Application of random-forest machine learning algorithm for mineral predictive mapping of Fe-Mn crusts in the World Ocean
Application of random-forest machine learning algorithm for mineral predictive mapping of Fe-Mn crusts in the World Ocean
复制标题
DOI：
10.1016/j.oregeorev.2023.105671
复制
发表时间：
2023-09
影响因子：
3.3
通讯作者：
Pierre Josso;Alex Hall;Christopher Williams;Tim Le Bas;P. Lusty;B. Murton
中科院分区：
文献类型：
--
作者：
Pierre Josso;Alex Hall;Christopher Williams;Tim Le Bas;P. Lusty;B. Murton
矿产远景图是一个有效的工具，可用来划定最感兴趣的区域，以指导今后的勘探。在过去几十年中，在绘制深海铁锰结壳远景图时采用了多种知识驱动的方法。报告介绍了一种数据驱动方法的结果，该方法利用了关于世界海洋铁锰结壳出现情况的广泛数据收集工作以及最近全球海洋数据集的增加。应用随机森林机器学习算法，并将结果与先前建立的专家驱动的地图进行比较。该算法的最佳预测条件是：（i）森林大小上级一百棵树，（ii）训练数据集大于10%，以及（iii）用作节点的预测因子数量上级两个。其余未使用数据的混淆矩阵和袋外误差突出表明，训练模型具有出色的预测能力，铁锰结壳的预测准确率为87.2%，非结壳位置为98.2%，Kohen K指数为0.84，验证了其在世界范围内预测的应用。海底坡度、沉积物厚度、沉积物类型、生物生产力和深海山脉是预测铁锰结壳出现的五个最有力的解释变量。大多数“手绘”知识驱动的远景区也被随机森林算法认为是远景区，但美洲大陆沿海沿着有明显的例外。然而，不良的相关性观察到知识驱动的GIS为基础的标准映射的随机森林认为非前瞻性的GIS方法的目标地区。总体而言，随机森林预测表现更好地预测在伊萨许可的区域铁锰结壳发生的机会高比GIS的方法，这构成了随机森林模型的预测质量的外部验证。
Mineral prospectivity mapping constitutes an efficient tool for delineating areas of highest interest to guide future exploration. Multiple knowledge-driven approaches have been applied for the creation of prospectivity maps for deep-sea ferromanganese (Fe-Mn) crusts over the last decades. The results of a data-driven approach making use of an extensive data collection exercise on occurrences of Fe-Mn crusts in the World Ocean and recent increase in global marine datasets are presented. A Random Forest machine learning algorithm is applied, and results compared with previously established expert-driven maps. Optimal predictive conditions for the algorithm are observed for (i) a forest size superior to a hundred trees, (ii) a training dataset larger than 10%, and (iii) a number of predictors to be used as nodes superior to two. The confusion matrix and out-of-bag errors on the remaining unused data highlight excellent predictive capabilities of the trained model with a prediction accuracy for Fe-Mn crusts of 87.2% and 98.2% for non-crusts locations, with a Kohen’s K index of 0.84, validating its application for prediction at the World scale. The slope of the seafloor, sediment thickness, sediment type, biological productivity, and abyssal mountain constitute the five strongest explanatory variables in predicting the occurrence of Fe-Mn crusts. Most ‘hand-drawn’ knowledge-driven prospective areas are also considered prospective by the random forest algorithm with notable exceptions along the coast of the American continent. However, poor correlation is observed with knowledge-driven GIS-based criterion mapping as the Random Forest considers un-prospective most target areas from the GIS approach. Overall, the Random Forest prediction performs better in predicting a high chance of Fe-Mn crust occurrence in ISA licensed area than the GIS approach, which constitutes an external validation of the predictive quality of the random forest model.