Macro-scale ore-controlling faults revealed by micro-geochemical anomalies

Whereas the mechanism of fluid flow, and thus structural control, linked with mineral deposit formation is quite understood, the specific structures that likely provided controls on mineralization at certain geographic scales are not readily known for a given region unless it is well-explored. This contributes uncertainty in mineral prospectivity analysis in poorly-explored regions (or greenfields). Here, because the spatial distribution of mineral deposits has been postulated to be fractals (i.e., the patterns of these features are self-similar across a range of spatial scales), we show for the first time that micro-geochemical anomalies (as proxies of micro-scale patterns of ore minerals), from few discrete parts of the Sossego iron-oxide copper-gold (IOCG) deposit in the Carajás Mineral Province (CMP) of Brazil, exhibit trends of macro-scale faults that are known to have controlled IOCG mineralization in the CMP. The methodology described here, which led to this novel finding, would help towards detecting mineral exploration targets as well as help towards understanding structural controls on mineralization in greenfields.

www.nature.com/scientificreports www.nature.com/scientificreports/ (Fig. 2a). Local-scale faults and shear zones at Sossego mostly trend ENE-WSW and WNW-ESE (Fig. 2a,b) and exhibit fractal distribution (Fig. 2d), and the Sossego deposit has adopted a main WNW-ESE trend (Fig. 2c) as well as a fractal distribution (Fig. 2e). However, there are other local-scale trends in IOCG mineralization that do not reflect the main local-scale trends faults and shear zones, e.g., the nearly N-S trend (Fig. 2c), which is related to nearly N-S-trending structures as at sample SQR-021 (Fig. 2a).
The similarity of local-scale trends of faults and shear zones at Sossego to the regional-scale trends of such structures in the CMP as well as the similarity of local-scale trends of IOCG mineralization to regional-scale trends of IOCG deposits clearly show that either set of objects is a fractal (i.e., the parts of a pattern look similar to the whole pattern). The similarity of local-scale trends of IOCG mineralization (Fig. 2c) to regional-scale trends of faults and shear zones (Fig. 1b), which is consistent with studies on IOCG and other types of mineralization elsewhere [32][33][34][35][36] , reflects structural control by regional-scale structures on mineralization at local scales.
The similarity of micro-scale trends of geochemical anomalies (as proxies of micro-scale patterns of ore/ gangue minerals) to local-scale trends of structures at/near mineral deposits is illustrated from nine thin sections of oriented samples of mineralized rocks that we collected from different parts of the Sossego deposit along its longitudinal and transverse axes (Fig. 2a).

Results
Micro-scale geochemical anomalies in sample SQR-006 (Fig. 3a) show: (a) a major NE-SW trend, which is likely linked to the south-westward extension of a NE-SW-trending shear zone located ~200 m NE of this sample (Fig. 2a); and (b) minor NW-SE and WNW-ESE trends, which reflect control by nearby faults with the same trends.
Micro-scale geochemical anomalies in sample SQR-008 (Fig. 3a) show: (a) major NNW-SSE to NW-SE trends, which are likely linked to the NNW-SSE-to NW-SE-trending faults located ~50 to ~100 m north of this sample (Fig. 2a); (b) a major N-S trend, which suggests the presence of N-S-trending shear zone at this sample location similar to the one located ~150 m to the east; (c) minor E-W trend, reflecting control by the nearly E-W-trending fault at the locations of this sample; and (d) minor NE-SW trend, which suggests the presence of NE-SW-trending shear zone at this sample location similar to the one located ~300 m to the northeast.
Micro-geochemical anomalies in sample SQR-021 (Fig. 3b) show: (a) a major NE-SW trend, reflecting control by a major NE-SW-trending fault at this sample location (Fig. 2a); (b) secondary N-S trends, reflecting control by www.nature.com/scientificreports www.nature.com/scientificreports/ a NE-SW-trending shear zones at this sample location; and (c) secondary NW-SE-to WNW-ESE trends, which suggest the presence of structures with the same trends at/around this sample location.
