Automatic Classification of Microseismic Events With Similar Features and Noise Interference Using RSREL-Stacking

Microseismic events must be classified to obtain effective disaster precursor information. To further improve the accuracy and efficiency of microseismic event classification, and based on the microseismic data of rockburst monitoring, combined with time–frequency analysis theory, the main factors affecting microseismic event classification were analyzed. For the first time, the rectangle histogram of oriented gradient (R-HOG) and stacking technologies were effectively combined to establish a new stacking integrated learning model (RSREL-stacking) for classifying microseismic events. Finally, the classification performances of RSREL-stacking, a deep learning model, and other models were tested. The results showed the following: 1) noisy signal interference and some events that have similar characteristics are the main factors leading to the high misjudgment rate of events; 2) RSREL-stacking can accurately distinguish the spectrum of useful signals from complex noise interference and effectively extract the contour, region, and spatial position feature information of the useful signal spectrum; and 3) through different experiments, it is confirmed that RSREL-stacking effectively combines the advantages of many independent models, can deeply recognize the subtle differences and features of similar events, and provides increased accuracy of event classification. Compared with other methods, RSREL-stacking combines the advantages of efficient and accurate classification of microseismic events, which guarantees quickly obtaining effective disaster information.