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GP-GE-1SRIZSIGeologyOpen for request

Mineral prospectivity multilayer mapper

A geospatial modelling system for combining geological, geochemical, geophysical, structural, and remote-sensing evidence into mineral-prospectivity maps.

  • Nuxt
  • Vue
  • FastAPI
  • PostgreSQL
  • PostGIS
  • Python
  • Rasterio
  • GeoPandas
  • scikit-learn
  • Docker

Project definition

Problem statement

Mineral exploration evidence comes from datasets with different scales, coverage, accuracy, and geological meaning. Combining layers without a deposit model or spatially independent validation can produce an attractive but unreliable map.

The engineering problem is to transform each layer into documented evidence, compare expert and data-driven integration methods, and report uncertainty and validation without claiming a mineral resource.

Project objectives

  • Define a selected mineral-system or deposit model and its expected geological controls.
  • Prepare public geology, structure, geochemistry, geophysics, terrain, remote-sensing, and occurrence layers.
  • Transform raw layers into reproducible proximity, anomaly, categorical, and continuous evidence rasters.
  • Compare weighted-overlay and selected machine-learning prospectivity models.
  • Validate using spatially separated known occurrences and analyse uncertainty and weight sensitivity.

System design

System modules

01

Layer catalogue

Records source, scale, resolution, projection, date, coverage, licence, quality, and geological interpretation.

02

Evidence processor

Creates aligned fault-distance, lithology, geochemical, geophysical, alteration, and terrain evidence layers.

03

Prospectivity modeller

Runs transparent weighted overlays and selected data-driven models under versioned configurations.

04

Spatial validator

Uses blocked or leave-area-out validation and compares predictions with held-out occurrence records.

05

Target dashboard

Shows evidence, prospectivity, uncertainty, sensitivity, ranked areas, and complete source provenance.

Methodology

System workflow

  1. 01
    Define model

    The student selects a prepared study area, target mineral system, conceptual controls, and validation plan.

  2. 02
    Prepare evidence

    Layers are cleaned, projected, resampled, transformed, and masked to comparable analysis cells.

  3. 03
    Build models

    Expert-weight and data-driven approaches combine the same evidence under saved configurations.

  4. 04
    Validate spatially

    Held-out areas or occurrence groups measure predictive ranking and reduce spatial leakage.

  5. 05
    Review targets

    High-scoring areas are compared by evidence support, uncertainty, sensitivity, access, and data gaps.

Demonstration scenario

A prepared study area combines lithology, fault distance, geochemistry, magnetics, and alteration evidence. Weighted and machine-learning models are trained outside one spatial block. The dashboard compares their held-out occurrence ranking, uncertainty, sensitivity, and the evidence supporting each target area.

Engineering

Technical architecture

Web application
Nuxt and Vue for layer control, map comparison, target profiles, sensitivity, and export.
Geospatial API
FastAPI for layer jobs, evidence transformations, modelling, validation, ranking, and tiles.
Spatial data layer
PostgreSQL and PostGIS for geology, occurrences, structures, targets, metadata, and model results.
Raster pipeline
Python, Rasterio, and GeoPandas for alignment, distance, interpolation, anomaly, zonal, and masking operations.
Modelling layer
Transparent weighted methods and scikit-learn models with spatial splits, calibration, and feature importance.

Testing

Evaluation

Evaluation measures

  • Spatial alignment and evidence-transformation correctness
  • Area under ROC and precision-recall curves under spatial validation
  • Success-rate and prediction-area curves for held-out occurrences
  • Target-rank stability under weight, threshold, resolution, and data-removal sensitivity
  • Calibration and uncertainty across evidence-coverage conditions
  • Processing time across layer count, raster resolution, and study-area size

System boundaries

  • The map shows modelled prospectivity and does not establish the presence, grade, quantity, ownership, or economic value of a mineral deposit.
  • Only public, licensed, synthetic, or explicitly permitted geoscience data is included.
  • Model results depend on the selected deposit concept, evidence coverage, resolution, and known-occurrence bias.
  • Exploration decisions require field mapping, geophysics, geochemistry, drilling, permissions, environmental review, and qualified geologists.

Included

  1. 01Geoscience layer and study-area interface
  2. 02Preprocessing, evidence, prospectivity, validation, and ranking modules
  3. 03Layer, target, uncertainty, sensitivity, and validation maps
  4. 04Prepared public geoscience data, occurrences, and results
  5. 05Complete source code in a private GitHub repository
  6. 06Complete project documentation with synopsis, abstract, methodology, geological workflow diagrams, prospectivity maps, screenshots, and conclusion
  7. 07Setup and usage guide

Project record

No buyer information is collected on this page.

Permanent project ID
GP-GE-1SRIZSI
Catalogued
21 Aug 2026
Completed
Pending
Verified
Pending
Demonstration
Added when ready

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.