Seismic Attribute Analysis with Saliency Detection in Fractional Fourier Transform Domain
- 67 Downloads
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
Unable to display preview. Download preview PDF.
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.
- Achanta, R., Hemami, S., Estrada, F., et al., 2009. Frequency-Tuned Salient Region Detection. IEEE Conference on Computer Vision and Pattern Recognition, 1597–1604. https://doi.org/10.1109/CVPR.2009.5206596Google Scholar
- Guo, C., Ma, Q., Zhang, L., 2008. Spatio-Temporal Saliency Detection Using Phase Spectrum of Quaternion Fourier Transform. IEEE Conference on Computer Vision and Pattern Recognition, 1–8. https://doi.org/10.1109/CVPR.2008.4587715Google Scholar
- Hou, X., Zhang, L., 2007. Saliency Detection: A Spectral Residual Approach. IEEE Conference on Computer Vision and Pattern Recognition, 1–8. https://doi.org/10.1109/CVPR.2007.383267Google Scholar
- Martin, G. S., 2004. The Marmousi 2 Model, Elastic Synthetic Data, and an Analysis of Imaging and AVO in a Structurally Complex Environment: [Dissertation]. University of Houston, Houston. 6–19Google Scholar
- Wang, C., Lu, Y. C., Huang, H. G., et al., 2015. New Seismic Attribute Technology for Predicting Dissolved Pore-Fracture of Deeply Buried Platform Margin Reef-Beach System in Northeast Sichuan Basin, China. Journal of Earth Science, 26(3): 373–383. https://doi.org/10.1007/s12583-015-0540-0CrossRefGoogle Scholar
- Yu, Y., Wang, B., Zhang, L., 2009. Pulse Discrete Cosine Transform for Saliency-Based Visual Attention. IEEE 8th International Conference on Development and Learning, 1–6. https://doi.org/10.1109/DEVLRN.2009.5175512Google Scholar