{"title":"A Novel Dynamic Recognition Method of Rock Burst Precursor Information Based on Adaptive Denoising and Object Detection","authors":"Shenglei Zhao, Jinxin Wang, Enyuan Wang, Qiming Zhang, Huihan Yang, Zhonghui Li","doi":"10.1007/s42461-024-01055-6","DOIUrl":null,"url":null,"abstract":"<p>Acoustic emission (AE) and electromagnetic radiation (EMR) can reflect the precursor information of rock burst and play important roles in rock burst monitoring, early warning, and prevention. However, the existing denoising methods of AE and EMR monitoring signals are poor, and the recognition of precursor information lacks comprehensiveness, accuracy, and real-time. This paper presents a novel method combining adaptive denoising and object detection to realize dynamic recognition of rock burst precursor information. Successive Variational Mode Decomposition (SVMD) adaptively decomposed the AE and EMR monitoring signals such as pulse and intensity into different mode components and Kalman Filter (KF) performed on each mode component to eliminate redundant noise. Furthermore, the YOLOX object detection algorithm recognizes the precursor information in the time–frequency domain after noise removal, including the time interval, frequency band, and energy. The case study illustrates that the precursor response of the AE and EMR monitoring signal in time–frequency domain is highlighted by denoising, and the average accuracy of different types of precursor recognition reaches 96%. Finally, the consistency of the identified precursor information and field records shows the feasibility and effectiveness of the method, which has practical guiding significance for improving the level of rock burst prevention.</p>","PeriodicalId":18588,"journal":{"name":"Mining, Metallurgy & Exploration","volume":"35 1","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mining, Metallurgy & Exploration","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s42461-024-01055-6","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"METALLURGY & METALLURGICAL ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
Acoustic emission (AE) and electromagnetic radiation (EMR) can reflect the precursor information of rock burst and play important roles in rock burst monitoring, early warning, and prevention. However, the existing denoising methods of AE and EMR monitoring signals are poor, and the recognition of precursor information lacks comprehensiveness, accuracy, and real-time. This paper presents a novel method combining adaptive denoising and object detection to realize dynamic recognition of rock burst precursor information. Successive Variational Mode Decomposition (SVMD) adaptively decomposed the AE and EMR monitoring signals such as pulse and intensity into different mode components and Kalman Filter (KF) performed on each mode component to eliminate redundant noise. Furthermore, the YOLOX object detection algorithm recognizes the precursor information in the time–frequency domain after noise removal, including the time interval, frequency band, and energy. The case study illustrates that the precursor response of the AE and EMR monitoring signal in time–frequency domain is highlighted by denoising, and the average accuracy of different types of precursor recognition reaches 96%. Finally, the consistency of the identified precursor information and field records shows the feasibility and effectiveness of the method, which has practical guiding significance for improving the level of rock burst prevention.
期刊介绍:
The aim of this international peer-reviewed journal of the Society for Mining, Metallurgy & Exploration (SME) is to provide a broad-based forum for the exchange of real-world and theoretical knowledge from academia, government and industry that is pertinent to mining, mineral/metallurgical processing, exploration and other fields served by the Society.
The journal publishes high-quality original research publications, in-depth special review articles, reviews of state-of-the-art and innovative technologies and industry methodologies, communications of work of topical and emerging interest, and other works that enhance understanding on both the fundamental and practical levels.