Interdisciplinary Institute in Artificial Intelligence

DESCRIPTION

With our current methodologies, the dynamics of Earth's processes remain hidden within rapidly accumulating seismic and geodetic data. AI will bridge the gap between the volume of observations and our incomplete understanding of these processes.
New weak signals shed light on the complex spatiotemporal evolution of the deformation. Our group is at the forefront of large-scale processing of seismological (passive imaging and monitoring) and geodetic data. Our goals are: to detect known and new classes of signals, to disentangle the physical processes at work, and to scale up our workflows.

ACTIVITIES

Multiscale representation of signals
Clustering of continuous records
Detection using vector autoregression
Ambient noise modeling
The applications include microearthquake detection and classification in mining and fluid injection environments, as well as the improvement of ambient noise-based imaging and monitoring—a technique developed by our group.
Our interdisciplinary group implements AI tools to address the complexity and the interplay of scales among the various interacting nonlinear processes acting on the solid and fluid components of the actual medium.

CHAIR EVENTS

Joint work seminars

American Geophysical Union, Fall Meeting 2019, abstract #S52A-05, “Towards Systematic Classification of Seismic Signals with Deep Neural Networks,” Beauce, E.; Cougoulat, G.; Poli, P.; Seydoux, L.; van der Hilst, R. D.; Campillo, M.

American Geophysical Union, Fall Meeting 2019, abstract #S52A-04
Seismic signals and noises clustering with unsupervised deep representation learning, Seydoux, L.; Balestriero, R.; Poli, P.; De Hoop, M. V.; Baraniuk, R.; Campillo, M.

European Geosciences Union General Assembly 2019 Unsupervised learning for identification of seismic signals Maarten de Hoop, Richard Baraniuk, Joan Bruna, Michel Campillo, Hope Jasperson, Stephane Mallat, Tan Nguyen, and Leonard Seydoux

SELECTED LIST OF PUBLICATIONS 

• El Bouch S. et al. (2021) “A Normality Test for Multivariate Dependent Samples,” submitted to the Journal of Computational Statistics and Data Analysis, Elsevier.

• El-Bouch, S., et al. “Joint Normality Test via Two-dimensional Projection.” R. Steinmann, L. Seydoux, and M. Campillo (2021), “Hierarchical exploration of single-station seismic data with unsupervised learning.”

• L. Seydoux, M. Campillo, R. Steinmann, R. Balestriero, M. de Hoop (2021) Observing seismic signatures of slow-slip events using unsupervised learning.

• R. Steinmann, L. Seydoux, M. Campillo (2021) Revealing the signature of ground frost in continuous seismic data using machine learning.

• L. Seydoux, R. Steinmann, M. Campillo, M. de Hoop, R. Balestriero (2021) Learning the signature of slow-slip events and slow earthquakes from seismic and geodetic data.