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 denoisi...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Systems Science & Control Engineering |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/21642583.2022.2087786 |
| _version_ | 1811240566138077184 |
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| author | Xuegui Li Shuo Feng Nan Hou Ruyi Wang Hanyang Li Ming Gao Siyuan Li |
| author_facet | Xuegui Li Shuo Feng Nan Hou Ruyi Wang Hanyang Li Ming Gao Siyuan Li |
| author_sort | Xuegui Li |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T13:22:29Z |
| format | Article |
| id | doaj.art-055f64efc3cc4f9889922930f9471ad2 |
| institution | Directory Open Access Journal |
| issn | 2164-2583 |
| language | English |
| last_indexed | 2024-04-12T13:22:29Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Systems Science & Control Engineering |
| spelling | doaj.art-055f64efc3cc4f9889922930f9471ad22022-12-22T03:31:26ZengTaylor & Francis GroupSystems Science & Control Engineering2164-25832022-12-0110161662810.1080/21642583.2022.2087786Surface microseismic data denoising based on sparse autoencoder and Kalman filterXuegui Li0Shuo Feng1Nan Hou2Ruyi Wang3Hanyang Li4Ming Gao5Siyuan Li6Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing, People's Republic of ChinaArtificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing, People's Republic of ChinaArtificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing, People's Republic of ChinaCNPC Engineering Technology R&D Company Limited, Beijing, People's Republic of ChinaArtificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing, People's Republic of ChinaArtificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing, People's Republic of ChinaArtificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing, People's Republic of ChinaMicroseismic 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.https://www.tandfonline.com/doi/10.1080/21642583.2022.2087786Sparse autoencodersurface micro-seismicdeep learningKalman filter |
| spellingShingle | Xuegui Li Shuo Feng Nan Hou Ruyi Wang Hanyang Li Ming Gao Siyuan Li Surface microseismic data denoising based on sparse autoencoder and Kalman filter Systems Science & Control Engineering Sparse autoencoder surface micro-seismic deep learning Kalman filter |
| title | Surface microseismic data denoising based on sparse autoencoder and Kalman filter |
| title_full | Surface microseismic data denoising based on sparse autoencoder and Kalman filter |
| title_fullStr | Surface microseismic data denoising based on sparse autoencoder and Kalman filter |
| title_full_unstemmed | Surface microseismic data denoising based on sparse autoencoder and Kalman filter |
| title_short | Surface microseismic data denoising based on sparse autoencoder and Kalman filter |
| title_sort | surface microseismic data denoising based on sparse autoencoder and kalman filter |
| topic | Sparse autoencoder surface micro-seismic deep learning Kalman filter |
| url | https://www.tandfonline.com/doi/10.1080/21642583.2022.2087786 |
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