| 000 | 02445cam a2200289zu 4500 | ||
|---|---|---|---|
| 001 | 88966122 | ||
| 003 | FRCYB88966122 | ||
| 005 | 20250429184346.0 | ||
| 006 | m o d | ||
| 007 | cr un | ||
| 008 | 250429s2022 fr | o|||||0|0|||eng d | ||
| 020 | _a9780128222959 | ||
| 035 | _aFRCYB88966122 | ||
| 040 |
_aFR-PaCSA _ben _c _erda |
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| 100 | 1 | _aBhattacharya, Shuvajit | |
| 245 | 0 | 1 |
_aAdvances in Subsurface Data Analytics _c['Bhattacharya, Shuvajit', 'Di, Haibin'] |
| 264 | 1 |
_bElsevier Science _c2022 |
|
| 300 | _a p. | ||
| 336 |
_btxt _2rdacontent |
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| 337 |
_bc _2rdamdedia |
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| 338 |
_bc _2rdacarrier |
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| 650 | 0 | _a | |
| 700 | 0 | _aBhattacharya, Shuvajit | |
| 700 | 0 | _aDi, Haibin | |
| 856 | 4 | 0 |
_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88966122 _qtext/html _a |
| 520 | _aAdvances in Subsurface Data Analytics: Traditional and Physics-Based Approaches brings together the fundamentals of popular and emerging machine learning (ML) algorithms with their applications in subsurface analysis, including geology, geophysics, petrophysics, and reservoir engineering. The book is divided into four parts: traditional ML, deep learning, physics-based ML, and new directions, with an increasing level of diversity and complexity of topics. Each chapter focuses on one ML algorithm with a detailed workflow for a specific application in geosciences. Some chapters also compare the results from an algorithm with others to better equip the readers with different strategies to implement automated workflows for subsurface analysis. Advances in Subsurface Data Analytics: Traditional and Physics-Based Approaches will help researchers in academia and professional geoscientists working on the subsurface-related problems (oil and gas, geothermal, carbon sequestration, and seismology) at different scales to understand and appreciate current trends in ML approaches, their applications, advances and limitations, and future potential in geosciences by bringing together several contributions in a single volume. - Covers fundamentals of simple machine learning and deep learning algorithms, and physics-based approaches written by practitioners in academia and industry - Presents detailed case studies of individual machine learning algorithms and optimal strategies in subsurface characterization around the world - Offers an analysis of future trends in machine learning in geosciences | ||
| 999 |
_c1331552 _d1331552 |
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