Identification of Lithium-Bearing Pegmatite Dikes Based on WorldView-3 Data: A Case Study of the Shaligou Area in Western Altyn

Abstract

Shaligou, located in the southwestern Altyn Mountains, is a newly discovered lithium-bearing pegmatite deposit that is driving increased exploration in the region. However, the challenging environmental conditions of the Altyn Mountains pose challenges for the exploration of lithium-bearing pegmatites. Consequently, remote sensing technology has emerged as an effective exploration tool. In this study, the spectral data of typical samples were collected to establish a spectral library of rocks and minerals in the study area capable of serving as a foundation for remote sensing analysis. Firstly, ASTER data were utilized successfully for lithological interpretation of the area. Secondly, high-resolution WorldView-3 data with a spatial resolution of 0.31 m were used to establish interpretation criteria for pegmatite dikes. Ground validation results were highly consistent with the remote sensing interpretations, confirming that the use of WorldView-3 data significantly enhances the accuracy of lithium-bearing pegmatite dike identification, providing valuable guidance for further exploration.

1. Introduction

In recent years, the exploration and prediction of pegmatite-type deposits, especially rare metal lithium deposits, have become a focus and frontier topic in mineral research. Lithium deposits with economic development potential include mainly brine-type, granite pegmatite-type, and granite-type deposits [1]. Among these, granite pegmatite-type lithium deposits have become the primary source of the global lithium supply because of their high grade, associated mature extraction and processing technologies, and short investment and construction cycles. Against this backdrop, domestic experts have discovered numerous potential mineralization zones of rare metals, such as lithium and beryllium, in the West Kunlun–Altyn Mountains region, revealing great exploration potential [2].

The western Kunlun region is renowned for its abundant mineral resources and large pegmatite deposits, which supply significant portions of the world’s rare metals and strategic minerals [3]. Moreover, as a significant tectonic belt, the western Kunlun region records crucial information on crustal evolution and provides key insights into the formation and distribution of pegmatite-type deposits [4]. The successful research experiences and exploration methods used in the region have provided valuable references for geological prospecting in adjacent areas, particularly in the search for critical metals such as lithium and beryllium [5].

The Altyn Tagh tectonic belt, an extension of the western Kunlun region, also holds potential for rare metal resources [6]. Its complex geology and magmatic history create favorable conditions for pegmatite formation, though challenges such as surface coverage and difficult transportation hinder resource identification and evaluation efforts [7]. Among regions in the Altyn Tagh tectonic belt, the Shaligou area, as an important prospecting target, has attracted widespread attention because of its unique geological features and potential lithium resources.

Shaligou, located in the southwestern segment of the Altyn Tagh Mountains, is a newly discovered significant lithium deposit, following the Dahongliutan deposit, in the western Kunlun region. Its discovery has further fueled the exploration boom for pegmatite-type lithium deposits. However, the pegmatite veins in this area exhibit complex occurrence characteristics, including small-scale veins typically 1–5 m wide and significant aeolian sand cover. Traditional gravity, magnetic, and electrical exploration methods have limited effectiveness in this circumstance. In addition, the Altyn Tagh region has an extremely harsh environment, with rugged terrain, crisscrossing canyons, and poor transportation conditions, all of which severely limit the effective application of traditional geological methods. Nevertheless, remote sensing technology has demonstrated significant application potential to overcome these challenges [8].

Focusing on the Shaligou area, this study addresses the challenges of the aeolian sand cover and narrow mineral veins in the region. High-resolution WorldView-3 data (spatial resolution of 0.31 m), in combination with spectral mineral mapping techniques, are used to differentiate between mineralized and non-mineralized pegmatite veins. ASTER data are used initially to identify the lithologies in the region, followed by the use of WorldView-3 data for preliminary identification of larger-scale pegmatite veins. Subsequently, the Spectral Angle Mapper (SAM) method is used to extract spatial distribution information of minerals such as spodumene and lepidolite. Finally, through an overlay analysis of the distribution of pegmatite veins and the spectral mineral indicators, high-precision identification of lithium-bearing pegmatite veins is achieved. This study provides scientific data and technical support for prospecting efforts in the Altyn Tagh Mountains and the western Kunlun–Altyn tectonic belt.

