Dynamic structural modelling can be a powerful and adaptive tool in brownfield mineral exploration, offering a framework to integrate diverse, multi-scale geological datasets into evolving 3D models. While dynamic structural modelling is often used for strongly deformed systems such as orogenic gold or volcanogenic massive sulphide deposits, it is equally valuable for banded iron fomation (BIF)-hosted deposits such a those found in the Pilbara region of Western Australia. In these deposits, faults, folds, and shear zones can upgrade and redistribute mineralization, shaping ore geometry and continuity.
In brownfield contexts, exploration must reconcile legacy data with new inputs from active mining. Dynamic modelling facilitates this by fusing data streams such as Unmanned Aerial Vehicle (UAV) photogrammetry for structural mapping of continuously exposed benches, blasthole geochemistry, Reverse Circulation (RC) drilling with downhole geophysical logs (e.g., gamma), and targeted diamond drillholes with Acoustic Televiewer (ATV)/Optical Televiewer (OTV) datasets for fracture/fault characterization. These are integrated within a multi-user geodatabase (e.g., Leapfrog, Vulcan) that supports iterative, version-controlled updates as interpretations evolve. This dynamic environment enables geoscientists to test hypotheses, quantify uncertainty, and prioritize targets with greater precision and confidence.
Even though BIFs are stratiform in origin, structural processes play a major role in concentrating high-grade zones of iron ore mineralization—so understanding deformation is critical. When applied effectively, structural modelling enhances understanding of fault offsets, fold amplitudes and wavelengths, and brittle-ductile regimes that influence ore distribution, mineral hydration states, and grade variability. SRK has applied dynamic modelling techniques to numerous iron ore operating pits in the Western Australian in Pilbara region. Using UAV imagery and Vulcan software, high-confidence structural data were extracted from continuously exposed benches and integrated with crest and pit mapping, blasthole geochemistry, and drillhole datasets. This composite dataset validated geological conformance. Discrepancies triggered immediate model updates, refining understanding of how faulting and folding localize high-grade, hydrated ore. This data-driven approach guided infill drilling campaigns, optimizing resource definition within active pit shells.
Importantly, dynamic structural modelling aligns with the principle that geological models must evolve alongside exploration and mining. As resource extraction progresses— through new exploration zones, pit extensions and cutbacks, and continuous mining—new data such as point cloud mapping, in situ assays, and production drilling results need to be fed back into the structural framework, capturing incremental morphological changes in the ore body. The modelling workflow proceeds hierarchically, from regional-scale geometry to deposit-scale deformation features and from well-constrained to poorly known domains, progressively reducing uncertainty through targeted data acquisition and model calibration.
Overall, dynamic structural modelling is a powerful tool for brownfield exploration. It supports adaptive decision-making, integrates evolving datasets, and enhances geological understanding, especially in mineral systems where stratigraphy and structure interact to control mineralization. Geotechnical risk assessment for pit design and optimization, as well as hydrogeological analyses, also benefit from this approach.
