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.
Highlights
P ATTERN recognition methods have ever been used to analyze seismic exploration data, earthquake data, and geophysical events [1]–[18]
The prediction was based on a seismic anomaly in the high amplitude of reflection that was referred as the bright spot
We propose a fusion learning rule by combining optimal learning-rate parameter, the normalized property of wc, and fractional correction parameter
Summary
P ATTERN recognition methods have ever been used to analyze seismic exploration data, earthquake data, and geophysical events [1]–[18]. There is a polarity reversal in the wavelet for the negative reflection coefficient [19], [20] Those three attributes are the seismic anomaly. A fusion learning rule is proposed here In the experiments, these rules are applied to the detection of a seismic anomaly in the simulated seismogram and the convergence speeds are compared. The major part of the energy of wavelets in real seismic data is close to the central part of the zerophase Ricker wavelet [10], [12] It is used in the simulated seismogram. Through the envelope and instantaneous frequency processing, the central part of the zerophase Ricker and the front part of the minimum-phase wavelet can have the same abnormal properties.
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