Modified Gauss-Newton with sparse updates

TitleModified {Gauss-Newton} with sparse updates
Publication TypeConference
Year of Publication2011
AuthorsXiang Li, Felix J. Herrmann, Tristan van Leeuwen, Aleksandr Y. Aravkin
Conference NameSBGF
KeywordsFull-waveform inversion, SBGF

Full-waveform inversion (FWI) is a data fitting procedure that relies on the collection of seismic data volumes and sophisticated computing to create high-resolution models.With the advent of FWI, the improvements in acquisition and inversion have been substantial, but these improvements come at a high cost because FWI involves extremely large multi-experiment data volumes. The main obstacle is the ‘curse of dimensionality’ exemplified by Nyquist’s sampling criterion, which puts a disproportionate strain on current acquisition and processing systems as the size and desired resolution increases. In this paper, we address the ‘curse of dimensionality’ by using randomized dimensionality reduction of the FWI problem, coupled with a modified Gauss-Newton (GN) method designed to promote curvelet-domain sparsity of model updates. We solve for these updates using the spectral projected gradient method, implemented in the SPG￿1 software package. Our approach is successful because it reduces the size of seismic data volumes without loss of information. With this reduction, we can compute Gauss-Newton updates with the reduced data volume at the cost of roughly one gradient update for the fully sampled wavefield

Citation Keyli2011SBGFmgnsu