Compressed sensing and sparse recovery in exploration seismology

TitleCompressed sensing and sparse recovery in exploration seismology
Publication TypeConference
Year of Publication2009
AuthorsFelix J. Herrmann
Conference NamePIMS
KeywordsPresentation
Abstract

In this course, I will present how recent results from compressed sensing and sparse recovery apply to exploration seismology. During the first lecture, I will present the basic principles of compressive sensing; the importance of random jitter sampling and sparsifying transforms; and large-scale one-norm solvers. I will discuss the application of these techniques to missing trace interpolation. The second lecture will be devoted to coherent signal separation based on curveletdomain matched filtering and Bayesian separation with sparsity promotion. Applications of these techniques to the primary-multiple wavefield-separation problem on real data will be discussed as well. The third lecture will be devoted towards sparse recovery in seismic modeling and imaging and includes the problem of preconditioning the imaging operators, and the recovery from simultaneous source-acquired data.

Notes

Lecture III presented at the PIMS Summer School on Seismic Imaging, Seattle

Presentation
Citation Keyherrmann2009PIMScssr3