Seismic Signal Classification Using Perceptron with Different Learning Rules

Abstract

Perceptron is adopted to classify the Ricker wavelets and to detect the seismic anomaly in a seismogram. Three learning rules are used in the training of perceptron to solve the decision boundary. The optimal learning-rate parameter is derived. The lower and upper bounds of the learning-rate parameter are derived. It can provide that the learning can converge when the parameter is within the range. The normalized learning rule is derived also. Combining learning rules, a fusion learning rule is proposed. In the experiments, these rules are applied to the detection of a seismic anomaly in the simulated seismogram and to compare the convergence speed. The fusion learning rule has the fastest convergence and is applied to the real seismogram. The seismic anomaly can be detected successfully. It can improve the seismic interpretation.