南方科技大学知识苑(SUSTech KC): Quantifying Continental Crust Thickness Using the Machine Learning Method

题名	Quantifying Continental Crust Thickness Using the Machine Learning Method
作者	Guo, Peng1,2 ; Yang, Ting1,2,3
通讯作者	Yang, Ting
发表日期	2023-03-01
DOI	10.1029/2022JB025970
发表期刊	JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH 影响因子和分区
ISSN	2169-9313
EISSN	2169-9356
卷号	128 期号:3
摘要	Crustal thickness plays a key role in many geological processes. However, it remains challenging to quantify crustal thickness in the geological past. Here we propose an Extremely Randomized Trees algorithm-based machine learning model to recover crustal thickness of old geological regions. The model is trained using major oxide and trace element compositions of 1,480 young intermediate to felsic rocks from global arcs and collisional orogens and geophysical measurements of crustal thickness. The model provides better estimations of crustal thickness than the commonly used methods based on Sr/Y and (La/Yb)(N) when applied to the testing data. The validity of this model is further demonstrated by its applications to the Kohistan-Ladakh, Gangdese and Talkeetna arcs, where paleocrustal thicknesses have been well constrained. We then use this model to construct the Mesozoic crustal thickness evolution of the Erguna Block in the southeast of the Mongol-Okhotsk suture belt. The closure time of the suture zone is still debated. Our results suggest that the crustal thickness of the Erguna Block increased from 43 +/- 9 km at 210 Ma to 62 +/- 7 km at 180 Ma, remained constant between 180 and 150 Ma, and then thinned to 36 +/- 4 km at 120 Ma. These results suggest that the Mongol-Okhotsk Ocean closed in the Early-Middle Jurassic and the thickened crust was stretched during the Cretaceous. We show that the thick crust and compression-extension transition seem to be favorable for the formation of porphyry copper deposits in the Erguna Block during the Late Jurassic.
相关链接	[来源记录]
收录类别	SCI
语种	英语
学校署名	第一 ; 通讯
资助项目	National Natural Science Foundation of China["42002046","41904088","42174105"] ; Guangdong Basic and Applied Basic Research Foundation[2021A1515011356]
WOS研究方向	Geochemistry & Geophysics
WOS类目	Geochemistry & Geophysics
WOS记录号	WOS:000953059900001
出版者	AMER GEOPHYSICAL UNION
ESI学科分类	GEOSCIENCES
来源库	Web of Science
引用统计	被引频次[WOS]：5
成果类型	期刊论文
条目标识符	http://sustech.caswiz.com/handle/2SGJ60CL/524026
专题	理学院_地球与空间科学系
作者单位	1.Southern Univ Sci & Technol, Dept Earth & Space Sci, Shenzhen, Peoples R China 2.Southern Marine Sci & Engn Guangdong Lab Guangzhou, Guangzhou, Peoples R China 3.Southern Univ Sci & Technol, Guangdong Prov Key Lab Geophys High Resolut Imagin, Shenzhen, Peoples R China
第一作者单位	地球与空间科学系
通讯作者单位	地球与空间科学系; 南方科技大学
第一作者的第一单位	地球与空间科学系
推荐引用方式 GB/T 7714	Guo, Peng,Yang, Ting. Quantifying Continental Crust Thickness Using the Machine Learning Method[J]. JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH,2023,128(3).
APA	Guo, Peng,&Yang, Ting.(2023).Quantifying Continental Crust Thickness Using the Machine Learning Method.JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH,128(3).
MLA	Guo, Peng,et al."Quantifying Continental Crust Thickness Using the Machine Learning Method".JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH 128.3(2023).