Seismic Attribute Analysis with Saliency Detection in Fractional Fourier Transform Domain
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Most image saliency detection models are dependent on prior knowledge and demand high computational cost. However, spectral residual (SR) and phase spectrum of the Fourier transform (PFT) models are simple and fast saliency detection approaches based on two-dimensional Fourier transform without the prior knowledge. For seismic data, the geological structure of the underground rock formation changes more obviously in the time direction. Therefore, one-dimensional Fourier transform is more suitable for seismic saliency detection. Fractional Fourier transform (FrFT) as an improved algorithm for Fourier transform, we propose the seismic SR and PFT models in one-dimensional FrFT domain to obtain more detailed saliency maps. These two models use the amplitude and phase information in FrFT domain to construct the corresponding saliency maps in spatial domain. By means of these two models, several saliency maps at different fractional orders can be obtained for seismic attribute analysis. These saliency maps can characterize the detailed features and highlight the object areas, which is more conducive to determine the location of reservoirs. The performance of the proposed method is assessed on both simulated and real seismic data. The results indicate that our method is effective and convenient for seismic attribute extraction with good noise immunity.
Key wordssaliency detection spectral residual phase spectrum fractional Fourier transform (FrFT) attribute extraction seismic data
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This work was supported by the National Natural Science Foundation of China (Nos. 61571096, 61775030, 41274127, 41301460, and 40874066). The final publication is available at Springer via https://doi.org/10.1007/s12583-017-0811-z.
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