Remote Sensing and Image Processing in Mineralogy

Cover
Title Page
Copyright Page
Dedication
Preface
Table of Contents
1. Principles of Mineralogy, Oil and Gas
	1.1 What is a Mineral?
	1.2 What is the Relationship between Atoms, Elements, Minerals and Rocks?
	1.3 Atom Structure
	1.4 Minerals in Periodic Table
	1.5 Chemical Bonding
	1.6 Valence and Charge
	1.7 Ionic Bonding
	1.8 Covalent Bonding
	1.9 Natural Crystallization of Minerals
		1.9.1 Isometric
		1.9.2 Hexagonal
		1.9.3 Tetragonal
		1.9.4 Orthorhombic
		1.9.5 Monoclinic
		1.9.6 Triclinic
		1.9.7 Trigonal or Rhombohedral
	1.10 Occurrence and Formation
	1.11 How are Minerals Categorized?
		1.11.1 Silicate Minerals
		1.11.2 The Dark Ferromagnesian Silicates
		1.11.3 Pyroxene Family
		1.11.4 Amphibole Minerals
		1.11.5 Sheet Silicates
		1.11.6 Framework Silicates
	1.12 Non-Silicate Minerals
		1.12.1 Carbonate
		1.12.2 Oxides
		1.12.3 Halides
		1.12.4 Sulfides
		1.12.5 Phosphate Minerals
		1.12.6 Native Element Minerals
	1.13 Oil and Gas Formation
	References
2. Quantization of Minerals and their Interactions with Remote Sensing Photons
	2.1 Quantization in the Atom
		2.1.1 Principal Quantum Number
		2.1.2 Angular Momentum Quantum
		2.1.3 Magnetic Quantum Number
		2.1.4 Spin Quantum Number
	2.2 Quantum Mechanics of Bonding
	2.3 Quantum Mechanics of Mineral Atomics
	2.4 Energy Variations Based on Schrödinger Wavefunction
	2.5 What is Quantum Influences?
	2.6 Quantization of Minerals from Point View of Wavefunction
	2.7 Antiferromagnetic Spin-frustrated Layers of Minerals
	2.8 General Quantization of Mineral Remote Sensing Imagines
		2.8.1 Plank Quanta
		2.8.2 Requantization of Photoelectric Effect
		2.8.3 The Uncertainty Principle
		2.8.4 Photovoltaic Effect
		2.8.5 De Broglie’s Wavelength
	2.9 Quantization of Blackbody Radiation
	2.10 Quantization of Spectral Signature
	2.11 How can We Establish a New Definition of Remote Sensing for Mineral Identification?
	References
3. Quantum Computing of Image Processing
	3.1 What is Meant by Quantum Computing?
	3.2 What is Meant by Quantization?
	3.3 What are Quantum Computers and How do they Work?
		3.3.1 Qubits and Superposition
		3.3.2 Quantum Registers
		3.3.3 Quantum Gates
			3.3.3.1 NOT Gate
			3.3.3.2 Controlled-NOT Gate
			3.3.3.3 Hadamard Gate
		3.3.4 Entanglement
	3.4 Quantum Image Processing
	3.5 Flexible Representation for Quantum Images
	3.6 Fast Geometric Transformations on FRQI Quantum Images
	3.7 Efficient Colour Transformations on FRQI Quantum Image
	3.8 Multi-Channel Representation for Quantum Images
	3.9 Novel Enhanced Quantum Image Representation (NEQR)
	References
4. Quantum Spectral Libraries of Minerals in Optical Remote
Sensing Data
	4.1 How do Spectral Libraries Build Up?
	4.2 Jablonski Energy Diagram
	4.3 Infrared Absorption Spectroscopy
	4.4 Spectral Regions Relevant to Mineralogy
	4.5 Entanglement by Absorption
	4.6 How Does Entanglement Form Spectral Libraries?
	4.7 How Does Quantum Teleportation Establish the Spectral Libraries?
	4.8 Modeling of Quantum Mineral Spectral Libraries
	4.9 Image Storage
	4.10 Tested Remote Sensing Data
	4.11 Example of Reflectance Spectra
