Key points are not available for this paper at this time.
In geothermal resource assessment, terrestrial heat flow is a crucial metric, providing key insights into lithospheric thermal states and energy balance, with significant implications for geology, geophysics, and geodynamics. This study focuses on modeling geothermal heat flow (GHF) in Germany, where direct borehole temperature gradient data is limited, i.e. only 595 unevenly distributed published heat flow points exist. To address this data scarcity, our approach utilizes indirect methodologies for GHF estimation. We integrate various geophysical and geological datasets, including gravity, magnetics, upper mantle velocity structure, topography, crustal and lithospheric thickness, fault distribution, proximity to volcanoes, and compositional data. This multi-faceted approach allows us to overcome the spatial constraints inherent in single-data reliance and more accurately reconstruct measured heat flow, e.g. in comparison to classical curie depth estimations. Given the complexities in direct geophysical and geological data representation, our project utilizes a probabilistic, multi-geophysical inversion method. This not only enhances our understanding of Germany's geothermal potential but also allows for a detailed quantification of uncertainties. We have developed a new heat flow map with a high resolution. This map more accurately and effectively illustrates the terrestrial heat flow distribution in the study area, providing a more detailed depiction than previous interpolation results. Our preliminary results have successfully identified high heat flow zones in regions such as the Rhenish Massif, Molasse Basin, and Upper Rhine Graben.
Sobh et al. (Fri,) studied this question.