GreenPhase以轻量高效方式实现地震波相位精准识别，兼顾性能与可持续性。
GreenPhase: A Green Learning Approach for Earthquake Phase Picking
- 基于格林学习框架的多分辨率前馈模型，无需反向传播训练。
- 在STEAD数据集上检测F1达1.0，P/S波拾取分别达0.98/0.96。
- 推理计算量降低约83%，适合大规模地震监测应用。
地震检测与波形相位拾取因信噪比低、波形变异和事件重叠而极具挑战。现有深度学习模型虽表现优异，但依赖大样本和耗能的反向传播训练，带来效率与可持续性问题。本文提出GreenPhase，一种基于格林学习框架的多分辨率、前馈式可解释模型。其包含三个分辨率层级，每层融合无监督表征学习、有监督特征学习与决策学习。前馈结构避免反向传播，支持模块独立优化，训练稳定且结果可解释。预测从粗到精逐步细化，计算仅限候选区域。在斯坦福地震数据集（STEAD）上，绿相位实现检测F1=1.0，P波拾取F1=0.98，S波拾取F1=0.96，同时推理计算量（FLOPs）相较顶尖模型降低约83%。结果表明，该模型为大规模地震监测提供了高效、可解释且可持续的替代方案。
原文摘要 · Abstract (English)
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.
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