GEOFCM: a new method for statistical classification of geochemical data using spatial contextual information

DOI Web Site 被引用文献1件 参考文献31件
  • YOSHIDA Kenta
    Japan Agency for Marine–Earth Science and Technology (JAMSTEC)
  • KUWATANI Tatsu
    Japan Agency for Marine–Earth Science and Technology (JAMSTEC) PRESTO, Japan Science and Technology Agency (JST)
  • YASUMOTO Atsushi
    Department of Geology and Mineralogy, Kyoto University
  • HARAGUCHI Satoru
    Japan Agency for Marine–Earth Science and Technology (JAMSTEC)
  • UEKI Kenta
    Japan Agency for Marine–Earth Science and Technology (JAMSTEC)
  • IWAMORI Hikaru
    Japan Agency for Marine–Earth Science and Technology (JAMSTEC) Earthquake Research Institute, The University of Tokyo Department of Earth and Planetary Sciences, Tokyo Institute of Technology

書誌事項

公開日
2018
資源種別
journal article
DOI
  • 10.2465/jmps.171127
公開者
一般社団法人　日本鉱物科学会

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説明

<p>Conventional clustering algorithms such as k–means and fuzzy c–means (FCM) cluster analysis do not fully utilize the spatial distribution information of geologic samples. In this paper, we propose GEOFCM, a new clustering method for geochemical datasets with location coordinates. A spatial FCM algorithm originally constructed for image segmentation was modified for application to a sparse and unequally–spaced dataset. The proposed algorithm evaluates the membership function of each sample using neighboring samples as a weighting function. To test the proposed algorithm, a synthetic dataset was analyzed by several hyper–parameter settings. Applying this algorithm to a geochemical dataset of granitoids in the Ina–Mikawa district of the Ryoke belt shows that samples collected from the same geological unit are likely to be classified as the same cluster. Moreover, overlapping geochemical trends are classified consistently with spatial distribution, and the result is more robust against noise addition than standard FCM analysis. The proposed method is a powerful tool to use with geological datasets with location coordinates, which are becoming increasingly available, and can help find overviews of complicated multidimensional data structure.</p>

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