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Thursday, March 5 • 15:15 - 17:15
Poster: 'Accelerating Extended Least Squares Migration with an approximate inverse to the extended Born modeling operator,' Jie Hou, Rice University

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Least Squares Migration (LSM) iteratively achieves a mean square best fit to seismic reflection data, provided that a kinematically accurate velocity model is supplied. The subsurface offset extension adds an extra degree of freedom to the model, thereby allowing LSM to fit the data even in the event of significant velocity error. This type of extension also implies additional expense per iteration from cross-correlating source and receiver wavefields over the subsurface offset, and therefore places a premium on rapid convergence. We accelerate the convergence of Extended Least Squares Migration (ELSM), by combining a modified Conjugate Gradient algorithm with a suitable preconditioner. The preconditioner uses an approximate inverse to the extended Born modeling operator. Numerical examples demonstrate that the proposed algorithm dramatically reduces the number of iterations required to achieve a given level of fit or gradient reduction, compared to ELSM by unpreconditioned conjugate gradient iteration.


Thursday March 5, 2015 15:15 - 17:15 CST
BioScience Research Collaborative 6500 Main Street, Houston, Tx 77005

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