Automatic Waveform Quality Control for Surface Waves Using Machine Learning

Journal Article · 09 February 2022 · Seismological Research Letters

DOI: https://doi.org/10.1785/0220210302 · OSTI ID:1865756

Chai, Chengping  ^[1]; Kintner, Jonas  ^[2]; Cleveland, Kenneth M.  ^[2]; Luo, Jingyi ^[3]; Maceira, Monica  ^[1]; Ammon, Charles J.  ^[4]

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1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
3. Univ. of Virginia, Charlottesville, VA (United States)
4. Pennsylvania State Univ., University Park, PA (United States)

Surface-wave seismograms are widely used by researchers to study Earth’s interior and earthquakes. To extract information reliably and robustly from a suite of surface waveforms, the signals require quality control screening to reduce artifacts from signal complexity and noise. This process has usually been completed by human experts labeling each waveform visually, which is time consuming and tedious for large data sets. 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. To speed up signal quality assessment, we trained these five machine learning (ML) methods using nearly 400,000 human-labeled waveforms. The ANN and RF models outperformed other algorithms and achieved a test accuracy of 92%. We evaluated these two best-performing models using seismic events from geographic regions not used for training. The results show that the two trained models agree with labels from human analysts but required only 0.4% of the time. Although the original (human) quality assignments assessed general waveform signal-to-noise, the ANN or RF labels can help facilitate detailed waveform analysis. Our investigations demonstrate the capability of the automated processing using these two ML models to reduce outliers in surface-wave-related measurements without human quality control screening.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Office of Fossil Energy (FE)
• Grant/Contract Number: AC05-00OR22725; 89233218CNA000001
• OSTI ID: 1865756
• Journal Information: Seismological Research Letters, Journal Name: Seismological Research Letters Journal Issue: 3 Vol. 93; ISSN 0895-0695
• Publisher: Seismological Society of AmericaCopyright Statement
• Country of Publication: United States
• Language: English