Reliability of resistivity quantification for shallow subsurface water processes

TL;DR

This study assesses the reliability of electrical resistivity tomography (ERT) in quantifying shallow subsurface water resistivities, highlighting the influence of resistivity contrast and proposing an ensemble clustering method to improve interpretation.

Contribution

Introduces an ensemble and clustering approach for analyzing ERT inversion models, enhancing interpretation reliability in high contrast resistivity scenarios.

Findings

Resistivity contrast significantly affects quantification accuracy.

Ensemble clustering helps distinguish persistent features from artifacts.

Quantification can be unreliable in high contrast, high sensitivity areas.

Abstract

The reliability of surface-based electrical resistivity tomography (ERT) for quantifying resistivities for shallow subsurface water processes is analysed. A method comprising numerical simulations of water movement in soil and forward-inverse modeling of ERT surveys for two synthetic data sets is presented. Resistivity contrast, e.g. by changing water content, is shown to have large influence on the resistivity quantification. An ensemble and clustering approach is introduced in which ensembles of 50 different inversion models for one data set are created by randomly varying the parameters for a regularisation based inversion routine. The ensemble members are sorted into five clusters of similar models and the mean model for each cluster is computed. Distinguishing persisting features in the mean models from singular artifacts in individual tomograms can improve the interpretation of inversion results. Especially in the presence of large resistivity contrasts in high sensitivity areas, the quantification of resistivities can be unreliable. The ensemble approach shows that this is an inherent problem present for all models inverted with the regularisation based routine. The results also suggest that the combination of hydrological and electrical modeling might lead to better results.