Геологические науки

Narzuloeva Manizha

Master student of

Xinjiang Institute of Ecology and Geography,

Chinese Academy of Sciences.

Kefa Zhou

Professor of

Xinjiang Institute of Ecology and Geography,

Chinese Academy of Sciences

Jinlin Wan

Professor of

Xinjiang Institute of Ecology and Geography,

Chinese Academy of Sciences

Borbugulov Esen

Master student of

Xinjiang Research Center for Mineral Resources,

Xinjiang Institute of Ecology and Geography,

Chinese Academy of Sciences

Abdudzhaborzoda Bakhromshokh

Master student of

Xinjiang Institute of Ecology and Geography,

Chinese Academy of Sciences.

Integrating Landsat – 8 OLI Data for Hydrothermal Alteration Mineral Mapping in Suoerkuduke Cu – Mo skarn ore deposit in East Junggar, NW China.

Abstract

Remote sensing is the science of acquiring, processing, and interpreting images and related data, acquired from aircraft and satellites that record the interconnection between material and electromagnetic energy. Remote sensing images usually used for mineral exploration for geology mapping and allocate ore deposits, secondly to recognize hydrothermally altered rocks by their spectral signatures. Landsat – 8 OLI (Operation land imager – thermal infrared sensor) are widely used to hydrothermal alteration minerals; iron minerals and clays. This method was used to detect alteration zones associated with well prospected skarn copper deposits in the study area. The band ratios, principal component analysis (PCA), false color composition (FCC) of Landsat 8 OLI data significantly improved the visual interpretation for detailed mapping of the Suoerkuduke region in Eastern Jungar in NW China. The advancement of this investigation should have considerable implication for geologists to implement Landsat – 8 Operational Land Imager (OLI) data for skarn copper exploration and geological objects.

Key words: Landsat-8 OLI, Hydrothermal alteration, Band ratios, False color composition, Principal component analysis, Skarn copper deposit;

Introduction

Remote sensing technology has been used in diverse aspects of Earth science, geography, archeology and environmental sciences [1, 2]. New Generation of advanced remote sensing data have been used by Earth scientists over last decades. They have focused on global experiences in environmental geology, mineral and hydrocarbon exploration. In the initial stage of remote sensing technology development, geological mapping and mineral exploration were among the most prominent applications [3-6]. Multispectral and hyperspectral remote sensing sensors were used for geological applications, ranging from a few spectral bands to more than 100 contiguous band, covering the visible to the shortwave infrared regions of the electromagnetic spectrum [7-15].

The Landsat satellites era that began in 1972 will become a nearly 42-years global land record with the successful launch and operation of the Landsat Data Continuity Mission (LDCM). Two generations of Landsat satellites have been launched by National Aeronautics and Space Administration (NASA) and the U.S. Geological Survey (USGS). The first generation (Landsat 1, 2, and 3) operated from 1972 to 1985 and is essentially replaced by the second generation (Landsat 4, 5 and 7), which began in 1982 and continues to the present. Landsat 6 of the second generation was launched in 1993, but failed to reach orbit [16]. The LDCM is a partnership formed between the NASA and the USGS to place the next Landsat satellite in orbit.

The Advanced Land Imager (ALI) sensor was launched on 21 November of 200 as archetype for the next production Landsat satellites, the multispectral characteristics maintains to Enhanced Thematic Mapper Plus (ETM+) sensor on Landsat – 7 with a spatial resolution of 30 meter, but the swath width is 37 kilometers [17].

Landsat – 8 was launched more than 10 years ago from Vandenberg Air Force Base in California. It is a free-flyer spacecraft carrying two sensors, the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). They have high signal-to-noise radiometer performance, allowing 12-bit quantification of data, thus providing more bits for better land-cover characterization. Landsat – 8 provides moderate-resolution imagery, from 15 to 100 meters of the Earth’s surface and polar regions [18-20]. The characteristics of Landsat – 8 shown in a table 1.

Table 1. Sensor details of Landsat – 8 OLI/TIRs.

