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Earthquake detection through computationally efficient similarity search.

Clara E Yoon1, Ossian O'Reilly1, Karianne J Bergen2

  • 1Department of Geophysics, Stanford University, Stanford, CA 94305, USA.

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|December 15, 2015
PubMed
Summary

A new earthquake detection method, Fingerprint And Similarity Thresholding (FAST), analyzes seismic data 140x faster than traditional methods. This efficient approach improves earthquake monitoring and understanding of seismic events.

Keywords:
computational seismologyearthquake detectionearthquakeslocality-sensitive hashingseismic monitoringseismologysimilarity search

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Area of Science:

  • Seismology
  • Geophysics
  • Data Mining

Background:

  • Seismic data volume is rapidly increasing, outpacing current processing algorithm development.
  • Earthquake detection is crucial for observational seismology and understanding seismic events.

Purpose of the Study:

  • To develop an efficient earthquake detection method using waveform similarity.
  • To overcome limitations of existing seismic event identification techniques.

Main Methods:

  • Developed Fingerprint And Similarity Thresholding (FAST), a novel algorithm adapting data mining techniques.
  • FAST creates waveform "fingerprints," groups similar ones, and identifies seismic events.
  • Analyzed one week of continuous seismic data in under 2 hours, significantly faster than autocorrelation.

Main Results:

  • FAST detected 21 of 24 cataloged earthquakes and 68 uncataloged events in one week of data.
  • Achieved detection performance comparable to autocorrelation, with a small number of false detections.
  • Demonstrated a processing speed 140 times faster than autocorrelation.

Conclusions:

  • FAST offers an efficient and scalable solution for earthquake detection in large seismic datasets.
  • Potential to enhance seismic monitoring, discover new seismic signals, and advance earthquake process understanding.
  • Widespread application over distributed networks is expected to yield significant benefits.