Micro-scale geochemical anomalies in sample SQR-010 ( Fig. 3c) show: (a) major NNW-SSE to secondary NNE-SSW trends, which can be linked to NNE-SSW-trending shear zones south and east of this sample location (Fig. 2a); and (b) secondary ENE-WSW trends, which can be linked to faults with the same trend at this sample location.
Micro-geochemical anomalies in sample SQR-011 ( Fig. 3c) show: (a) a major NNE-SSW trend, which can be linked to shear zones with the same trend at this sample location (Fig. 2a); and (b) secondary WNW-ESE to ENE-WSW trends, which reflect control by WNW-ESE-trending shear zones similar to the one located south of this sample point.
Micro-scale geochemical anomalies at sample SQR-018 ( Fig. 3c) show: (a) a major ENE-WSW trend, which reflects control by faults with the same trend located at and SW of this sample location (Fig. 2a); (b) a secondary NNW-SSE trend, which suggests control by faults of the same structure at this sample location.
Micro-scale geochemical anomalies in sample SQR-012 ( Fig. 3d) show: (a) a major NE-SW trend, which is likely linked to a NNE-SSW-trending shear zone or suggesting the presence of unmapped NW-SE-trending structures at this sample location (Fig. 2a); (b) secondary WNW-ESE trend, which reflects control by a major fault with the same trend at this sample location; and (c) secondary NNW-SSE and ENE-WSW trends, which suggest the presence of structures with these trends at/near this sample location.
Micro-scale geochemical anomalies in sample SQR-016 ( Fig. 3d) show: (a) a major NNE-SSW trend, which reflects control by a shear zone with the same trend at this sample location like the one present ~120 m east of it (Fig. 2a); (b) secondary NW-SE trends, which suggest the presence at this sample location of faults with the same trends like the one present ~50 m south of it; and (c) secondary ENE-WSW to NE-SW trends, which reflect control by a fault with the same trend at this sample location.
Micro-scale geochemical anomalies in sample SSG-002 ( Fig. 3e) show: (a) major WNW-ESE to NW-SE trends, which suggest the presence of and reflect control by faults with the same trends like those ~150 m southwest and ~100 m northeast of this sample location (Fig. 2a); and (b) secondary NE -SW trends, which suggest the presence of and reflect control by faults with the same trends like the one ~100 m southeast of this sample location.

Discussion
The above observations clearly illustrate for the first time that trends of micro-geochemical anomalies of IOCGs are similar to (a) trends of local-scale faults and shear zones, supporting the knowledge of structural control on IOCG mineralization at Sossego 28,31,37 , and (b) local-to regional-scale trends of IOCG mineralization (Table 1), supporting the proposition that the spatial distributions of mineral deposits of specific types are fractals [1][2][3][4] . The trends of micro-geochemical anomalies that we report here are strongly consistent with the trends of chalcopyrite grains that we have measured in thin sections 38 and with the trends of regional-, district-and local-scale faults www.nature.com/scientificreports www.nature.com/scientificreports/ and shear zones that are currently considered for publication in another journal. In this forthcoming paper, our analyses suggest that the initial phase of hydrothermal alteration at Sossego was coeval with the generation of several local-scale dilational jogs. In addition, our earlier regional-scale Fry analysis of the CMP IOCG deposits, including Sossego, reveals a sigmoidal pattern 28 that is consistent with the Carajás sigmoid that was interpreted 39 to have formed during 2.7-2.6 Ga under dextral transtension by the subsidence of supracrustal units into dilatational jogs. These novel findings are critical to geological mapping, understanding of mineralization controls and mineral exploration in frontier regions (or so-called greenfields) where the geology is poorly-mapped. In such regions, the collection of oriented samples at outcrops of mineral showings and the application of the methodology described here can result in the recognition of trends of unmapped faults and, thus, can guide detection and mapping of geological structures, say by remote sensing using satellite imagery and/or airborne geophysical data. In greenfields where structural control on mineralization is poorly-understood, the application of the methodology presented here can result in the recognition of trends of structures that are probably macro-scale controls of mineralization, which, in turn, can aid in predictive mapping of exploration targets 16,40,41 .