Remote sensing plays an important role in mineral exploration. Advances in the acquisition and processing of remote sensing data is an effective method for lithological mapping, especially in arid and semi-arid areas. Identification of hydrothermally modified rocks by means of remote sensing has been widely and successfully used to study the epithermal deposits of gold and skarn copper [16, 21-25].

The object of the study was to use Landsat – 8 OLI data to map the lithological units and alteration mineral zones associated with skarn copper mineralization in Suoerkuduke Cu-Mo deposit. The major part of this belt has well-exposed and sparse vegetated surface which is ideal for remote sensing investigations. The lithological information and alteration zones associated with porphyry copper mineralization was extracted by the methods of band ratios and Principal Component Analyses (PCA).

Regional geology

The fundamental questions of the formation of the Central Asian Orogenic belt as a major tectonic structures of the Eurasian continent for a long time occupied and continue to occupy the mind of lot of Scientists from different countries [26-31].

The Central Asian Orogenic Belt, also named “Altaids”. It’s the product of the successive accretion and amalgamation of Precambrian continental blocks, ancient island arcs, accretionary complexes, ophiolites, and passive continental margins since the Neoproterozoic [30, 32-39].

The East Junggar orogeny is a part of Central Asian Orogenic Belt and bound by the Altai orogeny to the north and the Tianshan orogeny to the south, and it extends eastwards into Mongolia. The East Junggar rocks comprise extensive Devonian mafic-intermediate lavas and tuffs and minor sandstones, limestone lenses, cherts, and conglomerates, Carboniferous intermediate volcanic and sedimentary rocks, and minor Silurian sedimentary and Permian volcanic rocks [40, 41]. The Devonian and Carboniferous volcanic rocks are characterized by their calc-alkaline geochemistry, including some adakitic, NB-rich basalts and high-temperature High-Mg andesite [41-44].

The East Junggar contains many types of ore deposit, including porphyry Cu-Mo-Au, orogenic gold, magmatic Cu-Ni, and skarn Cu-Mo. Because of the predominance of porphyry ore deposits, this is termed the East Junggar porphyry belt [45]. Chronological studies of the ore deposits indicate that they formed from the middle Devonian to the Late Permian. The porphyry ore deposits formed in two epochs: the first in the mid-Devonian and the second in the Late Carboniferous [43, 46, 47]. The magmatic Cu-Ni deposits formed in the early Permian [48], and the orogenic gold deposits from the Permian to Triassic (Fig. 1).

Fig. 1 Sketch map showing distribution of ore deposits in the Junggar area of Xinjiang (NW China). Figure modified after [49-52].

Most economic skarn ore deposits develop contemporaneously via metasomatism of their host intrusive rocks, the hydrothermal fluids proving the source of the ore-forming fluids and metals [53, 54]. The Suoerkuduke Cu-Mo deposit formed in the early Permian [55], but there are very few Permian intrusions in East Junggar, and certainly no Permian intrusions in the mine area. In contrast, late Carboniferous granites are widespread in East Junggar, and a late Carboniferous porphyry Cu-Mo deposit is located 10 kilometer to the north of the Suoerkuduke skarn deposit.

The Suoerkuduke Cu-Mo ore is a medium-size skarn deposit. The mine area is mainly composed of mid-Devonian volcaniclastic and sandstones of the Beitashan Formation (BFm), as well as intrusive granites and gabbros [56]. The BFm consist of an association of marine limestone, conglomerated, tuffaceous sandstone, pyroclastic sandstone, andesite, andesite porphyry, pyroxene andesite porphyry and basaltic porphyry with a low regional metamorphic grade [41] (Figure 2). Intrusive rocks in the eastern part of mine area are mostly granites that consist of 45-50% K-fieldspar. 5-10% plagioclase, 40-45% quartz and 1-4% biotite [57]. The margins of the granitic bodies are composed of fine-grained granitic porphyry. Between the granites and the volcanic wall rocks is a narrow contact metamorphic andalusite + fieldspar + quartz hornfels, which is completely different from the skarn that hosts the ores in the Suoerkuduke deposit; hence these granites are not considered to be the source of the skarn ore [41, 55].

