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author:

Rao, Dijun (Rao, Dijun.) [1] | Huang, Min (Huang, Min.) [2] | Shi, Xiuzhi (Shi, Xiuzhi.) [3] | Yu, Zhi (Yu, Zhi.) [4] | He, Zhengxiang (He, Zhengxiang.) [5]

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Abstract:

The denoising of microseismic signals is a prerequisite for subsequent analysis and research. In this research, a new microseismic signal denoising algorithm called the Black Widow Optimization Algorithm (BWOA) optimized Variational Mode Decomposition (VMD) joint Wavelet Threshold Denoising (WTD) algorithm (BVW) is proposed. The BVW algorithm integrates VMD and WTD, both of which are optimized by BWOA. Specifically, this algorithm utilizes VMD to decompose the microseismic signal to be denoised into several Band-Limited Intrinsic Mode Functions (BLIMFs). Subsequently, these BLIMFs whose correlation coefficients with the microseismic signal to be denoised are higher than a threshold are selected as the effective mode functions, and the effective mode functions are denoised using WTD to filter out the residual low- and intermediate-frequency noise. Finally, the denoised microseismic signal is obtained through reconstruction. The ideal values of VMD parameters and WTD parameters are acquired by searching with BWOA to achieve the best VMD decomposition performance and solve the problem of relying on experience and requiring a large workload in the application of the WTD algorithm. The outcomes of simulated experiments indicate that this algorithm is capable of achieving good denoising performance under noise of different intensities, and the denoising performance is significantly better than the commonly used VMD and Empirical Mode Decomposition (EMD) algorithms. The BVW algorithm is more efficient in filtering noise, the waveform after denoising is smoother, the amplitude of the waveform is the closest to the original signal, and the signal-to-noise ratio (SNR) and the root mean square error after denoising are more satisfying. The case based on Fankou Lead-Zinc Mine shows that for microseismic signals with different intensities of noise monitored on-site, compared with VMD and EMD, the BVW algorithm is more efficient in filtering noise, and the SNR after denoising is higher. © 2024 The Authors. Published by Tech Science Press.

Keyword:

Adaptive boosting Empirical mode decomposition Image coding Image segmentation Image thinning Microseismic monitoring Signal denoising Signal to noise ratio Variational mode decomposition Variational techniques Wavelet decomposition Wiener filtering Zinc mines

Community:

  • [ 1 ] [Rao, Dijun]Zijin Mining Group Co., Ltd., Xiamen; 361016, China
  • [ 2 ] [Rao, Dijun]Zijin (Changsha) Engineering Technology Co., Ltd., Changsha; 410006, China
  • [ 3 ] [Rao, Dijun]State Key Laboratory of Comprehensive Utilization of Low-Grade Refractory Gold Ores, Shanghang; 364204, China
  • [ 4 ] [Rao, Dijun]School of Resources and Safety Engineering, Central South University, Changsha; 410083, China
  • [ 5 ] [Huang, Min]Zijin Mining Group Co., Ltd., Xiamen; 361016, China
  • [ 6 ] [Huang, Min]Zijin (Changsha) Engineering Technology Co., Ltd., Changsha; 410006, China
  • [ 7 ] [Huang, Min]State Key Laboratory of Comprehensive Utilization of Low-Grade Refractory Gold Ores, Shanghang; 364204, China
  • [ 8 ] [Huang, Min]School of Resources & Environment Engineering, Jiangxi University of Science and Technology, Ganzhou; 341000, China
  • [ 9 ] [Shi, Xiuzhi]School of Resources and Safety Engineering, Central South University, Changsha; 410083, China
  • [ 10 ] [Yu, Zhi]Zijin School of Geology and Mining, Fuzhou University, Fuzhou; 350116, China
  • [ 11 ] [He, Zhengxiang]State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, China University of Mining and Technology, Xuzhou; 221116, China

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Source :

CMES - Computer Modeling in Engineering and Sciences

ISSN: 1526-1492

Year: 2024

Issue: 1

Volume: 141

Page: 187-217

2 . 2 0 0

JCR@2023

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ESI Highly Cited Papers on the List: 0 Unfold All

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30 Days PV: 0

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