Exploration algorithms and ‘missing’ assays: data lineage design notes for geologists
Reviewed by Joe Ashwell
First reported on International Mining – News
30 Second Briefing
Digital exploration workflows are exposing a new failure mode where assay values ingested into drillhole databases lose their evidentiary trail, allowing mis-keyed grades, OCR errors from scanned lab certificates, or misaligned historical drill logs to appear as authoritative inputs to machine-learning targeting. Joshua argues that algorithms ranking targets, interpolating block models, or driving prospectivity maps must treat each assay with an explicit provenance score, distinguishing original lab CSVs from hand-typed transcriptions or reconstructed intervals. For geologists and data engineers, this means redesigning schemas to store document lineage, QA/QC context and uncertainty flags, not just numeric assay fields.
Technical Brief
- Core failure mode arises when assay values are decoupled from original paper or PDF evidence.
- Once ingested, legacy lab certificates and drill logs often become untraceable within centralised exploration databases.
Our Take
International Mining also features prominently in coverage of IMARC and the World Mining Congress 2026, signalling that its editorial line on AI in exploration is being shaped alongside high-level conference debates on data integrity and critical minerals supply risk.
With IMARC summaries in our database already highlighting ‘rapid deployment of AI in exploration’, this article suggests operators will increasingly need formal QA/QC protocols not just for sampling and assaying, but for how machine-learning models treat missing, corrupted or fabricated drillhole data.
Prepared by collating external sources, AI-assisted tools, and Geomechanics.io’s proprietary mining database, then reviewed for technical accuracy & edited by our geotechnical team.
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