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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Aminzadeh, F., Burkhard, N., Kunz, T., Nicoletis, L., Rocca, F.: 3-D modeling project: 3rd report. Lead. Edge 14(2), 125–128 (1995)
Baysal, E., Kosloff, D.D., Sherwood, J.W.C.: Reverse time migration. Geophysics 48(11), 1514–1524 (1983)
Berenger, J.P.: A perfectly matched layer for the absorption of electromagnetic waves. J. Comput. Phys. 114(2), 185–200 (1994)
Chew, W.C., Liu, Q.H.: Perfectly matched layers for elastodynamics: a new absorbing boundary condition. J. Comput. Acoust. 4(4), 341–359 (1996)
Chua, L.O.: CNN: a vision of complexity. Int. J. Bifurcat. Chaos 7(10), 2219–2425 (1997)
Chua, L.O., Roska, T.: Cellular Neural Networks and Visual Computing. Cambridge University Press, Cambridge (2002)
Chua, L.O., Yang, L.: Cellular neural networks: theory. IEEE Trans. Circ. Syst. 35(30), 1257–1290 (1988)
Claerbout, J.F.: Toward a unified theory of reflector mapping. Geophysics 36(3), 467 (1971)
Clapp, R.G.: Reverse time migration: saving the boundaries. In: Stanford Exploration Project, p. 136 (2008)
Dussaud, E., et al.: Computational strategies for reverse-time migration. SEG Techn. Progr. Expanded Abs. 27(1), 2267–2271 (2008)
Fornberg, B.: The pseudospectral method: comparisons with finite differences for the elastic wave equation. Geophysics 52(4), 483–501 (1987)
Hemon, C.H.: Equations D’onde et Modeles. Geophys. Prospect. 26(4), 790–821 (1978)
Lecomte, J.C., Campbell, E., Letouzey, J.: Building the SEG/EAEG overthrust velocity macro model. In: EAEG/SEG Summer Workshop-Construction of 3-D Macro Velocity-Depth Models, European Association of Exploration Geophysicists (1994)
Liu, Q.H.: The PSTD algorithm: a time-domain method requiring only two cells per wavelength. Microwave Opt. Technol. Lett. 15(3), 158–165 (1997)
Liu, Q.H.: The pseudospectral time-domain (PSTD) algorithm for acoustic waves in absorptive media. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 45(4), 1044–1055 (1998)
Liu, Q.H.: Large-scale simulations of electromagnetic and acoustic measurements using the pseudospectral time-domain (PSTD) algorithm. IEEE Trans. Geosci. Remote Sens. 37(2), 917–926 (1999)
Liu, Q.H., Tao, J.P.: The perfectly matched layer for acoustic waves in absorptive media. J. Acoust. Soc. Am. 102(4), 2072–2082 (1997)
Xie, J., Guo, Z., Liu, H., Liu, Q.H.: GPU acceleration of time gating based reverse time migration using the pseudospectral time-domain algorithm. Comput. Geosci. 117, 57–62 (2018)
Yang, H.N., Li, T.J., Li, N., He, Z.M., Liu, Q.H.: Time gating based time reversal imaging for impulse borehole radar in layered media. IEEE Trans. Geosci. Remote Sens. PP(99), 1 (2015)
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-24274-9_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-24273-2
Online ISBN: 978-3-030-24274-9
eBook Packages: Computer ScienceComputer Science (R0)