000			03376nam a2200397Ki 4500
001			ocn827944810
003			OCoLC
005			20240726105320.0
008			130218s2013 enk ob 001 0 eng d
040			_aNT _beng _erda _cNT
020			_a9781139625098 _q((electronic)l(electronic)ctronic)l((electronic)l(electronic)ctronic)ctronic bk.
050	0	4	_aQE43 _b.G563 2013
049			_aNTA
100	1		_aSen, Mrinal K. _e1
245	1	0	_aGlobal optimization methods in geophysical inversionMrinal K. Sen, Paul L. Stoffa, The University of Texas at Austin, Institute for Geophysics, J.J. Pickle Research Campus.
250			_asecond edition.
260			_aCambridge : _bCambridge University Press, _c(c)2013.
300			_a1 online resource (pages cm.)
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_adata file _2rda
520	0		_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.
504			_a2
530			_a2 _ub
650		0	_aGeological modeling.
650		0	_aGeophysics _xMathematical models.
650		0	_aInverse problems (Differential equations)
650		0	_aMathematical optimization.
655		1	_aElectronic Books.
700	1		_aStoffa, Paul L., _d1948-
856	4	0	_uhttps://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=508363&site=eds-live&custid=s3260518 _zClick to access digital title | log in using your CIU ID number and my.ciu.edu password
942			_cOB _D _eEB _hQE _m2013 _QOL _R _x _8NFIC _2LOC
994			_a02 _bNT
999			_c96268 _d96268
902			_a1 _bCynthia Snell _c1 _dCynthia Snell