Denise Degen , Karen Veroy, Jessica Freymark, Magdalena Scheck-Wenderoth, Florian Wellmann 

Calibrating geothermal simulations is a critical step, both in scientific and industrial contexts, with suitable model parameterizations being optimised to reduce discrepancies between simulated and measured temperatures. Here we present a methodology to identify unaccounted physical processes in the process and overcome the problem of measurement sparsity. With an application to the Upper Rhine Graben, we demonstrate the essential need for global sensitivity studies to robustly calibrate geothermal models, showing that local studies overestimate the influence of some parameters. We ensure the feasibility of the study through a physics-based machine learning approach, reducing computation time by several orders of magnitude.