{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T12:03:24Z","timestamp":1777982604621,"version":"3.51.4"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T00:00:00Z","timestamp":1774310400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T00:00:00Z","timestamp":1774310400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the Natural Science Foundation of Hebei Province of China","award":["D2022107001"],"award-info":[{"award-number":["D2022107001"]}]},{"name":"the Youth Foundation of Northeast Petroleum University","award":["2020QNQ-01"],"award-info":[{"award-number":["2020QNQ-01"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Earth Sci Inform"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s12145-026-02103-z","type":"journal-article","created":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T09:00:54Z","timestamp":1774342854000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["3-D Seismic fault identification using dual-decoder network with multi-feature fusion"],"prefix":"10.1007","volume":"19","author":[{"given":"Lili","family":"Zeng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Limin","family":"Dai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyuan","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianpeng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaonan","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,24]]},"reference":[{"key":"2103_CR2","doi-asserted-by":"crossref","unstructured":"Alcalde J, Bond CE, Johnson G et al (2019) Fault interpretation in seismic reflection data: An experiment analysing the impact of conceptual model anchoring and vertical exaggeration. 10(5):1651\u20131662","DOI":"10.5194\/se-10-1651-2019"},{"key":"2103_CR3","doi-asserted-by":"crossref","unstructured":"Bahorich M, Farmer SJTL (1995) 3-D seismic discontinuity for faults and stratigraphic features: the coherence cube. 14(10):1053\u20131058E","DOI":"10.1190\/1.1437077"},{"issue":"12","key":"2103_CR12","first-page":"87","volume":"37","author":"B Bai","year":"2017","unstructured":"Bai B, Qin Z (2017) Research and application of seismic coherent volume algorithm. Chin Pet Chem Stand Q 37(12):87\u201389","journal-title":"Chin Pet Chem Stand Q"},{"key":"2103_CR32","doi-asserted-by":"crossref","unstructured":"B\u00f6nke W, Alaei B et al (2024) Data augmentation for 3D seismic fault interpretation using deep learning. 162:106706","DOI":"10.1016\/j.marpetgeo.2024.106706"},{"key":"2103_CR30","unstructured":"Cai Y (2020) Intelligent recognition and reconstruction of 3D seismic faults"},{"key":"2103_CR29","doi-asserted-by":"crossref","unstructured":"Choi B, Pyun S, Choi W et al (2025) Synthetic seismic data generation with pix2pix for enhanced fault detection model training. 105879","DOI":"10.1016\/j.cageo.2025.105879"},{"key":"2103_CR10","doi-asserted-by":"crossref","unstructured":"Gao DJG (2013) Integrating 3D seismic curvature and curvature gradient attributes for fracture characterization: Methodologies and interpretational implications. 78(2): O21\u2013O31","DOI":"10.1190\/geo2012-0190.1"},{"key":"2103_CR23","doi-asserted-by":"crossref","unstructured":"Gao K, Huang L, Zheng YJITOG et al (2021) Fault detection on seismic structural images using a nested residual U-Net. 60:1\u201315","DOI":"10.1109\/TGRS.2021.3073840"},{"key":"2103_CR34","doi-asserted-by":"publisher","first-page":"110449","DOI":"10.1016\/j.ress.2024.110449","volume":"252","author":"T Gao","year":"2024","unstructured":"Gao T, Yang J, Wang W et al (2024) A domain feature decoupling network for rotating machinery fault diagnosis under unseen operating conditions. Reliab Eng Syst Saf 252:110449","journal-title":"Reliab Eng Syst Saf"},{"key":"2103_CR9","unstructured":"Gogoi M, Ghosh GJTJOIGI (2017) Interpretation of Seismic data for thrust\/fault identification using variance and inverse of variance attribute analysis. 