Mine Microseismic Signal Denoising Based on a Deep Convolutional Autoencoder

IF 1.2 4区 工程技术 Q3 ACOUSTICS
Ting Hu, Bin Xu, Yongfa Wang, Jiayi Zhu, Jiang Zhou, Zhongyi Wan
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引用次数: 0

Abstract

Mine microseismic signal denoising is a basic and crucial link in microseismic data processing, which influences the accuracy and reliability of the monitoring system, and is of great significance with regard to safety during mining. Therefore, this study introduces a deep learning method to improve the mapping function and sparsity of signals in the time-frequency domain and constructs a denoising framework based on a deep convolutional autoencoder to address the denoising problem of mine microseismic signals. First, all noisy microseismic signals are normalized to ensure the nonlinear expression ability of the constructed denoising framework. Then, the normalized signals are transformed into the time-frequency domain using the short-time Fourier transform (STFT), and the real and imaginary parts of time-frequency coefficients serve as the input of the deep convolutional autoencoder to output the masks of the effective and noise signals. Next, these masks are applied to the time-frequency coefficients of the noisy microseismic signals, and the time-frequency coefficients of the potentially effective and noise signals are estimated. Finally, inverse STFT is used to transform these time-frequency coefficients to the time domain to obtain the final denoised effective and noise signals. The constructed framework automatically learns rich features from synthetic data to separate the effective and noise signals, thereby achieving the purpose of fast and automatic denoising. The experimental results show that compared with the wavelet threshold and ensemble empirical mode decomposition, the denoising framework considerably improves the signal-to-noise ratio of mine microseismic signals with less waveform distortion. Moreover, it can achieve a better denoising effect efficiently even in the case of a low SNR, which has obvious advantages. The constructed denoising framework is suitable for microseismic monitoring signals of various mine dynamic disasters and provides strong technical support for intelligent monitoring and early warning concerning production risks in mines.
基于深度卷积自动编码器的矿井微震信号去噪
矿井微震信号去噪是微震数据处理的基础和关键环节,影响着监测系统的准确性和可靠性,对矿井安全生产具有重要意义。因此,本研究引入深度学习方法,改进信号在时频域的映射函数和稀疏性,构建基于深度卷积自编码器的去噪框架,解决矿山微震信号的去噪问题。首先,对微震噪声信号进行归一化处理,保证所构建的去噪框架的非线性表达能力。然后利用短时傅里叶变换(STFT)将归一化后的信号变换到时频域,将时频系数的实部和虚部作为深度卷积自编码器的输入,输出有效信号和噪声信号的掩模。然后,将这些掩模应用于噪声微震信号的时频系数,估计潜在有效信号和噪声信号的时频系数。最后,利用逆STFT将这些时频系数变换到时域,得到去噪后的有效信号和噪声信号。构建的框架自动从合成数据中学习丰富的特征,分离有效信号和噪声信号,从而达到快速自动去噪的目的。实验结果表明,与小波阈值和集合经验模态分解相比,该去噪框架显著提高了矿井微震信号的信噪比,且波形失真较小。而且,即使在低信噪比的情况下,也能有效地取得较好的去噪效果,具有明显的优势。所构建的去噪框架适用于各种矿山动力灾害的微震监测信号,为矿山生产风险的智能监测预警提供了强有力的技术支持。
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来源期刊
Shock and Vibration
Shock and Vibration 物理-工程:机械
CiteScore
3.40
自引率
6.20%
发文量
384
审稿时长
3 months
期刊介绍: Shock and Vibration publishes papers on all aspects of shock and vibration, especially in relation to civil, mechanical and aerospace engineering applications, as well as transport, materials and geoscience. Papers may be theoretical or experimental, and either fundamental or highly applied.
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