Exploration of Machine Learning Methods to Seismic Event Discrimination in the Pacific Northwest

Accurately separating tectonic, anthropogenic, and geomorphologic seismic sources is essential for Pacific Northwest (PNW) monitoring but remains difficult as networks densify and signals overlap. Prior work largely treats binary discrimination and seldom compares classical machine learning (feature-engineered) and deep learning (end-to-end) approaches under a common, multi-class setting with operational constraints. We evaluate methods and features for four-way source discrimination – earthquakes, explosions, surface events, and noise – and identify models that are both accurate and deployable. Using ∼200k three-component waveforms from >70k events in an AI-curated PNW dataset, we test random-forest classifiers on TSFEL, physics-informed, and scattering features, and CNNs that ingest time series (1D) or spectrograms (2D); we benchmark on a balanced common test set, a 10k-event network dataset, and out-of-domain data (global surface events; near-field blasts). CNNs taking spectrograms lead with accuracy performance over 92% for within-domain (as a short-and-fat CNN SeismicCNN 2D) and out-of-domain (as a long and skinny CNN QuakeXNet 2D), versus 89% for the best random forest; performance remains strong at low signal-to-noise ratio (SNR) and longer distances, and generalizes to independent network and global datasets. QuakeXNet (2D) is lightweight (70k parameters; 1.2 MB) and integrated into SeisBench. On commodity hardware, it processes a full day of 100 Hz three-component data in 9 s. These results show spectrogram-based CNNs provide state-of-the-art accuracy, efficiency, and robustness for real-time PNW operations and transferable surface-event monitoring.