The stimulus behind the methodology discussed here is the synthesis of ideas regarding (a) fluid flow in porous media as a factor of mineralization 42,43 , (b) imaging microscopy of porous media to model micro-scale fluid flow 44,45 , (c) singularity as a fractal property of anomalous amount of energy release or mass (i.e., metal/element) accumulation during the formation of a mineral deposit 26,27 , and (d) point pattern analysis 15 of micro-scale 'centres of geochemical anomalies' (i.e., singularities as proxies of loci of multi-element accumulation at/along pores in rocks). This paper has shown the usefulness of SXAM images 24 for the purpose of this study. This study underscores the need for the orientation of rock samples from outcrops in the field 22 and the orientation of drill cores as standard rock sampling protocols in geology. This research proves graphically the fractal nature of mineral deposits, which has important implications not only for economic geology but for the geosciences in general.
www.nature.com/scientificreports www.nature.com/scientificreports/ Methods elemental imaging. From oriented samples of mineralized rocks (Extended Data Fig. 1), we prepared polished thin sections (Extended Data Fig. 2) from which we acquired images of element concentrations using the SXAM at Gifu University in Japan.
The SXAM system that we used (model XGT-2000V by Horiba Scientific) is an X-ray fluorescence analyser that uses a continuous high energy X-ray beam (Rh anode 50 kV 1 mA), 100 μm in diameter, focused with a guide tube and irradiated perpendicular to the surface of a sample 23,24 . X-ray fluorescence from the surface of sample is observed by a high-purity Si detector of an energy-dispersive spectrometer. In this SXAM system, a sample is mounted on a PC-controllable motor-driven X-Y stage placed in an open space outside the vacuum chamber. Therefore, among the elements that SXAM can analyze 46 (i.e., sodium to uranium), it is difficult to obtain compositional images for the lightest elements (e.g., Al), especially if these are present in very low concentrations, because low-energy fluorescence X-rays are absorbed by air and the film of the window. However, compared to other elemental imaging methods like electron probe micro-analyser or scanning electron microscopy with energy dispersive X-ray analyser, the SXAM can measure two-dimensional distribution of chemical elements over a significantly wider range of scales (e.g., it can acquire elemental images for a sample set on a standard size 24 × 46 mm petrographic thin section) because the scanning area can be set from 2.56 × 2.56 mm with a resolution of 10 mm, to roughly 200 × 400 mm with a resolution of 0.78 mm. The count number of fluorescence X-rays of each element is stored as a digital image that consists of pixels; each pixel corresponding to a position on the sample surface.

Principal component analysis. To derive from each set of SXAM images of element concentrations a
micro-scale image of geochemical signature (i.e., multi-element association) depicting the mineralization, we followed the traditional approach of applying principal component (PC) analysis to maps/images of concentrations of multiple elements to derive maps/images of multi-element associations (i.e., geochemical signatures) depicting geological processes of interest 48,49 . Nowadays this approach makes use of logratio-transformed geochemical data 50,51 because element compositions that are measured/expressed as ratios (i.e., as %, ppm, ppb, etc.) are constrained to a constant sum and, thus, represent closed number systems, which render spurious results in multivariate (e.g., PC) analysis 52 . However, logratio transformation of element concentrations in the SXAM images is not necessary because these data (i.e., X-ray counts) do not represent closed number systems.
Each set of SXAM images of element concentration per thin section sample (Extended Data Figs 3-11) is subjected to PC analysis, yielding a number of PCs equal to the number of input images. The PC depicting the geochemical signature of mineralization is interpreted according to the signs and magnitudes of the loadings (eigenvectors) of each element on a PC. For each pixel in an image, a PC score can be calculated as the linear sum of products between element loading and element concentration. We performed the PC analysis in the GIS (geographic information system) software ILWIS, to which we imported the digital data of the SXAM images saved as ASCII (or tab delimited text) files.
