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GP-GE-1SRIZSIGeologyReady

Northern Territory Zinc-Lead Prospectivity Concordance and Critical Co-Product Evidence Mapping

A completed geology study comparing published Northern Territory clastic-dominated and Mississippi Valley-type zinc-lead prospectivity surfaces and auditing critical co-product evidence.

Northern Territory Zinc-Lead Prospectivity Concordance and Critical Co-Product Evidence Mapping project visual
GP-GE-1SRIZSI · Geology
  • Python 3.14
  • GeoPandas
  • H3
  • Pandas
  • NumPy
  • SciPy
  • Shapely
  • Matplotlib

Software compatibility

Python 3.14 and the retained CMMI data snapshot only

The completed workflow uses checksummed Northern Territory extracts from the Geoscience Australia Critical Minerals Mapping Initiative and the pinned Python 3.14 packages. Replacing the source data, deposit classes, region, or thresholds requires a new provenance and interpretation review.

Project definition

Problem statement

Published regional prospectivity models can overlap, disagree, and inherit some of the same training information. Treating a high-scoring cell as a discovery ignores model lineage, retained-area rules, spatial dependence, and geological uncertainty.

The geology problem is to compare two related zinc-lead model surfaces, measure their agreement and sensitivity, and separate critical co-product data coverage from any claim of an economic deposit.

Project objectives

  • Verify and retain the Northern Territory CD, MVT, and deposit-geochemistry extracts with exact hashes.
  • Measure shared cells, union cells, Jaccard agreement, percentile concordance, and published geological attributes.
  • Test descriptive capture of represented target deposits at 10, 25, and 50 km distances.
  • Build connected high-priority screening clusters and retain their area, support, centroid, and score evidence.
  • Audit positive, below-detection, zero, and missing Ag, Cd, Ga, Ge, and In analyses.

Project structure

Project components

01

Source validator

Checks exact hashes, coordinate systems, geometry, required fields, and published source counts before analysis.

02

Model concordance

Aligns H3 cells and measures presence, percentile agreement, exact overlap, and geological category summaries.

03

Deposit capture

Deduplicates represented target deposits and calculates distance-based capture with a seeded bootstrap summary.

04

Cluster builder

Groups neighbouring high-support cells into deterministic connected screening clusters with CSV and GIS outputs.

05

Co-product audit

Separates available positive assays from below-detection encodings and missing values for five critical co-products.

Methodology

Project workflow

  1. 01
    Verify the sources

    The workflow checks three retained GeoJSON extracts against declared SHA-256 hashes and required schemas.

  2. 02
    Align the models

    CD and MVT H3 cells are joined without discarding model-only areas, and percentile evidence is kept separately.

  3. 03
    Measure agreement

    Exact overlap, Jaccard concordance, model presence, threshold sensitivity, and geological summaries are calculated.

  4. 04
    Build screening clusters

    High-support neighbouring cells form deterministic clusters with reproducible IDs, areas, centroids, and scores.

  5. 05
    Appraise limitations

    Deposit capture, bootstrap results, data completeness, model lineage, and claim boundaries are reported together.

Demonstration scenario

The retained Northern Territory data contain 44,792 CD cells and 22,106 MVT cells. Their 10,550 shared cells give a Jaccard index of 0.187229 across a 56,348-cell union. The workflow identifies 1,011 connected screening clusters. All three represented target deposits lie within 10 km of both surfaces, but the report treats this as weak descriptive evidence because the sample is very small and may share model lineage.

Engineering

Tools and method

Tools
The project uses Python 3.14, GeoPandas, H3, Pandas, NumPy, SciPy, Shapely, Matplotlib for subject analysis, simulation, and results.
Geospatial layer
GeoPandas, Pyogrio, PyProj, and Shapely validate, project, combine, dissolve, measure, and export the source geometry.
Index layer
H3 identifiers provide exact cell matching and deterministic neighbourhood connectivity.
Analysis layer
Pandas, NumPy, and SciPy calculate percentile ranks, overlap, capture, bootstrap intervals, sensitivity, and assay summaries.
Evidence layer
CSV, GeoJSON, JSON, and PNG retain every numerical result, screening cluster, map, and chart used in the report.
Verification
Automated tests, branch coverage, dependency audit, deterministic reruns, Docker, document checks, and repository validation verify the delivery.

Testing

Evaluation

Evaluation measures

  • Exact CD, MVT, shared, and union cell counts
  • Jaccard agreement and percentile concordance between the published surfaces
  • Capture of represented target deposits at 10, 25, and 50 km
  • Cluster area, cell count, mean percentile, maximum percentile, and dual-model support
  • Threshold sensitivity across the 75th, 85th, 90th, and 95th retained-score percentiles
  • Positive, below-detection, zero, and missing Ag, Cd, Ga, Ge, and In records

Project boundaries

  • The published services retain only their high-scoring cells, and model scores are not calibrated deposit probabilities.
  • The three represented target deposits are too few for strong independent predictive validation and may have influenced the original model lineage.
  • Co-product assay availability does not establish mineralogical association, recoverability, continuity, grade, tonnage, or economic value.
  • A screening cluster is not evidence of a deposit, resource, reserve, ownership, access permission, or exploration feasibility.

Included

  1. 01Complete Python and geospatial source code
  2. 02Three checksummed public Geoscience Australia source extracts
  3. 03Complete concordance evidence for 56,348 union cells
  4. 04One thousand and eleven connected screening-cluster records in CSV and GeoJSON
  5. 05Known-deposit capture, bootstrap, and threshold-sensitivity tables
  6. 06Silver, cadmium, gallium, germanium, and indium evidence audit
  7. 07Eighteen generated figures and three attributed literature images
  8. 08Forty-five annotated references, including current 2026 research
  9. 09Seventeen automated tests with 98.72 percent branch-aware coverage
  10. 10Complete project files, calculations, results, and analysis material in a private GitHub repository
  11. 1183-page project documentation in PDF and editable Word formats
  12. 123-page setup and usage guide in PDF and editable Word formats

Project record

No information is collected on this page.

Permanent project ID
GP-GE-1SRIZSI
Catalogued
21 Aug 2026
Completed
28 Aug 2026
Verified
28 Aug 2026
Demonstration
Included in repository

Handover

After purchase

  1. 01
    Payment is confirmed

    The project is marked unavailable and cannot be purchased again.

  2. 02
    Repository access is granted

    The buyer's submitted GitHub account receives access to the private repository.

  3. 03
    The purchase record is delivered

    The certification sheet is prepared from the reviewed buyer details and sent privately by email.