Application of soft computing and intelligent methods in geophysics:
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| Main Authors: | , |
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| Format: | Book |
| Language: | English |
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Springer
[2018]
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| Series: | Springer geophysics
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xvii, 533 Seiten Illustrationen, Diagramme |
| ISBN: | 9783319665313 |
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| 245 | 1 | 0 | |a Application of soft computing and intelligent methods in geophysics |c Alireza Hajian, Peter Styles |
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| 650 | 4 | |a Geophysics/Geodesy | |
| 650 | 4 | |a Geotechnical Engineering & Applied Earth Sciences | |
| 650 | 4 | |a Mathematical Applications in the Physical Sciences | |
| 650 | 4 | |a Math Applications in Computer Science | |
| 650 | 4 | |a Artificial Intelligence (incl. Robotics) | |
| 650 | 4 | |a Earth sciences | |
| 650 | 4 | |a Geophysics | |
| 650 | 4 | |a Geotechnical engineering | |
| 650 | 4 | |a Computer science / Mathematics | |
| 650 | 4 | |a Artificial intelligence | |
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| _version_ | 1804178800353738752 |
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| adam_text | Contents
Part I Neural Networks
1 Artificial Neural Networks............................................... 3
1.1 Introduction.................................................... 3
1.2 A Brief Review of ANN Applications in Geophysics.................. 4
1.3 Natural Neural Networks........................................... 6
1.4 Definition of Artificial Neural Network (ANN)..................... 7
1.5 From Natural Neuron to a Mathematical Model of an Artificial
Neuron........................................................... 10
1.6 Classification into Two Groups as an Example..................... 16
1.7 Extracting the Delta-Rule as the Basis of Learning Algorithms ... 18
1.8 Momentum and Learning Rate....................................... 19
1.9 Statistical Indexes as a Measure of Learning Error............... 20
1.10 Feed-Forward Back-Propagation Neural Networks.................... 20
1.11 A Guidance Checklist for Step-by-Step Design of a Neural
Network........................................................ 24
1.12 Important Factors in Designing a MLP Neural Network.............. 24
1.12.1 Determining the Number of Hidden Layers.............. 25
1.12.2 Determination of the Number of Hidden Neurons........ 25
1.13 How Good Are Multi-layer Per Feed-Forward Networks?.............. 26
1.14 Under Training and Over Fitting.................................. 27
1.15 To Stop or not to Stop, that Is the Question!
(When Should Training Be Stopped?!).............................. 27
1.16 The Effect of the Number of Learning Samples..................... 28
1.17 The Effect of the Number of Hidden Units......................... 29
1.18 The Optimum Number of Hidden Neurons............................. 30
1.19 The Multi-start Approach......................................... 30
1.20 Test of a Trained Neural Network................................. 32
1.20.1 The Training Set....................................... 32
1.20.2 The Validation Set....................................... 32
ix
X
Contents
1.20.3 The Test Set......................................... 33
1.20.4 Random Partitioning.................................. 33
1.20.5 User-Defined Partitioning.............................. 33
1.20.6 Partition with Oversampling............................ 34
1.20.7 Data Partition to Test Neural Networks for Geophysical
Approaches............................................. 34
1.21 The General Procedure for Testing of a Designed Neural
Network in Geophysical Applications ......................... 35
1.22 Competitive Networks—The Kohonen Self-organising Map .... 36
1.22.1 Learning in Biological Systems—The Self-organising
Paradigm............................................... 37
1.22.2 The Architecture of the Kohonen Network................ 37
1.22.3 The Kohonen Network in Operation....................... 37
1.22.4 Derivation of the Learning Rule
for the Kohonen Net.................................... 39
1.22.5 Training the Kohonen Network........................... 39
1.22.6 Training Issues in Kohonen Neural Nets............... . 40
1.22.7 Application of the Kohonen Network in Speech
Processing—Kohonen’s Phonetic Typewrite................ 41
1.23 Hopfield Network............................................. 41
1.24 Generalized Regression Neural Network (GRNN)................... 43
1.24.1 GRNN Architecture...................................... 43
1.24.2 Algorithm for Training of a GRNN...................... 44
1.24.3 GRNN Compared to MLP................................... 45
1.25 Radial Basis Function (RBF) Neural Networks.................... 45
1.25.1 Radial Functions..................................... 45
1.25.2 RBF Neural Networks Architecture...................... 46
1.26 Modular Neural Networks ....................................... 48
1.27 Neural Network Design and Testing in MATLAB................... 50
References......................................................... 66
2 Prior Applications of Neural Networks in Geophysics.................. 71
2.1 Introduction................................................... 71
2.2 Application of Neural Networks in Gravity..................... 72
2.2.1 Depth Estimation of Buried Qanats Using a Hopfield
Network.............................................. 73
2.2.2 Depth Estimation of Salt Domes Using Gravity
Anomalies Through General Regression Neural
Networks............................................. 79
2.2.3 Simultaneous Estimation of Depth and Shape Factor
of Subsurface Cavities................................. 95
2.2.4 Modeling Anticlinal Structures Through Neural
Networks Using Residual Gravity Data.................. 105
xi
110
115
121
126
130
136
137
141
141
141
143
145
147
147
153
154
154
156
156
157
159
161
161
161
163
164
166
166
168
170
173
176
178
Application of ANN for Inversion of Self-potential
Anomalies....................................................
