煤田地质与勘探2024，Vol.52Issue(5)：107-117,11.DOI:10.12363/issn.1001-1986.23.10.0641
机器学习方法在矿井水防治理论体系研究中的应用思考
Some reflections on the application of machine learning to research into the theoretical system of mine water prevention and control
摘要
Abstract
The theoretical system of mine water prevention and control encompasses three fundamental aspects:disaster-causing mechanisms,risk evaluation,and disaster prediction.This theoretical system,having undergone rapid develop-ment over the past 20 years,aims to gain insights into the behavior characteristics of mine water and predict its evolu-tionary trend,thus serving the prevention and control of water disasters in mining areas.Applying machine learning,a powerful tool for data analysis and mining in the era of big data,to research into the theoretical system has garnered con-siderable attention.This study focuses on the specific applications of machine learning to the three fundamental aspects of the theoretical system.Specifically,this study offered a brief introduction to the current status of research on disaster-causing mechanisms based on the classification of varying water disasters,proposing that the application gap of ma-chine learning to the mechanism research is due to its incapacity to make assumptions.This study posited that future re-search on disaster-causing mechanisms will still primarily rely on conventional methods like theoretical analysis,numer-ical simulation,and similarity simulation,with machine learning facilitating the acquisition and processing of geologic data.The analysis of method advantages reveals that the application of machine learning to the risk evaluation primarily via processing unstructured data and enriching evaluation methods.For disaster prediction,this study analyzed the draw-backs of prediction modes based merely on physics or data and expounded on the necessity of combining physical mod-els with data-driven approaches.Accordingly,this study presented three methods for achieving the model-data dual-driv-en prediction mode.Additionally,this study explored the feasibility of image-based disaster prediction methods.With the increasing abundance of production and geologic data,machine learning will accelerate the development of the the-oretical system,contributing to research on the systematic methodology for mine water prevention and control.关键词
机器学习/矿井突水/矿井水防治/理论体系/大数据Key words
machine learning/mine water inrush/mine water prevention and control/theoretical system/big data分类
矿业与冶金引用本文复制引用
姚辉,尹慧超,梁满玉,尹尚先,侯恩科,连会青,夏向学,张金福,吴传实..机器学习方法在矿井水防治理论体系研究中的应用思考[J].煤田地质与勘探,2024,52(5):107-117,11.基金项目
国家自然科学基金项目(51974126) （51974126）
河北省自然科学基金重点项目(D2017508099) （D2017508099）
