3 papers
physics.geo-ph2023
A stable deep adversarial learning approach for geological facies generation
Ferdinand Bhavsar, Nicolas Desassis, Fabien Ors +1
The simulation of geological facies in an unobservable volume is essential in various geoscience applications. Given the complexity of the problem, deep generative learning is a pr…
stat.ME2022
The SPDE approach for spatio-temporal datasets with advection and diffusion
Lucia Clarotto, Denis Allard, Thomas Romary +1
In the task of predicting spatio-temporal fields in environmental science using statistical methods, introducing statistical models inspired by the physics of the underlying phenom…
math.ST2018
Combining covariance tapering and lasso driven low rank decomposition for the kriging of large spatial datasets
Thomas Romary, Nicolas Desassis
Large spatial datasets are becoming ubiquitous in environmental sciences with the explosion in the amount of data produced by sensors that monitor and measure the Earth system. Con…