Probabilistic Cover-Basement Interface Characterization in Cloncurry, Australia, using Magnetotelluric Soundings

Cloncurry is located in the Mount Isa province in Queensland, NE Australia. The Mount Isa Province is a well-known metallogenic province in Australia which hosts many IOCG deposits. One of them is the Ernest Henry IOCG deposit, which was found below cover in the 90’s. The cover in this area comprises of regolith and the Jurassic-Cretaceous sediments of Eromanga and Carpentaria Basins. This deposit appears to belong to a complex mineral system which extend over the entire Cloncurry District.

A magnetotelluric (MT) survey was conducted in 2016 by Geoscience Australia and The Geological Survey of Queensland in the vicinity of the Ernest Henry IOCG deposit, in order to characterize the electrical properties of the mineral system beneath it. The derived 3D electrical conductivity model highlights the variable cover thickness over the area, and a correlation between conductors located in the upper crust and known mineral occurrences such as the Ernest Henry mine.

The use of 3D deterministic inversions of MT data is very powerful to image the electrical structure of the mineral system at the crustal scale but lacks resolution to image a realistic sharp cover-basement interface and precludes quantitative assessment of uncertainty around the results.

In this work we propose a workflow to image a geologically realistic cover-basement interface and bring insights on the reliability and robustness of different parts of the model using a probabilistic inversion approach.

We selected a subset of the MT survey and for each site we ran a probabilistic 1D trans-dimensional Markov chain Monte Carlo sampler for estimating subsurface conductivity and its associated uncertainty. These inversions are designed to be robust to non-1D effects present in the data. Next, we performed a petrophysical analysis using available down hole measurements to derive constraints on the electrical conductivities of the different lithologies found in the area. Then these petrophysical constraints, coupled to spatial lateral constraints, are used to fuse the 1D probabilistic ensembles into a 3D posterior ensemble.

The pseudo 3D model obtained is compared to a 3D model derived from a conventional 3D deterministic inversion using the same data to assess the value and validity of the workflow. Preliminary interpretation of the results is performed using petrophysical data and established local geology knowledge. Conclusions around the benefits of this workflow to give a different perspective on the characterization of a mineral system located under cover and to provide basis for future survey planning are presented.