Title :
Real-time Detection for Anomaly Data in Microseismic Monitoring System
Author :
Li-li, Liu ; Chang-peng, Ji
Author_Institution :
Sch. of Electron. & Inf. Eng., Liaoning Tech. Univ., Huludao, China
Abstract :
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.
Keywords :
condition monitoring; microsensors; regression analysis; seismology; abnormal event detection; anomaly data; high sensitivity seismic sensor; linear self-regression analysis; microseismic activities; microseismic monitoring system; mine earthquake; mine microseisms positioning system; real-time detection; rock physical properties; Acoustic noise; Data mining; Earthquakes; Event detection; Interference; Intrusion detection; Linear regression; Monitoring; Predictive models; Real time systems; anomaly data detection; anomaly events; microseismic monitoring; real-time prediction mechanism;
Conference_Titel :
Computational Intelligence and Natural Computing, 2009. CINC '09. International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-0-7695-3645-3
DOI :
10.1109/CINC.2009.44
