3D multiple point geostatistical simulation of joint subsurface redox and geological architectures

Abstract

Abstract. Nitrate contamination of subsurface aquifers is an ongoing environmental challenge due to nitrogen (N) losses from intensive N fertilization and management on agricultural fields. The distribution and fate of nitrate in aquifers are primarily governed by geological, hydrological and geochemical conditions of the subsurface. Therefore, we propose a novel approach to model both geology and redox architectures simultaneously in high resolution 3D (25 m × 25 m × 2 m) using multiple point geostatistical simulation (MPS). Data consists of 1) mainly resistivities of the subsurface mapped with towed transient electromagnetic measurements (tTEM), and 2) information of lithology and redox conditions obtained from the borehole observations at point scale interpreted from the geological descriptions and colors of sediment samples, and chemistry analyses of water samples. The conceptual understandings of the geological and redox architectures of the study system were introduced to the simulation as training images. These data were combined with detailed soil maps and digital elevation models to identify main geological elements defined as volumes within the subsurface. From a geological perspective, these data are considered independent from each other in terms of formation. This approach became computationally attractive by simulating smaller geological elements individually instead of the entire catchment. The final realizations were stitched together using simulations of the individual geological elements, resulting in an ensemble of realizations representing a quantification of the uncertainty for the given setup. The joint simulation of geological and redox architectures, which is one of the strengths of the MPS simulations compared to other geostatistical methods, secures that the two architectures in general show coherent patterns. Despite the inherent subjectivity of interpretations of the training images and geological element boundaries, they enable an easy and intuitive incorporation of qualitative knowledge of geology and geochemistry in quantitative simulations of the subsurface architectures. Altogether, we conclude that our approach effectively simulates the coherent geological and redox architectures of the subsurface that can be used for hydrological modelling with N-transport, which may be fundamental to better understanding of the nitrogen (N) retention of the subsurface and to future more targeted regulation of agriculture.

Choosing between Gaussian and MPS simulation: the role of data information content—a case study using uncertain interpretation data points

“…Cognitive geological modeling is the primary method used for 2D and 3D interpretations of the subsurface in Denmark (Jørgensen and Sandersen 2006;Sandersen et al 2009;Høyer et al 2015). Recently, efforts in mapping the hydrogeology of the substratum in Denmark also include geostatistical simulation (Høyer et al 2017;Barfod et al 2018;Vilhelmsen et al 2019;Madsen et al 2020b). Here, we focus on a study area situated just outside the town of Horsens, Denmark (Fig.…”

The influence of layer and voxel geological modelling strategy on groundwater modelling results

“…The second group of hydrogeological models is the (data-driven) geostatistical methods, where a number of equally plausible realizations of the geological setting is calculated based on a given set of data, conditions and assumptions. Recently, the use of geostatistics for hydrogeological modelling of areas with geophysical data has been a focus of research using several different geostatistical approaches (e.g., Vilhelmsen et al 2019;Barfod et al 2018;Madsen et al 2021;Gunnink and Siemon 2015). Geostatistical modelling has proven to be relevant in relation to numerical groundwater modelling and research (He et al 2013;Refsgaard et al 2012;Feyen and Caers 2006).…”

The influence of layer and voxel geological modelling strategy on groundwater modelling results

“…Recently, the use of geostatistics for hydrogeological modelling of areas with geophysical data has been a focus of research using several different geostatistical approaches (e.g. Vilhelmsen et al 2019;Barfod et al 2018;Madsen et al 2021;Gunnink and Siemon 2015). Geostatistical modelling has proven to be relevant in relation to numerical groundwater modelling and research (He et al 2013;Refsgaard et al 2012;Feyen and Caers 2006).…”