Efficient seismic sparse decomposition based on multiple kernel-based models
Fu Lihua1, Li Hongwei1, Liu Zhihui1, Zhao Haolan2,3
1. School of Mathematics and Physics, China University of Geosciences (Wuhan), Wuhan, Hubei 430074, China;
2. College of Physical Science and Technology, Central China Normal University, Wuhan, Hubei 430079, China;
3. R&D Department/Network Business Unit, Fiberhome Telecommunication Technologies Ltd., Wuhan, Hubei 430073, China
Abstract：To enhance the efficiency and sparsity of seismic signal decomposition, multiple kernels are used for the adaptive sparse decomposition of seismic signals. At first, the global k-means clustering algorithm is utilized to generate the preselected behavioral parameters in the dictionary. Then the signal is reconstructed with orthogonal least squares method. The experiments both on synthetic and real data were conducted to evaluate the performance. The results show that multiple kernel-based models greatly improve the sparsity with the similar accuracy.
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