对地震伤亡人口的预测需要同时考虑地震破裂特征本身、灾区人口及其生活环境等特征,其是一个典型的复杂预测系统。本文基于深度学习神经网络方法和1976—2020年间78次地震伤亡事件,构建了中国大陆地震伤亡预测模型,综合使用发震年代、发震时刻、发震季节、Ⅵ度及以上区域受灾面积、Ⅵ度及以上区域受灾人口数、震源深度、极震区烈度和震源机制类型等8个参数,对包括2008年汶川8.0级和2010年玉树7.1级地震在内的9次地震事件进行了预测检验。结果显示,该预测模型能够较好地反映出中小地震的伤亡人口特征,除汶川地震和玉树地震外的7次地震伤亡事件预测值与实际值误差均在一个数量级上,对于2008年汶川8.0级和2010年玉树7.1级地震,预测值明显小于实际伤亡人口;其中玉树地震发震断层位于玉树州府结古镇之下,造成了相对较多的人口伤亡数量;汶川地震的伤亡人口数量不仅由地震直接导致,还包括了地震滑坡等次生灾害引起的伤亡数量。

The rapid assessment of the number of casualties after an earthquake requires to consider not only the characteristics of the earthquake fault but also the population distribution in the disaster area and their living environment. Therefore,the assessment of earthquake casualties is a typical complex prediction system. In this article,we constructed an earthquake casualty assessment model for mainland China,based on the Deep Learning Neural Network method with fatalities of 78 earthquake events during 1976-2020. We applied eight critical factors in the model,i.e.,the date,the time,and the season of occurrence,earthquake-affected population and area,the epicenter,and the focal mechanism of the earthquakes. To test the effectiveness of the model,we used nine events,including the 2008 Wenchuan M_S8.0 earthquake and the 2010 Yushu M_S7.1 earthquake,to compare the estimated numbers of casualties with the true values from the investigation. Our results show that the estimation of fatalities for the seven intermediate earthquakes is good enough,with the error of estimated and investigated numbers in the same order. But for the 2010 Yushu earthquake and the 2008 Wenchuan earthquake,the estimated numbers are significantly smaller than the real ones. The seismogenic fault of the 2010 Yushu earthquake,located directly below the Jiegu town,the capital of the Yushu autonomous prefecture,caused more casualties because of high population density. Moreover,it is also because that the fatalities in the 2008 Wenchuan earthquake was caused not only by the earthquake,but also the secondary disasters.

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国家自然科学基金(42074064、41941016、U2039201)、中央级公益性科研院所基本科研业务专项(ZDJ2020-14)共同资助