The strata in the mine are were deformed by folds with NNW/SSE – trending axes, and detailed exploration indicates that more than 10 orebodies of different size are located on the SW limb of an anticline [58]. Another part of ore bodies are mainly distributed in the southeastern section of the mine area, all these stratiform or lenticular and are mostly bordered by pyroxene andesite, andesitic porphyry and tuff of the BFm [41, 59, 60]. All the ores are closely associated with calc-silicate skarn minerals. The skarn in the mine area is stratiform or lenticular, and consists of different assemblages of garnet, epidote-garnet, epidote and diopside. The garnet zone is mainly in the mine center, and the epidote-garnet zone is in the eastern and western areas of the mine. The epidote zone is also in the eastern and western areas of the mine, but it is discontinuous along strike. The diopside skarn only crops out in the eastern side of the mine. Most ore is in epidote and epidote-garnet, with epidote predominant. The garnets are calcium-rich andradite and grossular [41, 55, 59, 61].

The ores are mainly composed of chalcopyrite and purity with subordinate molybdenite and pyrrhotite, minor magnetite and native gold. The ores occur in two forms: veinlets and disseminated. The sulfides are angedral, microgranilar and unequigranular. They were oxidized ate the surface and subsurface and altered to covellite, limonite, malachite, chrysocolla, azurite and hematite [41, 55, 61].

Fig. 2 Geological map of the Suoerkuduke Cu — Mo skarn ore deposit. Modified after [40, 41].

Methodology

Landsat 8 OLI data

Landsat-8 OLI image LC81420282018103LGN00 was obtained through the Geospatial Data Cloud (http://www.gscloud.cn/sources/?cdataid=263&pdataid=10). It was acquired on April 13, 2018 for the Suoerkuduke. The image map projection is UTM zone 45 North (Polar Stereographic for Antarctica) using the WGS-84 datum. Landsat-8 OLI collects data from nine spectral bands. Seven of the nine bands are consistent with the Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) sensors found on earlier Landsat satellites, providing for compatibility with the historical Landsat data, while also improving measurement capabilities. Two new spectral bands, a deep blue coastal / aerosol band and a shortwave-infrared cirrus band will be collected, allowing scientists to measure water quality and improve detection of high, thin clouds. Band 1-7 spatial resolutions of 30 m were utilized in the study.

Data pre-processing

The Landsat-8 image of the target site was processed with Environment for Visualizing Images version 5.1 software. In the research, in order to extract effective spectral and favorable prospecting information, pretreatment of the image including radiometric correction, geometric correction, mask, mosaic, resize. In view of the interference with the result of the extraction of changes, then cloud, shadow and vegetation must be removed through a mask. It was found that there is little information about disturbances in vegetation and shade, except for clouds in the research area. The pre-processing procedures are essential to obtain spatially and radiometrically corrected images in order to analyze and compare spectral data. Landsat – 8 OLI image are calibrated to surface reflectance using the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercube (FLAASH) atmospheric correction model, which incorporates Moderate Resolution Transmittance (MODTRAN) radiation transfer code to remove the atmospheric attenuations to produce reflectance imagery.

Since the FLAASH model produces negative minimums and multiplies reflectance by ten thousand, the gained results were converted to reflection values in the range of 0 to 1 that minimizes processing time of the imagery.

Data processing

Image processing techniques including band ratios, principal component analysis (PCA), false-color composition (FCC) have been applied for detailed mapping of the lithological units and alteration zones in the study area.

The band ratio is a technique that has been used for many years in remote sensing to display spectral variations effectively [4]. Band rationing is a technique where the digital number value of one band is divided by the digital number value of another band. Band ratios are very useful for highlighting certain features or materials that cannot be seen in the raw bands. The band ratio images are known for enhancement of spectral contrasts among the bands considered in the ratio operation and have successfully been used in mapping of alteration zones [62] from the theoretical knowledge of mineral’s spectral properties, it is well recognized that the Landsat – 8 OLI bands ratios of 6/7, 6/5, 4/2 are analyzed for iron oxides, hydroxyl bearing minerals, ferrous oxides, respectively. Landsat – 8 OLI bands 4/2 can detect the smallest amount of ferric iron-bearing surfaces of hydrothermally altered rocks, sedimentary rocks, metamorphic rocks containing weathered, iron-bearing mafic minerals such as hornblende, biotite, sand deposits, and alluvium derived from such rocks will be identified with this index [63]. Clay mineralization is detected using the ratio 6/7 indicating that band 6 has reflectance in contrast to band 7 which has a high absorption. Band ratio 6/5 is used in order to detect locations with hydroxyl mineralization, which is another indicator of hydrothermally altered zones [20, 64].

