Stylization of a Seismic Image Profile Based on a Convolutional Neural Network

Huiting Hu, Wenxin Lian (), Rui Su (), Chongyu Ren and Juan Zhang
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Huiting Hu: School of Earth Sciences, Northeast Petroleum University, Daqing 163318, China
Wenxin Lian: School of Earth Sciences, Northeast Petroleum University, Daqing 163318, China
Rui Su: School of Earth Sciences, Northeast Petroleum University, Daqing 163318, China
Chongyu Ren: School of Earth Sciences, Northeast Petroleum University, Daqing 163318, China
Juan Zhang: School of Earth Sciences, Northeast Petroleum University, Daqing 163318, China

Energies, 2022, vol. 15, issue 16, 1-16

Abstract: Seismic data are widely used in oil, gas, and other kinds of mineral exploration and development. However, due to low artificial interpretation accuracy and small sample sizes, seismic data may not meet the needs of convolutional neural network training. There are major differences between optical image and seismic data, making it difficult for a model to learn seismic data characteristics. Therefore, a style transfer network is necessary to make the styles of optical image and seismic data more similar. Since the stylization effect of a seismic section is similar to that of most art styles, based on an in-depth study of image style transfer, this paper compared the effects of various style transfer models, and selected a Laplacian pyramid network to carry out a study of seismic section stylization. It transmits low-resolution global style patterns through a drafting network, revises high-resolution local details through correction networks, and aggregates all pyramid layers to output final stylized images of seismic profiles. Experiments show that this method can effectively convey the whole style pattern without losing the original image content. This style transfer method, based on the Laplacian pyramid network, provides theoretical guidance for the fast and objective application of the model to seismic data features.

Keywords: style transfer; deep learning; Laplacian pyramid network; seismic section stylization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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