Introduction

Geological mapping is the basis of geological study. Because structure-fluid magmatism and potential metallogenesis are often concentrated in metamorphic rock areas, metamorphic rocks are of scientific significance and can provide evidence for parsing the evolutionary process of orogenic belts, wherein geological mapping is even more important (Fry 1984). To conduct geological mapping in metamorphic rock areas, it is necessary to obtain the distribution of metamorphic rocks and their metamorphic degrees. Geologists have already completed considerable research on metamorphic rocks. For example, Spalla et al. (2002) used regional-scale correlation of the structural and metamorphic succession of events to identify the crustal portions recording coherent thermal and tectonic evolution; Aoki et al. (2011) recognized the Shimanto HP metamorphic belt within the traditional Sanbagawa HP metamorphic belt in SW Japan using high-precision analytical equipment to provide more precise age constraints for accretion and metamorphism of geological units; and Kakar et al. (2015) presented the petrology and geochemistry of the dominant lithologies, such as garnetiferous amphibolites, amphibolites, and greenschists, from the metamorphic sole of the Muslim Bagh ophiolite (Pakistan) to determine their protolith and the type of tectonic setting in which they formed. Additionally, Philippon et al. (2015) used 3D modeling to characterize post-foliation deformation in metamorphic terrains (Syros, Cyclades, and Greece).

The minerals of metamorphic rocks contain inherited minerals (quartz, orthoclase, anorthose, etc.) and metamorphic minerals (epidote, andalusite, staurolite, etc.). The inherited minerals are the original minerals in the rock, while the metamorphic minerals formed during the complex process of metamorphism. The diagnostic metamorphic minerals represent the type of metamorphism that occurred, as they have a stable range indicating metamorphic conditions (temperature, pressure, and sometimes the composition of original rock). For example, epidote, chlorite, actinolite, muscovite, and biotite form during low-grade metamorphism and indicate that metamorphic conditions were of low temperature and pressure; hornblende, andalusite, staurolite, and cordierite form during medium-grade metamorphism and indicate that metamorphic conditions were of medium temperature and pressure; and sillimanite forms during high-grade metamorphism and indicates that metamorphic conditions were of high temperature and high pressure. Therefore, these diagnostic metamorphic minerals can be good indicators of the distribution of metamorphic rocks and their metamorphic degrees.

The Wuliangshan area is rich in complex metamorphic rocks. Xue (1988) proposed that the metamorphism is of a regional dynamothermal metamorphic type. Hou et al. (2013) thought that the metamorphic belt is a progressive metamorphic zone and has the characteristics of inhomogeneity and uncontinuative isograde, which is different from regional metamorphism of homogeneity and isograde. Initially, the study area was classified as the Wuliangshan metamorphic belt (Genyao and Jiaju 1996). As research progressed, the area was roughly divided into two metamorphic belts (Yunnan Geological Mineral Bureau, 1990). Until now, there has been no more literature published on the further subdivision of metamorphic belts in the Wuliangshan area because of the extremely rugged topography and difficult travel conditions. Therefore, further subdivision of the metamorphic rocks needed to be completed, which can be obtained from information on the diagnostic metamorphic minerals.

Remote sensing techniques have been widely and successfully used for geological applications, such as alteration mapping, lithological discrimination, and mineral detection (Ferrier et al. 2002; Gabr et al. 2015; Kratt et al. 2010; Meer et al. 2012; Rowan et al. 1977; Sabins 1999; Zoheir and Emam 2014). The multifractal theory (Cheng 1999) has been successfully used to recognize weak but complex geochemical anomalies in areas covered by deserts, regolith, or vegetation. Various successful case studies using multifractal models for processing geochemical data have been reported (Agterberg 2014; Agterberg 2012; Cao and Cheng 2012; Carranza 2009; Carranza 2010; Chen and Cheng 2016; Cheng 2007; Cheng 2008; Cheng 2012; Cheng and Agterberg 1996; Cheng and Agterberg 2009; Cheng et al. 1994; Cheng et al. 2010; Cheng et al. 1999; Cheng et al. 2000; Li et al. 2003; Lima et al. 2003a; Lima et al. 2008; Lima et al. 2003b; Liu et al. 2017; Wang et al. 2012; Xu and Cheng 2001; Zuo et al. 2012; Zuo et al. 2013a; Zuo et al. 2013b). Based on the same principle, the multifractal theory is also introduced in the remote sensing data processing in this study.

