Prediction of marine heat flow based on the random forest method and geological and geophysical features
Prediction of marine heat flow based on the random forest method and geological and geophysical features
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基于随机森林法和地质地球物理特征的海洋热流预测
DOI：
10.1007/s11001-021-09452-y
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发表时间：
2021
影响因子：
1.4
通讯作者：
Yufeng Deng
中科院分区：
文献类型：
--
作者：
Min Li;Song Huang;Miao Dong;Ya Xu;Tianyao Hao;Xueshan Wu;Yufeng Deng
地热热流作为表征地球热状态的参数，记录了深层的热力学过程。然而，洋壳热流（HF）的测量相对稀疏，并且容易受到热液循环等表面活动的影响。我们提出了一种机器学习方法来预测海洋高频 (MHF)。我们应用随机森林（RF）回归算法来训练能够将多个地质和地球物理特征映射到 MHF 的预测器，从而实现全球洋壳中的 HF 预测。我们生成三个具有不同质量的高频测量数据集用于训练。最佳预测器的精度为 ~ 0.13（归一化均方根误差）或 ~ 0.07（归一化平均绝对误差），预测的全球洋壳HF图反映了MHF分布的基本模式。通过比较我们发现，高频测量的质量会影响预测结果。然后，我们使用交叉预测方案筛选出“低估”的实测高频案例，这些案例大多位于洋中脊和弧后盆地等构造环境中，与热液循环表现出较高的空间相关性。此外，我们对新的全球热流（NGHF）数据库的海洋部分中低估案例的范围和比例进行了实验计算；例如，低估程度大于50%的记录比例约为30.8%。这些计算可以为NGHF数据库中MHF记录的选择和应用提供参考信息。
Geothermal heat flow, as a parameter characterizing the Earth's thermal state, records deep thermodynamic processes. However, measurements of heat flow (HF) in the oceanic crust are relatively sparse and susceptible to surface activity such as hydrothermal circulation. We propose a machine learning approach to predict marine HF (MHF). We apply the random forest (RF) regression algorithm to train predictors capable of mapping multiple geological and geophysical features to MHF, thus enabling HF prediction in the global oceanic crust. We generate three data sets with different qualities of HF measurements for training. The best predictor has an accuracy of ~ 0.13 (normalized root mean squared error) or ~ 0.07 (normalized mean absolute error), and the predicted global oceanic crust HF map reflects the basic pattern of the MHF distribution. We find by comparison that the quality of HF measurement affects the prediction results. Then, we use a cross-prediction scheme to screen out the "underestimated" measured HF cases, which are mostly located in tectonic environments such as mid-ocean ridges and back-arc basins and show high spatial correlation with hydrothermal circulation. Furthermore, we conduct experimental calculations for the extent and proportion of underestimated cases in the oceanic part of a new global heat flow (NGHF) database; for example, the proportion of records with the degree of underestimation greater than 50% is approximately 30.8%. These calculations can provide reference information for the selection and application of MHF records in the NGHF database.