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Geothermal heat flow is inferred from the gradient of temperature values in boreholes or short-penetration probe measurements. Such measurements are expensive and logistically challenging in remote locations and, therefore, often targeted to regions of economic interest. As a result, measurements are not distributed evenly. Some tectonic, geologic and even topographic settings are overrepresented in global heat flow compilations; other settings are underrepresented or completely missing. These limitations in representation have implications for empirical heat flow models that use catalogue data to assign heat flow by the similarity of observables. In this contribution, we analyse the sampling bias in the Global Heat Flow database of the International Heat Flow Commission; the most recent and extensive heat flow catalogue, and discuss the implications for accurate prediction and global appraisals. We also suggest correction weights to reduce the bias when the catalogue is used for empirical modelling. From comparison with auxiliary variables, we find that each of the following settings is highly overrepresented for heat flow measurements; continental crust, sedimentary rocks, volcanic rocks, and Phanerozoic regions with hydrocarbon exploration. Oceanic crust, cratons, and metamorphic rocks are underrepresented. The findings also suggest a general tendency to measure heat flow in areas where the values are elevated; however, this conclusion depends on which auxiliary variable is under consideration to determine the settings. We anticipate that using our correction weights to balance disproportional representation will improve empirical heat flow models for remote regions and assist in the ongoing assessment of the Global Heat Flow database.

Geothermal heat flux under the Antarctic ice is one of the least known parameters. Different methods (based on e.g., magnetic or seismic data) have been applied in recent years to quantify the thermal structure and the geothermal heat flux, resulting in vastly different estimates. In this study, we use a Bayesian Monte-Carlo-Markov-Chain approach to explore the consistency of such models and to which degree lateral variations of the thermal parameters are required. Hereby, we evaluate the input from different lithospheric models and how they influence surface heat flux. We demonstrate that both Curie isotherm and heat production are dominating parameters for the thermal calculation and that use of incorrect models or sparsely available data lead to unreliable results. As an alternative approach, geological information should be coupled with geophysical data analysis, as we demonstrate for the Antarctic Peninsula.