Advanced Algorithms for Mineral and Hydrocarbon Exploration Using Synthetic Aperture Radar

Description

Advanced Algorithms for Mineral and Hydrocarbon Exploration Using Synthetic Aperture Radar is a research- and practically-based reference that bridges the gap between the remote sensing industry and the mineral and hydrocarbon exploration industry. In this context, the book explains how to commercialize the applications of synthetic aperture radar and quantum interferometry synthetic aperture radar (QInSAR) for mineral and hydrocarbon exploration. This multidisciplinary reference is useful for oil and gas companies, the mining industry, geoscientists, and coastal and petroleum engineers. Front Cover ADVANCED ALGORITHMS FOR MINERAL AND HYDROCARBON EXPLORATION USING SYNTHETIC APERTURE RADAR ADVANCED ALGORITHMS FOR MINERAL AND HYDROCARBON EXPLORATION USING SYNTHETIC APERTURE RADAR Copyright Dedication Contents Preface 1 - An introduction to minerals, rocks, and mineral deposits 1.1 What is the denotation of minerals? 1.2 Queries of understanding minerals 1.2.1 Why are minerals in rocks important? 1.2.2 How are minerals and rocks classified? 1.2.3 What are the minerals found in rocks? 1.2.4 How do minerals contribute to rock? 1.3 What makes a mineral as mineral? 1.4 Properties of minerals 1.4.1 Crystal form 1.4.2 Hardness 1.4.3 Fracture and cleavage 1.4.4 Luster 1.4.5 Color 1.4.6 Streak 1.4.7 Density 1.4.8 Tenacity 1.5 How are minerals classified? 1.5.1 Silicates 1.5.2 Sulfides 1.5.3 Carbonates 1.5.4 Oxides 1.5.5 Halides 1.5.6 Sulfates 1.5.7 Phosphates 1.5.8 Native elements 1.6 What is meant by ores? 1.7 What are igneous rocks? 1.8 How do igneous rocks form? 1.9 Types of igneous rocks 1.10 What are meant of sedimentary rocks? 1.10.1 Common sedimentary rocks 1.10.2 Clastic sedimentary rocks 1.10.3 Biologic sedimentary rocks 1.11 What are meant of metamorphic rocks? 1.11.1 Process of metamorphism 1.11.2 Foliated metamorphic rocks 1.11.3 Nonfoliated metamorphic rocks 1.12 Is glacier ice a type of rock? 1.13 Bowen's reaction series in understanding formation of igneous rock References 2 - Structural geology of mineral, oil and gas explorations 2.1 What is mean by structural geology? 2.2 Categorization of geological structures 2.2.1 Geometry 2.2.2 Geologic significance 2.2.3 Timing of formation 2.2.4 Deformation mechanism 2.2.5 Mesoscopic cohesiveness 2.2.6 Strain significance 2.2.7 Rock volume deformation 2.3 Folds 2.3.1 Folds formation 2.3.2 Anticlines and synclines 2.3.3 Dipping anticlines and synclines 2.3.4 Synclines 2.3.5 Faults 2.3.6 Reverse and thrust faults 2.4 Stress and mountain building 2.5 Mechanical principles in structural geology 2.5.1 Stress in Earth's crust 2.5.2 Stress and fault sorts 2.5.3 Strain 2.5.4 Ductile and Brittle Strain 2.6 Shear criteria and kinematic analysis 2.6.1 Helicitic inclusions in porphyroblasts 2.6.2 Mica fish 2.7 Ore deposits as a function of structural geology 2.7.1 Ore deposits and plate tectonics 2.7.1.1 Deposits at oceanic ridges (divergent plate margins) 2.7.1.2 Deposits at convergent plate margins 2.7.1.3 Deposits at collision boundaries 2.7.1.4 Deposits in cratonic rift systems 2.7.1.5 Deposits in cratonic basins 2.8 Veins and hydrothermal deposits 2.9 Mesothermal 2.10 Epithermal 2.11 Metamorphic processes 2.12 Surficial processes (exogenous) 2.13 Genesis of common ores 2.13.1 Iron 2.13.2 Lead zinc silver 2.13.3 Gold 2.13.4 Platinum 2.13.5 Nickel 2.13.6 Uranium 2.13.7 Titanium and zirconium 2.13.8 Tin, tungsten, and molybdenum 2.13.9 Rare Earth elements, niobium, tantalum, and lithium 2.13.9.1 Phosphate 2.13.9.2 Vanadium 2.14 Oil and gas formation 2.15 Type of oil and gas traps 2.15.1 Structural trap 2.15.1.1 Synthetic fault; antithetic fault and horst 2.15.1.2 Growth fault and hydrocarbon traps 2.15.1.3 Normal fault (gravity fault), reverse fault and listric faults 2.15.2 Anticline trap 2.15.3 Salt dome 2.15.4 Stratigraphic trap References Further reading 3 - Theories of microwave synthetic aperture radar 3.1 Maxwell's equations 3.1.1 Integral forms of Maxwell's equations 3.1.2 Differential forms of Maxwell's equations 3.2 Electromagnetic waves 3.3 Microwave beam 3.4 Microwave photon beams 3.5 Generation of electromagnetic wave by antenna 3.6 Radio detection and ranging (RADAR) 3.7 How does a radar measure the distance of an object? 