ORCID Profile
0000-0002-0742-9681
Current Organisation
Fortescue Metals Group Ltd.
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Publisher: Society of Exploration Geophysicists
Date: 11-2020
Abstract: Identifying the location of intrusions is a key component in exploration for porphyry Cu ± Mo ± Au deposits. In typical porphyry terrains, in the absence of outcrop, intrusions can be difficult to discriminate from the compositionally similar volcanic and volcanoclastic sedimentary rocks in which they are emplaced. The ability to produce lithological maps at an early exploration stage can significantly reduce costs by assisting in planning and prioritization of detailed mapping and s ling. Additionally, a data-driven strategy provides opportunity for the discovery of intrusions not identified during conventional mapping and interpretation. We used random forests (RF), a supervised machine-learning algorithm, to classify rock types throughout the Kliyul porphyry prospect in British Columbia, Canada. Rock types determined at geochemical s ling sites were used as training data. Airborne magnetic and radiometric data, geochemistry, and topographic data were used in classification. Results were validated using First Quantum Minerals’ geologic map, which includes additional detail from targeted location and transect mapping. The petrophysical and compositional similarity of rock types resulted in a noisy classification. Intrusions, particularly the more discrete, were inconsistently predicted, likely due to their limited extent relative to data s ling intervals. Closer examination of class membership probabilities (CMPs) identified locations where the probability of an intrusion being present was elevated significantly above the background. Indeed, a large proportion of mapped intrusions correspond to areas of elevated probability and, importantly, areas were highlighted as potential intrusions that were not identified in geologic mapping. The RF classification produced a reasonable lithological map, if lacking in resolution, but more significantly, great benefit comes from the insights drawn from the RF CMPs. Mapping the spatial distribution of elevated intrusion CMP, a soft classifier approach, produced a map product that can target intrusions and prioritize detailed mapping for mineral exploration.
Publisher: Elsevier BV
Date: 09-2019
Publisher: Society of Exploration Geophysicists
Date: 09-2020
Abstract: The Heazlewood-Luina-Waratah area is a prospective region for minerals in northwest Tasmania, Australia, associated with historically important ore deposits related to the emplacement of granite intrusions and/or ultramafic complexes. The geology of the area is poorly understood due to the difficult terrain and dense vegetation. We have constructed an initial high-resolution 3D geologic model of this area using constraints from geologic maps and geologic and geophysical cross sections. This initial model is improved upon by integrating results from 3D geometry and physical property inversion of potential field (gravity and magnetic) data, petrophysical measurements, and updated field mapping. Geometry inversion reveals that the Devonian granites in the south are thicker than previously thought, possibly connecting to deep sources of mineralization. In addition, we identified gravity anomalies to the northeast that could be caused by near-surface granite cupolas. A newly discovered ultramafic complex linking the Heazlewood and Mount Stewart Ultramafic Complexes in the southwest also has been modeled. This implies a greater volume of ultramafic material in the Cambrian successions and points to a larger obducted component than previously thought. The newly inferred granite cupolas and ultramafic complexes are targets for future mineral exploration. Petrophysical property inversion reveals a high degree of variation in these properties within the ultramafic complexes indicating a variable degree of serpentinization. Sensitivity tests suggest maximum depths of 2–3 km for the contact aureole that surrounds major granitic intrusions in the southeast, whereas the Heazlewood River complex is likely to have a deeper source up to 4 km. We have demonstrated the value of adding geologic and petrophysical constraints to 3D modeling for the purpose of guiding mineral exploration. This is particularly important for the refinement of geologic structures in tectonically complex areas that have lithology units with contrasting magnetic and density characteristics.
Publisher: Society of Exploration Geophysicists
Date: 07-2018
Abstract: The Eastern Goldfields of Western Australia is one of the world’s premier gold-producing regions however, large areas of prospective bedrock are under cover and lack detailed lithologic mapping. Away from the near-mine environment, exploration for new gold prospects requires mapping geology using the limited data available with robust estimates of uncertainty. We used the machine learning algorithm Random Forests (RF) to classify the lithology of an underexplored area adjacent to the historically significant Junction gold mine, using geophysical and remote-sensing data, with no geochemical s ling available at this reconnaissance stage. Using a sparse training s le, 1.6% of the total ground area, we produce a refined lithologic map. The classification is stable, despite including parts of the study area with later intrusions and variable cover depth, and it preserves the stratigraphic units defined in the training data. We assess the uncertainty associated with this new RF classification using information entropy, identifying those areas of the refined map that are most likely to be incorrectly classified. We find that information entropy correlates well with inaccuracy, providing a mechanism for explorers to direct future expenditure toward areas most likely to be incorrectly mapped or geologically complex. We conclude that the method can be an effective additional tool available to geoscientists in a greenfield, orogenic gold setting when confronted with limited data. We determine that the method could be used either to substantially improve an existing map, or produce a new map, taking sparse observations as a starting point. It can be implemented in similar situations (with limited outcrop information and no geochemical data) as an objective, data-driven alternative to conventional interpretation with the additional value of quantifying uncertainty.
Publisher: Society of Economic Geologists
Date: 06-2019
DOI: 10.5382/ECONGEO.4654
No related grants have been discovered for Stephen Kuhn.