Application of ANN for Sea Level Prediction..................
Application of Neural Network for Mineral Prospectivity
Mapping......................................................
Application of NN for SP Inversion Using MLP.................
Determination of Facies from Well Logs
Using Modular Neural Networks................................
Estimation of Surface Settlement Due to Tunneling............
2.8.1 Introduction.........................................
2.8.2 The Finite Element Method in Plaxis Software.........
2.8.3 The Available Elements for Modeling..................
2.8.4 Soil and Rock Behavior Models........................
2.8.5 The Studied Route of the Mashhad Subway
Line 2 Project.......................................
2.8.6 Characteristics of the Tunnel........................
2.8.7 The Surface Settlement Measurement Operations........
2.8.8 Surface Settlement Prediction Using ANN..............
2.8.9 Surface Settlement Calculation Using FEM ............
2.8.10 Results..............................................
2.8.11 Conclusions..........................................
Comparison of Neural Networks for Predicting the Penetration
Rate of Different Models for Tunnel Boring
Machines (TBM)...............................................
2.9.1 Literature Review of the Prediction of the Penetration
Rate of TBM..........................................
2.9.2 Case Study of the Golab Tunnel.......................
2.9.3 Geomorphology........................................
2.9.4 The TBM Machine Used for the Golab Project...........
2.9.5 Data Collection......................................
2.9.6 A Static Model for Predicting the Penetration Rate
2.9.7 Input Parameters.....................................
2.9.8 ANN Topology.........................................
2.9.9 Testing and Validation of the ANN Model..............
Application of Neural Network Cascade Correlation Algorithm
for Picking Seismic First-Breaks.............................
2.10.1 The Improvement of CC Algorithm. . ..................
2.10.2 Attribute Extraction for Neural Network Training.....
Application of Neural Networks to Engineering Geodesy;
Predicting the Vertical Displacement of Structures...........
Attenuation of Random Seismic Noise Using Neural
Networks and Wavelet Package Analysis........................
