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Inversion Of Geo-acoustic Parameters And Uncertainty Analysis Of Propagation Loss Based On Kriging Surrogate Model

Posted on:2018-05-09Degree:MasterType:Thesis
Country:ChinaCandidate:X P GuoFull Text:PDF
GTID:2370330623950620Subject:Computer Science and Technology
Abstract/Summary:PDF Full Text Request
The uncertainty of acoustic propagation loss and parameter inversion are two classic topics in the field of underwater acoustic.In the above two studies,the computational complexity of the acoustic model is one of the difficulties to be overcome.It is of great research value to improve the computational efficiency of underwater acoustic acoustics under the premise of ensuring a certain accuracy of calculation.Kriging model is the best,linear,unbiased geostatistical model with excellent fitting performance.In this paper,the Kriging surrogate model is introduced to calculate the uncertainty of underwater acoustic propagation loss and geo-acoustic parameter inversion.A fast acoustic field algorithm based on Kriging substitution model is proposed.Through the single frequency FOR3 D model calculation,multi-frequency FOR3 D model calculation and Kraken model calculation,the correctness and feasibility of the surrogate model are verified.The research shows that the Kriging surrogate model can significantly improve the computational efficiency under the premise of ensuring the accuracy of calculation.Based on the surrogate model,the uncertainty of acoustic propagation loss is quantified,and the better experimental results are obtained with less computational cost,which lays the foundation for efficient computation of uncertainty analysis.In addition,by using differential evolution algorithm and matching field inversion,an inversion model of geoacoustic parameters based on surrogate model is established.The parameters of seafloor sound velocity and seafloor density are inverted,and the probability density distribution of inversion parameters is solved.Finally,an efficient and accurate parameter inversion calculation is realized.
Keywords/Search Tags:Kriging surrogate model, Propagation loss uncertainty quantification, Differential evolution algorithm, Inversion of geo-acoustic parameters
PDF Full Text Request
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