A Novel Dynamic Recognition Method of Rock Burst Precursor Information Based on Adaptive Denoising and Object Detection

IF 1.5 4区工程技术 Q3 METALLURGY & METALLURGICAL ENGINEERING

Mining, Metallurgy & Exploration Pub Date : 2024-08-30 DOI:10.1007/s42461-024-01055-6

Shenglei Zhao, Jinxin Wang, Enyuan Wang, Qiming Zhang, Huihan Yang, Zhonghui Li

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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.

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基于自适应去噪和物体检测的岩爆前兆信息动态识别新方法

声发射（AE）和电磁辐射（EMR）可以反映岩爆的前兆信息，在岩爆监测、预警和预防中发挥着重要作用。然而，现有的声发射（AE）和电磁辐射（EMR）监测信号去噪方法效果不佳，对前兆信息的识别缺乏全面性、准确性和实时性。本文提出了一种结合自适应去噪和目标检测的新方法，以实现岩爆前兆信息的动态识别。连续变异模式分解（SVMD）将脉冲和强度等 AE 和 EMR 监测信号自适应地分解为不同的模式分量，并对每个模式分量进行卡尔曼滤波（KF）以消除冗余噪声。此外，YOLOX 物体检测算法还能识别除噪后时频域中的前兆信息，包括时间间隔、频段和能量。案例研究表明，通过去噪，AE 和 EMR 监测信号在时频域的前兆响应得到了突出，不同类型的前兆识别平均准确率达到 96%。最后，识别出的前兆信息与现场记录的一致性说明了该方法的可行性和有效性，对提高岩爆防治水平具有现实指导意义。

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来源期刊

Mining, Metallurgy & Exploration Materials Science-Materials Chemistry

CiteScore

3.50

自引率

10.50%

发文量

177

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.