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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.

Desert seismic random noise reduction based on LDA effective signal detection

Ma Haitao, Yan Jie, Li Yue, Zhang Chao, Lin Hongbo

Acta Geophysica

2019

Vol. 67, no. 1

109--121

At present, the seismic exploration of mineral resources such as unknown oil fields and natural gas fields has become the focus and difficulty. The Tarim Oilfield located in the desert area of northwest China has many uncertainties due to complicated geological structure and resource burial conditions. And the seismic record collected carries various noises, especially random noise with complex features, including non-stationary, non-Gaussian, nonlinear and low frequency. The seismic events are contaminated by random noise. Also the effective signal of desert seismic record is in the same frequency band as the random noise. These situations have brought great difficulties in denoising by conventional methods. In this paper, a noise reduction framework based on linear discriminant analysis effective signal detection in desert seismic record is proposed to solve this problem. At first, the method utilizes the difference between the effective signals and the noise in the low-dimensional space. The seismic data are divided into the effective signal cluster and the noise cluster. Then, the effective signal is extracted to realize the position of the seismic events. Finally, the conventional filter is matched to obtain better denoising results. The framework is applied to synthetic desert seismic records and real desert seismic records. The experimental results show that denoising capability after detecting effective signals is obviously better than those of conventional denoising methods. The accuracy of the seismic effective signal detection is higher, and the seismic events’ continuity is maintained better.