Authors / CoAuthors
Ford, A.
Abstract
In the first half of 2019, a collaborative mineral potential mapping project was undertaken between the Geological Survey of New South Wales (GSNSW) and Kenex to examine the mineral potential in the eastern Lachlan Orogen (ELO; Ford et al., 2019b). This project was part of a broader state-wide study that utilised the high quality publicly available geoscience data provided by the GSNSW to generate data-driven mineral potential maps using the weights of evidence (WofE) technique for different mineral systems in key metallogenic districts within NSW (Ford et al., 2019a). The aim of this collaborative project was to deliver a product that could be used to provide justifiable land use planning advice to key government stakeholders, as well as to highlight the exploration potential for key mineral systems at a regional scale. One key mineral system that was included in the 2019 ELO study was the porphyry Cu-Au mineral system, which was constrained to the Macquarie Arc. The results of the WofE mineral potential mapping for this porphyry model were broadly successful in terms of predicting the location of both the training data used in the WofE model, as well as a separate set of validation porphyry Cu-Au occurrences. However, the model failed to predict the location of one of the training points, Kaiser, in the prospective area. This failure to predict Kaiser led to a re-evaluation of the data using a variety of different machine learning techniques, in particular random forests (RF; Ford, 2020) and neural networks (NN). No additional or updated data was incorporated, and the maps used in the machine learning were the same maps made as part of the initial WofE study in 2019. The results show that the use of input maps that have been pre-classified to determine optimal thresholds outperform input maps that have had no favourability criteria applied when typical benchmarks for exploration targeting are considered. In addition, the NN analysis shows strong evidence of overfitting to the training data when a large number of input maps are used. A moderate degree of success for targeting under cover was achieved when only geophysical maps were included in the models. Abstract presented at the 8th Mines & Wines Conference 2022 (https://www.aig.org.au/events/8th-mines-wines-conference-2022/)
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document
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146453
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Keywords
- theme.ANZRC Fields of Research.rdf
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- Knowledge Representation and Machine LearningEARTH SCIENCES
- ( Project )
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- Exploring For The Future
- ( Project )
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- EFTF
- ( Place )
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- Eastern Lachlan Orogen
- ( Theme )
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- Porphyry copper-gold
- ( Theme )
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- machine learning
- ( Theme )
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- mineral potential mapping
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- Published_Internal
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geoscientificInformation
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8th Mines and Wines Conference 9-13 May 2022, Orange, NSW
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