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http://dx.doi.org/10.7582/GGE.2020.23.3.00192

Research Trend analysis for Seismic Data Interpolation Methods using Machine Learning  

Bae, Wooram (Department of Energy Resources Engineering, Pukyong National University)
Kwon, Yeji (Department of Energy Resources Engineering, Pukyong National University)
Ha, Wansoo (Department of Energy Resources Engineering, Pukyong National University)
Publication Information
Geophysics and Geophysical Exploration / v.23, no.3, 2020 , pp. 192-207 More about this Journal
Abstract
We acquire seismic data with regularly or irregularly missing traces, due to economic, environmental, and mechanical problems. Since these missing data adversely affect the results of seismic data processing and analysis, we need to reconstruct the missing data before subsequent processing. However, there are economic and temporal burdens to conducting further exploration and reconstructing missing parts. Many researchers have been studying interpolation methods to accurately reconstruct missing data. Recently, various machine learning technologies such as support vector regression, autoencoder, U-Net, ResNet, and generative adversarial network (GAN) have been applied in seismic data interpolation. In this study, by reviewing these studies, we found that not only neural network models, but also support vector regression models that have relatively simple structures can interpolate missing parts of seismic data effectively. We expect that future research can improve the interpolation performance of these machine learning models by using open-source field data, data augmentation, transfer learning, and regularization based on conventional interpolation technologies.
Keywords
seismic data interpolation; machine learning; support vector machine; U-Net; ResNet; GAN;
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