基于稀疏自动编码器和卡尔曼滤波器的地表微震数据去噪

IF 3.2 Q2 AUTOMATION & CONTROL SYSTEMS
Xuegui Li, Shuo Feng, Nan Hou, Ruyi Wang, Hanyang Li, Ming Gao, Siyuan Li
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引用次数: 14

摘要

微震技术广泛应用于非常规油气生产。微震降噪对于识别微震事件、确定震源位置和提高非常规油气产量具有重要意义。本文提出了一种基于稀疏自动编码器和卡尔曼滤波的去噪滤波器。首先,对稀疏自动编码器进行预训练,以学习微震数据的特征。稀疏自动编码是一种基于无监督学习的反向传播神经网络算法,分为三层:输入层、隐藏层和输出层。隐藏层是多余的,这使得算法更好地学习特征,在恶劣环境中表示样本,并有效地降维。此外,还采用卡尔曼滤波器对不确定因素进行处理。使用600个地表微震综合道和模拟噪声的数据集。稀疏自动编码器和卡尔曼滤波被训练来抑制噪声。基于稀疏自动编码器和卡尔曼滤波器模型的去噪滤波器比传统模型获得了更高的信噪比。地面微震信号滤波实验结果表明了该方法的可行性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Surface microseismic data denoising based on sparse autoencoder and Kalman filter
Microseismic technology is widely used in unconventional oil and gas production. Microseismic noise reduction is of great significance for the identification of microseismic events, the location of seismic sources and the improvement of unconventional oil and gas production. In this paper, a denoising filter is proposed based on sparse autoencoder and Kalman filtering. Firstly, a sparse autoencoder is pre-trained to learn the feature of the microseismic data. Sparse autoencoding is a back-propagation neural network algorithm based on unsupervised learning, in which there are three layers: the input layer, the hidden layer and the output layer. The hidden layer is the spare, which makes the algorithm learn features better, represents samples in harsh environments and reduces dimensionality effectively. Besides, Kalman filter is used to deal with the uncertainty factors. Using a dataset of 600 surface microseismic synthesis traces and simulation noise. Sparse autoencoders and Kalman filtering are trained to suppress noise. The denoising filter based on sparse autoencoder and Kalman filter model obtains a higher signal noise ratio than the conventional model. The experiment results for the filtering of surface microseismic signals show the feasibility and effectiveness of the proposed method.
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来源期刊
Systems Science & Control Engineering
Systems Science & Control Engineering AUTOMATION & CONTROL SYSTEMS-
CiteScore
9.50
自引率
2.40%
发文量
70
审稿时长
29 weeks
期刊介绍: Systems Science & Control Engineering is a world-leading fully open access journal covering all areas of theoretical and applied systems science and control engineering. The journal encourages the submission of original articles, reviews and short communications in areas including, but not limited to: · artificial intelligence · complex systems · complex networks · control theory · control applications · cybernetics · dynamical systems theory · operations research · systems biology · systems dynamics · systems ecology · systems engineering · systems psychology · systems theory
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