44 The analysis incorporated a heterogeneous set of exploration data. Inputs included legacy drilling data from adjacent areas, comprising lithological logs, wireline measurements, and geochemical assay data. These were complemented by multiple geophysical datasets—including gravity, magnetotellurics (MT), direct current induced polarization (DCIP), and transient electromagnetics (TEM)—as well as surface lithological maps, topography, and relevant regional geological literature. AI-based modeling was applied through a neural-network-driven data fusion and generative modeling framework, producing probabilistic ensembles of three-dimensional subsurface models. These ensembles were used to generate resource estimates across multiple statistical quantiles, explicitly representing uncertainty in geometry, grade distribution, and total contained copper. A sequential, stochastic drilling optimization approach was then employed to generate drill targets designed to maximize expected uncertainty reduction in total contained copper above a specified economic cutoff. Performance was evaluated through comparison with conventional exploration workflows. First, AI-driven resource estimates were compared against traditional resource modeling results to assess relative precision, contrasting objective, probabilistic estimates with expert-driven assessments produced independently by different practitioners. Second, drilling performance was evaluated by measuring the amount of drilling required for the AI-optimized strategy to reach comparable levels of precision and accuracy to the resource model derived from the full set of conventionally targeted drilling. This dual comparison enabled assessment of both modeling effectiveness and exploration efficiency. 3.2. Results First, we compared the efficacy of the multi-modal generative modeling by comparing the resulting resource estimate to an expert-developed resource estimate using the same data. Figure 1 shows the resource distribution generated by the AI system using the available legacy data, prior to any additional drilling on the target. The histogram was constructed by computing total contained copper for each geological realization in an ensemble of approximately 1,000 probabilistic subsurface models. All realizations achieved approximately 85% agreement with the input data and exhibited geologically plausible variability away from data-constrained regions. For comparison, the conventional uncertainty assessment is shown below the histogram as a boxand-whisker plot, with box quantiles defined at P30 and P70 and whiskers extending to the minimum and maximum case values. While both representations incorporate the same expert geological input, the histogram encodes substantially more information by explicitly weighting each realization according to data fit within the AI-driven framework. As a result, the P30–P70 spread of the AI-derived resource distribution is approximately three times narrower than that of the conventional expert-based estimate.
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