# Reduction of conceptual model uncertainty using ground-penetrating radar profiles: Field-demonstration for a braided-river aquifer

## TL;DR

This study presents a method to reduce uncertainty in geological models of aquifers by comparing ground-penetrating radar data with simulations, effectively discarding unlikely models and focusing on plausible scenarios.

## Contribution

It introduces a quantitative approach using global distance measures to evaluate and rank geological models based on GPR data, improving model selection efficiency.

## Key findings

- Seven out of nine models were discarded as incompatible with field data.
- Wavelet-decomposition distance measure was the most effective for comparison.
- The approach successfully identified models reproducing key geological features.

## Abstract

Hydrogeological flow and transport strongly depend on the connectivity of subsurface properties. Uncertainty concerning the underlying geological setting, due to a lack of field data and prior knowledge, calls for an evaluation of alternative geological conceptual models. To reduce the computational costs associated with inversions (parameter estimation for a given conceptual model), it is beneficial to rank and discard unlikely conceptual models prior to inversion. Here, we demonstrate an approach based on a quantitative comparison of ground-penetrating radar (GPR) sections obtained from field data with corresponding simulation results arising from various geological scenarios. The comparison is based on three global distance measures related to wavelet decomposition, multiple-point histograms, and connectivity that capture geometrical characteristics of geophysical reflection images. Using field data from the Tagliamento braided river system, Italy, we demonstrate that seven out of nine considered geological scenarios can be discarded as they produce GPR sections that are incompatible with those observed in the field. The retained scenarios reproduce important features such as cross-stratified deposits and irregular property interfaces. The most convenient distance measure of those considered is the one based on wavelet-decomposition. Direct analysis of the distances is the most intuitive and fastest way to compare scenarios.