Affiliation:
1. College of Geoexploration Science and Technology, Jilin University, Changchun 130026, China
Abstract
The incorporation of effective denoising techniques is a crucial requirement for seismic data processing during the acquisition phase due to the inherent susceptibility of the seismic data acquisition process to various forms of interference, such as random and coherent noise. For random noise, the Residual Neural Network (Resnet), with its notable ability to effectively suppress noise in seismic data, has garnered widespread utilization in removing unwanted disturbances or interference due to its elegant simplicity and outstanding performance. Despite the considerable advancements achieved by conventional Resnet in the field of suppressing noise, it is irrefutable that there is still room for amelioration in their ability to filter out unwanted disturbances. As a result, this paper puts forth a novel attention-based methodology for Resnet, intended to overcome the present constraints and attain an optimal seismic signal enhancement. Specifically, we add the convolutional block attention module (CBAM) after the convolutional layer of the residual module and add channel attention on the shortcut connections to filter out the disturbance. We replace the commonly used ReLU activation function in the network with ELU, which is better suited for suppressing seismic noise. Empirical assessments conducted on both synthetic and authentic datasets have demonstrated the efficacy of the proposed methodology in amplifying the denoising prowess of Resnet. Our proposed method remains stable even when dealing with seismic data that has complex waveforms. The findings of this investigation evince that the recommended approach furnishes a substantial augmentation in the signal-to-noise ratio (SNR), thereby facilitating the efficient and robust extraction of the underlying signal from the noisy observations.
Funder
National Natural Science Foundation of China
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference30 articles.
1. Expand dimensional of seismic data and random noise attenuation using low-rank estimation;Mafakheri;IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.,2022
2. Random Noise Attenuation in Seismic Data Using Hankel Sparse Low-Rank Approximation;Anvari;Comput. Geosci.,2021
3. Guan, X.Z., Wang, J.X., Wang, X.J., Xue, D., and Sun, W.B. (2021, January 27–29). Research on intelligent denoising technology of marine seismic data based on Resnet. Proceedings of the Chinese Petroleum Society 2021 Geophysical Exploration Technology Seminar, Chengdu, China.
4. An Efficient Undersampled High-Resolution Radon Transform for Exploration Seismic Data Processing;Latif;IEEE Trans. Geosci. Remote Sens.,2017
5. Comparisons of Wavelets, Contourlets and Curvelets in Seismic Denoising;Shan;J. Appl. Geophys.,2009
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献