Detection of alteration zones using hyperspectral remote sensing data from Dapingliang skarn copper deposit and its surrounding area, Shanshan County, Xinjiang Uygur autonomous region, China

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Abstract

In application of hyperspectral remote sensing, alteration zones are primarily detected by identifying alteration mineral assemblages, but the interpretation of alteration mineral maps is often complicated by surface materials and by minerals not directly associated with alteration. This study was conducted in the Dapingliang skarn copper deposit and its surrounding area, the Shanshan County of the Xinjiang Uygur autonomous region, China. In order to successfully detect alteration zones associated with skarns, this study identified skarns rather than alteration minerals using field spectra of skarn outcrops. In this study, skarn in pixels was identified from spectral overall shape and spectral shapes of absorption-bands; SAM (spectral angle mapper) was applied in the identification. When SAM scores of spectral overall shape were less than 0.022 rad, the identified skarns were mainly distributed in the contact zones of intrusive rocks and carbonates; in particular, the three identified skarns areas (R1, R2 and R3) were consistent with the skarn areas in the geological map of the study area. The field inspection of skarns showed that the identified objects of the three marked targets (A, B and C) were almost consistent with the ground objects. These obtained results demonstrated that using the field spectra, it was possible to identify skarns in the hyperspectral image. To evaluate the identified skarn zones for use in mineral exploration, the end-member spectra extracted from the image were analysed, and alteration zones were detected using these end-member spectra. Compared with these alteration zones, the identified skarn zones were more reliable for mineral exploration of the study area.

Introduction

The driver for application of hyperspectral remote sensing in geology is mineral mapping and the retrieval of surface compositional information for mineral exploration, in particular to hydrothermal systems [1]. In mineral exploration, alteration zones are typically detected in hyperspectral imagery by identifying alteration mineral assemblages [2], [3], [4], [5], [6], [7], [8], [9]. However, the interpretation of alteration mineral maps is often complicated by surface materials and by minerals not directly associated with alteration. In order to successfully detect alteration zones, for example, altered rocks related to the genesis of mineral deposit have to be understood, and the altered rock zones are an importance clue for mineral exploration.

The study area is located in the Dapingliang copper deposit and its surrounding area, the Shanshan County of the Xinjiang Uygur autonomous region, China. Wind-blown sand and soil of this area is ubiquitous and forms an obscuring veneer on some outcrops. Previous geologic studies have shown that copper orebodies are hosted entirely in skarns [10], [11], and thus skarn zones are an importance clue for mineral exploration in the study area.

In application of remote sensing (RS), published work on skarn alteration mainly focused on the spectral features of alteration minerals and aimed to extract the information on these minerals from the image [12], [13], [14]. It is possible that many spectral features of alteration minerals are not sensed because of the hampering effect of surface material (such as sand and soil). In addition, some minerals are found in both alteration and unaltered assemblages (for example, albite is not only present in altered rocks, but also in primary rock), and certain minerals (such as chlorite and serpentine) can be formed not only in a skarn deposit, but also in altered wallrocks which are not related to the genesis of skarn deposit. Therefore, not all information on alteration minerals extracted from the image is useful for mineral exploration. Skarns are related to the genesis of skarn deposit in the study area, thus the extracted information on skarns is more useful for mineral exploration than that on alteration minerals. Accordingly, a new method is put forward to detect alteration zones associated with skarns in this study.

Because of the hampering effect of surface material (such as sand and soil), some end-member spectra of skarns or alteration minerals may be not effectively extracted from the image or those extracted spectra are generally similar so that cannot be selected as reference spectra to detect alteration zones [15]. Without effective end-member spectra extracted from the image, some typical field spectra of skarn outcrops collected on the ground may be considered as reference spectra to characterise skarns in the image. This study tried to detect skarns in the image from spectral overall shape and absorption-bands, using those field spectra.

The main objective of this research is to detect skarns in the hyperspectral image using reflectance spectra of skarn outcrops, and to evaluate the identified skarn zones for use in mineral exploration. In evaluation of these skarn zones, the end-member spectra extracted from the image were analysed, and alteration zones were detected using these end-member spectra and compared with those skarn zones.

