Desert seismic noise suppression based on multimodal residual convolutional neural network

• Autorzy: Wang Shengnan , Li Yue , Zhao Yuxing

Identyfikatory

DOI

10.1007/s11600-020-00405-4

• Języki publikacji: EN

Abstrakty

EN

Seismic exploration is an important means of oil and gas detection, but afected by complex surface and near-surface conditions, and the seismic records are polluted by noise seriously. Particularly in the desert areas, due to the infuence of wind and human activities, the complex desert noise with low-frequency, nonstationary and non-Gaussian characteristics is produced. It is difcult to extract efective signals from strong noise using existing denoising methods. To address this issue, the paper proposes a new denoising method, called multimodal residual convolutional neural network (MRCNN). MRCNN combines convolutional neural network (CNN) with variational modal decomposition (VMD) and adopts residual learning method to suppress desert noise. Since CNN-based denoisers can extract data features based on massive training set, the impact of noise types and intensity on the denoised results can be ignored. In addition, VMD algorithm can sparsely decompose signal, which will facilitate the feature extraction of CNN. Therefore, using VMD algorithm to optimize the input data will conducive to the performance of the network denoising. Moreover, MRCNN adopts reversible downsampling operator to improve running speed, achieving a good trade-of between denoising results and efciency. Extensive experiments on synthetic and real noisy records are conducted to evaluate MRCNN in comparison with existing denoisers. The extensive experiments demonstrate that the MRCNN can exhibit good efectiveness in seismic denoising tasks.

Słowa kluczowe

EN

residual learning; desert seismic record; noise suppression; variational mode decomposition

PL

uczenie się resztkowe; zapis sejsmiczny pustyni; wyciszenie dźwięku; dekompozycja w trybie wariacyjnym

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2020
• Tom: Vol. 68, no. 2
• Strony: 389--401
• Opis fizyczny: Bibliogr. 30 poz.

Twórcy

autor

Wang Shengnan

• Department of Information, College of Communication Engineering, Jilin University, Changchun 130012, China

autor

Li Yue

• Department of Information, College of Communication Engineering, Jilin University, Changchun 130012, China

autor

Zhao Yuxing

• Department of Information, College of Communication Engineering, Jilin University, Changchun 130012, China

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021)