A Hybrid CNN-LSTM Deep Learning Approach for Basement Depth Prediction in Structurally Complex Sedimentary Basins Using Gravity Data

A Hybrid CNN-LSTM Deep Learning Approach for Basement Depth Prediction in Structurally Complex Sedimentary Basins Using Gravity Data | 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 A Hybrid CNN-LSTM Deep Learning Approach for Basement Depth Prediction in Structurally Complex Sedimentary Basins Using Gravity Data Osama Elghrabawy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7794259/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Mar, 2026 Read the published version in Earth Science Informatics → Version 1 posted 11 You are reading this latest preprint version Abstract The inversion of gravity data can be used to detect sediment-basement interfaces, which is crucial for understanding basin architecture and tectonic evolution. It is true that traditional inversion techniques are powerful, but they have issues related to their non-uniqueness, their computational cost, and their reliance on initial models. In this paper, a new deep learning-based method for inverting gravity anomaly profiles using Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks is presented. Synthetic gravity data were generated from 30,000 forward-modeled basin geometries with stochastic variability. With synthetic profiles, CNN-LSTM architecture is trained and validated based on normalized Bouguer anomalies. In synthetic validation data, a hybrid CNN-LSTM architecture successfully recovered both basin shapes and high-frequency structural features to high extent and reveals an average error less than 10%. A comparison of the model's accuracy with seismic-derived depths in Wadi Kharit Basin (Eastern Desert, Egypt) reveals an average error of 15%. Hybrid inversion outperformed conventional depth methods such as Source Parameter Imaging (SPI) and slightly exceeded constrained inversion for forward gravity match accuracy. The hybrid deep learning approach offers an alternative to conventional gravity inversion. These results illustrate the potential for deep learning-based inversion to enhance subsurface structural interpretation, especially in structurally complex or data-limited circumstances. Gravity Inversion CNN LSTM Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 27 Mar, 2026 Read the published version in Earth Science Informatics → Version 1 posted Editorial decision: Revision requested 25 Nov, 2025 Reviews received at journal 25 Nov, 2025 Reviews received at journal 10 Nov, 2025 Reviewers agreed at journal 01 Nov, 2025 Reviewers agreed at journal 31 Oct, 2025 Reviewers agreed at journal 30 Oct, 2025 Reviewers agreed at journal 30 Oct, 2025 Reviewers invited by journal 30 Oct, 2025 Editor assigned by journal 30 Oct, 2025 Submission checks completed at journal 08 Oct, 2025 First submitted to journal 06 Oct, 2025 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. 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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-7794259","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":542649214,"identity":"da21e64a-26ad-4586-875c-536698df8c4b","order_by":0,"name":"Osama 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