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.