基于DBSCAN聚类算法的ERT反演剖面数据挖掘方法
A data mining approach for improved interpretation of ERT inverted sections using the DBSCAN clustering algorithm
摘要：
使用反演程序的地球物理成像是探测地球地下的有力工具。然而，由于产生平滑截面的现有反演算法的固有限制，反演图像的解释有时可能是困难的。为了提高和自动化的处理和解释的反演地球物理模型，我们提出了一种方法的灵感来自数据挖掘。我们选择了一个算法称为DBSCAN（基于密度的空间聚类的应用程序与噪声）进行聚类的反演地球物理部分。该方法依赖于数据的自动分类和聚类。DBSCAN在没有簇的数量的先验知识的情况下检测反转的电阻率值中的簇。该算法的优点是仅由两个参数定义：数据空间中的点的邻域，以及该邻域中的最小数据点数量。我们提出了一个客观的程序来确定这两个参数。这里描述的概念证明适用于模拟的ERT（电阻率层析成像）部分，对于以下三种情况：两层台阶，两层反弹，两层异常嵌入在上层。为了验证这种方法，对上述两个参数进行了灵敏度研究，以及评估噪声对算法性能的影响。最后，这种方法进行了测试，真实的现场数据。DBSCAN在反演的电阻率模型中检测到簇，然后将前者与各种类型的地球材料相关联，从而可以确定勘探区域的结构。所提出的数据挖掘算法被证明是有效的，并提高了反演ERT截面的解释。这种新方法具有相当大的潜力，因为它可以应用于以剖面或地图形式表示的任何地球物理数据。
英文摘要：
SUMMARY Geophysical imaging using the inversion procedure is a powerful tool for the exploration of the Earth's subsurface. However, the interpretation of inverted images can sometimes be difficult, due to the inherent limitations of existing inversion algorithms, which produce smoothed sections. In order to improve and automate the processing and interpretation of inverted geophysical models, we propose an approach inspired from data mining. We selected an algorithm known as DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to perform clustering of inverted geophysical sections. The methodology relies on the automatic sorting and clustering of data. DBSCAN detects clusters in the inverted electrical resistivity values, with no prior knowledge of the number of clusters. This algorithm has the advantage of being defined by only two parameters: the neighbourhood of a point in the data space, and the minimum number of data points in this neighbourhood. We propose an objective procedure for the determination of these two parameters. The proof of concept described here is applied to simulated ERT (electrical resistivity tomography) sections, for the following three cases: two layers with a step, two layers with a rebound, and two layers with an anomaly embedded in the upper layer. To validate this approach, sensitivity studies were carried out on both of the above parameters, as well as to assess the influence of noise on the algorithm's performance. Finally, this methodology was tested on real field data. DBSCAN detects clusters in the inverted electrical resistivity models, and the former are then associated with various types of earth materials, thus allowing the structure of the prospected area to be determined. The proposed data-mining algorithm is shown to be effective, and to improve the interpretation of the inverted ERT sections. This new approach has considerable potential, as it can be applied to any geophysical data represented in the form of sections or maps.
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