地震经济损失快速评估是应急救灾的重要决策依据。本文选取了震级、极震区烈度、极震区烈度和抗震设防烈度之差ΔI、人口密度、人均GDP等5个指标作为输入层节点，将地震灾害的直接经济损失作为输出层节点，通过对1996—2013年的地震灾害损失资料进行训练和仿真分析，构建了Elman神经网络地震经济损失快速估计模型。运用该模型，对近年来的7个破坏性地震的直接经济损失进行评估分析，评估结果和实际直接经济损失有较好的一致性，该方法为地震经济损失快速评估提供了一种新思路。

Rapid assessment of earthquake economic loss is an important basis for disaster reduction and relief. In this article we choose magnitude, focal depth, intensity of the epicenter, seismic fortification intensity, the difference between the intensity of earthquake epicenter and the intensity of earthquake fortification, per capita GDP as the input index, which are used as input layer nodes. the direct economic loss of earthquake is used as output layer node. After the training and simulation analysis of earthquake disaster loss data from 2000 to 2013, the Elman neural network earthquake economic loss assessment model was constructed. By using this model, the direct economic losses of six major destructive earthquakes in recent years are calculated and analyzed, and the results are in good consistent with the actual direct economic losses from field investigation. This model provides a new way to quickly assess the economic losses after the earthquake.

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国家重点研发计划子课题（2016YFC0803109）、国家重点研发计划（2018YFC1504506）、福建省地震局青年科技基金专项（Y201905）共同资助