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Noise suppression of distributed acoustic sensing vertical seismic profile data based on time-frequency analysis

Shao Dan, Li Tonglin, Han Liguo, Li Yue

Acta Geophysica

2022

Vol. 70, no 4

1539--1549

Distributed acoustic sensing (DAS) technology is a novel technology applied in vertical seismic profile (VSP) exploration, which has many advantages, such as low cost, high precision, strong tolerance to harsh acquisition environment. However, the field DAS-VSP data are often disturbed by complex background noise and coupling noise with strong energy, affecting the quality of seismic data seriously. Therefore, we develop a time–frequency analysis method based on low-rank and sparse matrix decomposition (LSMD) and data position points distribution maps (DPM) to separate signals from noise. We adopt Multisynchrosqueezing Transform to construct the approximate ideal time–frequency representation of DAS data, which reduces the difficulty of signal to noise separation and avoids the loss of some effective information to a certain extent. The LSMD is performed to separate the signal component and noise component preliminarily. In addition, combined with the separated low-rank matrix and sparse matrix, we propose the DPM to improve the accuracy of signal component extraction and the recovery ability of the method for weak signals through the joint analysis of the maps in time domain and frequency domain. Both synthetic and field experiments show that the proposed method can suppress coupling noise and background noise and recover weak energy signals in DAS VSP data effectively.

Desert seismic noise suppression based on multimodal residual convolutional neural network

Wang Shengnan, Li Yue, Zhao Yuxing

Acta Geophysica

2020

Vol. 68, no. 2

389--401

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.