Linking Antarctic geological observations and geophysical data in a probabilistic space

• Main Authors: Staal, T, Reading, A, Halpin, J, Whittaker, J
• Format: Conference Object
• Language: English
• Published: . 2019
• Subjects: Earth Sciences; Geophysics; Seismology and seismic exploration; Antarctic; The Antarctic; Antarc*
• Online Access: https://www.isaes2019.org:12090/home/; http://ecite.utas.edu.au/136135

To understand the development of the Antarctic continent, and study properties of its crust andlithosphere, we have access to mainly sparse geological observations and extensive, but low resolution,geophysical data. Early models are often based on only one or a few datasets, andinterpretations can be non-unique. With a multivariate and stochastic model, we can better constrainambiguities and depict interpretations of the Antarctic crust and lithosphere robustly and in arepeatable, shareable, way. Recently, a number of improved geophysical datasets have been published. Data includes gravityacceleration from satellites, airborne measurements of the magnetic field, and maps of subglacialtopography. Seismic models of the crust and lithosphere have also been refined, with new data andimproved processing methods. Similar progress is seen in geological studies: new geological datahave been acquired, and older data are reviewed and compiled. Observations from outcrops havebeen extended by marine core data and studies of glacial erratics to suggest properties of thesubglacial terranes. Interpretations are supported by tectonic reconstructions of the East Antarcticcontinental margin. We present an example of Antarctic basement/lithospheric terranes interpreted by linking geologicalobservations with geophysical data utilising a probabilistic and multidimensional grid model. We usethese domains to generate subglacial heat flow maps of the catchment area of the Aurora Basin forwider interdisciplinary use. The models are based on age-constrained crustal heat production, andwe also include thermal properties from observations in adjoint Gondwanan margins, whereavailable. We populate the domains with properties as age and provenance from geological data anduse geophysics to extrapolate domain boundaries into the Antarctic interior. The probabilistic approach illustrated in this presentation provides a robust and repeatable workflow.Our results and process are shareable with the broader community to use for interdisciplinarystudies, and as a platform that will allow ongoing refinement.