Grant: Discovery Grants Program - Individual

The goal of the proposed research programme is to develop improved computer modelling and interpretation methods for geophysical survey data that can be truly integrated with modern computerized geological model-building techniques.It is generally recognized that the easy-to-find ore deposits have all been discovered. Deposits are often missed because of the complexity of the geological environment in which they are hosted, especially when the geometrical and structural relationships of the host rocks are complicated and hard to unravel.In the last decade or so, the use of geophysical inversion software that can construct 3D subsurface distributions of a physical property (i.e., a geophysical Earth model) from most types of geophysical survey data has become widespread. Existing inversion technology has proven to be successful in providing reliable, robust quantitative information about the location, depth, size and composition of ore deposits when the geological environment is not particularly complicated. However, on its own, existing inversion technology is limited in its ability to distinguish features when the subsurface is more complex, and it is not suited for integration with geological modelling methods.Recently, methodology and software for constructing 3D geological models, i.e, computer models comprising wireframe surfaces that represent contacts between rock units, faults, and outlines of ore deposits, have been developed. Such models, which are essentially 3D maps of the subsurface, can then be used to better understand the spatial and structural relationships between an ore deposit and its surrounding geological features, and to provide quantitative estimates of the location, depth and size of a mineral deposit. However, the data that such geological model-building methods use is limited to very sparse point measurements and the constructed surfaces are extrapolated throughout large expanses of the subsurface where no geological data exists.Combining these two processes into a single, integrated procedure will result in Earth modelling capabilities that can handle significantly more complex subsurface scenarios than is currently possible: the structural geological information will reduce the non-uniqueness and indistinctness of the geophysical inversion, and the geophysical information about the larger-scale, averaged physical property distribution will guide extrapolation of the geological surfaces throughout the subsurface,As such, the new methods will be of particular benefit to the mineral exploration industry, which is a significant contributor to Canada's economy, and its search for new ore deposits. The research will also significantly advance our knowledge and understanding of computational geophysical methods.