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Orebit Assay

Statistics, validation, compositing for Orebit Resource

Professional EDA + compositing workflow Standards-referenced, CP-review required 12 tab workflow data not loaded
Composites
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Elements
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Domains
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Lithologies
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Next stepLoad or upload compositesConfirm data quality before statistics, top-cut and compositing decisions.

Goal: Prepare a traceable drillhole composite dataset for professional EDA and Resource estimation screening.

Scope: data QA, support regularisation, distribution review, top-cut candidates, geological control, and domain handoff. Final interpretation remains with the Competent Person.

Statistics summary

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Domain summary

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Workflow guide — 11 steps
  1. 1. Import Data — Upload. Gate: Parsed CSV + column mapping
  2. 2. Visual Inspection — Data. Gate: Welllog + sample table
  3. 3. Bivariate EDA — Bivariate. Gate: Correlation heatmap + scatter matrix
  4. 4. Multivariate EDA — Multivariate. Gate: PCA biplot + domain boxplots
  5. 5. Spacing — Spacing. Gate: NN distance distribution + density map
  6. 6. Compositing — Composite. Gate: 1m weighted-average composites
  7. 7. EDA Statistics — Stats. Gate: n / mean / median / SD / CV / P10–P99
  8. 8. Top-Cut decision — Top-Cut. Gate: CV-based P95/P98/P99 candidates + impact
  9. 9. Grade by Litho — Litho. Gate: Per-litho stats + boxplot ranking
  10. 10. Domain Modeling — Domain. Gate: Geological boundaries + grade continuity
  11. 11. Report + Handoff — Report. Gate: Working summary + standards-referenced commentary

Upload Data — Import CSV

Format: CSV with a header row. Columns are detected automatically and case-insensitively (hole_id, HoleID and BHID are all recognised).

Multi-file: Upload collar.csv + assay.csv + litho.csv together; the system auto-merges by hole_id.

Privacy: All processing runs client-side. Data is never sent to a server.

Click or drag & drop a CSV file here

Multi-file support: collar.csv + assay.csv + litho.csv

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💡 Move data from Orebit Core: in Core use Project Manager → Export to produce a .orebit file, then Import .orebit bundle above. (Importing a CSV directly is also supported.)

Recognized Column Formats

FieldAliases (auto-detected)Required?
hole_idHoleID, BHID, DHID, HOLE, hole✓
from_mFROM, from, From, depth_from, fr✓
to_mTO, to, To, depth_to✓
au_gptAU, Au, au_ppm, AU_GPT, gold✓ (or the primary commodity)
cu_pctCU, Cu, cu_ppm, copperoptional
lithologyLITH, lith, rock_type, geologyoptional (warns if missing)
midx, midy, midzx, y, z, easting, northing, rl, elevationoptional (for spatial plots)
density, recovery_pct, rqd_pctSG, density_t/m3, rec, RQDoptional (QA/QC)

Reset

Restore the built-in sample data (1,241 composites across 40 holes, Thalanga RGMWP).

Data — Well Log & Samples

Embedded dataset: 1,241 composites · 40 holes · 16 columns · Thalanga VMS (RGMWP prospect, output of Core).

Welllog — Multi-element Trace

Per-hole multi-element subplot. Colour-coded bars are grades; side-by-side bars let you compare mineralization zoning across elements. The lithology colour overlay sits to the left of each track.

Data Table

Bivariate Analysis — Correlation & Scatter Matrix

Correlation heatmap + pairwise scatter plots to identify relationships between numeric variables. High correlation → redundancy or mineral assemblage; low correlation → independent processes.

Multivariate Analysis — PCA & Domain Boxplots

Principal Component Analysis (PCA) reduces dimensionality. PC1 is usually grade-dominated; PC2 can reveal secondary processes (alteration, litho control). Boxplot by domain validates domain definitions.

Spacing & Density Analysis

Industry rule: median spacing ≤ 50% of variogram range. Drill density drives the resource classification level (Measured / Indicated / Inferred).

Compositing — Weighted by Length

Compositing standardizes assay intervals to a fixed length. Method: length-weighted average. Default 1m (matches porphyry/disseminated SMU).

EDA Stats — Comprehensive Distribution Analysis

A high CV (>1.5) flags the need for a top-cut. Log-skewed distributions are candidates for a log-transform before kriging.

Auto-Pilot

Runs a P99 top-cut, K-Means clustering, and ANOVA validation in one go.

Top-Cut Decision

Decision panel: review CV-based P95/P98/P99 candidates, affected samples, metal loss, geological outliers, and domain consistency. No percentile is automatically approved.

Grade by Lithology

Per-lithology statistics → identify rock types hosting mineralization. Prerequisite for geological domain modeling.

Domain Modeling — Combined Geological + Grade

Domain MUST be defined by geological boundaries + grade continuity, NOT grade shells alone.

Report & Working Commentary

Export Options

A multi-page PDF: summary, visualizations, and a standards-referenced working checklist.

CPI Disclaimer

This output is not a final KCMI Table 1. The commentary must be reviewed and signed off by a CPI registered with PERHAPI/IAGI before it is published officially.

Standards-Referenced Working Commentary

Section 1: Sampling Techniques and Data

Data Preview

First 10 rows of the composite dataset that will be exported to Resource.

Export History

Audit trail of every download this session (max 20). Persists in browser localStorage.