Paper No. 122-3
Presentation Time: 9:30 AM
THE AUSTRALIAN REMANENT ANOMALIES DATABASE – A POSSIBLE TEMPLATE FOR MAPPING GLOBAL CONTINENTAL CRUSTAL MAGNETIZATION
Across most of Australia basement geology is beneath cover and/or deep weathering, and magnetic field imagery is of crucial importance for geological mapping and mineral exploration. To map magnetization directions, and thereby the igneous, thermal and alteration events they result from, we have created a database of magnetic anomalies that are due substantially to remanent magnetization. This database in part compensates for corresponding scarcity of direct palaeomagnetic studies, and assists geoscientists to reduce remanent magnetization hazards in borehole design. Presently the database consists of 250 user-guided parametric inversions, and 30,000 automated search solutions. Geo-located images of the selected anomaly inversions with resulting models can be downloaded in formats suitable for 2D and 3D GIS packages, together with workspaces for a free industry viewer. Fundamental statistics can be interrogated for the automated solutions. The database is available through the AuScope portal, which provides web-based access to various national and regional geoscientific data and imagery (link at https://wiki.csiro.au/confluence/display/cmfr/home). Presently we are improving generation of the automated solutions, and validating those against the inversions and sparse direct magnetization measurements. We intend to open the database for upload from the community of government, industry and academic geoscientists. We also believe that this national resource provides a template for future global coverage, and as an initial test, have uploaded the EMAG2 grid. Few of the discrete, mostly shallow-sourced magnetic anomalies inverted from the national magnetic coverage are well defined in the EMAG2 data, but we have uploaded models from inversion of 6 EMAG2 anomalies over southern Australia, with similar results to inversion of those primary anomalies in the national dataset. Presently we are experimenting with application of the automated search algorithm to the EAMG2 grid.