The Antarctic lithosphere revealed by multivariate analysis
The Antarctic continent, at 14 million km2, is larger than Australia; yet, due to the ice cover and inaccessibility, its geology and lithospheric structure are to a large extent unknown. During recent decades, particularly since the International Polar Year of 2007-08, a growing number of studies ha...
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University of Tasmania
2021
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| Online Access: | https://dx.doi.org/10.25959/100.00038553 https://eprints.utas.edu.au/id/eprint/38553 |
| Summary: | The Antarctic continent, at 14 million km2, is larger than Australia; yet, due to the ice cover and inaccessibility, its geology and lithospheric structure are to a large extent unknown. During recent decades, particularly since the International Polar Year of 2007-08, a growing number of studies have provided new and improved datasets of the continent’s surface, cryosphere, crust and upper mantle. The new data enable new or refined questions to be addressed in the Antarctic Earth sciences. For instance, how does large-scale geophysics correlate with sparse geological observations and interpretations? What are the extents of tectonic domains and affiliations with former neighbours in Gondwana? What is the spatial distribution of geothermal heat flow in the deep interior? Advancing our understanding of the Antarctic continent addresses fundamental knowledge gaps in plate tectonics, the dynamic foundation for our planet. Understanding the Antarctic lithosphere is also of urgent relevance due to ongoing anthropogenic climate change and the consequent need to better constrain interactions between the solid Earth and the cryosphere. Challenges to build on existing research include the lack of agreement between different studies, and uncertainties that are difficult to constrain. Methodologies previously employed are generally univariate, modelling the solid Earth structure or character from only one observable. However, the growing number of datasets affords an opportunity to combine constraints from multiple observables, embrace the uncertainties, and draw new, considered interpretations. In this thesis, studies that employ multivariate syntheses of recently compiled data are presented, with a focus on combining geophysics and geology. A new 3D model and software framework for spatial multivariate and multidimensional computation is presented. This is enabled by a newly developed software package, agrid, which contains methods for data import, visualisation, and export of results in compatible formats. Using this toolbox, a grid model of continental Antarctica is created from geophysics and geology combined. A range of illustrative maps of lithospheric properties are generated to exemplify the functionality of the framework. This includes a new isostatic model from seismic tomography data and a new approach to calculate geothermal heat flow from energy balance based on geophysics and geology. The dynamic and flexible 3D model of the lithosphere is designed with research addressing solid Earth and cryosphere interaction and feedbacks in mind. Multivariate methodology is used to investigate the presence of deep-seated lithospheric boundaries in East Antarctica. Three independent datasets are utilised: seismic shear wave speed at 150 km depth, free air gravity anomaly, and surface elevation. From each dataset, boundaries that indicate transitions in value, gradient, frequency, or pattern are suggested, with rated uncertainty and resolution. A range of likelihood maps is generated; the most conservative maps show regions where we are confident that an upper mantle boundary exists, whereas the least conservative maps contain a greater number of less confidently suggested boundaries. When boundary likelihood is compared with observed crustal geology, we find a good match. The East Antarctic lithosphere is revealed to comprise multiple domains, and internal geological complexity. Domains in the subglacial interior, with no geological outcrop, are very likely. The computational framework, agrid, is used to generate a geothermal heat flow map using over 15 datasets as input observables. A multivariate similarity method is applied, carefully modified for application to the datasets available for Antarctica. The new map, Aq1, is of higher resolution than previous heat flow maps of the continent, and robustly constrained with quantified uncertainty. The map confirms higher heat flow in West Antarctica, and lower heat flow in East Antarctica. The highest values are computed for the Thwaites Glacier region and the Siple Coast, locally over 150 mWm |
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