Random Forest Prediction of Time to Failure for Granite Hydraulic Fracturing Using Nanoseismic Signals
Published: 24 June 2024| Version 2 | DOI: 10.17632/8scy63d3tw.2
Contributors:
, , Description
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.
Files
Steps to reproduce
Major steps are shown in the ".ipynb" file with labelled waveforms in "data" folder and best models checkpoints in "models" folder.
Institutions
Western University Faculty of Engineering
Categories
Machine Learning, Hydraulic Fracturing, Induced Seismicity, Acoustic Emission