THEORETICAL DEVELOPMENT IN REAL TIME CONDITIONAL SIMULATION OF EARTHQUAKE GROUND MOTION

This paper presents a method is for simulating earthquake ground motions at local-field points under the condition that the recorded motions are specified at several locations and the stochastic characteristics of the field also are designated. The simulated motions at observation points coincide with observed records. The state equation expressing the spatial and temporal field of earthquake ground motion compatible with the modeled stochastic field is derived by using the autoregressive process. A method is developed to identify the unknown coefficient matrices of the autoregressive process from recorded time series of earthquake motions at observation points. The Kalman filtering technique is used to identify the best adaptive estimator of a stochastic field for observations made at discrete spatial and temporal points. This analytical method is very promising for real-time conditional simulation of earthquake motion at unobserved locations.