Developing an explainable rockburst risk prediction method using monitored microseismicity based on interpretable machine learning approach
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11600-024-01338-y.pdf
Reference59 articles.
1. Askaripour M, Saeidi A, Rouleau A, Mercier-Langevin P (2022) Rockburst in underground excavations: a review of mechanism, classification, and prediction methods. Underground Space. https://doi.org/10.1016/j.undsp.2021.11.008
2. Boatwright J, Fletcher JB (1984) The partition of radiated energy between p and s waves. Bull Seismol Soc Am 74(2):361–376
3. Breiman L (2001) Random forests. Mach Learn 45(1):5–32
4. Bruning TD (2018) A combined experimental and theoretical investigation of the damage process in hard rock with application to rockburst
5. Chen BR, Feng XT, Li QP, Luo RZ, Li SJ (2015) Rockburst intensity classification based on the radiated energy with damage intensity at jinping ii hydropower station, china. Rock Mech and Rock Eng 48(1):289–303
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1. Applying machine learning approach in predicting short-term rockburst risks using microseismic information: a comparison of parametric and non-parametric models;Natural Hazards;2024-08-06
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