Principal component analysis is a multispectral statistical method, the result of which is presented in the form of bright or dark pixels, predicting in different principal components, in accordance with the eigenvectors and magnitude [65].

The Crosta method is known as principal component selection. This technique can be applied on four bands of the Landsat – 8 OLI data. By analyzing of the eigenvector values, it allows to identify the principal components containing spectral information about specific minerals. Each of the original bands in the component has spectral information of materials of interest [66].

The false color composite (FCC) image create from the Landsat – 8 OLI band ratio combination and PCA image by assigning RGB (red, green and blue) colors to discriminate lithological units presented in research area.

Results and Discussion

Images with an RGB band ratio (6/7: 6/5: 4/2) are used in the current study and have proven to be highly effective in the lithological distinction between serpentinites and related rocks. The combination of band ratio image is shown in Fig. 3. This combination improves the texture of the relief, as well as the spectral color of each lithology, allows to distinguish each of them.

Fig. 3 Color composite of the Landsat – 8 OLI band ratios (6/7: 6/5: 4/2) in red, green and blue (RGB).

Band ratio image of false color composition bands (6/7: 6/5: 4/2) the granite-porphyry, myotite, and diorite-porphyry represented in bluish-green color, the Beishan formation in the western part is represented in bright red color.

The result showed that the lithological units differed in the study area, and the contacts on the PCA images (PC1, PC2, PC3) were identified as red, green and blue (R, G, B) (Fig. 4). Granite-porphyry, myotite, pyroxene-diorite-porphyry have a dark purple color, Beshan deposits in the western part are red and orange, andesite, sandstone tuff, marine limestone and basalt porphyry and quaternary green. This image turned out to be important for distinguishing stones, in which each type of rocks has its own color.

Fig. 4 Color composite of the Principal Component Analysis (PC1, PC2, and PC3) in red, green and blue of the Landsat – 8 OLI.

False color composition of band ratios and principal component analysis were used to produce lithological units and compare the results of the band ratios and the PCA color composite images, the PCA of Landsat – 8 OLI in RGB is the best suited to distinguish the lithological units of the research area.

The geological interpretation of the PCA (PC1, PC2, PC3) and Landsat – 8 OLI image band ratio (6/7: 6/5: 4:2) was used to create the lithological map of the study area.

The principal component analysis was carried out in two sets of band 2456 and 2567 combinations. For each data set, statistical data are computed and the values of the covariance eigenvalues are investigated. The values of the PCA eigenvectors are given in Table 2 and Table 3.

In Table 2 PC4 which reinforces the iron oxide minerals, has a higher stress on bands 4 and 5, but with opposite signs. It has a positive contribution to band 4 (0.651741) and a negative contribution to band 5 (-0.704159). This indicates that the iron oxide minerals are displayed as dark pixels and are superimposed in blue on band 1 of Landsat – 8 OLI (Fig. 5)

Table 2. Eigenvalues calculated for principal components of data for Iron-Oxide minerals on Landsat – 8 OLI

A similar analysis of PC4 in table 3 for hydroxyl minerals shows that the most important contributions are related to band 6 (-0.723843) and band 7 (0.510952). This indicates that the hydroxyl image of hydroxyl minerals will be dark pixels and in false color composite image the areas of hydroxyl minerals are shown in red color (Fig. 5).

Table 3. Eigenvalues calculated for principal components of data for Hydroxyl minerals on Landsat – 8 OLI.

Fig. 5 Color composite of abundance images for Hydroxyl and Iron-Oxide minerals in RGB draped over on Landsat 8 OLI band 1.

Metallogenic belt was found in Suoerkuduke-Xilekuduke Cu-Mo, magnetite mineralization is discovered in boreholes. These volcanic-sedimentary formations are favorably ore bearing layers.