As the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data contain the spectral characteristics of minerals, this principal component analysis (PCA) and spectrum-area (S-A) methods, based on multifractal theory, were adopted to identify metamorphic minerals, such as actinolite, chlorite, epidote, biotite, muscovite, hornblende, andalusite, staurolite, cordierite, and sillimanite with the ASTER data in the Wuliangshan area. Furthermore, to better identify the metamorphic rocks of the Wuliangshan area, the extracted metamorphic mineral anomalies were used to analyze the distribution of metamorphic rocks and compared with the previous studies.

Geological setting

The study area is in western Yunnan Province, China, in the Simao Basin in the middle of the Sanjiang orogenic belt (BaoLong et al. 2008; JunLai et al. 2006; Wang et al. 2015). The latitude and longitude coordinates of the study area are 100° 30′ 00″ E–100° 45′ 00″ E and 24° 20′ 00″–24° 40′ 00″ N respectively and the area covers 900 km2. With a high altitude, extremely rugged topography and difficult travel conditions, in which the highest altitude is 3306 m and most slopes are greater than 35°, it is almost impossible to conduct conventional geological investigation in this area (Fig. 1).

Fig. 1
figure 1

Map of the study area (Yunnan Geological Mineral Bureau, 1990)

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According to the regional geological background, there are relatively complete formation outcrops in the study area, which span from the Palaeoproterozoic era to the Cenozoic era. The main strata are the Palaeozoic Wuliangshan (Pzw.) and Daxinshan Formations (Pz2dx.); the Triassic Manghuai (T2m), Sanhedong (T3sh), Waluba (T3wl), and Maichuqing Formations (T3m); the Triassic–Jurassic Xiaodingxi Formation (TJxd1); the Jurassic Yangjiang (J1y), Huakaizuo (J1h), and Bazhulu Formations (J1b); the Cretaceous Jingxing (K1j) and Nanxin Formations (K1n); the Neogene Pliocene Sanying Formation (N2s); and Quatemary–Holocene sediment (Qh). The Palaeozoic Wuliangshan Formation (Pzw.) is in the central part of the study area; the Palaeozoic Daxinshan Formation (Pz2dx.), the Triassic–Jurassic Xiandingxi Formation (TJxd1) and Quatemary–Holocene sediment (Qh) are in the southwest part of the study area; the Triassic Manghuai = (T2 m), Sanhedong (T3sh), Waluba = (T3wl), and Maichuqing Formations (T3m); the Jurassic Yangjiang (J1y), Huakaizuo (J1h), and Bazhulu Formations (J1b); the Cretaceous Jingxing (K1j) and Nanxin Formations (K1n); and the Neogene–Pliocene Sanying Formation (N2s) are in the northeast part of the study area. The distribution of these strata are controlled by NW-trending faults (Fig. 2).

Fig. 2
figure 2

Geological map of the study area

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The Palaeozoic Daxinshan Formation (Pz2dx.) and the Wuliangshan Formation (Pzw.) have a high concentration of metamorphic minerals. Two metamorphic belts were defined in previous study according to metamorphic characteristics and metamorphic types. These are the Yunling-Jinghong metamorphic belt and the Wuliangshan metamorphic belt (Yunnan Geological Mineral Bureau, 1990). Detailed information regarding the metamorphic belts is listed as follows:

The Yunling-Jinghong metamorphic belt

This metamorphic belt is distributed on both sides of the Lancang River in a NW-trending direction. The metamorphic strata include the upper Palaeozoic Daxinshan Formation (Pz2dx.). The lower segment lithology combination is ash and grey-black sericitic slate, and the upper segment is slate and sandy slate, which is 220–850 m thick. The metamorphic mineral assemblage is composed of sericite and chlorite, and the metamorphic degree is low greenschist facies. The metamorphic period is the end of the Variscan orogeny (Fig. 3a).

Fig. 3
figure 3

a Interbedding of purple grey silty slate and argillic slate (Pz2dx.). b Biotite plagioclase granulite (Pz1W.1). c Interbedding of grey-white layered biotite quartzite and grey two-mica quartz schist (Pz1W.2). d Interbedding of grey greisenized epidote biotite plagioclase granulite and grey muscovite schist (Pz1W.3). e Interbedding of striated muscovite quartzite and greyish green two-mica quartz schist (Pz1W.4)

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The Wuliangshan metamorphic belt

This metamorphic belt is near the eastern part of the Lancang River in a NW-trending direction. The metamorphic strata include the Palaeozoic Wuliangshan Formation. The Palaeozoic Wuliangshan Formation is divided into four members (Pz1W.1, Pz1W.2, Pz2W.3, and Pz2W.4) according to the lithologic characteristics and metamorphic degree.

