基于聚类算法的ERT污染区域识别方法

本文提出将聚类算法引入到ERT监测系统中，采用K均值（K-means）聚类、模糊C均值算法（FCM）以及混合高斯模型（GMM）3种常用聚类算法对ERT检测结果进行污染区域识别，通过一个数值模型分析了3种算法的识别效果.研究结果表明当污染区域与背景土壤的电阻率区分度较大时（电阻率差异性大于30%），采用3种聚类算法都可以识别出污染区域，K-means和FCM的识别效果优于GMM算法.最后，给出一个实际场地调查的应用案例.

An ERT pollution area identification method based on clustering algorithm

Electrical resistivity tomography (ERT) has been used for pollution monitoring in contaminated sites in recent years because it is low cost and relatively fast. However, ERT monitoring data sets are usually analyzed and processed manually, as a result, a lot of manpower is required and the efficiency and accuracy of the ERT data identification are difficult to guarantee. This represents a strong limitation for the application of ERT monitoring system in the fields. To address this problem, clustering algorithms for ERT data analysis was introduced. A numerical model was used to research the effectiveness of contaminated areas recognition using K-means, fuzzy C-means (FCM), and Gaussian mixture model (GMM). The results showed that the three clustering algorithms identified the contaminated area effectively when the difference in resistivity values between the contaminated area and the background soil was larger than 30%. The recognition accuracy of the K-means and FCM algorithms was better than that of the GMM. Finally, a case of the clustering algorithms in ERT survey of a contaminated site was presented.