Data Provenance for Geologists: Trace Every Assay to Its Source

Mon, 26 Oct 2026 00:00:00 +0800

Establishing geological data provenance links every reported grade and drillhole interval back to certified laboratory analysis and immutable field evidence.

Key Takeaways

The task today: Exploration geologists must establish data provenance for every drillhole interval and assay in their database. Traditional exploration teams rely on digital spreadsheets or desktop GIS databases to trace measurements back to certified laboratory assay sheets.
The problem: Geological datasets often lose raw assay certificates and original drill logs during legacy migrations and property transfers. When downstream AI systems or resource estimators consume unverified numbers without an audit trail, rounding adjustments and unchecked coordinate shifts distort geological block models.
What the tool does: GeoCluster is a browser-based AI agent for geological investigation, available in open-source and Enterprise editions. It maintains strict evidence traceability between interpretations and source data, processes drill intervals with length-weighted means, and flags below-detection assays at half the analytical limit.

TL;DR

Data provenance in geology establishes an unbroken, auditable chain linking every assay, borehole interval, and structural interpretation back to its primary source certificate or field log. Exploration teams frequently encounter legacy databases where raw analytical measurements were overwritten by adjusted or converted values, obscuring original detection limits and coordinate systems. By recording persistent identifiers, laboratory analytical methods, and transformation steps in structured geochemical databases, geologists defend resource estimates against audit challenges and enable automated AI models to reason over verifiable evidence.

Geochemical Anomaly Detection: z-score, MAD and Background Ratio

Mon, 26 Oct 2026 00:00:00 +0800

Comparing parametric z-score, non-parametric MAD, and domain-specific background ratios allows geochemists to detect subtle mineralisation halos without outlier masking.

Key Takeaways

The task today: Exploration geologists must identify every geochemical anomaly in soil, rock chip, and drillhole assay tables. Exploration teams routinely use statistical calculations in desktop software to distinguish subtle mineralisation signatures from barren regional background.
The problem: Parametric methods like z-score assume normal distributions and fail when skewed populations or extreme outliers distort sample means. In contrast, rigid thresholding flags false positives from regolith variations or misinterprets below-detection analytical noise as genuine exploration targets.
What the tool does: GeoCluster is a browser-based AI agent for geological data analysis, available in open-source and Enterprise editions. It compares anomaly rankings across z-score, median absolute deviation, and background-ratio models while preserving complete evidence traceability back to laboratory certificates.

TL;DR

A geochemical anomaly represents an elemental concentration that significantly departs from expected barren background values, indicating potential mineralisation, weathering enrichment, or contamination. Standard parametric methods like mean plus two standard deviations (z-score) assume a normal distribution, making them vulnerable to skewness and outlier masking in multi-element datasets. Robust non-parametric alternatives such as median absolute deviation (MAD) and local background ratios resist extreme values and provide reliable thresholding when anomalies constitute less than ten percent of samples. By validating statistical outliers against geological domains, regolith context, and below-detection assay flags, exploration teams prioritize defensible drilling targets.

Geochemistry on Eigenform AI Geo Tooltips

Data Provenance for Geologists: Trace Every Assay to Its Source

Key Takeaways

TL;DR

Geochemical Anomaly Detection: z-score, MAD and Background Ratio

Key Takeaways

TL;DR