A machine learning approach to tracking crustal thickness variations in the eastern North China Craton
The variation of crustal thickness is a critical index to reveal how the continental crust evolved over its four billion years. Generally, ratios of whole-rock trace elements, such as Sr/Y, (La/Yb)n and Ce/Y, are used to characterize crustal thicknesses. However, sometimes confusing results are obta...
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doaj-06cdff88f3014132a0f6138af136a4fb2021-09-03T04:44:05ZengElsevierGeoscience Frontiers1674-98712021-09-01125101195A machine learning approach to tracking crustal thickness variations in the eastern North China CratonShaohao Zou0Xilian Chen1Deru Xu2Matthew J. Brzozowski3Feng Lai4Yubing Bian5Zhilin Wang6Teng Deng7State Key Laboratory of Nuclear Resources and Environment, East China University of Technology, Nanchang 330013, China; Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, School of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaState Key Laboratory of Nuclear Resources and Environment, East China University of Technology, Nanchang 330013, China; Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, School of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaState Key Laboratory of Nuclear Resources and Environment, East China University of Technology, Nanchang 330013, China; Corresponding author.State Key Laboratory for Mineral Deposits Research, School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023, ChinaState Key Laboratory of Nuclear Resources and Environment, East China University of Technology, Nanchang 330013, ChinaState Key Laboratory of Nuclear Resources and Environment, East China University of Technology, Nanchang 330013, ChinaKey Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, School of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaState Key Laboratory of Nuclear Resources and Environment, East China University of Technology, Nanchang 330013, ChinaThe variation of crustal thickness is a critical index to reveal how the continental crust evolved over its four billion years. Generally, ratios of whole-rock trace elements, such as Sr/Y, (La/Yb)n and Ce/Y, are used to characterize crustal thicknesses. However, sometimes confusing results are obtained since there is no enough filtered data. Here, a state-of-the-art approach, based on a machine-learning algorithm, is proposed to predict crustal thickness using global major- and trace-element geochemical data of intermediate arc rocks and intraplate basalts, and their corresponding crustal thicknesses. After the validation processes, the root-mean-square error (RMSE) and the coefficient of determination (R2) score were used to evaluate the performance of the machine learning algorithm based on the learning dataset which has never been used during the training phase. The results demonstrate that the machine learning algorithm is more reliable in predicting crustal thickness than the conventional methods. The trained model predicts that the crustal thickness of the eastern North China Craton (ENCC) was ~45 km from the Late Triassic to the Early Cretaceous, but ~35 km from the Early Cretaceous, which corresponds to the paleo-elevation of 3.0 ± 1.5 km at Early Mesozoic, and decease to the present-day elevation in the ENCC. The estimates are generally consistent with the previous studies on xenoliths from the lower crust and on the paleoenvironment of the coastal mountain of the ENCC, which indicates that the lower crust of the ENCC was delaminated abruptly at the Early Cretaceous.http://www.sciencedirect.com/science/article/pii/S1674987121000591Machine learningGeochemical databaseCrustal thicknessEastern North China Craton |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shaohao Zou Xilian Chen Deru Xu Matthew J. Brzozowski Feng Lai Yubing Bian Zhilin Wang Teng Deng |
spellingShingle |
Shaohao Zou Xilian Chen Deru Xu Matthew J. Brzozowski Feng Lai Yubing Bian Zhilin Wang Teng Deng A machine learning approach to tracking crustal thickness variations in the eastern North China Craton Geoscience Frontiers Machine learning Geochemical database Crustal thickness Eastern North China Craton |
author_facet |
Shaohao Zou Xilian Chen Deru Xu Matthew J. Brzozowski Feng Lai Yubing Bian Zhilin Wang Teng Deng |
author_sort |
Shaohao Zou |
title |
A machine learning approach to tracking crustal thickness variations in the eastern North China Craton |
title_short |
A machine learning approach to tracking crustal thickness variations in the eastern North China Craton |
title_full |
A machine learning approach to tracking crustal thickness variations in the eastern North China Craton |
title_fullStr |
A machine learning approach to tracking crustal thickness variations in the eastern North China Craton |
title_full_unstemmed |
A machine learning approach to tracking crustal thickness variations in the eastern North China Craton |
title_sort |
machine learning approach to tracking crustal thickness variations in the eastern north china craton |
publisher |
Elsevier |
series |
Geoscience Frontiers |
issn |
1674-9871 |
publishDate |
2021-09-01 |
description |
The variation of crustal thickness is a critical index to reveal how the continental crust evolved over its four billion years. Generally, ratios of whole-rock trace elements, such as Sr/Y, (La/Yb)n and Ce/Y, are used to characterize crustal thicknesses. However, sometimes confusing results are obtained since there is no enough filtered data. Here, a state-of-the-art approach, based on a machine-learning algorithm, is proposed to predict crustal thickness using global major- and trace-element geochemical data of intermediate arc rocks and intraplate basalts, and their corresponding crustal thicknesses. After the validation processes, the root-mean-square error (RMSE) and the coefficient of determination (R2) score were used to evaluate the performance of the machine learning algorithm based on the learning dataset which has never been used during the training phase. The results demonstrate that the machine learning algorithm is more reliable in predicting crustal thickness than the conventional methods. The trained model predicts that the crustal thickness of the eastern North China Craton (ENCC) was ~45 km from the Late Triassic to the Early Cretaceous, but ~35 km from the Early Cretaceous, which corresponds to the paleo-elevation of 3.0 ± 1.5 km at Early Mesozoic, and decease to the present-day elevation in the ENCC. The estimates are generally consistent with the previous studies on xenoliths from the lower crust and on the paleoenvironment of the coastal mountain of the ENCC, which indicates that the lower crust of the ENCC was delaminated abruptly at the Early Cretaceous. |
topic |
Machine learning Geochemical database Crustal thickness Eastern North China Craton |
url |
http://www.sciencedirect.com/science/article/pii/S1674987121000591 |
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