Figure 4. Impact of Top-Capping on Kriging vs. GeaAI. 5. CONCLUSION This study presents a validated framework for integrating Machine Learning into mineral resource estimation without sacrificing geostatistical rigor. The GeaAI system demonstrates that: 1. Geometry Matters: Incorporating explicit geometric features (Curvature, Wavelets) allows the model to correct the smoothing effect of Kriging. 2. Value Recovery: The hybrid approach recovers over 50% of the metal value in high-grade zones that traditional methods underestimate. 3. Efficiency: The Rust-based engine makes it feasible to compute complex fractal and spectral features on an industrial scale. We conclude that hybrid architectures represent the future of resource estimation. However, they must be accompanied by rigorous "White Box" validation methods—such as the Swath Plots and Spacing Analysis presented here—to ensure they meet the transparency standards required by international reporting codes. REFERENCES Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246-1266. Rossi, M. E., & Deutsch, C. V. (2014). Mineral Resource Estimation. Springer Science & Business Media. Chilès, J. P., & Delfiner, P. (2012). Geostatistics: Modeling Spatial Uncertainty. John Wiley & Sons. Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. KDD '16. Mandelbrot, B. B. (1982). The Fractal Geometry of Nature. W. H. Freeman.
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