Data for Copula-Enhanced Machine Learning for Labquake Prediction and Risk Assessment
收藏Mendeley Data2026-04-09 收录
下载链接：
https://data.mendeley.com/datasets/nsm4zbfwf3/1
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资源简介：
Data from experiments 5268–5271 (see also the online repository at Chris Marone's labs http://psudata.s3-website.us-east-2.amazonaws.com/p5271/index.html) and the Python implementations of Bi-LSTM, LSTM, Transformer, and XGBoost models used to predict key slip indicators, including time to failure (TTF), time since failure (TSF), and shear stress (SS).
本数据集包含实验5268至5271的相关数据（亦可通过Chris Marone实验室的在线存储库访问：http://psudata.s3-website.us-east-2.amazonaws.com/p5271/index.html），同时包含用于预测关键滑移指标的双向长短期记忆网络（Bi-LSTM）、长短期记忆网络（LSTM）、Transformer及XGBoost模型的Python实现代码，其中待预测的关键滑移指标包括失效时间（time to failure, TTF）、失效后时间（time since failure, TSF）与剪应力（shear stress, SS）。
