Tomogram-based comparison of geostatistical models: Application to the MAcro-Dispersion Experiment (MADE) site
N. Linde, T. Lochb\"uhler, M. Dogan, R. L. Van Dam

TL;DR
This paper introduces a novel framework for comparing geostatistical models using tomograms derived from geophysical data, applied to the MADE site, to evaluate which models best explain observed data.
Contribution
The study develops a new method combining tomogram inversion and direct sampling to assess and compare geostatistical models based on geophysical data fit.
Findings
Multi-Gaussian models can explain geophysical data.
Training image based on an aquifer analog fits data better.
High conductivity zones are underrepresented in local outcrop models.
Abstract
We propose a new framework to compare alternative geostatistical descriptions of a given site. Multiple realizations of each of the considered geostatistical models and their corresponding tomograms (based on inversion of noise-contaminated simulated data) are used as a multivariate training image. The training image is scanned with a direct sampling algorithm to obtain conditional realizations of hydraulic conductivity that are not only in agreement with the geostatistical model, but also honor the spatially varying resolution of the site-specific tomogram. Model comparison is based on the quality of the simulated geophysical data from the ensemble of conditional realizations. The tomogram in this study is obtained by inversion of cross-hole ground-penetrating radar (GPR) first-arrival travel time data acquired at the MAcro-Dispersion Experiment (MADE) site in Mississippi (USA).…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
