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Published as Geophysics, 83, A27-A32, (2018)

EMD-seislet transform

Yangkang Chen% latex2html id marker 1542 \setcounter{footnote}{1}\fnsymbol{footnote}and Sergey Fomel% latex2html id marker 1543 \setcounter{footnote}{2}\fnsymbol{footnote}
% latex2html id marker 1544 \setcounter{footnote}{1}\fnsymbol{footnote}Previously: John A. and Katherine G. Jackson School of Geosciences
The University of Texas at Austin
University Station
Box X, Austin, TX 78713-8924
Currently: National Center for Computational Sciences
Oak Ridge National Laboratory
One Bethel Valley Road
Oak Ridge, TN 37831-6008
chenyk2016@gmail.com&cheny@ornl.gov
% latex2html id marker 1545 \setcounter{footnote}{2}\fnsymbol{footnote}John A. and Katherine G. Jackson School of Geosciences
The University of Texas at Austin
University Station
Box X, Austin, TX 78713-8924
sergey.fomel@beg.utexas.edu

Abstract:

The seislet transform uses a prediction operator which is connected to the local slope or frequency of seismic events. In this paper, we propose combining the 1D non-stationary seislet transform with empirical mode decomposition (EMD) in the $ f-x$ domain. We use the EMD to decompose data into smoothly variable frequency components for the following 1D seislet transform. The resultant representation shows remarkable sparsity. We introduce the detailed algorithm and use a field example to demonstrate the application of the new seislet transform for sparsity-promoting seismic data processing.


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2019-02-12