2. Regional Geology

The Altyn Mountains in northwestern China form a tectonic belt along the northeastern edge of the Tibetan Plateau extending along the Altyn Fault Zone (Figure 1). The region is geologically complex with frequent crustal activity [9]. The study area is located in the western section of the Altyn Mountains, where the exposed strata primarily include the Archean Milan Group (Ar_1–2M), Paleoproterozoic Altyn Group (Pt₂A), Changcheng System Beiketank Group (Chb), Jixian System Jinyanshan Group (Jxj), and Quaternary strata (Q) (Figure 2). The Milan Group consists of highly deformed and metamorphosed clastic rocks, carbonates, and volcanic rocks, mainly distributed in the southeastern part of the study area [6]. The Altyn Group, which is widely exposed in the central and southwestern parts of the study area, comprises greenschist to amphibolite facies metamorphic rocks, including metamorphosed clastic, carbonate, and metavolcanic rocks. The Changcheng System Beiketank Group is composed of metamorphic rocks, and the Jixian System Jinyanshan Group, distributed along both sides of the Kumukejiakou Valley and trending approximately east–west, is dominated by marble.

The Shaligou lithium-beryllium mining area, located in the central part of the study region, hosts numerous granite pegmatite dikes that typically appear as relatively regular, dike-like, or lenticular forms exposed on the surface with an approximate east–west orientation. The main ore minerals include lepidolite, spodumene, and beryl, and the gangue minerals are primarily quartz, sodium feldspar, potassium feldspar, and mica. Spodumene is the predominant lithium-rich mineral, followed by lepidolite, and beryl is the main beryllium-rich mineral.

3. Materials and Methods

3.1. Remote Sensing Images

3.1.1. Aster

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is a satellite instrument developed through a collaboration between NASA and Japan [10]. It is equipped with 14 spectral bands that span visible, near-infrared, shortwave infrared, and thermal infrared wavelengths with spatial resolutions ranging from 15 to 90 m. ASTER is utilized extensively in mineral resource exploration, geological analysis, and environmental monitoring, in which it offers valuable insights into surface mineral composition and structure [11]. The availability of free data download channels facilitates convenient access for remote sensing applications. In this study, ASTER-L1T level data were selected (

Table 1. Aster-related parameters [11].

Spectral Region	Band	Spectral Range	Spatial Resolution
Visible-near infrared (VNIR)	1	520–600 nm	15 m
	2	630–690 nm
	3	780–860 nm
Short-wave infrared (SWIR)	4	1600–1700 nm	30 m
	5	2150–2190 nm
	6	2190–2230 nm
	7	2240–2290 nm
	8	2300–2370 nm
	9	2360–2430 nm
Thermal infrared (TIR)	10	8130–8480 nm	90 m
	11	8480–8830 nm
	12	8930–9280 nm
	13	10,250–10,950 nm
	14	10,950–11,650 nm

). This data level has undergone precise terrain correction and sensor radiometric calibration, providing superior geometric accuracy in comparison with the more commonly used L1B level data [12].

3.1.2. WorldView-3

Currently, multispectral remote sensing for alteration information extraction often relies on data from sources such as ASTER and ETM+, which have been proven effective [13]. However, WorldView-3, with its highest spatial resolution among commercial multispectral satellites, offers substantial advantages over WorldView-2, ETM+, ASTER, and similar datasets. The 0.31-m spatial resolution of WorldView-3 significantly enhances the ability to identify large-scale pegmatite dikes within the study area [14]. Furthermore, WorldView-3 introduces eight new shortwave infrared (SWIR) bands beyond WorldView-2, expanding the imagery’s applicability (Figure 3 and

Table 2. WorldView-3 related parameters [14].

