Chengping Chai1, Jonas Kintner2, Kenneth M. Cleveland2, Jingyi Luo3, Monica Maceira1, and Charles J. Ammon4
1Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA.
2Los Alamos National Laboratory, Los Alamos, New Mexico, USA.
3School of Data Science, University of Virginia, Charlottesville, Virginia, USA.
4Department of Geosciences, Pennsylvania State University, University Park, Pennsylvania, USA.
Corresponding author: Chengping Chai (chaic@ornl.gov)
Key Points:
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.