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

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 groundpenetrating 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.

Blockworlds 0.1.0: A demonstration of anti-aliased geophysics for probabilistic inversions of implicit and kinematic geological models

“…Pirot et al, 2019b), enables selection among competing conceptual models (e.g. Brunetti et al, 2019;Pirot et al, 2019a), and optimizes management of risk in decision-making over possible outcomes (e.g. Varouchakis et al, 2019).…”

A Systematic Approach to Hydrogeological Conceptual Model Testing, Combining Remote Sensing and Geophysical Data

“…A Popper‐Bayes framework has been applied by researchers in various fields of water resources (Hermans et al, 2015; Park et al, 2013; Pirot et al, 2019; Scheidt et al, 2015). The framework is however still largely an academic exercise, prompting the delineation of a workflow that can readily be adopted by practitioners.…”

Convolutional neural networks (CNN) for feature-based model calibration under uncertain geologic scenarios

“…Two classes of methods have been proposed in the literature to implement model calibration under uncertainty in the geologic scenario. The first class performs scenario falsification before model calibration [6,12,17,37,38,43]. The second class of methods performs scenario falsification after (and in some methods, simultaneously) model calibration [4,10,16,18,20].…”

“…In the first class of methods, some authors have used kernel density estimation on simulated data (from realizations of multiple scenarios) where distance of observed data to distributions of labeled data is used to infer its likelihood (online method) [6,12,37,38]. In Scheidt et al [43], decomposition via wavelet transform is used to analyze differences in patterns for measuring global similarity between simulated and observed data.…”

“…Classification and regression tree (CART) method has also been utilized to find the probability of each outcome (e.g., water breakthrough trend) given uncertain geologic parameters [17]. Pirot et al [38] use multiple global spatial statistics of simulated data from multiple scenarios to the observed data to identify plausible scenarios. In the second class of methods, Brunetti et al [4] perform scenario selection after posterior sampling and acknowledge that the method is computationally expensive for large models.…”