{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T10:16:09Z","timestamp":1783160169188,"version":"3.54.6"},"reference-count":44,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62576325"],"award-info":[{"award-number":["62576325"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.eswa.2026.132585","type":"journal-article","created":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T18:46:32Z","timestamp":1777315592000},"page":"132585","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A domain-aware deep learning framework for unified seismic analysis: integrating frequency-spatial-temporal features with trace attention"],"prefix":"10.1016","volume":"324","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-4992-307X","authenticated-orcid":false,"given":"Hao","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7462-4238","authenticated-orcid":false,"given":"Hong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5555-0734","authenticated-orcid":false,"given":"Xiang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9071-3618","authenticated-orcid":false,"given":"Ming","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1610-6865","authenticated-orcid":false,"given":"Wenyin","family":"Gong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2026.132585_b0005","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3210899","article-title":"Seismic velocity model building using recurrent neural networks: A frequency-stepping approach","volume":"60","author":"Alzahrani","year":"2022","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"10.1016\/j.eswa.2026.132585_b0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.cageo.2025.105916","article-title":"Unsupervised seismic reconstruction via deep learning with one-dimensional signal representation","volume":"200","author":"Chen","year":"2025","journal-title":"Computers & Geosciences"},{"key":"10.1016\/j.eswa.2026.132585_b0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.128973","article-title":"A conditional masked autoencoder network based on efficient multiple-head self-attention for characterizing heterogeneous reservoirs","volume":"296","author":"Chen","year":"2026","journal-title":"Expert Systems with Applications"},{"issue":"2","key":"10.1016\/j.eswa.2026.132585_b0020","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1007\/s10712-023-09801-z","article-title":"Self-supervised multistep seismic data deblending","volume":"45","author":"Chen","year":"2024","journal-title":"Surveys in Geophysics"},{"key":"10.1016\/j.eswa.2026.132585_b0025","series-title":"SEG\/AAPG international meeting for applied geoscience & energy","article-title":"Seismic resolution enhancement with self-supervised learning","author":"Cheng","year":"2024"},{"key":"10.1016\/j.eswa.2026.132585_b0030","first-page":"1","article-title":"Self-supervised seismic resolution enhancement","volume":"63","author":"Cheng","year":"2025","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"issue":"5","key":"10.1016\/j.eswa.2026.132585_b0035","doi-asserted-by":"crossref","DOI":"10.1190\/geo2023-0508.1","article-title":"Self-supervised learning for efficient seismic facies classification","volume":"89","author":"Chikhaoui","year":"2024","journal-title":"Geophysics"},{"issue":"11","key":"10.1016\/j.eswa.2026.132585_b0040","doi-asserted-by":"crossref","DOI":"10.1029\/2024EA003565","article-title":"Low-frequency reconstruction for full waveform inversion by unsupervised learning","volume":"11","author":"Cui","year":"2024","journal-title":"Earth and Space Science"},{"key":"10.1016\/j.eswa.2026.132585_b0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.123617","article-title":"3D seismic fault detection via contrastive-reconstruction representation learning","volume":"249","author":"Dou","year":"2024","journal-title":"Expert Systems with Applications"},{"issue":"3","key":"10.1016\/j.eswa.2026.132585_b0050","doi-asserted-by":"crossref","first-page":"M79","DOI":"10.1190\/geo2023-0550.1","article-title":"FaultSSL: Seismic fault detection via semisupervised learning","volume":"89","author":"Dou","year":"2024","journal-title":"Geophysics"},{"key":"10.1016\/j.eswa.2026.132585_b0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.cageo.2024.105731","article-title":"MAMCL: Multi-attributes Masking Contrastive Learning for explainable seismic facies analysis","volume":"193","author":"Han","year":"2024","journal-title":"Computers & Geosciences"},{"key":"10.1016\/j.eswa.2026.132585_b0060","first-page":"1","article-title":"Seismic full waveform inversion with uncertainty analysis using unsupervised variational deep learning","volume":"63","author":"Jia","year":"2025","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"10.1016\/j.eswa.2026.132585_b0065","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2024.3440508","article-title":"Semi-supervised seismic impedance inversion with convolutional neural network and lightweight