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Remote Sensing and Image Processing in Mineralogy [Kõva köide]

(FACULTY OF GEOSPATIAL & REAL ESTATE, University Geomatica College, Kuala Lumpur, Malaysia)
  • Formaat: Hardback, 300 pages, kõrgus x laius: 234x156 mm, kaal: 720 g, 13 Tables, black and white; 38 Line drawings, black and white; 5 Halftones, color; 197 Halftones, black and white; 5 Illustrations, color; 235 Illustrations, black and white
  • Ilmumisaeg: 03-Mar-2022
  • Kirjastus: CRC Press
  • ISBN-10: 0367896702
  • ISBN-13: 9780367896706
Teised raamatud teemal:
Remote Sensing and Image Processing in MineralogySuurem pilt
  • Formaat: Hardback, 300 pages, kõrgus x laius: 234x156 mm, kaal: 720 g, 13 Tables, black and white; 38 Line drawings, black and white; 5 Halftones, color; 197 Halftones, black and white; 5 Illustrations, color; 235 Illustrations, black and white
  • Ilmumisaeg: 03-Mar-2022
  • Kirjastus: CRC Press
  • ISBN-10: 0367896702
  • ISBN-13: 9780367896706
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780367896706.html
Märksõnad:
Remote Sensing and Image Processing in Mineralogy reveals the critical tools required to comprehend the latest technology surrounding the remote sensing imaging of mineralogy, oil and gas explorations. It particularly focusses on multispectral, hyperspectral and microwave radar, as the foremost sources to understand, analyze and apply concepts in the field of mineralogy. Filling the gap between modern physics quantum theory and image processing applications of remote sensing imaging of geological features, mineralogy, oil and gas explorations, this reference is packed with technical details associated with the potentiality of multispectral, hyperspectral and synthetic aperture radar (SAR). The book also includes key methods needed to extract the value-added information necessary, such as lineaments, gold and copper minings. This book also reveals novel speculation of quantum spectral mineral signature identifications, named as quantized Marghanys mineral spectral or Marghany Quantum Spectral Algorithms for Mineral identifications (MQSA).

Rounding out with practical simulations of 4-D open-pit mining identification and monitoring using the hologram radar interferometry technique, this book brings an effective new source of technology and applications for todays minerology and petroleum engineers.

Key Features Helps develop new algorithms for retrieving mineral mining potential zones in remote sensing data. Solves specific problems surrounding the spectral signature libraries of different minerals in multispectral and hyperspectral data. Includes over 200 equations that illustrate how to follow examples in the book.
Preface v
1 Principles of Mineralogy, Oil and Gas
1(29)
1.1 What is a Mineral?
1(1)
1.2 What is the Relationship between Atoms, Elements, Minerals and Rocks?
2(1)
1.3 Atom Structure
2(2)
1.4 Minerals in Periodic Table
4(3)
1.5 Chemical Bonding
7(1)
1.6 Valence and Charge
7(2)
1.7 Ionic Bonding
9(1)
1.8 Covalent Bonding
9(1)
1.9 Natural Crystallization of Minerals
10(5)
1.9.1 Isometric
11(1)
1.9.2 Hexagonal
12(1)
1.9.3 Tetragonal
12(1)
1.9.4 Orthorhombic
13(1)
1.9.5 Monoclinic
13(1)
1.9.6 Triclinic
14(1)
1.9.7 Trigonal or Rhombohedral
14(1)
1.10 Occurrence and Formation
15(1)
1.11 How are Minerals Categorized?
16(6)
1.11.1 Silicate Minerals
16(2)
1.11.2 The Dark Ferromagnesian Silicates
18(1)
1.11.3 Pyroxene Family
19(1)
1.11.4 Amphibole Minerals
20(1)
1.11.5 Sheet Silicates
21(1)
1.11.6 Framework Silicates
21(1)
1.12 Non-Silicate Minerals
22(3)
1.12.1 Carbonate
23(1)
1.12.2 Oxides
24(1)
1.12.3 Halides
24(1)
1.12.4 Sulfides
24(1)
1.12.5 Phosphate Minerals
25(1)
1.12.6 Native Element Minerals
25(1)
1.13 Oil and Gas Formation
25(3)
References
28(2)
2 Quantization of Minerals and their Interactions with Remote Sensing Photons
30(33)
2.1 Quantization in the Atom
30(3)
2.1.1 Principal Quantum Number
31(1)
2.1.2 Angular Momentum Quantum
31(1)
2.1.3 Magnetic Quantum Number
32(1)
2.1.4 Spin Quantum Number
32(1)
2.2 Quantum Mechanics of Bonding
33(2)
2.3 Quantum Mechanics of Mineral Atomics
35(2)
2.4 Energy Variations Based on Schrodinger Wavefunction
37(1)
