The NuTS (Numérique en Terre Solide) network is organizing a thematic event focused on the intersection of artificial intelligence and Earth sciences. This two-day gathering will be held at the Institut de Physique du Globe de Paris from Monday noon, July 7, to early afternoon on Tuesday, July 8. The event features two half-day sessions with invited speakers designed to foster interdisciplinary dialogue: one emphasizing scientific challenges in the geosciences community, and the other highlighting methodological advances in AI. A convivial gathering between the sessions will provide opportunities for informal exchange and networking. The primary goal is to stimulate new collaborations between researchers in Earth sciences and artificial intelligence, encouraging mutual understanding and joint innovation. The organizers also intend this event to be the first in a recurring series that strengthens ties between these communities. Participation in the event is free, with venue and refreshments covered by the organizers; however, attendees are expected to use their own research funds for travel and accommodation. Attendees will have the opportunity to present their work in form of a poster during the poster session.
Please consider this as a tentative program. The titles will be announced later.
Monday, July 7
13:55 — Welcoming speech
Session AI in Earth Sciences
14h
Quentin Bletery — AI-driven earthquake and tsunami early warning
Charles Le Losq — Combining artificial neural networks and Gaussian process for tackling regression problems: example of magma viscosity on exoplanets.
Jannes Münchmeyer — What can deep learning earthquake catalogs actually tell us?
Catherine Pothier — Unsupervised data mining technique to extract patterns corresponding to aseismic SLOW slip events: application to the Izmit section of the north Anatolian fault
15h30
Spotlight poster presentations
Coffee break
16h30
Antoine Lucas — Can AI be leveraged to revisit unsolved problems in critical zone research?
Giuseppe Costantino — Signal extraction from noisy geospatial data: detecting transient deformations through spatiotemporal deep learning
Joachim Rimpot — Self-Supervised learning for the exploration of large seismological datasets
Andrea Tomasi — Towards the prediction of elastic (seismic) anisotropy in the upper mantle using supervised deep-learning
18h Cocktail
Tuesday, July 8
Session AI
9h
Christophe Rigotti — TBD
Sylvain Lobry — Foundation models for remote sensing: which one(s)?
11h Coffee break
11h30
Erwan Allys — Unsupervised modeling and separation of seismic signals with scattering transforms
Stéphanie Allassonnière — Taking advantage of geometry in Variational Autoencoders for data representation and augmentation