LA-ICP-MS trace element mapping: Element mobility of hydrothermal magnetite from the giant Beiya Fe-Au skarn deposit, SW China

Accepted Manuscript LA-ICP-MS trace element mapping: Element mobility of hydrothermal magnetite from the giant Beiya Fe-Au skarn deposit, SW China Dengfeng Li, Yu Fu, Xiaoming Sun, Pete Hollings, Jianlin Liao, Qiaofen Liu, Yuzhou Feng, Ying Liu, Chunkit Lai PII: DOI: Reference:

S0169-1368(17)30576-0 https://doi.org/10.1016/j.oregeorev.2017.11.027 OREGEO 2413

To appear in:

Ore Geology Reviews

Received Date: Revised Date: Accepted Date:

28 July 2017 20 November 2017 27 November 2017

Please cite this article as: D. Li, Y. Fu, X. Sun, P. Hollings, J. Liao, Q. Liu, Y. Feng, Y. Liu, C. Lai, LA-ICP-MS trace element mapping: Element mobility of hydrothermal magnetite from the giant Beiya Fe-Au skarn deposit, SW China, Ore Geology Reviews (2017), doi: https://doi.org/10.1016/j.oregeorev.2017.11.027

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LA-ICP-MS trace element mapping: Element mobility of hydrothermal magnetite from the giant Beiya Fe-Au skarn deposit, SW China Dengfeng Lia, b*, Yu Fu a, b, Xiaoming Suna, b, c, Pete Hollingsd, Jianlin Liaoa, Qiaofen Liua, Yuzhou Fenge, Ying Liua, b, Chunkit Laif, g a. School of Marine Sciences, Sun Yat-sen University, Guangzhou 510006, China b. Guangdong Provincial Key Laboratory of Marine Resources and Coastal Engineering, Guangzhou 510006, China c. School of Earth Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China d. Department of Geology, Lakehead University, 955 Oliver Road, Thunder Bay, Ontario P7B 5E1, Canada e. School of Geosciences and Info-Physics, Central South University, Changsha 410083, China f. Faculty of Science, University Brunei Darussalam, Gadong BE1410, Brunei Darussalam g. ARC Centre of Excellence in Ore Deposits (CODES), University of Tasmania, Hobart, Tasmania 7001, Australia

*Corresponding author: D-F Li. Address: Sun Yat-sen University, No. 135, Xingang Xi Road, Guangzhou, 510275, P. R. China E-mail: [email protected] (D.F. Li)

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Abstract Skarn alteration occurs over a wide range of temperatures and geological settings, and hydrothermal magnetite (a common mineral in skarn) represents a potentially useful temperature and tectonic indicator. Here we describe a new 2D/3D quantitative element mapping approach to evaluate the element mobility of magnetite during skarn alteration/mineralization. Magnetite grains from the giant Beiya Fe-Au skarn deposit (Yunnan, SW China) are either coarse-grained euhedral (hexagonal) with core-rim zoning (type I) or subhedral unzoned (type II) coexisting with (some enclosing) garnet. Concentrations of Na, Mg, Al, Si, Ca, Sr, Cr and Mn in the type I magnetite core and rims are drastically different, which are high in the core and outer rim and low in the inner rim. This suggests fluid geochemical fluctuations, probably led by multiple phases of ore fluid incursions that are common in many world-class skarn deposits. Concentrations of Mg, Al, Si, Ca, Mn and Sr in the garnet are higher than those in type II magnetite. This is likely because the earlier garnet crystallization (during the prograde skarn alteration) had depleted these elements in the later magnetite ore fluids. Both the garnet and the magnetite core are characterized by high Ti concentration. The high Ti concentration in the garnet is governed by the crystal structure, while that of Ti in hydrothermal magnetite is governed by Ti mobility. The 3D-mapping and simulation modeling have shown clear element variation patterns in some magnetite grains that were previously determined to be compositionally homogeneous by spot analysis. Our new 2D/3D-mapping have revealed the presence of mineral inclusions such as garnet and calcite as constrained by the trace element mobility during the replacement of garnet by magnetite, as shown in the euhedral zoned magnetite in garnet pseudomorph. Keywords: Hydrothermal magnetite; 2D/3D geochemical mapping; Element mobility; Giant Beiya Fe-Au deposit; SW China

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Introduction Magnetite is common in a wide variety of rocks and ores, and can incorporate various trace

elements in its cubic spinel structure (Dare et al., 2014b; Nadoll et al., 2012). Many recent LA-ICP-MS studies suggest that trace element geochemistry of hydrothermal magnetite is dependent on the ore fluid compositions and physicochemical conditions, and can thus discriminate the ore deposit types (Acosta-Góngora et al., 2014; Chen et al., 2015; Chung et al., 2015; Dare et al., 2014a, 2012; Hu et al., 2015; Nadoll, 2011; Nadoll et al., 2014b; Smithies and Champion, 2000), yet some other studies question the reliability of some of these magnetite geochemical discrimination diagrams, as they may not fully apprehend the element behaviors during fluid-rock interactions (Hu et al., 2015; Huang et al., 2015). To better understand the element behaviors (e.g., element mobility, partitioning and migration) during hydrothermal alteration/mineralization, accurate determination of the element spatial distribution among and within (across zonation) the ore/gangue minerals is required (Barker et al., 2009; Landtwing and Pettke, 2005; Paquette and Reeder, 1995), which can be achieved by high resolution 2D/3D geochemical mapping. Depending on the resolution (detection limit) and quantity of analyses needed, 2D/3D geochemical mapping can be performed by using SEM/EDS, EPMA, TEM, SIMS or LA-ICP-MS (Barker et al., 2009; Herwegh, 2000; Raimondo et al., 2017). For instance, LA-ICP-MS line/plan scan can generate high resolution 2D/3D-geochemical maps to unravel trace element compositional variations along and across microstructures and chemical/physical phase boundaries (Park et al., 2015). Previous LA-ICP-MS geochemical mapping is mostly focused on sulfides and silicates (George and Cook, 2015; Ismail et al., 2014; Kontonikas-Charos et al., 2014; Lockington et al., 2014; Paul et al., 2014; Petrelli et al., 2016; Sharrad et al., 2014; Ulrich et al., 2009). This study focuses on the hydrothermal magnetite from the giant Beiya porphyry-skarn Fe-Au deposit (323 t Au @ 2.47 g/t) in western Yunnan (SW China), which ranks the largest Au skarn deposit (and the third largest Au deposit) in China (Fig. 1). Previous research on the Beiya deposit suggests that the skarn alteration/mineralization there is similar to many important