2.5 What is Quantum Influences?
38(1)
2.6 Quantization of Minerals from Point View of Wavefunction
39(1)
2.7 Antiferromagnetic Spin-frustrated Layers of Minerals
40(5)
2.8 General Quantization of Mineral Remote Sensing Imagines
45(9)
2.8.1 Plank Quanta
45(2)
2.8.2 Requantization of Photoelectric Effect
47(3)
2.8.3 The Uncertainty Principle
50(1)
2.8.4 Photovoltaic Effect
50(1)
2.8.5 De Broglie's Wavelength
51(3)
2.9 Quantization of Blackbody Radiation
54(3)
2.10 Quantization of Spectral Signature
57(3)
2.11 How can We Establish a New Definition of Remote Sensing for Mineral Identification?
60(1)
References
61(2)
3 Quantum Computing of Image Processing
63(16)
3.1 What is Meant by Quantum Computing?
63(1)
3.2 What is Meant by Quantization?
64(1)
3.3 What are Quantum Computers and How do they Work?
65(3)
3.3.1 Qubits and Superposition
65(1)
3.3.2 Quantum Registers
66(1)
3.3.3 Quantum Gates
67(1)
3.3.3.1 NOT Gate
67(1)
3.3.3.2 Controlled-NOT Gate
67(1)
3.3.3.3 Hadamard Gate
67(1)
3.3.4 Entanglement
68(1)
3.4 Quantum Image Processing
68(1)
3.5 Flexible Representation for Quantum Images
69(2)
3.6 Fast Geometric Transformations on FRQI Quantum Images
71(1)
3.7 Efficient Colour Transformations on FRQI Quantum Image
72(2)
3.8 Multi-Channel Representation for Quantum Images
74(2)
3.9 Novel Enhanced Quantum Image Representation (NEQR)
76(1)
References
77(2)
4 Quantum Spectral Libraries of Minerals in Optical Remote Sensing Data
79(22)
4.1 How do Spectral Libraries Build Up?
79(2)
4.2 Jablonski Energy Diagram
81(3)
4.3 Infrared Absorption Spectroscopy
84(1)
4.4 Spectral Regions Relevant to Mineralogy
85(3)
4.5 Entanglement by Absorption
88(1)
4.6 How Does Entanglement Form Spectral Libraries?
89(3)
4.7 How Does Quantum Teleportation Establish the Spectral Libraries?
92(3)
4.8 Modeling of Quantum Mineral Spectral Libraries
95(1)
4.9 Image Storage
95(1)
4.10 Tested Remote Sensing Data
96(1)
4.11 Example of Reflectance Spectra
97(2)
References
99(2)
5 Quantum Multispectral and Hyperspectral Remote Sensing Imaging of Alteration Minerals
101(25)
5.1 What is an Alteration?
101(3)
5.1.1 Potassic Alteration
102(1)
5.1.2 Propylitic Alteration
103(1)
5.1.3 Phyllic (Sericitic) Alteration
103(1)
5.1.4 Argillic Alteration
103(1)
5.1.5 Silicification
104(1)
5.1.6 Carbonatization and Greisenization
104(1)
5.2 Multispectral and Hyperspectral Remote Sensing Sensors
104(2)
5.3 Mineral Exploration from Space
106(4)
5.3.1 Multispectral Satellite Sensors
107(1)
5.3.2 Hyperspectral Satellite Sensors
108(2)
5.4 Why Does The Spectral Analyst Tool Work Properly in Some Cases and Not At All in Others?
110(1)
5.5 Quantization of Multispectral and Hyperspectral Data
111(3)
5.6 Spectral Reflectance Quantum Image Formation (SRQIF)
114(1)
5.7 Marghany Quantum Spectral Algorithms for Mineral Identifications (MQSA)
115(2)
5.8 Selected Investigation Area for MQSA Application
117(1)
5.9 MQSA Application of Different Minerals in Landsat and ASTER Images
117(4)
