[2603.03344] GreenPhase: A Green Learning Approach for Earthquake Phase Picking

Physics > Geophysics arXiv:2603.03344 (physics) [Submitted on 23 Feb 2026] Title:GreenPhase: A Green Learning Approach for Earthquake Phase Picking Authors:Yixing Wu, Shiou-Ya Wang, Dingyi Nie, Sanket Kumbhar, Yun-Tung Hsieh, Yun-Cheng Wang, Po-Chyi Su, C.-C. Jay Kuo View a PDF of the paper titled GreenPhase: A Green Learning Approach for Earthquake Phase Picking, by Yixing Wu and 7 other authors View PDF HTML (experimental) Abstract:Earthquake detection and seismic phase picking are fundamental yet challenging tasks in seismology due to low signal-to-noise ratios, waveform variability, and overlapping events. Recent deep-learning models achieve strong results but rely on large datasets and heavy backpropagation training, raising concerns over efficiency, interpretability, and sustainability. We propose GreenPhase, a multi-resolution, feed-forward, and mathematically interpretable model based on the Green Learning framework. GreenPhase comprises three resolution levels, each integrating unsupervised representation learning, supervised feature learning, and decision learning. Its feed-forward design eliminates backpropagation, enabling independent module optimization with stable training and clear interpretability. Predictions are refined from coarse to fine resolutions while computation is restricted to candidate regions. On the Stanford Earthquake Dataset (STEAD), GreenPhase achieves excellent performance with F1 scores of 1.0 for detection, 0.98 for P-wave picking, and 0...