Seislet-based morphological component analysis using scale-dependent exponential shrinkage

Published as Journal of Applied Geophysics, doi: 10.1016/j.jappgeo.2015.04.003 (2015)

Seislet-based morphological component analysis using scale-dependent exponential shrinkage

Pengliang Yangand Sergey Fomel
Xi'an Jiaotong University
National Engineering Laboratory for Offshore Oil Exploration
Xi'an 710049, China
Bureau of Economic Geology,
John A. and Katherine G. Jackson School of Geosciences
The University of Texas at Austin
University Station, Box X
Austin, TX 78713-8924, USA

Abstract:

Morphological component analysis (MCA) is a powerful tool used in image processing to separate different geometrical components (cartoons and textures, curves and points etc). MCA is based on the observation that many complex signals may not be sparsely represented using only one dictionary/transform, however can have sparse representation by combining several over-complete dictionaries/transforms. In this paper we propose seislet-based MCA for seismic data processing. MCA algorithm is reformulated in the shaping-regularization framework. Successful seislet-based MCA depends on reliable slope estimation of seismic events, which is done by plane-wave destruction (PWD) filters. An exponential shrinkage operator unifies many existing thresholding operators and is adopted in scale-dependent shaping regularization to promote sparsity. Numerical examples demonstrate a superior performance of the proposed exponential shrinkage operator and the potential of seislet-based MCA in application to trace interpolation and multiple removal.

Seislet-based morphological component analysis using scale-dependent exponential shrinkage