Coordinate-Based Seismic Interpolation in Irregular Land Survey: A Deep Internal Learning Approach

Goyes-Peñafiel, Paul; Vargas, Edwin; Correa, Claudia V.; Sun, Yu; Kamilov, Ulugbek S.; Wohlberg, Brendt Egon; Arguello, Henry

doi:10.1109/tgrs.2023.3290468

Coordinate-Based Seismic Interpolation in Irregular Land Survey: A Deep Internal Learning Approach

Journal Article · Wed Jun 28 00:00:00 EDT 2023 · IEEE Transactions on Geoscience and Remote Sensing

DOI:https://doi.org/10.1109/tgrs.2023.3290468· OSTI ID:2217488

^[1]; ^[1]; ^[1]; ^[2]; ^[3]; ^[4]; ^[1]

Industrial University of Santander (UIS), Bucaramanga (Colombia)
Washington Univ., St. Louis, MO (United States); California Institute of Technology (CalTech), Pasadena, CA (United States)
Washington Univ., St. Louis, MO (United States)
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Physical and budget constraints often result in irregular sampling, which complicates accurate subsurface imaging. Preprocessing approaches, such as missing trace or shot interpolation, are typically employed to enhance seismic data in such cases. Recently, deep learning has been used to address the trace interpolation problem at the expense of large amounts of training data to adequately represent typical seismic events. Nonetheless, most research in this area has focused on trace reconstruction, with little attention having been devoted to shot interpolation. Furthermore, existing methods assume regularly spaced receivers/sources failing in approximating seismic data from real (irregular) surveys. This work presents a novel shot gather interpolation approach which uses a continuous coordinate-based representation of the acquired seismic wavefield parameterized by a neural network. The proposed unsupervised approach, which we call coordinate-based seismic interpolation (CoBSI), enables the prediction of specific seismic characteristics in irregular land surveys without using external data during neural network training. Importantly, experimental results on real and synthetic 3-D data validate the ability of the proposed method to estimate continuous smooth seismic events in the time-space and frequency-wavenumber domains, improving sparsity or low-rank-based interpolation methods.

View Accepted Manuscript (DOE)

Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 2217488

Report Number(s):: LA-UR--22-24453

Journal Information:: IEEE Transactions on Geoscience and Remote Sensing, Journal Name: IEEE Transactions on Geoscience and Remote Sensing Vol. 61; ISSN 0196-2892

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

References (45)

