With the increase of emergencies in large public places, emergency evacuation research has become an important and urgent issue. This paper first proposes a tree hierarchical evacuation network. According to the hierarchical path selection strategy, the evacuation routes are obtained and sorted by the length of route. This hierarchical path selection strategy is more realistic than using the straight line distance. An evacuation model based on hierarchical directed evacuation network is presented in this paper, and a hierarchical directed artificial fish swarm algorithm is proposed to solve the evacuation problem. The model simulates the movements of pedestrians by means of preying, swarming, following and waiting behaviors of artificial fish swarm algorithm. During the evacuation process, the effects of congestion, retrograde and blocking time on evacuation speed and route selection are considered. The simulation results show that the proposed model and algorithm can effectively improve the evacuation efficiency in a stadium, and provide scientific and reasonable path guidance.

Seismic amplitude variation with offset (AVO) inversion is well-known as a popular and pragmatic tool used for the prediction of elastic parameters in the geosciences. Low frequencies missing from conventional seismic data are conventionally recovered from other geophysical information, such as well-log data, for estimating the absolute rock properties, which results in biased inversion results in cases of complex heterogeneous geologic targets or plays with sparse well-log data, such as marine or deep stratum. Broadband seismic data bring new opportunities to estimate the low-frequency components of the elastic parameters without well-log data. We have developed a novel AVO inversion approach with the Bayesian inference for broadband seismic data. The low-frequency components of the elastic parameters are initially estimated with the proposed broadband AVO inversion approach with the Bayesian inference in the complex frequency domain because seismic inversion in the complex frequency domain is helpful to recover the long-wavelength structures of the elastic models. Gaussian and Cauchy probability distribution density functions are used for the likelihood function and the prior information of model parameters, respectively. The maximum a posteriori probability solution is resolved to estimate the low-frequency components of the elastic parameters in the complex frequency domain. Furthermore, with those low-frequency components as initial models and constraints, the conventional AVO inversion method with the Bayesian inference in the time domain is further implemented to estimate the final absolute elastic parameters. Synthetic and field data examples demonstrate that the proposed AVO inversion in the complex frequency domain is able to predict the low-frequency components of elastic parameters well, and that those low-frequency components set a good foundation for the final estimation of the absolute elastic parameters.

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