Open AccessDesert seismic signal denoising by 2D compact variational mode decomposition
Author(s) -
Yue Li,
Linlin Li,
Chao Zhang
Publication year - 2019
Publication title -
journal of geophysics and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.623
H-Index - 38
eISSN - 1742-2140
pISSN - 1742-2132
DOI - 10.1093/jge/gxz065
Subject(s) - noise reduction , desert (philosophy) , noise (video) , algorithm , signal (programming language) , hilbert–huang transform , seismic noise , computer science , norm (philosophy) , mode (computer interface) , vibration , acoustics , geology , seismology , artificial intelligence , physics , white noise , telecommunications , philosophy , epistemology , political science , law , image (mathematics) , programming language , operating system
Noise suppression and effective signal recovery are very important for seismic signal processing. The random noise in desert areas has complex characteristics due to the complex geographical environment; noise characteristics such as non-stationary, non-linear and low frequency. These make it difficult for conventional denoising methods to remove random noise in desert seismic records. To address the problem, this paper proposes a two-dimensional compact variational mode decomposition (2D-CVMD) algorithm for desert seismic noise attenuation. This model decomposes the complex desert seismic data into an finite number of intrinsic mode functions with specific directions and vibration characteristics. The algorithm introduces binary support functions, which can detect the edge region of the signal in each mode by penalizing the support function through the L1 and total variation (TV) norm. Finally, the signal can be reconstructed by the support functions and the decomposed modes. We apply the 2D-CVMD algorithm to synthetic and real seismic data. The results show that the 2D-CVMD algorithm can not only suppress desert low-frequency noise, but also recover the weak effective signal.