Real-time Detection for Anomaly Data in Microseismic Monitoring System

被引：2
|
作者：
Ji Chang-peng [1 ]
Liu Li-li [2 ]
机构：
[1] Liaoning Tech Univ, Sch Elect & Informat Engn, Huludao, Peoples R China
[2] Liaoning Tech Univ, Inst Grad, Fuxing, Peoples R China
来源：
PROCEEDINGS OF THE 2009 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND NATURAL COMPUTING, VOL II | 2009年
关键词：
anomaly data detection; anomaly events; microseismic monitoring; real-time prediction mechanism;
D O I：
10.1109/CINC.2009.44
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Microseismic monitoring means to records microseismic activities caused by the changes of the rock physical properties continuously through the high sensitivity seismic sensor placed in mine. How to make real-time detection of abnormal data in mine microseisms positioning system is a extremely important task. Forecast model and mechanism of data stream in the mine microcosmic monitoring system are given through the linear self-regression analysis. Based on this prediction model, a detection method of abnormal data is proposed. This method detects whether real-time data is abnormal by calculating the ratio of real-time forecast error and average forecast error and making a comparison between the ratio and predefined threshold. Experimental results verified correctness and effectiveness of the prediction model to show that the model can realize real-time detection of abnormal event in mine earthquake.
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页码：307 / +
页数：2
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