	References
5. Quantum Multispectral and Hyperspectral Remote Sensing
Imaging of Alteration Minerals
	5.1 What is an Alteration?
		5.1.1 Potassic Alteration
		5.1.2 Propylitic Alteration
		5.1.3 Phyllic (Sericitic) Alteration
		5.1.4 Argillic Alteration
		5.1.5 Silicification
		5.1.6 Carbonatization and Greisenization
	5.2 Multispectral and Hyperspectral Remote Sensing Sensors
	5.3 Mineral Exploration from Space
		5.3.1 Multispectral Satellite Sensors
		5.3.2 Hyperspectral Satellite Sensors
	5.4 Why Does The Spectral Analyst Tool Work Properly in Some Cases and Not At All in Others?
	5.5 Quantization of Multispectral and Hyperspectral Data
	5.6 Spectral Reflectance Quantum Image Formation (SRQIF)
	5.7 Marghany Quantum Spectral Algorithms for Mineral Identifications (MQSA)
	5.8 Selected Investigation Area for MQSA Application
	5.9 MQSA Application of Different Minerals in Landsat and ASTER Images
	5.10 Why Marghany Quantum Spectral Algorithms (MQSA) Identify Accurate Quantum Mineral Images?
	References
6. Evolving Quantum Image Processing Tool for Lineament
Automatic Detection in Optical Remote Sensing Satellite Data
	6.1 What is Meant by Lineament?
	6.2 What is the Magic of Lineament?
	6.3 What are the Sorts of Lineaments?
	6.4 Satellite Remote Sensing and Image Processing for Lineament Features’ Detection
	6.5 How do Multispectral Remote Sensing Data Identify the Lineaments?
	6.6 Problems for Geological Features’ Extraction from Remote Sensing Data
	6.7 Can Digital Elevation Model be Utilized in Lineament Delineation?
	6.8 What is the Main Question?
	6.9 The Fuzzy B-splines Algorithm for Digital Elevation Model Reconstruction
	6.10 Entanglement of Fuzzy Quantum for DEM Reconstruction
	6.11 Quantum Edge Detection Algorithm for Lineament Mapping
	References
7. Quantum Support Vector Machine in Retrieving Clay Mineral
Saturation in Multispectral Sentinel-2 Satellite Data
	7.1 Salinity, Soil and Geological Minerals
	7.2 Mineral Soil Classifications
	7.3 Remote Sensing of Mineral Soils
	7.4 Can Marshlands be Indicator for Mineral Occurrences?
	7.5 How to Compute Cation Exchange Capacity in Laboratory?
	7.6 Sentinel-2 Satellite Data
	7.7 How to Retrieve Clay Potential Percentage in Remote Sensing Data?
	7.8 Quantized Marghany Clay Saturation Algorithm in Al-Hawizeh Marsh
	7.9 Support Vector Machines
	7.10 Quantum Support Vector Machines
	7.11 Why Does QSVM Entangle Quantized Marghany’s Clay Saturation Algorithm?
	References
8. Automatic Detection of Oil Seeps in Synthetic Aperture Radar
Using Quantum Immune Fast Spectral Clustering
	8.1 What are Oil Seeps?
	8.2 Behaviour of Oil and Gas Jets and Plumes Below the Sea Water Surface
	8.3 Onshore Seep Occurrences
	8.4 Offshore Seep Occurrences
	8.5 Sort of Seeps
	8.6 How Does Remote Sensing Technology Identify Natural Oil and Gas Seeps?
	8.7 Why Do Microwave Data Have Advantages on Top of Optical Data in Seep Monitoring?
	8.8 Offshore Seep Imagine in SAR Data
	8.9 What are the Physical Seep Parameters Identified in SAR Data?
	8.10 SAR Polarization Signals
	8.11 Quantum Fully-polarized SAR Image Processing
