| Issue |
E3S Web Conf.
Volume 700, 2026
Journées Scientifiques AGAP Qualité 2026
|
|
|---|---|---|
| Article Number | 00001 | |
| Number of page(s) | 40 | |
| DOI | https://doi.org/10.1051/e3sconf/202670000001 | |
| Published online | 23 March 2026 | |
Geophysics and Artificial Intelligence: What Revolutions?
Géophysique et intelligence artificielle : quelles révolutions ?
Earth Resource Management Services (ERM.S) 77210 Avon, France
* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract
The relationship between Geophysics and Artificial Intelligence is examined through the lens of the ongoing digital transformation, driven by three major revolutions that have reshaped human interaction with the natural environment: the computer revolution, the quantum revolution, and the probabilistic revolution. Within this framework, Earth Intelligence emerges as the mathematical and conceptual bridge that reconciles Geophysics with Artificial Intelligence. By integrating physical laws, probabilistic modelling, and data-driven learning, Earth Intelligence provides a unified approach that enhances the ability of geophysics to deliver more accurate, scalable, and uncertainty-aware representations of the subsurface.
Résumé
La relation entre la géophysique et l’intelligence artificielle est analysée à travers le prisme de la transformation numérique en cours, portée par trois grandes révolutions qui ont profondément modifié l’interaction de l’être humain avec son environnement naturel : la révolution informatique, la révolution quantique et la révolution probabiliste. Dans ce cadre, l’Earth Intelligence apparaît comme le pont mathématique et conceptuel permettant de réconcilier la géophysique et l’intelligence artificielle. En intégrant les lois physiques, la modélisation probabiliste et l’apprentissage fondé sur les données, l’Earth Intelligence propose une approche unifiée qui renforce la capacité de la géophysique à produire des représentations du sous-sol plus précises, plus évolutives et explicitement conscientes des incertitudes.
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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