transformer","volume":"62","author":"Lang","year":"2024","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"10.1016\/j.eswa.2026.132585_b0070","first-page":"1","article-title":"Simultaneous off-the-grid deblending and data reconstruction via unsupervised deep learning","volume":"63","author":"Li","year":"2025","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"issue":"6","key":"10.1016\/j.eswa.2026.132585_b0075","doi-asserted-by":"crossref","first-page":"V537","DOI":"10.1190\/geo2024-0098.1","article-title":"Robust unsupervised 5D seismic data reconstruction on regular and irregular grids","volume":"89","author":"Li","year":"2024","journal-title":"Geophysics"},{"issue":"5","key":"10.1016\/j.eswa.2026.132585_b0080","doi-asserted-by":"crossref","first-page":"V437","DOI":"10.1190\/geo2023-0762.1","article-title":"Robust seismic data denoising via self-supervised deep learning","volume":"89","author":"Li","year":"2024","journal-title":"Geophysics"},{"key":"10.1016\/j.eswa.2026.132585_b0085","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2024.111894","article-title":"A method for seismic fault identification based on self-training with high-stability pseudo-labels","volume":"163","author":"Li","year":"2024","journal-title":"Applied Soft Computing"},{"issue":"2","key":"10.1016\/j.eswa.2026.132585_b0090","doi-asserted-by":"crossref","first-page":"M17","DOI":"10.1190\/geo2023-0214.1","article-title":"Probabilistic physics-informed neural network for seismic petrophysical inversion","volume":"89","author":"Li","year":"2024","journal-title":"Geophysics"},{"issue":"3","key":"10.1016\/j.eswa.2026.132585_b0095","doi-asserted-by":"crossref","first-page":"V223","DOI":"10.1190\/geo2024-0502.1","article-title":"From shallow to deep: Enhancing seismic resolution with weak supervision","volume":"90","author":"Liu","year":"2025","journal-title":"Geophysics"},{"key":"10.1016\/j.eswa.2026.132585_b0100","series-title":"Advances in subsurface data analytics","first-page":"95","article-title":"Recurrent neural network for seismic reservoir characterization","author":"Liu","year":"2022"},{"issue":"20","key":"10.1016\/j.eswa.2026.132585_b0105","doi-asserted-by":"crossref","first-page":"7452","DOI":"10.3390\/en15207452","article-title":"Analysis of deep learning neural networks for seismic impedance inversion: A benchmark study","volume":"15","author":"Marques","year":"2022","journal-title":"Energies"},{"key":"10.1016\/j.eswa.2026.132585_b0110","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.119256","article-title":"Fast convex set projection with deep prior for seismic interpolation","volume":"213","author":"Min","year":"2023","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132585_b0115","doi-asserted-by":"crossref","unstructured":"Saad, O. M., & Alkhalifah, T. (2025). Self-supervised deep learning framework for multi-source full wave inversion. In 86th EAGE Annual Conference & Exhibition (Vol. 2025, pp. 1-5). European Association of Geoscientists & Engineers. https:\/\/doi.org\/10.3997\/2214-4609.202510046.","DOI":"10.3997\/2214-4609.202510046"},{"issue":"4","key":"10.1016\/j.eswa.2026.132585_b0120","doi-asserted-by":"crossref","first-page":"V319","DOI":"10.1190\/geo2023-0637.1","article-title":"Unsupervised deep-learning framework for 5D seismic denoising and interpolation","volume":"89","author":"Saad","year":"2024","journal-title":"Geophysics"},{"key":"10.1016\/j.eswa.2026.132585_b0125","doi-asserted-by":"crossref","DOI":"10.1016\/j.petrol.2021.109549","article-title":"Semi-supervised learning seismic inversion based on Spatio-temporal sequence residual modeling neural network","volume":"208","author":"Song","year":"2022","journal-title":"Journal of Petroleum Science and Engineering"},{"key":"10.1016\/j.eswa.2026.132585_b0130","first-page":"1","article-title":"Unsupervised seismic data denoising using diffusion denoising model","volume":"63","author":"Sun","year":"2025","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"10.1016\/j.eswa.2026.132585_b0135","article-title":"Intelligent AVA inversion using a convolution neural network trained with pseudo-well datasets","volume":"1\u201331","author":"Sun","year":"2023","journal-title":"Surveys in Geophysics"},{"issue":"2","key":"10.1016\/j.eswa.2026.132585_b0140","doi-asserted-by":"crossref","first-page":"M87","DOI":"10.1190\/geo2022-0314.1","article-title":"Prestack seismic inversion for elastic parameters using model-data-driven generative adversarial networks","volume":"88","author":"Sun","year":"2023","journal-title":"Geophysics"},{"issue":"4","key":"10.1016\/j.eswa.2026.132585_b0145","doi-asserted-by":"crossref","first-page":"891","DOI":"10.3390\/rs15040891","article-title":"Acoustic impedance inversion from seismic imaging profiles using self attention U-Net","volume":"15","author":"Tao","year":"2023","journal-title":"Remote Sensing"},{"issue":"3","key":"10.1016\/j.eswa.2026.132585_b0150","doi-asserted-by":"crossref","first-page":"1098","DOI":"10.1016\/j.petsci.2024.12.023","article-title":"Self-supervised