Lithologic Mapping in the Karamaili Ophiolite-Mélange Belt in Xinjiang, China, with Machine Learning and Integration of SDGSAT-1 TIS, Landsat-8 OLI and ASTER-GDEM
文献类型：期刊论文
| 作者 | Zhang, Zhao4; Yin, Fang2; Zhu, Yunqiang1; Liu, Lei3,4 |
| 刊名 | NATURAL RESOURCES RESEARCH
 |
| 出版日期 | 2025-02-13 |
| 卷号 | N/A |
| 关键词 | SDGSAT-1 TIS Rock-forming minerals Karamaili ophiolitem & eacute lange belt Lithology mapping Machine learning algorithms |
| ISSN号 | 1520-7439 |
| DOI | 10.1007/s11053-025-10467-0 |
| 产权排序 | 4 |
| 文献子类 | Article ; Early Access |
| 英文摘要 | Lithological mapping is an effective tool for geological surveys and mineral exploration. However, it faces challenges in identifying complex rock types and improving classification accuracy. We mapped lithological units in the Karamaili ophiolite-m & eacute;lange belt of Xinjiang using integrated machine learning algorithms, including artificial neural network (ANN), Mahalanobis distance (MD), support vector machine (SVM), and random forest (RF). These algorithms were utilized to process remote sensing datasets acquired by the Sustainable Development Science Satellite 1 Thermal Infrared Spectrometer (SDGSAT-1 TIS), Landsat-8 Operational Land Imager (OLI), and Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER-GDEM). The results indicated that the overall accuracies of ANN, MD, SVM, and RF were 68.87%, 78.98%, 93.4%, and 98.36%, respectively. The SVM and RF effectively mapped the lithological units. The SDGSAT-1 TIS data helped to identify mafic-ultramafic and feldspar-rich rocks, while Landsat-8 OLI helped to successfully delineate granitoid and complex lithologies. The ASTER-GDEM data helped improve mapping accuracy by providing detailed topographic information. Thus, this study confirmed the efficacy of the implemented approaches to delineate mineralization zones and to discriminate lithological units. This study provides detailed geological data for lithological mapping and serves as a significant reference for geological surveys and environmental monitoring. |
| URL标识 | 查看原文 |
| WOS关键词 | SPACEBORNE THERMAL EMISSION ; REMOTE-SENSING DATA ; ULTRAMAFIC COMPLEX ; MOUNTAIN PASS ; RANDOM FOREST ; DISCRIMINATION ; CLASSIFICATION ; GRANITOIDS ; CALIFORNIA ; GEOLOGY |
| WOS研究方向 | Geology |
| 语种 | 英语 |
| WOS记录号 | WOS:001420091700001 |
| 出版者 | SPRINGER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/212324]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Yin, Fang; Zhu, Yunqiang |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Changan Univ, Sch Land Engn, Shaanxi Key Lab Land Consolidat, Xian 710054, Peoples R China; 3.Minist Nat Resources, New Energy Minerals & Resources Informat Engn Tech, Xian 710054, Peoples R China; 4.Changan Univ, Sch Earth Sci & Resources, Xian 710054, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Zhang, Zhao,Yin, Fang,Zhu, Yunqiang,et al. Lithologic Mapping in the Karamaili Ophiolite-Mélange Belt in Xinjiang, China, with Machine Learning and Integration of SDGSAT-1 TIS, Landsat-8 OLI and ASTER-GDEM[J]. NATURAL RESOURCES RESEARCH,2025,N/A. |
| APA | Zhang, Zhao,Yin, Fang,Zhu, Yunqiang,&Liu, Lei.(2025).Lithologic Mapping in the Karamaili Ophiolite-Mélange Belt in Xinjiang, China, with Machine Learning and Integration of SDGSAT-1 TIS, Landsat-8 OLI and ASTER-GDEM.NATURAL RESOURCES RESEARCH,N/A. |
| MLA | Zhang, Zhao,et al."Lithologic Mapping in the Karamaili Ophiolite-Mélange Belt in Xinjiang, China, with Machine Learning and Integration of SDGSAT-1 TIS, Landsat-8 OLI and ASTER-GDEM".NATURAL RESOURCES RESEARCH N/A(2025). |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。