In addition to the large distribution of andradite and diopsite in the skarn of Suoerkuduke, the distribution of relatively small grossular and henbergites shows a relatively oxidized state during the formation of scarn [67].

The results obtained for the Suoerkuduke region indicate that PCA can obtain detailed mineralogical information from Landsat – 8 OLI multispectral data to obtain abundant images for some alteration minerals, such as iron and hydroxyl oxides commonly used in the Cu-Mo skarn ore deposit.

Conclusions

We evaluated the feasibility of using Landsat – 8 OLI data to obtain geological information about the hydrothermal changes associated with Cu-Mo skarn ore deposit and lithological mapping using selected image processing methods.

In Suoerkuduke region Landsat – 8 OLI bands provide spectral information to identify iorn oxide and hydroxyl minerals and lithological units for the investigation of Cu-MO skarn ore deposits. Landsat – 8 OLI data were used to enrich regions with hydroxyl and iron oxide minerals in the Suoerkuduke region. The principal component analysis was selected to produce images showing iron oxide and hydroxyl minerals (Crosta technique). PC4 shows the contribution of iron oxide, and PC4 shows the contribution of hydroxyl minerals. It can be concluded that the Crosta technique can be used very reliable methods for improving hydrothermal alteration zones as a fast and cost-effective tool for exploration of mineralization in the Suoerkuduke area of NW China.

Acknowledgement

I would like to acknowledge the financial, academic and technical support of the Chinese Academy of Science, particularly for awarding me a full research studentship that provided me the basic necessary financial support for this research. The library facilities and computer facilities of the Institute, I also thank the Department for their supporting and assistance since the start of my work.

References

1. Ranjbar, H., M. Honarmand, and Z. Moezifar, Application of the Crosta technique for porphyry copper alteration mapping, using ETM+ data in the southern part of the Iranian volcanic sedimentary belt. Journal of Asian Earth Sciences, 2004. 24(2): p. 237-243.

2. Rowan, L.C., et al., Mapping hydrothermally altered rocks by analyzing hyperspectral image (AVIRIS) data of forested areas in the Southeastern United States. Journal of Geochemical Exploration, 2000. 68(3): p. 145-166.

3. Rowan et, a., Discrimination of rock types and detection of hydrothermally altered areas in south-Nevada. U.S. Geolofical Surbey Professional Paper, 1974. 883(34).

4. Goetz, A.F.H., B.N. Rock, and L.C. Rowan, Remote sensing for exploration; an overview. Economic Geology, 1983. 78(4): p. 573-590.

5. J. Abrams, M., et al., Remote sensing for porphyry copper deposits in southern Arizona. Vol. 78. 1983. 591-604.

6. Rowan, L.C., A.F.H. Goetz, and R.P. Ashley, Discrimination of hydrothermal altered and unaltered rocks in visible and near infrared multispectral images. Geophysics, 1977. 42(3): p. 522-535.

7. Crowley, J.K., D.W. Brickey, and L.C. Rowan, Airborne imaging spectrometer data of the Ruby Mountains, Montana: Mineral discrimination using relative absorption band-depth images. Remote Sensing of Environment, 1989. 29(2): p. 121-134.

8. Kruse, F.A., et al., The spectral image processing system (SIPS)—interactive visualization and analysis of imaging spectrometer data. Remote Sensing of Environment, 1993. 44(2): p. 145-163.

9. Goetz, A.F.H., Three decades of hyperspectral remote sensing of the Earth: A personal view. Remote Sensing of Environment, 2009. 113: p. S5-S16.

10. Pour, A.B., M. Hashim, and J. van Genderen, Detection of hydrothermal alteration zones in a tropical region using satellite remote sensing data: Bau goldfield, Sarawak, Malaysia. Ore Geology Reviews, 2013. 54: p. 181-196.

11. Pour, A.B., M. Hashim, and M. Marghany, Exploration of gold mineralization in a tropical region using Earth Observing-1 (EO1) and JERS-1 SAR data: a case study from Bau gold field, Sarawak, Malaysia. Arabian Journal of Geosciences, 2014. 7(6): p. 2393-2406.

