E3S Web Conf.
Volume 303, 2021The 10th Anniversary Russian-Chinese Symposium “Clean Coal Technologies: Mining, Processing, Safety, and Ecology” 2021
|Number of page(s)||13|
|Published online||17 September 2021|
Super-resolution reconstruction of seismic section image via multi-scale convolution neural network
1 College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China.
2 Shandong Province Key Laboratory of Wisdom Mine Information Technology, Shandong University of Science and Technology, Qingdao 266590, China.
* Corresponding author: firstname.lastname@example.org
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.
© The Authors, published by EDP Sciences, 2021
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