Mineral Prospectivity
Alteration minerals, structures and host rock combined into targets for sampling.
Draw an area and the tool ranks where hydrothermal alteration, structures and favourable host rock coincide: clay–hydroxyl, iron-oxide and ferrous-silicate anomalies from Sentinel-2 with vegetation, fields, water and towns masked, host-rock favourability, distance to lineaments and lineament density. The layers are weighted by the analytic hierarchy process and the map is checked against known occurrences.
Overview
This is knowledge-driven prospectivity mapping. The spectral layers are band ratios rated by standard deviations above the mean of the unmasked area, so anomalies are relative to your area. Lineaments are extracted automatically from the elevation model or taken from your own file. Host rock comes from the geological map and can be re-rated by you unit by unit.
Satellites cannot see gold or any metal itself, only clay and iron minerals that sometimes accompany ore, and most alteration in the world is barren. The result is a set of targets for field mapping, rock and stream-sediment sampling and geochemistry, not proven ore.
What you enter · What you get
What you enter
- Area of study (4–2500 km²)
- Imagery year (dry season)
- Importance of each layer (1–9)
- Your lineaments (GeoJSON, optional)
- Known occurrences (CSV, optional)
What you get
- Prospectivity map in five classes with areas
- Alteration, structure and host-rock layers
- AHP weights with the consistency ratio
- Success-rate and ROC curves, frequency ratios
- Framed map sheet, layer figure, ASCII grid, CSV
How it works in the field
Method and equations
Alteration ratios
Sentinel-2 surface reflectance, dry season; pixels with NDVI > 0.25 or mapped as trees, crops, built-up, water or wetland are masked.
Ratings
Spectral layers by standard deviations above the area mean (< 0, 0–0.5, 0.5–1, 1–2, > 2σ); distance to lineaments 250 / 500 / 1000 / 2000 m.
Analytic hierarchy process
Saaty pairwise matrix from the importance scores, principal eigenvector, CR < 0.10.
Accuracy and error limits
- The map ranks relative favourability; it does not give ore probability. Its success is measured on known occurrences: an area under the success curve of 0.7–0.8 is typical for knowledge-driven models.
- Weights come from expert judgement; spectral layers miss alteration under soil and vegetation, and lineaments from imagery include roads, fields and drainage.
- To reduce the error: validate with known occurrences, test the effect of changing the weights, confirm targets by mapping and sampling, and add geochemistry or geophysics where available.
Who uses it
Standards and references
- Bonham-Carter, G.F. (1994). Geographic Information Systems for Geoscientists: Modelling with GIS. Pergamon, Oxford.
- Carranza, E.J.M. (2009). Geochemical Anomaly and Mineral Prospectivity Mapping in GIS. Handbook of Exploration and Environmental Geochemistry 11. Elsevier.
- Sabins, F.F. (1999). Remote sensing for mineral exploration. Ore Geology Reviews, 14, 157–183.
- van der Meer, F.D., van der Werff, H.M.A. & van Ruitenbeek, F.J.A. (2014). Potential of ESA’s Sentinel-2 for geological applications. Remote Sensing of Environment, 148, 124–133.
- Pour, A.B. & Hashim, M. (2012). The application of ASTER remote sensing data to porphyry copper and epithermal gold deposits. Ore Geology Reviews, 44, 1–9.
- Saaty, T.L. (1980). The Analytic Hierarchy Process. McGraw-Hill, New York.
- Copernicus Sentinel-2 Level-2A, Copernicus DEM GLO-30, ESA WorldCover 10 m, Macrostrat geological map (CC BY 4.0); served by the Microsoft Planetary Computer.
At a glance
- Category
- Remote sensing
- Runs on
- Phone, tablet and desktop browser
- Export
- Map sheet PNG, layer figure PNG, ASCII grid, CSV, PDF report
- Access
- Subscribers
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