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| DOI | https://doi.org/10.1594/PANGAEA.911692 |
| Deep-sea sediments of the global ocean mapped with Random Forest machine learning algorithm | |
| Diesing; Markus | |
| 发布日期 | 2020-02-03 |
| 数据集类型 | dataset |
| 英文关键词 | accuracy ; confidence ; Deep-sea ; lithology ; map ; seafloor ; sediment ; spatial prediction |
| 英文简介 | The seafloor lithology of deep-sea sediments of the global ocean was spatially predicted. Seven lithology classes were predicted: Calcareous sediment, Clay, Diatom ooze, Lithogenous sediment, Mixed calcareous-siliceous ooze, Radiolarian ooze and Siliceous mud. The dataset contains probability surfaces of the seven seafloor lithologies, the probability of the most probable class (maximum probability) and the predicted seafloor lithology. The results are presented as geo-referenced floating-point TIFF-files with a spatial resolution of 10 km and Wagner IV equal-area projection as spatial reference. |
| 语种 | 英语 |
| 国家 | 国际 |
| 学科大类 | 气候变化 |
| 学科子类 | 气候变化 |
| 文献类型 | 数据集 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/215675 |
| 推荐引用方式 GB/T 7714 | Diesing,Markus. Deep-sea sediments of the global ocean mapped with Random Forest machine learning algorithm.2020-02-03.https://doi.org/10.1594/PANGAEA.911692. |
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