The Use of Data Mining Techniques in Rockburst Risk Assessment

目前在世界范围内的很多地下矿山,岩爆已经成为一个与矿山采矿生产密切相关的重要现象。深入理解这类现象,不仅有助于岩爆管理,而且还有可能节约采矿成本,减少人身伤亡事故。其中,实验室实验是深入研究岩爆机理的一个重要途径。在本文作者前期的研究中,已经建立了实验室岩爆实验数据库。与此同时,借助于数字采矿技术,也建立了岩爆最大应力和岩爆风险指数的预测模型。为实现基于矿山地质条件和矿山井巷建筑结构特性对岩爆类型即岩爆强度等级的准确预测,本文的重点是,基于对岩爆实例的分析来建立岩爆影响矩阵,明确岩爆现象的诱发因子,并厘清这些影响因子之间的相互关系。运用人工神经网络(ANN)和初始贝叶斯分类器等数字矿山技术,对矿山岩爆数据库进行了更深入的研究。最后给出了研究得出的各项结论。

Rockburst is an important phenomenon that has affected many deep underground mines around the world. An understanding of this phenomenon is relevant to the management of such events, which can lead to saving both costs and lives. Laboratory experiments are one way to obtain a deeper and better understanding of the mechanisms of rockburst. In a previous study by these authors, a database of rockburst laboratory tests was created; in addition, with the use of data mining （DM） techniques, models to predict rockburst maximum stress and rockburst risk indexes were developed. In this paper, we focus on the analysis of a database of in situ cases of rockburst in order to build influence diagrams, list the factors that interact in the occurrence of rockburst, and understand the relationships between these factors. The in situ rockburst database was further analyzed using different DM techniques ranging from artificial neural networks （ANNs） to naive Bayesian classifiers. The aim was to predict the type of rockburst-that is, the rockburst level-based on geologic and construction characteristics of the mine or tunnel. Conclusions are drawn at the end of the paper.