5.10 Why Marghany Quantum Spectral Algorithms (MQSA) Identify Accurate Quantum Mineral Images?
121(3)
References
124(2)
6 Evolving Quantum Image Processing Tool for Lineament Automatic Detection in Optical Remote Sensing Satellite Data
126(22)
6.1 What is Meant by Lineament?
126(3)
6.2 What is the Magic of Lineament?
129(1)
6.3 What are the Sorts of Lineaments?
130(1)
6.4 Satellite Remote Sensing and Image Processing for Lineament Features' Detection
131(2)
6.5 How do Multispectral Remote Sensing Data Identify the Lineaments?
133(3)
6.6 Problems for Geological Features' Extraction from Remote Sensing Data
136(1)
6.7 Can Digital Elevation Model be Utilized in Lineament Delineation?
137(2)
6.8 What is the Main Question?
139(1)
6.9 The Fuzzy B-splines Algorithm for Digital Elevation Model Reconstruction
139(3)
6.10 Entanglement of Fuzzy Quantum for DEM Reconstruction
142(1)
6.11 Quantum Edge Detection Algorithm for Lineament Mapping
143(3)
References
146(2)
7 Quantum Support Vector Machine in Retrieving Clay Mineral Saturation in Multispectral Sentinel-2 Satellite Data
148(20)
7.1 Salinity, Soil and Geological Minerals
148(1)
7.2 Mineral Soil Classifications
149(1)
7.3 Remote Sensing of Mineral Soils
150(1)
7.4 Can Marshlands be Indicator for Mineral Occurrences?
151(1)
7.5 How to Compute Cation Exchange Capacity in Laboratory?
152(2)
7.6 Sentinel-2 Satellite Data
154(2)
7.7 How to Retrieve Clay Potential Percentage in Remote Sensing Data?
156(1)
7.8 Quantized Marghany Clay Saturation Algorithm in Al-Hawizeh Marsh
157(3)
7.9 Support Vector Machines
160(3)
7.10 Quantum Support Vector Machines
163(1)
7.11 Why Does QSVM Entangle Quantized Marghany's Clay Saturation Algorithm?
164(2)
References
166(2)
8 Automatic Detection of Oil Seeps in Synthetic Aperture Radar Using Quantum Immune Fast Spectral Clustering
168(25)
8.1 What are Oil Seeps?
168(2)
8.2 Behaviour of Oil and Gas Jets and Plumes Below the Sea Water Surface
170(1)
8.3 Onshore Seep Occurrences
171(1)
8.4 Offshore Seep Occurrences
171(1)
8.5 Sort of Seeps
171(1)
8.6 How Does Remote Sensing Technology Identify Natural Oil and Gas Seeps?
172(1)
8.7 Why Do Microwave Data Have Advantages on Top of Optical Data in Seep Monitoring?
173(1)
8.8 Offshore Seep Imagine in SAR Data
174(1)
8.9 What are the Physical Seep Parameters Identified in SAR Data?
174(1)
8.10 SAR Polarization Signals
175(2)
8.11 Quantum Fully-polarized SAR Image Processing
177(3)
8.12 Quantum Immune Fast Spectral Clustering
180(1)
8.13 Quantum Immune Operation
181(2)
8.14 Spectral Embedding
183(1)
8.15 Automatic Detection of Oil Seep in Full Polarimetric SAR
184(2)
8.16 Applications of QIFSC to Other Satellite Polarimetric SAR Sensors
186(3)
8.17 Why Can QIFSC Precisely Cluster Different Kinds of Oil Seep?
189(2)
References
191(2)
9 Quantum Interferometry Radar for Oil and Gas Explorations
193(2)
9.1 What is Reservoir Geomechanics?
193(1)
9.2 What is the Role of Reservoir Geomechanics in Oil and Gas Explorations?
194(1)
93 Physics of Interferometry
195(20)
9.4 What is Synthetic Aperture Interferometry?
197(2)
9.5 Interferograms
199(1)
9.6 Phase Unwrapping
200(1)
9.7 How to Understand SAR Interferograms?
201(2)
9.8 Quantum of Differential-InSAR (QD-InSAR)
203(2)
9.9 Quantum Hopfield Algorithm for DInSAR Phase Unwrapping
205(3)
9.10 Application of Quantum DInSAR Hopfield Algorithm in Land Deformation Owing to Oil and Gas Explorations
208(5)
References
213(2)
10 Quantum Machine Learning Algorithm for Iron, Gold, and Copper Detection in Optical Remote Sensing Data
215(32)
