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The Wilkes Subglacial Basin hosts potentially the largest unstable sector of the East Antarctica Ice Sheet due to the depth of the ice bed below sea level. Ice covering such basins poses a potentially high, but poorly constrained risk for future sea-level rise, as it is more vulnerable to melting by warming of the surrounding ocean. Such melting could potentially trigger mechanisms of unstable retreat. The neighbouring Transantarctic Mountains are the largest non-contractional mountain range on Earth. Traditionally, the Transantarctic Mountains are viewed as dividing the ancient East Antarctic craton from the younger West Antarctic Rift system. However, petrological samples and previous geophysical mapping suggest that the craton boundary is further west, following the western edge of the Wilkes Subglacial Basin. Subglacial geology influences geothermal heat flow and bed roughness, and therefore to better understand the past, present and possible future behaviour of the East Antarctic Ice Sheet improved understanding of the subglacial geology on which it flows, especially in the Wilkes Subglacial Basin and Transantarctic Mountains region, is important.We present a new 3D crustal model of the Wilkes Subglacial Basin and the Transantarctic Mountains based on joint inversion of airborne gravity and magnetic data using the mutual information inversion algorithm incorporated in the software JIF3D. Our model shows a large intrusive body located in the interior of the Wilkes Subglacial Basin and suggests a tectonically complex area west of the Basin, which could potentially indicate the transition zone at the margin of the Terre Adélie Craton. Geological units are inferred by clustering of inverted susceptibility and density distribution and are validated against sparse petrological samples from the Transantarctic Mountains region and along the George V Land and Terre Adélie coasts. Our inferred crustal properties model can provide crucial insight into the heterogeneity of subglacial geology in terms of thermal conductivity and crustal heat production, which could influence the geothermal heat flow in this area and therefore make the overlying ice sheet more vulnerable than commonly thought.

We established a new Geothermal Heat Flow (GHF) model for Antarctica by using a machine learning approach. GHF is substantially related to the geodynamic setting of the plates, and global geophysical and geological data sets can provide information for remote regions like Antarctica, where only sparse direct measurements exist. We applied a Gradient Boosted Regression Tree algorithm in order to build an optimal prediction model relating GHF to the observables.Employed data sets are reviewed for their reliability and quality in polar regions and we emphasize the need for adding reasonable data to the algorithm. The validity of our approach is indicated by predictions for Australia, where an extensive database of GHF measurements exists. Our new estimated GHF map exhibits rather moderate values compared to previous models, ranging from 35 to 156 mWm-2, and shows visible connections to the conjugate margins in Australia, Africa, and India.Such estimates on the geothermal structure of Antarctica are for example needed for studies on ice sheet modeling. The internal thermal structure and the mass balance of the modeled Antarctic ice sheet (AIS) are significantly affected by the prescribed GHF distribution. Applying a wide range of possible GHF maps within estimated uncertainties to ice-sheet-shelf set-ups, the influence of GHF on the modeled AIS response to a variety of climate scenarios is quantified.

The lithospheric structure of Greenland is still poorly known due to its thick ice sheet, the sparseness of seismological stations, and the limitation of geological outcrops near coastal areas. As only a few geothermal measurements are available for Greenland, one must rely on geophysical models. Such models of Moho and LAB depths and sub-ice geothermal heat-flow vary largely.Our approach is to model the lithospheric architecture by geophysical-petrological modelling with LitMod3D. The model is built to reproduce gravity observations, the observed elevation with isostasy assumptions and the velocities from a tomography model. Furthermore, we adjust the thermal parameters and the temperature structure of the model to agree with different geothermal heat flow models. We use three different heat flow models, one from machine learning, one from a spectral analysis of magnetic data and another one which is compiled from a similarity study with tomography data.For the latter, a new shear wave tomography model of Greenland is used. Vs-depth profiles from Greenland are compared with velocity profiles from the US Array, where a statistical link between Vs profiles and surface heat flow has been established. A similarity function determines the most similar areas in the U.S. and assigns the mean heat-flow from these areas to the corresponding area in Greenland.The geothermal heat flow models will be further used to discuss the influence on ice sheet dynamics by comparison to friction heat and viscous heat dissipation from surface meltwater.

Improving the understanding of geothermal heat flux in Antarctica is crucial for ice-sheet modelling and glacial isostatic adjustment. It affects the ice rheology and can lead to basal melting, thereby promoting ice flow. Direct measurements are sparse and models inferred from e.g. magnetic or seismological data differ immensely. By Bayesian inversion, we evaluated the uncertainties of some of these models and studied the interdependencies of the thermal parameters. In contrast to previous studies, our method allows the parameters to vary laterally, which leads to a heterogeneous West- and a slightly more homogeneous East Antarctica with overall lower surface heat flux. The Curie isotherm depth and radiogenic heat production have the strongest impact on our results but both parameters have a high uncertainty.To overcome such shortcomings, we adopt a machine learning approach, more specifically a Gradient Boosted Regression Tree model, in order to find an optimal predictor for locations with sparse measurements. However, this approach largely relies on global data sets, which are notoriously unreliable in Antarctica. Therefore, validity and quality of the data sets is reviewed and discussed. Using regional and more detailed data sets of Antarctica’s Gondwana neighbors might improve the predictions due to their similar tectonic history. The performance of the machine learning algorithm can then be examined by comparing the predictions to the existing measurements. From our study, we expect to get new insights in the geothermal structure of Antarctica, which will help with future studies on the coupling of Solid Earth and Cryosphere.