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The Gibi Project is an advanced-stage iron ore exploration program progressing toward production through staged drilling in Liberia.
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Integrating artisanal mining activity and gold-grain analysis with geological and geospatial data can significantly enhance greenfield exploration by improving targeting and identification of primary gold sources.
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This issue covers several approaches currently used to investigate mineral systems that are not readily exposed at the surface.
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For prospectivity analysis, SRK typically uses a hybrid approach or develops both model types for comparison. The approaches are considered complementary, as demonstrated in a recent prospectivity- mapping project across Western Australia (WA).
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This article shows that graph-based machine learning—particularly GCNs—outperforms traditional models in producing geologically coherent mineral prospectivity maps for copper deposits in southern Peru.
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The key objective of the study was to provide MIND ID with a maturity assessment at the strategic, tactical, and operational levels of its exploration capability, departmental processes, and performance.
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The study provided a revised interpretation of the Arabian Shield’s geological history, integrating multidisciplinary datasets and expert knowledge.
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Feature engineering guided by geology is a key step in applying machine learning to mineral exploration.
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SRK is adapting in turn by pursuing specialised generative work for major mining companies with leaner geological teams, developing new analytical approaches, and integrating digital and data-driven capabilities.
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Implementing this methodology across different mineral deposits has allowed SRK to assess the spatial continuity of structurally controlled geological models more accurately.
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