  • Pz1W.1: The lithology consists of grey and dark grey biotite plagioclase granulite, staurolite two-mica schist, and two-mica quartz schist, with a thickness of 824.9–850.5 m (Fig. 3b).

  • Pz1W.2: The lithology consists of grey and grey-white biotite quartzite, two-mica quartzite, epidote biotite plagioclase granulite, and two-mica quartz schist, with a thickness of 1025.4–1261.3 m (Fig. 3c).

  • Pz2W.3: The lithology consists of grey greisenized epidote biotite plagioclase granulite, two-mica quartz schist, and muscovite schist, with a thickness of 692.7–747.5 m (Fig. 3d).

  • Pz2W.4: The lithology consists of grey to light grey muscovite quartzite, biotite quartzite, epidote quartzite, and two-mica quartz schist, with a thickness of 481.5–770.4 m (Fig. 3e).

The main metamorphic minerals of Pz1W.1 and Pz1W.2 are muscovite, biotite, chlorite, epidote, andalusite, cordierite, staurolite, and kyanite, and the metamorphic degree is generally high greenschist facies-low amphibolite facies. The main metamorphic minerals of Pz2W.3 and Pz2W.4 are muscovite, biotite, chlorite, and epidote, and the metamorphic degree is low greenschist facies.

Methods

Data acquisition and processing

Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is a multispectral imaging sensor on board the Terra satellite, which was launched on December 18, 1999, as part of NASA’s Earth Observing System program. It can measure reflected and emitted electromagnetic radiation from the Earth’s surface and atmosphere in 14 bands. There are three groups of bands: (i) three recording visible and near infrared (VNIR) bands ranging from 0.52 to 0.86 μm at a spatial resolution of 15 m; (ii) six recording portions of shortwave infrared (SWIR) bands from 1.6 to 2.43 μm at a spatial resolution of 30 m; and (iii) five recording thermal infrared (TIR) bands from 8.125 to 11.65 μm at a spatial resolution of 90 m. The ASTER swath width is 60 km (each scene is 60 × 60 km) (Table 1).

Table 1 Spectral characteristics and wavelength ranges of ASTER data
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Two scenes of ASTER image data were acquired in this study with good quality and minimal cloud coverage. The acquisition dates were February 23, 2002, and February 2, 2006.

In this study, the ENVI® 5.3 and ArcGIS®10.3 software packages were used to process the ASTER images. The main processing of the ASTER data included atmospheric correction (Perkins et al., 2005; Nguyen et al., 2015; Rani et al., 2017), data re-sampling, layer stacking, and geometric correction. Figure 4 shows the final image, with a combination of bands 7, 3 N, and 1 after data processing.

Fig. 4
figure 4

ASTER image of the study area with combination of R (Band 7), G (Band 3 N), B (Band 1)

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Analysis of metamorphic mineral spectral response using ASTER data

The minerals’ spectral response features are the basis for mineral extraction. Epidote was used as an example to analyze the mineral spectral response features in the ASTER data as follows:

The spectral curve of epidote in the ASTER data is shown in Figs. 5 and 6. The spectral response of epidote is reflected as absorption valleys in Bands 8 (B8) and 12 (B12) and strong reflections in Bands 5 (B5) and 9 (B9). Therefore, ASTER Bands 5, 8, 9, and 12 can be taken as epidote characteristic spectra. In this manner, the characteristic spectra of other metamorphic minerals can be summarized as follows: ASTER Bands 1,2, 9, and 13 are the characteristic spectra of chlorite; ASTER Bands 1, 8, 10, and 12 are the characteristic spectra of biotite; ASTER Bands 6, 9, 12, and 13 are the characteristic spectra of muscovite; ASTER Bands 7, 8, 10, and 11 are the characteristic spectra of hornblende; ASTER Bands 1, 5, 11, and 12 are the characteristic spectra of andalusite; ASTER Bands 1, 2, 11, and 14 are the characteristic spectra of staurolite; ASTER Bands 2, 6, 11, and 12 are the characteristic spectra of cordierite; ASTER Bands 3, 4, 11, and 12 are the characteristic spectra of actinolite; and ASTER Bands 5, 7, 10, and 11 are the characteristic spectra of sillimanite (Table 3).