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porphyry-related skarn mineral systems worldwide (Deng et al., 2015; Fu et al., 2016a; He et al., 2015; Liu et al., 2015). The Beiya Au skarn mineralization was interpreted to have formed by at least two phases of Au migration in Bi-rich ore fluids during magnetite formation (Zhou et al., 2016), a hypothesis still under debate due to the poor constraints of the element mobility in magnetite at Beiya. To resolve this issue, we present a user-friendly, GeoLasPro-based method of trace element mapping and data reduction for magnetite, using the Beiya magnetite as a case study. High-resolution 2D/3D trace element mapping and modeling are combined with existing nano-mineralogy and EPMA major element analysis, on the purpose of revealing these interesting but complex trace element distribution patterns in magnetite during skarn mineralization.

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Geological background The Beiya deposit is mined since the 1400s AD (Ming Dynasty) and was re-explored by a

detailed drilling program since 2000. The Beiya Fe-Au skarn deposit is approximately 90 km north of Dali (western Yunnan; Fig. 1). Geologically, the deposit is located in the Jinshajiang alkalic porphyry belt (tectonically the eastern Jinshajiang suture zone) of the Sanjiang Tethyan mineral province. Exposed stratigraphy in the Beiya mining district comprises the Lower Triassic Qingtianbao Fm. (Formation) (175–350 m), the Middle Triassic Beiya Fm. and Quaternary sediments. The Qingtianbao Fm. contains sandstone, greywacke and mafic volcaniclastic rocks, whereas the Beiya Fm. contains limestone interbedded with dolomite (138–531 m, the main ore host). These sedimentary sequences are N–trending as a result of the N-S-trending folding (syncline) in the basin (He et al., 2015). Magmatic rocks emplaced in/around the ore district include the Upper Permian Emeishan flood basalt and the locally widespread Cenozoic (Himalayan) alkalic porphyries. The alkalic porphyries (e.g., syenite and albite porphyries) are rarely exposed (six small outcrops) around

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Beiya, yet they are interpreted to be closely related to the Beiya Fe-Au skarn mineralization (He et al., 2015; Liu et al., 2015; Xu et al., 2007, 2006; Xue et al., 2008). Two major fault systems are well developed in the ore district, one N-striking (dominant) and the other E-striking. The Ma’anshan Fault, a branch of the Jinshajiang-Honghe fault system, extends across the western Beiya district and its subsidiary faults/fractures are interpreted to be ore-controlling (Li et al., 2016). The E-striking fault system was likely post–mineralized and may have dissected/destroyed some orebodies (He, 2014). The Beiya district comprises two major parts (eastern and western parts) with six ore segments. The eastern part contains the Bijiashan, Guogaishan and Weiganpo segments and the western part contains the Jingouba, Hongnitang and Wandongshan segments. Among these, the Wandongshan segment is the largest and contains a resource of 99 Mt ore @ 2.6 g/t Au and 170.0 Mt @ 33.34% Fe (Deng et al., 2015).

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Ore deposit geology The Beiya Fe-Au orebodies mostly occur along the intrusive contact between the alkalic

porphyries and the Beiya Fm. carbonates, and comprise stratabound, veinlet and laterite ores. These stratabound orebodies are hosted in the interlayer fractures or breccia zones of the Beiya Fm. carbonates, as well as along the boundary between the Beiya Fm. carbonates and the underlying Qingtianbao Fm. sandstone. Veinlet ores commonly occur inside the porphyries (Li et al., 2016). Among the three major mineralization styles at Beiya (i.e. porphyry, skarn and supergene) (Zhou et al., 2016), skarn mineralization is the most important. The KT52 skarn orebody (Wandongshan section), for instance, is the largest and most economic orebody at Beiya, with proven resources of 87.2 Mt ore @ 2.35 g/t Au, 90.27 Mt @ 34 wt.% Fe, and 111.8 Mt @ 0.34 wt.% Cu (Li et al., 2016). Previous studies have divided the skarn alteration/mineralization into the prograde skarn (I), retrograde alteration (II), sulfide mineralization (III) and supergene mineralization (IV) stages. Prograde skarn mineral assemblage comprises mainly anhydrous minerals (e.g., garnet and diopside) that formed at ca. 36.9–33.3 Ma (zircon U-Pb ages of the ore-related alkalic porphyries)

