Hidden Sources of Bias in Mineral Resource Databases: Common Issues and Consequences

Poorly governed geoscience databases introduce subtle but systematic biases that propagate through mineral resource estimation workflows, distorting grade continuity and resource classification. While the importance of data quality in mining is widely acknowledged, quantitative demonstrations of how specific data-management decisions translate into downstream technical and economic impacts remain limited. This study addresses that gap through three case studies derived from real-world industry examples. The first case study quantifies operator-dependent data extraction bias by comparing two independently generated datasets sourced from the same database on the same day. Despite nominal equivalence, the datasets differed materially in record counts and grade distributions, producing only negligible global volumetric differences (~0.1%) but up to ~5% local geometric variability and a 19.5% difference in estimated copper grade, resulting in a ~36% divergence in projected revenue. The second case study evaluates analytical uncertainty near detection limits using duplicate assay pairs, demonstrating that relative error increases markedly at low concentrations and that this behavior reflects inherent analytical limitations rather than laboratory non-compliance. The third case study examines the common practice of assigning detection-limit placeholder values to un-assayed intervals, showing that such substitutions can artificially generate ore in barren domains, whereas retaining null values and applying assignments during post-processing yields geologically and statistically coherent results. Collectively, these case studies demonstrate that hidden data biases can exert a stronger influence on resource outcomes than estimation methodology alone. The results highlight the need for standardized extraction workflows, explicit treatment of low-grade uncertainty, and validation practices that extend beyond global reconciliation metrics. Robust data governance and transparent, reproducible workflows are essential to reducing compounding uncertainty and improving confidence in mineral resource models. All datasets have been anonymized, scaled, or modified to prevent identification of any specific project, company, or operation. No confidential or propriety datasets are disclosed.