Spectral Region	Band	Spectral Range	Spatial Resolution
Multispectral (MUL)	VNIR1	400–450 nm	1.2 m
	VNIR2	450–510 nm
	VNIR3	510–580 nm
	VNIR4	585–625 nm
	VNIR5	630–690 nm
	VNIR6	705–745 nm
	VNIR7	770–895 nm
	VNIR8	860–1040 nm
Short-wave infrared (SWIR)	SWIR1	1195–1225 nm	3.7 m
	SWIR2	1550–1590 nm
	SWIR3	1640–1680 nm
	SWIR4	1710–1750 nm
	SWIR5	2145–2185 nm
	SWIR6	2185–2225 nm
	SWIR7	2235–2285 nm
	SWIR8	2295–2365 nm
Panchromatic (PAN)	Pan	450–800 nm	0.31 m

). These SWIR bands are particularly valuable for extracting alteration information because they reveal distinct absorption features and spectral variations for alteration minerals that are difficult to discern in visible and near-infrared bands [15]. This enhanced capability allows for more accurate differentiation of mineral types. For optimal data support in spectral information extraction for this study area, specific parameters are outlined in Table WorldView-3-related parameters.

3.1.3. Image Preprocessing

The original remote sensing imagery, with only minimal processing, often exhibits limited information and low average brightness [16]. Geological features, such as body edges and structures, appear blurred and poorly defined. To address these issues, it is essential to enhance the brightness levels and minimize errors introduced during image acquisition through preprocessing. This will ensure a high-quality data foundation for accurate and effective subsequent information extraction.

In this study, the visible–near-infrared (VNIR) and SWIR data from ASTER were radiometrically calibrated, with the SWIR data resampled to 15 m to align with the near-infrared data. These datasets were then fused, and the fused data were subjected to atmospheric correction using the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) method [17]. The FLAASH correction module, utilizing the MODTRAN4+ radiative transfer model code, removes the effects of atmospheric and lighting conditions, thus restoring the true physical parameters of the surface, such as surface reflectance, radiance, and surface temperature. In addition to these steps, the processing of WorldView-3(WV3) imagery involves an extra step of geometric correction. This correction addresses geometric distortions in the remote sensing images and integrates the multispectral (MUL) and shortwave infrared (SWIR) bands with the panchromatic (PAN) band, enhancing accuracy and detail.

3.2. Spectral Basis

The spectral characteristics of rocks and minerals form the core foundation for extracting remote sensing information. They are crucial for differentiating surface material types and their distribution. A widely used reference is the USGS spectral library, which provides spectral data measured under controlled conditions and accurately represents the reflection spectra of pure minerals. It provides valuable references for remote sensing analysis, especially in the absence of local measurement data. However, remote sensing data can exhibit regional variability [18]. The same mineral may exhibit spectral variability across different geographic settings due to the inherent spectral mixing effect, especially in non-diagnostic spectral regions where mixed pixel signatures predominate [19]. Although diagnostic absorption features are generally stable, non-diagnostic spectral bands can be influenced by environmental factors such as weathering and compositional mixing [20]. Therefore, measured spectra more accurately reflect the true characteristics of local minerals, helping to mitigate potential errors arising from relying solely on standard spectral libraries [21].

This study employed a convenient spectrometer, the SVC-HR-1024, to perform multiple measurements of typical rock and mineral samples in the study area, using a professional laboratory lighting source in a darkroom with a light source response range of 350–2500 nm and a diffuse reflectance reference whiteboard, thereby obtain their average reflectance spectra. These measured data reflect the actual environmental conditions more accurately, addressing the limitations of regional variability inherent in published reference spectral libraries. This approach provides robust support for remote-sensing information extraction and regional geological research (Figure 4).

3.3. Methodology

3.3.1. Independent Component Analysis

In remote sensing data processing, independent component analysis (ICA) is a powerful signal separation technique used to extract mutually independent land or mineral components from mixed spectral data [22]. ICA operates under the assumption that the spectral signals of different land cover types are statistically independent [23]. By applying linear decomposition to remote sensing images, ICA can independently extract the spectral features of various land cover types. The key advantage of ICA in remote sensing data processing is its ability to effectively distinguish the distinct spectral features of different land cover types, particularly in complex surface or mixed-pixel environments [24].