3.8 Line-of-sight of a radar 3.9 Radar-microwave characteristics 3.10 Radar equation 3.11 Real aperture radar 3.11.1 Real aperture radar: range resolution 3.11.2 Real aperture radar: azimuth resolution 3.12 Synthetic aperture radar (SAR) 3.13 Slant and ground range resolution 3.14 Kinds of SAR 3.15 Theoretical limits for strip map SAR 3.16 What is the effect of object width on strip map SAR resolution? 3.17 SAR polarization References 4 - Marghany Adaptive Modification Algorithm for lineament automatic detection in Synthetic aperture radar data 4.1 What is meant by lineament? 4.2 What is the magic of lineament? 4.3 What are the sorts of lineaments? 4.4 Can implement lineaments as tool for mineral and hydrocarbon explorations? 4.5 What is mechanism of Synthetic Aperture Radar in imagine lineaments? 4.6 How far does the incidence angle effect lineament imaging in SAR image? 4.7 How far does the look-azimuth impact lineament imaging in SAR? 4.8 Radar speckle noise 4.9 Multilook processing and speckle 4.10 Marghany Adaptive Modification Algorithm for lineament detection 4.11 Automatic lineament detection using Marghany Adaptive Modification Algorithm References 5 - Mechanism of imaging structural geology features in synthetic aperture radar 5.1 SAR imaging geometry 5.2 How do different view geometries effect geology imaging? 5.3 How far the wavelength and incident angle effect geological feature imaging? 5.4 How far beam polarization impact geology feature imaging mechanisms in SAR sensors? 5.5 Subsurface imaging mechanisms in SAR image 5.6 Complex dielectric constant impact in mineral deposit imaging in SAR image? 5.7 SAR geometric distortion mechanisms 5.8 Complex volume-scattering mechanisms 5.9 Imagine mechanisms of terrain categories in SAR images 5.10 Geological structure imaging in SAR images 5.11 SAR stereoscopy mechanism for geological structure imagings References Further reading 6 - Retrieving Minerals and Rocks in polarimetry Microwave Remote Sensing data 6.1 What is the magic of polarimetry in nature? 6.2 How polarization pattern can identify mineral deposits? 6.3 What is meant by state of polarization? 6.4 What are the sorts of approaches to describe state of polarization? 6.5 Mechanism of polarimetric scattering 6.5.1 The scattering matrix 6.5.2 The connection between sinclair matrix and Jones matrix 6.6 Mueller matrix 6.7 The covariance and coherency matrixes 6.8 Circular polarization 6.9 Airborne synthetic aperture radar (AIRSAR) 6.10 Geological polariemetry signatures in AIRSAR data 6.11 Marghany's technique for rock and mineral deposit detections in AIRSAR data 6.12 Freeman–Durden polarization decomposition for mineral deposit detections References 7 - Texture and quantum entropy algorithms for mineral and hydrocarbon explorations in synthetic aperture radar images 7.1 What is the magic of mineral deposits in hydrocarbon explorations? 7.2 How to exploit clay minerals in oil and gas exploration? 7.3 How remote sensing imagine hydrocarbon as based on alteration minerals? 7.4 SAR image texture 7.5 Texture SAR image algorithms 7.6 What is the correlation between GLCM and SAR image pixels? 7.7 How do GLCM operate in SAR image? 7.8 How SAR image turns into symmetrical matrix using GLCM? 7.9 How to normalize symmetrical SAR GLMC matrix? 7.10 How to create SAR texture image? 7.11 What is the mathematical form of cooccurrence matrix? 7.11.1 Contrast category 7.11.2 Homogeneity 7.11.3 Dissimilarity 7.11.4 Angular second moment (ASM) and energy 7.11.5 Statistics of GLCM 7.11.6 Entropy 7.12 Can GLCM accurately detect mineral deposit indices for hydrocarbon potential zones? 7.13 Can quantum entropy perform better than entropy for the automatic detection of diversity of mineral deposits and hydrocarbo ... References Further reading 8 - Mahalanobis classifier and neural network algorithms for mineral exploration 8.1 SAR imagine mineral deposits 8.2 What is the exact issue for mineral extraction in SAR data? 8.3 Hypotheses 8.4 Selected SAR data acquisition 8.5 Mahalanobis algorithm 8.6 Lineament detection by Mahalanobis classifier 8.7 Mineralization automatic detection by Mahalanobis classifier 8.8 Artificial intelligent for mineral sorts automatic detection 8.9 How do the mineral exploration indices select in SAR data into neural network? 8.10 Frame structure of neural network for mineral deposits automatic detection 8.11 Back-propagation learning algorithm for automatic detection of mineral deposits 8.12 Back-propagation training algorithm 8.13 Mineral deposit detections by NN algorithm 8.14 Comparison between Mahalanobis classifier and neural networks References Further reading 9 - Fractal dimension algorithm for automatic detection of gold mineralization 9.1 How is gold formed? 