2.12.1 Methodology..........................................
Lumems
2.12.2 Experimental Philosophy .............................. 182
2.12.3 Conclusion ........................................... 189
References......................................................... 193
Part II Fuzzy Logic
3 Fuzzy Logic.......................................................... 201
3.1 Introduction.................................................. 201
3.2 Motivation for Using Fuzzy Logic in Geophysics............... 202
3.2.1 First Viewpoint....................................... 202
3.2.2 The Second Viewpoint................................ 208
3.2.3 Geophysical Data Fusion Based on Fuzzy
Logic Rules........................................... 210
3.3 Fuzzy Sets.................................................. 210
3.3.1 The Concept of a Fuzzy Set............................ 211
3.3.2 Definition of a Fuzzy Set............................. 214
3.3.3 Different Types of Fuzzy Sets According to Their
Membership Functions.................................. 219
3.3.4 Connecting Classical Set Theory
to Fuzzy Set Theory................................... 232
3.4 Operations on Fuzzy Sets.................................... 235
3.4.1 Standard Union........................................ 235
3.4.2 Standard Intersection................................. 236
3.4.3 Standard Complement................................... 236
3.4.4 Applications of the Intersection of Fuzzy Set......... 239
3.4.5 Fuzzy Averaging Operations............................ 240
3.4.6 Matlab Codes for Fuzzy Operations..................... 241
3.4.7 Other Operations on Fuzzy Sets........................ 241
3.4.8 Cartesian Product..................................... 245
3.5 Fuzzy Relationships........................................... 246
3.5.1 Definition of Fuzzy Relationship...................... 246
3.5.2 Domain and Range of Fuzzy Relationship................ 248
3.5.3 Operations on Fuzzy Relationships..................... 249
3.5.4 Projection of Fuzzy Relationship and Cylindrical
Extension............................................. 250
3.5.5 Composition of Fuzzy Relations........................ 252
3.5.6 Matlab Coding for Fuzzy Relations..................... 256
3.5.7 Properties of Fuzzy Relations......................... 257
3.5.8 a-cut of a Fuzzy Relation............................ 259
3.5.9 a-cut of Equivalent Fuzzy Relationship............... 260
3.6 Fuzzy Numbers............................................. 261
3.6.1 Further Description of the Extension Principle........ 261
3.6.2 Generalized Extension Principle or Multi-variate
Extension Principle................................... 263
Contents
xm
3.6.3 Philosophy of Fuzzy Numbers............................ 264
3.6.4 Definition of a Fuzzy Number . ,......................... 264
3.6.5 LR Representation of Fuzzy Numbers....................... 266
3.6.6 Operations on LR Fuzzy Numbers .......................... 268
3.6.7 Triangular Fuzzy Numbers................................. 269
3.6.8 a-cut of Fuzzy Number.................................... 269
3.7 Definition of Some Basic Concepts of Fuzzy Sets................. 272
3.8 T-Norm........................................................... 275
3.9 S-Norm........................................................... 276
3.10 If-then Fuzzy Rules.............................................. 277
3.11 Fuzzy Statement.................................................. 277
3.12 Linguistic Variable.............................................. 277
3.13 Fuzzy Conditional Proposition (Fuzzy if-then Rule)............... 278
3.13.1 Definition with Example in Geophysics.................... 278
3.13.2 Interpretation of Fuzzy if-then Rule..................... 280
3.14 Approximate Reasoning............................................ 281
3.14.1 Fuzzy Inference.......................................... 281
3.14.2 Fuzzy Extended Exceptional Deduction Rule................ 283
3.15 Fuzzy Rules Base............................................... 286
3.15.1 Definition Assume F1? G1? 1=1, 2,..., N Are Fixed
Fuzzy Sets Over Set U then............................... 286
3.15.2 FATI Method.............................................. 286
3.15.3 FITA Method............................................. 287
3.16 Defuzzification.................................................. 287
3.16.1 Center of Gravity (Centroid of Area) Defuzzification . . . 287
3.16.2 Center of Sum Method................................... 289
3.16.3 Mean of Max Method....................................... 290
3.16.4 Height Method............................................ 290
3.16.5 Bisector Defuzzification................................. 291
3.16.6 Smallest of Maximum Defuzzification...................... 293
3.16.7 Largest of Maximum Defuzzification....................... 293
3.16.8 Weighted Average Defuzzification Method.................. 293