Fig. 5
figure 5

Spectral curves of metamorphic minerals from the visible to shortwave infrared spectrum in ASTER Bands 1–9: a Spectral curves of ten metamorphic minerals from the United States Geological Survey (USGS). b Spectral curve of epidote in ASTER Bands 1–9

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Fig. 6
figure 6

Spectral curves of metamorphic minerals in the thermal infrared wave infrared spectrum in ASTER Bands 10–14: a Spectral curves of ten metamorphic minerals from the United States Geological Survey (USGS). b Spectral curve of epidote in ASTER Bands 10–14

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Extraction of metamorphic mineral anomalies

The principle component analysis (PCA) and spectrum-area (S-A) methods were used to extract the metamorphic mineral anomalies.

Principle component analysis

Images generated by digital data from various wavelength bands often appear similar and convey essentially the same information (Arnous and Sultan, 2014). The principle component analysis (PCA) method is typically applied to enhance information extraction in remote sensing data processing, which is based on the statistical characteristics of images as shown in Formula (1) via multi-dimensional orthogonal linear transformation. After the transformation, the linear combination of the original image is generated as a new set of components.

$$ Y= AX $$
(1)

In Formula (1), X represents the multispectral vectors before the PCA transformation, Y represents the principal components after the PCA transformation, and A represents the transformation matrix, which is the eigenvector matrix of the covariances of X.

The 10 analyzed characteristic spectra of the metamorphic minerals in the ASTER Bands were processed using principal component analysis to concentrate the information of metamorphic mineral anomalies into a single PC.

The PCA method was adopted for the epidote remote sensing anomalies as follows:

The ASTER Bands 5, 8, 9, and 12 were used in the PCA transformation based on the spectral properties listed above, and PCs (principal components) representing the epidote remote sensing anomalies should have the characteristics that obtain positive contributions from ASTER Bands 5 and 9 and negative contributions from ASTER Bands 8 and 12. Thus, the PC4 was determined to represent the epidote remote sensing anomalies PC (Table 2).In the same principle, Table 3 shows the parameters of the characteristic spectra and principle components of the other nine metamorphic minerals for anomaly extraction.

Table 2 Contributions of principal component of epidote from ASTER bands
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Table 3 The characteristic spectra, anomaly principle components, and S-A fractal filters (threshold value) in ten metamorphic mineral anomaly extractions
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Spectrum-area

Multifractal theory is effectively applied in the enhanced extraction of weak mineralization minerals and is based on the principle of generalized self-similarity and local singularity. The spectrum-area (S-A) method, which is taken as the typical application of the multifractal theory, can separate weak mineralization anomalies from the geological background (Cheng et al. 1994, Cheng and Agterberg, 1996, Cheng et al., 1999, 2000, Cheng, 2012). The S-A method measures anisotropic generalized self-similarity in the Fourier spectrum space and identifies different generalized self-similarities of the energy spectra distribution by which the background and anomalies can be divided into different filters.

Self-similarity can be expressed using the following formula:

$$ A\left(>S\right)\propto {S}^{-\beta } $$
(2)

In Formula (2), S is the spectral energy density, and A is the area of the spectral energy density greater than a certain critical value (S0). β is available in the log A (> S0) – log (S) diagram. All straight segments usually obey this formula in the log-log diagram. Different straight segments correspond to different fractal relationships, with the intersection of two straight spectral density lines representing the threshold of the fractal filter. A number of abnormal filter models can be produced according to the threshold value, by which the Fourier inverse transformation is used to separate mineral anomalies from the background.

The S-A method was used to obtain the epidote anomalies as follows: the Fourier transformation was applied to transfer the epidote anomalies PC(PC4) from the space domain to the frequency domain at the beginning, and the log-log diagram between the frequency (S) and number of pixels (A) in PC4 was generated in the frequency domain (Fig. 7a). Based on the log-log fractal characteristics diagram in the frequency domain space, the straight segment was then adopted to represent different fractal characteristics for the background and the epidote remote sensing anomalies. That is, the fractal filter threshold, demonstrated by the intersection of the two straight lines of the spectral density values, could be obtained. In the log-log diagram of the epidote remote sensing anomalies, the fractal filter threshold was 1.4, which was determined to distinguish the epidote remote sensing anomalies from the geological background. Accordingly, the filter model was designed as Fig. 7b, in which the black area represents the geological background and the green area represents the epidote anomalies. In the same principle, Table 3 shows the parameters of the S-A fractal filters (threshold value) in the other nine metamorphic mineral anomaly extractions.

