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E-raamat: Application of Soft Computing and Intelligent Methods in Geophysics

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  • Formaat: PDF+DRM
  • Sari: Springer Geophysics
  • Ilmumisaeg: 21-Jun-2018
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319665320
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Application of Soft Computing and Intelligent Methods in GeophysicsSuurem pilt
  • Formaat: PDF+DRM
  • Sari: Springer Geophysics
  • Ilmumisaeg: 21-Jun-2018
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319665320
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This book provides a practical guide to applying soft-computing methods to interpret geophysical data. It discusses the design of neural networks with Matlab for geophysical data, as well as fuzzy logic and neuro-fuzzy concepts and their applications. In addition, it describes genetic algorithms for the automatic and/or intelligent processing and interpretation of geophysical data. The book’s closing chapter outlines the future of soft-computing methods in geophysics.

Arvustused

This is certainly a useful book offering novel approaches of computational techniques related to a variety of geophysical applications widely using Matlab and its toolbox algorithms. It can be interesting both for those active in pure computational techniques as well as for professional researchers . The book is also a highly instructive literature for graduate students as it provides many exercises and practical examples of how to use and apply the described numerical methods in treating concrete geophysical problems. (Vladimir ade, zbMATH 1400.86001, 2019)

Part I Neural Networks
1 Artificial Neural Networks
3(68)
1.1 Introduction
3(1)
1.2 A Brief Review of ANN Applications in Geophysics
4(2)
1.3 Natural Neural Networks
6(1)
1.4 Definition of Artificial Neural Network (ANN)
7(3)
1.5 From Natural Neuron to a Mathematical Model of an Artificial Neuron
10(6)
1.6 Classification into Two Groups as an Example
16(2)
1.7 Extracting the Delta-Rule as the Basis of Learning Algorithms
18(1)
1.8 Momentum and Learning Rate
19(1)
1.9 Statistical Indexes as a Measure of Learning Error
20(1)
1.10 Feed-Forward Back-Propagation Neural Networks
20(4)
1.11 A Guidance Checklist for Step-by-Step Design of a Neural Network
24(1)
1.12 Important Factors in Designing a MLP Neural Network
24(2)
1.12.1 Determining the Number of Hidden Layers
25(1)
1.12.2 Determination of the Number of Hidden Neurons
25(1)
1.13 How Good Are Multi-layer Per Feed-Forward Networks9
26(1)
1.14 Under Training and Over Fitting
27(1)
1.15 To Stop or not to Stop, that Is the Question! (When Should Training Be Stopped?!)
27(1)
1.16 The Effect of the Number of Learning Samples
28(1)
1.17 The Effect of the Number of Hidden Units
29(1)
1.18 The Optimum Number of Hidden Neurons
30(1)
1.19 The Multi-start Approach
30(2)
1.20 Test of a Trained Neural Network
32(3)
1.20.1 The Training Set
32(1)
1.20.2 The Validation Set
32(1)
1.20.3 The Test Set
33(1)
1.20.4 Random Partitioning
33(1)
1.20.5 User-Defined Partitioning
33(1)
1.20.6 Partition with Oversampling