3.17 Fuzzifiers....................................................... 294
3.17.1 Singleton Fuzzifier...................................... 294
3.17.2 Triangular Fuzzifier..................................... 294
3.18 Fuzzy Modeling Using the Matlab Toolbox.......................... 295
3.18.1 Fuzzy Inference System (FIS) Editor...................... 296
3.18.2 Membership Function Editor............................... 296
3.18.3 Rule Editor.............................................. 297
3.18.4 Rule Viewer.............................................. 298
3.18.5 Surface Viewer........................................... 298
References........................................................... 299
XIV
Contents
4 Applications of Fuzzy Logic in Geophysics.......................... 301
4.1 Introduction.............................. .................... 301
4.2 Fuzzy Logic for Classification of Volcanic Activities......... 301
4.3 Fuzzy Logic for Integrated Mineral Exploration................ 302
4.4 Shape Factors and Depth Estimation of Microgravity Anomalies
via Combination of Artificial Neural Networks and Fuzzy Rules
Based System (FRBS)......................................... 310
4.4.1 Introduction............................................ 312
4.4.2 Extracting Suitable Fuzzy Sets and Fuzzy Rules
for Cavities Shape Estimation........................... 312
4.4.3 The Fuzzy Rule Based System (FRBS) for Depth
and Shape Estimation with Related
Membership Degree....................................... 319
4.4.4 Test of the Fuzzy Rule-Based Model
with Real Data........................................ 319
4.5 Application of Fuzzy Logic in Remote Sensing: Change
Detection Through Fuzzy Sets Using Multi Temporal
Landsal Thematic Mapper Data.................................... 320
4.5.1 Introduction.......................................... . 320
4.6 Fuzzy Transitive Closure Algorithm for the Analysis
of Geomagnetic Field Data....................................... 330
4.6.1 Classical and Fuzzy Clustering.......................... 330
4.6.2 Fuzzy Transitive Closure Method......................... 332
4.6.3 Fuzzy Equivalence Relations............................. 333
4.6.4 Fuzzy Transitive Closure Algorithm...................... 334
4.6.5 Application to for Geomagnetic Storm Data............... 334
4.7 Geophysical Data Fusion by Fuzzy Logic to Image Mechanical
Behavior of Mudslides........................................... 339
4.8 Automatic Fuzzy-Logic Recognition of Anomalous Activity
on Geophysical Log Records.................................... 348
4.8.1 Description of the Research............................. 348
4.8.2 Difference Recognition Algorithm
for Signals (DRAS)...................................... 350
4.8.3 Application of the DRAS Algorithm
to Observational Data................................... 355
4.9 Operational Earthquake Forecasting Using Linguistic Fuzzy
Rule-Based Models from Imprecise Data........................... 359
References............................................................ 367
Contents
xv
Part III Combination of Neural Networks and Fuzzy Logic
5 Neuro-fuzzy Systems................................................. 375
5.1 Hybrid Systems................................................ 375
5.1.1 Introduction........................................... 375
5.1.2 Cooperative Neuro-fuzzy Systems........................ 377
5.1.3 Concurrent Neuro-fuzzy Systems....................... 377
5.1.4 Hybrid Neuro-fuzzy Systems............................. 378
5.2 Neural Expert Systems.......................................... 380
5.2.1 The Inference Engine................................. 380
5.2.2 Approximate Reasoning.................................. 381
5.2.3 Rule Extraction........................................ 381
5.2.4 The Neural Knowledge Base.............................. 381
5.2.5 Multi-layer Knowledge Base............................. 383
5.3 Neuro-fuzzy Systems............................................ 383
5.3.1 Synergy of Neural and Fuzzy Systems.................... 384
5.3.2 Training of a Neuro-fuzzy System....................... 387
5.3.3 Good and Bad Rules from Expert Systems................. 388
5.4 Adaptive Neuro-fuzzy Inference System: ANFIS................... 389
5.4.1 Structure of ANFIS..................................... 389
5.4.2 Learning in the ANFIS Model............................ 392
5.4.3 Function Approximation Using the ANFIS Model......... 394
5.5 ANFIS Design and Testing Using the Matlab Fuzzy Logic
Toolbox........................................................ 395
5.5.1 Introduction........................................... 395
5.5.2 ANFIS Graphical User Interference...................... 397
References........................................................... 414
6 Application of Neuro-Fuzzy Systems in Geophysics................... 417
6.1 Depth Estimation of Cavities from Microgravity Data
Using Multi Adaptive Neuro Fuzzy Interference Systems........ 417
