4.5 Article

Insights and Lessons from 3D Geological and Geophysical Modeling of Mineralized Terranes in Tasmania

Journal

MINERALS
Volume 11, Issue 11, Pages -

Publisher

MDPI
DOI: 10.3390/min11111195

Keywords

3D modeling; potential field; gravity; magnetics; inversion

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Over the past two decades, Mineral Resources Tasmania has developed regional 3D geological and geophysical models to improve the understanding of controls on ore-forming processes and prospectivity. These models, based on high-quality potential field data sets, are crucial for 3D modeling workflows that allow rapid hypothesis testing while satisfying the constraints of observed data.
Over the last two decades, Mineral Resources Tasmania has been developing regional 3D geological and geophysical models for prospective terranes at a range of scales and extents as part of its suite of precompetitive geoscience products. These have evolved in conjunction with developments in 3D modeling technology over that time. Commencing with a jurisdiction-wide 3D model in 2002, subsequent modeling projects have explored a range of approaches to the development of 3D models as a vehicle for the better synthesis and understanding of controls on ore-forming processes and prospectivity. These models are built on high-quality potential field data sets. Assignment of bulk properties derived from previous well-constrained geophysical modeling and an extensive rock property database has enabled the identification of anomalous features that have been targeted for follow-up mineral exploration. An aspect of this effort has been the generation of uncertainty estimates for model features. Our experience is that this process can be hindered by models that are too large or too detailed to be interrogated easily, especially when modeling techniques do not readily permit significant geometric changes. The most effective 3D modeling workflow for insights into mineral exploration is that which facilitates the rapid hypothesis testing of a wide range of scenarios whilst satisfying the constraints of observed data.

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