1 Introduction

Major Ni sulfide deposition occurred globally between the Archaean and Triassic, and these deposits are often associated with ultramafic rocks (e.g., komatiitic volcanic flows and/or related intrusions) and tholeiitic rift-related Ni–Cu–Cr sulfides in layered intrusions [1]. Prospective criteria for sulfide deposits have been developed, e.g., the creation of exploration guidelines for Ni sulfide deposits in Australia [1,2,3,4,5], in which geochemical characterization remains critical.

In Daxinganling, China, the recent identification of the medium-scale Gaxian Ni sulfide deposit occurring in ultramafic rocks suggests that the prospectivity of Ni sulfide is closely related to that of ultramafic rocks. However, ultramafic rocks are relatively difficult to locate due to heavy vegetation, relatively small-scale serpentinization, and extensive weathering. A 1:200,000 regional geochemical survey of this region has been completed, which includes the analysis of 39 elements and oxides and provides a solid foundation for prospecting and exploration.

In recent years, spectroscopy has emerged as a new method to identify regional lithologies. The use of multispectral and hyperspectral data to identify lithologies can be based on either the analysis of all spectral bands or the investigation of bands with specific absorption characteristics, both of which can be completed using image processing software. The spectral angle mapper (SAM) method is used to analyze the whole wavelength range [6,7,8], while the multirange spectral feature fitting (MRSFF) method is used to analyze multiple spectral bands with specific absorption characteristics [9, 10].

Traditionally, geochemical prospecting is based on geochemical anomalies [11]. In recent years, pattern recognition [12] has been used to study metallogenic systems, that is, to identify the geochemical distribution pattern of mineralization by considering enrichments and depletions at the same time [13, 14]. Then, the whole mineralization process is simulated. These approaches need some new functions (operations) for pattern recognition. The SAM and MRSFF methods are essentially pattern recognition tools; the former is equivalent to the cosine similarity measure (CSM) and has been applied in mineral prospectivity map** [15, 16].

The main aim of this study was to provide a prospective area for Ni sulfide deposits in the Daxinganling area using the idea of pattern recognition. Rasterized regional geochemical data were treated as spectral-like data, and the SAM algorithm was used to identify the locations of ultramafic rocks, while the MRSFF algorithm was used for the prospective areas for Ni sulfide deposits.

2 Geological setting and geochemical data

2.1 Location and tectonic setting

The Daxinganling region is located in northeastern China (Fig. 1) and adjoins Russia to the north and Mongolia to the west. Daxinganling is located at the eastern end of the Central Asian orogenic belt, sandwiched between the North China plate and the Siberian plate [17]. The crystalline basement is composed of Precambrian strata, which were metamorphosed from a series of felsic volcanic rocks and carbonate rocks in an active continental margin tectonic setting. The cap rock is a combination of Paleozoic clastic and carbonate rocks. Wide areas are covered by volcanic rocks from the Late Jurassic to Early Cretaceous, within which two large volcanic eruption cycles can be classified. The early cycle began with mafic rock emplacement and ended with felsic volcanic eruptions. The second cycle formed only mafic volcanic rocks. Intermediate-mafic and intermediate-felsic volcanic rocks dominate in the region, with ultramafic rocks accounting for only a very small proportion.

2.2 Regional ultramafic rocks and Ni sulfide mineralization

Most ultramafic rocks in Daxinganling are found within ophiolite belts. Ultramafic rocks in the north belong to the ** and its implementation in the Asia-Pacific region. Appl Geochem 16(11–12):1309–1321" href="/article/10.1007/s42452-021-04308-x#ref-CR22" id="ref-link-section-d106143167e584">22].

3 Methods

Several steps were adopted to determine the prospective areas of Ni sulfide deposits. First, regional geochemical data were rasterized and treated as spectral-like data. Then, the SAM method for accurately identifying ultramafic rocks was applied. After locating the possible areas where ultramafic rocks may occur, we used the MRSFF method to classify the areas with alteration characteristics similar to those of typical Ni sulfide deposits.