6.1.1 Why Use Neuro-Fuzzy Methods for Microgravity
Interpretation?........................................ 417
6.1.2 Multiple Adaptive Neuro Fuzzy Interference
SYSTEM (MANFIS)........................................ 418
6.1.3 Procedure of Gravity Interpretation Using MANFIS .... 420
6.1.4 Training Strategies and MANFIS Network
Architecture........................................... 421
6.1.5 Test of MANFIS in Present of Noise
and for Real Data.................................... . 426
XVI
Contents
6.2 Surface Settlement Prediction Using ANFIS
for a Metro Tunnel.............................................. 427
6.2.1 ANFIS Structure......................................... 427
6.2.2 ANFIS Training and Testing.............................. 429
6.2.3 Conclusion ............................................. 431
6.3 The Use of the ANFIS Method for the Characterization
of North Sea Reservoirs......................................... 432
6.3.1 Introduction............................................ 432
6.3.2 Literature Review..................................... 433
6.3.3 Geological Setting...................................... 433
6.3.4 Data Set................................................ 435
6.3.5 Preprocessing to Select the Most Suitable Attributes . . . 436
6.3.6 Reservoir Characterization Using ANFIS and PFE........ 441
6.4 Neuro-Fuzzy Approach for the Prediction of Longitudinal
Wave Velocity................................................... 441
6.4.1 Introduction............................................ 441
6.4.2 Training of the Neuro-Fuzzy Model....................... 442
6.4.3 ANFIS Testing........................................... 446
6.4.4 Conclusion.............................................. 446
6.5 Estimation of Electrical Earth Structure Using an Adaptive
Neuro-Fuzzy Inference System (Anfis)........................... 450
6.5.1 Introduction............................................ 450
6.5.2 Data Collection......................................... 451
6.5.3 ANFIS Training.......................................... 451
6.5.4 ANFIS Performance Validation Using Real Data........... 454
6.5.5 Conclusion.............................................. 457
6.6 Discrimination Between Quarry Blasts and Micro-earthquakes
Using Adaptive Neuro-Fuzzy Inference Systems.................... 457
6.6.1 Literature Review....................................... 457
6.6.2 Feature Selection....................................... 457
6.6.3 Spectral Characteristics................................ 458
6.6.4 Training and Test of ANFIS.............................. 460
6.7 Application of Neuro-Fuzzy Pattern Recognition Methods
in Borehole Geophysics.......................................... 461
6.7.1 Literature............................................ 461
6.7.2 Inputs-Output Structure of the Designed ANFIS .......... 462
6.7.3 Training of ANFIS....................................... 463
6.7.4 Training of ANFIS Performance........................... 463
6.7.5 Validation of ANFIS Performance......................... 464
6.7.6 Application of ANFIS Methods to Real Borehole
Geophysics Data......................................... 465
Contents
xvu
6 8 A Fuzzy Interference System for the Prediction of Earth
Rotation Parameters............................................ 466
6.8.1 Introduction........................................... 466
6.8.2 Prediction of Earth Rotation Parameters by ANFIS .... 468
6.8.3 Patterns for Polar Motion Components x and y.......... 468
6.8.4 Design of ANFIS Structure.............................. 470
6.8.5 Test of ANFIS for Real Data........................... 471
6.9 Coherent-Event-Preserving Random Noise Attenuation
Using Wiener-Aniis Filtering in Seismic Data Processing....... 473
6.9.1 Literature Review..................................... 473
6.9.2 Wiener-ANFIS Filtering................................. 475
6.9.3 Application to a Real Stacked Seismic Section.......... 476
6.9.4 Conclusions............................................ 478
References............................................................ 480
Part IV Genetic Algorithm
7 Genetic Algorithm with Applications in Geophysics.................. 487
7.1 Introduction................................................... 487
7.2 Optimization................................................... 490
7.3 Genetic Algorithm.............................................. 492
7.3.1 Model Representation................................... 492
7.3.2 Model Selection........................................ 494
7.3.3 Crossover and Mutation................................. 494
7.4 Applications .................................................. 495
7.4.1 Multi-scale GA for Trans-Dimensional Inversion........ 495
7.4.2 Multi-objective Optimization........................... 496
7.4.3 The Future of Multi-objective Optimization
in Geophysics.......................................... 519
References............................................................ 531
