How to Validate Drillhole Data Before Resource Estimation

A resource model is only as good as the data underneath it. And in 12 years of looking at exploration databases, the same five problems keep showing up: duplicate collar IDs, surveys that disagree with downhole depth, assay intervals that overlap, lithology codes nobody documented, and the silent killer — partial data that looks fine until you actually start estimating.

This post walks through the 6-step validation checklist I use on every project before starting EDA. It’s not exhaustive. It’s the minimum to avoid embarrassing errors later.

Why this matters

JORC 2012 Table 1 Section 1 and KCMI 2017 both require you to disclose your data validation procedures. “I trusted the database” is not a procedure. Your Competent Person needs to be able to point at concrete checks — and so do you, when an investor’s technical advisor starts asking questions.

More practically: a bad assay interval at 80m depth in one hole won’t change your global mean. A systematic error — like all REVERSE-CIRCULATION holes having a survey offset of 3° because the survey tool wasn’t zeroed — will silently shift your entire variogram and skew your kriging.

The 6-step checklist

1. Collar validation

Before you load anything else, the collar table must be clean.

Checks:

• Unique hole IDs — no duplicates. If you see TRRC014 twice, one is wrong.
• Coordinate ranges — are easting/northing within the project area? An 8000 km offset is a real thing that happens when someone enters UTM zone wrong.
• Elevation sanity — does collar elevation match topography (DEM check)? Holes that “start underground” are a red flag.
• Hole type — DDH, RC, AC labeled consistently. Mixed casing breaks downstream filters.

Decision gate: 100% unique IDs, all coordinates in project bounding box, elevation within ±10m of DEM.

2. Survey validation

Surveys are where projection errors hide.

Checks:

• Azimuth range: 0–360°. Any 999, -1, or blank is a flag.
• Dip range: -90° to 0° for downhole drilling (most common convention). Positive dips? Re-check sign convention.
• Depth monotonic: surveys must go DOWN, not jump backward. [15, 30, 25, 60] is broken.
• Survey interval: typical 30m for DDH, 6m for RC. Gaps >50m without explanation are suspect.
• Final depth match: last survey depth ≤ final hole depth. Not the other way around.

Decision gate: All azimuths 0–360°, all dips negative or zero, depths monotonic, no unexplained gaps.

3. Assay validation

This is where most issues live, because assay tables are the largest.

Checks:

• From-To overlaps: [10–15, 12–18] is broken. Adjacent intervals can share a boundary, never overlap it.
• From-To gaps: gaps without a “no sample” reason recorded? Investigate.
• BDL flags: below detection limit values — are they encoded as -1, 0, half-detection-limit, or text "BDL"? Pick one convention and verify.
• Negative grades: should never exist (except as BDL placeholder).
• Duplicate samples: same hole-from-to with different grade? Investigation, not deletion.
• Unit consistency: gram-per-tonne (Au) vs ppm (base metals) — don’t mix.

Decision gate: 0 overlaps, BDL convention documented, 0 unexpected negatives, duplicate sample IDs reconciled.

4. Geology validation

Lithology drives domain separation. Bad lithology = bad domains = bad estimates.

Checks:

• Code consistency: GRT, Granite, granite are three different codes to a database. Standardize.
• Missing intervals: every depth interval should have a lithology code. null lithology in the middle of a hole is a data entry error.
• Code count: typical project has 5-15 lithology codes. 50+ codes means someone got creative — consolidate.
• Major-Minor structure: if you have MAJOR_LITH and MINOR_LITH columns, both should be coded consistently.

Decision gate: Lithology code dictionary documented, 0 missing intervals, ≤20 unique codes (after consolidation).

5. Visual inspection

Numbers can pass all of the above and still be visually wrong. Eyes catch what scripts miss.

Checks:

• Strip log per hole (random sample of 5-10 holes): do lithology bands and grade curves look geologically reasonable?
• Cross-section (at least 2 azimuths): are holes spatially clustered as expected? Outliers in 3D space?
• Hole length distribution: histogram of final_depth. Any holes <5m? That’s likely an aborted hole — flag or remove.
• Sample interval distribution: histogram of to - from. Bimodal distributions usually mean someone changed sampling protocol mid-project.

Decision gate: Subjective — but you should be able to say “I looked at strip logs for holes X, Y, Z and they’re consistent with the geological model.”

6. Cross-table joins

The final check is whether your tables actually connect.

Checks:

• Collar ↔ Survey: every hole in collar table has at least one survey record. Holes with no survey can’t be desurveyed.
• Collar ↔ Assay: every assay record points to a valid hole ID. Orphan assays from deleted holes are a thing.
• Hole length consistency: max assay depth ≤ collar final_depth ≤ max survey depth. Inconsistency = data entry mismatch.

Decision gate: 100% join completeness across collar/survey/assay/geology.

What good validation output looks like

For each project, I output a 1-page validation summary:

That table is what gets attached to your Table 1 Section 1 disclosure. No table = no data validation = JORC concern.

Automating this

Manual validation on a 40-hole project is a half-day. On a 400-hole project it’s a week. That’s why I built Orebit Drillhole Prep (Phase 01) — every check above runs automatically when you upload your CSVs:

• Auto-detects column mappings (no manual schema setup)
• Flags overlaps, gaps, BDL values, code inconsistencies
• Generates strip logs and cross-sections in one click
• Outputs a JORC-aligned validation report (PDF + CSV)
• 100% offline — your data never leaves your laptop

Phase 01 covers steps 1-6 above. Phase 02 (Drilling EDA) extends into bivariate/multivariate analysis. Phase 03 (Resource Estimation) handles variography and kriging.

One-time payment: IDR 49K per module / IDR 99K full bundle. No subscription. Buy once, use forever.

Bottom line

Drillhole data validation isn’t glamorous, but it’s the work that separates a Resource Estimation Report you can defend from one that gets re-done by an external auditor. Six steps. Half a day with manual tools. Three minutes with Orebit.

Either way — do the work.

Got a validation horror story or a check I missed? Email me at hello@orebit.id. I read everything.