34(1)
1.20.7 Data Partition to Test Neural Networks for Geophysical Approaches
34(1)
1.21 The General Procedure for Testing of a Designed Neural Network in Geophysical Applications
35(1)
1.22 Competitive Networks-The Kohonen Self-organising Map
36(5)
1.22.1 Learning in Biological Systems-The Self-organising Paradigm
37(1)
1.22.2 The Architecture of the Kohonen Network
37(1)
1.22.3 The Kohonen Network in Operation
37(2)
1.22.4 Derivation of the Learning Rule for the Kohonen Net
39(1)
1.22.5 Training the Kohonen Network
39(1)
1.22.6 Training Issues in Kohonen Neural Nets
40(1)
1.22.7 Application of the Kohonen Network in Speech Processing-Kohonen's Phonetic Typewrite
41(1)
1.23 Hopfield Network
41(2)
1.24 Generalized Regression Neural Network (GRNN)
43(2)
1.24.1 GRNN Architecture
43(1)
1.24.2 Algorithm for Training of a GRNN
44(1)
1.24.3 GRNN Compared to MLP
45(1)
1.25 Radial Basis Function (RBF) Neural Networks
45(3)
1.25.1 Radial Functions
45(1)
1.25.2 RBF Neural Networks Architecture
46(2)
1.26 Modular Neural Networks
48(2)
1.27 Neural Network Design and Testing in MATLAB
50(16)
References
66(5)
2 Prior Applications of Neural Networks in Geophysics
71(130)
2.1 Introduction
71(1)
2.2 Application of Neural Networks in Gravity
72(38)
2.2.1 Depth Estimation of Buried Qanats Using a Hopfield Network
73(6)
2.2.2 Depth Estimation of Salt Domes Using Gravity Anomalies Through General Regression Neural Networks
79(16)
2.2.3 Simultaneous Estimation of Depth and Shape Factor of Subsurface Cavities
95(10)
2.2.4 Modeling Anticlinal Structures Through Neural Networks Using Residual Gravity Data
105(5)
2.3 Application of ANN for Inversion of Self-potential Anomalies
110(5)
2.4 Application of ANN for Sea Level Prediction
115(6)
2.5 Application of Neural Network for Mineral Prospectivity Mapping
121(5)
2.6 Application of NN for SP Inversion Using MLP
126(4)
2.7 Determination of Facies from Well Logs Using Modular Neural Networks
130(6)
2.8 Estimation of Surface Settlement Due to Tunneling
136(20)
2.8.1 Introduction
137(4)
2.8.2 The Finite Element Method in Plaxis Software
141(1)
2.8.3 The Available Elements for Modeling
141(1)
2.8.4 Soil and Rock Behavior Models
141(2)
2.8.5 The Studied Route of the Mashhad Subway Line 2 Project
143(2)
2.8.6 Characteristics of the Tunnel
145(2)
2.8.7 The Surface Settlement Measurement Operations
147(1)
2.8.8 Surface Settlement Prediction Using ANN
147(6)
2.8.9 Surface Settlement Calculation Using FEM
153(1)
2.8.10 Results
154(1)
2.8.11 Conclusions
154(2)
2.9 Comparison of Neural Networks for Predicting the Penetration Rate of Different Models for Tunnel Boring Machines (TBM)
156(10)
2.9.1 Literature Review of the Prediction of the Penetration Rate of TBM
156(1)
2.9.2 Case Study of the Golab Tunnel
157(2)
2.9.3 Geomorphology
159(2)
2.9.4 The TBM Machine Used for the Golab Project
161(1)
2.9.5 Data Collection
161(1)
2.9.6 A Static Model for Predicting the Penetration Rate
161(2)
2.9.7 Input Parameters
163(1)
2.9.8 ANN Topology
164(2)
2.9.9 Testing and Validation of the ANN Model
166(1)
2.10 Application of Neural Network Cascade Correlation Algorithm for Picking Seismic First-Breaks
166(7)
