Hough transform neural network for pattern detection and seismic applications

Abstract

Hough transform neural network is adopted to detect the line pattern of direct wave and the hyperbolic pattern of reflection wave in a one-shot seismogram. We use time difference from point to hyperbola and line as the distance in the pattern detection of seismic direct and reflection waves. This distance calculation makes the parameter learning feasible. One set of parameters represents one pattern. Many sets of parameters represent many patterns. The neural network can calculate the distances from point to many patterns as total error. The parameter learning rule is derived by gradient descent method to minimize the total error. The network is applied to three kinds of data in the experiments. One is the line and hyperbolic pattern in the image data. The second is the simulated one-shot seismic data. And the last is the real one-shot seismic data. Experimental results show that lines and hyperbolas can be detected correctly in three kinds of data. The method can also tolerate certain level of noise data. The detection results in the one-shot seismogram can improve the seismic interpretation and further seismic data processing. (C) 2008 Elsevier B.V. All rights reserved.