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  2. 地质与地球物理研究所
  3. 中国科学院地质与地球物理研究所
  4. 中国科学院油气资源研究重点实验室
Prediction of marine heat flow based on the random forest method and geological and geophysical features

文献类型：期刊论文

作者Li, Min1,2,3; Huang, Song1,2,3; Dong, Miao1,2,3; Xu, Ya1,2,3; Hao, Tianyao1,2,3; Wu, Xueshan1,2,3; Deng, Yufeng1,2,3
刊名MARINE GEOPHYSICAL RESEARCH
出版日期2021-09-01
卷号42期号:3页码:13
关键词Marine heat flow Machine learning Hydrothermal circulation Random forest method
ISSN号0025-3235
DOI10.1007/s11001-021-09452-y
英文摘要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 similar to 0.13 (normalized root mean squared error) or similar to 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.
WOS关键词MODEL ; EARTH
资助项目National natural science foundation of china[91858214] ; National natural science foundation of china[91858212]
WOS研究方向Geochemistry & Geophysics ; Oceanography
语种英语
WOS记录号WOS:000695842100001
出版者SPRINGER
资助机构National natural science foundation of china ; National natural science foundation of china ; National natural science foundation of china ; National natural science foundation of china ; National natural science foundation of china ; National natural science foundation of china ; National natural science foundation of china ; National natural science foundation of china ; National natural science foundation of china ; National natural science foundation of china ; National natural science foundation of china ; National natural science foundation of china ; National natural science foundation of china ; National natural science foundation of china ; National natural science foundation of china ; National natural science foundation of china
源URL[http://ir.iggcas.ac.cn/handle/132A11/102658]  
专题地质与地球物理研究所_中国科学院油气资源研究重点实验室
通讯作者Huang, Song
作者单位1.Univ Chinese Acad Sci, Beijing, Peoples R China
2.Chinese Acad Sci, Innovat Acad Earth Sci, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Petr Resources Res, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Li, Min,Huang, Song,Dong, Miao,et al. Prediction of marine heat flow based on the random forest method and geological and geophysical features[J]. MARINE GEOPHYSICAL RESEARCH,2021,42(3):13.
APA Li, Min.,Huang, Song.,Dong, Miao.,Xu, Ya.,Hao, Tianyao.,...&Deng, Yufeng.(2021).Prediction of marine heat flow based on the random forest method and geological and geophysical features.MARINE GEOPHYSICAL RESEARCH,42(3),13.
MLA Li, Min,et al."Prediction of marine heat flow based on the random forest method and geological and geophysical features".MARINE GEOPHYSICAL RESEARCH 42.3(2021):13.

入库方式： OAI收割

来源：地质与地球物理研究所

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