Global optimization methods in geophysical inversion:
"Making inferences about systems in the Earth's subsurface from remotely-sensed, sparse measurements is a challenging task. Geophysical inversion aims to find models which explain geophysical observations - a model-based inversion method attempts to infer model parameters by iteratively fi...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
2013
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| Ausgabe: | 2. ed. |
| Schlagworte: | |
| Online-Zugang: | DE-91 Volltext |
| Zusammenfassung: | "Making inferences about systems in the Earth's subsurface from remotely-sensed, sparse measurements is a challenging task. Geophysical inversion aims to find models which explain geophysical observations - a model-based inversion method attempts to infer model parameters by iteratively fitting observations with theoretical predictions from trial models. Global optimization often enables the solution of non-linear models, employing a global search approach to find the absolute minimum of an objective function, so that predicted data best fits the observations. This new edition provides an up-to-date overview of the most popular global optimization methods, including a detailed description of the theoretical development underlying each method, and a thorough explanation of the design, implementation, and limitations of algorithms. A new chapter provides details of recently-developed methods, such as the neighborhood algorithm, and particle swarm optimization. An expanded chapter on uncertainty estimation includes a succinct description on how to use optimization methods for model space exploration to characterize uncertainty, and now discusses other new methods such as hybrid Monte Carlo and multi-chain MCMC methods. Other chapters include new examples of applications, from uncertainty in climate modeling to whole earth studies. Several different examples of geophysical inversion, including joint inversion of disparate geophysical datasets, are provided to help readers design algorithms for their own applications. This is an authoritative and valuable text for researchers and graduate students in geophysics, inverse theory, and exploration geoscience, and an important resource for professionals working in engineering and petroleum exploration. ".. |
| Beschreibung: | Includes bibliographical references (pages 268-278) and index |
| Beschreibung: | 1 Online-Ressource (xii, 289 pages) |
| ISBN: | 9780511997570 |
| DOI: | 10.1017/CBO9780511997570 |
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| 520 | |a "Making inferences about systems in the Earth's subsurface from remotely-sensed, sparse measurements is a challenging task. Geophysical inversion aims to find models which explain geophysical observations - a model-based inversion method attempts to infer model parameters by iteratively fitting observations with theoretical predictions from trial models. Global optimization often enables the solution of non-linear models, employing a global search approach to find the absolute minimum of an objective function, so that predicted data best fits the observations. This new edition provides an up-to-date overview of the most popular global optimization methods, including a detailed description of the theoretical development underlying each method, and a thorough explanation of the design, implementation, and limitations of algorithms. A new chapter provides details of recently-developed methods, such as the neighborhood algorithm, and particle swarm optimization. An expanded chapter on uncertainty estimation includes a succinct description on how to use optimization methods for model space exploration to characterize uncertainty, and now discusses other new methods such as hybrid Monte Carlo and multi-chain MCMC methods. Other chapters include new examples of applications, from uncertainty in climate modeling to whole earth studies. Several different examples of geophysical inversion, including joint inversion of disparate geophysical datasets, are provided to help readers design algorithms for their own applications. This is an authoritative and valuable text for researchers and graduate students in geophysics, inverse theory, and exploration geoscience, and an important resource for professionals working in engineering and petroleum exploration. ".. | ||
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| language | English |
