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...

Full description

Bibliographic Details
Main Authors: Shaohao Zou, Xilian Chen, Deru Xu, Matthew J. Brzozowski, Feng Lai, Yubing Bian, Zhilin Wang, Teng Deng
Format: Article
Language:English
Published: Elsevier 2021-09-01
Series:Geoscience Frontiers
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S1674987121000591
id doaj-06cdff88f3014132a0f6138af136a4fb
record_format Article
spelling 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
work_keys_str_mv AT shaohaozou amachinelearningapproachtotrackingcrustalthicknessvariationsintheeasternnorthchinacraton
AT xilianchen amachinelearningapproachtotrackingcrustalthicknessvariationsintheeasternnorthchinacraton
AT deruxu amachinelearningapproachtotrackingcrustalthicknessvariationsintheeasternnorthchinacraton
AT matthewjbrzozowski amachinelearningapproachtotrackingcrustalthicknessvariationsintheeasternnorthchinacraton
AT fenglai amachinelearningapproachtotrackingcrustalthicknessvariationsintheeasternnorthchinacraton
AT yubingbian amachinelearningapproachtotrackingcrustalthicknessvariationsintheeasternnorthchinacraton
AT zhilinwang amachinelearningapproachtotrackingcrustalthicknessvariationsintheeasternnorthchinacraton
AT tengdeng amachinelearningapproachtotrackingcrustalthicknessvariationsintheeasternnorthchinacraton
AT shaohaozou machinelearningapproachtotrackingcrustalthicknessvariationsintheeasternnorthchinacraton
AT xilianchen machinelearningapproachtotrackingcrustalthicknessvariationsintheeasternnorthchinacraton
AT deruxu machinelearningapproachtotrackingcrustalthicknessvariationsintheeasternnorthchinacraton
AT matthewjbrzozowski machinelearningapproachtotrackingcrustalthicknessvariationsintheeasternnorthchinacraton
AT fenglai machinelearningapproachtotrackingcrustalthicknessvariationsintheeasternnorthchinacraton
AT yubingbian machinelearningapproachtotrackingcrustalthicknessvariationsintheeasternnorthchinacraton
AT zhilinwang machinelearningapproachtotrackingcrustalthicknessvariationsintheeasternnorthchinacraton
AT tengdeng machinelearningapproachtotrackingcrustalthicknessvariationsintheeasternnorthchinacraton
_version_ 1717818028111953920