A machine learning approach to tracking crustal thickness variations in the eastern North China Craton

[Display omitted] •Machine learning algorithms are constructed for predicating the crustal thickness.•Machine learning algorithms are more effective than the conventional methods.•The crustal thickness of ENCC was thick enough to be delaminated during the Early Cretaceous. The variation of crustal t...

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Veröffentlicht in:	Di xue qian yuan. 2021-09, Vol.12 (5), p.101195, Article 101195
Hauptverfasser:	Zou, Shaohao, Chen, Xilian, Xu, Deru, Brzozowski, Matthew J., Lai, Feng, Bian, Yubing, Wang, Zhilin, Deng, Teng
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Cretaceous Crustal thickness Eastern North China Craton Elevation Geochemical database Isotopes Machine learning Mountains Root-mean-square errors Thickness Trace elements Yttrium
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