RESEARCH ON OPTIMAL OBSERVATION SCALE FOR DAMAGED BUILDINGS AFTER EARTHQUAKE BASED ON OPTIMAL FEATURE SPACE

Early emergency response requires improving the utilization value of the data available in the early post-earthquake period. We proposed a method for assessing seismic intensities by analyzing early aftershock sequences using the robust locally weighted regression program. The seismic intensity map evaluated by the method can reflect the range of the hardest-hit areas and the spatial distribution of the possible property damage and casualties caused by the earthquake.

The spatial and temporal distribution characteristics of aftershocks around the fault are analyzed according to the stress changes after the main earthquake. The model can be used to predict the multi-timescale anisotropy distribution of aftershocks fairly. The finite fault model of the main earthquake is used in the construction of the prediction model. The model is a deep neural network; the inputs are the stress components of each point; and the output is the probability of an aftershock.