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(Deng et al., 2015; Fu et al., 2015; He et al., 2015, 2013, 2012; Jiang et al., 2013; Liu et al., 2015; Lu et al., 2012; Xu, 2007). Retrograde alteration at Beiya generated mainly hydrous minerals (e.g., epidote-group minerals, chlorite and biotite), magnetite and titanite, and minor scheelite, fluorite and feldspar. The hydrothermal titanite was U-Pb dated to be ~34.1–33.1 Ma, which is interpreted to be the upper limit of the ore formation (Fe mineralization) age (Fu et al., 2016b). The sulfide mineralization is characterized by the abundance of pyrite, pyrrhotite, chalcopyrite and minor molybdenite, and both the oxide (stage II) and sulfide (stage III) stages are associated with the gold mineralization (Fu et al., 2016b). Although the Stage II titanite U-Pb (33.1 ± 1.0 Ma; Fu et al., 2016c) and Stage III molybdenite Re-Os ages (34.7 ± 1.6 Ma; Fu et al., 2015) overlap within the analytical uncertainties, the relative age relationships are clearly demonstrated by the crosscutting of Stage II magnetite by Stage III pyrite veinlets. Many Stage IV oxidized minerals (e.g., limonite and hematite, likely formed from sulfide weathering) are gold-bearing, and constitute the main ore-bearing phase of the supergene mineralization at Beiya. Native gold and electrum in magnetite, pyrite, quartz, limonite and hematite cracks or veins are the major gold phases at Beiya. Gold is hosted by both Stage II magnetite and Stage III sulfide (pyrite, chalcopyrite and bismuthinite), indicating two phases of Au migration and precipitation (Zhou et al., 2016). Previous fluid inclusion studies indicated that the ore-forming fluids were characterized by medium homogenization temperatures (ca. 186–372 °C) and high salinities (ca. 8–20 wt.% NaCl equiv.), suggesting a magmatic-related hydrothermal system (He, 2014). The Beiya sulfides are commonly banded with magmatic-related isotopic signature, including δ34S (–2.4 to 4.5‰) (He, 2014; Li et al., 2016; Xiao et al., 2011), δ18O (–0.85 to 3.52‰) and δD (–78.6 to –88.6‰), suggesting an important magmatic influence during Stage II and III alteration/mineralization (He, 2014).

4 4.1

Methods Sample description Two representative Stage II magnetite samples from drill cores at Beiya (in contact with

Stage I garnet) were selected for the LA-ICP-MS geochemical mapping (Fig. 2), and were prepared as thick (~ 100 μm) sections for the mapping. The magnetite grains are euhedral with 6

distinct zoned (core-rim) texture (type I; Figs. 2A and C), they are euhedral with course-grained calcite interstitials. Electron probe microanalysis (EPMA) geochemical maps have been made to check the FeO variations within individual magnetite grains (Fig. B). Under the microscope, some garnet grains are unaltered and coexist with magnetite, whereas some are partially replaced by magnetite with their remnants enclosed in it (Fig. 2D).

4.2

EPMA and LA-ICP-MS geochemical mapping EPMA analysis, mainly including in-situ major elements analysis, EPMA mapping and

back-scattered electron (BSE) observation, was carried out at the School of Geosciences and Info-Physics of the Central South University, using a 1720 EPMA (Shimadzu Corporation, Japan). Analytical parameters include 15 kV (acc. voltage), 2.0 × 10-8 A (probe current) and 1 μm (spot size), and with 0.01wt% detection limit. The mapping was conducted at the School of Marine Sciences of the Sun Yat-sen University (Guangzhou, China), using a 193 nm ArF Excimer Laser Ablation system (GeoLasPro) coupled with an Agilent 7700X ICP–MS. Detailed equipment setting and analytical procedures are described as follows: The new GeoLasPro-based method: The ablation site is determined using the Matrix program (Fig. 3), which allows the programming of ablation sites in a rectangular grid pattern. The laser ablation parameters can be programmed together with the distance between the ablation rows and columns, which generates an evenly-spaced spot matrix (Figs. 2A and B). To produce the trace element map, successive adjacent spots were ablated with no spacing in-between, and a small overlap (2 μm) was ablated to avoid gaps (Zhu et al., 2016). The ablated area can be set to any size, but the shape should be kept consistent to simplify data reduction and thus the geochemical map production. In this study, no pre-ablation was necessary due to the careful sample cleaning with semiconductor grade solvents (Ubide et al., 2015). To test and optimize the technique, we carried out the matrix ablation experiment with the tested analytical parameters (Table S1): The ablation was carried out with an energy density of 5 J/cm2, a repetition rate of 8 Hz and spot size (diameter) of 24 μm. The number of pulses was set to six, and both the waiting time and delay after ablation times were set to zero for the successive 7

data collection by the ICP-MS. The length of the mapping area equals to the number of columns multiplied by the column widths. Similarly, the total width equals to the number of rows multiplied by the row height. The basic rule is that the distance of x/y-direction should be smaller than the spot size to create the small overlap (2 μm) (Figs. 2B and D). In the computer program (GeoLasPro), the position mode is selected as relative position, and the matrix file is saved for the first time. After that, the matrix icon is clicked to choose the file that is just saved, and a 2D map will be generated after the analysis is completed. A 3D map can be generated by repeating the matrix on multiple layers. The time marker needed for the 2D mapping can be estimated based on (6 pulses / 8 Hz + 0.5s delay of stage moving) second per spot × (x=column × y=row) spots, which can be used as the data collecting time of ICP-MS. Mapping of the zoned magnetite grains from the Beiya skarn Fe-Au deposit is presented as an example (Fig. 2). The Agilent Masshunter (G7201A A.01.01) software was used in time-resolved analysis opinion with each map as one single continuous experiment. The dwell time for the whole element analyses was 6 ms. It is more effective to conduct the mapping with multiple elements per experiment to maximize the dwell time for each element. The estimated time consuming for each run was based on the mapping size of the laser setting above.

