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原文摘要

Seismic station networks continuously record the ground velocity at several locations on Earth in the form of waveform data series, which seismologists analyze to detect various kinds of geophysical events, including earthquakes. Then, template events are defined to search the seismic data collection for matching events. This is template matching, a fundamental task in seismology for various seismic analyses. However, template matching requires extensive processing time, especially for large seismic collections that exceed the memory capacity of a single machine. This poses a significant challenge in seismology, in particular with the increasing size of seismological datasets. In this paper, we introduce TREMOR, a distributed data series processing framework for template matching that efficiently handles large waveform collections. We apply TREMOR to two representative real-world seismic use cases. Through an extensive experimental evaluation, we demonstrate its efficiency, with TREMOR being up to 6.7x faster than the best competing method on each workload, and up to 18x faster than the fastest distributed version of MASS, an FFT-based method popular in seismology, while returning the same exact results.