Aurore Joly

Crustal architecture of the Capricorn Orogen, Western Australia and associated metallogeny

A 581 km vibroseis-source, deep seismic reflection survey was acquired through the Capricorn Orogen of Western Australia and, for the first time, provides an unprecedented view of the deep crustal architecture of the West Australian Craton. The survey has imaged three principal suture zones, as well as several other lithospheric-scale faults. The suture zones separate four seismically distinct tectonic blocks, which include the Pilbara Craton, the Bandee Seismic Province (a previously unrecognised tectonic block), the Glenburgh Terrane of the Gascoyne Province and the Narryer Terrane of the Yilgarn Craton. In the upper crust, the survey imaged numerous Proterozoic granite batholiths as well as the architecture of the Mesoproterozoic Edmund and Collier basins. These features were formed during the punctuated reworking of the craton by the reactivation of the major crustal structures. The location and setting of gold, base metal and rare earth element deposits across the orogen are closely linked to the major lithospheric-scale structures, highlighting their importance to fluid flow within mineral systems by the transport of fluid and energy direct from the mantle into the upper crust.

Random Forest-Based Prospectivity Modelling of Greenfield Terrains Using Sparse Deposit Data: An Example from the Tanami Region, Western Australia

AbstractData-driven prospectivity modelling of greenfields terrains is challenging because very few deposits are available and the training data are overwhelmingly dominated by non-deposit samples. This could lead to biased estimates of model parameters. In the present study involving Random Forest (RF)-based gold prospectivity modelling of the Tanami region, a greenfields terrain in Western Australia, we apply the Synthetic Minority Over-sampling Technique to modify the initial dataset and bring the deposit-to-non-deposit ratio closer to 50:50. An optimal threshold range is determined objectively using statistical measures such as the data sensitivity, specificity, kappa and per cent correctly classified. The RF regression modelling with the modified dataset of close to 50:50 sample ratio of deposit to non-deposit delineates 4.67% of the study area as high prospectivity areas as compared to only 1.06% by the original dataset, implying that the original “sparse” dataset underestimates prospectivity.