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Data Mining and Knowledge Discovery for Geoscientists
- 1st Edition - October 9, 2013
- Author: Guangren Shi
- Language: English
- Hardback ISBN:9 7 8 - 0 - 1 2 - 4 1 0 4 3 7 - 2
- eBook ISBN:9 7 8 - 0 - 1 2 - 4 1 0 4 7 5 - 4
Currently there are major challenges in data mining applications in the geosciences. This is due primarily to the fact that there is a wealth of available mining data amid an ab… Read more
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Request a sales quoteCurrently there are major challenges in data mining applications in the geosciences. This is due primarily to the fact that there is a wealth of available mining data amid an absence of the knowledge and expertise necessary to analyze and accurately interpret the same data. Most geoscientists have no practical knowledge or experience using data mining techniques. For the few that do, they typically lack expertise in using data mining software and in selecting the most appropriate algorithms for a given application. This leads to a paradoxical scenario of "rich data but poor knowledge".
The true solution is to apply data mining techniques in geosciences databases and to modify these techniques for practical applications. Authored by a global thought leader in data mining, Data Mining and Knowledge Discovery for Geoscientists addresses these challenges by summarizing the latest developments in geosciences data mining and arming scientists with the ability to apply key concepts to effectively analyze and interpret vast amounts of critical information.
- Focuses on 22 of data mining’s most practical algorithms and popular application samples
- Features 36 case studies and end-of-chapter exercises unique to the geosciences to underscore key data mining applications
- Presents a practical and integrated system of data mining and knowledge discovery for geoscientists
- Rigorous yet broadly accessible to geoscientists, engineers, researchers and programmers in data mining
- Introduces widely used algorithms, their basic principles and conditions of applications, diverse case studies, and suggests algorithms that may be suitable for specific applications
The primary audience includes researchers in data mining, and scientists and engineers in the geosciences. A secondary audience includes scientists and engineers in computer science and information technology, and graduate students taking related coursework.
Preface
Chapter 1. Introduction
Abstract
1.1 INTRODUCTION TO DATA MINING
1.2 Data Systems Usable by Data Mining
1.3 Commonly Used Regression and Classification Algorithms
1.4 Data Mining System
Exercises
References
Chapter 2. Probability and Statistics
Abstract
2.1 Probability
2.2 Statistics
Exercises
References
Chapter 3. Artificial Neural Networks
Abstract
3.1 Methodology
3.2 Case Study 1: Integrated Evaluation of Oil and Gas-Trap Quality
3.3 Case Study 2: Fractures Prediction Using Conventional Well-Logging Data
Exercises
References
Chapter 4. Support Vector Machines
Abstract
4.1 Methodology
4.2 Case Study 1: Gas Layer Classification Based on Porosity, Permeability, and Gas Saturation
4.3 Case Study 2: Oil Layer Classification Based on Well-Logging Interpretation
4.4 Dimension-Reduction Procedure Using Machine Learning
Exercises
References
Chapter 5. Decision Trees
Abstract
5.1 Methodology
5.2 Case Study 1: Top Coal Caving Classification (Twenty-Nine Learning Samples)
5.3 Case Study 2: Top Coal Caving Classification (Twenty-Six Learning Samples and Three Prediction Samples)
Exercises
References
Chapter 6. Bayesian Classification
Abstract
6.1 Methodology
6.2 Case Study 1: Reservoir Classification in the Fuxin Uplift
6.3 Case Study 2: Reservoir Classification in the Baibao Oilfield
6.4 Case Study 3: Oil Layer Classification Based on Well-Logging Interpretation
6.5 Case Study 4: Integrated Evaluation of Oil and Gas Trap Quality
6.6 Case Study 5: Coal-Gas-Outburst Classification
6.7 Case Study 6: Top Coal Caving Classification (Twenty-Six Learning Samples and Three Prediction Samples)
Exercises
References
Chapter 7. Cluster Analysis
Abstract
7.1 Methodology
7.2 Case Study 1: Integrated Evaluation of Oil and Gas Trap Quality
7.3 Case Study 2: Oil Layer Classification Based on Well-Logging Interpretation
7.4 Case Study 3: Coal-Gas-Outburst Classification
7.5 Case Study 4: Reservoir Classification in the Baibao Oilfield
Exercises
References
Chapter 8. Kriging
Abstract
8.1 Preprocessing
8.2 Experimental Variogram
8.3 Optimal Fitting of Experimental Variogram
8.4 Cross-Validation of Kriging
8.5 Applications of Kriging
8.6 Summary and Conclusions
Exercises
References
Chapter 9. Other Soft Computing Algorithms for Geosciences
Abstract
9.1 Fuzzy Mathematics
9.2 Gray Systems
9.3 Fractal Geometry
9.4 Linear Programming
Exercises
References
Chapter 10. A Practical Software System of Data Mining and Knowledge Discovery for Geosciences
Abstract
10.1 Typical Case Study 1: Oil Layer Classification in the Keshang Formation
10.2 Typical Case Study 2: Oil Layer Classification in the Lower H3 Formation
10.3 Typical Case Study 3: Oil Layer Classification in the Xiefengqiao Anticline
10.4 A Practical System of Data Mining and Knowledge Discovery for Geosciences
Exercises
References
Appendix 1. Table of Unit Conversion
Appendix 2. Answers to Exercises
Answers to Chapter 1 Exercises
Answers to Chapter 2 Exercises
Answers to Chapter 3 Exercises
Answers to Chapter 4 Exercises
Answers to Chapter 5 Exercises
Answers to Chapter 6 Exercises
Answers to Chapter 7 Exercises
Answers to Chapter 8 Exercises
Answers to Chapter 9 Exercises
Answers to Chapter 10 Exercises
Index
- No. of pages: 376
- Language: English
- Edition: 1
- Published: October 9, 2013
- Imprint: Elsevier
- Hardback ISBN: 9780124104372
- eBook ISBN: 9780124104754
GS
Guangren Shi
Affiliations and expertise
Research Institute of Petroleum Exploration and Development at PetroChina, Beijing, ChinaRead Data Mining and Knowledge Discovery for Geoscientists on ScienceDirect