12. Pour, A., ALTERATION MINERAL MAPPING USING ETM+ AND HYPERION REMOTE SENSING DATA AT BAU GOLD FIELD, SARAWAK, MALAYSIA. 2013.

13. Pour, A.B. and M. Hashim, Structural geology mapping using PALSAR data in the Bau gold mining district, Sarawak, Malaysia. Advances in Space Research, 2014. 54(4): p. 644-654.

14. Beiranvand Pour, A. and M. Hashim, ASTER, ALI and Hyperion sensors data for lithological mapping and ore minerals exploration. SpringerPlus, 2014. 3(1): p. 130.

15. Pour, A.B. and M. Hashim, Structural mapping using PALSAR data in the Central Gold Belt, Peninsular Malaysia. Ore Geology Reviews, 2015. 64: p. 13-22.

16. Sabins, F.F., Remote sensing for mineral exploration. Ore Geology Reviews, 1999. 14(3): p. 157-183.

17. Wulder, M.A., et al., Landsat continuity: Issues and opportunities for land cover monitoring. Remote Sensing of Environment, 2008. 112(3): p. 955-969.

18. Hearn, D., et al. EO-1 Advanced Land Imager overview and spatial performance. in Geoscience and Remote Sensing Symposium, 2001. IGARSS’01. IEEE 2001 International. 2001. IEEE.

19. Irons, J.R., J.L. Dwyer, and J.A. Barsi, The next Landsat satellite: The Landsat data continuity mission. Remote Sensing of Environment, 2012. 122: p. 11-21.

20. Pour, A.B. and M. Hashim, Hydrothermal alteration mapping from Landsat-8 data, Sar Cheshmeh copper mining district, south-eastern Islamic Republic of Iran. Journal of Taibah University for Science, 2015. 9(2): p. 155-166.

21. Di Tommaso, I. and N. Rubinstein, Hydrothermal alteration mapping using ASTER data in the Infiernillo porphyry deposit, Argentina. Ore Geology Reviews, 2007. 32(1-2): p. 275-290.

22. Gabr, S., A. Ghulam, and T. Kusky, Detecting areas of high-potential gold mineralization using ASTER data. Ore Geology Reviews, 2010. 38(1-2): p. 59-69.

23. Bedini, E., Mineral mapping in the Kap Simpson complex, central East Greenland, using HyMap and ASTER remote sensing data. Advances in Space Research, 2011. 47(1): p. 60-73.

24. Hashim, M., M. Pournamdary, and A.B. Pour, Processing and interpretation of advanced space-borne thermal emission and reflection radiometer (ASTER) data for lithological mapping in ophiolite complex. International Journal of Physical Sciences, 2011. 6(28): p. 6410-6421.

25. Amer, R., T. Kusky, and A. El Mezayen, Remote sensing detection of gold related alteration zones in Um Rus area, Central Eastern Desert of Egypt. Advances in Space Research, 2012. 49(1): p. 121-134.

26. Jahn, B.-m., Massive granitoid generation in central Asia: Nd isotopic evidence and implication for continental growth in the Phanerozoic. Episodes, 2000. 23: p. 82-92.

27. Jahn, B.-m., The Central Asian Orogenic Belt and growth of the continental crust in the Phanerozoic. Geological Society, London, Special Publications, 2004. 226(1): p. 73-100.

28. Kröner, A., et al., Accretionary growth and crust formation in the Central Asian Orogenic Belt and comparison with the Arabian-Nubian shield. Geological Society of America Memoirs, 2007. 200: p. 181-209.

29. Şengör, A.M.C. and B.A. Natal’in, Turkic-type orogeny and its role in the making of the continental crust. Annual Review of Earth and Planetary Sciences, 1996. 24: p. 263-337.

30. Şengör, A.M.C., B.A. Natal’in, and V.S. Burtman, Evolution of the altaid tectonic collage and paleozoic crustal growth in eurasia. Nature, 1993. 364(6435): p. 299-307.

31. Windley, B.F., et al., Tectonic models for accretion of the Central Asian Orogenic Belt. Journal of the Geological Society, 2007. 164(1): p. 31-47.

