Geospatial Analysis Applied to Mineral Exploration

1: Introduction to mineral exploration

1.1. Mineral exploration

1.2. Stages

1.3. GIS toolbox and mineral mapping approaches

1.3.1. Input

13.1.1. Remote sensing

1.3.1.2. Geochemical data

13.1.3. Geophysical results

1.3.1.4. Geological data

1.3.2. Data layer processing

1.3.3. Interpretation

2: Remote sensing for mineral exploration

2.1. Introduction

2.2. Spectral features of hydrothermal alteration minerals and lithologies

2.2.1. Iron oxide minerals

2.2.2. Hydroxyl-bearing minerals and carbonates

2.2.3. Silicate minerals

2.3. Multi-spectral sensors

2.3.1. Landsat data

2.3.2. SPOT data

2.3.3. ASTER data

2.3.4. ALI data

2.3.5. Sentinel-2 MSI data

2.4. Hyper-spectral sensors

2.4.1. Spaceborne sensors

2.4.1.1. Hyperion data

2.4.1.2. PRISMA data

2.4.2. Airborne sensors

2.4.2.1. AVIRIS data

2.4.2.2. HyMap data

2.5. Unmanned Aerial Vehicle (UAV)

2.6. Synthetic Aperture Radar (SAR)

2.6.1. RADARSAT data

2.6.2. JERS-1 SAR data

2.6.3. ERS SAR data

2.6.4. ALOS PALSAR data

2.6.5. Sentinel-1 SAR data

2.7. Data acquisition

2.8. Pre-processing techniques

2.9. Image processing algorithms

2.10. Interpretation of remote sensing data for alteration mineral detection and lithological mapping

2.11. Structural analysis for mineral exploration

2.12. Case studies

3: The GIS toolbox for mineral exploration

3.1. Introduction

3.2. Geographic Information Systems

3.2.1. Descriptive models of mineral deposits

3.2.2. Mineral systems framework

3.3. Numerical techniques used for GIS-based mineral exploration

3.3.1. Conceptual frameworks

3.3.2. Empirical frameworks

3.4. Uncertainties in GIS-based mineral exploration

3.5. Case studies in quantification of uncertainties in GIS-based mineral exploration

4: Processing and interpretation of geochemical data for mineral exploration

4.1. Introduction

4.2. Geochemical sampling media

4.2.1. Rock chip geochemical samples

4.2.2 Soil geochemical samples

4.2.3. Till geochemical samples

4.2.4. Stream sediment geochemical samples

4.3. Analytical techniques applied to geochemical data

4.3.1. Chemical analysis

4.3.2. Quality control

4.4. Interpretation of geochemical data

4.4.1. Compositional data analysis

4.4.2. Techniques used for delineating geochemical anomalies

4.5. Case studies

5: Geophysical data for mineral exploration

5.1. Introduction to geophysical exploration

5.2. Geophysical methods

5.2.1. Gravity methods

5.2.2. Magnetic methods

5.2.3. Magnetotelluric methods

5.2.4. Seismic methods

5.2.5. Radiometric method

5.2.6. Ground penetrating radar (GPR)

5.2.7. Self-potential and induced polarization

5.3. Methodologies, processing and interpretation

5.4. Case studies

6: Geological data for mineral exploration

6.1. Introduction

6.2. Mineral deposits and occurrence

6.3. Geological mapping

6.3.1 Global positioning system (GPS) survey

6.3.2 Lithological mapping

6.3.2.1 Petrography

6.3.2.2 Stratigraphy

6.3.3. Structural mapping

6.3.3.1 Relation between geological structures and ore deposition

6.4. Laboratory analysis

6.4.1 Mineralogy and petrography analysis

6.4.2 X-ray diffraction (XRD) analysis

6.4.3 Analytical Spectral Devices (ASD)

6.5 Accuracy assessment techniques

6.6. Case studies

7: Artificial intelligence and mineral exploration

7.1. Introduction

7.2. Supervised machine learning tools

7.2.1. Problems

7.2.1.1. Data imbalance

7.2.1.2. Bias-variance tradeoff

7.2.2. Solutions

7.2.2.1. Geo-spatial data augmentation

7.2.2.2. Modulating the effects of bias-variance tradeoff

7.2.3. Algorithms

7.2.4. Case study examples

7.3. Unsupervised machine learning tools for mineral exploration

7.3.1. Clustering problems

7.3.2. Dimension reduction problems

7.3.3. Algorithms

7.4. Deep learning and big data analysis

7.5. Case studies in the application of artificial intelligence for GIS-based mineral exploration