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The important convolution properties include width, area, differentiation, and integration properties.
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Convolutional neural network for earthquake detection and location.

Thibaut Perol1,2, Michaël Gharbi3, Marine Denolle4

  • 1John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA.

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|March 1, 2018
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Summary

This study introduces ConvNetQuake, an AI tool that detects and locates earthquakes faster and more accurately than traditional methods. It significantly improves seismic hazard assessment by identifying 17 times more events in Oklahoma.

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Area of Science:

  • Geophysics
  • Artificial Intelligence
  • Seismology

Background:

  • Induced seismicity in the Central US requires comprehensive earthquake catalogs for accurate seismic hazard assessment.
  • Exponential growth in seismic data necessitates efficient algorithms for reliable earthquake detection and location.
  • Current methods often involve scanning continuous seismic records for repeating signals.

Purpose of the Study:

  • To develop and apply a highly scalable convolutional neural network (ConvNetQuake) for earthquake detection and location using single seismic waveforms.
  • To improve the efficiency and accuracy of earthquake cataloging, particularly for induced seismicity.

Main Methods:

  • Leveraging recent advances in artificial intelligence, specifically convolutional neural networks (CNNs).
  • Developing and implementing ConvNetQuake, a novel AI-based algorithm for seismic data analysis.
  • Applying the ConvNetQuake technique to analyze induced seismicity in Oklahoma, USA.

Main Results:

  • ConvNetQuake detected over 17 times more earthquakes than previously cataloged by the Oklahoma Geological Survey in the study area.
  • The algorithm demonstrated significantly higher speed, being orders of magnitude faster than established earthquake detection methods.
  • Successfully applied AI to enhance earthquake detection from continuous seismic records.

Conclusions:

  • ConvNetQuake offers a substantial improvement in earthquake detection and location capabilities, especially for induced seismicity.
  • The AI-driven approach significantly enhances the efficiency of seismic data analysis.
  • This advancement is crucial for improving seismic hazard assessments in data-rich regions.