Global Optimization Methods in Geophysical Inversion - download pdf or read online

By Mrinal Sen and Paul L. Stoffa (Eds.)

ISBN-10: 0444817670

ISBN-13: 9780444817679

One of many significant pursuits of geophysical inversion is to discover earth versions that designate the geophysical observations. therefore the department of arithmetic referred to as optimization has stumbled on major use in lots of geophysical applications.Both neighborhood and international optimization equipment are utilized in the estimation of fabric houses from geophysical facts. because the name of the e-book indicates, the purpose of this publication is to explain the appliance of a number of lately built worldwide optimization easy methods to geophysical difficulties. • the well-known linear and gradient dependent optimization equipment were summarized that allows you to clarify their merits and boundaries• the idea of simulated annealing and genetic algorithms were defined in adequate aspect for the readers to appreciate the underlying primary ideas upon which those algorithms are established• The algorithms were defined utilizing easy circulate charts (the algorithms are basic and will be utilized to a large choice of problemsStudents, researchers and practitioners should be capable of layout sensible algorithms to unravel their particular geophysical inversion difficulties. The e-book is nearly self-contained in order that there are not any necessities, aside from a basic mathematical heritage that features a uncomplicated realizing of linear algebra and calculus.

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Therefore, for stable estimates of model parameters, this information will have to be supplied independently. Also, any noise present in the data will cause errors in the estimation of reflection coefficients. These errors grow rapidly and accumulate with depth (because of recursion) and the algorithm becomes unstable. , 1989] to circumvent these problems, they are far from being satisfactory in practical situations where the data are band limited and contaminated with noise. A layer stripping type approach can be formulated for any problem for which a recursive type forward modeling formulation can be written down.

Uniqueness is also related to the problem of identifiability of the model by the data. For example, in seismic problems the information on seismic wave velocity is contained in the moveout (traveltime as a function of offset) in the data. Therefore, the use of only near offset traces does not reveal much information on velocity. This means that a suite of velocities will explain the data equally well resulting in non-unique estimates of velocities. In seismic tomography problems, the regions of the earth with no ray coverage cannot be resolved from the data and slowness estimates for these regions will result in non-unique values.

The theory of layer stripping is based on the assumption that the data are broad-band and noise free. , low and high frequency information are missing from the data. Therefore, for stable estimates of model parameters, this information will have to be supplied independently. Also, any noise present in the data will cause errors in the estimation of reflection coefficients. These errors grow rapidly and accumulate with depth (because of recursion) and the algorithm becomes unstable. , 1989] to circumvent these problems, they are far from being satisfactory in practical situations where the data are band limited and contaminated with noise.

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Global Optimization Methods in Geophysical Inversion by Mrinal Sen and Paul L. Stoffa (Eds.)


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