Classification of Microseismic Events and Blasts Using Deep Belief Network
作者单位：Northeastern University Shenyang University of Technology
会议名称：《第32届中国控制与决策会议》
会议日期：2020年
学科分类：12[管理学] 081801[工学-矿产普查与勘探] 081802[工学-地球探测与信息技术] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0818[工学-地质资源与地质工程] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
关 键 词：Microseismic monitoring Blasts Microseismic events Deep belief network Classification feature
摘 要：Identification and separation of microseismic（MS） events and blasting signals is typically a challenging issue. In particular, the extraction of features from large amounts of data using shallow neural networks is inefficient and not always complete. To address this, the use of a deep belief network model to identify MS events and blasting signals during construction processes is proposed. The aim is to take advantage of the strong learning and feature extraction abilities of such models. Using data recorded during the construction of the Jinping Hydropower Station II in China, nine typical source parameters are selected to use as characteristic parameters. This is accomplished by comparing and analyzing the differences and probability density distribution ranges of the different source parameters associated with MS and blasting events. The model uses the data collected for the source parameters to learn, train, and test. A classification accuracy of 94.4% was thus achieved, which is superior to that obtained using traditional support vector machine and Fisher classifiers. The results also show that the method has other advantages compared to the other methods: false positive rates are low, the characteristic parameters are easy to acquire, and the calculation speed is fast. It is thus an effective method of identifying MS and blasting events when using MS monitoring techniques.