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水氡浓度和环境参数的分层神经网络研究
引用本文:潘震宇,卓群,刘仲达,杨婕,丁俊芳.水氡浓度和环境参数的分层神经网络研究[J].地震地磁观测与研究,2012,33(3):110-114.
作者姓名:潘震宇  卓群  刘仲达  杨婕  丁俊芳
作者单位:1. 中国福建361003 厦门市地震局
2. 中国福建361003 厦门地震台
基金项目:福建省地震局科研基金资助项目
摘    要:采用分层神经网络(LNN)分析地下水的氡浓度,试图给出氡浓度和环境参数之间的函数关系。由于环境(例如:降雨量)对水氡浓度的影响可能是非线性的,与目前时间脉冲响应线性计算方法相比,该方法能够较准确的估计环境参数造成的氡浓度变化。

关 键 词:水氡浓度  环境参数  神经网络  地震预报

The study of layered neural networks based on radon concentration and environmental parameters in earthquake prediction
Pan Zhenyu , Zhuo Qun , Liu Zhongda , Yang Jie , Ding Junfang.The study of layered neural networks based on radon concentration and environmental parameters in earthquake prediction[J].Seismological and Geomagnetic Observation and Research,2012,33(3):110-114.
Authors:Pan Zhenyu  Zhuo Qun  Liu Zhongda  Yang Jie  Ding Junfang
Institution:l)Earthquake Administration of Xiamen City, Fujian Province 361003, China 2)Xiamen Seismic Station, Fujian Province 361003, China
Abstract:LNN had been used to analyze the current radon concentration in groundwater, attempting to find the function relationship between the radon concentration and environmental parame- ters. The influence of environment (for example, rainfall) on the radon concentration in groundwater may be nonlinear. The LNN can estimate more accurately the radon concentration by environmental parameters change, comparing with the linear computational technique (CLT). The analysis results of Radon observation data from the wells in Xiamen Dongfu show that LNN can accurately find out the change of radon concentration caused by the earthquake from environmental factors (for example, rainfall). In addition, LNN can tell the change of radon concentration by the environmental factors from other factors.
Keywords:water radon concentration  environmental parameters  neural network  earthquake prediction
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