Fig. 7
figure 7

Extraction of epidote remote sensing anomalies with PC4 by S-A method. a Log(S)-log(A) diagram of the epidote remote sensing anomalies in the frequency domain. b Fractal filter of the epidote remote sensing anomalies from the geological background

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Profile investigation and petrographic analysis

The identification of the aforementioned extracted metamorphic mineral anomalies was determined through profile investigation in the field and sample petrographic analysis using a microscope.

Profile investigation

The profile investigation should choose areas with complete formation, good outcrops, and simple structure. The profile investigation contains GPS locations, lithologic identification, sample collection, photography, and thin section production. Two profiles (PM28, PM33) were carefully investigated in this study.

PM28: This profile was investigated from April 18 to 20, 2014. The profile length was 4949 m, in which 61 layers were identified, 94 samples were collected, 62 photos were taken, and 94 thin sections were produced.

PM33: This profile was investigated from October 12 to 15, 2014. The profile length was 8061 m, in which 106 layers were identified, 122 samples were collected, 108 photos were taken, and 122 thin sections were produced.

Petrographic analysis

Petrographic analysis is a method to identify minerals and rocks under a microscope. The thin sections are used to analyze the mineral composition and the mineral structure in the minerals or rocks using a microscope. Hence, the minerals or rock types of 216 thin sections were confirmed by petrographic analysis.

Analysis of metamorphic mineral distribution

From the distribution of different metamorphic minerals, three metamorphic degree regions were identified in the study area. That is, a low metamorphic degree region, a medium metamorphic degree region, and a high metamorphic degree region. Furthermore, the difference between these results and the previous studies was studied further, and a new understanding of the background of the study area was obtained.

Results

Analyzing the information on the ten metamorphic mineral anomalies extracted using the aforementioned method (Fig. 8), the rules of the metamorphic mineral distribution become clear: biotite and muscovite are distributed over the whole study area; hornblende, andalusite, actinolite, and cordierite are distributed in the central and southwestern parts of the study area; a large number of epidote and chlorite minerals is distributed in the central part of the study area; and staurolite and sillimanite are widely distributed in the western and southern parts of the study area and concentrated in the central part in a NW-trending direction. That is, the overall trend of the metamorphic degree decreases from the central part to the outer parts in the study area.

Fig. 8
figure 8

Distribution map of extracted metamorphic minerals

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The analysis of the extracted metamorphic minerals anomalies was validated by the material of the profile investigation and petrography, which were provided by the Institute of Yunnan Geological Mineral Exploration in cooperation with our research group. A total of 185 of the 216 analysis results of the sample thin sections were identical to the extracted mineral anomaly results. That is, the extraction accuracy reached 85.65%. The epidote, biotite, and muscovite were related to the lower metamorphic degree; staurolite was related to medium metamorphic degree; and sillimanite was related to high metamorphic degree. To verify these metamorphic minerals which were related to the different metamorphic degree, Point 88-2 of profile PM33, Point 99-2 of profile PM33, and Point 11-2 of profile PM28 were selected to introduce the identification of extracted anomaly information and petrographic analysis in detail as follows:

Point 88-2 of profile PM33 has a concentration of extracted epidote and biotite anomaly information; Point 93-2 of profile PM33 has a concentration of extracted sillimanite, muscovite, and biotite anomaly information; Point 11-2 of profile PM28 has a concentration of extracted staurolite, muscovite, and biotite anomaly information. The petrographic analysis results showed that the rock contains epidote and biotite whose contents are 12 and 30% respectively in Point 88-2 of profile PM33; the rock contains sillimanite, muscovite, and biotite whose contents are 3, 20, and 7% respectively in Point 99-2 of profile PM33; the rock contains staurolite, muscovite, and biotite whose contents are 5, 12, and 18% respectively in Point 11-2 of profile PM28. Therefore, the extracted minerals anomalies are identical to the results of the rock-mineral appraisal (Table 4, Figs. 9, 10, 11).

