Geothermal Heat Flow Mapping of Germany Through Integration of
Multi-Geophysical and Geological Data with Uncertainty Quantification
Abstract
Geothermal heat flow (GHF) is a critical parameter for understanding the
thermal structure and dynamics of the lithosphere, offering key insights
into geophysical processes and geothermal energy potential. This study
investigates the spatial variability of GHF in Germany by applying a
Bayesian Markov Chain Monte Carlo method to estimate key thermal
parameters, including crustal and mantle thermal conductivities, crustal
heat production, and mantle heat flow. The analysis integrates data on
surface heat flow, surface temperatures, and the
lithosphere-asthenosphere boundary (LAB) depth.
To address the limitations posed by the sparse and uneven distribution
of direct borehole measurements, comprising only 595 GHF records, we
incorporated a wide range of geophysical and geological datasets, such
as gravity, magnetics, seismic velocity, topography, and proximity to
faults and volcanic regions. These datasets were analyzed using a
Quantile Regression Forest (QRF) approach, which enabled robust GHF
estimations while accounting for uncertainties and providing reliable
prediction intervals. This methodology significantly improves upon
traditional Curie depth-based methods, providing a more accurate and
comprehensive GHF model for Germany.
The probabilistic multi-geoobservable modelling presented here enhances
our understanding of the geothermal regime in Germany, contributing to a
more precise assessment of its geothermal resources and the thermal
state of its lithosphere.
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