Global optimization methods in geophysical inversion /
- 翻译题名：地球物理反演问题中的全球最优化方法（第2版）.
- 出版日期：2013.
- 出版者：Cambridge University Press,
- 页数：xii, 289 p. :
- 出版地：Cambridge :
- 第一责任说明：Mrinal K. Sen and Paul L. Stoffa.
- 尺寸：26 cm.
- 分类号：a630
- ISBN：978-1-107-01190-8(hdk.) :
MARC全文
02h0036937 20130806092752.0 130715s2013 enka frb |001|||eng | 978-1-107-01190-8(hdk.) : CNY750.00 NGL NGL NGL QE43 .S46 2013 550/.1/13 20 a630 aP3-05 Sen, Mrinal K. 地球物理反演问题中的全球最优化方法（第2版）. chi Global optimization methods in geophysical inversion / Mrinal K. Sen and Paul L. Stoffa. 2nd ed. Cambridge : Cambridge University Press, 2013. xii, 289 p. : ill. ; 26 cm. Includes bibliographic reference 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. "-- cProvided by publisher. Geological modeling. ; Geophysics ; Inverse problems (Differential equations) ; Mathematical optimization. Mathematical models. aStoffa, Paul L., ; d1948- aCN b010001 NGL 630 Se5/2 gljx1303 h1 ; rCNY750.00