Applying airborne electromagnetics in 3D stochastic geohydrological modelling for determining groundwater protection

Abstract

Airborne electromagnetic (AEM) measurements provide information regarding the electrical properties of the subsurface for large spatial coverage in a limited time. In mapping and modelling for geological and geohydrological purposes, electrical properties (e.g. resistivity) need to be converted to relevant parameters, like lithology. Helicopter‐borne frequency‐domain EM measurements from an area in the Netherlands were combined with borehole data to create a 3D model of two contrasting lithologies (sand and clay) that served as proxy for assessing the vulnerability of the aquifer to surface contamination. By comparing the lithology found in boreholes with the resistivity derived from AEM at that location, a probabilistic relationship between these two variables was determined. This relationship was used to convert the AEM resistivity models into a 3D model of clay probability. Using geostatistical Monte Carlo simulations, the boreholes (hard data) and the probability of clay from the AEM resistivity models (soft data) were combined. AEM improved the 3D model substantially, compared to using only borehole data. An independent validation dataset verified the improvement of the 3D model using AEM data. Areas with a high probability of clay occurrence could be distinguished and a clay thickness map with uncertainty (standard deviation) was calculated. Using a simple groundwater model, the capability of the clay to protect the underlying aquifer from contamination was quantified. This resulted in the delineation of distinct areas that are well protected due to the large travel time for infiltrating water from the surface to the aquifer.

Towards an improved geological interpretation of airborne electromagnetic data: a case study from the Cuxhaven tunnel valley and its Neogene host sediments (northwest Germany)

“…Our results show that AEM data provide excellent opportunities to map the subsurface geology as previously demonstrated by, for example, Newman et al (1986) Klimke et al (2013) and Gunnink & Siemon (2014). A good relationship between resistivity values and lithology enables the 3D imaging of the subsurface architecture.…”

Voxel inversion of airborne electromagnetic data for improved groundwater model construction and prediction accuracy

“…Alternatively, more objective and cost-efficient geostatistical modeling approaches (Carle and Fogg, 1996;Deutsch and Journel, 1998;Strebelle, 2002) are available for generating models from a combination of borehole information and AEMdetermined resistivity models. For example: He et al (2014) used a transition probability indicator simulation approach (Carle and Fogg, 1996), while Gunnink and Siemon (2015) used sequential indicator simulation (Deutsch, 2006). Marker et al (2015) used a deterministic strategy for the integration of AEM resistivity models into the hydrological modeling process.…”

Three-dimensional geological modelling of AEM resistivity data — A comparison of three methods

“…Lee et al, 2007). Geophysical data such as ground penetrating radar and seismic data have often been utilised in stochastic simulations (De Benedetto et al, 2012;Engdahl et al, 2010), but to our knowledge, AEM data have only been used in a few studies (Gunnink and Siemon, 2014;He et al, 2014aHe et al, , 2014b.…”