The geochemical signature of mineralization in sample SQR-006 is represented by PC1, which depicts a Fe-Ti association that explains about 64% of the multivariate data variance (Extended Data Table 1). The high negative loading on Fe in PC1 reflects pyrite (FeS 2 ) whereas the low negative loading on Ti reflects titanite (CaTiSiO 5 ) representing calcic alteration. Because of the negative loading on Fe in PC1, the PC1 scores are negated (i.e., multiplied by −1) so that low to high negated PC1 scores reflect increasing intensity of mineralization or metal enrichment. The spatial distribution of negated PC1 scores depicting the Fe-Ti association is shown in Extended Data Fig. 13a.
The geochemical signature of mineralization in sample SQR-008 is represented by PC2, which depicts an antipathetic association between Cu and Ca-Ti and explains about 7% of the multivariate data variance (Extended Data Table 2). The high negative loading on Cu in PC2 reflects chalcopyrite (CuFeS 2 ) whereas the intermediate and low positive loadings on Ca and Ti, respectively, reflect a suite of minerals (e.g., epidote (Ca 2 (Al,Fe) 2 (SiO4) 3 (OH)) and titanite (CaTiSiO 5 )) that comprise calcic alteration. Because of the negative loading on Cu in PC2, the PC2 scores are negated (i.e., multiplied by −1) so that low to high negated PC2 scores reflect increasing intensity of mineralization or metal enrichment. The spatial distribution of negated PC2 scores, which represent Cu mineralization, is shown in Extended Data Fig. 13b.
The geochemical signature of mineralization in sample SQR-010 is represented by PC1, which depicts mainly Cu and explains about 98% of the multivariate data variance (Extended Data Table 3). The high positive loading on Cu in PC1 reflects chalcopyrite (CuFeS 2 ). The spatial distribution of PC1 scores, which represent Cu mineralization, is shown in Extended Data Fig. 13c.
The geochemical signature of mineralization in sample SQR-011 is represented by PC1, which depicts a Fe-Cu association that is antipathetic with Ca and explains about 77% of the multivariate data variance (Extended Data Table 4). The high and low positive loading on Fe and Cu, respectively, in PC1 reflects pyrite (FeS 2 ) and chalcopyrite (CuFeS 2 ) whereas the intermediate negative loading on Ca reflects a suite of mineral representing calcic www.nature.com/scientificreports www.nature.com/scientificreports/ alteration. The spatial distribution of PC1 scores, which represents Fe-Cu mineralization, is shown in Extended Data Fig. 13d.
The geochemical signature of mineralization in sample SQR-012 is represented by PC1, which depicts mainly Cu and explains about 89% of the multivariate data variance (Extended Data Table 5). The high positive loading on Cu in PC1 reflects chalcopyrite (CuFeS 2 ). The spatial distribution of PC1, which represents Cu mineralization, in the thin section sample is shown in Extended Data Fig. 13e.
The geochemical signature of mineralization in sample SQR-016 is represented by PC4, which depicts Cu-Ti association and explains roughly 1% of the multivariate data variance (Extended Data Table 6). The high and intermediate negative loadings on Cu and Ti in PC1 reflect chalcopyrite (CuFeS 2 ) and titanite (CaTiSiO 5 ), respectively, the latter representing calcic alteration. The PC1, with high positive loading on Fe and explains about 96% of the multivariate data variance, mainly represents hematite (Fe 2 O 3 ) alteration and was not considered as the geochemical signature of mineralization. Because of the negative loading on Cu in PC4, the PC4 scores are negated (i.e., multiplied by −1) so that low to high negated PC4 scores reflect increasing intensity of mineralization or metal enrichment. The spatial distribution of PC4 scores, which represent Cu mineralization, is shown in Extended Data Fig. 13f.