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| spelling | Sen, Mrinal 1923-2018 Verfasser (DE-588)119015250 aut Global optimization methods in geophysical inversion Mrinal K. Sen ; Paul L. Stoffa 2. ed. 2013 1 Online-Ressource (xii, 289 pages) txt rdacontent c rdamedia cr rdacarrier Includes bibliographical references (pages 268-278) and index "Making inferences about systems in the Earth's subsurface from remotely-sensed, sparse measurements is a challenging task. Geophysical inversion aims to find models which explain geophysical observations - a model-based inversion method attempts to infer model parameters by iteratively fitting observations with theoretical predictions from trial models. Global optimization often enables the solution of non-linear models, employing a global search approach to find the absolute minimum of an objective function, so that predicted data best fits the observations. This new edition provides an up-to-date overview of the most popular global optimization methods, including a detailed description of the theoretical development underlying each method, and a thorough explanation of the design, implementation, and limitations of algorithms. A new chapter provides details of recently-developed methods, such as the neighborhood algorithm, and particle swarm optimization. An expanded chapter on uncertainty estimation includes a succinct description on how to use optimization methods for model space exploration to characterize uncertainty, and now discusses other new methods such as hybrid Monte Carlo and multi-chain MCMC methods. Other chapters include new examples of applications, from uncertainty in climate modeling to whole earth studies. Several different examples of geophysical inversion, including joint inversion of disparate geophysical datasets, are provided to help readers design algorithms for their own applications. This is an authoritative and valuable text for researchers and graduate students in geophysics, inverse theory, and exploration geoscience, and an important resource for professionals working in engineering and petroleum exploration. ".. Mathematisches Modell Geological modeling Geophysics Mathematical models Inverse problems (Differential equations) Mathematical optimization SCIENCE / Geophysics Inverse Methode (DE-588)4162226-1 gnd rswk-swf Inversion Mathematik (DE-588)4162235-2 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Daten (DE-588)4135391-2 gnd rswk-swf Inversionsalgorithmus (DE-588)4162237-6 gnd rswk-swf Inverses Problem (DE-588)4125161-1 gnd rswk-swf Inversion Geologie (DE-588)4393651-9 gnd rswk-swf Optimierung (DE-588)4043664-0 gnd rswk-swf Geophysik (DE-588)4020252-5 gnd rswk-swf Datenauswertung (DE-588)4131193-0 gnd rswk-swf Geophysik (DE-588)4020252-5 s Daten (DE-588)4135391-2 s Inversion Mathematik (DE-588)4162235-2 s Datenauswertung (DE-588)4131193-0 s Inverse Methode (DE-588)4162226-1 s Inverses Problem (DE-588)4125161-1 s 1\p DE-604 Datenanalyse (DE-588)4123037-1 s Inversion Geologie (DE-588)4393651-9 s Inversionsalgorithmus (DE-588)4162237-6 s Optimierung (DE-588)4043664-0 s 2\p DE-604 Stoffa, Paul L. 1948- Verfasser (DE-588)1041635044 aut https://doi.org/10.1017/CBO9780511997570 Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Sen, Mrinal 1923-2018 Stoffa, Paul L. 1948- Global optimization methods in geophysical inversion Mathematisches Modell Geological modeling Geophysics Mathematical models Inverse problems (Differential equations) Mathematical optimization SCIENCE / Geophysics Inverse Methode (DE-588)4162226-1 gnd Inversion Mathematik (DE-588)4162235-2 gnd Datenanalyse (DE-588)4123037-1 gnd Daten (DE-588)4135391-2 gnd Inversionsalgorithmus (DE-588)4162237-6 gnd Inverses Problem (DE-588)4125161-1 gnd Inversion Geologie (DE-588)4393651-9 gnd Optimierung (DE-588)4043664-0 gnd Geophysik (DE-588)4020252-5 gnd Datenauswertung (DE-588)4131193-0 gnd |
| subject_GND | (DE-588)4162226-1 (DE-588)4162235-2 (DE-588)4123037-1 (DE-588)4135391-2 (DE-588)4162237-6 (DE-588)4125161-1 (DE-588)4393651-9 (DE-588)4043664-0 (DE-588)4020252-5 (DE-588)4131193-0 |
| title | Global optimization methods in geophysical inversion |
| title_auth | Global optimization methods in geophysical inversion |
| title_exact_search | Global optimization methods in geophysical inversion |
| title_full | Global optimization methods in geophysical inversion Mrinal K. Sen ; Paul L. Stoffa |
| title_fullStr | Global optimization methods in geophysical inversion Mrinal K. Sen ; Paul L. Stoffa |
| title_full_unstemmed | Global optimization methods in geophysical inversion Mrinal K. Sen ; Paul L. Stoffa |
| title_short | Global optimization methods in geophysical inversion |
| title_sort | global optimization methods in geophysical inversion |
| topic | Mathematisches Modell Geological modeling Geophysics Mathematical models Inverse problems (Differential equations) Mathematical optimization SCIENCE / Geophysics Inverse Methode (DE-588)4162226-1 gnd Inversion Mathematik (DE-588)4162235-2 gnd Datenanalyse (DE-588)4123037-1 gnd Daten (DE-588)4135391-2 gnd Inversionsalgorithmus (DE-588)4162237-6 gnd Inverses Problem (DE-588)4125161-1 gnd Inversion Geologie (DE-588)4393651-9 gnd Optimierung (DE-588)4043664-0 gnd Geophysik (DE-588)4020252-5 gnd Datenauswertung (DE-588)4131193-0 gnd |
| topic_facet | Mathematisches Modell Geological modeling Geophysics Mathematical models Inverse problems (Differential equations) Mathematical optimization SCIENCE / Geophysics Inverse Methode Inversion Mathematik Datenanalyse Daten Inversionsalgorithmus Inverses Problem Inversion Geologie Optimierung Geophysik Datenauswertung |
| url | https://doi.org/10.1017/CBO9780511997570 |
| work_keys_str_mv | AT senmrinal globaloptimizationmethodsingeophysicalinversion AT stoffapaull globaloptimizationmethodsingeophysicalinversion |