	8.12 Quantum Immune Fast Spectral Clustering
	8.13 Quantum Immune Operation
	8.14 Spectral Embedding
	8.15 Automatic Detection of Oil Seep in Full Polarimetric SAR
	8.16 Applications of QIFSC to Other Satellite Polarimetric SAR Sensors
	8.17 Why Can QIFSC Precisely Cluster Different Kinds of Oil Seep?
	References
9. Quantum Interferometry Radar for Oil and Gas Explorations
	9.1 What is Reservoir Geomechanics?
	9.2 What is the Role of Reservoir Geomechanics in Oil and Gas Explorations?
	9.3 Physics of Interferometry
	9.4 What is Synthetic Aperture Interferometry?
	9.5 Interferograms
	9.6 Phase Unwrapping
	9.7 How to Understand SAR Interferograms?
	9.8 Quantum of Differential-InSAR (QD-InSAR)
	9.9 Quantum Hopfield Algorithm for DInSAR Phase Unwrapping
	9.10 Application of Quantum DInSAR Hopfield Algorithm in Land Deformation Owing to Oil and Gas Explorations
	References
10. Quantum Machine Learning Algorithm for Iron, Gold, and
Copper Detection in Optical Remote Sensing Data
	10.1 How Copper and Gold Form in the Earth?
	10.2 How Copper and Gold are Mined?
	10.3 What are the Characteristics of Copper and Gold?
	10.4 Remote Sensing for Copper and Gold Identifications
	10.5 Conventional Image Processing Techniques for Gold, Iron, and Copper Explorations
		10.5.1 Preprocessing
		10.5.2 Post Image Processing
			10.5.2.1 False Colour Composite
			10.5.2.2 Band Ratio
			10.5.2.3 Principal Component Analysis (PCA)
			10.5.2.4 Noise Fraction (MNF)
			10.5.2.5 Spectral Unmixing in n-dimensional Spectral Feature Space
	10.6 Quantum Machine Learning
	10.7 Classifier Architecture
	10.8 Classifier Training as a Supervised Learning Task
	10.9 Training Score and Classifier Bias
	10.10 Gold Mining Simulation Using Quantum Machine Learning
	10.11 Quantum Artificial Neural Network (QANN) for Gold Exploration
	10.12 QANN for Copper Mining Potential Zone
	10.13 Why Quantum Machine Learning can be Used for Mineral Exploration?
	References
11. Four-Dimensional Hologram Interferometry for Automatic
Detection of Copper Mineralization Using Terrasar-X
Satellite Data
	11.1 What is the Real Age of Copper?
	11.2 Occurrences of Copper
	11.3 Conventional Methods for Copper Extraction
	11.4 What is the Major Challenge with Optical Remote Sensing and Microwave Radar Data?
	11.5 Underground Mines and Open Pits Identification and Monitoring by InSAR
	11.6 InSAR Processing Challenges
	11.7 Why Do We Still Need to Identify Well-known Open-Pit Mining?
	11.8 What are the Advantages of TanDEM Data?
	11.9 What is Meant by Four-Dimensional and Why?
	11.10 Does N-dimensional Exist?
	11.11 What is Hologram Interferometry?
	11.12 Marghany’s 4-D Hologram Interferometry Theory for Copper Mineralization
	11.13 Marghany’ 4-D Phase Unwrapping Algorithm
	11.14 Particle Swarm Optimization Algorithm
		11.14.1 Optimization of 4-D Phase Unwrapping
		11.14.2 Optimization of Open-pit Mining Geometry Deformation
	11.15 Hamming Graph for 4-D Formation from Quantum Hologram Interferometry
	11.16 4D Hologram Interferometry of Open-Pit Mining
	11.17 Can Relativity Theory Explain 4-D Quantum Geometry Reconstruction?
	References
Index
About the Author