simultaneous deblending and interpolation of incomplete blended data using a multistep blind-trace U-Net","volume":"22","author":"Wang","year":"2025","journal-title":"Petroleum Science"},{"issue":"3","key":"10.1016\/j.eswa.2026.132585_b0155","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1007\/s11004-023-10089-3","article-title":"Seismic data denoising using a self-supervised deep learning network","volume":"56","author":"Wang","year":"2024","journal-title":"Mathematical Geosciences"},{"key":"10.1016\/j.eswa.2026.132585_b0160","article-title":"S-wave velocity inversion and prediction using a deep hybrid neural network","volume":"1\u201318","author":"Wang","year":"2022","journal-title":"Science China Earth Sciences"},{"key":"10.1016\/j.eswa.2026.132585_b0165","article-title":"Self-Supervised diffusion model for 3D seismic data reconstruction","volume":"1\u2013124","author":"Wang","year":"2025","journal-title":"Geophysics"},{"key":"10.1016\/j.eswa.2026.132585_b0170","doi-asserted-by":"crossref","first-page":"77522","DOI":"10.1109\/ACCESS.2025.3562544","article-title":"Efficient sedimentary facies recognition using vision transformer and weakly supervised deep multi-view clustering","volume":"13","author":"Wu","year":"2025","journal-title":"IEEE Access"},{"key":"10.1016\/j.eswa.2026.132585_b0175","doi-asserted-by":"crossref","DOI":"10.1109\/JSTARS.2023.3262679","article-title":"Attention and Hybrid loss guided 2D network for seismic impedance inversion","author":"Xie","year":"2023","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"key":"10.1016\/j.eswa.2026.132585_b0180","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.126249","article-title":"3D Saltseg-CL: Unsupervised embedding characterization based multi-task dense prediction method for 3D salt bodies","volume":"267","author":"Xu","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132585_b0185","first-page":"1","article-title":"Reflection coefficients inversion based on the bidirectional long short-term memory network","volume":"19","author":"Yang","year":"2022","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"issue":"2","key":"10.1016\/j.eswa.2026.132585_b0190","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1111\/1365-2478.13665","article-title":"Unsupervised learning inversion of seismic velocity models based on a multi\u2010scale strategy","volume":"73","author":"Yang","year":"2025","journal-title":"Geophysical Prospecting"},{"issue":"1","key":"10.1016\/j.eswa.2026.132585_b0195","doi-asserted-by":"crossref","first-page":"R95","DOI":"10.1190\/geo2021-0794.1","article-title":"Regularization of anisotropic full-waveform inversion with multiple parameters by adversarial neural networks","volume":"88","author":"Yao","year":"2023","journal-title":"Geophysics"},{"issue":"12","key":"10.1016\/j.eswa.2026.132585_b0200","doi-asserted-by":"crossref","DOI":"10.3390\/app14125214","article-title":"Seismic blind deconvolution based on self-supervised machine learning","volume":"14","author":"Yin","year":"2024","journal-title":"Applied Sciences"},{"key":"10.1016\/j.eswa.2026.132585_b0205","first-page":"1","article-title":"Unsupervised diffusion model for seismic deconvolution","volume":"22","author":"Yu","year":"2025","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"key":"10.1016\/j.eswa.2026.132585_b0210","first-page":"1","article-title":"Post-stack impedance inversion based on spatio-temporal neural network","volume":"19","author":"Zhang","year":"2022","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"issue":"5","key":"10.1016\/j.eswa.2026.132585_b0215","doi-asserted-by":"crossref","DOI":"10.3390\/rs16050922","article-title":"Improving seismic fault recognition with self-supervised pre-training: A study of 3D transformer-based with multi-scale decoding and fusion","volume":"16","author":"Zhang","year":"2024","journal-title":"Remote Sensing"},{"key":"10.1016\/j.eswa.2026.132585_b0220","first-page":"1","article-title":"Physical model and super-resolution theory-guided unsupervised deep learning deconvolution for seismic resolution enhancement","volume":"63","author":"Zhao","year":"2025","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426014983?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426014983?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T09:25:41Z","timestamp":1783157141000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417426014983"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":44,"alternative-id":["S0957417426014983"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132585","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A domain-aware deep learning framework for unified seismic analysis: integrating frequency-spatial-temporal features with trace attention","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132585","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"132585"}}