2.10.1 The Improvement of CC Algorithm
168(2)
2.10.2 Attribute Extraction for Neural Network Training
170(3)
2.11 Application of Neural Networks to Engineering Geodesy: Predicting the Vertical Displacement of Structures
173(3)
2.12 Attenuation of Random Seismic Noise Using Neural Networks and Wavelet Package Analysis
176(17)
2.12.1 Methodology
178(4)
2.12.2 Experimental Philosophy
182(7)
2.12.3 Conclusion
189(4)
References
193(8)
Part II Fuzzy Logic
3 Fuzzy Logic
201(100)
3.1 Introduction
201(1)
3.2 Motivation for Using Fuzzy Logic in Geophysics
202(8)
3.2.1 First Viewpoint
202(6)
3.2.2 The Second Viewpoint
208(2)
3.2.3 Geophysical Data Fusion Based on Fuzzy Logic Rules
210(1)
3.3 Fuzzy Sets
210(25)
3.3.1 The Concept of a Fuzzy Set
211(3)
3.3.2 Definition of a Fuzzy Set
214(5)
3.3.3 Different Types of Fuzzy Sets According to Their Membership Functions
219(13)
3.3.4 Connecting Classical Set Theory to Fuzzy Set Theory
232(3)
3.4 Operations on Fuzzy Sets
235(11)
3.4.1 Standard Union
235(1)
3.4.2 Standard Intersection
236(1)
3.4.3 Standard Complement
236(3)
3.4.4 Applications of the Intersection of Fuzzy Set
239(1)
3.4.5 Fuzzy Averaging Operations
240(1)
3.4.6 Matlab Codes for Fuzzy Operations
241(1)
3.4.7 Other Operations on Fuzzy Sets
241(4)
3.4.8 Cartesian Product
245(1)
3.5 Fuzzy Relationships
246(15)
3.5.1 Definition of Fuzzy Relationship
246(2)
3.5.2 Domain and Range of Fuzzy Relationship
248(1)
3.5.3 Operations on Fuzzy Relationships
249(1)
3.5.4 Projection of Fuzzy Relationship and Cylindrical Extension
250(2)
3.5.5 Composition of Fuzzy Relations
252(4)
3.5.6 Matlab Coding for Fuzzy Relations
256(1)
3.5.7 Properties of Fuzzy Relations
257(2)
3.5.8 α-cut of a Fuzzy Relation
259(1)
3.5.9 α-cut of Equivalent Fuzzy Relationship
260(1)
3.6 Fuzzy Numbers
261(11)
3.6.1 Further Description of the Extension Principle
261(2)
3.6.2 Generalized Extension Principle or Multivariate Extension Principle
263(1)
3.6.3 Philosophy of Fuzzy Numbers
264(1)
3.6.4 Definition of a Fuzzy Number
264(2)
3.6.5 LR Representation of Fuzzy Numbers
266(2)
3.6.6 Operations on LR Fuzzy Numbers
268(1)
3.6.7 Triangular Fuzzy Numbers
269(1)
3.6.8 α-cut of Fuzzy Number
269(3)
3.7 Definition of Some Basic Concepts of Fuzzy Sets
272(3)
3.8 T-Norm
275(1)
3.9 S-Norm
276(1)
3.10 If-then Fuzzy Rules
277(1)
3.11 Fuzzy Statement
277(1)
3.12 Linguistic Variable
277(1)
3.13 Fuzzy Conditional Proposition (Fuzzy if-then Rule)
278(3)
3.13.1 Definition with Example in Geophysics
278(2)
3.13.2 Interpretation of Fuzzy if-then Rule
280(1)
3.14 Approximate Reasoning
281(5)
3.14.1 Fuzzy Inference
281(2)
3.14.2 Fuzzy Extended Exceptional Deduction Rule
283(3)
3.15 Fuzzy Rules Base
286(1)
3.15.1 Definition Assume F1, G1, I = 1, N Are Fixed Fuzzy Sets Over Set U then
286(1)
3.15.2 FATI Method
286(1)
3.15.3 FITA Method
287(1)
3.16 Defuzzification
287(7)
3.16.1 Center of Gravity (Centroid of Area) Defuzzification
287(2)
3.16.2 Center of Sum Method
289(1)
3.16.3 Mean of Max Method
290(1)
3.16.4 Height Method
290(1)
3.16.5 Bisector Defuzzification
291(2)
3.16.6 Smallest of Maximum Defuzzification
293(1)
3.16.7 Largest of Maximum Defuzzification
293(1)
3.16.8 Weighted Average Defuzzification Method