|
| any_adam_object | 1 |
| author | Hajian, Alireza Styles, Peter |
| author_GND | (DE-588)1031212388 |
| author_facet | Hajian, Alireza Styles, Peter |
| author_role | aut aut |
| author_sort | Hajian, Alireza |
| author_variant | a h ah p s ps |
| building | Verbundindex |
| bvnumber | BV045136499 |
| classification_rvk | RB 10104 |
| ctrlnum | (OCoLC)1047873321 (DE-599)BVBBV045136499 |
| dewey-full | 550 526.1 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 550 - Earth sciences 526 - Mathematical geography |
| dewey-raw | 550 526.1 |
| dewey-search | 550 526.1 |
| dewey-sort | 3550 |
| dewey-tens | 550 - Earth sciences 520 - Astronomy and allied sciences |
| discipline | Geologie / Paläontologie Physik Geographie |
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| id | DE-604.BV045136499 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T08:09:43Z |
| institution | BVB |
| isbn | 9783319665313 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030526400 |
| oclc_num | 1047873321 |
| open_access_boolean | |
| owner | DE-384 DE-703 |
| owner_facet | DE-384 DE-703 |
| physical | xvii, 533 Seiten Illustrationen, Diagramme |
| publishDate | 2018 |
| publishDateSearch | 2018 |
| publishDateSort | 2018 |
| publisher | Springer |
| record_format | marc |
| series2 | Springer geophysics |
| spelling | Hajian, Alireza Verfasser aut Application of soft computing and intelligent methods in geophysics Alireza Hajian, Peter Styles Cham Springer [2018] xvii, 533 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Springer geophysics Earth Sciences Geophysics/Geodesy Geotechnical Engineering & Applied Earth Sciences Mathematical Applications in the Physical Sciences Math Applications in Computer Science Artificial Intelligence (incl. Robotics) Earth sciences Geophysics Geotechnical engineering Computer science / Mathematics Artificial intelligence Mathematical physics Datenanalyse (DE-588)4123037-1 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Geophysik (DE-588)4020252-5 gnd rswk-swf Geophysik (DE-588)4020252-5 s Datenanalyse (DE-588)4123037-1 s Künstliche Intelligenz (DE-588)4033447-8 s DE-604 Styles, Peter Verfasser (DE-588)1031212388 aut Erscheint auch als Online-Ausgabe 978-3-319-66532-0 Digitalisierung UB Augsburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030526400&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Hajian, Alireza Styles, Peter Application of soft computing and intelligent methods in geophysics Earth Sciences Geophysics/Geodesy Geotechnical Engineering & Applied Earth Sciences Mathematical Applications in the Physical Sciences Math Applications in Computer Science Artificial Intelligence (incl. Robotics) Earth sciences Geophysics Geotechnical engineering Computer science / Mathematics Artificial intelligence Mathematical physics Datenanalyse (DE-588)4123037-1 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Geophysik (DE-588)4020252-5 gnd |
| subject_GND | (DE-588)4123037-1 (DE-588)4033447-8 (DE-588)4020252-5 |
| title | Application of soft computing and intelligent methods in geophysics |
| title_auth | Application of soft computing and intelligent methods in geophysics |
| title_exact_search | Application of soft computing and intelligent methods in geophysics |
| title_full | Application of soft computing and intelligent methods in geophysics Alireza Hajian, Peter Styles |
| title_fullStr | Application of soft computing and intelligent methods in geophysics Alireza Hajian, Peter Styles |
| title_full_unstemmed | Application of soft computing and intelligent methods in geophysics Alireza Hajian, Peter Styles |
| title_short | Application of soft computing and intelligent methods in geophysics |
| title_sort | application of soft computing and intelligent methods in geophysics |
| topic | Earth Sciences Geophysics/Geodesy Geotechnical Engineering & Applied Earth Sciences Mathematical Applications in the Physical Sciences Math Applications in Computer Science Artificial Intelligence (incl. Robotics) Earth sciences Geophysics Geotechnical engineering Computer science / Mathematics Artificial intelligence Mathematical physics Datenanalyse (DE-588)4123037-1 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Geophysik (DE-588)4020252-5 gnd |
| topic_facet | Earth Sciences Geophysics/Geodesy Geotechnical Engineering & Applied Earth Sciences Mathematical Applications in the Physical Sciences Math Applications in Computer Science Artificial Intelligence (incl. Robotics) Earth sciences Geophysics Geotechnical engineering Computer science / Mathematics Artificial intelligence Mathematical physics Datenanalyse Künstliche Intelligenz Geophysik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030526400&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT hajianalireza applicationofsoftcomputingandintelligentmethodsingeophysics AT stylespeter applicationofsoftcomputingandintelligentmethodsingeophysics |