The geochemical signature of mineralization in sample SQR-018 is represented by PC2, which depicts Cu-K association and explains roughly 5% of the multivariate data variance (Extended Data Table 7). The high and low negative loadings on Cu and K in PC2 reflect chalcopyrite (CuFeS 2 ) and biotite (K(Mg,Fe) 3 (AlSi 3 O 10 )(F,OH) 2 ), respectively, the latter representing potassic alteration. The PC1, with high positive loading on Fe and explains about 94% of the multivariate data variance, mainly represents hematite (Fe 2 O 3 ) alteration and was not considered as the geochemical signature of mineralization. Because of the negative loading on Cu in PC2, the PC2 scores are negated (i.e., multiplied by −1) so that low to high negated PC2 scores reflect increasing intensity of mineralization or metal enrichment. The spatial distribution of PC2 scores, which represent Cu mineralization, is shown in Extended Data Fig. 13g.
The geochemical signature of mineralization in sample SQR-021 is represented by PC2, which depicts an antipathetic association between Cu and Fe and explains about 15% of the multivariate data variance (Extended Data Table 8). The high negative loading on Cu in PC2 reflects chalcopyrite (CuFeS 2 ) whereas the low positive loading on Fe represents hematite (Fe 2 O 3 ). Because of the negative loading on Cu in PC2, the PC2 scores are negated (i.e., multiplied by −1) so that low to high negated PC2 scores reflect increasing intensity of mineralization or metal enrichment. The spatial distribution of PC2 scores, which represent Cu mineralization, is shown in Extended Data Fig. 13h.
The geochemical signature of mineralization in sample SSG-002 is represented by PC1, which depicts an antipathetic association between Fe-Cu and Ca and explains about 70% of the multivariate data variance (Extended Data Table 9). The high and intermediate positive loadings on Fe and Cu in PC1 reflect pyrite (FeS 2 ) and chalcopyrite (CuFeS 2 ), respectively, whereas the intermediate negative loading on Ca represents a suite of minerals comprising calcic alteration. The spatial distribution of PC1 scores, which represent Fe-Cu mineralization, is shown in Extended Data Fig. 13i. Singularity mapping. We subjected each of the micro-scale PC images of geochemical signature of mineralization (Extended Data Fig. 13) to singularity analysis 26,27 in order to derive micro-scale images of geochemical anomalies. Singularity is defined, from a geoscience viewpoint, as a special geological event associated with anomalous energy release or material accumulation that take place during restricted spatial-temporal intervals 26 . The method for mapping singularity indices (denoted as α) has been developed and described by Cheng 26,27 and is not repeated here. However, we used a MATLAB-based software that is available from its developers 53 . To run the singularity mapping algorithm in this software, image data in raster (i.e., pixel-based) format must be converted to ASCII (or tab delimited text file) format. The output file of singularity indices is also in ASCII format, which can be imported to software for image analysis (and in this study we used the ILWIS GIS software).
The images of singularity indices are shown in Extended Data Fig. 14. In each of these images, pixels with α < 2 are positions where positive singularity exists (i.e., where element/metal enrichment increased as area decreased), pixels with α > 2 are positions where negative singularity exists (i.e., where element/metal enrichment decreased as area decreased), and pixels with α = 2 are positions where neither positive nor negative singularity did not occur 26 . Therefore, geochemical anomalies are defined by pixels with α < 2 (Extended Data Fig. 15).

Spatial neighbourhood analysis.
