Geothermal Heat Flux in Antarctica: Assessing Models and Observations by Bayesian Inversion

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

Properties and biases of the global heat flow compilation

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

The 3D Crustal Structure of the Wilkes Subglacial Basin, East Antarctica, Using Variation of Information Joint Inversion of Gravity and Magnetic Data

Direct geological information in Antarctica is limited to ice free regions along the coast, high mountain ranges, or isolated nunataks. Therefore, indirect methods are required to reveal subglacial geology and heterogeneities in crustal properties, which are critical steps toward interpreting geological history. We present a 3D crustal model of density and susceptibility distribution in the Wilkes Subglacial Basin (WSB) and the Transantarctic Mountains (TAM) based on joint inversion of airborne gravity and magnetic data. The applied “variation of information” technique enforces a coupling between inferred susceptibility and density, relating these quantities to the same gravity and magnetic sources to give an enhanced inversion result. Our model reveals a large body located in the interior of the WSB interpreted as a batholithic intrusive structure, as well as a linear dense body at the margin of the Terre Adélie Craton. Density and susceptibility relationships are used to inform the interpretation of petrophysical properties and the reconstruction of the origin of those crustal bodies. The petrophysical relationship indicates that the postulated batholitic intrusion is granitic, but independent from the Granite Harbor Igneous Complex described previously in the TAM area. Emplacement of a large volume of intrusive granites can potentially elevate local geothermal heat flow significantly. Finally, we present a new conceptual tectonic model based on the inversion results, which includes development of a passive continental margin with seaward dipping basalt horizons and magmatic underplating followed by two distinct intrusive events associated with the protracted Ross Orogen

Greenland Geothermal Heat Flow Database and Map (Version 1)

We compile and analyze all available geothermal heat flow measurements collected in and around Greenland into a new database of 419 sites and generate an accompanying spatial map. This database includes 290 sites previously reported by the International Heat Flow Commission (IHFC), for which we now standardize measurement and metadata quality. This database also includes 129 new sites, which have not been previously reported by the IHFC. These new sites consist of 88 offshore measurements and 41 onshore measurements, of which 24 are subglacial. We employ machine learning to synthesize these in situ measurements into a gridded geothermal heat flow model that is consistent across both continental and marine areas in and around Greenland. This model has a native horizontal resolution of 55ĝ€¯km. In comparison to five existing Greenland geothermal heat flow models, our model has the lowest mean geothermal heat flow for Greenland onshore areas. Our modeled heat flow in central North Greenland is highly sensitive to whether the NGRIP (North GReenland Ice core Project) elevated heat flow anomaly is included in the training dataset. Our model's most distinctive spatial feature is pronounced low geothermal heat flow (<ĝ€¯40ĝ€¯mWĝ€¯m-2) across the North Atlantic Craton of southern Greenland. Crucially, our model does not show an area of elevated heat flow that might be interpreted as remnant from the Icelandic plume track. Finally, we discuss the substantial influence of paleoclimatic and other corrections on geothermal heat flow measurements in Greenland. The in situ measurement database and gridded heat flow model, as well as other supporting materials, are freely available from the GEUS Dataverse (10.22008/FK2/F9P03L; Colgan and Wansing, 2021).publishedVersionPeer reviewe

Antarctic Geothermal Heat Flow: Investigations by Geophysical and Statistical Analyses

Antarctica’s contribution to past and future sea level changes is highly uncertain given the poor understanding of ice sheet dynamics in which solid Earth interactions play an important role. Geothermal heat flow (GHF) is one of the least constrained solid Earth components but has a significant influence on the visco-elastic behavior of the lithosphere and the thermal state at the base of the ice sheet. Estimations of Antarctic GHF seldom come from direct measurements and rely on indirect methods based on geophysical observations. Forward models using constraints on lithospheric isotherms and assumptions on uniform thermal parameters exhibit large differences, both in amplitude and spatial distribution of the calculated heat flow. The consistency of such models is explored using a Bayesian inversion approach in an effort to reconcile different modeled lithospheric structures. Further, a machine learning approach is adopted that enables a statistical derivation of GHF incorporating multiple global geophysical data sets and in situ heat flow measurements. Lastly, a novel joint inversion approach is applied to magnetic and gravity data to invert for the crustal structure of the Wilkes Land region in East Antarctica and South Australia. This improves the understanding of the subglacial geology and small-scale GHF contributions. The methods and results presented in this thesis are relevant for the thermal modeling of ice sheets and the lithosphere, especially with regard to understanding the coupling between ice and solid Earth

Predicted Antarctic Heat Flow and Uncertainties using Machine Learning

We predicted Antarctic Geothermal Heat Flow (GHF) using a machine learning approach. The adopted approach estimates GHF from multiple geophysical and geological data sets, assuming that GHF is substantially related to the geodynamic setting of the plates. We applied a Gradient Boosted Regression Tree algorithm to find an optimal prediction model relating GHF to the observables. In Antarctica, only a sparse number of direct GHF measurements are available, and therefore, in addition to the global models, we explore the use of regional data sets of Antarctica as well as its tectonic Gondwana neighbors to refine the predictions. We hereby demonstrated the need for adding reliable data to the machine learning approach. Here, we present a new geothermal heat flow map, which exhibits intermediate values compared to previous models, ranging from 35 to 156 mW/m2 and showing visible connections to the conjugate margins in Australia, Africa, and India. Also, the data set contains minimum and maximum heat flow values and maximum absolute differences, resulting from calculating three additional heat flow models with different feature set-ups to assess the direct uncertainties