气候变化领域集成服务门户(CCPortal): Chromite-Bearing Peridotite Identification, Based on Spectral Analysis and Machine Learning: A Case Study of the Luobusa Area, Tibet, China

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DOI	10.3390/app13169325
	Chromite-Bearing Peridotite Identification, Based on Spectral Analysis and Machine Learning: A Case Study of the Luobusa Area, Tibet, China
	Yang, Weiguang; Zheng, Youye; Chen, Shizhong; Duan, Xingxing; Zhou, Yu; Xu, Xiaokuan
发表日期	2023
EISSN	2076-3417
卷号	13 期号:16
英文摘要	Chromite is a strategic mineral resource for many countries, and chromite deposit occurrences are widespread in the ultramafic rocks of the Yarlung Zangbo ophiolite belt, particularly in the harzburgite unit of the mantle section. Conducting field surveys in complex and poorly accessible terrain is challenging, expensive, and time-consuming. Remote sensing is an advanced method of achieving modern geological work and is a powerful technical means of geological research and mineral exploration. In order to delineate outcrops of chromite-bearing mantle peridotite, the present research study integrates seven image-enhancement techniques, including optimal band combination, decorrelation stretching, band ratio, independent component analysis, principal component analysis, minimum noise fraction, and false color composite, for the interpretation of Landsat8 OLI and WorldView-2 satellite data. This integrated approach allows the effective discrimination of chromite-containing peridotite outcrops in the Luobusa area, Tibet. The interpretation results derived from these integrated image-processing techniques were systematically verified in the field and formed the basis of the feature selection process of different lithologies supported by the support vector machine algorithm. Furthermore, the distribution range of the ferric contamination anomaly is detected through the de-interference abnormal principal component thresholding technique, which shows a high spatial matching relationship with mantle peridotite. This is the first study to utilize Landsat8 OLI and WorldView-2 remote sensing satellite data to explore the largest chromite deposit in China, which enriches the research methods for the chromite deposits in the Luobusa area. Accordingly, the results of this investigation indicate that the integration of information extracted from image-processing algorithms using remote sensing data could be a broadly applicable tool for prospecting chromite ore deposits associated with ophiolitic complexes in mountainous and inaccessible regions such as Tibet's ophiolitic zones.
关键词	remote sensing spectral enhancement techniques support vector machine mantle peridotite chromite deposit
英文关键词	SUPPORT VECTOR MACHINES; SOUTH EASTERN DESERT; REMOTE-SENSING TECHNIQUES; ZANGBO SUTURE ZONE; OPHIOLITE COMPLEX; LANDSAT TM; PODIFORM CHROMITITES; MANTLE PERIDOTITES; MINERALIZED ZONES; INTEGRATED FIELD
WOS研究方向	Chemistry, Multidisciplinary ; Engineering, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS记录号	WOS:001056020000001
来源期刊	APPLIED SCIENCES-BASEL
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/283141
作者单位	China Geological Survey; China University of Geosciences - Beijing; China University of Geosciences - Wuhan; China Geological Survey; University of Canberra
推荐引用方式 GB/T 7714	Yang, Weiguang,Zheng, Youye,Chen, Shizhong,et al. Chromite-Bearing Peridotite Identification, Based on Spectral Analysis and Machine Learning: A Case Study of the Luobusa Area, Tibet, China[J],2023,13(16).
APA	Yang, Weiguang,Zheng, Youye,Chen, Shizhong,Duan, Xingxing,Zhou, Yu,&Xu, Xiaokuan.(2023).Chromite-Bearing Peridotite Identification, Based on Spectral Analysis and Machine Learning: A Case Study of the Luobusa Area, Tibet, China.APPLIED SCIENCES-BASEL,13(16).
MLA	Yang, Weiguang,et al."Chromite-Bearing Peridotite Identification, Based on Spectral Analysis and Machine Learning: A Case Study of the Luobusa Area, Tibet, China".APPLIED SCIENCES-BASEL 13.16(2023).