The thesis presents the application of the magnetotelluric (MT) sounding method to image Earth’s crust in Oman and South Australia. The aim of these MT surveys is to provide constraints on the geological interpretation of emplacement scenarios and the tectonic evolution of the geological domain. The thesis concentrates on the methodological aspects of the MT technique, e.g. the data analysis and modelling of electromagnetic fields. The phase tensor approach by Caldwell et al. (2004) is applied to the data and provides insights into the dimensionality of the MT data in even complex and electrically distorted terranes. Modelling and inversion of the MT data is performed with various 2-D and 3-D codes to show how the interpretation of the data can benefit from multiple modelling approaches. Data collected in a 2-D survey across the Oman ophiolite mountains show complex behaviour and 2-D inversion and 3-D forward modelling resolve ambiguities in the emplacement scenario of the Oman ophiolite. It is believed that initial underthrusting of the Jurassic-Cretaceous oceanic lithosphere was followed by exhumation. Further oceanic thrusting subsequently led to rising of lower-plate eclogites and eventually gravitational collapse of the ophiolite onto the margin (Gray et al., 2000). The 3-D inversion code by (Siripunvaraporn et al., 2005a) was expanded to incorporate static shift corrections and inversion model misfits have therefore improved significantly compared to inversion models without static shift correction. 2-D and 3-D surveys across the South Australian Gawler Craton reveal deep crustal conductors which are connected to near surface mineralisation systems of the IOCG Olympic Dam deposit in the north-eastern part of the craton and the Au-dominated central Gawler Craton provinces. / Thesis (Ph.D.) -- University of Adelaide, School of Earth and Environmental Sciences, 2008
| Identifer | oai:union.ndltd.org:ADTP/264517 |
| Date | January 2008 |
| Creators | Thiel, Stephan |
| Source Sets | Australiasian Digital Theses Program |
| Detected Language | English |
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