Gauss-Newton Inversion of CSEM Data via the Adjoint-State Method

Gauss-Newton Inversion of CSEM Data via the Adjoint-State Method | 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 Gauss-Newton Inversion of CSEM Data via the Adjoint-State Method Adriany R. Valente, Deivid Nascimento, Jessé Costa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9186377/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 The Marine Controlled-Source Electromagnetic (CSEM) method plays an important role in offshore hydrocarbon reservoir exploration, serving as a valuable complement to seismic methods for imaging and quantitative characterization of subsurface targets.However, intrinsic electromagnetic signal attenuation, the complexity of the seafloor environment, measurement uncertainties, the demand for higher resolution and more accurate geological information, and the increasing size of data sets pose significant computational challenges in terms of both storage and efficiency.We present a methodology that combines fast finite-difference fictitious time-domainelectromagnetic modeling with a matrix-free second-order Gauss-Newton inversion, based on the adjoint-state method for computing Hessian-vector products in the model domain. For electromagnetic field modeling, we apply the correspondence principle to transform Maxwell\textquotesingle s quasi-static equations into an equivalent formulation in a fictitious dielectric medium, in which electromagnetic fields propagate in a non-conductive medium. This approach enables multi-frequency simulations within a single run while reducing memory requirements.The proposed second-order Gauss-Newton formulation, based on Hessian-vector products, is demonstrated to be feasible through validation and numerical experiments. To properly scale the damping parameter in the second-order inversion,we employ a stochastic estimator of the trace of the Hessian matrix. This stabilization strategy incorporates information about the curvature of the misfit function and proves essential for CSEM data inversion using second-order methods, guiding the weighting of the information contained-in the Hessian matrix. The results show that second-order inversions using Newton-class methods, combined with forward and adjoint modeling in the fictitious time domain, yield models that are representative of the subsurface medium, with few artifacts in the final model, while ensures a balance between memory cost and computational performance. CSEM Gauss-Newton inversion Adjoint-state method Correspondence principle 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-9186377","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":626869503,"identity":"0e91a932-debb-468d-96c6-1f7fed50511a","order_by":0,"name":"Adriany R. 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