The largest contiguous former opencast lignite mining district in the EU is located in Lusatia (Germany). Hydrochemical contaminations such as acid mine drainage from opencast mines as well as increased susceptibility to soil instabilities pose major challenges for the reclamation of the Lusatian mining district in the coming decades. Therefore, time- and cost-efficient methods investigating former opencast lignite mines are vital for a sustainable remediation and reclamation.
In Lusatia, loose Cenozoic sediments (sands, silts, clays, and glacial tills) form several tens of metres of heterogeneous lithological successions in extensive opencast dumps. However, the effects of newly formed depositional structures, spatial heterogeneities and pore water mineralisation of the opencast dumps on groundwater flow and mass transport remain largely unknown in the post-mining areas of Lusatia.
In this study, we developed a workflow that makes use of spatially variable and heterogeneous input data ranging from airborne geophysics, geological maps and drilling logs. In a first step, we created 3D-representations of an exemplary post-mining area in Lusatia by using both classical geological interpretations as well as machine learning approaches. In a second step, the 3D representations are compared to each other and are used as input data for numerical groundwater modelling in order to analyse the effect on the simulated groundwater flow. This approach allows us to optimise the 3D-modelling workflow and to refine our understanding of the flow and mass transport processes between groundwater and pit lakes in the subsurface of post-mining areas.