3.1 Pre-processing of geochemical data

Data in this study were taken from a 1:200,000-scale regional geochemical database. The geochemical data were rasterized using the “rasterize point data” drop-down menu in ENVI software (version 4.4, Research Systems, Inc., Boulder, CO, USA). ENVI is a tool for imaging and geographic information system (GIS) processing. Its gridding function uses Delaunay triangulation of a planar set of points. After the irregularly gridded data points were triangulated, they were interpolated to a regular grid. Interpolation with a linear quintic polynomial was chosen to enable the resulting surface to pass through all given data points, and the value of each point does not change [23]. In our study, the output projection was determined, and the output X/Y size was selected as 1000 m (i.e., the spatial resolution of the rasterized images was 1000 m). After the above steps, images of single elements were formed (e.g., Fig. 2). To use a spectroscopic approach, geochemical elements were organized according to families in the periodic table, as these elements possess similar chemical properties on Earth (i.e., Li, Na2O, K2O, Be, MgO, CaO, Sr, Ba, Y, La, Th, U, Ti, Zr, V, Nb, Cr, Mo, W, Mn, Fe2O3, Co, Ni, Cu, Ag, Au, Zn, Cd, Hg, B, Al2O3, SiO2, Sn, Pb, P, As, Sb, Bi, and F).

Fig. 2
figure 2

Example of a geochemical image (Li contents in ppm) for the Daxinganling area

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3.2 Conversion of geochemical images into spectral-like images

Rasterized data were transformed to spectral-like data using ENVI software, in which a wavelength was assigned to each element to run the MRSFF method. For example, the first element (Li) was assigned a value of 0.1 µm, the second (Na) was assigned 0.2 µm, and so on until all elements had been assigned a wavelength.

As geochemical data differ significantly from reflectance data (some by more than 1), they need to be normalized. Minimum–maximum normalization was calculated by the following formula:

$${\text{E}}{{\text{C}}_{{\text{out}}}}\, = \,\left( {{\text{E}}{{\text{C}}_{{\text{original}}}} - {\text{mi}}{{\text{n}}_c}} \right)/\left( {{\text{ma}}{{\text{x}}_c} - {\text{mi}}{{\text{n}}_c}} \right),$$

where ECoriginal is the original content of an element/oxide in the image, minc is the minimum value of the element, maxc is the maximum value of the element, and ECout is the output value. After the calculation, the data values were normalized between 0 and 1.

The emulated spectra (i.e., the spectra of geochemical elements) of the Gaxian deposit (e.g., Ni sulfide deposits) were extracted by locating previously identified deposits on the 1:200,000 regional geological map. The geographical coordinates at the central position of a deposit were extracted, and the “Z profile” method was used to extract the relevant emulated spectra from the spectral-like data.

The positions of known ultramafic rocks, including ** at Bodie, California, using AVIRIS hyperspectral data. Remote Sens Environ 65:309–319" href="/article/10.1007/s42452-021-04308-x#ref-CR7" id="ref-link-section-d106143167e2811">7]. The same principle can be applied to geochemical data as long as the geochemical data have been transformed into spectral-like data. We treated the geochemical spectra as vectors in multidimensional space (with the number of dimensions equal to the number of elements), which allowed the angle between the reference emulated spectra and pixel spectra to be calculated and provided a measure of the similarity. The SAM method was applied to all geochemical images, with the threshold of the angle fixed at a value of 0.1, and an angle larger than this value was not classified. The generated rule images were then used for emulated spectral analysis and post-classification.

The emulated spectrum of ** of imaging spectrometer data using the complete band shape least-squares algorithm simultaneously fit to multiple spectral features from multiple materials. In: Proceedings of the third airborne visible/infrared imaging Spectrometer (AVTRIS) Workshop, JPL Publication, pp 90–54, 176–186" href="/article/10.1007/s42452-021-04308-x#ref-CR9" id="ref-link-section-d106143167e3310">9]. MRSFF is an improvement on the treatment of several spectral wavelength ranges and the assignment of corresponding weights and can yield a better performance [24]. When the main metallogenetic elements are treated as absorption features, MRSFF can be used to identify ore-bearing altered rocks and relate them spatially to prospective areas.