9.2 How earthquakes and volcanoes play vital roles in forming gold atoms 9.3 What are the characteristics of gold? 9.4 Mineral exploration in SAR data 9.5 What is meant by fractal? 9.6 Fractal dimensions 9.7 How to calculate the fractal? 9.8 Computing of hurst exponent 9.9 Fractal algorithm for gold mining identification 9.10 Examined SAR satellite data and in situ data collection 9.11 Backscatter pattern variations as a function of incidence angles and wind speed 9.12 Fractal map of Bau anticline and Bau gold field 9.13 How far can fractal algorithm detect a Bau gold field? References Further reading 10 - Quantum finite automaton algorithm for mineral and hydrocarbon exploration in Sentinel-1A SAR data 10.1 What is the main problem in the field of geology image processing? 10.2 What is meant by quantum computing? 10.3 What is meant by quantization? 10.4 What are quantum computers, and how do they work? 10.4.1 Qubits and superposition 10.4.2 Quantum registers 10.5 What is meant by quantum image processing? 10.6 What is meant by probabilistic automaton? 10.7 Quantum finite automaton algorithm 10.8 Automatic detection of altered minerals and hydrocarbon micro seepage 10.9 Quantum operator gate 10.10 In situ measurement and SAR satellite data 10.11 Altered mineral detection in SAR image 10.12 Training score and automatic classifier bias References 11 - Quantum cellular automata algorithm for automatic detection of hydrocarbon exploration zones 11.1 How surface geomorphic anomalies are key index of hydrocarbon exploration potential zones? 11.2 What are the drainage patterns visual in radar images? 11.2.1 Dendritic drainage pattern 11.2.2 Parallel drainage pattern 11.2.3 Trellis drainage pattern 11.2.4 Rectangular drainage pattern 11.2.5 Radial drainage pattern 11.2.6 Centripetal drainage pattern 11.2.7 Deranged drainage pattern 11.3 What is the significance of drainage network? 11.4 How do SAR sensors imagine drainage? 11.5 How shuttle radar topography mission (SRTM) imagine the drainage trapped oil and gas? 11.6 Principles of quantum-dot cellular automata 11.7 Quantum cellular automata cell construction 11.8 QCA adder with five gates for automatic detection of hydrocarbon zones 11.9 Cellular automata for automatic detection of hydrocarbon zones 11.10 5 explored SAR image 11.11 Automatic detection of hydrocarbon zone using quantum cellular automata 11.12 Why QCA can detect potential zone of hydrocarbon automatically? References Further reading 12 - Multiobjective quantum evolutionary algorithm for oil seep detection from synthetic aperture radar 12.1 What is meant by oil seep? 12.2 What are the differences between onshore and offshore seeps? 12.3 Types of seeps 12.4 Chronological fluctuations in seeps 12.5 How do microwave remote sensing data identify oil seeps? 12.6 What are the physical seep parameters distinguish in SAR data? 12.7 What is the main obstacle in seep automatic detection in SAR images? 12.8 Can a quantum algorithm automatically detect natural oil seeps in SAR images? 12.9 Marghany's quantum oil spill imagine in SAR images 12.9.1 Multiobjective quantum evolutionary algorithm of oil seeps automatic detection 12.10 Quantum pareto optimal solution 12.11 Automatic detection of oil seep in SAR images 12.12 Applications of MOQEA-based quantum pareto front to other satellite SAR sensors 12.13 Why does MOQE success to cluster different types of oil seep? References 13 - Four-dimensional radar quantum interferometry for oil and gas explorations 13.1 How do satellites aid in oil exploration? 13.2 What is meant by reservoir geomechanics? 13.3 What is the vital role of geomechanics in mature oil and gas fields? 13.4 What is the role of borehole integrity? 13.5 What is the main role of geomechanical in fracking operations? 13.6 How do subsurface stresses and strengths form? 13.7 What is the role of reservoir geomechanics in oil and gas explorations? 13.8 What is meant by interferometry? 13.9 How to demonstrate InSAR using double-slit experiment 13.10 What is meant by synthetic aperture interferometry (InSAR)? 13.11 What are the sorts of radar interferometry? 13.12 How InSAR works? 13.13 What are InSAR processing challenges? 13.14 Marghany's speculation for quantization of InSAR 13.15 Marghany's 4D quantized phase unwrapping algorithm 13.16 Application of Marghany's 4D quantized phase unwrapping for retrieving land deformation: study case Kharg Island, Iran References Further reading Index A B C D E F G H I J K L M N O P Q R S T U V W X Z Back Cover