Estimating Geothermal Heat Flow in Ice-Covered Regions Using Bayesian Inversion and SMOS Satellite Data

Geothermal heat flow (GHF) plays a critical role in influencing ice sheet dynamics, making accurate estimation essential for understanding the thermal structure of the lithosphere. However, direct observations in ice-covered regions are sparse, and geophysical data interpolations often have high uncertainties, particularly in remote areas like Antarctica. To address these challenges, we propose an approach for estimating and reconciling GHF by integrating multiple sources of information.

Our methodology combines Solid Earth models with ice temperature profiles derived from remote sensing data provided by the Soil Moisture and Ocean Salinity (SMOS) satellite mission. To estimate GHF from the ice temperature profiles, a Bayesian inversion framework is used, treating the geothermal heat flow as a free parameter. This allows us to derive posterior distributions, quantifying uncertainties and exploring the parameter space of possible GHF values. Subsequently, stationary thermal modelling is employed to achieve convergence between ice and lithospheric temperature models at the base of the ice sheet.

With our inversion we focus on the Solid Earth parameters, such as radiogenic heat production, using the GHF posterior distributions derived from ice temperature profiles as prior. We apply this approach to the Dome A region in Antarctica, where GHF has previously been estimated using glaciological constraints from ice-penetrating radar. This independent dataset enables validation of SMOS-derived observations with respect to amplitude and wavelength. Our approach demonstrates the potential of integrating remote sensing data and Solid Earth models to overcome data scarcity in ice-covered regions, offering a robust framework for improving GHF estimation and reducing uncertainties in regions critical to ice sheet dynamics.