Tutorial 2: Simple Mineral Prospectivity Maps with Kalpa

In this tutorial, we will use Kalpa to build a mineral prospectivity map. The process utilizes three types of datasets:

• Geophysical data (e.g., Bouguer Gravity Anomaly, Magnetic Anomaly)

• Geological data (e.g., geological age, folds, faults, lineaments)

• Satellite data (e.g., ASTER, SRTM, and LANDSAT)

The goal is to create prospectivity maps for copper deposits based on the known occurrences of 96 mineral deposits in the study region.

We will sample various datasets at the locations of known copper occurrences, create an unlabeled dataset for contrast, and use Random Forest (RF) classifier and regressor to label and map prospectivity.

Dataset Overview

The dataset can be downloaded here.

Geophysical Data

Gravity Data:

• Observed Gravity

• Theoretical Gravity

• Bouguer Anomaly

• Upward Continued Bouguer Anomaly

• Directional Derivatives of Bouguer Anomaly

Magnetic Data:

• Observed Magnetism

• IGRF

• Magnetic Anomaly

• Upward Continued Magnetic Anomaly

• Directional Derivatives of Magnetic Anomaly

Geological Data

• Tectonic Age

• Folds

• Faults

• Lineaments

Satellite Data

LANDSAT8 Bands:

• Multispectral bands (3 to 8) as NetCDF files

SRTM Elevation Data:

• Elevation values from the Shuttle Radar Topography Mission (SRTM)

ASTER Data:

• Multispectral bands (1 to 14) as NetCDF files

Mineral Occurrences:

• The dataset includes 96 known occurrences of mineral deposits. For this tutorial, we focus on copper deposits.

Workflow

Step 1: Loading Data and Visualization

1. Start Kalpa and choose Cartesian coordinates as the working coordinate system.

2. Create a new folder named MineralProspectivity to save the dataset, figures, etc.

Import Vector Data:

• Navigate to: File -> Import -> Vector

• Load the following files:

  • ShapeFiles/Fault_Tectonic.shp

  • ShapeFiles/Fold_Tectonic.shp

  • ShapeFiles/Lineament_Tectonic.shp

  • ShapeFiles/Tectonic_Framework.shp

  • Deposits/Deposits.shp

• Adjust colormap, feature width, and range as needed.

  

Import Raster Data:

• Navigate to: File -> Import -> Raster

• Load the following files:

  • GeophysicalProcessed/Raster/BOUGUER_AN_Kriging.nc

  • GeophysicalProcessed/Raster/MAGNETIC_A_Kriging.nc

  • GeophysicalProcessed/Raster/Grad_Gravity/*

  • GeophysicalProcessed/Raster/Grad_Magnetic/*

  • GeophysicalProcessed/Raster/UC_Gravity/*

  • GeophysicalProcessed/Raster/UC_Magnetic/*

  • SatelliteData/SRTM/SRTM_Raj_India.nc

  • SatelliteData/LANDSAT/Band_3_LANDSAT.nc to Band_8_LANDSAT.nc

    

Step 2: Feature Generation

1. Generate Positive Labels from Known Deposits:

   • Go to: Plugins -> MPM -> Feature Generation

   • AOI/ROI: Deposits

   • Select all layers except Tectonic Framework and Deposits

   Layer Config:

   • Euclidean Distance for vectors: - Fault_Tectonic - Fold_Tectonic - Lineament_Tectonic

   • Band Value for rasters: - BOUGUER_AN_Kriging.nc - MAGNETIC_A_Kriging.nc - Grad_Gravity/* - Grad_Magnetic/* - UC_Gravity/* - UC_Magnetic/*

   • Sampling method: regular with 100 seed points

   

   • Click Generate → Save as: deposit_sampled.gpkg

   

2. Generate Label Column:

   • Go to: Tools -> Vector Calculator

   • Layer: deposit_sampled

   • Condition: == 1

   • New column: Label

     

   • Click Apply → Save as: deposit_positive_label.gpkg

   

3. Create Bounding Box:

   • From Create Bounding Box, choose layer: BOUGUER_AN_Kriging

   

   • Click Create → Save as: bounding_box.gpkg

   

4. Generate Unlabelled (Random) Features:

   • AOI: bounding_box

   • Method: Random, 1000 seed points

   Layer Config:

   • Euclidean Distance for: - Fault_Tectonic - Fold_Tectonic - Lineament_Tectonic

   • Band Value for: - BOUGUER_AN_Kriging.nc - MAGNETIC_A_Kriging.nc - Grad_Gravity/* - Grad_Magnetic/* - UC_Gravity/* - UC_Magnetic/*

   

   • Click Generate → Save as: randomly_sampled.gpkg

   

This workflow now generates randomly sampled data which in this workflow, the width is set to 1 for visualisation and positive samples as thicker dots with width 3

5. Label Random Features as 0:

   • Go to: Vector Calculator

   • Layer: randomly_sampled

   • Condition: == 0

   • Column name: Label

   • Save as: deposits_unlabelled.gpkg

   

6. Merge Positive and Unlabelled Datasets:

   • Remove deposit_sampled and randomly_sampled layers (optional)

   • Go to: Merge Layers

     

   • Merge: deposits_positive_label + deposits_unlabelled

   • Save as: training_data.gpkg

   • Visualization tip: Set positive samples (label = 1) with width 3, others with width 1

Now you can see that the positive (1) are in green color and unlabelled in brown. You may edit this in cmap

Step 3: Create PU Bagging Model

1. Go to: Plugins -> MPM -> Models -> PU Bagging -> Create New Model

   

2. Training data: training_data.gpkg

   

3. (Optional) Generate correlation report to examine feature correlation

   

   Click close

4. Target feature: Label

5. Select all other features as training features (except Label)

   

6. Enable: scaled probabilities

7. Click Start Training

8. Save model as: PU Model

   

9. Go to: Plugins -> MPM -> Feature Generation -> Bounding Box

   

10. Select all layers except:

    • Tectonic Framework

    • Deposit

    • deposits_positive_label

    • bounding_box

    • deposits_unlabelled

    • training_data

      

11. Repeat selection of Band Value for rasters and Euclidean Distance for vectors

12. Set resolution to: 0.005 degrees. This may be of your choice

13. Click Generate → Save as: predicted_dataset.gpkg. This may take a while depending on resolution and hardware specs.

    

14. Go to: Plugins -> MPM -> Models -> PU Bagging -> Existing Model

15. Choose: predicted_dataset.gpkg as prediction input

    

16. Click Predict

Voila! You now have your PU Bagging prediction.

Final Step: Select a colormap of your choice for visualizing predictions.