We subjected each of the micro-scale images of geochemical anomalies (Extended Data Fig. 15) to spatial neighbourhood analysis to locate positions that are likely "geochemical anomaly centres" or "loci of metal enrichment". In a neighbourhood of pixels in an image of singularity indices, the "geochemical anomaly centre" or "locus of metal enrichment" is a pixel with the lowest singularity index because geochemical anomalies are defined by pixels with α < 2. To locate such pixels (denoted as "pits"), we ran a spatial neighbourhood algorithm, namely Pit = NBMINP(MAP#) = 5, where MAP# is a neighbourhood matrix (i.e., a 3 × 3 kernel filter) will move over input map an image (each of the SXAM images) and position (denoted by #) found by the matrix will be 'retrieved' according to the command NBMINP, which returns the position of the neighbour that has the smallest value. This algorithm is commonly used to identify local pits in a digital elevation model. In a 3 × 3 kernel filter, the pixels are coded with numbers 1 to 9 from left to right and from top to bottom; thus, the central pixel is coded 5. If the "Pit" algorithm finds multiple neighbour pixels having the same smallest value, then the precedence for retrieving the "pit" is: central pixel number 5, and then pixels 1, 2, 3, 4, 6, 7, 8, 9. The positions of "geochemical anomaly centres" or "loci of metal enrichment" in individual thin section specimen are shown in Extended Data Fig. 16. www.nature.com/scientificreports www.nature.com/scientificreports/ Fry analysis. To describe trends of "geochemical anomaly centres" per oriented thin section sample, we used Fry analysis 15 . This is a graphical method of spatial autocorrelation analysis of objects depicted as points, whereby each and every point is used as origin for translation. The method plots translations (so-called Fry plots) of point objects by using each and every point as a centre or origin for translation. Detailed description, with illustration, for creating Fry plots can be found in Carranza 16 and is not repeated here. Fry plots have been developed, in the late 1970s, originally for the investigation of strain and strain partitioning in rocks 15,54 . Since two decades later, Fry plots have been used to analyse spatial distributions of mineral deposits 16,28,[55][56][57][58] and geothermal fields 59 in order to infer their structural controls.
To visualize trends in Fry plots a rose diagram can be created for (a) all pairs of translated points and (b) pairs of translated point within a specified distance from each other. The former case may reveal trends due to processes operating at macro-scales but may also show a trend that is an artefact of the shape of the study area (e.g., a major trend due to the longer axis of a thin section), whereas the latter case may reveal trends due to processes operating at micro-scales. For the latter case, it is instructive to use a distance within which there is maximum probability of only two neighbouring points (i.e., analysis of trends between any two neighbouring "geochemical anomaly centres"). This distance can be determined via point pattern analysis 60 .
The Fry plots of "geochemical anomaly centres" and corresponding rose diagrams of trends between pairs of Fry points at specified distances in the thin sections of the studied samples are shown in Extended Data Figs 17-25. Pairs of Fry points at specified distances in thin sections of samples SQR-006, SQR-008, SQR-010 SQR-011, SQR-016, and SQR-021 show major trends that follow the trends of the thin sections' long axes. However, these major trends are likely real because pairs of Fry points at specified distances in thin sections of samples SQR-012, SQR-018, SQR-021, and SSG-002 show major trends that do not follow the trends of the thin sections' long axes.

Data Availability
For trends of regional-scale faults and shear zones in the CMP (Fig. 1), we obtained the data from regional lithological-structural map of the CMP (Vasquez, M.L. & Rosa-Costa, L.T. Geologia e Recursos Minerais do Estado do Pará: Sistema de Informações Geográficas -SIG: texto explicativo dos mapas Geológico e Tectônico e de Recursos Minerais do Estado do Pará. CPRM (Companhia de Pesquisa de Recursos Minerais), Belém (328 pp) (2008). For trends of local-scale faults and shear zones at Sossego, we obtained data from the geological map of Sossego (Fig. 2) that is available in Monteiro et al. 31 . For trends of micro-scale geochemical anomalies, we collected oriented samples of rocks from which we obtained micro-scale images of geochemical anomalies (Extended Data Figs 1-11 and 13-16). The digital data of the SXAM images, from which we obtained micro-scale images of geochemical anomalies, are available as ASCII (or tab delimited text) files at https://osf.io/5cvhz/.