基岩
中国
地质学
梯度升压
地理信息系统
克里金
自然地理学
随机森林
水文学(农业)
地貌学
遥感
计算机科学
地理
岩土工程
人工智能
机器学习
考古
作者
Fapeng Yan,Wei Shangguan,Jing Zhang,Bifeng Hu
标识
DOI:10.1038/s41597-019-0345-6
摘要
Abstract Depth to bedrock influences or controls many of the Earth’s physical and chemical processes. It plays important roles in soil science, geology, hydrology, land surface processes, civil engineering, and other related fields. However, information about depth to bedrock in China is very deficient, and there is no independent map of depth to bedrock in China currently. This paper describes the materials and methods to produce high-resolution (100 m) depth-to-bedrock maps of China. For different research and application needs, two sets of data are provided for users. One is the prediction by the ensemble of the random forests and gradient boosting tree models, and the other is the prediction and the uncertainty of prediction based on quantile regression forests model. In comparison with depth-to-bedrock maps of China extracted from previous global predictions, our predictions showed higher accuracy and more spatial details. These data sets can provide more accurate information for Earth system research compared with previous depth-to-bedrock maps.
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