2019
DOI: 10.1190/geo2017-0495.1
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Abstract: Seismic data interpolation is a longstanding issue. Most current methods are only suitable for randomly missing cases. To deal with regularly missing cases, an antialiasing strategy should be included. However, seismic survey design using a random distribution of shots and receivers is always operationally challenging and impractical. We have used deep-learning-based approaches for seismic data antialiasing interpolation, which could extract deeper features of the training data in a nonlinear way by self-learn… Show more

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Cited by 241 publications
(50 citation statements)
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“…Within the area of seismic data interpolation and reconstruction, Wang et al . (,b) introduced an eight‐layer residual learning network (ResNet) based on CNNs to interpolate seismic data without aliasing. Mandelli et al .…”
Section: Introductionmentioning
confidence: 99%
“…Within the area of seismic data interpolation and reconstruction, Wang et al . (,b) introduced an eight‐layer residual learning network (ResNet) based on CNNs to interpolate seismic data without aliasing. Mandelli et al .…”
Section: Introductionmentioning
confidence: 99%
“…Yang [28] adapted the DL method to velocity estimation without requiring any prior information. Wang [29] introduced the DL strategy into seismic data interpolation to provide accurately reconstructed dense data. Hu [30] proposed the identification of the first arrivals by using convolution networks.…”
Section: Introductionmentioning
confidence: 99%
“…Based on a variety of principles and assumptions, important advances have been made in seismic data reconstruction. Some of them addressed interpolating regularly sampled data [13][14][15]20 , while some of them attacked non-uniformly sampled interpolation 21 . There are techniques developed for both irregularly and regularly missing data reconstruction 22 .…”
mentioning
confidence: 99%
“…Jia et al 14 proposed an intelligent interpolation method for regularly sampled data by Monte Carlo ML. Wang et al 15 proposed a DL-based approach for regularly sampled seismic data antialiasing interpolation. Based on CNNs, Wang et al 15 designed eight-layer residual networks (ResNets) with a better back-propagation property for interpolation, which extract feature maps of the training data in a non-linear way.…”
mentioning
confidence: 99%
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