Abstract Geothermal heat flux (GHF) is a crucial boundary condition for making accurate predictions of ice sheet mass loss, yet it is poorly known in Greenland due to inaccessibility of the bedrock. Here we use a machine learning algorithm on a large collection of relevant geologic features and global GHF measurements and produce a GHF map of Greenland that we argue is within ∼15% accuracy. The main features of our predicted GHF map include a large region with high GHF in central-north Greenland surrounding the NorthGRIP ice core site, and hot spots in the Jakobshavn Isbrae catchment, upstream of Petermann Gletscher, and near the terminus of Nioghalvfjerdsfjorden glacier. Our model also captures the trajectory of Greenland movement over the Icelandic plume by predicting a stripe of elevated GHF in central-east Greenland. Finally, we show that our model can produce substantially more accurate predictions if additional measurements of GHF in Greenland are provided. The heat generated at the interior regions of Earth (geothermal heat flux, GHF) can be high enough to melt the bottom layers of ice sheets, decrease friction between ice and bedrock, and increase ice discharge to the ocean. This heat, however, cannot be directly measured in ice sheets because the bedrock is inaccessible. Here we present a novel approach to estimate this heat. We combine all the available geologic, tectonic, and GHF data that are available on all continents. We then establish a complex relationship between GHF and all the geologic-tectonic features using machine learning techniques and then predict the GHF for the Greenland Ice Sheet. We utilize all information from available ice cores and bedrock boreholes to improve the GHF prediction in Greenland. Thus, the new GHF map honors tectonic settings, regional geology, and measurements from ice cores and can be used as an important input parameter to numerical ice sheet models that aim at lowering the uncertainties of future sea level rise predictions.
Prediction of Greenland geothermal heat flux is investigated using machine learning in the article REF .
Predicting the geothermal heat flux in Greenland: a Machine Learning approach
Geophysical Research Letters
Geology