用深度学习从地震记录预测地下30米剪切波速，提升震害评估精度。
Deep Sequence Models for Predicting Average Shear Wave Velocity from Strong Motion Records
- 结合卷积与LSTM网络，捕捉地震信号时空特征。
- 改进P波初至时间模型后，预测准确率显著提升。
- 适合地震工程与地质建模研究者参考使用。
本研究探索利用深度学习方法，基于土耳其强震台站的地震记录预测地表下30米内平均剪切波速（$V_{s30}$）。$V_{s30}$是场地分类与地震危险性评估的关键参数，但常因缺乏直接测量而难以获取，通常依赖经验公式估算。然而这些公式难以反映复杂、场地特异的变异，亟需数据驱动方法。本文采用卷积神经网络（CNN）与长短期记忆网络（LSTM）融合的混合模型，同时捕捉地震信号中的空间与时间依赖性。进一步分析不同信号段对模型性能的影响，结果表明该混合模型能有效学习地震信号中的非线性关系。改进后的P波初至时间模型显著提升了$V_{s30}$预测精度。研究为利用CNN-LSTM框架提升场地表征能力提供了重要启示。代码已公开于：https://github.com/brsylmz23/CNNLSTM_DeepEQ。
原文摘要 · Abstract (English)
This study explores the use of deep learning for predicting the time averaged shear wave velocity in the top 30 m of the subsurface ($V_{s30}$) at strong motion recording stations in Türkiye. $V_{s30}$ is a key parameter in site characterization and, as a result for seismic hazard assessment. However, it is often unavailable due to the lack of direct measurements and is therefore estimated using empirical correlations. Such correlations however are commonly inadequate in capturing complex, site-specific variability and this motivates the need for data-driven approaches. In this study, we employ a hybrid deep learning model combining convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to capture both spatial and temporal dependencies in strong motion records. Furthermore, we explore how using different parts of the signal influence our deep learning model. Our results suggest that the hybrid approach effectively learns complex, nonlinear relationships within seismic signals. We observed that an improved P-wave arrival time model increased the prediction accuracy of $V_{s30}$. We believe the study provides valuable insights into improving $V_{s30}$ predictions using a CNN-LSTM framework, demonstrating its potential for improving site characterization for seismic studies. Our codes are available via this repo: https://github.com/brsylmz23/CNNLSTM_DeepEQ
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