Surface microseismic data denoising based on sparse autoencoder and Kalman filter. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Li, Xuegui; Feng, Shuo; Hou, Nan; Wang, Ruyi; Li, Hanyang; Gao, Ming; Li, Siyuan
• Is Part Of: Systems science & control engineering. Volume 10:Issue 1(2022)
• Page Start: 616
• Page End: 628
• Publication Date: 2022-12-31
• Subjects: Sparse autoencoder -- surface micro-seismic -- deep learning -- Kalman filter; System theory -- Periodicals; Automatic control -- Periodicals; 003.05
• DOI: 10.1080/21642583.2022.2087786
• Languages: English
• ISSNs: 2164-2583

Abstract

Abstract : 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.