4.3

Data reduction, mapping and 3D modeling After the data acquisition, data reduction was performed using the software Iolite (Hellstrom

et al., 2008; Kemp et al., 2007; Paton et al., 2011), an add-in run within Igor Pro (version 6.37, WaveMetrics). Mass bias and instrument drift was carefully corrected by setting a linear fit with a selected standard calibrating of both start and end of a single mapping run, and the standard chosen in this study was NIST 612. Iolite was designed to conduct a single raw data reduction in the following procedure: 1) Import the raw data, choose the machine brand which enables Iolite to identify the file type of different brand ICP-MS; 2) Select the baseline by holding the CTRL key and choose representative baseline, 3) then selected the standards and the samples in order, 4) Data reduction is calibrated to the external standard NIST 612, then calibrated with the internal standard the average Fe value (acquired by EPMA) of the whole map.

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By using an in-house Excel add-in (auto_coordinate_attribute.xls), the relative coordinates were assigned (z-coordinates needed for 3D mapping) for the raw data at each spot. The production

of

elemental

distribution

maps

was

performed

with

IoGAS

v6.1

(http://reflexnow.com/iogas/downloads/). IoGAS can detect the patterns and anomalies in the geochemical data, and allows element distribution comparison by using attribute maps to generate gridded images. After importing the raw data with coordinates into IoGAS, an attribute map for each element can be produced. By using the grid function, the cell size (equal to the pixel size) and other parameters such as smoothing radius and coloring operation is set to simulate the actual element distribution (Fig. 3; Table S2). For 3D mapping, a stack of 2D trace element mapping was conducted to generate the 3D model with the set of volumetric datasets, which are defined to add depth that equals to the actual ablated depth (voxelization) (each run removed ~0.4 μm; Ubide et al., 2015). Before ablating the next layer, the z-axis needs to be focused manually. After ablating the first layer, the subsequent layers were calibrated with the same internal standard as the first layer. This is because the subsequent layers are not polished, and their Fe contents (internal standard) cannot be measured by EPMA. EPMA mapping shows that the FeO contents are similar across the magnetite surface (Fig. 3D). In 3D mapping, these voxels then underwent opacity transformation, which stacks up these 2D surface maps into a 3D view. This visualization method was adopted to generate overprints of multiple element distributions on a 2D surface (Fig. 3). Fiji, also known as ImageJ, is an open-source software which enables relatively rapid prototyping for scientific image analysis (Schindelin et al., 2012), by inputting the mapping layers into the FIJI, it enables us to build a 3D spatial map modeling based on the 2D image and provide the profile in every direction. The spatial distribution of these highlight area in the 2D image is connected which provide spatial modeling of fluid flow path. The 3D view and volume view were used to generate the 3D element distribution models in this study (Fig. 3). Elemental fractionation for the 3D maps should be insignificant because the coherent 193 nm excimer laser was applied for the sample ablation. This allows better coupling and dissociation of the sample material, and produces a more even particle size and a greater applicability over a

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wider energy range. Our ablation matrix involves moving the laser beam across the surface of each ablation layers to maintain the initial energy density and thus minimize elemental fractionation. The pre-ablation manual focus adjustment can ensure that the actual energy density on the sample surface is consistent with that of the automatic density setting (i.e. 5J/cm3). Before the ablation, we used pre-ablation on the shield to warm up the laser. Before we moved to ablate the next layer, 5% HNO3 is used to remove any sample particles left in the laser craters on the sample surface (Horstwood et al., 2003).

5 5.1

Results 2D-mapping Representative element maps are present in Figures 4 and 5. Type I magnetite can further be

divided into the zoned (I-1) and unzoned (I-2) subtype. Core-rim zoning in subtype I-1 magnetite are most distinct in the Na, Mg, Al, Si, Ca, Sr, Cr and Mn geochemical maps (Figs. 4A–I), with the core and outer rim show higher elemental concentrations than the inner rim . The lack of zoning in subtype I-2 magnetite are distinct in the Ti, V, Bi, Co, Sn and Ga geochemical maps. For all Type I magnetite, Ti concentration decreases progressively from core to rim, whilst Bi concentration is slightly higher in the core than the rim (Fig. 4L). Type II magnetite shows similar geochemical patterns: Concentrations of certain elements (i.e. Na, Cr and Co) are positively correlated with Fe, whereas some other elements (Mg, Al, Si, Ca, Ti, V, Mn, Sr and REEs; Fig. 5B) are negatively correlated with Fe. Elements such as Cr and Bi show no obvious correlations with Fe (Figs. 5J and O). The unaltered garnet contains higher concentrations of Al, Si, Ca, V, Ti, Mn and Sr, and lower concentrations of Na, Mg and Co than the garnet remnants in the magnetite (Fig. 5).

5.2

3D-mapping and simulation Stacking up of 2D images of the euhedral zoned magnetite produces 3D maps and enables

visualization of the element variation across layers (Fig. 6). 2D geochemical maps of Ti, V, Ca and Sr are shown in Figures 4J, K, F and G, respectively. Although all map layers show higher Ti concentrations in the core, the degrees of high Ti concentrations differ in different layers (Fig. 6A), indicating the inhomogeneous distribution of Ti in magnetite grain. In contrast, the V 10

concentrations are rather consistent within and across different layers (Fig. 6B). The stacking up of Ca and Sr maps reveal clear 3D zoning pattern, as characterized by the higher Ca and Sr concentrations in the core and outer rim, and lower concentrations in the inner rim (Figs. 6C and D). A volume viewer (a function for 3D visualization in FIJI) was used to check the 3D elemental concentration variations, by connecting the equal values as the contour maps and simulating the actual element distribution in 3D space, the garnet pseudomorph between the successive ablation layers were clearly presented (Fig. 7). In the simulating figures, most, but not all, of the high (in warm colors) and low concentration (in cold colors) areas of the elements analyzed (such as Ti, V, Ca and Sr) align with those of the adjacent layers. This indicates the presence of 3D elemental concentration variations, and question the homogeneity assumed by spot analysis. Besides, all of these elemental maps clearly show the garnet pseudomorph, which is now replaced by magnetite.

