Random Forest Prediction of Time to Failure for Granite Hydraulic Fracturing Using Nanoseismic Signals

The data include nanoseismic seismic waveforms associated with time to failure parsed from from Li et al. (2019)'s (https://doi.org/10.1016/j.engfracmech.2019.01.034) hydraulic fracturing experiments and statistical features extracted following Rouet-Leduc et al. (2017) (https://doi.org/10.1002/2017GL074677). The code to load the data and trained models is enclosed in the Jupyter Notebook.本数据集包含从Li等人（2019年，https://doi.org/10.1016/j.engfracmech.2019.01.034）的水力压裂实验中解析得到的、与失效时间相关的纳米地震波形，以及依据Rouet-Leduc等人（2017年，https://doi.org/10.1002/2017GL074677）的方法提取的统计特征。用于加载该数据集与训练好的模型的代码已封装于Jupyter Notebook中。