03422nam a2200433 a 4500001001100000003000700011005001700018006001900035007001500054008004100069010001700110020002900127020003500156035002300191035002400214035002500238035002300263035002100286040002700307050002000334082002100354100002700375245011700402250001200519260005200531300002400583504005100607520180400658533014702462650003302609650004502642650005402687650003802741655002902779700003602808710002802844856009702872999001902969EBC1099853MiAaPQ20250821123344.0m     o  d |      cr cn|||||||||121116s2013    enka    sb    001 0 eng d  z  2012033212  z9781107011908 (hardback)  a9781139612074 (electronic bk.)  a(MiAaPQ)EBC1099853  a(Au-PeEL)EBL1099853  a(CaPaEBR)ebr10659320  a(CaONFJC)MIL457014  a(OCoLC)827944810  aMiAaPQcMiAaPQdMiAaPQ 4aQE43b.S46 201304a550.1/5153572231 aSen, Mrinal K.947439910aGlobal optimization methods in geophysical inversionh[electronic resource] /cMrinal K. Sen and Paul L. Stoffa.  a2nd ed.  aCambridge :bCambridge University Press,c2013.  axii, 289 p. :bill.  aIncludes bibliographical references and index.  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.  aElectronic reproduction. Ann Arbor, MI : ProQuest, 2015. Available via World Wide Web. Access may be limited to ProQuest affiliated libraries. 0aGeological modeling.9474400 0aGeophysicsxMathematical models.9474401 0aInverse problems (Differential equations)9474402 0aMathematical optimization.936731 4aElectronic books.9351421 aStoffa, Paul L.,d1948-94744032 aProQuest (Firm)947440440uhttps://ebookcentral.proquest.com/lib/kuct-ebooks/detail.action?docID=1099853zClick to View  c295633d295633