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Data Mining and Knowledge Discovery for Geoscientists
Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Publisher: Amsterdam ; Boston : Elsevier, 2013Description: Online-Ressource (377 p)ISBN:- 9780124104372
- 9781306120005
- 1306120004
- 9780124104754
- 006.3/12 006.312
- 006.3/12 23
- 006.312
- QE48.8 .S54 2014
- QE48.8
Contents:
Summary: 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"". TPPN: PPN: 772611521Package identifier: Produktsigel: ZDB-30-PAD | ZDB-30-PQE | ZDB-26-MYL
Front Cover; Data Mining and Knowledge Discovery for Geoscientists; Copyright; Contents; Preface; Chapter 1 - Introduction; 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; 2.1 PROBABILITY; 2.2 STATISTICS; EXERCISES; References; Chapter 3 - Artificial Neural Networks; 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 DATAEXERCISES; References; Chapter 4 - Support Vector Machines; 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; 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; 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; 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; 8.1 PREPROCESSING; 8.2 EXPERIMENTAL VARIOGRAM; 8.3 OPTIMAL FITTING OF EXPERIMENTAL VARIOGRAM
8.4 CROSS-VALIDATION OF KRIGING8.5 APPLICATIONS OF KRIGING; 8.6 SUMMARY AND CONCLUSIONS; EXERCISES; References; Chapter 9 - Other Soft Computing Algorithms for Geosciences; 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; 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
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