首页 | 本学科首页   官方微博 | 高级检索  
     检索      


Sparse Linear Algebra and Geophysical Migration: A Review of Direct and Iterative Methods
Authors:Yann-Hervé De Roeck
Institution:(1) IFREMER Centre de Brest, BP 70 Technopôle Iroise, 29280 Plouzané, France;(2) INRIA/IRISA, Campus de Beaulieu, 35042 Rennes Cedex, France
Abstract:The pre-stack depth migration of reflection seismic data can be expressed, in the framework of waveform inversion, as a linear least squares problem. Together with the precise definition of this operator, we detail additional main characteristics of the forward model, like its huge size, its sparsity and the composition with convolution. It ends up with a so-called discrete ill-posed problem, whose acceptable solutions have to undergo a regularization procedure. Both direct and iterative methods have been implemented with specific attention to the convolution, and then applied to a given data set: a synthetic 2-dimensional profile of revealing size with some added noise. The efficiency with regard to computational effort and storage requirements is evaluated. The needed regularization of the solution is thoroughly studied in both cases. From the point of the global inverse problem, the extra feature of providing a solution that can be differentiated with respect to a parameter such as background velocity is also discussed.
Keywords:large sparse linear least squares  geophysical depth migration  deconvolution  regularization  inverse problem  differentiation  QR decomposition  conjugate gradient
本文献已被 SpringerLink 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号