Physics-aware Bayesian Vision Transformer enables structure-level uncertainty in seismic inversion

A Physics-aware Bayesian Vision Transformer for Seismic AVO Inversion: Towards an Embodied Structural Intelligence Framework with Structure-aware Uncertainty Modeling | 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 Physics-aware Bayesian Vision Transformer for Seismic AVO Inversion: Towards an Embodied Structural Intelligence Framework with Structure-aware Uncertainty Modeling Zhen Liu, Junhua Zhang, Yongrui Chen, Deyong Feng, Liang Qi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7097139/v3 This work is licensed under a CC BY 4.0 License Status: Posted Version 3 posted You are reading this latest preprint version Show more versions Abstract Traditional seismic inversion frameworks struggle to preserve spatial structure and to quantify model reliability. We present a next-generation pathway that progresses from a convolutional Physics-Informed Neural Network (PINN) to a Bayesian PINN (BPINN) with uncertainty modeling, and culminates in a Bayesian Physics-Informed Vision Transformer (BPI-ViT) that enables structure-level uncertainty quantification. In our formulation, PINN “training data” are equation-domain samples used to minimize physical residuals—supporting physics-driven, data-agnostic generalization—while BPI-ViT integrates multi-layer self-attention and Bayesian inference to transition from pixel-level optimization to structure-aware collaboration. Consistent evaluation on the Marmousi2 benchmark and validation on field-scale CO₂ EOR monitoring data show that BPI-ViT outperforms prior methods in target-horizon recovery, fault and anomaly detection, spatial continuity, and uncertainty quantification, while maintaining physical consistency. These results establish a structural-intelligent paradigm that moves seismic inversion beyond error minimization toward structure-aware, reliable, and cognitively informed modeling, and provide a foundation for future multi-physics and complex-geology applications. Earth and environmental sciences/Solid Earth sciences/Geophysics Earth and environmental sciences/Solid Earth sciences/Seismology Pre-stack AVO inversion Physics-Informed Neural Networks (PINN) Bayesian PINN (BPINN) BPI-ViT Vision Transformer uncertainty quantification structure-aware modeling Zoeppritz equations interpretability. Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 3 posted You are reading this latest preprint version Show more versions 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. 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