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|2 23
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| 100 |
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|a Misra, Siddharth,
|e author.
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|a Machine learning for subsurface characterization /
|c Siddharth Misra, Hao Li, Jiabo He.
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| 250 |
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|a First edition.
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| 264 |
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|a San Diego :
|b Gulf Professional Publishing, an imprint of Elsevier,
|c 2019.
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| 300 |
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|a 1 online resource ( 442 pages)
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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|a online resource
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|2 rdacarrier
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| 504 |
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|a Includes bibliographical references and index.
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|a Front Cover; Machine Learning for Subsurface Characterization; Copyright; Dedication; Contents; Contributors; Preface; Machine learning-driven success stories; Challenges and precautions when using machine learning; Recommendations for harnessing the power of machine learning; Concluding remarks; References; Further reading; Acknowledgment; Chapter 1: Unsupervised outlier detection techniques for well logs and geophysical data; 1. Introduction; 1.1. Basic terminologies in machine learning and data-driven models; 1.2. Types of machine learning techniques; 1.3. Types of outliers
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|a 2. Outlier detection techniques3. Unsupervised outlier detection techniques; 3.1. Isolation forest; 3.2. One-class SVM; 3.3. DBSCAN; 3.4. Local outlier factor; 3.5. Influence of hyperparameters on the unsupervised ODTs; 4. Comparative study of unsupervised outlier detection methods on well logs; 4.1. Description of the dataset used for the comparative study of unsupervised ODTs; 4.2. Data preprocessing; 4.2.1. Feature transformation: Convert R to log(R); 4.2.2. Feature scaling: Use of robust scaler; 4.3. Validation dataset; 4.3.1. Dataset #1: Containing noisy measurements
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|a 4.3.2. Dataset #2: Containing bad holes4.3.3. Dataset #3: Containing shaly layers and bad holes with noisy measurements; 4.3.4. Dataset #4: Containing manually labeled outliers; 4.4. Metrics/scores for the assessment of the performances of unsupervised ODTs on the conventional logs; 4.4.1. Recall; 4.4.2. Specificity; 4.4.3. Balanced accuracy score; 4.4.4. Precision; 4.4.5. F1 score; 4.4.6. Receiver operating characteristics (ROC) curve and ROC-AUC score; 4.4.7. Precision-recall (PR) curve and PR-AUC score; 5. Performance of unsupervised ODTs on the four validation datasets
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|a 5.1. Performance on Dataset #1 containing noisy measurements5.2. Performance on Dataset #2 containing measurements affected by bad holes; 5.3. Performance on Dataset #3 containing shaly layers and bad holes with noisy measurements; 5.4. Performance on Dataset #4 containing manually labeled outliers; 6. Conclusions; Appendix A. Popular methods for outlier detection; Appendix B. Confusion matrix to quantify the inlier and outlier detections by the unsupervised ODTs; Appendix C. Values of important hyperparameters of the unsupervised ODT models
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|a Appendix D. Receiver operating characteristics (ROC) and precision-recall (PR) curves for various unsupervised ODTs on th ...Acknowledgments; References; Chapter 2: Unsupervised clustering methods for noninvasive characterization of fracture-induced geomechanical alterations; 1. Introduction; 2. Objective of this study; 3. Laboratory setup and measurements; 4. Clustering methods for the proposed noninvasive visualization of geomechanical alterations; 4.1. K-means clustering; 4.2. Agglomerative clustering; 4.3. DBSCAN
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|a 5. Features/attributes for the proposed noninvasive visualization of geomechanical alteration.
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|a Description based upon print version of record.
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|a Electronic resource.
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|a Geophysics
|x Data processing.
|0 http://id.loc.gov/authorities/subjects/sh2002005961
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|a Machine learning
|x Industrial applications.
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| 650 |
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|a Big data.
|0 http://id.loc.gov/authorities/subjects/sh2012003227
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|a Petroleum engineering
|x Data processing.
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|a Electronic books.
|2 local
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| 700 |
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|a Li, Hao,
|e author.
|0 http://id.loc.gov/authorities/names/n85114463
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| 700 |
1 |
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|a He, Jiabo,
|e author.
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| 710 |
2 |
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|a EBSCOhost.
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| 776 |
1 |
8 |
|i Print version:
|a Misra, Siddharth
|t Machine Learning for Subsurface Characterization
|d San Diego : Elsevier Science & Technology,c2019
|z 9780128177365
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| 856 |
4 |
0 |
|u http://proxy.library.tamu.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=1951550
|z Connect to the full text of this electronic book
|t 0
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| 880 |
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|6 520-00/$1
|a Machine Learning for Subsurface Characterization develops and applies neural networks, random forests, deep learning, unsupervised learning, Bayesian frameworks, and clustering methods for subsurface characterization. Machine learning (ML) focusses on developing computational methods/algorithms that learn to recognize patterns and quantify functional relationships by processing large data sets, also referred to as the "big data." Deep learning (DL) is a subset of machine learning that processes "big data" to construct numerous layers of abstraction to accomplish the learning task. DL methods do not require the manual step of extracting/engineering features; however, it requires us to provide large amounts of data along with high-performance computing to obtain reliable results in a timely manner. This reference helps the engineers, geophysicists, and geoscientists get familiar with data science and analytics terminology relevant to subsurface characterization and demonstrates the use of data-driven methods for outlier detection, geomechanical/electromagnetic characterization, image analysis, fluid saturation estimation, and pore-scale characterization in the subsurface. Learn from 13 practical case studies using field, laboratory, and simulation dataBecome knowledgeable with data science and analytics terminology relevant to subsurface characterization Learn frameworks, concepts, and methods important for the engineer’s and geoscientist’s toolbox needed to support
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|a Texas A&M University
|b College Station
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|h Library of Congress classification
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