6 6.1

Discussion LA-ICP-MS mapping technique Signal smearing, spatial resolution and accuracy are some of the critical factors needed to

consider before conducting geological interpretation on these magnetite trace element maps (Raimondo et al., 2017). There are generally two factors that contribute to the signal smearing: 1) accumulation of linear ridges generated by ablation, which contaminates the sample tank and affects the subsequence rasterization. This problem could be solved by reducing the beam size or the scanning speed (Ubide et al., 2015) or by pre-ablation (Raimondo et al., 2017); 2) the affection of washout caused by the design characteristic of sample tank of laser machine, we are running the mapping method on the assumption that, laser ablation spots are strict synchronous with the data, in other words, the data matches with the ablation area. However, some elements (such as the REEs, Ti and Zr etc.) are significantly higher than the baseline signal, and can generate significant signal spikes with relatively slow decay. By mapping the Cu-Pt grid wires, Ubide et al. (2015) has demonstrated that the signals are carried over in the smoothing and tubing device, and 11

the signal smearing would be reduced by removing these devices. Additionally, the experiment also showed that the ablation direction cannot affect the real element distribution patterns (Ubide et al., 2015). The spatial resolution and accuracy have been discussed in detail before, the spatial resolution of the 2D maps is co-determine by the number of pixel in the x- and y-direction. The former is defined by the stage moving speed and how many cycles of running thought the whole mass range selected in ICP-MS, while the pixel values are defined by the spot size minus the overlapping of laser spot. Nevertheless, the gridding function of ioGAS provided great advantage for generating maps, these gridded maps are created by attributing the data onto a rectangle or matrix grid, and then define each grid with continuous color based on the lowest to the highest value of the rectangle or matrix over to the number of the grids. To get rid of the unexpected spikes caused by signal smear, the Search Radius (cells) should be used to smooth the image, and this function defines the each pixel value equal to the weighted average of neighboring cell ( = Radius) values. For better element concentration calculation, the total beam (total counts per second) of the standard (e.g., NIST 612) should be similar to that of the samples. As such, a test running is necessary before the geochemical mapping to ensure their concentrations are above the detection limits. Due to the size of laser beam, the LA maps will smooth the sample boundaries with some of the features that are smaller than the laser spot size disappeared (Figs. 5 and 6), which is consistent with previous study (Raimondo et al., 2017)

6.2

Geological implications

6.2.1

Magnetite petrography

Both type I and II magnetite are generated during the retrograde alteration stage (mineral assemblage: magnetite + quartz + chlorite + epidote) after the prograde-skarn-stage garnet, as evidenced by the replacement of the latter by the former. This shows that both type I and II magnetite are hydrothermal rather than magmatic, as further supported by their lower Ti concentration than typical magmatic magnetite (>2%; Dare et al., 2014b).

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Type I hydrothermal magnetite at Beiya is characterized by its hexagonal shape (120º triple junction) and zoned texture, features common in magnetite from Fe-rich skarn (Hu et al., 2015). The hexagonal crystal shape is likely inherited from the prograde-stage garnet that the magnetite replaced (Fig. 2B). 6.2.2

Element mobility in hydrothermal magnetite

The Beiya magnetite grains provide several visual insights into the mobility and origins of the trace element distribution in the magnetite and their geological signature. The magnetite grains demonstrate the decoupling of the major and trace element during the contact thermal metamorphism of the successive skarn mineralization. These maps show different variations in magnetite with core-rim texture and magnetite with garnet grains which should be attributed to different element mobility habit in the hydrothermal fluid during the fluid-rock interaction. The petrography of type I and type II magnetite has some pale red (hematite) bands in microscale (~ 10 μm) (Fig. 4A), even though it is not clear what kind of physical-chemical changes occur during magnetite precipitation that causes these fluctuations in the banded color of individual growth zones on the micrometer scale. It is unlikely, however, that the variations in color are caused by element variations, as no oscillatory zoning map was found in the element distribution maps, which is consistent with the EPMA maps. These tiny red bands within the magnetite rims at Beiya are found to be hematite, these hematite bands should not be secondary iron-oxides due to the following reasons: If the magnetite was partially oxidized, the rim should be oxidized before the core and contains more hematite, the samples analyzed show no elemental concentration variation in the core and rim, and homogeneous in element distributed with hematite bands; and some of the tiny grains presented in the previous studies has hematite core with magnetite rim (Zhou et al., 2017). All of these evidences indicated the hematite bands should be primary rather than secondary, which reflect high oxygen fugacity during the precipitation of these primary hematite. This conclusion is also supported by studies on the gold enrichment regime at Beiya, which suggest that of the occurrences of bright and red pale-red bands inside the magnetite grains are likely to be governed by oxygen fugacity (ƒO2) fluctuations.

