Dictionary learning methods for seismic data
(09/2015 - 08/2017)
Individual research grant of Chinese Scholarship Council
We propose a new regularization method for the sparse representation and denoising of seismic data. Our approach is based on a sparse data representation in a learned dictionary and a similarity measure for image patches that is evaluated using the Laplacian matrix of a graph. Dictionary learning methods aim to find a data-dependent basis or a frame that admits a sparse data representation while capturing the characteristics of the given data. Our algorithms for dictionary learning are based on clustering and singular value decomposition. We achieve very good denoising performance for seismic data, both in terms of peak signal-to-noise ratio values and visual estimation of weak event preservation.
|Sparse graph-regularized dictionary learning for suppressing
random seismic noise|
Lina Liu, Jianwei Ma, Gerlind Plonka
Geophysics 83(3), pp. V215-V231, 2018, preprint as download.
|Seismic data interpolation and denoising by learning a tensor tight frame|
Lina Liu, Gerlind Plonka, Jianwei Ma
Inverse Problems 33(10), 105011, 2017, preprint as download.