{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T16:50:35Z","timestamp":1779295835848,"version":"3.51.4"},"reference-count":30,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T00:00:00Z","timestamp":1671580800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"TotalEnergies EP R&amp;T USA"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Inverting seismic data to build 3D geological structures is a challenging task due to the overwhelming amount of acquired seismic data, and the very-high computational load due to iterative numerical solutions of the wave equation, as required by industry-standard tools such as Full Waveform Inversion (FWI). For example, in an area with surface dimensions of 4.5 km \u00d7 4.5 km, hundreds of seismic shot-gather cubes are required for 3D model reconstruction, leading to Terabytes of recorded data. This paper presents a deep learning solution for the reconstruction of realistic 3D models in the presence of field noise recorded in seismic surveys. We implement and analyze a convolutional encoder\u2013decoder architecture that efficiently processes the entire collection of hundreds of seismic shot-gather cubes. The proposed solution demonstrates that realistic 3D models can be reconstructed with a structural similarity index measure (SSIM) of 0.9143 (out of 1.0) in the presence of field noise at 10 dB signal-to-noise ratio.<\/jats:p>","DOI":"10.3390\/s23010061","type":"journal-article","created":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T02:31:11Z","timestamp":1671676271000},"page":"61","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Encoder\u2013Decoder Architecture for 3D Seismic Inversion"],"prefix":"10.3390","volume":"23","author":[{"given":"Maayan","family":"Gelboim","sequence":"first","affiliation":[{"name":"Electrical Engineering Department, Braude College of Engineering, Karmiel 2161002, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amir","family":"Adler","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, Braude College of Engineering, Karmiel 2161002, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yen","family":"Sun","sequence":"additional","affiliation":[{"name":"TotalEnergies, EP R&T, Houston, TX 77002, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mauricio","family":"Araya-Polo","sequence":"additional","affiliation":[{"name":"TotalEnergies, EP R&T, Houston, TX 77002, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,21]]},"reference":[{"key":"ref_1","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4509","DOI":"10.1109\/TIP.2017.2713099","article-title":"Deep Convolutional Neural Network for Inverse Problems in Imaging","volume":"26","author":"Jin","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1109\/MSP.2020.3037429","article-title":"Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows","volume":"38","author":"Adler","year":"2021","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"eabm4470","DOI":"10.1126\/science.abm4470","article-title":"Deep-learning seismology","volume":"377","author":"Mousavi","year":"2022","journal-title":"Science"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1190\/tle37010058.1","article-title":"Deep-learning tomography","volume":"37","author":"Jennings","year":"2018","journal-title":"Lead. Edge"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"R583","DOI":"10.1190\/geo2018-0249.1","article-title":"Deep-learning inversion: A next generation seismic velocity-model building method","volume":"84","author":"Yang","year":"2019","journal-title":"Geophysics"},{"key":"ref_7","first-page":"1","article-title":"Deep recurrent architectures for seismic tomography","volume":"Volume 2019","author":"Adler","year":"2019","journal-title":"Proceedings of the 81st EAGE Conference and Exhibition 2019"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Araya-Polo, M., Adler, A., Farris, S., and Jennings, J. (2020). Fast and accurate seismic tomography via deep learning. Deep Learning: Algorithms and Applications, Springer.","DOI":"10.1007\/978-3-030-31760-7_5"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"V33","DOI":"10.1190\/geo2018-0870.1","article-title":"Automatic velocity analysis using convolutional neural network and transfer learning","volume":"85","author":"Park","year":"2020","journal-title":"Geophysics"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, Z., and Alkhalifah, T. (2022). Regularized elastic full-waveform inversion using deep learning. Advances in Subsurface Data Analytics, Elsevier.","DOI":"10.1016\/B978-0-12-822295-9.00009-1"},{"key":"ref_11","first-page":"1","article-title":"Target-Oriented Time-Lapse Elastic Full-Waveform Inversion Constrained by Deep Learning-Based Prior Model","volume":"60","author":"Li","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"U21","DOI":"10.1190\/geo2018-0786.1","article-title":"Seismic velocity estimation: A deep recurrent neural-network approach","volume":"85","author":"Sarkar","year":"2020","journal-title":"Geophysics"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"R93","DOI":"10.1190\/geo2020-0933.1","article-title":"Integrating deep neural networks with full-waveform inversion: Reparameterization, regularization, and uncertainty quantification","volume":"87","author":"Zhu","year":"2022","journal-title":"Geophysics"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"R869","DOI":"10.1190\/geo2018-0838.1","article-title":"Convolutional neural network for seismic impedance inversion","volume":"84","author":"Das","year":"2019","journal-title":"Geophysics"},{"key":"ref_15","first-page":"1","article-title":"Physics-constrained seismic impedance inversion based on deep learning","volume":"19","author":"Wang","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.petsci.2021.09.038","article-title":"Seismic impedance inversion based on cycle-consistent generative adversarial network","volume":"19","author":"Wang","year":"2022","journal-title":"Pet. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"894","DOI":"10.1190\/tle37120894.1","article-title":"Geophysical inversion versus machine learning in inverse problems","volume":"37","author":"Kim","year":"2018","journal-title":"Lead. Edge"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"SE161","DOI":"10.1190\/INT-2018-0236.1","article-title":"Prestack and poststack inversion using a physics-guided convolutional neural network","volume":"7","author":"Biswas","year":"2019","journal-title":"Interpretation"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1190\/tle38070512.1","article-title":"Machine learning and geophysical inversion\u2014A numerical study","volume":"38","author":"Russell","year":"2019","journal-title":"Lead. Edge"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Sun, H., and Demanet, L. (2018). Low frequency extrapolation with deep learning. SEG Technical Program Expanded Abstracts 2018, Society of Exploration Geophysicists.","DOI":"10.1190\/segam2018-2997928.1"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"R989","DOI":"10.1190\/geo2018-0884.1","article-title":"Deep learning for low-frequency extrapolation from multioffset seismic data","volume":"84","author":"Ovcharenko","year":"2019","journal-title":"Geophysics"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3185794","article-title":"Multi-Task Learning for Low-Frequency Extrapolation and Elastic Model Building From Seismic Data","volume":"60","author":"Ovcharenko","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"R275","DOI":"10.1190\/geo2019-0195.1","article-title":"Extrapolated full-waveform inversion with deep learning","volume":"85","author":"Sun","year":"2020","journal-title":"Geophysics"},{"key":"ref_24","first-page":"1","article-title":"Deep Learning for Low-Frequency Extrapolation of Multicomponent Data in Elastic FWI","volume":"60","author":"Sun","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1259","DOI":"10.1190\/1.1441754","article-title":"Inversion of seismic reflection data in the acoustic approximation","volume":"49","author":"Tarantola","year":"1984","journal-title":"Geophysics"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Schuster, G.T. (2017). Seismic Inversion, Society of Exploration Geophysicists.","DOI":"10.1190\/1.9781560803423"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015: 18th International Conference, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Akhiyarov, D., Gherbi, A., and Araya-Polo, M. (2020, January 22\u201324). Machine Learning Scalability Requires High Performance Computing Strategies. Proceedings of the First EAGE Conference on Machine Learning in Americas, Online.","DOI":"10.3997\/2214-4609.202084018"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1318","DOI":"10.1121\/1.3290999","article-title":"Convolutional perfectly matched layer for elastic second-order wave equation","volume":"127","author":"Li","year":"2010","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/1\/61\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:45:30Z","timestamp":1760147130000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/1\/61"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,21]]},"references-count":30,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["s23010061"],"URL":"https:\/\/doi.org\/10.3390\/s23010061","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,21]]}}}