The emulated spectra of Gaxian2 were chosen to represent the geochemical characteristics of the deposits. The spectra of Gaxian2 had high concentrations of Mg, Ca, Sr, Ti, Cu, Ni, Al, and P (Fig. 3). Using the emulated spectra of the Gaxian deposit as the reference spectra, we subtracted the normalized data from “1,” and the elements with higher concentrations then fell into the absorption ranges of the emulated spectra. The weight of the factor assigned to Cu and Ni was 10, while that for Mg, Ca, Sr, Ti, Al, and P was 5, reflecting the effects of ore formation on these elements. Using these values, we generated a scale image with the MRSFF method (Fig. 5). A scale image and root mean square (RMS) image were output. The images were related to material abundances, where brighter pixels indicated a better match to the Gaxian2 spectra, which represent the geochemical characteristics of the Gaxian deposit.

Fig. 5
figure 5

Enhanced scale image constructed using the multirange spectral feature fitting (MRSFF) method. The reference emulated spectra are from Gaxian2

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3.5 Prospective area classification

We classified prospective areas from among the Ni sulfide deposits based on three criteria: (1) possible location of identified ultramafic rocks classified by SAM, (2) a high MRSFF scale value, and (3) distribution of prospective areas along a structural belt.

When applying the first of these criteria, the classification of ultramafic rocks was limited to just 15% of the region (i.e., the cumulative frequency percentage was less than 15%, and the spectral values were less than 0.70; Fig. 4) to limit the scope of the potential ultramafic rocks.

Prospective area classifications of MRSFF scale images for Ni sulfide deposits were performed using the density slice method, which is a thresholding technique used in image classifications to select data ranges and colors for highlighting areas in a greyscale image. In this study, prospective areas were classified according to cumulative frequency statistics. Cumulative frequency statistics were obtained by computing parameters from the histogram statistics of MRSFF scale images. The pixel values corresponding to the cumulative frequency statistics of 97.5%, 92%, and 85% in the scale image were taken as the threshold values, which is typical for the classification of geochemical exploration anomalies [25]. Level A corresponded to scale values of > 0.85, level B corresponded to scale values of 0.71–0.85, and level C corresponded to scale values of 0.57–0.71 (Fig. 6). Different colors were then assigned to levels A, B, and C.

Fig. 6
figure 6

The grouped prospective areas in Daxinganling and the contrast among geochemical anomalies and prospective areas. Levels A, B, and C represent threshold values of the scale image from the MRSFF method, which are equal to 97.5%, 92%, and 85% (i.e., Level A corresponds to scale values > 0.85, level B corresponds to scale values from 0.71 to 0.85, and level C corresponds to scale values of 0.57–0.71). Prospective areas include 1. Gaxian; 2. Wunuer; 3. Dahongshan; 4. Badaogoumen; 5. Fangbaotukacha; 6. Heremutu; and 7. Wutonghua

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4 Results and discussion

4.1 Characteristics of prospective areas for Ni sulfide deposits

The scale image for MRSFF was classified using three levels. After the levels were limited according to the possible ultramafic rocks identified from the SAM method, seven prospective areas for Ni sulfide mineralization were grouped (Fig. 6) based on the concentrations of the levels. The characteristics of the predictive areas were assessed against known ultramafic rocks and mineralization (Table 3). The prospective areas included Gaxian, Dahongshan, Badaogoumen, Fangbaotukacha, Heremutu, and Wutonghua, the lithologies of which are komatiite, peridotite, pyroxene peridotite, gabbro, pyroxene, and lherzolite. In other prospective areas, spilite keratophyres and veins of gabbro occurred. Ni sulfide mineralization in Fangbaotukacha occurred in feather-like fractures.

