Abstract
We apply a cellular neural network (CNN) based contour detection to exact the main reflective interface in seismic image. The seismic image is generated by reverse time migration (RTM) using the pseudospectral time domain (PSTD) method. According to Nyquist sampling theorem, the PSTD algorithm requires only two points per minimum wavelength rather than the traditional high order finite difference time domain (FDTD) which needs more than eight points per minimum wavelength to get the same accuracy. Thus, RTM using the PSTD algorithm can reduce the computing costs greatly when comparing with the traditional RTM based on the FDTD algorithm. To get more clear reflective interface in imaging result of reverse time migration, we use a contour detection algorithm based on CNN which calculates the template parameters by the gray-scale and spatial relationship between the central pixel and the other neighboring pixels in the current local window. The simulation results shows that the proposed method have good efficiency and imaging quality, and the contour detection method makes the reflective interface easier to distinguish.
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Acknowledgments
This work was supported in part by National Natural Science Foundation of China (Grant No. 41504111) and by Undergraduate Teaching Quality and Reform Programme of Guangdong Province in 2015: Teaching Team Construction Project of Information Security in the Open University of Guangdong (Grant No. STZL201502).
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Xie, J., He, G., Xiao, X. (2019). Cellular Neural Network Based Contour Detection for Seismic Image. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11632. Springer, Cham. https://doi.org/10.1007/978-3-030-24274-9_29
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DOI: https://doi.org/10.1007/978-3-030-24274-9_29
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