What is it about?
Coal mine revelation data is constructed as a dataset, and two data pre-processing methods, PCA and SOM, and three SVM parameter search methods, GA, PSO and GWO, are used for experimental comparison. The combined model is used for mine fault identification, and the SOM-GWO-SVM achieves optimal fault interpretation accuracy.
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Why is it important?
提高断层解释效率和精度，促进煤矿勘探智能化发展
Perspectives
Machine learning is a new idea for intelligent development of coal mine structural exploration
Yufei 宫
Read the Original
This page is a summary of: Seismic fault identification in coal mines based on the self-organizing map-gray wolf optimizer-support vector machine algorithm, Interpretation, January 2024, Society of Exploration Geophysicists,
DOI: 10.1190/int-2023-0025.1.
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