Lateritic Ni–Co Prospectivity Modeling in Eastern Australia Using an Enhanced Generative Adversarial Network and Positive-Unlabeled Bagging DOI Creative Commons
N. Wake, Ehsan Farahbakhsh, R. Dietmar Müller

et al.

Natural Resources Research, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 18, 2024

Abstract The surging demand for Ni and Co, driven by the acceleration of clean energy transitions, has sparked interest in Lachlan Orogen New South Wales its potential lateritic Ni–Co resources. Despite recent discoveries, a substantial knowledge gap exists understanding full scope these critical metals this geological province. This study employed machine learning-based framework, integrating multidimensional datasets to create prospectivity maps deposits within specific segment. framework generated variety data-driven models incorporating (rock units, metamorphic facies), structural, geophysical (magnetics, gravity, radiometrics, remote sensing spectroscopy) data layers. These ranged from comprehensive that use all available layers fine-tuned restricted high-ranking features. Additionally, two hybrid (knowledge-data-driven) distinguished between hypogene supergene components mineral systems. implemented augmentation methods tackled imbalances training samples using SMOTE–GAN method, addressing common learning challenges with sparse data. overcame difficulties defining negative translating into proxy employing positive unlabeled bagging technique. revealed robust spatial correlation high probabilities known occurrences, projecting extensions sites identifying greenfield areas future exploration Orogen. high-accuracy developed utilizing Random Forest classifier enhanced mineralization processes promising region.

Language: Английский

Lateritic Ni–Co Prospectivity Modeling in Eastern Australia Using an Enhanced Generative Adversarial Network and Positive-Unlabeled Bagging DOI Creative Commons
N. Wake, Ehsan Farahbakhsh, R. Dietmar Müller

et al.

Natural Resources Research, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 18, 2024

Abstract The surging demand for Ni and Co, driven by the acceleration of clean energy transitions, has sparked interest in Lachlan Orogen New South Wales its potential lateritic Ni–Co resources. Despite recent discoveries, a substantial knowledge gap exists understanding full scope these critical metals this geological province. This study employed machine learning-based framework, integrating multidimensional datasets to create prospectivity maps deposits within specific segment. framework generated variety data-driven models incorporating (rock units, metamorphic facies), structural, geophysical (magnetics, gravity, radiometrics, remote sensing spectroscopy) data layers. These ranged from comprehensive that use all available layers fine-tuned restricted high-ranking features. Additionally, two hybrid (knowledge-data-driven) distinguished between hypogene supergene components mineral systems. implemented augmentation methods tackled imbalances training samples using SMOTE–GAN method, addressing common learning challenges with sparse data. overcame difficulties defining negative translating into proxy employing positive unlabeled bagging technique. revealed robust spatial correlation high probabilities known occurrences, projecting extensions sites identifying greenfield areas future exploration Orogen. high-accuracy developed utilizing Random Forest classifier enhanced mineralization processes promising region.

Language: Английский

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