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Random noise attenuation using f-x regularized nonstationary autoregression

Liu Guochang, Chen Xiaohong, Du Jing and Wu Kailong
Random noise attenuation using f-x regularized nonstationary autoregression
Geophysics (April 2012) 77 (2): V61-V69

Abstract

We have developed a novel method for random noise attenuation in seismic data by applying regularized nonstationary autoregression (RNA) in the frequency-space (f-x) domain. The method adaptively predicts the signal with spatial changes in dip or amplitude using f-x RNA. The key idea is to overcome the assumption of linearity and stationarity of the signal in conventional f-x domain prediction technique. The conventional f-x domain prediction technique uses short temporal and spatial analysis windows to cope with the nonstationary of the seismic data. The new method does not require windowing strategies in spatial direction. We implement the algorithm by an iterated scheme using the conjugate-gradient method. We constrain the coefficients of nonstationary autoregression (NA) to be smooth along space and frequency in the f-x domain. The shaping regularization in least-square inversion controls the smoothness of the coefficients of f-x RNA. There are two key parameters in the proposed method: filter length and radius of shaping operator. Tests on synthetic and field data examples showed that, compared with f-x domain and time-space domain prediction methods, f-x RNA can be more effective in suppressing random noise and preserving the signals, especially for complex geological structure.


ISSN: 0016-8033
EISSN: 1942-2156
Coden: GPYSA7
Serial Title: Geophysics
Serial Volume: 77
Serial Issue: 2
Title: Random noise attenuation using f-x regularized nonstationary autoregression
Affiliation: China University of Petroleum (Beijing), National Engineering Laboratory for Offshore Oil Exploration, Beijing, China
Pages: V61-V69
Published: 201204
Text Language: English
Publisher: Society of Exploration Geophysicists, Tulsa, OK, United States
References: 22
Accession Number: 2012-059478
Categories: Applied geophysics
Document Type: Serial
Bibliographic Level: Analytic
Illustration Description: illus.
Secondary Affiliation: China Petroleum and Chemical Corporation, CHN, China
Country of Publication: United States
Secondary Affiliation: GeoRef, Copyright 2018, American Geosciences Institute. Reference includes data from GeoScienceWorld, Alexandria, VA, United States. Reference includes data supplied by Society of Exploration Geophysicists, Tulsa, OK, United States
Update Code: 201231
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