{"paper_id":"2b600287-b966-4e2b-a5f7-dda855baa4a0","body_text":"Automatic Salt Segmentation Using Deep Learning Techniques | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Automatic Salt Segmentation Using Deep Learning Techniques Gaurang Jadhav, Jeeya Shah, Dhruv Vaghani, Jyoti Wadmare This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4360581/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Detecting salt boundaries in seismic images is critical for subsurface reservoir characterization and oil and gas exploration. The presence of salt in seismic data often indicates the presence of valuable hydrocarbon resources, which could lead to significant oil discoveries. Traditional manual interpretation methods have limitations, prompting the industry to embrace deep learning techniques. Our proposed system extensively evaluates the effectiveness of deep learning models in salt boundary detection. Our approach involves developing a custom residual encoder-decoder model and comparing it against two existing models: Res-UNet and UNet. The advantage of our custom-built residual encoder-decoder model lies in its utilization of transposed convolutions for image segmentation. Unlike regular convolutions that extract features and reduce image size, transposed convolutions expand the image, potentially introducing new information from seismic data. The custom model emerges superior to UNet and Res-UNet models, exhibiting an accuracy of 94.28% and a precision score of 0.86. A series of comparative analysis is drawn with a main focus on transforming the automated salt segmentation process. CNN(Convolutional Neural Network) Salt boundary detection Deep learning models Seismic Images ResUNet Unet Residual Encoder-Decoder Comparative Analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-4360581\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":298573882,\"identity\":\"1927fdfd-8020-473d-9df5-e1bba008e389\",\"order_by\":0,\"name\":\"Gaurang 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