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Fluctuations of ƒO2 on the crystallizing magnetite surface is regarded to have facilitated the Au precipitation (Zhou et al., 2017). Type I magnetite elemental maps of Na, Mg, Al, Si, Ca, Sr, Cr and Mn in group I-1 are generally featured with obvious core and rim texture, which have fluctuated high concentration in the core and connection between core and rim, it is unlikely to be crystalized in a single successive process, as which will be progressively deceasing or increasing in element concentrations and consequently generating maps with gradual changes from core to the rim. Trace elements in magnetite can be significantly modified by oxy-exsolution, the DRP (dissolution re-precipitation process) and recrystallization during the successive mineralization process. As oxy-exsolution generally occurs in magmatic systems, and the recrystallization process occurs in a higher pressure and/or temperature than the original mineral precipitation conditions, whereas the recrystallized magnetite tends to be more uniform in composition (Hu et al., 2015, 2014). Previous research showed that many low-Ti magnetite grains are characterized by well-developed oscillatory zoning and show extensive re-equilibration texture (hexagonal euhedral magnetite with 120º triple junction) by DRP processes, with examples including the Terezia Mica skarn Fe deposit, Romania (Hu et al., 2015), which are features also found in the Beiya magnetite in this study. During the DRP processes, Na, Mg, Al, Si, Ca, Sr, Cr and Mn are mobile and sensitive to hydrothermal P-T changes, and their concentrations likely decrease progressively with the cooling of the hydrothermal system. In contrast to a progressive decrease in concentrations of the mobile elements, the Beiya magnetite 2D/3D geochemical maps are characterized by higher concentrations of these elements in the core and the outer rim. This suggests that the zoned magnetite were probably formed from two hydrothermal phases, and multiphase hydrothermal activities are common in many world-class magmatic hydrothermal deposits (example such as Yerington deposit in Nevada and Bingham Canyon Mine in the Utah; Chelle-Michou et al., 2017; Nadeau, 2015). As titanium is sensitive to temperature, two thermal events at Beiya would have generated high Ti concentrations in both the core and the outer rim, yet our Ti map only shows high concentration in the core, which is possible due to the moderate Ti mobility during the DRP processes. Vanadium is relative immobile and shows different

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pattern with Ti, and consequently they show different element distribution patterns from the mobile elements, V concentrations are relatively consistent within and across different layers (Figs. 4J and K). These Bi, Co, Sn and Ga maps show no variations during the DRP process and even distribution with the magnetite lattice, they show less sensitive to the physical-chemical changes in the hydrothermal fluid, due to their large ionic radius and lack of halogen make it difficult to corporates into the magnetite lattice (Yin et al., 2017).

6.2.3

Crystal structure control on element mobility

It becomes more complicated when forming the type II magnetite, Na concentration in the fluid is relatively stable during the garnet and magnetite precipitation as these Na concentration maps show no zoning patterns, but Figure 5B shows Na concentration in magnetite is higher than that in the garnet, possibly due to the lack of hydrothermal fluids in the prograde skarn alteration process (Whitney, 2002). The Mg, Al, Si, Ca, Mn and Sr shows similar pattern but different from the Na, which could be resulted from the different crystal structure of garnet and magnetite. The universal formula of garnet is X3Y2[SiO4]3, and the ‘X’ could be replaced by Mg2+, Fe2+, Mn2+, Ca2+ and Y+, with ‘Y’ could be replaced by Al 3+, Fe3+, Cr 3+, V3+, Ti4+ and Zr4+ etc. Therefore, the Mg, Al, Si, Ca, Mn and Sr situated into the slightly early formed garnet structure rather than the magnetite (Fig. 5) and these element maps are highlighted in the garnet area. It is well known that the oxygen fugacity (ƒO2) is a major factor that governs element behavior and possibly influenced Au segregation during the ore-forming process, the expression of V can be used to estimate the relative oxygen fugacity during the ore-forming process (Acosta-Góngora et al., 2014; Nadoll et al., 2014a, 2014b), as the oxidation state of V in the joint fugacity range permits the occurrence of V3+, V4+, and V5+. The reduced valence of V3+ has the highest compatibility with the spinel structure of magnetite than these of oxidized valence V5+, which means if the fluid oxygen fugacity is level with reduced V 3+, the magnetite will absorb more V from the reduced ore fluid and vice versa. And the V content in magnetite and their relationship with oxygen fugacity have been tested by the silicate melt experiments, which has demonstrated that the fractionation of V into magnetite, in other words, the D(V), is a function of ƒO2 (Righter et al., 2006). The magnetite/liquid partition coefficient for V or D(V) decreases 15

with increasing ƒO2 (V3+ to V5+) as V3+ is less stable under these conditions. But the garnet is controlled by the crystal structure discussed above rather than ƒO2 variations, which means that the garnet can absorb the V in the fluid in either condition, so the V concentration in the decrease from garnet fragments to magnetite. And the V content in magnetite is substantially governed by the oxygen fugacity, as showing downward trend from right up to left down (Log V = 1.54 to 0.82 ppm), which suggests one of the following or both 1) the V content are significantly controlled by the garnet crystal structure leading to depletion of V in the ore fluid when precipitating magnetite and 2) the ƒO2 are gradually increased in the ore-forming fluid, resulted in V exhaustion in the later formed magnetite grains. Note the REE distributions in the magnetite crystal are different from that of the garnet fragments, the REE concentrations in these of garnet are almost twice as much as that in the magnetite, which should result from structural variation of the substitution of Ca2+ by REE, garnet such as grandite is a Ca-rich mineral which could incorporate REE to substitute Ca 2+ (Hönig et al., 2014), resulting in high concentration in the garnet. In contrast, magnetite is a Ca-poor mineral and has very low REE contents, these characters could be discriminated from magmatic magnetite and consistent with hydrothermal magnetite (Knipping et al., 2015). Bismuth concentration is found to be nearly uniform within the garnet fragments and magnetite grains, different from many other elements such as Sr, Sn, Ca and REEs. These magnetite grains are found to have relatively consistent Bi concentrations (~ 10 ppm) across the grains. The Bi in the garnet and magnetite should be mainly hosted in the crystal lattice rather than Bi-mineral phase, as the nearly uniformly Bi concentration in the garnet and magnetite should represent the ore-forming fluid Bi characters. Bi was carried by the magnetite and they were precipitated as Bi-minerals at the very start of the sulfide stage (Zhou et al., 2016). The incorporation of Bi in magnetite is beneficial for gold migration and concentration (Kim et al., 2012), which consequently increased the ore grade. Gold at Beiya was interpreted to have been transported by Bi-bearing fluids. Bismuth can precipitate as nm- to µm-scale minerals in the magnetite (Zhou et al., 2017, 2016). This study has revealed that the Bi and Au are still in the hydrothermal fluids when the magnetite precipitated.