293(1)
3.17 Fuzzifiers
294(1)
3.17.1 Singleton Fuzzifier
294(1)
3.17.2 Triangular Fuzzifier
294(1)
3.18 Fuzzy Modeling Using the Matlab Toolbox
295(4)
3.18.1 Fuzzy Inference System (FIS) Editor
296(1)
3.18.2 Membership Function Editor
296(1)
3.18.3 Rule Editor
297(1)
3.18.4 Rule Viewer
298(1)
3.18.5 Surface Viewer
298(1)
References
299(2)
4 Applications of Fuzzy Logic in Geophysics
301(74)
4.1 Introduction
301(1)
4.2 Fuzzy Logic for Classification of Volcanic Activities
301(1)
4.3 Fuzzy Logic for Integrated Mineral Exploration
302(8)
4.4 Shape Factors and Depth Estimation of Microgravity Anomalies via Combination of Artificial Neural Networks and Fuzzy Rules Based System (FRBS)
310(10)
4.4.1 Introduction
312(1)
4.4.2 Extracting Suitable Fuzzy Sets and Fuzzy Rules for Cavities Shape Estimation
312(7)
4.4.3 The Fuzzy Rule Based System (FRBS) for Depth and Shape Estimation with Related Membership Degree
319(1)
4.4.4 Test of the Fuzzy Rule-Based Model with Real Data
319(1)
4.5 Application of Fuzzy Logic in Remote Sensing: Change Detection Through Fuzzy Sets Using Multi Temporal Landsat Thematic Mapper Data
320(10)
4.5.1 Introduction
320(10)
4.6 Fuzzy Transitive Closure Algorithm for the Analysis of Geomagnetic Field Data
330(9)
4.6.1 Classical and Fuzzy Clustering
330(2)
4.6.2 Fuzzy Transitive Closure Method
332(1)
4.6.3 Fuzzy Equivalence Relations
333(1)
4.6.4 Fuzzy Transitive Closure Algorithm
334(1)
4.6.5 Application to for Geomagnetic Storm Data
334(5)
4.7 Geophysical Data Fusion by Fuzzy Logic to Image Mechanical Behavior of Mudslides
339(9)
4.8 Automatic Fuzzy-Logic Recognition of Anomalous Activity on Geophysical Log Records
348(11)
4.8.1 Description of the Research
348(2)
4.8.2 Difference Recognition Algorithm for Signals (DRAS)
350(5)
4.8.3 Application of the DRAS Algorithm to Observational Data
355(4)
4.9 Operational Earthquake Forecasting Using Linguistic Fuzzy Rule-Based Models from Imprecise Data
359(8)
References
367(8)
Part III Combination of Neural Networks and Fuzzy Logic
5 Neuro-fuzzy Systems
375(42)
5.1 Hybrid Systems
375(5)
5.1.1 Introduction
375(2)
5.1.2 Cooperative Neuro-fuzzy Systems
377(1)
5.1.3 Concurrent Neuro-fuzzy Systems
377(1)
5.1.4 Hybrid Neuro-fuzzy Systems
378(2)
5.2 Neural Expert Systems
380(3)
5.2.1 The Inference Engine
380(1)
5.2.2 Approximate Reasoning
381(1)
5.2.3 Rule Extraction
381(1)
5.2.4 The Neural Knowledge Base
381(2)
5.2.5 Multi-layer Knowledge Base
383(1)
5.3 Neuro-fuzzy Systems
383(6)
5.3.1 Synergy of Neural and Fuzzy Systems
384(3)
5.3.2 Training of a Neuro-fuzzy System
387(1)
5.3.3 Good and Bad Rules from Expert Systems
388(1)
5.4 Adaptive Neuro-fuzzy Inference System: ANFIS
389(6)
5.4.1 Structure of ANFIS
389(3)
5.4.2 Learning in the ANFIS Model
392(2)
5.4.3 Function Approximation Using the ANFIS Model
394(1)
5.5 ANFIS Design and Testing Using the Matlab Fuzzy Logic Toolbox
395(19)
5.5.1 Introduction
395(2)
5.5.2 ANFIS Graphical User Interference
397(17)
References
414(3)
6 Application of Neuro-Fuzzy Systems in Geophysics
417(70)
6.1 Depth Estimation of Cavities from Microgravity Data Using Multi Adaptive Neuro Fuzzy Interference Systems
417(10)