10.1 How Copper and Gold Form in the Earth?
215(1)
10.2 How Copper and Gold are Mined?
216(1)
10.3 What are the Characteristics of Copper and Gold?
217(2)
10.4 Remote Sensing for Copper and Gold Identifications
219(2)
10.5 Conventional Image Processing Techniques for Gold, Iron, and Copper Explorations
221(6)
10.5.1 Preprocessing
221(1)
10.5.2 Post Image Processing
222(1)
10.5.2.1 False Colour Composite
222(1)
10.5.2.2 Band Ratio
222(2)
10.5.2.3 Principal Component Analysis (PCA)
224(1)
10.5.2.4 Noise Fraction (MNF)
225(1)
10.5.2.5 Spectral Unmixing in n-dimensional Spectral Feature Space
225(2)
10.6 Quantum Machine Learning
227(1)
10.7 Classifier Architecture
227(3)
10.8 Classifier Training as a Supervised Learning Task
230(2)
10.9 Training Score and Classifier Bias
232(2)
10.10 Gold Mining Simulation Using Quantum Machine Learning
234(2)
10.11 Quantum Artificial Neural Network (QANN) for Gold Exploration
236(5)
10.12 QANN for Copper Mining Potential Zone
241(1)
10.13 Why Quantum Machine Learning can be Used for Mineral Exploration?
242(2)
References
244(3)
11 Four-Dimensional Hologram Interferometry for Automatic Detection of Copper Mineralization Using Terrasar-X Satellite Data
247(30)
11.1 What is the Real Age of Copper?
247(1)
11.2 Occurrences of Copper
247(1)
11.3 Conventional Methods for Copper Extraction
248(2)
11.4 What is the Major Challenge with Optical Remote Sensing and Microwave Radar Data?
250(1)
11.5 Underground Mines and Open Pits Identifications and Monitoring by InSAR
251(1)
11.6 InSAR Processing Challenges
251(2)
11.7 Why Do We Still Need to Identify Well-known Open-Pit Mining?
253(1)
11.8 What are the Advantages of TanDEM Data?
254(1)
11.9 What is Meant by Four-Dimensional and Why?
255(1)
11.10 Does N-dimensional Exist?
256(1)
11.11 What is Hologram Interferometry?
257(1)
11.12 Marghany's 4-D Hologram Interferometry Theory for Copper Mineralization
258(4)
11.13 Marghany' 4-D Phase Unwrapping Algorithm
262(2)
11.14 Particle Swarm Optimization Algorithm
264(3)
11.14.1 Optimization of 4-D Phase Unwrapping
264(2)
11.14.2 Optimization of Open-pit Mining Geometry Deformation
266(1)
11.15 Hamming Graph for 4-D Formation from Quantum Hologram Interferometry
267(2)
11.16 4D Hologram Interferometry of Open-Pit Mining
269(2)
11.17 Can Relativity Theory Explain 4-D Quantum Geometry Reconstruction?
271(4)
References
275(2)
Index 277(6)
About the Author 283
Prof. Maged Marghany is currently the director of Global Geoinformation, Sdn. Bhd. In 2020 he was ranked amongst the top 2 percent of scientists in a global list compiled by the prestigious Stanford University. He also ranked as the first oil spill scientist in a global list of over last 50 years compiled by the prestigious Universidade Estadual de Feira de Santana, Universidade Federal da Bahia, and Universidade Federal de Pernambuco; Brazil.

He is the author of 6 titles including: Advanced Remote Sensing Technology for Tsunami Modelling and Forecasting which is published by Routledge Taylor and Francis Group, CRC and Synthetic Aperture Radar Imaging Mechanism for Oil Spills, which is published by Elsevier, His research specializes in microwave remote sensing and remote sensing for mineralogy detection and mapping. Previously, he worked as a professor of remote sensing in Indonesian and Malaysian universities. Maged has earned many degrees including a post-doctoral in radar remote sensing from the International Institute for Aerospace Survey and Earth Sciences, a PhD in environmental remote sensing from the Universiti Putra Malaysia, a Master of Science in Physical oceanography from the University Pertanian Malaysia, general and special Diploma of Education and a Bachelor of Science in physical oceanography from the University of Alexandria in Egypt. Maged has published well over 250 papers in international conferences and journals and is active in International Geoinformatic, and the International Society for Photogrammetry and Remote Sensing (ISPRS).