Source fingerprinting loess deposits in Central Asia using elemental geochemistry with Bayesian and GLUE models

doi:10.1016/j.catena.2020.104808

IEECAS OpenIR > 古环境研究室

	Source fingerprinting loess deposits in Central Asia using elemental geochemistry with Bayesian and GLUE models
	Li, Yue 1,2,3; Gholami, Hamid 4; Song, Yougui1,2 ; Fathabadi, Aboalhasan 5; Malakooti, Hossein 6; Collins, Adrian L.7
通讯作者	Gholami, Hamid(hgholami@hormozgan.ac.ir)
	2020-11-01
发表期刊	CATENA
ISSN	0341-8162
卷号	194 页码:12
摘要	The provenance of loess deposits in Central Asia is largely unexplored. Accordingly, the goals of this research were to test and compare the performance of two different models (generalized likelihood uncertainty estimation - GLUE and a Bayesian model) for quantifying the uncertainty in source apportionment estimated for 46 target loess samples collected in the Ili basin, in eastern Central Asia. Model performance was evaluated using goodness-of-fit (GOF), mean absolute fit (MAF) and virtual mixtures (VM) in combination with root mean square error (RMSE) and mean absolute error (MAE). Our dataset comprised 132 surficial samples collected from three potential sources comprising river alluvium (n = 29), sand dunes (n = 35) and topsoils (n = 68). All samples were analysed for elemental geochemistry. Six geochemical properties (Co, Er, Y, Ga, Dy and Pb) were selected in a composite fingerprint which classified 83% of the samples from the three source categories correctly. Based on both models, source contributions to the loess samples were in the following order: topsoils > river alluvium > sand dunes. Based on the GOF and MAF tests, both models were accurate in predicting measured tracer concentrations in the loess samples. The Bayesian model was slightly more accurate (mean RMSE 1.6%, mean MAE 1.8%) than the GLUE (mean RMSE 5.0%, mean MAE 4.7%) model in predicting known source contributions. Overall, our results provide confirmation that application of source fingerprinting with elemental geochemistry and uncertainty modelling techniques is useful for identifying the provenance of loess sediments in arid and desert environments.
关键词	Source fingerprinting Uncertainty Virtual mixtures Ili basin Central Asia
DOI	10.1016/j.catena.2020.104808
关键词[WOS]	SEDIMENT SOURCES ; FINE SEDIMENT ; MIXING MODEL ; CATCHMENT ; PROVENANCE ; CHINA ; BASIN ; UNCERTAINTY ; RECONSTRUCTION ; TAJIKISTAN
收录类别	SCI ; SCI
语种	英语
资助项目	State Key Laboratory of Loess and Quaternary Geology, Chinese Academy of Sciences[SKLLQGPY2006] ; University of Hormozgan ; Institute of Earth Environmental, Chinese Academy of Sciences ; UKRI-BBSRC (UK Research and Innovation - Biotechnology and Biological Sciences Research Council) ; institute strategic programme Soil to Nutrition[BBS/E/C/000I0330]
WOS研究方向	Geology ; Agriculture ; Water Resources
项目资助者	State Key Laboratory of Loess and Quaternary Geology, Chinese Academy of Sciences ; University of Hormozgan ; Institute of Earth Environmental, Chinese Academy of Sciences ; UKRI-BBSRC (UK Research and Innovation - Biotechnology and Biological Sciences Research Council) ; institute strategic programme Soil to Nutrition
WOS类目	Geosciences, Multidisciplinary ; Soil Science ; Water Resources
WOS记录号	WOS:000566699000085
出版者	ELSEVIER
引用统计	被引频次：36[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ieecas.cn/handle/361006/15278
专题	古环境研究室第四纪科学与全球变化卓越创新中心
通讯作者	Gholami, Hamid
作者单位	1.Chinese Acad Sci, Inst Earth Environm, State Key Lab Loess & Quaternary Geol, Xian 710061, Peoples R China 2.CAS Ctr Excellence Quaternary Sci & Global Change, Xian 710061, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Univ Hormozgan, Dept Nat Resources Engn, Bandar Abbas, Hormozgan, Iran 5.Gonbad Kavous Univ, Dept Range & Watershed Management, Gonbad Kavous, Golestan Provin, Iran 6.Univ Hormozgan, Fac Marine Sci & Technol, Bandar Abbas, Hormozgan, Iran 7.Rothamsted Res, Sustainable Agr Sci Dept, Okehampton EX20 2SB, Devon, England
推荐引用方式 GB/T 7714	Li, Yue,Gholami, Hamid,Song, Yougui,et al. Source fingerprinting loess deposits in Central Asia using elemental geochemistry with Bayesian and GLUE models[J]. CATENA,2020,194:12.
APA	Li, Yue,Gholami, Hamid,Song, Yougui,Fathabadi, Aboalhasan,Malakooti, Hossein,&Collins, Adrian L..(2020).Source fingerprinting loess deposits in Central Asia using elemental geochemistry with Bayesian and GLUE models.CATENA,194,12.
MLA	Li, Yue,et al."Source fingerprinting loess deposits in Central Asia using elemental geochemistry with Bayesian and GLUE models".CATENA 194(2020):12.