Random noise attenuation using a structure oriented adaptive singular value decomposition

• Autorzy: Xu Yankai , Cao Siyuan , Pan Xiao , Liu Wei , Chen Hongling

Identyfikatory

DOI

10.1007/s11600-019-00301-6

• Języki publikacji: EN

Abstrakty

EN

Singular value decomposition (SVD) is an efficient method to suppress random noise in seismic data. The performance of noise attenuation is typically affected by choosing the rank of the estimated signal using SVD. That the rank is fixed limits noise attenuation especially for a low signal-to-noise ratio data. Therefore, we propose a modified approach to attenuate random noise based on structure-oriented adaptively choosing singular values. In this approach, we first estimate dominant local slopes, predict other traces from a reference trace using the plane-wave prediction and construct a 3D seismic volume which is composed of all predicted traces. Then, we remove noise from a 2D profile whose traces are predicted from different reference traces via adaptive SVD filter (ASVD), which adaptively chooses the rank of estimated signal by the singular value increments. Finally, we stack every 2D denoised profile to a stacking denoised trace and reconstruct the 2D denoised seismic data which are composed of all stacking denoised traces. Synthetic data and field data examples demonstrate that the proposed structure-oriented ASVD approach performs well in random noise suppression for the low SNR seismic data with dipping and hyperbolic events.

Słowa kluczowe

EN

adaptive singular value decomposition; singular value increments; random noise attenuation; plane-wave prediction

PL

adaptacyjny rozkład wartości pojedynczej; przyrosty wartości pojedynczych; losowe tłumienie hałasu; przewidywanie fali płaskiej

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2019
• Tom: Vol. 67, no. 4
• Strony: 1091--1106
• Opis fizyczny: Bibliogr. 32 poz.

Twórcy

autor

Xu Yankai

• College of Information Science and Engineering, China University of Petroleum-Beijing, Beijing, China

autor

Cao Siyuan

• College of Geophysics, China University of Petroleum-Beijing, Beijing, China

autor

Pan Xiao

• Department of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen, China
• School of Earth and Space Science, University of Science and Technology of China, Hefei, China

autor

Liu Wei

• College of Geophysics, China University of Petroleum-Beijing, Beijing, China

autor

Chen Hongling

• College of Geophysics, China University of Petroleum-Beijing, Beijing, China

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).