An open-source Matlab code package for improved rank-reduction 3D seismic data denoising and reconstruction Chen, Yangkang; Huang, Weilin; Zhang, Dong Computers & Geosciences, Vol. 95 https://doi.org/10.1016/j.cageo.2016.06.017	journal	October 2016
Seismic source reconstruction in an orthogonal geometry based on local and non-local information in the time slice domain Villarreal, Ofelia; León-López, Kareth; Espinosa, Dayanna Journal of Applied Geophysics, Vol. 170 https://doi.org/10.1016/j.jappgeo.2019.103846	journal	November 2019
Reconstruction of missing seismic traces based on sparse dictionary learning and the optimization of measurement matrices Sun, Hong-Mei; Jia, Rui-Sheng; Zhang, Xing-Li Journal of Petroleum Science and Engineering, Vol. 175 https://doi.org/10.1016/j.petrol.2019.01.016	journal	April 2019
Deep learning for irregularly and regularly missing data reconstruction Chai, Xintao; Gu, Hanming; Li, Feng Scientific Reports, Vol. 10, Issue 1 https://doi.org/10.1038/s41598-020-59801-x	journal	February 2020
Seismic data interpolation using deep internal learning Wang, Qin; Shen, Yuantong; Fu, Lihua Exploration Geophysics, Vol. 51, Issue 6 https://doi.org/10.1080/08123985.2020.1748496	journal	April 2020
Reconstructing seismic data by incorporating deep external and internal learning Wang, Qin; Fu, Lihua; Ruan, Shufen Exploration Geophysics, Vol. 53, Issue 6 https://doi.org/10.1080/08123985.2021.2024072	journal	January 2022
Fast dictionary learning for noise attenuation of multidimensional seismic data Chen, Yangkang Geophysical Journal International, Vol. 222, Issue 3 https://doi.org/10.1093/gji/ggaa184	journal	June 2020
Compressive sensing seismic acquisition by using regular sampling in an orthogonal grid Villarreal, Ofelia P.; Leon, Kareth; Espinosa, Dayanna 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) https://doi.org/10.1109/CAMSAP.2017.8313094	conference	December 2017
Zero-Shot Super-Resolution Using Deep Internal Learning Shocher, Assaf; Cohen, Nadav; Irani, Michal 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) https://doi.org/10.1109/CVPR.2018.00329	conference	June 2018
Learned Initializations for Optimizing Coordinate-Based Neural Representations Tancik, Matthew; Mildenhall, Ben; Wang, Terrance 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) https://doi.org/10.1109/CVPR46437.2021.00287	conference	June 2021
pixelNeRF: Neural Radiance Fields from One or Few Images Yu, Alex; Ye, Vickie; Tancik, Matthew 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) https://doi.org/10.1109/CVPR46437.2021.00455	conference	June 2021
GeoNeRF: Generalizing NeRF with Geometry Priors Johari, Mohammad Mahdi; Lepoittevin, Yann; Fleuret, Francois 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) https://doi.org/10.1109/CVPR52688.2022.01782	conference	June 2022
Plug-and-Play priors for model based reconstruction Venkatakrishnan, Singanallur V.; Bouman, Charles A.; Wohlberg, Brendt 2013 IEEE Global Conference on Signal and Information Processing (GlobalSIP) https://doi.org/10.1109/GlobalSIP.2013.6737048	conference	December 2013
An Internal Learning Approach to Video Inpainting Zhang, Haotian; Mai, Long; Jin, Hailin 2019 IEEE/CVF International Conference on Computer Vision (ICCV) https://doi.org/10.1109/ICCV.2019.00281	conference	October 2019
Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields Barron, Jonathan T.; Mildenhall, Ben; Tancik, Matthew 2021 IEEE/CVF International Conference on Computer Vision (ICCV) https://doi.org/10.1109/ICCV48922.2021.00580	conference	October 2021
Seismic Data Reconstruction Using Deep Bidirectional Long Short-Term Memory With Skip Connections Yoon, Daeung; Yeeh, Zeu; Byun, Joongmoo IEEE Geoscience and Remote Sensing Letters, Vol. 18, Issue 7 https://doi.org/10.1109/LGRS.2020.2993847	journal	July 2021
