Geospatial Analysis Applied to Mineral Exploration : Remote Sensing, GIS, Geochemical, and Geophysical Applications to Mineral Resources

Geospatial Analysis Applied to Mineral Exploration: Remote Sensing, GIS, Geochemical, and Geophysical Applications to Mineral Resources presents state-of-the-art approaches on recent remote sensing and GIS-based mineral prospectivity modeling for Earth scientists, researchers, mineral exploration communities and mining companies. This book will help readers solve high complexity issues in remote sensing data processing, geochemical data analysis, geophysical data analysis, and appropriate applications of GIS techniques for data fusion designed for mineral exploration purposes. It contains updated knowledge of remote sensing imagery, geochemistry, geophysics and geospatial techniques that can assist in delineating the signatures and patterns linked to deep-seated, covered, blind or buried mineral deposits. Covers advances in remote sensing data processing algorithms and geochemical data analysis Includes sections on geophysical data analysis and machine learning algorithms for mineral exploration Introduces the suite of geo-spatial tools currently available for mineral exploration Presents case studies to provide real-world examples of the theories covered Front Cover Geospatial Analysis Applied to Mineral Exploration Copyright Page Dedication Contents List of contributors Preface 1 Introduction to mineral exploration 1.1 Mineral exploration 1.2 Stages 1.2.1 Exploration strategy 1.2.2 Prospecting 1.2.3 Early-stage exploration 1.2.4 Detailed surveys 1.3 4D-geographic information system for mineral exploration 1.3.1 Input 1.3.1.1 Remote sensing 1.3.1.2 Exploration geochemistry 1.3.1.3 Exploration geophysics 1.3.1.4 Geological indicators 1.3.2 User-guided interpretation of datasets 1.3.3 Predictive modeling References 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 and lithologies 2.3 Multispectral sensors 2.3.1 Landsat data 2.3.2 SPOT data 2.3.3 Advanced Spaceborne Thermal Emission and Reflection Radiometer data 2.3.4 Advanced Land Imager data 2.3.5 Sentinel-2 MSI data 2.3.6 WorldView data 2.4 Hyperspectral sensors 2.4.1 Spaceborne sensors 2.4.1.1 Hyperion data 2.4.1.2 Prcursore IperSpettrale della Missione Applicativa data 2.4.2 Airborne sensors 2.4.2.1 Airborne Visible/IR Image Spectrometer data 2.4.2.2 HyMap data 2.5 Unmanned aerial vehicle 2.6 Synthetic aperture radar 2.6.1 RADARSAT data 2.6.2 JERS-1 synthetic aperture radar data 2.6.3 European Remote Sensing Satellite synthetic aperture radar data 2.6.4 Advanced Land Observing Satellite phased array–type L-band synthetic aperture radar data 2.6.5 Sentinel-1 synthetic aperture radar data 2.7 Data acquisition 2.8 Preprocessing techniques 2.8.1 Noise reduction/correction 2.8.2 Atmospheric corrections 2.8.2.1 Atmospheric correction methods 2.8.2.1.1 Dark subtract 2.8.2.1.2 Dark dense vegetation method 2.8.2.1.3 Flat field method 2.8.2.1.4 Internal average relative reflectance 2.8.2.1.5 Log residual method 2.8.2.1.6 Empirical line method 2.8.3 Geometric distortions of satellite images 2.8.3.1 Geometric correction 2.8.3.2 Systematic image rectification 2.8.3.3 Random geometric correction 2.9 Image-processing algorithms 2.9.1 Color combinations 2.9.2 Color space transforms 2.9.3 Contrast stretching 2.9.4 Nonlinear contrast stretching methods 2.9.5 Density slice 2.9.6 Band ratios 2.9.6.1 Vegetation detection 2.9.6.2 Minerals detection 2.9.7 Absorption band depth 2.9.8 Logical operators 2.9.9 Principal component analysis 2.9.10 Independent component analysis 2.9.11 Minimum noise fraction 2.9.12 Spectral angle mapper 2.9.13 Linear spectral unmixing 2.9.14 Matched filtering 2.9.15 Mixture-tuned matched filtering 2.9.16 Constrained energy minimization 2.9.17 Adaptive coherence estimator 2.9.18 Spectral feature fitting 2.9.19 Image classification 2.9.20 Supervised classification 2.9.20.1 Minimum distance to mean classification 2.9.20.2 Parallelepiped 2.9.20.3 Maximum likelihood classification 2.9.20.4 Support vector machines 2.9.20.4.1 Linear support vector machine 2.9.20.4.2 Nonlinear support vector machine 2.9.20.4.3 Nonlinear support vector machine 2.9.21 Methods of increasing the spatial resolution of satellite images 2.9.21.1 Brovey method 2.9.21.2 Intensity, Hue, and Saturation method 2.9.21.3 Principal component analysis method 2.9.21.4 Color normalization method 2.9.22 Image filtering 2.9.22.1 Low-pass filters 2.9.22.2 