02871cam a2200277 i 4500008004100000020001800041040000800059050002200067100002700089245016600116250001100282260005200293300001100345505001700356520178100373650003302154650004502187650005402232650003102286650003402317700003602351900001502387942001302402999001902415952015902434121116s2013    enk      b    001 0 eng    a9781107011908  aSDU00aQE43bS474 G 20131 aSen, Mrinal K.921085010aGlobal optimization methods in geophysical inversion /cMrinal K. Sen, University of Texas, Austin, USA and Paul L. Stoffa, The University of Texas, Austin, USA.  a2nd ed  aNew York : bCambridge University Press, c2013  a289 p.  aHKBU Library  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. "-- 0aGeological modeling.9210851 0aGeophysicsxMathematical models.9210852 0aInverse problems (Differential equations)9205854 0aMathematical optimization. 7aSCIENCE / Geophysics.92108531 aStoffa, Paul L.,d1948-9210854  a = C.1 SDU  cGBE2lcc  c109368d109368  00102lcc406QE0043 S474 G2013708CGB9283816aSDUbSDUcGEN3d2025-10-03e2l0oQE43 S474 G 2013p1000377562r2025-10-03 00:00:00tC.1w2025-10-03yGBE