利用多目标地球化学数据识别第四纪沉积物类型--基于概率神经网络方法

Identification of Quaternary Sediments Types Used by Multi-Purpose Geochemical Data——With Probabilistic Neural Networks Method

针对利用多目标地球化学数据研究第四纪沉积物类型问题,提出了基于概率神经网络的分类识别模型,并给出地球化学特征指标选取、指标归一化、神经网络设置和训练的具体方法、步骤。在吉林省中西部松嫩平原应用表明,该方法识别出8类不同成因的第四纪沉积物,较好地解决了该区第四纪沉积物成因归属问题。概率神经网络模型对第四纪沉积物类型的识别能力远高于常规多元统计方法,且结构简单、训练快捷。

Aiming at the issue of using multi-purpose geochemistry data to study the classification of quaternary sediment, recognition model based on prohabilistic neural networks was put forward and the method and steps of selection of geochemical index, index normalized and the setting and training of neural networks were given, Eight kinds of genetic quaternary sediments were distinguished availably by probabilistic neural networks in Songnen Plain,the central and western Jilin Province, and the problems for the genesis of quaternary sediments was solved well in this area, which showed that the method was of strong nonlinear recognition ability. The recognition ability of the probabilistic neural networks model for Quaternary sediments is higher than that of traditional multivariate statistical analysis. And this model with simple structure provided can he trained fast, thus alsois the valid method using multi-purpose geochemistry data to recognize the quaternary sediment types.