6.1.1 Why Use Neuro-Fuzzy Methods for Microgravity Interpretation?
417(1)
6.1.2 Multiple Adaptive Neuro Fuzzy Interference SYSTEM (MANFIS)
418(2)
6.1.3 Procedure of Gravity Interpretation Using MANFIS
420(1)
6.1.4 Training Strategies and MANFIS Network Architecture
421(5)
6.1.5 Test of MANFIS in Present of Noise and for Real Data
426(1)
6.2 Surface Settlement Prediction Using ANFIS for a Metro Tunnel
427(5)
6.2.1 ANFIS Structure
427(2)
6.2.2 ANFIS Training and Testing
429(2)
6.2.3 Conclusion
431(1)
6.3 The Use of the ANFIS Method for the Characterization of North Sea Reservoirs
432(9)
6.3.1 Introduction
432(1)
6.3.2 Literature Review
433(1)
6.3.3 Geological Setting
433(2)
6.3.4 Data Set
435(1)
6.3.5 Preprocessing to Select the Most Suitable Attributes
436(5)
6.3.6 Reservoir Characterization Using ANFIS and PFE
441(1)
6.4 Neuro-Fuzzy Approach for the Prediction of Longitudinal Wave Velocity
441(9)
6.4.1 Introduction
441(1)
6.4.2 Training of the Neuro-Fuzzy Model
442(4)
6.4.3 ANFIS Testing
446(1)
6.4.4 Conclusion
446(4)
6.5 Estimation of Electrical Earth Structure Using an Adaptive Neuro-Fuzzy Inference System (ANFIS)
450(7)
6.5.1 Introduction
450(1)
6.5.2 Data Collection
451(1)
6.5.3 ANFIS Training
451(3)
6.5.4 ANFIS Performance Validation Using Real Data
454(3)
6.5.5 Conclusion
457(1)
6.6 Discrimination Between Quarry Blasts and Micro-earthquakes Using Adaptive Neuro-Fuzzy Inference Systems
457(4)
6.6.1 Literature Review
457(1)
6.6.2 Feature Selection
457(1)
6.6.3 Spectral Characteristics
458(2)
6.6.4 Training and Test of ANFIS
460(1)
6.7 Application of Neuro-Fuzzy Pattern Recognition Methods in Borehole Geophysics
461(5)
6.7.1 Literature
461(1)
6.7.2 Inputs-Output Structure of the Designed ANFIS
462(1)
6.7.3 Training of ANFIS
463(1)
6.7.4 Training of ANFIS Performance
463(1)
6.7.5 Validation of ANFIS Performance
464(1)
6.7.6 Application of ANFIS Methods to Real Borehole Geophysics Data
465(1)
6.8 A Fuzzy Interference System for the Prediction of Earth Rotation Parameters
466(7)
6.8.1 Introduction
466(2)
6.8.2 Prediction of Earth Rotation Parameters by ANFIS
468(1)
6.8.3 Patterns for Polar Motion Components x and y
468(2)
6.8.4 Design of ANFIS Structure
470(1)
6.8.5 Test of ANFIS for Real Data
471(2)
6.9 Coherent-Event-Preserving Random Noise Attenuation Using Wiener-ANFIS Filtering in Seismic Data Processing
473(7)
6.9.1 Literature Review
473(2)
6.9.2 Wiener-ANFIS Filtering
475(1)
6.9.3 Application to a Real Stacked Seismic Section
476(2)
6.9.4 Conclusions
478(2)
References
480(7)
Part IV Genetic Algorithm
7 Genetic Algorithm with Applications in Geophysics
487
7.1 Introduction
487(3)
7.2 Optimization
490(2)
7.3 Genetic Algorithm
492(3)
7.3.1 Model Representation
492(2)
7.3.2 Model Selection
494(1)
7.3.3 Crossover and Mutation
494(1)
7.4 Applications
495(36)
7.4.1 Multi-scale GA for Trans-Dimensional Inversion
495(1)
7.4.2 Multi-objective Optimization
496(23)
7.4.3 The Future of Multi-objective Optimization in Geophysics
519(12)
References
531
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