A matrix-free approach to geostatistical filtering
Preprint, 2020

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 datasets. The approach is based on a finite element approximation of Gaussian random fields expressed as an expansion of the eigenfunctions of a Laplace--Beltrami operator defined to account for local anisotropies. The numerical approximation of the resulting random fields using a finite element approach is then leveraged to solve the scalability issue through a matrix-free approach. Finally, two cases of application of this approach, on simulated and real seismic data are presented.

Riemannian manifold

Factorial kriging

Gen-eralized random field

SPDE

Filtering

Matrix-free

Författare

Mike Pereira

Chalmers, Elektroteknik, System- och reglerteknik, Reglerteknik

Nicolas Desassis

Mines ParisTech

Cédric MAGNERON

ESTIMAGES

Nathan PALMER

Central Petroleum Ltd

STOchastic Traffic NEtworks (STONE)

Chalmers, 2020-02-01 -- 2022-01-31.

Chalmers AI-forskningscentrum (CHAIR), -- .

Ämneskategorier

Beräkningsmatematik

Sannolikhetsteori och statistik

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2021-01-11