32. Badarch, G., W.D. Cunningham, and B.F. Windley, A new terrane subdivision for Mongolia: implications for the Phanerozoic crustal growth of Central Asia. Journal of Asian Earth Sciences, 2002. 21(1): p. 87-110.

33. Buslov, M., et al., Evolution of the Paleo-Asian Ocean (Altai-Sayan Region, Central Asia) and collision of possible Gondwana-derived terranes with the southern marginal part of the Siberian continent. Geosciences Journal, 2001. 5(3): p. 203-224.

34. Dobretsov, N.L., M.M. Buslov, and U. Yu, Fragments of oceanic islands in accretion–collision areas of Gorny Altai and Salair, southern Siberia, Russia: early stages of continental crustal growth of the Siberian continent in Vendian–Early Cambrian time. Journal of Asian Earth Sciences, 2004. 23(5): p. 673-690.

35. Khain, E., et al., The most ancient ophiolite of the Central Asian fold belt: U–Pb and Pb–Pb zircon ages for the Dunzhugur Complex, Eastern Sayan, Siberia, and geodynamic implications. Earth and Planetary Science Letters, 2002. 199(3): p. 311-325.

36. Safonova, I.Y., et al., Fragments of Vendian-Early Carboniferous oceanic crust of the Paleo-Asian Ocean in foldbelts of the Altai-Sayan region of Central Asia: geochemistry, biostratigraphy and structural setting. Gondwana Research, 2004. 7(3): p. 771-790.

37. Xiao, W., et al., Accretion leading to collision and the Permian Solonker suture, Inner Mongolia, China: termination of the central Asian orogenic belt. Tectonics, 2003. 22(6).

38. Xiao, W.J., et al., Middle Cambrian to Permian subduction-related accretionary orogenesis of Northern Xinjiang, NW China: Implications for the tectonic evolution of central Asia. Journal of Asian Earth Sciences, 2008. 32(2-4): p. 102-117.

39. Xiao, W.J., et al., End-Permian to mid-Triassic termination of the accretionary processes of the southern Altaids: implications for the geodynamic evolution, Phanerozoic continental growth, and metallogeny of Central Asia. International Journal of Earth Sciences, 2009. 98(6): p. 1189-1217.

40. BGMRX, Bureau of Geology and Mineral Resources of Xinjiang Uygur Autonomous Region, 1993. Regional Geology of Xinjiang Uygur Autonomous Region, People’s Republic of China. In: M.O.G.A.M. Resources (Ed.), Geological Publishing House, Beijing, pp. 6–206 (in Chinese). 1993.

41. Wan, B., et al., Re–Os molybdenite age of the Cu–Mo skarn ore deposit at Suoerkuduke in East Junggar, NW China and its geological significance. Ore Geology Reviews, 2014. 56: p. 541-548.

42. Xiao, Y., et al., Late Paleozoic magmatic record of East Junggar, NW China and its significance: Implication from zircon U–Pb dating and Hf isotope. Vol. 20. 2011. 532-542.

43. Long, L.L., et al., Geochronology and geochemistry of the ore-bearing porphyry in Xilekuduke Cu-Mo deposit, Fuyun area, Xinjiang, China. Vol. 28. 2009. 1840-1851.

44. Niu, H., et al., Juxtaposition of adakite, boninite, high-TiO2 and low-TiO2 basalts in the Devonian southern Altay, Xinjiang, NW China. Journal of Asian Earth Sciences, 2006. 28(4): p. 439-456.

45. Wan, B., et al., Contrasting styles of mineralization in the Chinese Altai and East Junggar, NW China: implications for the accretionary history of the southern Altaids. Journal of the Geological Society, 2011. 168(6): p. 1311-1321.

46. Xue, C., et al., The multiperiodic superimposed porphyry copper mineralization in Central Asian tectonic region: A case study of geology, geochemistry and chronology of Halasu copper deposit, southeastern Altai, China. Vol. 17. 2010. 53-82.

47. Yang, F., et al., A review of the geological characteristics and geodynamic setting of Late Paleozoic porphyry copper deposits in the Junggar region, Xinjiang Uygur Autonomous Region, Northwest China. Vol. 49. 2012. 80-98.

