New CODES PhD candidate Alex Farrar, generously supported by First Quantum Minerals, aims to solve the mysteries surrounding geodynamic and structural controls, in particular the presence of trans-lithospheric faults (TLF), on the formation of giant porphyry copper deposits in the Central Andes. The Central Andes offers an excellent case study region due to its prolific metal production, well-documented geologic evolution and distinct spatial and temporal mineralisation events. Alex will use information that describes the location, timing, grade and structural setting of both economic and non-economic porphyry Cu deposits, combined with detailed structural mapping to answer the following research questions:

• What role does the inherited geologic architecture play in the localisation of giant porphyry camps and how do we best interpret it within multiple data sets and across scales representative of their length?
• Can proxies for pre-existing geologic architecture, i.e. TLF, be identified in the field to increase the level of confidence in the existence of structures?
• How does the evolution of the regional stress field focus, store and then release fertile magmas that form porphyry camps and can the paleo-stress fields be modelled in geographic space and through geological time?
• What are appropriate methods for transferring the information in geodynamic models to supervised machine learning methods for predicting porphyry mineralisation and how are the outputs best communicated?

We look forward to following Alex’s progress over the next few years as he tackles this exciting and ambitious project.

In collaboration with Geoscience Australia’s Minerals Division and the University of Canberra’s Institute of Applied Ecology, this project has developed methods for the meaningful integration of geoscience data for regional resource targeting and management. The focus of this project has been the investigation of approaches for appropriate handling of geoscience data with different scales and spatial supports (geometries). This has ultimately contributed to the development of novel pre-processing methods that facilitate bringing data together and unique visualisation and analysis methods that aid the interpretation of resulting models.

In 2018, collaborations with the University of Canberra have continued the application of unsupervised clustering for local- and regional-scale land management mapping projects within the Hydrogeological Landscape (HGL) Framework. The roll-out of automated HGL mapping in several regions across New South Wales is ongoing. While these computer-assisted mapping methods were specifically developed for defining land management zones the learnings gained from this project have implications for geologically focussed mapping projects.

In collaboration with Geoscience Australia’s Minerals Division and the University of Canberra’s Institute of Applied Ecology, this project has developed methods for the meaningful integration of geoscience data for regional resource targeting and management. The focus of this project has been the investigation of approaches for appropriate handling of geoscience data with different scales and spatial supports (geometries). This has ultimately contributed to the development of novel pre-processing methods that facilitate bringing data together, and unique visualisation and analysis methods that aid the interpretation of resulting models.

In 2017, unsupervised clustering algorithms were combined with robust geochemical data processing and extraction of multi-scale textural indices from geophysical imagery to highlight similarities between catchments in northern Australia. The geochemical and geophysical characteristics of the resulting catchment clusters were analysed in conjunction with gold deposit occurrence data and geological and geographic information. These results highlighted the importance of considering river system networks and sediment provenance when assessing regional-scale prospectivity models. The outcomes of this research culminated in the identification of several catchments highly prospective for gold mineralisation that will feed into pre-competitive geoscience data products delivered by Geoscience Australia.

In conjunction with researchers from the University of Canberra this project has investigated the use of unsupervised clustering for local- and regional-scale mapping applications. The automated construction of landscape ‘units’ based on a range of data representing geographical, geological and biological features was evaluated in conjunction with expert interpretations of landscape characteristics using the Hydrogeological Landscape (HGL) Framework. The successful outcomes of this research have led to the planned roll-out of automated HGL mapping in several regions across New South Wales. While these computer-assisted mapping methods were specifically developed for defining land management zones the learnings gained from this project have implications for geologically focussed mapping projects.