Modelling gold potential in the Granites-Tanami Orogen, NT, Australia: A comparative study using continuous and data-driven techniques

Roshanravan, Bijan; Kreuzer, Oliver P.; Bruce, Matt; Davis, James A.; Briggs, Matt

doi:10.1016/j.oregeorev.2020.103661

Cited by 15 publications

(4 citation statements)

References 62 publications

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“…Further study of the N. benthamiana population located in the region of The Granites and the surrounding land is of great scientific interest. This task remains challenging because of the remote location of the site, the uncertainty of rains and timing of germination of N. benthamiana seed, and the development of the site as a goldmine [88]. Of critical importance before such studies are undertaken is engagement with the traditional owners of the region.…”

Section: Discussionmentioning

confidence: 99%

Historical and Scientific Evidence for the Origin and Cultural Importance to Australia’s First-Nations Peoples of the Laboratory Accession of Nicotiana benthamiana, a Model for Plant Virology

Wylie

2022

Viruses

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Nicotiana benthamiana is an indigenous plant species distributed across northern Australia. The laboratory accession (LAB) of N. benthamiana has become widely adopted as a model host for plant viruses, and it is distinct from other accessions morphologically, physiologically, and by having an attenuation-of-function mutation in the RNA-dependent RNA polymerase 1 (NbRdr1) gene, referred to as NbRdr1m. Recent historical evidence suggested LAB was derived from a 1936 collection by John Cleland at The Granites of the Northern Territory, although no scientific evidence was provided. We provide scientific evidence and further historical evidence supporting the origin of LAB as The Granites. Analysis of a herbarium specimen of N. benthamiana collected by Cleland in 1936 revealed that The Granites population contains plants heterozygous for the NbRdr1 locus, having both the functional NbRdr1 and the mutant NbRdr1m alleles. N. benthamiana was an important cultural asset actively utilised as the narcotic Pituri (chewing tobacco) by the Warlpiri Aboriginal people at the site, who prevented women of child-bearing age from consuming it. We propose that Aboriginal people selected some of the unique traits of LAB that have subsequently facilitated its adoption as a model plant, such as lack of seed dormancy, fast maturity, low nornicotine content, and gracility.

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Section: Discussionmentioning

confidence: 99%

Historical and Scientific Evidence for the Origin and Cultural Importance to Australia’s First-Nations Peoples of the Laboratory Accession of Nicotiana benthamiana, a Model for Plant Virology

Wylie

2022

Viruses

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“…Although AI algorithms have matured and proved to be valid for the outputting predictions of mineral prospectivity, the input data for AI models are not sound enough to objectively represent the target mineral systems [20], which degrades the effectiveness of AI models in practical exploration activities. This is mainly attributed to the intrinsically complex and noisy nature of geological features employed in the MPM task.…”

Section: Introductionmentioning

confidence: 99%

Fractal-Based Multi-Criteria Feature Selection to Enhance Predictive Capability of AI-Driven Mineral Prospectivity Mapping

Sun,

Feng,

et al. 2024

Fractal Fract

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AI-driven mineral prospectivity mapping (MPM) is a valid and increasingly accepted tool for delineating the targets of mineral exploration, but it suffers from noisy and unrepresentative input features. In this study, a set of fractal and multifractal methods, including box-counting calculation, concentration–area fractal modeling, and multifractal analyses, were employed to excavate the underlying nonlinear mineralization-related information from geological features. Based on these methods, multiple feature selection criteria, namely prediction–area plot, K-means clustering, information gain, chi-square, and the Pearson correlation coefficient, were jointly applied to rank the relative importance of ore-related features and their fractal representations, so as to choose the optimal input feature dataset readily used for training predictive AI models. The results indicate that fault density, the multifractal spectrum width (∆α) of the Yanshanian intrusions, information dimension (D1) of magnetic anomalies, correlation dimension (D2) of iron-oxide alteration, and the D2 of argillic alteration serve as the most effective predictor features representative of the corresponding ore-controlling elements. The comparative results of the model assessment suggest that all the AI models trained by the fractal datasets outperform their counterparts trained by raw datasets, demonstrating a significant improvement in the predictive capability of fractal-trained AI models in terms of both classification accuracy and predictive efficiency. A Shapley additive explanation was employed to trace the contributions of these features and to explain the modeling results, which imply that fractal representations provide more discriminative and definitive feature values that enhance the cognitive capability of AI models trained by these data, thereby improving their predictive performance, especially for those indirect predictor features that show subtle correlations with mineralization in the raw dataset. In addition, fractal-trained models can benefit practical mineral exploration by outputting low-risk exploration targets that achieve higher capturing efficiency and by providing new mineralization clues extracted from remote sensing data. This study demonstrates that the fractal representations of geological features filtered by multi-criteria feature selection can provide a feasible and promising means of improving the predictive capability of AI-driven MPM.

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“…The methods are mainly divided into data-and knowledge-driven methods [11,12]. The data-driven methods, involving quantitative analysis of spatial associations between known mineral occurrences as training sites, contain logistic regression, weights of evidence model, evidential belief modeling, Bayesian network classifiers, artificial neural networks, support vector machines and random forests [12][13][14][15]. The knowledge-driven methods, based on the judgment of a geological expert, contain Boolean logic, index overlay, fuzzy analytical hierarchy process, fuzzy logic and restricted Boltzmann machine [8,16,17].…”

Section: Introductionmentioning

confidence: 99%

Application of Logistic Regression and Weights of Evidence Methods for Mapping Volcanic-Type Uranium Prospectivity

et al. 2023

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Pucheng district is a part of the Wuyi Mountain polymetallic metallogenic belt, which is constituted by Archean-Proterozoic metamorphic basements and Mesozoic volcanic-sedimentary covers. Uranium deposits are formed as volcanic-hosted and structural controls. In this study, the hybrid data-driven methods of logistic regression (LR) and weights of evidence (WofE) were applied for the mineral potential mapping of uranium in the Pucheng district. Evidential layers such as volcanic stratum, structure, igneous rock, alteration and radioactive anomaly were used in the mineral prospectivity analyses. The results show that the data-driven methods can not only measure the relative importance of each type of geological feature in uranium controls but also delineate prospective grounds for uranium exploration. The receiver operating characteristics (ROC) curve and under the ROC curve (AUC) were applied to measure the performance of the prospectivity models. The data-driven models are highly capable of mapping uranium prospectivity because AUC is close to 1. The results show that more than 90% of the known uranium deposits occur in regions with high probability. LR performs a little better than WofE in this area. The prospectivity mapping confirmed that there is significant potential for uranium mineralization for further exploration.

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Modelling gold potential in the Granites-Tanami Orogen, NT, Australia: A comparative study using continuous and data-driven techniques

Cited by 15 publications

References 62 publications

Historical and Scientific Evidence for the Origin and Cultural Importance to Australia’s First-Nations Peoples of the Laboratory Accession of Nicotiana benthamiana, a Model for Plant Virology

Historical and Scientific Evidence for the Origin and Cultural Importance to Australia’s First-Nations Peoples of the Laboratory Accession of Nicotiana benthamiana, a Model for Plant Virology

Fractal-Based Multi-Criteria Feature Selection to Enhance Predictive Capability of AI-Driven Mineral Prospectivity Mapping

Application of Logistic Regression and Weights of Evidence Methods for Mapping Volcanic-Type Uranium Prospectivity

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