Super-resolution reconstruction of seismic section image via multi-scale convolution neural network

Meng-Di Deng,Xing-Li Zhang,Hong-Mei Sun,Rui-Sheng Jia,Q Yao,W Qiao,S Chen,S Gao,L Chen

doi:10.1051/e3sconf/202130301058

Meng-Di Deng, Xing-Li Zhang + Show 7 more

Open Access

https://doi.org/10.1051/e3sconf/202130301058

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Abstract

The resolution of seismic section images can directly affect the subsequent interpretation of seismic data. In order to improve the spatial resolution of low-resolution seismic section images, a super-resolution reconstruction method based on multi-scale convolution is proposed. This method designs a multi-scale convolutional neural network to learn high-low resolution image feature pairs, and realizes mapping learning from low-resolution seismic section images to high-resolution seismic section images. This multi-scale convolutional neural network model consists of four convolutional layers and a sub-pixel convolutional layer. Convolution operations are used to learn abundant seismic section image features, and sub-pixel convolution layer is used to reconstruct high-resolution seismic section image. The experimental results show that the proposed method is superior to the comparison method in peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). In the total training time and reconstruction time, our method is about 22% less than the FSRCNN method and about 18% less than the ESPCN method.

Highlights

IntroductionValidity of the seismic data, resulting in increased exploration costs. The super-resolution reconstruction technology can reconstruct high-resolution seismic section images with fewer sensors, which can effectively save seismic exploration costs
The seismic section image is obtained by two-dimensional mapping of seismic data in a computer
In order to improve the spatial resolution of low-resolution seismic section images, a super-resolution reconstruction method based on multi-scale convolution is proposed

Summary

Introduction

Validity of the seismic data, resulting in increased exploration costs. The super-resolution reconstruction technology can reconstruct high-resolution seismic section images with fewer sensors, which can effectively save seismic exploration costs. The method uses three convolution layers to map image features from lowresolution space to high-resolution space It avoids the defect of artificial design features, realizes end-to-end learning, and has better reconstruction effect than other traditional method. In order to better reconstruct the texture detail information in the seismic section image, the texture detail enhancement should be fully considered when designing the super-resolution reconstruction method. To this end, this paper proposes a seismic section image reconstruction method based on multi-scale convolutional neural network. Multiple dimensionality reduction operations are performed in the model, which effectively reduces the complexity of the algorithm

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