21:500\u2013506"},{"key":"2103_CR22","doi-asserted-by":"crossref","unstructured":"Guo B, Li L, Luo Y (2018) A new method for automatic seismic fault detection using convolutional neural network; proceedings of the SEG International Exposition and Annual Meeting. SEG","DOI":"10.1190\/segam2018-2995894.1"},{"key":"2103_CR11","doi-asserted-by":"crossref","unstructured":"Jing Z, Yanqing Z, Zhigang C et al (2007) Detecting boundary of salt dome in seismic data with edge detection technique; proceedings of the SEG International Exposition and Annual Meeting. SEG","DOI":"10.1190\/1.2792759"},{"key":"2103_CR24","unstructured":"Kometa BK Hou J (2024) Automated seismic fault detection using computer vision and deep learning techniques. UIS"},{"key":"2103_CR8","doi-asserted-by":"crossref","unstructured":"Li J, He M, Cui G et al (2020) A novel method of seismic signal detection using waveform features. 10(8):2919","DOI":"10.3390\/app10082919"},{"key":"2103_CR14","doi-asserted-by":"crossref","unstructured":"Liu H (2013) Improvement of C3 coherence algorithm and its application in fault recognition","DOI":"10.11591\/telkomnika.v11i9.3308"},{"key":"2103_CR35","doi-asserted-by":"publisher","first-page":"103931","DOI":"10.1016\/j.aei.2025.103931","volume":"69","author":"Y Li","year":"2026","unstructured":"Li Y, Yang J, Wang W et al (2026) A joint collaborative adaptation network for fault diagnosis of rolling bearing under class imbalance and variable operating conditions. Adv Eng Inform 69:103931","journal-title":"Adv Eng Inform"},{"issue":"03","key":"2103_CR28","first-page":"1100","volume":"37","author":"F Lu","year":"2022","unstructured":"Lu F, Meng R, Zhang J et al (2022) Research on complex fault identification method combining UNet\u2009+\u2009+\u2009and transfer learning. Prog Geophy 37(03):1100\u20131111","journal-title":"Prog Geophy"},{"key":"2103_CR13","doi-asserted-by":"crossref","unstructured":"Marfurt KJ, Kirlin RL, Farmer SL et al (1998) 3-D seismic attributes using a semblance-based coherency algorithm. 63(4):1150\u20131165","DOI":"10.1190\/1.1444415"},{"key":"2103_CR7","doi-asserted-by":"crossref","unstructured":"Marfurt KJ, Sudhaker V et al (1999) Coherency calculations in the presence of structural dip. 64(1):104\u2013111","DOI":"10.1190\/1.1444508"},{"key":"2103_CR26","doi-asserted-by":"crossref","unstructured":"Ma X, Yao G, Zhang F et al (2023) 3-D seismic fault detection using recurrent convolutional neural networks with compound loss. 61:1\u201315","DOI":"10.1109\/TGRS.2023.3275951"},{"key":"2103_CR16","doi-asserted-by":"crossref","unstructured":"Pedersen SI, Randen T, S\u00f8nneland L et al (2002) Automatic fault extraction using artificial ants; proceedings of the Seg international exposition and annual meeting. SEG","DOI":"10.1190\/1.1817297"},{"key":"2103_CR17","doi-asserted-by":"crossref","unstructured":"Pedersen SI, Skov T, Hetlelid A et al (2003) New paradigm of fault interpretation; proceedings of the SEG international exposition and annual meeting. SEG","DOI":"10.3997\/2214-4609-pdb.6.B25"},{"key":"2103_CR15","doi-asserted-by":"crossref","unstructured":"Randen T, Pedersen SI, S\u00f8nneland L (2001) Automatic extraction of fault surfaces from three-dimensional seismic data; proceedings of the SEG International Exposition and Annual Meeting. SEG","DOI":"10.1190\/1.1816675"},{"key":"2103_CR20","doi-asserted-by":"crossref","unstructured":"Sun Q, Wang X et al (2023) Fault identification of U-Net based on enhanced feature fusion and attention mechanism. 12(12):2562","DOI":"10.3390\/electronics12122562"},{"key":"2103_CR31","doi-asserted-by":"crossref","unstructured":"Tang Z, Wu B, Wu W et al (2023) Fault detection via 2.5 d transformer u-net with seismic data pre-processing. 