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GreenPhase: A Green Learning Approach for Earthquake Phase Picking

Yixing Wu, Shiou-Ya Wang, Dingyi Nie, Sanket Kumbhar, Yun-Tung Hsieh, Yun-Cheng Wang, Po-Chyi Su, C. -C. Jay Kuo

TL;DR

GreenPhase is proposed, a multi-resolution, feed-forward, and mathematically interpretable model based on the Green Learning framework that provides an efficient, interpretable, and sustainable alternative for large-scale seismic monitoring.

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 0.96 for S-wave picking. This is accomplished while reducing the computational cost (FLOPs) for inference by approximately 83% compared to state-of-the-art models. These results demonstrate that the proposed model provides an efficient, interpretable, and sustainable alternative for large-scale seismic monitoring.

GreenPhase: A Green Learning Approach for Earthquake Phase Picking

TL;DR

GreenPhase is proposed, a multi-resolution, feed-forward, and mathematically interpretable model based on the Green Learning framework that provides an efficient, interpretable, and sustainable alternative for large-scale seismic monitoring.

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 0.96 for S-wave picking. This is accomplished while reducing the computational cost (FLOPs) for inference by approximately 83% compared to state-of-the-art models. These results demonstrate that the proposed model provides an efficient, interpretable, and sustainable alternative for large-scale seismic monitoring.
Paper Structure (16 sections, 6 equations, 9 figures, 6 tables)

This paper contains 16 sections, 6 equations, 9 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Preprocessing
  4. Multi-resolution Design
  5. Label Generation
  6. Sampling
  7. Representation Learning
  8. Feature Learning
  9. Decision Learning for P/S-wave Picking
  10. Seismic Wave Detection
  11. Results
  12. Dataset
  13. Detection Performance
  14. P/S Wave Picking Performance
  15. Discussion
  16. ...and 1 more sections

Figures (9)

  • Figure 1: Data preprocessing workflow prior to GreenPhase training.
  • Figure 2: Comparison of a seismic waveform before and after the preprocessing pipeline.
  • Figure 3: The architecture of P/S-wave picking
  • Figure 4: An example of the pseudo-label generation for a Level 1 waveform (1500-sample resolution). The top panel shows the transformed waveform with the P-arrival marked. The bottom panel displays the corresponding continuous pseudo-label for each time location.
  • Figure 5: The distribution of pseudo-label values for the Level 1 training set after stratified sampling. The histogram confirms that the three categories (noise, intermediate, and high-confidence) are equally represented, resulting in a balanced dataset for model training.
  • ...and 4 more figures