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Automatic Waveform Quality Control for Surface Waves Using Machine Learning
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  • Chengping Chai,
  • Jonas A. Kintner,
  • Kenneth M. Cleveland,
  • Jingyi Luo,
  • Monica Maceira,
  • Charles Ammon
Chengping Chai
Oak Ridge National Laboratory (DOE), Oak Ridge National Laboratory (DOE), Oak Ridge National Laboratory (DOE)

Corresponding Author:[email protected]

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Jonas A. Kintner
Los Alamos National Laboratory, Los Alamos National Laboratory, Los Alamos National Laboratory
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Kenneth M. Cleveland
Los Alamos National Laboratory, Los Alamos National Laboratory, Los Alamos National Laboratory
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Jingyi Luo
University of Virginia, University of Virginia, University of Virginia
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Monica Maceira
Oak Ridge National Laboratory (DOE), Oak Ridge National Laboratory (DOE), Oak Ridge National Laboratory (DOE)
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Charles Ammon
Pennsylvania State University, Pennsylvania State University, Pennsylvania State University
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Abstract

Surface-wave seismograms are widely used by researchers to study Earth’s interior and earthquakes. Reliable results require effective waveform quality control to reduce artifacts from signal complexity and noise, a task typically completed by human analysts. We explore automated approaches to improve the efficiency of waveform quality control processing by investigating logistic regression, support vector machines, k-nearest neighbors, random forests (RF), and artificial neural networks (ANN) algorithms. Trained using nearly 400,000 waveforms with human-assigned quality labels, the ANN and RF models outperformed other algorithms with a test accuracy of 92%. We evaluated the trained models using seismic events from geographic regions not used for training. The results show the trained models agree with labels from human analysts, but required only 0.5% time. Although the quality assignments assessed general waveform signal-to-noise, the ANN or RF labels can help facilitate detailed waveform analysis, reducing surface-wave measurement outliers without human intervention.
01 May 2022Published in Seismological Research Letters volume 93 issue 3 on pages 1683-1694. 10.1785/0220210302