The DEGREE Project: A Digital Laboratory for Geothermal Exploration in the Eifel Region

Recently, the active Eifel volcanic region has received increasing interest due to the occurrence of deep low-frequency earthquakes, often interpreted as a sign of rising volatiles in the crust. Additionally, recent tomographic models have resolved vertically inclined low-velocity anomalies beneath the Laacher See volcano, which may indicate enhanced fluid ascent. These observations raise the question of whether volcanic activity in the region is increasing and whether such activity may be beneficial for geothermal exploration.

To address these questions, the DEGREE project is developing a digital laboratory that enhances predictive capabilities by combining geophysical data with geological and numerical models. The laboratory includes workflows that couple data assimilation, geological modeling, and numerical simulations into a single process. A key challenge is the propagation of uncertainties in the input data and parameters through the entire workflow. This allows us to obtain quantitive uncertainties for derived quantities to support decision making.

The foundation of the laboratory is a collection of diverse datasets compiled during the project. An extensive seismic dataset acquired by the Eifel Large-N network, deployed between September 2023 and September 2024, is used to investigate subsurface structure and active geodynamic processes in the Eifel region. We employ seismic tomography methods to resolve crustal thickness variations and velocity anomalies, together with moment tensor inversion to constrain fault geometries and deformation mechanisms.

Surface geological maps, digital elevation models, and geological cross-sections are used to build 3D structural geological models using the open-source software GemPy. Model construction follows a stepwise approach, starting from a simplified stratigraphic framework and gradually adding geological complexity, such as time-equivalent units and major fault structures. Although the steps are applied sequentially, the geological model is constructed from the input data and is therefore reproducible, enabling integration into subsequent workflow steps.

GemPy addresses uncertainty in geological models by generating ensembles of realizations through sampling input parameters from probability distributions. These ensembles serve as inputs for numerical simulations of physical quantities. Computing adjoint sensitivity kernels allows us to assess how each realization affects model outputs and to identify which models best match available observations, integrating structural uncertainty with process-based simulations. The numerical simulations are performed with LaMEM and its bindings for the Julia programming language. As GemPy is written in Python, the GemPy.jl package has been developed to expose its functionality in Julia.

The resulting geological and geophysical models may serve as the basis for a Play Fairway Analysis (PFA) which identifies regions with high potential for geothermal exploration. Crucially, this type of analysis requires uncertainty estimations for the modeled physical quantities, which our workflow provides.

From an implementation perspective, the digital laboratory consists of three main parts: a repository to collect data and models and their metadata, workflows and infrastructure for automatic processing, and an interface for visualization and interaction with the results. To demonstrate the feasibility, we develop the first prototype in JupyterLab which accommodates different computing environments and enables an interactive development process.