Neural Kriging Implementation Checklist

Neural Kriging bridges the gap between parametric geostatistics and flexible deep learning by encoding spatial dependence not via hand-crafted variograms but through differentiable neural surrogates—typically coordinate-based MLPs or attention-driven architectures—that map spatial coordinates (x,y,z) and auxiliary features (e.g., lithology, assay history, geophysics) to grade estimates and predictive uncertainties. A core principle is *geostatistical fidelity*: the model is trained with loss functions incorporating kriging-like constraints—such as conditional unbiasedness (E[Ŷ|X] = Y_true), reproducibility of sample means, and penalization of violation of the generalized covariance consistency condition—often enforced via regularization or custom layers (e.g., kriging-aware attention masks or variogram-mimicking spectral losses). In practice, Neural Kriging excels in complex, non-stationary orebodies where traditional variogram modeling fails—such as folded stratiform deposits or hydrothermal breccia systems—with improved extrapolation robustness and quantified epistemic/aleatoric uncertainty via Monte Carlo dropout or deep ensembles. Deployment requires rigorous validation against cross-validated kriging benchmarks, reconciliation with drill-hole composites, and integration into mine planning workflows (e.g., block model conditioning, reserve classification under NI 43-101 or JORC standards).