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AI fusion: Better geoscience models and seismic interpretation. For more videos about geology, geochemistry, AI, and much more, please visit and subscribe for free here: Golden droplets- https://shorturl.at/fetV1 Geovoices- https://tinyurl.com/m23pp4pb News about geology- https://tinyurl.com/3979urhy This video explores integrated geosciences, highlighting the synergy of geology and geophysics through multiphysics data fusion and advanced joint inversion techniques, critical for modern geological exploration and subsurface imaging. Learn how AI and machine learning (ML), including physics-informed AI and generative AI models, are accelerating seismic interpretation and improving geophysical modeling by leveraging petrophysical constraints and mathematical approaches like Gramian constraints to solve ambiguity. The seamless synthesis of geological observation and geophysical interpretation is essential for the robust construction of an accurate, predictive model of the Earth. A key example is the application of Gramian-based structural constraints to combine airborne electromagnetic (AEM) and total magnetic intensity (TMI) data for enhanced mineral exploration at the Reid-Mahaffy test site, yielding models with enhanced structural similarity compared to standalone inversions. In energy applications, this integrated approach is paramount for hydrocarbon exploration and geothermal exploration, where systematic de-risking relies on unifying geological basin models with subsurface seismic data to define trapping structures and fluid conduits. The bridge between Academy and Industry! P. Geo. Ricardo A Valls, M. Sc. and Geo Gadfly Valls Geoconsultant ORCID ID- https://orcid.org/0000-0002-5421-0914 Scopus Author ID: 7003369619/35335510700 ResearcherID: S-6604-2018 If you like this content, please "buy me a coffee" https://www.buymeacoffee.com/goldendroplets #valls_geoconsultant #JointInversion #GeophysicalModeling #AIinGeology References 1. Alaska Division of Geological & Geophysical Surveys. (n.d.). Energy resources - Cook Inlet. Retrieved from Alaska Division of Geological & Geophysical Surveys. 2. Close, D. I. (2012, February). Geophysical constraints in geostatistical modelling. [Source Name Missing], 37(2). 3. Earth Science Stack Exchange. (n.d.). What is the difference between a geologist and a geophysicist? [Online forum post]. Retrieved from Earth Science Stack Exchange. 4. Fairfield Geotechnologies. (2025, July 31). Human insight, machine intelligence: The next chapter in seismic interpretation. Retrieved from Fairfield Geotechnologies. 5. GeoMark Research. (n.d.). Basin modeling. Retrieved from GeoMark Research. 6. Johnsen, E. (2008, August 7). Incorporating geophysics into geologic models. GeoExpro. 7. Light, S. (2008). Geological constraints on geophysical modelling and inversion. CRC LEME. 8. Ma, X. (2021). Data science for geoscience: Recent progress and future trends from the perspective of a data life cycle [Preprint]. EarthArXiv. 9. Morris, W. A., Spicer, B., Tschirhart, P., Tschirhart, V., Lee, M. D., & Ugalde, H. (2012). Integrating geological constraints in geophysical models. SEG Technical Program Expanded Abstracts. https://doi.org/10.1190/segam2012-0487.1 10. Rafiq, J., Abu-Mahfouz, I. S., Chavanidis, K., & Soupios, P. (2025). Integrated structural analysis for geothermal exploration: A new protocol combining remote sensing and aeromagnetic geophysical data. MethodsX, 14, 103189. https://doi.org/10.1016/j.mex.2025.103189 11. State of California. (2008). Consumer guide to geological and geophysical services publications for consumers. Retrieved from State of California. 12. The integrated geoscience imperative: Synergy, discovery, and the computational future. (n.d.). [Unpublished manuscript]. 13. Wilson, H. (2021). The hydrocarbon exploration process. In H. Wilson, K. Nunn, & M. Luheshi (Eds.), Integration of geophysical technologies in the petroleum industry (pp. 7–38). Cambridge University Press. https://doi.org/10.1017/9781108913256.002 14. Zhao, T., Wang, S., Ouyang, C., Chen, M., Liu, C., Zhang, J., Yu, L., Wang, F., Xie, Y., Li, J., Wang, F., Grunwald, S., Wong, B. M., Zhang, F., Qian, Z., Xu, Y., Yu, C., Han, W., Sun, T., . . . Wang, L. (2024). Artificial intelligence for geoscience: Progress, challenges, and perspectives. [Journal Name Missing], 5(5), 100691. https://doi.org/10.1016/j.xinn.2024.100691 15. Zhdanov, M. S., Jorgensen, M., & Cox, L. H. (2021). Advanced methods of joint inversion of multiphysics data for mineral exploration. Geosciences, 11(6), 262. https://doi.org/10.3390/geosciences11060262