降低DOAS系统探测限的新型反演算法研究

Modified Retrieval Algorithm on Decreasing Detection Limits in Differential Optical Absorption Spectroscopy

差分吸收光谱法(DOAS)是一种高灵敏测量大气痕量气体成分含量的有效的光学遥感方法,该方法基于最小二乘拟合模型,利用获得的痕量气体的差分吸收光学密度与标准的吸收截面进行拟合,反演待测气体的浓度。建立了基于径向基(RBF)神经网络的痕量气体浓度反演的新模型,对网络的隐层参数采用改进最近邻聚类学习算法训练,对输出层权值的训练采用梯度下降算法,使得网络收敛快,能更好地实时、在线反演测量光谱。并针对DOAS技术的特点,把拟合残差输入网络集中训练,使得RBF网络在反演真实痕量气体吸收时,效果更佳。实验结果表明该新型反演方法提高了DOAS系统的反演精度,降低了DOAS系统的探测限。

Differential optical absorption spectroscopy （DOAS） has become a widely used method to measure trace gases in the atmosphere. It can identify trace gases through narrow-band molecular absorption. Concentration of trace gases is retrieved using method of least-squares fits of reference spectra to the measurement spectra. A novel retrieval method based on radial basis function （RBF） neural network was developed to retrieve the concentration of trace gases in DOAS system. The coefficient of the hidden layer was trained by modified nearest neighbor clustering algorithm, and that of the output layer was trained by gradient descent algorithm. These result in a fast speed of convergence of network. At last, there was comparison between the novel retrieval method and the conventional least-squares fitting. The experimental results show that the reliability and accuracy of DOAS are improved and detection limits are decreased by using the novel retrieval method.