Data Mining and Knowledge Discovery for Geoscientists

1st Edition

Authors: Guangren Shi
Hardcover ISBN: 9780124104372
eBook ISBN: 9780124104754
Imprint: Elsevier
Published Date: 5th November 2013
Page Count: 376
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Description

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 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.

Key Features

  • 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

Readership

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.

Table of Contents

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

Details

No. of pages:
376
Language:
English
Copyright:
© Elsevier 2014
Published:
Imprint:
Elsevier
eBook ISBN:
9780124104754
Hardcover ISBN:
9780124104372

About the Author

Guangren Shi

Affiliations and Expertise

Research Institute of Petroleum Exploration and Development at PetroChina, Beijing, China

Reviews

"Shi introduces geological scientists to algorithms that are widely used for data mining and knowledge discovery, describes how they have been and could be applied in the geosciences, and surveys some successful applications. The algorithms fall into the categories of probability and statistics, artificial neural networks, support vector machines, decision trees, Bayesian classification, cluster analysis, the Kriging method, and fuzzy mathematics…"-ProtoView.com, February 2014