Abstract
AbstractWe present a machine learning approach to statistically derive geothermal heat flow (GHF) for Antarctica. 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 apply a Gradient Boosted Regression Tree algorithm to find an optimal prediction model relating GHF to the observables. The geophysical and geological features are primarily global data sets, which are often unreliable in polar regions due to limited data coverage. Quality and reliability of the data sets are reviewed and discussed in line with the estimated GHF model. Predictions for Australia, where an extensive database of GHF measurements exists, demonstrate the validity of the approach. In Antarctica, only a sparse number of direct GHF measurements are available. Therefore, we explore the use of regional data sets of Antarctica and its tectonic Gondwana neighbors to refine the predictions. With this, we demonstrate the need for adding reliable data to the machine learning approach. Finally, we present a new geothermal heat flow map, which exhibits intermediate values compared to previous models, ranging from 35 to 156 mW/m2, and visible connections to the conjugate margins in Australia, Africa, and India.
Highlights
geothermal heat flow (GHF) is a crucial and poorly constrained parameter for ice sheet modeling, and glacial isostatic adjustment calculations
We present a machine learning approach to statistically derive geothermal heat flow (GHF)
The validity of the machine learning approach is tested for Australia
Summary
GHF is a crucial and poorly constrained parameter for ice sheet modeling, and glacial isostatic adjustment calculations It affects the ice rheology and can lead to basal melting, thereby promoting ice flow (e.g., Larour et al, 2012; Pittard et al, 2016; Winsborrow et al, 2010). The results differ immensely, for example, between magnetic and seismological data (e.g., An et al, 2015b; Martos et al, 2017), and the underlying assumptions cannot be combined (Lösing et al, 2020) While these different approaches usually take the respective sensitivity ranges into account, simplifications like a definition of laterally constant thermal parameters are not considered in the assessment. In ice-covered regions, different geophysical models show no consensus on magnitude and spatial distribution of heat flow (Rezvanbehbahani et al, 2019; Van Liefferinge, 2018)
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