Fast "online" migration with Compressive Sensing

TitleFast "online" migration with {Compressive} {Sensing}
Publication TypeConference
Year of Publication2015
AuthorsFelix J. Herrmann, Ning Tu, Ernie Esser
Conference NameEAGE Annual Conference Proceedings
Month06
OrganizationUBC
KeywordsEAGE, LSRTM
Abstract

We present a novel adaptation of a recently developed relatively simple iterative algorithm to solve large-scale sparsity-promoting optimization problems. Our algorithm is particularly suitable to large-scale geophysical inversion problems, such as sparse least-squares reverse-time migration or Kirchoff migration since it allows for a tradeoff between parallel computations, memory allocation, and turnaround times, by working on subsets of the data with different sizes. Comparison of the proposed method for sparse least-squares imaging shows a performance that rivals and even exceeds the performance of state-of-the art one-norm solvers that are able to carry out least-squares migration at the cost of a single migration with all data.

Notes

(EAGE, Madrid)

URLhttps://www.slim.eos.ubc.ca/Publications/Public/Conferences/EAGE/2015/herrmann2015EAGEfom/herrmann2015EAGEfom.html
DOI10.3997/2214-4609.201412942
Presentation

https://www.slim.eos.ubc.ca/Publications/Public/Conferences/EAGE/2015/he...

Citation Keyherrmann2015EAGEfom