Improved 3D Geological Modelling with Geophysical Data and Markov-Type Categorical Prediction

Airborne electromagnetics (AEM) is a key tool for 3D subsurface imaging, enabling fast, efficient collection of large datasets for hydrogeological studies (Deleersnyder et al., 2023; Madsen et al., 2022). Combined with geostatistical modelling techniques, AEM data generates geologically realistic, data-consistent subsurface models (Hermans et al., 2015). Geostatistics integrates diverse data, captures geological variability, and addresses parameter uncertainties. This study integrates AEM inversion results with the Markov-type categorical prediction (MCP) method to improve subsurface modelling, using a 3D hydrogeological site in Denmark.

The study area has 13 lithological layers, ranging from Quaternary sands and clays to Miocene and Paleogene clays, as well as a limestone layer at the base (Madsen et al., 2022). A practical workflow was developed to create a lithological model using AEM data and borehole observations. The process starts by extracting 100 2D transects from an existing 3D lithological model. These transects are used to calculate 2D bivariate probabilities, which describe the spatial relationships between different lithological units (Benoit et al. 2018). The 100 individual probabilities are then merged into a single bivariate probability distribution, which is used to calculate conditional probabilities in the Markov-type categorical prediction (MCP) method.

AEM data were integrated with borehole observations to enhance the accuracy of the lithological modelling. A stochastic petrophysical model linked lithological classes to inverted AEM resistivity values. The permanence of ratios concept combined MCP-derived conditional probabilities with geophysical data, ensuring consistent relative contributions.

Figure 1: Overview of Integrating Borehole and TEM Data into MCP-Based Geological Modelling

The real-world application to the Danish hydrogeological site highlighted the robustness of the integrated approach. Cross-sections from the 3D model showed clear improvements in lithological delineation compared to non-constrain simulations. These results present the potential of geophysically constrained MCP simulations to support resource management and groundwater modelling in complex geological settings.

References
Benoit, N., Marcotte, D., Boucher, A., D’Or, D., Bajc, A. and Rezaee, H., (2018). Directional hydrostratigraphic units simulation using MCP algorithm. Stochastic environmental research and risk assessment, 32, 1435-1455.

Deleersnyder, W., Maveau, B., Hermans, T., & Dudal, D. (2023). Flexible quasi-2D inversion of time-domain AEM data, using a wavelet-based complexity measure. Geophysical Journal International, 233(3), 1847–1862.

Hermans, T., Nguyen, F. and Caers, J., (2015). Uncertainty in training image‐based inversion of hydraulic head data constrained to ERT data: Workflow and case study. Water Resources Research, 51(7), 5332-5352.

Madsen, R. B., Høyer, A.-S., Andersen, L. T., Møller, I., & Hansen, T. M. (2022). Geology-driven modeling: A new probabilistic approach for incorporating uncertain geological interpretations in 3D geological modeling. Geological Survey of Denmark and Greenland. Institute for Geoscience, University of Aarhus.