Abstract
Hough transform neural network is adopted to detect line pattern of direct wave and hyperbola pattern of reflection wave in a seismogram. The distance calculation from point to hyperbola is calculated from the time difference. This calculation makes the parameter learning feasible. The neural network can calculate the total error for distance from point to patterns. The parameter learning rule is derived by gradient descent method to minimize the total error. Experimental results show that line and hyperbola can be detected in both simulated and real seismic data. The network can get a fast convergence. The detection results can improve the seismic interpretation.
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© 2006 Springer-Verlag Berlin Heidelberg
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Huang, KY., You, JD., Chen, KJ., Lai, HL., Don, AJ. (2006). Hough Transform Neural Network for Seismic Pattern Detection. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_7
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DOI: https://doi.org/10.1007/11893257_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46481-5
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