Quantification of groundwater storage heterogeneity in weathered/fractured basement rock aquifers using electrical resistivity tomography: Sensitivity and uncertainty associated with petrophysical modelling

Abstract

Quantifying groundwater storage in weathered/fractured basement rock aquifers can be challenging owing to both their high degree of heterogeneity and their overall low storage capacity.Therefore, in these aquifers, the use of direct borehole hydraulic data is usually insufficient. Here we assessed the popular method of electrical resistivity tomography (ERT), combined with borehole data and including associated uncertainties, to resolve the spatial variability of groundwater storage properties at high resolution within a fractured mica schist aquifer in Ireland. Porosity distributions across both the saturated and unsaturated zones were calculated from two-dimensional (2D) ERT resistivities using two standard petrophysical models, Archie and Waxman & Smits (WS), the latter accounting for the influence of clay minerals on resistivity data. Our results demonstrated the importance of the hydrogeological conceptual constraints provided by ERT when parametrizing the 2D petrophysical models from borehole point data. They also confirmed the importance of accounting for clay minerals (the products of bedrock weathering processes) in the WS model, whereas predictions from Archie's model produced unrealistically high porosity values of over an order of magnitude higher than the WS model. The WS model predicted porosities decreasing exponentially with depth, with values ranging from a few % in the shallowest, most-weathered part of the bedrock (upper 5 m on average) and deep fractured zones (to about 20 m deep), to less than 1% in the underlying fissured aquifer, and possibly down another order of magnitude in the deep massive bedrock. WS-derived porosities were in agreement with independent vertical water content profiles derived from magnetic resonance sounding (MRS), as well as point storativity values estimated from borehole hydraulic testing at the study site, with particularly good matches in the upper weathered/fractured bedrock and deeply weathered/fractured zones associated with regional faults.Detailed comparison suggested that WS provides an upper-bound estimate of groundwater storage in this environment. In the deep massive, un-weathered, and poorly fractured bedrock, however, discrepancies between groundwater storage estimate obtained from the three methods (ERT, MRS, and hydraulic) prevented reliable storage quantification, owing to the methods' inherent technical limitations in such low porosity rocks. Our results demonstrated the suitability of resistivity tomography to quantify groundwater storage heterogeneity in weathered/fractured basement rock aquifers at high resolution and with reasonable overall uncertainty given the relative high uncertainties in petrophysical parameters at the kilometric scale. The results are promising for better characterization of groundwater storage variations in these hydrogeological systems, which are crucial to predict their response to climate variability and human exploitation.

Field surveys in heterogeneous rock masses aimed at hydraulic conductivity assessment

“…[25]), and hard rock aquifers [26]. Other studies have also provided additional quantitative information on the aquifer properties, such as storage properties and recharge processes [27,28]. However, due to their complexity and lack of data on petrophysical model input parameters, only a small number of studies have evaluated the relationship between geophysical measures and hydraulic properties in hard rock aquifers [29,30].…”

Surface parameters and bedrock properties covary across a mountainous watershed: Insights from machine learning and geophysics

“…1 ). We assume that variations in the subsurface electrical properties, as imaged by surface and airborne geophysical methods, are predominantly driven by variations in lithology, the effective porosity, and fracture density or weathering of the underlying rock [e.g., ( 41 )]. To eliminate the effect of varying lithology, we are focusing our analysis on the dominant lithological unit (Mancos Shale).…”

Assessing the Impact of Fractured Zones Imaged by ERT on Groundwater Model Prediction: A Case Study in a Chalk Aquifer in Voort (Belgium)

“…The characterization of fractured aquifers remains particularly complicated. In recent years, many efforts have been made to characterize fractures (e.g., Guevara-Mansilla et al, 2020;Molron et al, 2020;Mézquita González et al, 2021), to model them using discrete fracture network (Maillot et al, 2016;Medici et al, 2021) or to include those characteristics in calibration processes (Ringel et al, 2019;Medici et al, 2021). However, the identification of individual fractures can only be made locally using borehole data or high-resolution geophysical methods such as ground penetrating radar (Molron et al, 2020).…”

“…In this context, geophysical methods can bring relevant information to characterize the structure of the aquifer and reveal the presence of fractured zones (e.g., Robert et al, 2011;Binley et al, 2015;Van Hoorde et al, 2017). Given their sensitivity to the porosity and water content, electromagnetic methods are particularly suited for the characterization of fractures inducing a secondary porosity (Schmutz et al, 2011;Mézquita González et al, 2021). Recently, airborne electromagnetic surveys have gained popularity as they can provide dense information at the catchment-scale up to a depth of several hundred meters (e.g., Barfod et al, 2018;Knight et al, 2018;Vilhelmsen et al, 2018;Minsley et al, 2021).…”