Karianne Jodine Bergen

Towards data-driven earthquake detection: Extracting weak seismic signals with locality-sensitive hashing

Extracting weak earthquake signals from continuous waveform data recorded by sensors in a seismic network is a fundamental and challenging task in seismology. In this talk, I will present Fingerprint and Similarity Thresholding (FAST; Yoon et al, 2015), a computationally efficient method for large-scale earthquake detection. FAST adapts technology used for rapid audio identification to the problem of extracting weak earthquake signals in continuous seismic data. FAST uses locality-sensitive hashing, a data mining technique for efficiently identifying similar items in large data sets, to detect similar waveforms (candidate earthquakes) in continuous seismic data. A distinguishing feature of our approach is that FAST is an unsupervised detector; FAST can discover new sources without any template waveforms or waveform characteristics available as training data – a common situation for seismic data sets. In our recent work, we have extended FAST to enable earthquake detection using data from multiple sensors spaced tens or hundreds of kilometers apart (Bergen and Beroza, 2018), and optimized the FAST software for detection at scale (Rong et al., 2018). FAST can now detect earthquakes with previously unknown sources in 10-year, multi-sensor seismic data sets without training data – a capability that was not previously available for seismic data analysis.