FAST fingerprint extraction method with existing fingerprinting techniques designed for audio identification. To overcome the challenges associated with using limited or incomplete event catalogs to evaluate detection algorithms, we propose a framework for quantifying the performance of different fingerprint extraction methods in the context of blind similarity-based detection. Our framework uses computational experiments on benchmark data sets, constructed with known event waveforms, to compute a measure of fingerprint effectiveness. We use this framework to show that, among the audio fingerprinting schemes considered in this work, our proposed FAST fingerprint extraction method achieves the most consistent performance in distinguishing similar, low signal-to-noise earthquake waveforms from noise in waveform data sets from eight stations in the Northern California Seismic Network.