The Improvement of Neural Network Cascade-Correlation Algorithm and Its Application in Picking Seismic First Break

Abstract

Neural Network is a kind of widely used seismic wave travel time auto-picking method. Most commercial software such as Promax often uses Back Propagation (BP) neural network. Here we introduce a cascade-correlation algorithm for constructing neural network. The algorithm’s convergence is faster than BP algorithm and can determine its own network architecture according to training samples, in addition, it can be able to expand network topology to learn new samples. The cascaded-correlation algorithm is improved. Different from the standard cascade-correlation algorithm, improved algorithm starts at an appropriate BP network architecture (exits hidden units), but the standard one’s initial network only includes input layer and output layer. In addition, in order to prevent weight-illgrowth, adding regularization term to the objective function when training candidate hidden units can decay weights. The simulation experiment demonstrates that improved cascade-correlation algorithm is faster convergence speed and stronger generalization ability. Analytically study five attributes, including instantaneous intensity ratio, amplitude, frequency, curve length ratio, adjacent seismic channel correlation. Intersection figure shows that these five attributes have distinctiveness of first break and stability. The neural network first break picking method of this paper has achieved good effect in testing actual seismic data. Key words : Neural network; Cascade-correlation algorithm; Picking seismic first break