Nicolas Desassis

Biography

Nicolas Desassis is a researcher specializing in geostatistics and spatial modeling, with significant expertise in the development of advanced statistical methods for the analysis and simulation of geological and environmental phenomena. His work focuses in particular on the application of stochastic partial differential equations (SPDEs) for modeling Gaussian random fields, as well as on the integration of deep learning (generative neural networks, GANs) to improve uncertainty management in geological models. His research also addresses issues related to the non-stationarity of spatial data, the conditional simulation of geological reservoirs, and the optimization of kriging methods for complex datasets. His approach combines mathematical rigor with practical applications, particularly in the fields of hydrogeology, seismology, and the characterization of natural resources.

Publication(s)

2025
Kriging Alluvial Thicknesses in Valley Bottoms Using Nonstationary Geometric Anisotropies DOI : 10.1007/s11004-025-10200-w
2025
Two-Dimensional Stochastic Structural Geomodeling with Deep Generative Adversarial Networks DOI : 10.1007/s11004-025-10188-3
2024
A stable deep adversarial learning approach for geological facies generation DOI : 10.1016/j.cageo.2024.105638
2024
The SPDE approach for spatio-temporal datasets with advection and diffusion DOI : 10.1016/j.spasta.2024.100847
2024
Deep Generative Models for Stochastic Seismic Imaging and Uncertainty Quantification
2022
First Steps in the Application of Physics-Informed Neural Networks to Full Waveform Inversion
2022
Geostatistics for Large Datasets on Riemannian Manifolds: A Matrix-Free Approach DOI : 10.6339/22-JDS1075
2021
A general framework for SPDE-based stationary random fields DOI : 10.3150/20-BEJ1317
2020
Analysing Temporal Variability in Spatial Distributions Using Min–Max Autocorrelation Factors: Sardine Eggs in the Bay of Biscay DOI : 10.1007/s11004-019-09845-1
2020
Kriging DOI : 10.1007/978-3-030-26050-7_18-2
2019
Technical note: Water table mapping accounting for river-aquifer connectivity and human pressure DOI : 10.5194/hess-23-4835-2019
2019
Efficient simulation of Gaussian Markov random fields by Chebyshev polynomial approximation DOI : 10.1016/j.spasta.2019.100359
2019
Inference of plurigaussian model parameters in SPDE framework DOI : 10.3997/2214-4609.201902171
2019
Plurigaussian simulations with the stochastic partial differential equation (SPDE) approach DOI : 10.3997/2214-4609.201902174
2019
Geostatistical filtering of noisy seismic data using Stochastic Partial Differential Equations (SPDE) DOI : 10.3997/2214-4609.201902264
2018
Fifty years of kriging DOI : 10.1007/978-3-319-78999-6_29
2017
Uncertainty estimation by probabilistic first arrival time tomography using Markov Chain Monte Carlo sampling DOI : 10.3997/2214-4609.201701694
2016
A generalized convolution model and estimation for non-stationary random functions DOI : 10.1016/j.spasta.2016.01.002
2015
Estimation of space deformation model for non-stationary random functions DOI : 10.1016/j.spasta.2015.05.001
2015
Propagation of the velocity model uncertainties to the seismic event location DOI : 10.1093/gji/ggu374
2014
An automated MCEM algorithm for hierarchical models with multivariate and multitype response variables DOI : 10.1080/03610926.2012.700372
2013
Microseismic monitoring - Consequences of velocity model uncertainties on event location uncertainties DOI : 10.3997/2214-4609.20131027
2013
Automatic Variogram Modeling by Iterative Least Squares: Univariate and Multivariate Cases DOI : 10.1007/s11004-012-9434-1
2011
Microseismic monitoring: Consequences of velocity model uncertainties on location uncertainties
2010
Aggregation patterns in hierarchy/proximity spaces DOI : 10.1016/j.ecocom.2009.03.012
2009
Inference with a contrast-based posterior distribution and application in spatial statistics DOI : 10.1016/j.stamet.2009.03.003
2008
Pollen flow in the wildservice tree, Sorbus torminalis (L.) Crantz. IV. Whole interindividual variance of male fecundity estimated jointly with the dispersal kernel DOI : 10.1111/j.1365-294X.2008.03809.x
2007
Model-based estimation of the link between the daily survival probability and a time-varying covariate, application to mosquitofish survival data DOI : 10.1016/j.mbs.2007.06.005

Teaching

Inverse Problems

Guest Lecturer

Introduction: Origin of Inverse Problems. Study of Linear Problems: Conditioning, Least Squares, Singular Value Decomposition. Why There Is Noise in Measurements: The Example of the CCD Sensor. Regularization: Tikhonov Method, LASSO. Optimization Problems and Associated Algorithms. Problems governed by a differential equation: the adjoint method for gradient calculation Statistics: a Bayesian perspective. A posteriori estimation, least-squares estimator. Use of neural networks: the Plug-and-Play framework Each half-day will consist of one lecture session and one tutorial or lab session.

Geostatistics

Lecturer

General Introduction and Introduction to the R Software (www.r-project.org) Random Function Models, Inference, and Prediction (Kriging and Simulations) Spatiotemporal Modeling

PhD supervision

2025 Automatic generation of visual animations from musical sources BUGUET Romain
2025 Effective methods for spatio-temporal model estimation LORET Alexandre
2022 Stochastic two-dimensional structural geomodeling using deep generative adversarial networks GARAYT Charlie
2022 The impact of deep generative models for solving nonlinear inverse problems: application to seismic imaging XIE Yuke
2021 Deep Generative Models for Conditional Spatial Simulation BHAVSAR Ferdinand
2020 Machine learning and compliance with physical laws: application to seismic imaging DE SOUZA Léo
2020 Spatio-temporal prediction by stochastic partial differential equations CLAROTTO Lucia
2016 Generalized random fields defined on Riemannian manifolds: theory and practice PEREIRA Mike
2015 Stochastic partial differential equation-based development of geostatistical models CARRIZO VERGARA Ricardo

Keywords

Team