Mean filter 2.9.22.3 High-pass filters 2.9.22.3.1 Filters that enhance features in all directions 2.9.22.3.2 Filters that enhance features in a specific direction 2.10 Accuracy assessment techniques 2.10.1 Principle of accuracy assessment 2.10.2 Validation of the classified image 2.10.3 Determining the number of samples 2.10.4 Confusion matrix 2.10.5 Overall accuracy 2.10.6 User’s accuracy and commission error 2.10.7 Producer’s Accuracy and Omission Error 2.10.8 Kappa coefficient 2.11 Interpretation of remote sensing data for alteration mineral mapping 2.12 Remote sensing structural analysis for mineral exploration 2.13 Case studies References 3 The geographical information system toolbox for mineral exploration 3.1 The geographical information system toolbox for mineral exploration 3.1.1 Introduction 3.2 Geographical information system 3.2.1 Descriptive models of mineral deposits 3.2.2 Mineral systems framework 3.3 Mathematical frameworks used for geographical information system-based mineral prospectivity mapping 3.3.1 Knowledge-driven methods 3.3.2 Data-driven methods 3.4 Uncertainty in geographical information system-based mineral exploration 3.5 Case studies 3.6 Summary and conclusion References 4 Processing and interpretation of geochemical data for mineral exploration 4.1 Introduction 4.2 Geochemical sampling media 4.2.1 Water samples 4.2.2 Rock chip samples 4.2.3 Soil geochemical samples 4.2.4 Till geochemical samples 4.2.5 Stream sediment geochemical samples 4.3 Instrumental techniques applied to geochemical data 4.3.1 Instrumental techniques 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 study References 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 5.2.2.1 Instrumentation 5.2.3 Magnetotelluric methods 5.2.4 Seismic methods 5.2.5 Radiometric method 5.2.6 Ground-penetrating radar 5.2.7 Self-potential and induced polarization 5.2.7.1 Self-potential method 5.2.7.2 Induced polarization method 5.3 Methodologies, processing, and interpretation 5.3.1 Gravity 5.3.2 Magnetic 5.3.3 Inversion of magnetotelluric data 5.3.4 Seismic attributes contributing to geology interpretation 5.3.5 Radiometric data 5.3.6 Self-potential 5.3.7 Induced polarization 5.4 Case studies 5.4.1 Voisey’s Bay deposit (gravity data) 5.4.2 Gabal Semna region, Eastern Desert of Egypt (magnetic data) 5.4.2.1 3D magnetic inversion 5.4.3 Cloncurry–Georgetown–Charters Towers, North Queensland, Australia (seismic and magnetotelluric) 5.4.3.1 Results 5.4.3.1.1 Magnetotelluric analysis 5.4.3.1.2 Seismic analysis 5.4.3.1.3 Integration 5.4.3.2 Conclusion 5.4.4 Gebel El-Bakriyah—Wadi El Batur area, Central Eastern Desert, Egypt (radiometry) 5.4.5 Wadi El-Ghawaby, Central Eastern Desert, Egypt (induced polarization) References 6 Geological data for mineral exploration 6.1 Introduction 6.1.1 Definitions 6.1.2 Mineral and rock 6.1.3 Stages of mineral exploration 6.1.4 Mineral system 6.2 Mineral deposits and occurrence 6.2.1 Definition and formation of mineral deposits 6.2.2 Criteria for the mineral deposits classification 6.2.3 Mineral deposits classification 6.2.3.1 Industrial classification 6.2.3.2 Geological classification based on origin or genetic and occurrence 6.2.3.3 Classification of chromite deposits 6.2.3.4 Placer deposits 6.3 Geological mapping 6.3.1 Lithological mapping 6.3.1.1 Petrography 6.3.1.1.1 Igneous Rocks Ultrabasic igneous rocks Basic rocks igneous rocks Intermediate igneous rocks Acidic igneous rocks 6.3.1.1.2 Sedimentary rocks Classification of sedimentary rocks 6.3.1.1.3 Metamorphic rocks 6.4 The foliated rocks 6.4.1 Schists 6.4.1.1 Tourmaline-bearing schists 6.4.1.2 Tourmalinites 6.4.1.3 Sillimanite-bearing schists 6.4.1.4 Garnetiferous schists 6.5 The nonfoliated rocks 6.5.1 Greisens 6.5.2 Chromite in serpentinites 6.5.3 Talc 6.5.3.1 Stratigraphy 6.5.4 Structural mapping 6.5.4.1 Relation between geological structures and ore deposition 6.5.4.2 Mapping techniques of structural controlled mineralization 6.6 Laboratory analysis 6.6.1 X-ray diffraction analysis 6.6.2 Analytical spectral devices 6.7 Case studies References 7 Machine learning for analysis of geo-exploration data 7.1 Introduction 7.2 Supervised algorithms 7.3 Examples of using supervised algorithms in mineral exploration 7.3.1 Predictive modeling of volcanic-hosted massive sulfide deposits using supervised algorithms 7.4 Unsupervised algorithms 7.4.1 Examples of using unsupervised algorithms in mineral exploration References Index Back Cover