Integrated data analysis for mineral exploration: A case study of clustering satellite imagery, airborne gamma-ray, and regional geochemical data suites
Integrated data analysis for mineral exploration: A case study of clustering satellite imagery, airborne gamma-ray, and regional geochemical data suites
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DOI：
10.1190/geo2011-0063.1
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发表时间：
2012-06
期刊：
影响因子：
3.3
通讯作者：
D. Eberle;H. Paasche
中科院分区：
文献类型：
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作者：
D. Eberle;H. Paasche
摘要划分聚类算法是数据驱动的大型地学数据库集成的有力工具。我们使用模糊Gustafson-Kessel聚类分析，整合陆地卫星图像，航空辐射，区域地球化学数据，以帮助多方法数据库的解释。调查区面积超过3700平方公里，位于南非北开普省。我们仔细选择了五个变量进行聚类分析，以避免聚类结果被我们数据库中存在的空间高度相关的数据集所主导。与其他更流行的聚类算法（如k-means或fuzzy c-means）不同，Gustafson-Kessel算法不需要预聚类数据处理，如缩放或调整直方图数据分布。聚类分析的结果是一个分类地图，描绘了突出的近地表结构。为了增加分类图的价值，我们将检测到的结构与映射的地质和构造进行了比较。
ABSTRACTPartitioning cluster algorithms have proven to be powerful tools for data-driven integration of large geoscientific databases. We used fuzzy Gustafson-Kessel cluster analysis to integrate Landsat imagery, airborne radiometric, and regional geochemical data to aid in the interpretation of a multimethod database. The survey area extends over 3700 km2 and is located in the Northern Cape Province, South Africa. We carefully selected five variables for cluster analysis to avoid the clustering results being dominated by spatially high-correlated data sets that were present in our database. Unlike other, more popular cluster algorithms, such as k-means or fuzzy c-means, the Gustafson-Kessel algorithm requires no preclustering data processing, such as scaling or adjustment of histographic data distributions. The outcome of cluster analysis was a classified map that delineates prominent near-to-surface structures. To add value to the classified map, we compared the detected structures to mapped geology an...