GreenPhase: A Green Learning Approach for Earthquake Phase Picking

2026-02-23

AI Summary

This work addresses the challenges of seismic detection and phase picking under low signal-to-noise ratios, waveform variability, and overlapping events. Existing deep learning approaches suffer from high computational costs, poor interpretability, and reliance on extensive labeled data and backpropagation. To overcome these limitations, we propose GreenPhase, a model grounded in a green learning framework that employs a multi-resolution feedforward architecture. GreenPhase integrates unsupervised representation learning, supervised feature learning, and decision learning in a coarse-to-fine hierarchical optimization scheme, performing computations only within candidate regions. By eliminating backpropagation and enabling module-wise independent optimization, the method offers mathematical interpretability and drastically reduces computational overhead. Evaluated on the STEAD dataset, GreenPhase achieves an F1 score of 1.0 for event detection, 0.98 for P-phase picking, and 0.96 for S-phase picking, while reducing inference computation by approximately 83% compared to state-of-the-art models.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Natural Language Processing: Learning & Optimization for NLP
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• earthquake detection
• seismic phase picking
• low signal-to-noise ratio
• waveform variability
• overlapping events

Innovation

• Green Learning
• feed-forward architecture
• multi-resolution learning
• interpretable AI
• seismic phase picking