Abstract：Most conventional seismic data reconstruction algorithms use the overall reconstruction mode. Inspired by digital image reconstruction, we propose in this paper a novel seismic data reconstruction algorithm based on the high-order expansion fast marching method. The algorithm uses local reconstruction mode. Firstly, the missing seismic data is mapped into seismic image and the quantization error in the mapping is analyzed. Then the seismic image is subsequently decomposed by wavelet transform with two down-sampling. Decomposed low frequency part is reconstructed point by point with the high-order expansion fast marching method. High frequency part is reconstructed based on the horizontal, vertical and diagonal prediction filtering of the low frequency part. After that the reconstructed seismic image is obtained by the inverse wavelet transform. Finally, the seismic image is mapped into the seismic data. Prestack and poststack seismic data reconstruction experiments verify the feasibility of the proposed algorithm. In comparison with morphological component analysis (MCA) and the K-SVD dictionary learning, the proposed algorithm is faster and more accurate in data reconstruction.
周亚同, 滕琳琳, 李玲玲. 基于高阶扩展快速行进法的缺失地震数据重建[J]. 石油地球物理勘探, 2015, 50(5): 873-880.
Zhou Yatong, Teng Linlin, Li Lingling. Seismic data reconstruction with the high-order expansion fast marching method. OGP, 2015, 50(5): 873-880.
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