7

Conclusion 16

LA-ICP-MS 2D/3D mapping can reveal the element distribution of minerals in petrographic thin sections. This study has tested the possibility of this new analytical setup on geological samples (such as garnet and magnetite) by using the GeolasPro laser machine. The zoned mobile element maps of magnetite (such as Na, Mg, Al, Si, Ca, Sr, Cr and Mn) are consistent with the zoned magnetite texture, which was probably formed from two hydrothermal phases, as multiphase hydrothermal activities are common in many world-class skarn deposits. These of moderated mobile element of magnetite such as Ti and V, show different element distribution patterns from the mobile elements, Ti and V concentrations are relatively high in the cores, and decrease progressively to the rim. These immobile elements (Bi, Co, Sn and Ga) in magnetite tend to show no discernible intra-grain variation, suggesting that concentrations of these elements in the hydrothermal fluids remain fairly constant throughout the magnetite precipitation. The high partition coefficients for elements such as Mg, Al, Si, Ca, Mn, Sr and REEs into garnet likely results in their relative depletions in the later-formed hydrothermal magnetite.

Acknowledgements This work was financially supported by the National Natural Science Foundation of China (NSFC) (U1302233 and 41602067), the Fundamental Research Funds for the Central Universities (20174200031610052) and the Higher School Specialized Research Fund for the Doctoral Program Funding Issue (Grant No. 200805580031). Drs. Wenchang Li and Zhangrong Liu are thanked for their support during the field trip. Three anonymous reviewers are thanked for constructive reviews that greatly improved the quality of this paper.

References Acosta-Góngora, P., Gleeson, S.A., Samson, I.M., Ootes, L., Corriveau, L., 2014. Trace element geochemistry of magnetite and its relationship to Cu-Bi-Co-Au-Ag-U-W mineralization in the great

bear

magmatic

zone,

NWT,

Canada.

Econ.

Geol.

109,

1901–1928.

doi:10.2113/econgeo.109.7.1901 Barker, S.L.L., Hickey, K.A., Cline, J.S., Dipple, G.M., Kilburn, M.R., Vaughan, J.R., Longo, A.A., 2009. Uncloaking invisible gold: use of nanoSIMS to evaluate gold, trace elements, and sulfur

17

isotopes in pyrite from Carlin-type gold deposits. Econ. Geol. 104, 897–904. Chelle-Michou, C., Rottier, B., Caricchi, L., Simpson, G., 2017. Tempo of magma degassing and the genesis of porphyry copper deposits. Sci. Rep. 7, 40566. doi:10.1038/srep40566 Chen, W.T., Zhou, M.-F., Gao, J.-F., Hu, R., 2015. Geochemistry of magnetite from Proterozoic Fe-Cu deposits in the Kangdian metallogenic province, SW China. Miner. Depos. 1–15. doi:10.1007/s00126-014-0575-7 Chung, D., Zhou, M.-F., Gao, J.-F., Chen, W.T., 2015. In-situ LA–ICP-MS trace elemental analyses of magnetite: The late Palaeoproterozoic Sokoman Iron Formation in the Labrador Trough, Canada. Ore Geol. Rev. 65, 917–928. doi:10.1016/j.oregeorev.2014.09.030 Dare, S.A.S., Ames, D.E.E., Lightfoot, P.C.C., Barnes, S.J.-J., Beaudoin, G., 2014a. Mineral chemistry and supporting databases for TGI4 project on “Trace elements in Fe-oxides from fertile and barren igneous complexes: Investigating their use as a vectoring tool in the intrusions that host Ni-Cu-PGE deposits” 12. doi:10.4095/293640 Dare, S.A.S., Barnes, S., Beaudoin, G., 2012. Variation in trace element content of magnetite crystallized from a fractionating sulfide liquid , Sudbury , Canada : Implications for provenance discrimination. Geochim. Cosmochim. Acta 88, 27–50. doi:10.1016/j.gca.2012.04.032 Dare, S.A.S., Barnes, S.J., Beaudoin, G., Méric, J., Boutroy, E., Potvin-Doucet, C., 2014b. Trace elements

in

magnetite

as

petrogenetic

indicators.

Miner.

Depos.

49,

785–796.

doi:10.1007/s00126-014-0529-0 Deng, J., Wang, Q., Li, G., Hou, Z., Jiang, C., Danyushevsky, L., 2015. Geology and genesis of the giant Beiya porphyry-skarn gold deposit, northwestern Yangtze Block, China. Ore Geol. Rev. 70, 457–485. doi:10.1016/j.oregeorev.2015.02.015 Fu, Y., Sun, X., Lin, H., Zhou, H., Li, X., Ouyang, X., Jiang, L., Shi, G., Liang, Y., 2015. Geochronology of the giant Beiya gold-polymetallic deposit in Yunnan Province, Southwest China and its relationship with the petrogenesis of alkaline porphyry. Ore Geol. Rev. 71, 138–149. doi:10.1016/j.oregeorev.2015.05.016 Fu, Y., Sun, X., Zhou, H., Lin, H., Yang, T., 2016a. In-situ LA-ICP-MS trace elements analysis of scheelites from the giant Beiya gold-polymetallic deposit in Yunnan Province, Southwest China and

its

metallogenic

implications.

Ore

Geol.

Rev.

