Over the past 150 years, lignite mining in the Lausitz has created a cultural landscape under intensive human control. With the implementation of the Act to Reduce and End Coal-Fired Power Generation (KVBG), the region now faces a range of geotechnical, hydrogeological, hydrological and energy-related challenges that demand renewed attention.

The deposits within the Lausitz are characterised by thick Cenozoic sequences, which locally up to 350 m thick, rest on a pre-Tertiary bedrock surface dipping northward. These strata were formed through an interplay of repeated marine transgressions and regressions, marsh formation, and terrestrial sedimentation processes. Following Quaternary glacial systems produced heterogenic sedimentary systems with interglacial layers. Particularly, deeply eroded channels and extensive glacial deformation zones lead to highly complex geological units.

The aim of the Project is to provide a detailed 3D model of the geological situation within the Lusatian area, independent of national borders and associated political responsibilities. The model will incorporate detailed stratigraphic, lithological and structural parameters of the Cenozoic deposits. In the current phase of the project, the focus is on developing the methodological approach, data management, statistical analysis and technical data processing.

Ultimately, the 3D model will cover both active and former opencast mining fields, as well as the previously underrepresented areas in between them. It will provide a spatially resolved representation of the Quaternary and Tertiary successions, serving as a fundamental tool for subsequent modelling-such as groundwater flow simulations for transboundary groundwater management in Saxony and Brandenburg, shallow geothermal energy assessments, and addressing further geotechnical issues.

The increasing demand for reliable geothermal resources highlights the necessity for robust geological models of the deep subsurface. In this context, we present a pilot study aimed at constructing a regional 3D geological model of the Bavarian segment of the North Alpine Foreland Basin (NAFB). This work, conducted under the auspices of the Bavarian Environment Agency (LfU), integrates legacy 2D and 3D seismic data, well logs, and borehole information into a unified geoscientific framework enabled by recent advances in data availability (geological data ect) and modelling infrastructure.

The methodological workflow encompasses seismic reprocessing, log correlation, synthetic seismogram generation, and reflector calibration through seismic-to-well ties. A consistent seismostratigraphic framework is derived by correlating predefined key reflectors and regionally traceable unconformities across heterogeneous lithological domains.

The iterative integration of geophysical and geological datasets enables precise depth-to-time conversion and enhances both structural and stratigraphic resolution. A preliminary velocity model supports time-depth conversion and volumetric parametrization. The approach facilitates the incorporation of external datasets (e.g., geothermal assessments) and promotes standardized modelling practices across institutional and scientific domains.

This pilot application within a defined test area serves to validate internal workflows and model components, ultimately contributing to a comprehensive 3D subsurface framework for future geothermal exploration and reservoir characterization in the Bavarian Molasse Basin.

The Uppermost Maastrichtian calcarenites in the North German Basin are considered a promising mid-depth geothermal reservoir. However, their subsurface architecture is highly variable due to a complex tectono-stratigraphic evolution, making it difficult to predict reservoir geometry and quality based solely on borehole data, especially in structurally heterogeneous areas.

To enhance spatial understanding and reduce geological uncertainty, 3D seismic data originally acquired for hydrocarbon exploration were reinterpreted and analyzed using OpendTect software. The seismic interpretation workflow involved the application of various filtering techniques aimed at reducing noise and improving the visibility of geological structures. Filters were applied to highlight discontinuities and improve the resolution of fault patterns. In addition, parametrized volumes were generated to assist in identifying and delineating potential fault zones. A suite of seismic attributes was used to further analyze and characterize the reservoir’s structural and stratigraphic framework. Key reservoir horizons were tracked using the ‘Inversion+’ method, allowing for more accurate and geologically consistent horizon interpretation.

These methods significantly improved the visualization of critical geological features, including fault systems, stratigraphic boundaries, and lateral thickness variations. By integrating seismic interpretation with borehole data, a detailed 3D subsurface model was developed. Based on the model, the evolution of the reservoir should be reconstructed, the structural framework should be checked for consistency, and zones with enhanced geothermal potential should be identified. The combined approach enables more accurate reservoir characterization and helps reduce exploration risk in complex geological settings. Our presentation will provide initial insights into our ongoing work.