3.3.2. Mineral Mapping

Mineral mapping involves analyzing remote sensing data to identify and map the distribution of minerals on the Earth’s surface [25]. Common methods include spectral matching, endmember decomposition, and spectral angle mapping (SAM) [26]. Spectral matching compares the spectral curves in remote sensing images with a spectral library of known minerals to identify the most similar mineral types. This method relies on the unique spectral signatures of minerals and can accurately identify specific mineral species. Endmember extraction, on the other hand, is used to decompose mixed pixels in remote sensing data [27]. This technique assumes that each pixel contains a linear mixture of different minerals. By isolating the spectra of each “endmember”, it is possible to determine the relative abundance of various minerals, which makes this method particularly useful for areas with mixed mineral distributions. SAM calculates the spectral angle between the pixel spectrum and known mineral spectra to determine the mineral type within remote sensing pixels. A smaller angle indicates a closer match between the spectra. SAM is effective for identifying minerals with similar spectral characteristics and is less sensitive to variations in spectral intensity. Considering the specific conditions of the study area, the SAM method was selected for mineral mapping, as it effectively identifies minerals with similar spectral characteristics, such as spodumene and lepidolite, which are commonly found in the region.

4. Identification of Li-Bearing Pegmatite Dikes

4.1. Identifying Granitic Rocks

ASTER imagery offers unique capabilities for mineral and rock remote sensing interpretation and is used extensively to differentiate lithological units [28]. To enhance both efficiency and accuracy, preprocessed ASTER data were employed for lithological identification and extraction [29]. ICA was conducted using ASTER bands 6, 7, and 8. The resulting color composite image demonstrated that the lithological information largely aligns with the geological map. Granite was prominently displayed in red, and the two-mica granite to the south was depicted in dark green [30]. The color combination of PC3 (R), PC4 (G), and PC5 (B) from the ICA facilitates their distinction from other geological units (Figure 5). The granite outcrops are marked with dashed yellow lines on the map. Key differences included the following: (1) The Ordovician monzogranite in the southeast, being present only on the western side of a north–south oriented valley, has a reduced distribution area compared to the area shown on the geological map. (2) Two new granite bodies, inferred to be two-mica granites, were identified in the southern part of the study area.

4.2. Identification of Pegmatite Dikes

Remote sensing interpretation encompasses both visual and digital data analysis techniques. Visual interpretation involves using cognitive skills to assess spatial patterns in imagery qualitatively and remains one of the most prevalent methods in remote sensing [31]. Given that many interpretation tasks rely heavily on the subjective judgment of image features, visual interpretation continues to play a crucial role in practical applications. In this study, visual interpretation methods were employed to analyze pegmatite dikes within the research area [32]. By systematically examining image data and integrating it with data from geological maps and field survey reports, the fundamental characteristics of the pegmatite dikes were identified and interpreted, providing essential data support for subsequent mineralization analyses [33].

The key distinguishing feature of pegmatite dikes, compared to other dikes, is their notable resistance to weathering relative to the surrounding rock. In the aeolian sand-covered areas of the study region, pegmatite dikes appear as long, point-like protrusions and linear patterns. Additionally, in areas with abrupt terrain changes, these dikes are disrupted and exhibit white material in a discontinuous linear arrangement [34]. These visual characteristics are typical for pegmatite dikes in the region and can be used to recognize the majority of such dikes. The ASTER data in Figure 6, which have undergone histogram equalization, help highlight these pegmatite dikes, making their spatial distribution characteristics easier to distinguish. Based on these observations, 249 significant pegmatite dikes were identified (Figure 6a). The pegmatite dikes exhibit various forms, including linear band-like distributions (Figure 6b) and branching patterns (Figure 6c).

