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Seismic Random Noise Attenuation Using Hessian MatrixNormal access

Authors: K. Jiang, B. Zhao, S. Cao, H. Mai, H. Wang and X. Sun
Event name: 81st EAGE Conference and Exhibition 2019
Session: Poster: Noise Attenuation A
Publication date: 03 June 2019
DOI: 10.3997/2214-4609.201901357
Organisations: EAGE
Language: English
Info: Extended abstract, PDF ( 6.29Mb )
Price: € 20

Summary:
Hessian matrix based method is an effective algorithm for curve detection in image processing and computer vision. In this abstract, we use the concepts of linear scale space theory and consider seismic signals as curves of different scales, using the good performance of Hessian matrix in curve detection to suppress random noise, that we call the multi-scale Hessian based method. Compared with the traditional method, the proposed method is not limited by the dip angle of the formation, so it can process seismic data with complex structures very well. Tests on synthetic and field data examples showed that, compared with F-X deconvolution method, the proposed method can be more effective in suppressing random noise and preserving the signals, especially for complex geological structure.


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