Geological Reasonableness Audit Worksheet

Geological reasonableness auditing bridges the gap between computational efficiency and geological fidelity in modern resource estimation. As AI and ML techniques (e.g., random forests, neural networks, or kriging variants) increasingly automate orebody modeling, they risk generating statistically optimal but geologically nonsensical outcomes—such as high-grade zones cutting across impermeable lithologies, grade reversals violating paragenetic sequences, or contact geometries inconsistent with regional structural trends. The audit worksheet operationalizes domain knowledge by embedding checklists, rule-based logic, and conditional formatting that compare model outputs against predefined geological constraints—including lithological competence, structural controls (e.g., fold axial traces, fault offsets), alteration zonation patterns, and empirical grade-thickness relationships. Each audit item is scored for compliance (e.g., 'Pass', 'Warning', 'Fail') and annotated with rationale, enabling traceable decision-making and regulatory defensibility under standards such as JORC, NI 43-101, or PERC. Critically, the worksheet is not static: it evolves with deposit understanding and supports feedback loops where audit failures trigger retraining of AI models with geologically constrained feature engineering or bias mitigation strategies.