Seismic interpretation is inherently subjective, yet it underpins critical decisions concerning various uses of the subsurface—from the oil industry to the emerging challenges of the energy transition, including projects such as carbon storage and nuclear waste disposal. It serves as the fundamental building blocks (bricks) for constructing 3D models of the subsurface, shaping how it is visualised, modelled and understood. This study investigates how different interpreters, applying their own workflows to the same 3D seismic dataset, produce interpretations that diverge and converge and what these patterns reveal about the workflows. Two interpreters with similar levels of experience were independently tasked with generating a 3D interpretation of a salt body, selected horizons, and faults using the same seismic volume. Each interpreter's workflow was documented, including their use of tools, attribute analysis, and decision-making rationale.

The resulting interpretations will be compared using spatial juxtaposition, geospatial feature quantification, and heat maps. Preliminary results indicate that while some consistent interpretational patterns are emerging, notable variations are observed in fault positioning and the complexity of the salt body. These differences correlate with specific workflow choices such as sampling density preferences, attribute usage, and assumptions. Our findings demonstrate that interpretation outcomes are shaped not only by the data but also by the interpreter’s methodological framing.

By better understanding the sources and consequences of interpretation uncertainty, we can improve the reliability of 3D subsurface models, contributing to more robust and transparent geological assessments for critical subsurface applications.

Structural geological modeling is often an integral part of larger geoscientific workflows. However, when creating these workflows, challenges arise not only in constructing models but also in the knowledge and availability of software, coding skills (especially for open-source solutions), and expertise across multiple domains. These hurdles hinder the exploration and comparison of various methods, compelling users to develop workflows tailored to specific scenarios that frequently require manual adjustments—making them difficult to reuse and labor-intensive.

To address these issues, we present a workbench for digital geosystems that employs a component and connector software architecture alongside both textual and graphical domain-specific languages (DSLs) to establish a modular framework. Within this framework, we define fixed interface formats for each workflow step, allowing components responsible for specific tasks to be interchangeable.

This design enhances workflow creation accessibility while promoting comparability and reusability. New components can be easily integrated into the workflow as long as they adhere to the established interface formats.

The current version of our workbench focuses on workflows spanning structural geological modeling, meshing, and numerical process simulations. Additionally, it incorporates integrated visualization capabilities in extended reality (XR), virtual reality (VR), and augmented reality (AR) for each step of the workflow.

We will showcase the software architecture and DSL system through a series of simple models with an emphasis on structural geological modeling.

In structural geological modeling, interfaces between different rock units are typically represented as sharp boundaries. However, geophysical methods such as electrical resistivity tomography (ERT) often depict these interfaces as smooth transitions of geophysical properties. This representation can obscure the precise locations of geological features that are critical for utilizing the models. To address this challenge, we present GeoBUS (Geological modeling by Bayesian Updating of Scalar fields), a novel workflow for creating probabilistic geological models that seamlessly incorporates information from probabilistic geophysical inversion results through Bayesian updates.

The GeoBUS workflow consists of three key steps: (1) We employ the Kalman Ensemble Generator (KEG) to invert geophysical data and generate probabilistic images in terms of a geophysical property. (2) We perform implicit structural geological modeling by creating an ensemble of scalar fields based on interface point information for geological units while accounting for associated uncertainties. This ensemble serves as the foundation for our probabilistic model. (3) We use the same subsurface discretization as in geophysical inverse modeling and assign probabilistic scalar field values to each cell based on our ensemble of geological models to build a discrete prior for a second KEG application. Based on literature-derived probability density functions for geophysical properties across different geological units, we formulate a corresponding likelihood. The KEG then returns a discretized posterior scalar field that integrates petrophysical likelihoods with geophysical inversion data. We demonstrate this novel workflow for ERT data and simple 2D structural geological models.