Zobrazeno 1 - 10
of 97
pro vyhledávání: '"Desassis , Nicolas"'
To obtain high-resolution images of subsurface structures from seismic data, seismic imaging techniques such as Full Waveform Inversion (FWI) serve as crucial tools. However, FWI involves solving a nonlinear and often non-unique inverse problem, pres
Externí odkaz:
http://arxiv.org/abs/2406.04859
Autor:
Beauchamp, Maxime, Desassis, Nicolas, Johnson, J. Emmanuel, Benaichouche, Simon, Tandeo, Pierre, Fablet, Ronan
The spatio-temporal interpolation of large geophysical datasets has historically been adressed by Optimal Interpolation (OI) and more sophisticated model-based or data-driven DA techniques. In the last ten years, the link established between Stochast
Externí odkaz:
http://arxiv.org/abs/2402.01855
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 promising approach to overcome the limitations of traditional geostatist
Externí odkaz:
http://arxiv.org/abs/2305.13318
In the task of predicting spatio-temporal fields in environmental science using statistical methods, introducing statistical models inspired by the physics of the underlying phenomena that are numerically efficient is of growing interest. Large space
Externí odkaz:
http://arxiv.org/abs/2208.14015
Large or very large spatial (and spatio-temporal) datasets have become common place in many environmental and climate studies. These data are often collected in non-Euclidean spaces (such as the planet Earth) and they often present non-stationary ani
Externí odkaz:
http://arxiv.org/abs/2208.12501
Publikováno v:
In Spatial Statistics August 2024 62
Publikováno v:
In Computers and Geosciences August 2024 190
In this paper, we present a novel approach to geostatistical filtering which tackles two challenges encountered when applying this method to complex spatial datasets: modeling the non-stationarity of the data while still being able to work with large
Externí odkaz:
http://arxiv.org/abs/2004.02799
Autor:
Pereira, Mike, Desassis, Nicolas
In this paper, we present finite element approximations of a class of Generalized random fields defined over a bounded domain of R d or a smooth d-dimensional Riemannian manifold (d $\ge$ 1). An explicit expression for the covariance matrix of the we
Externí odkaz:
http://arxiv.org/abs/1811.03004
This paper presents theoretical advances in the application of the Stochastic Partial Differential Equation (SPDE) approach in geostatistics. We show a general approach to construct stationary models related to a wide class of linear SPDEs, with appl
Externí odkaz:
http://arxiv.org/abs/1806.04999