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Seismic Tomography Inversion Research Based On Sequential Monte Carlo

Posted on:2017-01-09Degree:MasterType:Thesis
Country:ChinaCandidate:X F JiaFull Text:PDF
GTID:2180330482978515Subject:Mathematics
Abstract/Summary:PDF Full Text Request
Recent years by other engineering disciplines and many technology field application requirements, geophysical inverse problem has attracted great attention of scholars both domestic and abroad.Seismic tomography has become a new field of geophysical research,it has become one of the effective methods to study the earth’s interior structure.Generally speaking, the forward problem is comparatively perfect, the causal relationship is clear comparing with the inversion problem.But the inversion problem is rather difficult in practice.The main objective of seismic tomography is to find the error as small as possible between the data and observation data. So the quality of the sample data determined the stability and precision of the inversion problem.This article using Sequential Monte Carlo method to take samples and calculate the earthquake observation data.This method based on the simulation.It can well calculate the posterior distribution, easy to implement and can be parallel implementation, shorten the calculation time, so as to achieve the effect of the global optimization.This article mainly studied the fast marching method in earthquake forward proble-m.Mainly stated the related theory of the Sequential Monte Carlo in earthquake inversi-on problem. And give the calculate detailed process.To verify the validity of the method, this article used the numerical simulation in the end.Used two different kinds of velocity model, completed imaging technology by the fast marching method combined with Seq-uential Monte Carlo inversion method.The numerical calculation results show that:Seq-uential Monte Carlo can be effectively used in seismic tomography inversion and make the calculation result better.
Keywords/Search Tags:seismic tomography inversion, Fast Marching method, Sequen- tial Monte Carlo, Numerical simulation
PDF Full Text Request
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