Abstract
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, a task
typically completed by human analysts. This process has usually been
done by experts labeling each waveform visually, which is time-consuming
and tedious for large datasets. 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 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 the two trained models agree with labels from human
analysts but required only 0.4% time. Although the quality assignments
assessed general waveform signal-to-noise, the ANN or RF labels can help
facilitate detailed waveform analysis. Our analyses demonstrate the
capability of the automated processing using these two machine learning
models to reduce outliers in surface-wave-related measurements without
human quality control screening.