Lithium‐bearing Pegmatite Exploration in Western Altun, Xinjiang, using Remote‐Sensing Technology

Abstract

Western Altun in Xinjiang is an important area, where lithium (Li)‐bearing pegmatites have been found in recent years. However, the complex terrain and harsh environment of western Altun exacerbates in prospecting for Li‐bearing pegmatites. Therefore, remote‐sensing techniques can be an effective means for prospecting Li‐bearing pegmatites. In this study, the fault information and lithologyical information in the region were obtained using the median‐resolution remote‐sensing image Landsat‐8, the radar image Sentinel‐1 and hyperspectral data GF‐5. Using Landsat‐8 data, the hydroxyl alteration information closely related to pegmatite in the region was extracted by principal component analysis, pseudo‐anomaly processing and other methods. The high spatial resolution remote‐sensing data WorldView‐2 and WorldView‐3 short‐wave infrared images were used and analyzed by principal component analysis (PCA), the band ratio method and multi‐class machine learning (ML), combined with conventional thresholds specified the algorithms used to automatically extract Li‐bearing pegmatite information. Finally, the Li‐bearing pegmatite exploration area was determined, based on a comprehensive analysis of the faults, hydroxyl alteration lithology and Li‐bearing pegmatite information. Field investigations have verified that the distribution of pegmatites in the central part of the study area is consistent with that of Li‐bearing pegmatites extracted in this study. This study provides a new technique for prospecting Li‐bearing pegmatites, which shows that remote‐sensing technology possesses great potential for identifying lithium‐bearing pegmatites, especially in areas that are not readily accessible.

Prospecting Prediction for the Yulong Metallogenic Belt in Tibet Based on Remote Sensing Alteration Information and Structural Interpretation

“…The extraction of that alteration information using remote sensing data can effectively reduce the costs of prospecting, especially in the alpine and high-altitude areas, with few people in the area. In recent years, remote sensing technology has played an important role in the identification of minerals, geological mapping, alteration anomaly zoning, prospecting prediction and other fields [37][38][39][40][41]. However, remote sensing data are easily affected by artificial ground objects, vegetation and other factors, and using a single data source risks the production of significant errors, which leads to failure in meeting the needs of practical work.…”

Quantitative Study of the Morphological Variations among Dzieduszyckia Populations from the Devonian Rongxian Formation, South China and their Influencing Factors

“…To visualize shell outline variation, PCA was employed in this study. PCA is a statistical method that reduces the dimensionality of high-dimensional data by transforming the data into a new set of linearly uncorrelated variables called principal components, which account for most of the variability in the data (Jolliffe, 2002;Tang et al, 2019;Jiang et al, 2023). A total of 101, 99, and 95 well-preserved specimens were selected for PCA analyses from the Dazhai, Dalong, and Longmi populations, respectively.…”