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Real-time estimation of strong motion seismic waves

An approach for real-time analyses, estimation and prognoses of strong motion seismic waves with stochastic modeling and neural network is presented. As input information 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 is determined the beginning of destructive phase of strong motion acceleration. The suggested approach gives possibility to classify seismic waves from recorded part of the wave to certain class, according to developed seismic waves classification. For different kind of classified waves are suggested different kind prognoses models. The prognoses is realized with the help of neural network, build on the principle of vector quantization. For prognoses of destructive phase of strong motion waves is suggested scene-oriented model. The determined statistical function of density distribution of recorded data from accelerogram are generating in real time. The received destructive phase prognoses of strong motion waves can be used in devices for structural control. Examples of received prognoses are compared with real data of strong motion waves. Simulation and numerical results are shown.