The anomaly recognition of spatial electric field is an important issue in studying the ionospheric disturbance caused by earthquakes.As a random digital signal,the UltraLow Frequency (ULF) space electric field data can be disrobed by the four features of the mean,variance,skewness and kurtosis.The ULF data recorded before the 2008 Wenchuan earthquake was used as original data to train the improved BP neural network,the identification model is established for the classification of abnormal signal of space electric field,which is verified by SOM neural network.The calculation results show that the abnormal signal is concentrated in the area between 5°～ 25°N and 88°～ 120°E,which lie within the influence scope of and may be caused by the Wenchuan earthquake.This fact is consistent with previous research results and proves that the improved BP neural network model is reasonable.%空间电场信号异常识别是研究地震引起电离层扰动的重要内容.将空间超低频电场电位数据看作随机数字信号,以均值、均方差、偏度和峰度等四个指标进行描述,采用“5· 12”汶川大地震前空间超低频电场电位数据作为原始数据,训练改进型BP神经网络,建立了空间电场信号异常分类识别模型,并以SOM神经网络进行验证.计算结果显示,空间超低频电场电位异常信号主要集中在5°～25°N,88°～120°E之间的区域,汶川大地震影响范围内的电离层扰动,可能是汶川地震发生前引起的,这与前人研究一致,说明采用改进型BP神经网络异常分类识别模型研究地震引起的电离层扰动是可行的.
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