Lithium-Bearing Pegmatite Identification, Based on Spectral Analysis and Machine Learning: A Case Study of the Dahongliutan Area, NW China

Author:

Chen Li,Zhang NannanORCID,Zhao Tongyang,Zhang Hao,Chang JinyuORCID,Tao Jintao,Chi Yujin

Abstract

Lithium (Li) resources are widely used in many strategic emerging fields; recently, several large-scale to super-large-scale pegmatite-type lithium deposits have been discovered in Dahongliutan, NW China. However, the natural environmental conditions in the Dahongliutan area are extremely harsh; hence, manpower in field exploration is difficult to achieve. Efficient and rapid methods for identifying Li-rich pegmatites, based on hyperspectral remote sensing technology, have great potential for promoting the discovery of lithium resources. Ground spectral research is the cornerstone of regional hyperspectral imaging (HSI) for geological mapping. Direct observation and analysis by the naked eye are part of a process that is mainly dependent upon abundant experience and knowledge from experts. Machine learning (ML) technology has the advantages of automatic feature extraction and relationship characterization. Therefore, identifying the spectral features of Li-rich pegmatite via ML can accurately and efficiently distinguish the spectral characteristics of Li-rich pegmatites and Li-poor pegmatites, enabling further excavation to identify the strongest predictors of Li-pegmatite and laying a foundation for the accurate extraction of Li-rich pegmatites in the West Kunlun region using HSI. The spectral characteristics of pegmatite in the visible near-infrared and shortwave infrared (VNIR–SWIR) spectra were observed and analyzed. Li-rich pegmatite was identified based on the diagnostic spectral waveform characteristic parameters of the local wavelength range. The results demonstrated that the pegmatite ML recognition model was based on spectral characteristic parameters of the local wavelength range, with good model explicability, and the area under the curve (AUC) calculated for the model is 0.843. A recognition model based on full-range spectrum data achieved a higher precision, and the AUC value was up to 0.977. The evaluation of the Gini coefficient presented the strongest predictors, which were used to map the spatial distribution lithology, based on GF-5, in Akesayi and the 509 mines, producing encouraging lithological mapping results (Kappa > 0.9, OA > 94%).

Funder

Chinese Academy of Sciences

Xinjiang Science Foundation for Distinguished Young Scholars

Major Science and Technology Project of Xinjiang Uygur Autonomous Region

Geological and Mineral Exploration and Development Bureau of Xinjiang Uygur Autonomous Region

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3