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Seismic Data Restoration by the Weighted MCANormal access

Authors: M. Sun, Z. Li, C. Li and X. Li
Event name: 80th EAGE Conference and Exhibition 2018
Session: Poster: Seismic interpolation and Regularization A
Publication date: 11 June 2018
DOI: 10.3997/2214-4609.201801277
Organisations: EAGE
Language: English
Info: Extended abstract, PDF ( 1013.43Kb )
Price: € 20

The regular reconstruction of seismic data is one of the most important and fundamental aspects of seismic data processing. The deep reflection seismic profile is mostly arranged in a complicated geological tectonic section and more complicated wave group characteristics on the deep reflection seismic profile are resultant due to the complicated geological structure. Consequently, the internal structure characteristics of data cannot be represented effectively by using a single sparse transformation. The morphological component analysis (MAC) method decomposes a signal into several components with distinguished morphological features to approximate the complex internal structure of data, however, the various characteristics of the complex data still cannot be described effectively through a simple addition of the signal components. We improved the MCA method, which is weighted by sparse dictionaries. By making use of the morphological differences among signal components, the seismic signal was divided into two forms. In the process of reconstruction, the weight of the two components is constantly adjusted to achieve the recovery of the complex missing seismic data. Experimental results shows that the weighted MCA method can be used to reconstruct the irregular and large span data and eliminate the spatial aliasing.

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