Potential Field Modeling at Global to Prospect Scales - Adding Value to the Geological, Seismic, Gravity, Magnetic and Rock Property Datasets of Australia

Abstract:

At Geoscience Australia, we are upgrading our gravity and magnetic modeling tools to provide new insights into the composition, properties, and structure of the subsurface. The scale of the investigations varies from the size of tectonic plates to the size of a mineral prospect. To accurately model potential field data at all of these scales, we require modeling software that can operate in both spherical and Cartesian coordinate frameworks.

The models are in the form of a mesh, with spherical prismatic (tesseroid) elements for spherical coordinate models of large volumes, and rectangular prisms for smaller volumes evaluated in a Cartesian coordinate framework. The software can compute the forward response of supplied rock property models and can perform inversions using constraints that vary from weak generic smoothness through to very specific reference models compiled from various types of "hard facts" (i.e., surface mapping, drilling information, crustal seismic interpretations). To operate efficiently, the software is being specifically developed to make use of the resources of the National Computational Infrastructure (NCI) at the Australian National University (ANU). The development of these tools is been carried out in collaboration with researchers from the Colorado School of Mines (CSM) and the China University of Geosciences (CUG) and is at the stage of advanced testing.

The creation of individual 3D geological models will provide immediate insights. Users will also be able to combine models, either by stitching them together or by nesting smaller and more detailed models within a larger model. Comparison of the potential field response of a composite model with the observed fields will give users a sense of how comprehensively these models account for the observations. Users will also be able to model the residual fields (i.e., the observed minus calculated response) to discover features that are not represented in the input composite model.