000			03358nam a2200433 a 4500
001			EBC1099853
003			MiAaPQ
005			20240120134309.0
006			m o d |
007			cr cn|||||||||
008			121116s2013 enka sb 001 0 eng d
010			_z 2012033212
020			_z9781107011908 (hardback)
020			_a9781139612074 (electronic bk.)
035			_a(MiAaPQ)EBC1099853
035			_a(Au-PeEL)EBL1099853
035			_a(CaPaEBR)ebr10659320
035			_a(CaONFJC)MIL457014
035			_a(OCoLC)827944810
040			_aMiAaPQ _cMiAaPQ _dMiAaPQ
050		4	_aQE43 _b.S46 2013
082	0	4	_a550.1/515357 _223
100	1		_aSen, Mrinal K.
245	1	0	_aGlobal optimization methods in geophysical inversion _h[electronic resource] / _cMrinal K. Sen and Paul L. Stoffa.
250			_a2nd ed.
260			_aCambridge : _bCambridge University Press, _c2013.
300			_axii, 289 p. : _bill.
504			_aIncludes bibliographical references and index.
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."-- _cProvided by publisher.
533			_aElectronic reproduction. Ann Arbor, MI : ProQuest, 2015. Available via World Wide Web. Access may be limited to ProQuest affiliated libraries.
650		0	_aGeological modeling.
650		0	_aGeophysics _xMathematical models.
650		0	_aInverse problems (Differential equations)
650		0	_aMathematical optimization.
655		4	_aElectronic books.
700	1		_aStoffa, Paul L., _d1948-
710	2		_aProQuest (Firm)
856	4	0	_uhttps://ebookcentral.proquest.com/lib/bacm-ebooks/detail.action?docID=1099853 _zClick to View
999			_c88851 _d88851