Machine Learning for Subsurface Characterization (notice n° 1330479)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02373cam a2200301zu 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | FRCYB88965550 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250429183924.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250429s2019 fr | o|||||0|0|||eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9780128177365 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | FRCYB88965550 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | FR-PaCSA |
| Language of cataloging | en |
| Transcribing agency | |
| Description conventions | rda |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Misra, Siddharth |
| 245 01 - TITLE STATEMENT | |
| Title | Machine Learning for Subsurface Characterization |
| Statement of responsibility, etc. | ['Misra, Siddharth', 'Li, Hao', 'He, Jiabo'] |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Name of producer, publisher, distributor, manufacturer | Gulf Professional Publishing |
| Date of production, publication, distribution, manufacture, or copyright notice | 2019 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | p. |
| 336 ## - CONTENT TYPE | |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type code | c |
| Source | rdamdedia |
| 338 ## - CARRIER TYPE | |
| Carrier type code | c |
| Source | rdacarrier |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | 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 data - Become 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 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Misra, Siddharth |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Li, Hao |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | He, Jiabo |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Access method | Cyberlibris |
| Uniform Resource Identifier | <a href="https://international.scholarvox.com/netsen/book/88965550">https://international.scholarvox.com/netsen/book/88965550</a> |
| Electronic format type | text/html |
| Host name | |
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