标题：Seismic Data Interpolation Based on Spectrally Normalized Generative Adversarial Network

入藏号：WOS:001064718000017

中国科学院文献情报中心期刊分区：地球科学1区TOP（2023）

本校作者：赵铭鑫，潘晓*，肖世鹏，张雨强，唐超，文晓涛

来源出版物：IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING  卷: 61  文献号: 5915611

出版年：2023  

第一地址： 成都理工大学

关键词：Deep learning;seismic data interpolation;spectral normalization generative adversarial network (SN-GAN);training set

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摘要：Missing traces are a common problem in seismic data acquisition, which can affect the quality of subsequent processing and interpretation. Therefore, seismic data interpolation is an essential step to recover the missing information. Recently, deep learning has emerged as a powerful tool for seismic data interpolation, especially generative adversarial networks (GANs). GAN can generate realistic data by learning from existing samples. In this article, we propose an improved GAN for seismic data interpolation. The generator is set as U-Net, which could extract more features from the input data via skip connections. For the discriminator, we add a spectral normalization layer to preserve the information content of the discriminator's weights. The Wasserstein loss function is used to stabilize the training process. With those changes, the improved GAN outperforms the traditional GAN. Both synthetic and field data tests demonstrate its effectiveness. Our proposed network can intelligently interpolate seismic data with a high signal-to-noise ratio (SNR) and enhance the efficiency of seismic data processing and analysis.

文章链接地址: https://ieeexplore.ieee.org/document/10201923