Predicting Stick-Slips in Sheared Granular Fault Using Machine Learning Optimized Dense Fault Dynamics Data
Abstract
:1. Introduction
2. Methods
2.1. FDEM Simulation of Stick-Slips
2.2. LightGBM (Light Gradient Boosting Machine) Approach
2.3. SHAP (Shapley Additive exPlanation) Value
3. Predictions
3.1. Input Feature Data Optimization
3.2. Prediction Using Optimized Data
3.3. Prediction Using Optimized Data and Their Statistics
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Huang, W.; Gao, K.; Feng, Y. Predicting Stick-Slips in Sheared Granular Fault Using Machine Learning Optimized Dense Fault Dynamics Data. J. Mar. Sci. Eng. 2024, 12, 246. https://doi.org/10.3390/jmse12020246
Huang W, Gao K, Feng Y. Predicting Stick-Slips in Sheared Granular Fault Using Machine Learning Optimized Dense Fault Dynamics Data. Journal of Marine Science and Engineering. 2024; 12(2):246. https://doi.org/10.3390/jmse12020246
Chicago/Turabian StyleHuang, Weihan, Ke Gao, and Yu Feng. 2024. "Predicting Stick-Slips in Sheared Granular Fault Using Machine Learning Optimized Dense Fault Dynamics Data" Journal of Marine Science and Engineering 12, no. 2: 246. https://doi.org/10.3390/jmse12020246