Integrated Inversion of Electromagnetic and Geological Data for Regolith Characterisation

Abstract

An increase in demand for commodities coupled with a decrease in world-class ore deposit discoveries in the last three decades is the driving force for exploring for a new generation of world-class ore deposits at depth. Exploration through cover is becoming one of the critical challenges for the mineral exploration industry.

This paper explores methods of integrating geophysical, geological and landscape data so as to reduce uncertainty in landscape evolution models interpreted from inversion of electromagnetic (EM) data. Inversion of EM data is, in general, non-unique: many different models will be able to fit the EM data equally well, resulting in large uncertainty. However, EM model uncertainty can be significantly constrained when geological and landscape context are taken into account. This study aims to characterise the regolith (weathered and transported cover), and understand how its conductivity varies with the landscape and with the regolith architecture. This is assisted by logging information from 104 boreholes penetrating through the regolith and into the basement rocks. The study area is associated with the DeGrussa Cu-Au deposit located in the Capricorn Orogen of Western Australia, a regolith-dominated terrain where the regolith varies in thickness between <5 and ~150m.

We first select EM decay curves, extracted from an airborne EM survey, whose locations are close to those of the boreholes. We then use a layered-earth (1D) forward model, and invert those data for the electrical resistivities of each of the lithologies identified on the geological borehole logs. Layer boundary depths are fixed to the borehole depths. We show how the non-uniqueness associated with EM inversion can be reduced by the inclusion of decay curves from geology with different layer thickness ratios.

Lithological models derived from the integration of electromagnetic, geological and landscape data show less uncertainty and are therefore more reliable for mineral exploration targeting.