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Structural complexity-guided predictive filtering |
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| Authors: | Bin Liu Chao Fu Yuxiao Ren Qingsong Zhang Xinji Xu Yangkang Chen |
| Institution: | 1. Geotechnical and Structural Engineering Research Center, Shandong University, Jinan, Shandong, 250061 China
School of Qilu Transportation, Shandong University, Jinan, Shandong, 250002 China Data Science Institute, Shandong University, Jinan, Shandong, 250100 China;2. Geotechnical and Structural Engineering Research Center, Shandong University, Jinan, Shandong, 250061 China;3. School of Earth Sciences, Zhejiang University, Hangzhou, Zhejiang, 310027 China |
| Abstract: | Random noise attenuation utilizing predictive filtering achieves great performance in denoising seismic data. Conventional predictive filtering methods are based on fixed filter operators and neglect the complexity of structures. In this way, the denoised data cannot meet the requirement of balancing the signal preservation and noise removal. In this study, we proposed a structural complexity-guided predictive filtering method that utilizes an adapted filter operator to adjust the changes of structural complexity. The proposed structural complexity-guided predictive filtering mainly consists of two stages. A slope field information is acquired according to plane-wave destruction to assess the structural complexity. In addition, an adaptive filter operator is obtained to denoise the seismic data according to the adaptive factor. Both synthetic data and real seismic profiles are employed to examine the denoising capacity and flexibility of the refined predictive filtering using adaptive lengths. The analysis of the predicted results shows that adaptive predictive filtering is powerful and has the ability to eliminate random noises with negligible distortions. |
| Keywords: | 2D structures Seismic data processing Signal processing |