48. Zhang, Z., et al., Re–Os dating of two Cu–Ni sulfide deposits in northern Xinjiang, NW China and its geological significance. Journal of Asian Earth Sciences, 2008. 32(2): p. 204-217.

49. Wang, Z., et al., Types, characteristics and metallogenic geodynamic evolution of the Paleozoic polymetallic copper-gold deposits in the West Tianshan Mountains. Acta Geol Sin, 2004. 78: p. 836-847.

50. Chen, H., et al., Chemical composition of fluid inclusions of the Sawayardun gold deposit, Xinjiang and its implications for metallogeny and exploration. Acta Petrol Sin, 2007. 23: p. 2189-2197.

51. Xiao, W., et al., Middle Cambrian to Permian subduction-related accretionary orogenesis of Northern Xinjiang, NW China: implications for the tectonic evolution of central Asia. Journal of Asian Earth Sciences, 2008. 32(2-4): p. 102-117.

52. Chen, Y.J., et al., Epithermal deposits in North Xinjiang, NW China. International Journal of Earth Sciences, 2012. 101(4): p. 889-917.

53. Meinert, L.D., et al., Geology, zonation, and fluid evolution of the big Gossan Cu-Au skarn deposit, Ertsberg district: Iran Jaya. Economic Geology, 1997. 92: p. 509-534.

54. Meinert, L., G. Dipple, and S. Nicolescu, World Skarn Deposits. 2005. p. 299–336.

55. Zhou, S., H. Cai, and C.F. Xie, Geochronology on Suoerkuduke copper (molybdenum) deposit, northern Xinjiang. Vol. 4. 1996. 52-56.

56. Liu, X., et al., Multiple sources and genesis of the Suoerkuduke Cu-Mo deposit during the Carboniferous, East Junggar: Insights from zircon U-Pb age and C-O-S-Pb isotopes. 2016.

57. Zhao, L., et al., REE Geochemistry of the Suoerkuduke Cu-Mo Deposit and its Metallogenic Implications. Vol. 41. 2017. 1078-1096.

58. Liu, X., W. Liu, and C. Si, Fluid metasomatic genesis of stratiform skarn in the Suoerkuduke Cu-Mo deposit, East Junggar, NW China. Vol. 78. 2016.

59. Liu, T.G., et al., Genesis of Sorkuduk Cu-Mo deposit based on geochemical characteristics. Vol. 10. 1992. 176-183.

60. Rui, Z.Y., et al., The multiple metallogenic model of the porphyry-epithermal copper-gold deposits in Eastern Jilin and Heilongjiang. Vol. 14. 1995. 174-184.

61. Chen, R.Y., Y.L. Liu, and Z.Y. Rui, Geological characteristics and genesis of the Xorkuduk skarnoid copper (molybdenum) deposit, Xinjiang. Vol. 41. 1995. 165-173.

62. Segal, D.B., Use of Landsat multispectral scanner data for the definition of limonitic exposures in heavily vegetated areas. Economic Geology, 1983. 78(4): p. 711-722.

63. Goward, S.N., et al., The Landsat 7 mission: Terrestrial research and applications for the 21st century. Remote Sensing of Environment, 2001. 78(1-2): p. 3-12.

64. Pour, A.B., M. Hashim, and M. Marghany, Using spectral mapping techniques on short wave infrared bands of ASTER remote sensing data for alteration mineral mapping in SE Iran. International Journal of Physical Sciences, 2011. 6(4): p. 917-929.

65. Jolliffe, I.T., Choosing a subset of principal components or variables, in Principal Component Analysis. 1986, Springer. p. 92-114.

66. Crosta, A.P. and J.M. MOORE, Geological mapping using Landsat thematic mapper imagery in Almeria Province, South-east Spain. International Journal of Remote Sensing, 1989. 10(3): p. 505-514.

67. Liu, X., W. Liu, and C. Si, Fluid metasomatic genesis of stratiform skarn in the Suoerkuduke Cu-Mo deposit, East Junggar, NW China. Ore Geology Reviews, 2016. 78: p. 239-251.