Unsupervised learning on scientific ocean drilling datasets from the South China Sea
在从华南海钻数据集的科学海洋上的无指导的学习作者机构:Department of Earth SciencesThe University of Hong KongPokfulamHong KongChina Department of Geography and Centre for Geo-computation StudiesHong Kong Baptist UniversityKowloon TongHong KongChina Department of Earth SciencesUniversity of TorontoTorontoON M5S 2M8Canada Department of Electrical and Electronic EngineeringThe University of Hong KongPokfulamHong KongChina
出 版 物:《Frontiers of Earth Science》 (地球科学前沿(英文版))
年 卷 期:2019年第13卷第1期
页 面:180-190页
核心收录:
学科分类:07[理学]
主 题:machine learning unsupervised learning ODP IODP clustering
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 *** 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 *** 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 *** 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 *** 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.