SeisDeNet: an intelligent seismic data Denoising network for the internet of things
Abstract Deep learning (DL) has attracted tremendous interest in various fields in last few years. Convolutional neural networks (CNNs) based DL architectures have been successfully applied in computer vision, medical image processing, remote sensing, and many other fields. A recent work has proved...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-03-01
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| Series: | Journal of Cloud Computing: Advances, Systems and Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13677-022-00378-3 |
| _version_ | 1827982727504199680 |
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| author | Yu Sang Yanfei Peng Mingde Lu Chen Zhao Liquan Li Tianjiao Ma |
| author_facet | Yu Sang Yanfei Peng Mingde Lu Chen Zhao Liquan Li Tianjiao Ma |
| author_sort | Yu Sang |
| collection | DOAJ |
| description | Abstract Deep learning (DL) has attracted tremendous interest in various fields in last few years. Convolutional neural networks (CNNs) based DL architectures have been successfully applied in computer vision, medical image processing, remote sensing, and many other fields. A recent work has proved that CNNs based models can also be used to handle geophysical problems. Due to noises in seismic signals acquired by geophone equipment this kind of important multimedia resources cannot be effectively utilized in practice. To this end, from the perspective of seismic exploration informatization, this paper takes informatization data in seismic signal acquisition and energy exploration field using cutting-edge technologies such as Internet of things and cloud computing as the research object, presenting a novel CNNs based seismic data denoising (SeisDeNet) architecture is suggested. Firstly, a multi-scale residual dense (MSRD) block is built to leverage the characteristics of seismic data. Then, a deep MSRD network (MSRDN) is proposed to restore the noisy seismic data in a coarse-to-fine manner by using cascading MSRDs. Additionally, the denoising problem is formulated into predicting transform-domain coefficients, by which noises can be further removed by MSRDNs while richer structure details are preserved comparing with the results in spatial domain. By using synthetic seismic records, public SEG and EAGE salt and overthrust seismic model and real field seismic data, the proposed method is qualitatively and quantitatively compared with other leading edge schemes to evaluate it performance, and some results shows that the proposed scheme can produce data with higher quality evaluation while maintaining far more useful data comparing with other schemes. The feasibility of this approach is confirmed by the denoising results, and this approach is shown to be promising in suppressing the seismic noise automatically. |
| first_indexed | 2024-04-09T22:36:48Z |
| format | Article |
| id | doaj.art-17337f43adf34da0b2604c926e1edcd3 |
| institution | Directory Open Access Journal |
| issn | 2192-113X |
| language | English |
| last_indexed | 2024-04-09T22:36:48Z |
| publishDate | 2023-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Cloud Computing: Advances, Systems and Applications |
| spelling | doaj.art-17337f43adf34da0b2604c926e1edcd32023-03-22T12:23:34ZengSpringerOpenJournal of Cloud Computing: Advances, Systems and Applications2192-113X2023-03-0112111210.1186/s13677-022-00378-3SeisDeNet: an intelligent seismic data Denoising network for the internet of thingsYu Sang0Yanfei Peng1Mingde Lu2Chen Zhao3Liquan Li4Tianjiao Ma5School of Electronic and Information Engineering, Liaoning Technical UniversitySchool of Electronic and Information Engineering, Liaoning Technical UniversityInstitute of Computing Technology, Research Institute of Liaohe Oilfield Company, CNPCSchool of Electronic and Information Engineering, Liaoning Technical UniversitySchool of Electronic and Information Engineering, Liaoning Technical UniversitySchool of Electronic and Information Engineering, Liaoning Technical UniversityAbstract Deep learning (DL) has attracted tremendous interest in various fields in last few years. Convolutional neural networks (CNNs) based DL architectures have been successfully applied in computer vision, medical image processing, remote sensing, and many other fields. A recent work has proved that CNNs based models can also be used to handle geophysical problems. Due to noises in seismic signals acquired by geophone equipment this kind of important multimedia resources cannot be effectively utilized in practice. To this end, from the perspective of seismic exploration informatization, this paper takes informatization data in seismic signal acquisition and energy exploration field using cutting-edge technologies such as Internet of things and cloud computing as the research object, presenting a novel CNNs based seismic data denoising (SeisDeNet) architecture is suggested. Firstly, a multi-scale residual dense (MSRD) block is built to leverage the characteristics of seismic data. Then, a deep MSRD network (MSRDN) is proposed to restore the noisy seismic data in a coarse-to-fine manner by using cascading MSRDs. Additionally, the denoising problem is formulated into predicting transform-domain coefficients, by which noises can be further removed by MSRDNs while richer structure details are preserved comparing with the results in spatial domain. By using synthetic seismic records, public SEG and EAGE salt and overthrust seismic model and real field seismic data, the proposed method is qualitatively and quantitatively compared with other leading edge schemes to evaluate it performance, and some results shows that the proposed scheme can produce data with higher quality evaluation while maintaining far more useful data comparing with other schemes. The feasibility of this approach is confirmed by the denoising results, and this approach is shown to be promising in suppressing the seismic noise automatically.https://doi.org/10.1186/s13677-022-00378-3Seismic dataInternet of thingsDenoisingCNNsMSRDNTransform domain |
| spellingShingle | Yu Sang Yanfei Peng Mingde Lu Chen Zhao Liquan Li Tianjiao Ma SeisDeNet: an intelligent seismic data Denoising network for the internet of things Journal of Cloud Computing: Advances, Systems and Applications Seismic data Internet of things Denoising CNNs MSRDN Transform domain |
| title | SeisDeNet: an intelligent seismic data Denoising network for the internet of things |
| title_full | SeisDeNet: an intelligent seismic data Denoising network for the internet of things |
| title_fullStr | SeisDeNet: an intelligent seismic data Denoising network for the internet of things |
| title_full_unstemmed | SeisDeNet: an intelligent seismic data Denoising network for the internet of things |
| title_short | SeisDeNet: an intelligent seismic data Denoising network for the internet of things |
| title_sort | seisdenet an intelligent seismic data denoising network for the internet of things |
| topic | Seismic data Internet of things Denoising CNNs MSRDN Transform domain |
| url | https://doi.org/10.1186/s13677-022-00378-3 |
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