Table 4 Petrographic analysis of thin sections
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Fig. 9
figure 9

The verification of point 88-2. a PM33-88-2 ⊥ schistosity S, micrograph, single polarization; b extracted metamorphic mineral anomaly; c profile map of PM33

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Fig. 10
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The verification of point 93-2. a Extracted metamorphic minerals; b PM33-93-2 ⊥ schistosity S, ∥ lineation L, micrograph, single polarization; c profile map of PM33

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Fig. 11
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The verification of point 88-2. a Extracted metamorphic minerals; b PM28-11-2 ⊥ schistosity S, micrograph, single polarization; c profile map PM28

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Discussion

Previous studies (Yunnan Geological Mineral Bureau, 1990) suggest that the metamorphic degree of the Yunling-Jinghong metamorphic belt is low greenschist facies, the metamorphic degree of sections 1 and 2 is generally high greenschist facies-low amphibolite facies, and the metamorphic degree of sections 3 and 4 is generally low greenschist facies in the Wuliangshan metamorphic belt. Metamorphic minerals such as muscovite, biotite, chlorite, epidote, andalusite, cordierite, staurolite, and kyanite are abundant in the strata of Pz1W.1 and Pz1W.2, while muscovite, biotite, chlorite, and epidote are abundant in the strata of Pz2dx., Pz2W.3, and Pz2W.4. The metamorphic degree decreases from the central part to the outer part of the study area.

In this study, seven concentration regions of metamorphic minerals were delineated based on the extracted metamorphic mineral anomalies. The metamorphic mineral anomalies of Region 1 are of muscovite, biotite, and epidote, which indicated that the metamorphic degree is low; the metamorphic mineral anomalies of Region 2 are of staurolite, muscovite, biotite, and epidote, which indicated that the metamorphic degree is medium; the metamorphic mineral anomalies of Region 3 are of muscovite and biotite, which indicated that metamorphic degree is low; the metamorphic mineral anomalies of Region 4 are of actinolite, muscovite, biotite, epidote, and hornblende, which indicated that metamorphic degree is low-medium; the metamorphic mineral anomalies of Region 5 are of muscovite biotite, chlorite, hornblende, and cordierite, which indicated that metamorphic degree is low-medium; the metamorphic mineral anomalies of Region 6 are of sillimanite, muscovite, biotite, epidote, hornblende, and andalusite, which indicated that metamorphic degree is medium-high; and the metamorphic mineral anomalies of Region 7 are of sillimanite and andalusite, which indicated that metamorphic degree is medium-high (Fig. 12).

Fig. 12
figure 12

Analysis of metamorphic regions

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Comparing the extracted metamorphic mineral anomalies with previous study results, it can be seen that the overall trend of the metamorphic mineral information is consistent, with some differences. For example, Region 1, related to Pz2W.3, is of low metamorphic degree; Region 2, related to Pz1W.1 and Pz1W.2, is of medium metamorphic degree; Region 3, related to Pz2W.4, is of low metamorphic degree; Region 4, related to Pz2dx., is of low metamorphic degree; and Region 6, related to Pz1W.1 and Pz1W.2, is of high metamorphic degree. The grade of the metamorphic degree is 6 > 2 > 1 > 4 > 3, which is consistent with the previous study results that the metamorphic degree decreases from the central part to the outer part of the study area. However, the distribution of the metamorphic mineral anomalies in Region 5 and Region 7 is different from previous study results. Moreover, Region 5 was not recognized as a metamorphic rock region in previous study results, but numerous medium- and high-grade metamorphic mineral anomalies were extracted in this area. The metamorphic degree of Region 7, related to Pz1W.2 and Pz2W.3, was recognized as medium-high, but a large amount of sillimanite, which is of high metamorphic degree, was extracted in this region.

Conclusions

In this study, the anomaly information of ten metamorphic minerals, actinolite, chlorite, epidote, biotite, muscovite, hornblende, andalusite, staurolite, cordierite, and sillimanite, was successfully extracted by the PCA and S-A methods based on the multifractal theory with ASTER data in the Wuliangshan area, Yunnan Province, China. Profile investigation and petrographic analysis showed that the identification accuracy of the diagnostic metamorphic minerals reached 85.65%. Therefore, the proposed methods in this study have proven to be valuable for metamorphic minerals anomalies extraction. Meanwhile, based on the extracted metamorphic mineral anomalies, seven concentrations of metamorphic rocks were delineated. Compared with previous studies, the Regions 1, 2, 3, 4, and 6 are consistent with existing data, whereas the Regions 5 and 7 are different, which can provide a useful reference for geological survey in the Wuliangshan area. Further detailed research, particularly on the extracted anomalies, needs to be completed in the future to obtain useful insight for geological mapping and a better understanding of the metamorphic rocks.