4.3. Identification of Dike Mineralization

The primary ore minerals in the pegmatite-type lithium deposits of the Shaligou area are lepidolite and spodumene, and the gangue minerals consist predominantly of quartz, potassium feldspar, plagioclase, albite, and mica. The pegmatites generally exhibit stronger lithium mineralization farther from the host rock. Lithium minerals are often absent near the host rock. Notably, the mineralogical assemblages and spatial distribution of spodumene, albite, mica, and quartz play a critical role in the mineralization process. Field verification and studies of typical spodumene-type lithium deposits in the region have revealed that outcropping mineralized pegmatite dikes contain key indicator minerals including spodumene, lepidolite, quartz, and albite.

4.3.1. Mapping of Typical Minerals

Measured spectra from the study area were utilized to apply the traditional SAM method, which was used to map key minerals such as lepidolite, spodumene, mica, and albite.

The mineral mapping results (Figure 7) reveal that spodumene is significantly less abundant than lepidolite, only about one-tenth as abundant. The spodumene is distributed in a discontinuous, band-like pattern, extending in a northwest–southeast direction, along both the eastern and western sides of the granite. On the western side of the granite, a distinct zonation with spodumene, albite, and mica forming different zones is observed. From west to east, the sequence of zones includes an albite-mica-lepidolite-spodumene zone, an albite-mica zone with minor lepidolite, and an albite-mica-spodumene zone. On the eastern side, the predominant minerals are mica and albite. The widespread occurrence of albite near the granite is attributed to the prevalence of monzonitic granite in the region, which results in a more extensive albite distribution. Furthermore, the endmember spectrum of albite shows shallow absorption features around 2200 nm, making it susceptible to interference from other aluminum-hydroxide minerals, which can affect the accuracy of mineralization anomaly detection.

4.3.2. Analysis of Mineral Potential

Through mineral mapping, the pegmatite dikes were classified precisely based on their mineralization potential, leading to the identification of 22 lithium-bearing pegmatite dikes. By analyzing the zoning and distribution characteristics of the mineral compositions, four distinct targets were delineated for comparative mineralization analysis. Of these, Targets I and II were found to contain lithium minerals, whereas Targets III and IV showed no evidence of lithium mineralization.

Target I is located to the southwest of the monzogranite body and east of the two-mica granite, though at a considerable distance from it. The mineral extraction results indicate strong signs of mineralization. In the northeastern part of the target, a sodium feldspar-spodumene (minor quantities) zone is dominant, but it gradually transitions into a mica-sodium feldspar-spodumene zone toward the southwest. The central region of this target is heavily covered by wind-blown sand, which obscures any identifiable mineralization. This target encompasses the known lithium deposit in Shaligou, located south of Waxi Gorge (Figure 7a).

Target II is located at the boundary between the diorite granite body and the Altyn Tagh rock formations. The northeastern section of this target has been identified as part of the diorite granite body, which is characterized by a high concentration of sodium feldspar and small amounts of mica, consistent with the typical mineral composition of diorite granite. The anomalous region shows strong lithium mineralization, with sodium feldspar widely distributed. In the non-granitic sections, the dominant mineral association is a sodium feldspar-lithium pyroxene belt, with the presence of lithium pyroxene diminishing with greater proximity to the granite body. WV3 imagery revealed a series of northeast–southwest trending pegmatite dikes. Among these, the pegmatite dikes in the southern part of the granite body display significant lithium mineralization anomalies, and those closer to or within the granite body show weaker lithium mineralization.

Targets III and IV are located on the eastern side of the granite body, where multiple fault zones intersect, resulting in a complex structural setting. Using WV3 imagery, several large-scale pegmatite dikes were identified both within the anomalous zone and its surroundings. In these regions, the dominant mineral assemblage consists mainly of sodium feldspar and mica, however, all identified pegmatite dikes are non-mineralized. This suggests that lithium mineralization effectively differentiates between mineralized and non-mineralized pegmatite dikes.

