Earthquakes generate seismic waves that travel through the Earth’s interior, carrying critical information about the Earth’s structure and the source event. These waves are mainly divided into two types: P-waves and S-waves. P-waves, or primary waves, travel fastest and arrive first at seismic stations, while S-waves, or secondary waves, follow with a slower speed but higher amplitude, often causing greater damage. Accurately identifying the arrival times of these waves — a process known as phase picking — is fundamental for earthquake localization, magnitude estimation, and early warning systems. However, seismic recordings are often contaminated by complex background noise, overlapping signals, and variable station conditions, which make manual picking both time-consuming and subjective. Automated phase picking, therefore, plays an increasingly vital role in modern seismology, enabling real-time earthquake detection and large-scale waveform analysis. Despite significant progress, achieving high precision and robustness across diverse seismic environments remains a major challenge for automated systems.
GreenPhase is a multi-resolution Green Learning framework for seismic phase picking. It aims to achieve high accuracy while maintaining interpretability and computational efficiency. The model operates across three resolution levels — from coarse to fine — progressively narrowing down candidate regions for P and S arrivals. At each level, GreenPhase extracts spectral–temporal features using the Saab transform and refines them through supervised feature selection and XGBoost regression. A pseudo-label generation and balanced sampling strategy further enhance training stability. Compared with deep networks such as PhaseNet and EQTransformer, GreenPhase requires far fewer training samples while achieving comparable performance. It is trained in a fully feedforward manner, balancing performance, efficiency, and interpretability. For the detection task, GreenPhase achieves an F1 score of 1.00; for P-phase picking, 0.98; and for S-phase picking, 0.96. The differences from EQTransformer, the state-of-the-art model, are only 0.01 and 0.02, respectively, while requiring about 10× fewer FLOPs. Training takes roughly 8 hours on a 16-core CPU, and inference on over 120,000 testing waveforms completes in just 18 minutes.
