P AND S TOMOGRAPHY USING NORMALMODE AND SURFACE WAVES DATA WITH A NEIGHBOURHOOD ALGORITHM
 DSpace Home
 →
 Геология России
 →
 ELibrary
 →
 View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.
P AND S TOMOGRAPHY USING NORMALMODE AND SURFACE WAVES DATA WITH A NEIGHBOURHOOD ALGORITHM
Beghein C.; Resovsky J.S.; Trampert J.
xmlui.dri2xhtml.METS1.0.itemcitation:
Geophysical Journal International, 2002, 149, 3, 646658
Date:
2002
Abstract:
Traditionally P  and S wave tomography has been based on the inversion of data that are sensitive to the desired Earth structure, and model covariance is estimated from imperfect resolution and data error propagation. This analysis ignores the usually large spaces, and hence the significant nonuniqueness of the solution encountered in seismic tomography problems. Here we perform a model space search for P  and S velocity structure to find acceptable fits to recent normalmode splitting and fundamentalmode phase velocity data. The survey of the model space employs the neighbourhood algorithm of Sambridge, which preferentially samples the good datafitting regions. A Bayesian approach is used subsequently to extract robust information from the ensemble of models. We particularly focus on posterior marginal probability density functions and covariances for the various model parameters. The covariance matrix obtained is very useful in providing insights into the tradeoffs between the different variables and the uncertainties associated with them. We stay within the framework of perturbation theory, meaning that our emphasis is on the space of the linear inverse problem rather than the neglected nonlinearity. The whole model space (including the space) is sampled within reasonable parameter bounds, and hence the error bars are determined by all fitting models rather than subjective prior information. We estimated P and S models for spherical harmonic degree two only. The uncertainties are quite large and corresponding relative errors can exceed 100 per cent in the midmantle for V p . We find a good correlation of our most likely S model with previous models but some small changes in amplitude. Our most likely P model differs quite strongly from the recent P model SB10L18 and the correlation between our most likely P and S models is small. Among all the good datafitting models, there are, however, many that have a significant V p V s correlation. We compute d ln V s /d ln V p from the models that correlate significantly. We find an increase with depth in the top 1500 km. Deeper in the mantle, normalmode data prefer modest values compared with traveltime data.
Files in this item
This item appears in the following Collection(s)

ELibrary
Метаданные публикаций с сайта https://www.elibrary.ru
Search DSpace
Browse

All of DSpace

This Collection