Non-parametric seismic data recovery with curvelet frames

TitleNon-parametric seismic data recovery with curvelet frames
Publication TypeJournal Article
Year of Publication2008
AuthorsFelix J. Herrmann, Gilles Hennenfent
JournalGeophysical Journal International
Volume173
Page233-248
Month04
KeywordsAcquisition, curvelet transform, reconstruction, SLIM
Abstract

Seismic data recovery from data with missing traces on otherwise regular acquisition grids forms a crucial step in the seismic processing flow. For instance, unsuccessful recovery leads to imaging artifacts and to erroneous predictions for the multiples, adversely affecting the performance of multiple elimination. A non-parametric transform-based recovery method is presented that exploits the compression of seismic data volumes by recently developed curvelet frames. The elements of this transform are multidimensional and directional and locally resemble wavefronts present in the data, which leads to a compressible representation for seismic data. This compression enables us to formulate a new curvelet-based seismic data recovery algorithm through sparsity-promoting inversion. The concept of sparsity-promoting inversion is in itself not new to geophysics. However, the recent insights from the field of ‘compressed sensing’ are new since they clearly identify the three main ingredients that go into a successful formulation of a recovery problem, namely a sparsifying transform, a sampling strategy that subdues coherent aliases and a sparsity-promoting program that recovers the largest entries of the curvelet-domain vector while explaining the measurements. These concepts are illustrated with a stylized experiment that stresses the importance of the degree of compression by the sparsifying transform. With these findings, a curvelet-based recovery algorithms is developed, which recovers seismic wavefields from seismic data volumes with large percentages of traces missing. During this construction, we benefit from the main three ingredients of compressive sampling, namely the curvelet compression of seismic data, the existence of a favorable sampling scheme and the formulation of a large-scale sparsity-promoting solver based on a cooling method. The recovery performs well on synthetic as well as real data and performs better by virtue of the sparsifying property of curvelets. Our results are applicable to other areas such as global seismology.

URLhttps://www.slim.eos.ubc.ca/Publications/Public/Journals/GeophysicalJournalInternational/2008/herrmann2008GJInps.pdf
DOI10.1111/j.1365-246X.2007.03698.x
Citation Keyherrmann2008GJInps