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Open Access

ARTICLE

A Microseismic Signal Denoising Algorithm Combining VMD and Wavelet Threshold Denoising Optimized by BWOA

Dijun Rao1,2,3,4, Min Huang1,2,3,5, Xiuzhi Shi4, Zhi Yu6,*, Zhengxiang He7
1 Zijin Mining Group Co., Ltd., Xiamen, 361016, China
2 Zijin (Changsha) Engineering Technology Co., Ltd., Changsha, 410006, China
3 State Key Laboratory of Comprehensive Utilization of Low-Grade Refractory Gold Ores, Shanghang, 364204, China
4 School of Resources and Safety Engineering, Central South University, Changsha, 410083, China
5 School of Resources & Environment Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, China
6 Zijin School of Geology and Mining, Fuzhou University, Fuzhou, 350116, China
7 State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, China University of Mining and Technology, Xuzhou, 221116, China
* Corresponding Author: Zhi Yu. Email: email
(This article belongs to the Special Issue: Bio-inspired Optimization in Engineering and Sciences)

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2024.051402

Received 05 March 2024; Accepted 14 June 2024; Published online 08 July 2024

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.

Keywords

Variational mode decomposition; microseismic signal; denoising; wavelet threshold denoising; black widow optimization algorithm

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