High-resolution seismic signal processing method based on matching pursuit and kernel principal component analysis
Resolution is an important factor affecting the interpretation of seismic data.Low seismic signal reso-lution can lead to difficulties in identifying small faults and thin interbeds.To address this issue,this study pro-poses a high-resolution seismic signal processing method based on a matching pursuit algorithm and kernel prin-cipal component analysis(KPCA).Firstly,the matching pursuit algorithm is utilized to iteratively obtain the most effective information on seismic signals through sparse decomposition.Next,the wavelet is replaced by a wideband Ricker wavelet for shaping processing,effectively suppressing the side lobes of the wavelet and im-proving the resolution of seismic data.Finally,the original seismic signals are mapped to a high-dimensional space through nonlinear mapping using KPCA,and the seismic signals are reconstructed in the high-dimen-sional space to eliminate redundant information.Practical applications demonstrate that the seismic signals pro-cessed by this method exhibit clearer waveforms and richer details,which are beneficial for fault identification and characterization of thin bed,thereby providing a data foundation for subsequent geological data interpreta-tion and reservoir prediction.
matching pursuithigh resolutionwavelet shapingkernel principal component analysis
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