Deep Prior-Based Unsupervised Reconstruction of Irregularly Sampled Seismic Data Kong, Fantong; Picetti, Francesco; Lipari, Vincenzo IEEE Geoscience and Remote Sensing Letters, Vol. 19 https://doi.org/10.1109/LGRS.2020.3044455	journal	January 2022
A Consensus Equilibrium Approach for 3-D Land Seismic Shots Recovery Goyes-Penafiel, Paul; Vargas, Edwin; Correa, Claudia V. IEEE Geoscience and Remote Sensing Letters, Vol. 19 https://doi.org/10.1109/LGRS.2021.3082421	journal	January 2022
CoIL: Coordinate-Based Internal Learning for Tomographic Imaging Sun, Yu; Liu, Jiaming; Xie, Mingyang IEEE Transactions on Computational Imaging, Vol. 7 https://doi.org/10.1109/TCI.2021.3125564	journal	January 2021
Intelligent Missing Shots’ Reconstruction Using the Spatial Reciprocity of Green’s Function Based on Deep Learning Wang, Benfeng; Zhang, Ning; Lu, Wenkai IEEE Transactions on Geoscience and Remote Sensing, Vol. 58, Issue 3 https://doi.org/10.1109/TGRS.2019.2947085	journal	March 2020
Deep Learning for Irregularly and Regularly Missing 3-D Data Reconstruction Chai, Xintao; Tang, Genyang; Wang, Shangxu IEEE Transactions on Geoscience and Remote Sensing, Vol. 59, Issue 7 https://doi.org/10.1109/TGRS.2020.3016343	journal	July 2021
Fast Dictionary Learning for High-Dimensional Seismic Reconstruction Wang, Hang; Chen, Wei; Zhang, Quan IEEE Transactions on Geoscience and Remote Sensing, Vol. 59, Issue 8 https://doi.org/10.1109/TGRS.2020.3030740	journal	August 2021
DTAE: Deep Tensor Autoencoder for 3-D Seismic Data Interpolation Qian, Feng; Liu, Zhangbo; Wang, Yan IEEE Transactions on Geoscience and Remote Sensing, Vol. 60 https://doi.org/10.1109/TGRS.2021.3075968	journal	January 2022
Seismic Intelligent Deblending via Plug and Play Method With Blended CSGs Trained Deep CNN Gaussian Denoiser Xu, Weiwei; Zhou, Yanhui; Liu, Dawei IEEE Transactions on Geoscience and Remote Sensing, Vol. 60 https://doi.org/10.1109/TGRS.2022.3166029	journal	January 2022
Image Quality Assessment: From Error Visibility to Structural Similarity Wang, Z.; Bovik, A. C.; Sheikh, H. R. IEEE Transactions on Image Processing, Vol. 13, Issue 4 https://doi.org/10.1109/TIP.2003.819861	journal	April 2004
Efficient least-squares imaging with sparsity promotion and compressive sensing: Compressive imaging Herrmann, Felix J.; Li, Xiang Geophysical Prospecting, Vol. 60, Issue 4 https://doi.org/10.1111/j.1365-2478.2011.01041.x	journal	January 2012
Plug-and-Play Unplugged: Optimization-Free Reconstruction Using Consensus Equilibrium Buzzard, Gregery T.; Chan, Stanley H.; Sreehari, Suhas SIAM Journal on Imaging Sciences, Vol. 11, Issue 3 https://doi.org/10.1137/17M1122451	journal	January 2018
Architecture and Performance of Devito, a System for Automated Stencil Computation Luporini, Fabio; Louboutin, Mathias; Lange, Michael ACM Transactions on Mathematical Software, Vol. 46, Issue 1 https://doi.org/10.1145/3374916	journal	March 2020
NeRF Mildenhall, Ben; Srinivasan, Pratul P.; Tancik, Matthew Communications of the ACM, Vol. 65, Issue 1 https://doi.org/10.1145/3503250	journal	January 2022
On the Kronecker Products and Their Applications Zhang, Huamin; Ding, Feng Journal of Applied Mathematics, Vol. 2013 https://doi.org/10.1155/2013/296185	journal	January 2013
Nonequispaced curvelet transform for seismic data reconstruction: A sparsity-promoting approach Hennenfent, Gilles; Fenelon, Lloyd; Herrmann, Felix J. GEOPHYSICS, Vol. 75, Issue 6 https://doi.org/10.1190/1.3494032	journal	November 2010
Seismic data restoration via data-driven tight frame Liang, Jingwei; Ma, Jianwei; Zhang, Xiaoqun GEOPHYSICS, Vol. 79, Issue 3 https://doi.org/10.1190/geo2013-0252.1	journal	May 2014