Section snippets

Geologic setting

The Kuluketage block, located in the northeastern Tarim Craton, is one of the largest Precambrian blocks in Xinjiang Uygur autonomous region (Fig. 1) [11], [16], [17]. The Dapingliang copper deposit was discovered in the eastern portion of the Kuluketage block over a decade ago [10] and geologic studies showed that it is a skarn deposit [10], [11].

At 830–800 Ma, in the Kuluketage block the tectonic transition from compression to extension occurred, with potassium-rich adakite (rich in metals

Hyperspectral image data

The hyperspectral image data were acquired from the Hyperion sensor, on board the EO-1 spacecraft, on August 15, 2009. Hyperion is the first space-borne hyperspectral sensor, launched in 2000 on board the National Aeronautics and Space Administration (NASA)’s Earth Observing Mission One (EO-1), and provides spectral coverage of 242 bands from 400 to 2500 nm at approximately 10 nm spectral resolution and 30 m spatial resolution, with 7.5 km coverage in the across-track direction [2]. Hyperion

Methods

The methodology adopted was chosen to detect alteration zones associated with skarns in the hyperspectral image. SAM (spectral angle mapper) [21], [22], one of the most frequently used spectrum-matching techniques, has been widely used in the spectral discrimination of minerals and rocks [8], [23], [24], [25], [26]. The spectrum matching techniques aim to express the similarity of reference (library or field spectra of known materials) to test (image) spectra [26]. SAM treats the two spectra as

Recognition results of matching spectral overall shape

The pixel of possible skarn was tested (i.e. with angle value of overall shape < 0.022 rad, and the angle values of all absorption-band positions < 0.020 rad) to demonstrate the effectiveness of our methods. For certain pixels, although the angle values of overall shape were small (such as the angle values < 0.022 rad), certain angle values of absorption-bands were > 0.020 rad. The ground objects in these pixels as well as their field environment were investigated and analysed to test the

Discussion

To evaluate the identified skarn zones for use in mineral exploration, alteration zones was detected using end-member spectra extracted from the image, and then were compared with those identified skarn zones.

Using the hyperspectral image after atmospheric correction, a procedure of identifying and mapping alteration minerals has been summarised, and it includes the Minimum Noise Fraction (MNF) transformation, determination of end-member occurrences, extraction of end-member spectra,

Conclusions

This study used both the hyperspectral image and skarn spectra to detect skarn zones. Skarn in the image was identified from spectral overall shape and spectral shapes of absorption-bands, and this procedure was implemented in a VC++ environment. When SAM scores of spectral overall shape were less than 0.022 rad, the identified skarns were mainly distributed in the contact zones of intrusive rocks and carbonates; in particular, the three skarns areas (R1, R2 and R3) were consistent with the

Conflict of interest

There is no conflict of interest.

Acknowledgments

This project is supported by the National Key R&D Program of China (No. 2017YFC0601504 and 2017YFC0601500), and the National Natural Science Foundation of China (No. 41872252).

Yuanjin Xu received the M.S. degree and Ph.D. degree from China University of Geosciences (Wuhan) in 2004 and 2009, respectively. Currently, he is a vice professor of China University of Geosciences (Wuhan). His research interests include remote sensing image processing and application of remote sensing in geology.

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    Yuanjin Xu received the M.S. degree and Ph.D. degree from China University of Geosciences (Wuhan) in 2004 and 2009, respectively. Currently, he is a vice professor of China University of Geosciences (Wuhan). His research interests include remote sensing image processing and application of remote sensing in geology.

    Jianguo Chen received the M.S. degree and Ph.D. degree from China University of Geosciences (Wuhan) in 1987 and 2002, respectively. Currently, he is a professor of China University of Geosciences (Wuhan). His research interests include mathematical geology and remote sensing geology.

    Pengyan Meng received the B.E. and M.S. degree from China University of Geosciences (Wuhan) in 2012 and 2015, respectively. His research interests include design and development of algorithms and remote sensing image processing.

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