Earthquake detection through computationally efficient similarity search

By Clara E. Yoon, Ossian O’Reilly, Karianne J. Bergen, Gregory C. Beroza. Science Advances, 2015

309 citations recorded by the source corpus.

Data mining techniques applied to continuous waveforms reveal many previously unknown small earthquakes. 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 that overcomes the 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 2 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 of 24) cataloged earthquakes and 68 uncataloged earthquakes in 1 week of continuous data from a station located near the Calaveras Fault in central California, achieving detection performance comparable to that of 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 the discovery of unexpected seismic signals, improve seismic monitoring, and promote a greater understanding of a variety of earthquake processes.

Subfields: Artificial Intelligence, Geophysics

View Paper: https://www.semanticscholar.org/paper/c9ee356b303a06b7a5475f076ffa2ef150d2bbb6

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Citations in the corpus

Matrix Profile I: All Pairs Similarity Joins for Time Series: A Unifying View That Includes Motifs, Discords and Shapelets (2016, methodology): “A recent paper notes that many fundamental problems in seismology can be solved by joining seismometer telemetry [23], including the discovery of foreshocks, aftershocks, triggered earthquakes, volcanic activity and induced seismicity.”
Physics‐Informed Neural Networks (PINNs) for Wave Propagation and Full Waveform Inversions (2021, background): “, 2019) to tackle problems such as earthquake detection, automatic phase picking (Mousavi et al., 2020; Ross et al., 2018; Yoon et al., 2015) and seismic signal denoising (Zhu et al.”
Earthquake Phase Association Using a Bayesian Gaussian Mixture Model (2021, background): “These high-resolution high-precision catalogs have the potential to reveal relationships among earthquakes and illuminate active structures that would otherwise remain hidden (Beroza et al., 2021; Hauksson et al., 2012; Park et al., 2020; Ross, Trugman, et al., 2019; Tan et al., 2021; Waldhauser & S”
P Wave Arrival Picking and First‐Motion Polarity Determination With Deep Learning (2018, unknown): “Automated earthquake phase detection has been improved dramatically with template-waveform-based methods that search continuous seismic data for signals that are similar to previously detected ones (Ross et al., 2017; Shelly et al., 2013; Skoumal et al., 2015; Yoon et al., 2015).”
Machine Learning in Seismology: Turning Data into Insights (2018, methodology): “Alternatively, the Fingerprinting and SimilarityThresholding (FAST) algorithm (Yoon et al. , 2015; Bergen and Beroza, 2018) is a data mining approach that converts an entire continuous waveform dataset into a database of binary fingerprints.”
A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality (2017, background): “…with the design of new drugs [78,79]; artificial intelligence with robots adapting like animals [80] and programs mastering the GO game [81]; psychology by profiling neuropsychosocial behavior [82]; climate change and its effects on food [83]; earthquake detection [84]; and economics [85].”
Machine learning for data-driven discovery in solid Earth geoscience (2019, methodology): “( 107 ) take an unsupervised pattern-mining approach to earthquake detection; the authors use a fast similarity search algorithm to search the continuous waveform data for similar or re-peating, allowing the method to discover new events with previously unknown sources.”