Lithium-Bearing Pegmatite Identification, Based on Spectral Analysis and Machine Learning: A Case Study of the Dahongliutan Area, NW China
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; hen...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/2/493 |
| _version_ | 1797437461904752640 |
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| author | Li Chen Nannan Zhang Tongyang Zhao Hao Zhang Jinyu Chang Jintao Tao Yujin Chi |
| author_facet | Li Chen Nannan Zhang Tongyang Zhao Hao Zhang Jinyu Chang Jintao Tao Yujin Chi |
| author_sort | Li Chen |
| collection | DOAJ |
| description | 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%). |
| first_indexed | 2024-03-09T11:19:36Z |
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| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T11:19:36Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d867403dae964c11b0cbe1c4858d3f8b2023-12-01T00:22:33ZengMDPI AGRemote Sensing2072-42922023-01-0115249310.3390/rs15020493Lithium-Bearing Pegmatite Identification, Based on Spectral Analysis and Machine Learning: A Case Study of the Dahongliutan Area, NW ChinaLi Chen0Nannan Zhang1Tongyang Zhao2Hao Zhang3Jinyu Chang4Jintao Tao5Yujin Chi6State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, ChinaState Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, ChinaXinjiang Research Center for Mineral Resources, Chinese Academy of Sciences, Urumqi 830011, ChinaState Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, ChinaState Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, ChinaState Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, ChinaState Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, ChinaLithium (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%).https://www.mdpi.com/2072-4292/15/2/493Li-bearing pegmatitemachine learningVNIR–SWIRDahongliutan areaGF-5 AHSI |
| spellingShingle | Li Chen Nannan Zhang Tongyang Zhao Hao Zhang Jinyu Chang Jintao Tao Yujin Chi Lithium-Bearing Pegmatite Identification, Based on Spectral Analysis and Machine Learning: A Case Study of the Dahongliutan Area, NW China Remote Sensing Li-bearing pegmatite machine learning VNIR–SWIR Dahongliutan area GF-5 AHSI |
| title | Lithium-Bearing Pegmatite Identification, Based on Spectral Analysis and Machine Learning: A Case Study of the Dahongliutan Area, NW China |
| title_full | Lithium-Bearing Pegmatite Identification, Based on Spectral Analysis and Machine Learning: A Case Study of the Dahongliutan Area, NW China |
| title_fullStr | Lithium-Bearing Pegmatite Identification, Based on Spectral Analysis and Machine Learning: A Case Study of the Dahongliutan Area, NW China |
| title_full_unstemmed | Lithium-Bearing Pegmatite Identification, Based on Spectral Analysis and Machine Learning: A Case Study of the Dahongliutan Area, NW China |
| title_short | Lithium-Bearing Pegmatite Identification, Based on Spectral Analysis and Machine Learning: A Case Study of the Dahongliutan Area, NW China |
| title_sort | lithium bearing pegmatite identification based on spectral analysis and machine learning a case study of the dahongliutan area nw china |
| topic | Li-bearing pegmatite machine learning VNIR–SWIR Dahongliutan area GF-5 AHSI |
| url | https://www.mdpi.com/2072-4292/15/2/493 |
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