4.4. Field Validation

For field verification, we surveyed a total of 12 points in Zones I and III. In the field, the pegmatite dikes predominantly appear white, with severely weathered and eroded outcrops showing a yellowish-white hue. Macroscopically, there is no noticeable difference between mineralized and non-mineralized dikes. They generally take the form of dike-like, lens-shaped, or irregular plate-like bodies interspersed within the surrounding rock. The width of the dikes typically ranges from 0.3 to 5 m. The length varies from a few meters to several hundreds of meters, and the dikes are linearly distributed along structural features. The pegmatite dikes display blocky or columnar crystals, exhibit strong mineral zoning, and contain abundant lithium-bearing minerals such as spodumene and lepidolite. On a microscopic scale, mineralized pegmatite dikes are characterized by larger crystal grains with clear mineral boundaries. Spodumene and lepidolite display distinct plate-like or needle-like structures, with spodumene having higher transparency and a glassy luster. The mineral distribution within the dikes is uneven, typically concentrated in the core or in specific targets, where it is accompanied by secondary minerals such as sodalite and mica. These minerals exhibit complex intergrowth structures and variations in mineral assemblage. A total of nine mineralized pegmatite dikes was identified via remote sensing across both targets. Field verification confirmed an 84% accuracy in identifying mineralization. However, further geochemical analysis is recommended. Although no spodumene, lepidolite, or beryl minerals may be detected, the target may still contain elevated concentrations of lithium and other pegmatite-associated metals.

We selected two of the most representative sampling points for detailed description: The first representative point is Point 5 in Target I, which is located within the Middle Proterozoic Altyn Tagh Formation primarily composed of biotite schist. Two barren pegmatite dikes trending at 110°, with widths ranging from 2 to 4 m, and extents of approximately 1000 m were observed macroscopically at this location (Figure 8a). The dikes generally dip steeply to the south, with some targets exhibiting steep northward dips at the surface. They display a relatively simple, large-dike morphology, while the eastern section of the mineral body shows branching and composite features. The exposed length at the surface is about 240 m, and the exposure thickens from east to west before becoming covered by Quaternary windblown sand and soil. Samples collected from the dikes include minerals such as lepidolite, mica, and sodium feldspar (Figure 8c,d). The surrounding host rock is garnet-bearing biotite quartz schist (Figure 8e).

The second representative point is Point 9 in Zone III, located within the Middle Proterozoic Altyn Group. The primary rock type at this point is biotite schist, which is intersected by a fault trending north-northeast. One barren pegmatite dike striking at 80°, with a width of 2 to 4 m, and an extent of approximately 1 km (Figure 8b) was identified macroscopically at this location. The samples collected from the dike reveal minerals such as albite, tourmaline, mica, and quartz (Figure 8f). The albite is typically grayish-white, occurs as tabular or prismatic crystals, and has sizes ranging from 5 to 30 mm. The tourmaline is black and often occurs in needle-like or radiating aggregates with lengths typically between 2 and 10 mm. Quartz is predominantly smoky gray or colorless, occurs in aggregate and blocky forms, exhibits a greasy luster, and has crystal sizes generally ranging from 2 to 8 mm.

5. Conclusions

(1)

Spectral measurements of samples from the Shaligou area were conducted to create a spectral library for typical rock and mineral types. This library includes minerals and rocks such as spodumene, lepidolite, albite, mica, two-mica granite, and monzonite granite.

(2)

Identifying the source rock body is essential to exploration of pegmatite-type rare mineral deposits. ASTER imagery, with its unique capabilities in mineral and rock remote sensing interpretation, effectively supports lithological identification. Four granite bodies were identified in the study area: two that correspond closely with the geological map and two new two-mica granite bodies discovered during this research.

(3)

In the study area, a total of 249 pegmatite dikes was identified. A detailed analysis of the mineral composition and spatial distribution of these dikes revealed the zoning patterns of key minerals such as spodumene, lepidolite, sodium feldspar, and mica. Building on this analysis, a detailed assessment of the mineralization of the pegmatite dikes was conducted, clarifying the mineralization levels and prospectivity across different regions.

(4)

This study uncovered the enrichment patterns of minerals in the region. It also provides a valuable reference for future prospecting efforts, helping to prioritize lithium-bearing dikes with higher economic potential.