Table 3 Summary of lithologies, mineralization, and geochemical anomalies in prospective area
Full size table

We contrasted the prospective areas with the geochemical background and anomalies. We classified the geochemical anomalies according to the anomaly threshold: mean + 2*standard deviations. The results showed that the three classified levels of MRSFF scale values were coincident with the anomalies. Figure 6 illustrates that in the Badaogoumen prospective area (No. 4), the levels of scale values coincided with Ni, Co, P, Ca, and Mg in most of the areas.

Geochemical element concentrations were extracted to contrast high-scale values created with the MRSFF method. We extracted the Z profiles of the central pixel in clustered pixels with high scale values and obtained the normalized values of different elements, where higher values corresponded to geochemical anomalies of the given element. Three types of element anomaly combinations were identified: (1) Fe, Co, Ni, Cu, V, and Cr, which were found in Dahongshan, Fangbaotukacha, **nshengmuchang, Heremutu, and Wutonghua; (2) Fe, Co, Ni, Cu, V, Cr, Au, As, and Sb, which were found in the Gaxian, Wunuer, and Badaogoumen areas; and (3) Na, Mg, and Ba, which were identified in the Wuniuhe prospective area. The first and second types were consistent with ultramafic rocks or ultramafic rock-related mineralization, although type 2 may have undergone later hydrothermal alteration. The genesis of the third combination type led to a high Na content, and rocks may contain minerals such as Na-rich plagioclase.

4.2 Discussion

In the past, spectral data have been used with the SAM and MRSFF methods, and an attempt has been made to apply these methods to geochemical data. The rasterized geochemical images were treated as vectors in multidimensional space, where the number of elements was equal to the number of dimensions. We assigned the order of elements according to families in the periodic table in this study. This arrangement was mainly combined with prior geological knowledge; that is, geochemical elements had the characteristics of enrichment or deletion. In fact, the results produced by the SAM and MRSFF methods are not affected by any element arrangement order.

One of the advantages of this study is that the linear quintic polynomial interpolation method can keep the geochemical values from sampling sites unchanged. We have made a comparison, if the chemical composition of a sampling site has a maximum value, and the actual value of its adjacent sites is much smaller, if adopting Kriging method, which is commonly used in geochemical anomaly the value of the surrounding sites will also become very high after interpolation, which leads to a big difference with the original chemical composition value. If linear quintic polynomial interpolation is used, the values of the surrounding sites are the same as the original values and do not change. Another advantage is that according to the mineralization characteristics of the surrounding rock (ultramafic rock) and ore body, regional deposits can be predicted by pattern recognition. This approach was different from the traditional method of selecting prospecting targets according to geochemical anomalies (or multielement comprehensive anomalies). However, if based on only copper and nickel anomalies, it was preferable to include deposits related to nonultramafic rocks such as mafic rocks, skarn deposits or low-temperature hydrothermal deposits. Therefore, the most important issue was how to eliminate the influence of these anomalies. The SAM method could reveal the distribution of major elements in ultramafic rocks and reduce the influence of other factors. Considering the multielement geochemical characteristics of the ore-forming system, the MRSFF method was used to classify the ore-forming system; this method reflected not only the anomaly intensity but also the overall trend of the whole mineralization and alteration system.

Nine prospective areas for Ni sulfide deposits were obtained using the SAM and MRSFF methods. All areas showed high prospectivity for ultramafic rocks or Ni mineralization, providing guidelines for future exploration. In the traditional approach, prospectivity maps are created using a geochemical block method that predicts across thousands of km2 [26]. In this study, the pixel size of the geochemical images is 1 km, which provides much better predictions than previous methods and has advantages in terms of accuracy and targeting locations [27].

5 Conclusions

This research represents the first attempt to use rasterized geochemical data for the selection of prospective areas, the SAM method to predict the locations of ultramafic rocks, and the MRSFF method to locate areas with geochemical characteristics similar to those of the reference Ni sulfide deposit. Then, prospective areas are classified according to these two methods. The classified MRSFF levels in the prospective areas have nearly the same positions as the geochemical anomalies, and ultramafic rocks and related mineralization also provide clues about the occurrence of Ni sulfide deposits.