Seismic data reconstruction based on partial convolution and attentional mechanism adversarial network model
FENG Yongji1,2, CHEN Xuehua1,2
1. State Key Laboratory of Oil & Gas Reservoir Geology and Exploitation, Chengdu University of Technology, Chengdu, Sichuan 610059, China; 2. Key Lab of Earth Exploration & Information Techniques of Ministryof Education, Chengdu University of Technology, Chengdu, Sichuan 610059, China
Abstract:The deep learning model represented by generative adversal network (GAN) has achieved good results in seismic data reconstruction,but the reconstruction results of ordinary GAN networks have some shortcomings such as ambiguity and false frequency. The main reasons are as follows: during convolution,part of the convolution kernel slides into the missing region,so that the convolution result is affected by the region without data; Secondly,due to the limitation of the size of the convolution kernel,the convolution result is mainly affected by the data in the convolution kernel,and the effective information of distant locations cannot be obtained. In order to solve these two problems,this paper uses the part of the convolution thought and attention to improve GAN network model,Firstly,a scale factor r is introduced into the convolution process to achieve partial convolution,so as to shrink the convolution result and strengthen the influence of the effective region on the convolution result. Secondly,the attention mechanism is used to select the background data with high cosine similarity,which can break through the convolution distance limit and make more effective background data promote the reconstruction of the missing foreground data region,The data processing results show that the proposed method can improve the problems of ambiguity and false frequency in reconstructed data.
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