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.

The Eastern United States has a complex geological history and hosts several seismic active regions. We investigate the subsurface structure beneath the broader eastern United States. To produce reliable images of the subsurface, we simultaneously invert smoothed P-wave receiver functions, Rayleigh-wave phase and group velocity measurements, and Bouguer gravity observations for the 3D shear wave speed. Using surface-wave observations (3-250 s) and spatially smoothed receiver functions, our velocity models are robust, reliable, and rich in detail. The shear-wave velocity models fit all three types of observations well. The resulting velocity model for the eastern U.S. shows thinner crust beneath New England, the east coast, and the Mississippi Embayment. A relatively thicker crust was found beneath the stable North America craton. A relatively slower upper mantle was imaged beneath New England, the east coast, and western Mississippi Embayment. A comparison of crust thickness derived from our model against four recent published models shows first-order consistency. A relatively small upper mantle low-speed region correlates with a published P-waves analysis that has associated the anomaly with a 75 Ma kimberlite volcanic site in Kentucky. We also explored the relationship between the subsurface structure and seismicity in the eastern U.S. We found earthquakes often locate near regions with seismic velocity variations, but not universally. Not all regions of significant subsurface wave speed changes are loci of seismicity. A weak correlation between upper mantle shear velocity and earthquake focal mechanism has been observed.