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Geological and structural modelling for better mineral resource estimation

A4   |   Letter

SRK News | Issue 57Exploration Geology: Keys to success

Ben Jupp, Geology and 3D geological modelling

Under the JORC Code, reporting a Mineral Resource requires sufficient confidence in the continuity of mineralisation to enable correlation between the sample points that form the basis of the resource. Achieving this confidence relies on understanding and interpreting the deposit’s mineral system, geological and structural framework, mineralisation timing, and controls. Understanding the underlying framework is critical for defining the position and geometry of mineralisation. Geological modelling is a powerful tool that helps geologists to better investigate and define a deposit’s mineral system and structure thereby improving confidence in interpretations, helping to reduce exploration risk, and improving the quality of resource domains for resource estimation.

Implicit modelling (Cowan et al., 2003) enables the integration and modelling of a diverse range of datasets in a more streamlined and versatile workflow than conventional wireframing methodologies (e.g., CAD-based approaches). Advanced visualisation and structural modelling toolsets allow geologists to better analyse and interrogate the datasets to explore the deposit structure, timing relationships, and mineralisation controls. By applying this methodology, improved confidence in the outputs are achieved, providing models and resource domains that honour the geology and the structure, not just link grade distribution.

The Maud Creek gold deposit in the Northern Territory is an example that highlights the importance of employing 3D structural modelling to underpin the resource domaining process (Figure 1).

The deposit is understood to be structurally controlled with mineralisation hosted within the north–south striking Maud Creek fault and lithological association with the mafic tuff sequences of the Dorothy Volcanic Member.

To fully understand the mineralisation controls of the deposit, all available drilling, historic pit mapping, regional geological mapping and geophysical datasets were integrated in Leapfrog’s 3D modelling system. Preliminary modelling of the gold grade using Leapfrog’s implicit modelling system (Figure 2) demonstrated a strong fault control on the gold distributions, with gold dipping along the main Maud Creek fault. It also illustrated a previously unrecognised south-east plunge associated with a cross-cutting fault linking it with a structure identified within pit mapping. Additionally, a north-south structure to the east of the main gold lode was interpreted to control steeply dipping mineralisation in this area and corresponding with regional mapping and geophysical interpretations in the area.

This modelling provided a better understanding of structure, lithological controls, and grade distributions. This additional knowledge was used to develop detailed wireframes of the host vein bodies. Vein geometries were constrained within host horizons, in alignment with geological and structural observations. Grade halos were developed to observe the same controls. This process ensured resource wireframes integrated the underlying geological understanding, which provided greater confidence in the final resource domains. We find 3D geological modelling is a powerful method for integrating and analysing datasets. Improving understanding of the structure and mineral systems of a deposit during the modelling process ensures geology is honoured and ultimately improves confidence in models developed for resource estimation.

Ben Jupp:

SRK China