15(4):1039","DOI":"10.3390\/rs15041039"},{"key":"2103_CR6","doi-asserted-by":"crossref","unstructured":"Tingdahl KM, De Rooij MJGP (2005) Semi-automatic detection of faults in 3D seismic data. 53(4):533\u2013542","DOI":"10.1111\/j.1365-2478.2005.00489.x"},{"key":"2103_CR4","doi-asserted-by":"crossref","unstructured":"Wu X, Geng Z, Shi Y et al (2020) Building realistic structure models to train convolutional neural networks for seismic structural interpretation. 85(4):WA27\u2013WA39","DOI":"10.1190\/geo2019-0375.1"},{"key":"2103_CR21","doi-asserted-by":"crossref","unstructured":"XIONG W, Ji X et al (2018) Seismic fault detection with convolutional neural network. 83(5):O97\u2013O103","DOI":"10.1190\/geo2017-0666.1"},{"issue":"12","key":"2103_CR27","first-page":"249","volume":"47","author":"Y Xi","year":"2021","unstructured":"Xi Y, Li K, Xu Y et al (2021) An SPD-UNet model for seismic tomography image recognition. J Comput Eng 47(12):249\u2013255","journal-title":"J Comput Eng"},{"issue":"04","key":"2103_CR18","first-page":"614","volume":"46","author":"Z Yan","year":"2011","unstructured":"Yan Z, Gu H, Cai C et al (2011) The directional constrained ant colony algorithm was used to identify faults. Geophys Prospect Pet 46(04):614\u2013620","journal-title":"Geophys Prospect Pet"},{"key":"2103_CR25","doi-asserted-by":"crossref","unstructured":"Yu T, Wang X, Chen T J et al (2022) Fault recognition method based on attention mechanism and the 3D-UNet. 2022(1):9856669","DOI":"10.1155\/2022\/9856669"},{"issue":"01","key":"2103_CR19","first-page":"350","volume":"32","author":"R Zhang","year":"2017","unstructured":"Zhang R, Wen X, Li S et al (2017) Application of frequency-division ant tracking to identify deep small faults. Prog Geophy 32(01):350\u2013356","journal-title":"Prog Geophy"},{"key":"2103_CR1","doi-asserted-by":"crossref","unstructured":"Zhang Z, Chen R, Ma JJRS (2024) Improving seismic fault recognition with self-supervised pre-training: a study of 3d transformer-based with multi-scale decoding and fusion. 16(5):922","DOI":"10.3390\/rs16050922"},{"key":"2103_CR5","doi-asserted-by":"crossref","unstructured":"Zhang Z, Yan Z, Jing J et al (2023) Generating paired seismic training data with cycle-consistent adversarial networks. 15(1):265","DOI":"10.3390\/rs15010265"},{"key":"2103_CR33","doi-asserted-by":"crossref","unstructured":"Zhou R, Yao X et al (2021) Learning from unlabelled real seismic data: Fault detection based on transfer learning. 69(6):1218\u20131234","DOI":"10.1111\/1365-2478.13097"}],"container-title":["Earth Science Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-026-02103-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12145-026-02103-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-026-02103-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T06:53:57Z","timestamp":1774680837000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12145-026-02103-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,24]]},"references-count":35,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["2103"],"URL":"https:\/\/doi.org\/10.1007\/s12145-026-02103-z","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-7622248\/v1","asserted-by":"object"}]},"ISSN":["1865-0473","1865-0481"],"issn-type":[{"value":"1865-0473","type":"print"},{"value":"1865-0481","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,24]]},"assertion":[{"value":"15 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and\/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"All participants provided written informed consent before recruitment.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Inform consent"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"51"}}