Damped multichannel singular spectrum analysis for 3D random noise attenuation Huang, Weilin; Wang, Runqiu; Chen, Yangkang GEOPHYSICS, Vol. 81, Issue 4 https://doi.org/10.1190/geo2015-0264.1	journal	July 2016
Deep-learning-based seismic data interpolation: A preliminary result Wang, Benfeng; Zhang, Ning; Lu, Wenkai GEOPHYSICS, Vol. 84, Issue 1 https://doi.org/10.1190/geo2017-0495.1	journal	January 2019
Seismic trace interpolation for irregularly spatial sampled data using convolutional autoencoder Wang, Yingying; Wang, Benfeng; Tu, Ning GEOPHYSICS, Vol. 85, Issue 2 https://doi.org/10.1190/geo2018-0699.1	journal	March 2020
Can learning from natural image denoising be used for seismic data interpolation? Zhang, Hao; Yang, Xiuyan; Ma, Jianwei GEOPHYSICS, Vol. 85, Issue 4 https://doi.org/10.1190/geo2019-0243.1	journal	May 2020
Deep-seismic-prior-based reconstruction of seismic data using convolutional neural networks Liu, Qun; Fu, Lihua; Zhang, Meng GEOPHYSICS, Vol. 86, Issue 2 https://doi.org/10.1190/geo2019-0570.1	journal	February 2021
Seismic data interpolation based on U-net with texture loss Fang, Wenqian; Fu, Lihua; Zhang, Meng GEOPHYSICS, Vol. 86, Issue 1 https://doi.org/10.1190/geo2019-0615.1	journal	January 2021
Unsupervised deep learning for 3D interpolation of highly incomplete data Saad, Omar M.; Fomel, Sergey; Abma, Raymond GEOPHYSICS, Vol. 88, Issue 1 https://doi.org/10.1190/geo2022-0232.1	journal	December 2022
Interpolated compressive sensing for seismic data reconstruction Li, Chengbo; Mosher, Charles C.; Kaplan, Sam T. SEG Technical Program Expanded Abstracts 2012 https://doi.org/10.1190/segam2012-1335.1	conference	September 2012
Seismic data interpolation through convolutional autoencoder Mandelli, Sara; Borra, Federico; Lipari, Vincenzo SEG Technical Program Expanded Abstracts 2018 https://doi.org/10.1190/segam2018-2995428.1	conference	August 2018
Seismic data interpolation using a POCS-guided deep image prior Park, Min Jun; Jennings, Joseph; Clapp, Bob SEG Technical Program Expanded Abstracts 2020 https://doi.org/10.1190/segam2020-3427320.1	conference	September 2020
Geologic model building in SEAM Phase II — Land seismic challenges Regone, Carl; Stefani, Joseph; Wang, Peter The Leading Edge, Vol. 36, Issue 9 https://doi.org/10.1190/tle36090738.1	journal	September 2017
Reconstruction of 2D Seismic Wavefields from Nonuniformly Sampled Sources Galvis, Laura; Ramírez, Juan M.; Vargas, Edwin Electronic Imaging, Vol. 32, Issue 14 https://doi.org/10.2352/ISSN.2470-1173.2020.14.COIMG-307	journal	January 2020
Devito (v3.1.0): an embedded domain-specific language for finite differences and geophysical exploration Louboutin, Mathias; Lange, Michael; Luporini, Fabio Geoscientific Model Development, Vol. 12, Issue 3 https://doi.org/10.5194/gmd-12-1165-2019	journal	January 2019

Similar Records

A Consensus Equilibrium Approach for 3-D Land Seismic Shots Recovery

Journal Article · Fri Jun 04 00:00:00 EDT 2021 · IEEE Geoscience and Remote Sensing Letters · OSTI ID:1825420

Artificial neural networks as a tool for porosity and permeability prediction

Journal Article · Sat Jun 01 00:00:00 EDT 1996 · AAPG Bulletin · OSTI ID:272631

Knowledge base interpolation of path-dependent data using irregularly spaced natural neighbors

Conference · Thu Aug 01 00:00:00 EDT 1996 · OSTI ID:266902

Related Subjects

58 GEOSCIENCES
deep internal learning
irregular land surveys
positional encodings
seismic shot interpolation

Coordinate-Based Seismic Interpolation in Irregular Land Survey: A Deep Internal Learning Approach

Citation Formats

References (45)

Similar Records

Related Subjects