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Workshop: AI for Earth and Space Science

Inferring Antarctica’s Geology with a Variation of Information Inversion and Machine Learning

Mareen Lösing · Max Moorkamp · Jörg Ebbing


Antarctic geothermal heat flow has often been derived indirectly from geophysical data with assumptions about a simplified and undifferentiated lithosphere, which resulted in weakly constrained and inconsistent models. From other continents, we know that thermal parameters and heat flow can exhibit large spatial variations depending on geology and tectonic history. Combining gravity and magnetic data in a joint inversion approach yields information on the crustal structure of Wilkes Land, East Antarctica, and possible geological features become more evident. Both datasets are combined through a coupling method which increases the mutual information to get similar and statistically compatible inversion results. Therefore, we minimize data misfit and variation of information under the coupling constraint. The results show matching features of high magnitude density and susceptibility anomalies. Prominent structures are visible in NE – SW direction along the edge of the Mawson craton and at the presumed Australo-Antarctic and Indo-Antarctic terrane boundary.Applying the same method to Australia, formerly connected to Wilkes Land, we can exploit the much better-known geology there and identify coherent structures along the adjacent margins. The inverted parameter relationship between susceptibility and density can be used as input for machine learning techniques to define a spatially variable heat production map, which in turn would lead to improved heat flow estimates. For this, we rely on existing petrophysical and geochemical databases to correlate and confine thermal parameters with our results.

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