Strong Motion Waves Classification and Prognoses with Neural Networks

The paper is devoted on the problem of strong motion waves classification and real-time prognoses with neural network. As input information for the neural network are given the parameters of recorded part of accelerogram, principle axis transform and spectral characteristics of the wave. With the help of stochastic long-range dependence time series analyses are determined the separated phases of strong motion acceleration. The boundaries between separated phases of seismic waves are determined with sceneoriented model. Determining the scene boundaries are based on the coefficient of variation for a sequence of consecutive accelerogram values. We add values to current scene until its weighted coefficient of variation is changing more than a preset value. The last added value is defined as the beginning of a new scene.