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Unsupervised learning on scientific ocean drilling datasets from the South China Sea

查看全文 作　　者：Kevin [1]C.TSE;Hon-Chim [2]CHIU;Man-Yin [3]TSANG;Yiliang [1]LI;Edmund [4]Y.LAM 高影响力作者 机构地区：[1]Department of Earth Sciences,The University of Hong Kong,Pokfulam,Hong Kong,China;[2]Department of Geography and Centre for Geo-computation Studies,Hong Kong Baptist University,Kowloon Tong,Hong Kong,China;[3]Department of Earth Sciences,University of Toronto,Toronto,ON M5S 2M8,Canada;[4]Department of Electrical and Electronic Engineering,The University of Hong Kong,Pokfulam,Hong Kong,China高影响力机构 出　　处：《Frontiers of Earth Science》索引2019年第13卷第1期,共11页高影响力期刊 摘　　要：Unsupervised learning methods were applied to explore data patterns in multivariate geophysical datasets collected from ocean floor sediment core samples coming from scientific ocean drilling in the South China Sea.Compared to studies on similar datasets,but using supervised learning methods which are designed to make predictions based on sample training data,unsupervised learning methods require no a priori information and focus only on the input data.In this study,popular unsupervised learning methods including K-means,self-organizing maps,hierarchical clustering and random forest were coupled with different distance metrics to form exploratory data clusters.The resulting data clusters were externally validated with lithologic units and geologic time scales assigned to the datasets by conventional methods. Compact and connected data clusters displayed varying degrees of correspondence with existing classification by lithologic units and geologic time scales.K-means and self-organizing maps were observed to perform better with lithologic units while random forest corresponded best with geologic time scales.This study sets a pioneering example of how unsupervised machine learning methods can be used as an automatic processing tool for the increasingly high volume of scientific ocean drilling data. 关 键 词：machine LEARNING UNSUPERVISED LEARNING ODP IODP clustering
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