77,

43–56.

doi:10.1016/j.oregeorev.2016.02.001 Fu, Y., Sun, X., Zhou, H., Lin, H., Yang, T., 2016b. In-situ LA-ICP-MS U-Pb geochronology and trace elements analysis of polygenetic titanite from the giant Beiya gold-polymetallic deposit in Yunnan

Province,

Southwest

China. 18

Ore

Geol.

Rev.

77,

43–56.

doi:10.1016/j.oregeorev.2016.02.001 Fu, Y., Sun, X., Zhou, H., Lin, H., Yang, T., 2016c. In-situ LA – ICP – MS U – Pb geochronology and trace elements analysis of polygenetic titanite from the giant Beiya gold – polymetallic deposit in Yunnan Province , Southwest China 77, 43–56. doi:10.1016/j.oregeorev.2016.02.001 George, L., Cook, N.J., 2015. George L , Cook NJ , Ciobanu CL ( 2015 ) Trace and minor elements in galena : A reconnaissance LA-ICP-MS study . American 548–569. He, W., Mo, X., He, Z., White, N.C., Chen, J., Yang, K., Wang, R., Yu, X., Dong, G., Huang, F., 2015. The geology and mineralogy of the Beiya skarn gold deposit in Yunnan, Southwest China. Econ. Geol. 110, 1625–1641. doi:10.1016/S0361-1124(78)80358-X He, W., Mo, X., Yu, X., He, Z., Dong, G., Liu, X., Su, G., Huang, X., 2013. Zircon U-Pb and molybdenite Re-Os dating for the Beiya gold-polymetallic deposit in the western Yunnan Province and its geological significance. Acta Petrol. Sin. 29, 1301–1310. He, W., Yu, X., Mo, X., He, Z., Li, Y., Huang, X., Su, G., 2012. Genetic type and the relationship between alkali-rich intrusion and mineralization of Beiya gold-polymetallic ore field, western Yunnan Province, China. Acta Geol. Sin. 28, 1401–1421. doi:10.1038/nature04506.Rowley He, W.Y., 2014. The Beiya giant gold-polymetallic deposit: magmatism and metallogenic model. China University of Geosciences (Beijing). Hellstrom, J., Paton, C., Woodhead, J., Hergt, J., 2008. Iolite: software for spatially resolved LA-(quad and MC) ICPMS analysis. Mineral. Assoc. Canada short course Ser. 40, 343–348. Herwegh, M., 2000. A new technique to automatically quantify microstructures of fine grained carbonate mylonites: Two-step etching combined with SEM imaging and image analysis. J. Struct. Geol. 22, 391–400. doi:10.1016/S0191-8141(99)00165-0 Hönig, S., Copjaková, R., Škoda, R., Novák, M., Dolejš, D., Leichmann, J., Galiová, M.V., 2014. Garnet as a major carrier of the y and REE in the granitic rocks: An example from the layered anorogenic granite in the Brno Batholith, Czech Republic. Am. Mineral. 99, 1922–1941. doi:10.2138/am-2014-4728 Horstwood, M.S.A., Foster, G.L., Parrish, R.R., Noble, S.R., Nowell, G.M., 2003. Common-Pb corrected in situ U–Pb accessory mineral geochronology by LA-MC-ICP-MS. R. Soc. Chem. 18, 837–846. doi:10.1039/b304365g Hu, H., Lentz, D., Li, J.-W.J.-W.J.W., McCarron, T., Zhao, X.F.X.-F.X.-F., Hall, D., 2015. Reequilibration Processes in Magnetite From Iron Skarn Deposits. Econ. Geol. 110, 1–8. doi:10.2113/econgeo.110.1.1 Hu,

H.,

Li, J.W.,

Lentz, D.,

Ren,

Z.,

Zhao, X.F., Deng, X.D., Hall, 19

D., 2014.

Dissolution-reprecipitation process of magnetite from the Chengchao iron deposit: Insights into ore genesis and implication for in-situ chemical analysis of magnetite. Ore Geol. Rev. 57, 393–405. doi:10.1016/j.oregeorev.2013.07.008 Huang, X.X.-W.W., Zhou, M.-F.F., Qiu, Y.Z., Qi, L., 2015. In-situ LA-ICP-MS trace elemental analyses of magnetite: The Bayan Obo Fe-REE-Nb deposit, North China. Ore Geol. Rev. 65, 884–899. doi:10.1016/j.oregeorev.2014.09.010 Ismail, R., Ciobanu, C.L., Cook, N.J., Teale, G.S., Giles, D., Mumm, A.S., Wade, B., 2014. Rare earths and other trace elements in minerals from skarn assemblages, Hillside iron oxide–copper–gold deposit, Yorke Peninsula, South Australia. Lithos 184, 456–477. Jiang, C., Wang, Q., Li, G., Ma, N., Hu, Z., 2013. Relative oxidation states of intrusions in Beiya gold-polymetallic deposit in Sanjiang area, Yunnan, SW China. Acta Geol. Sin. 29, 3925–3936. Kemp, A.I.S., Hawkesworth, C.J., Foster, G.L., Paterson, B.A., Woodhead, J.D., Hergt, J.M., Gray, C.M., Whitehouse, M.J., 2007. Magmatic and crustal differentiation history of granitic rocks from Hf-O isotopes in zircon. Science (80-. ). 315, 980–983. Kim, E.J., Park, M.E., White, N.C., 2012. Skarn gold mineralization at the Geodo Mine, South Korea. Econ. Geol. 107, 537–551. doi:10.2113/econgeo.107.3.537 Knipping, J.L., Bilenker, L.D., Simon, A.C., Reich, M., Barra, F., Deditius, A.P., Wälle, M., Heinrich, C.A., Holtz, F., Munizaga, R., 2015. Trace elements in magnetite from massive iron oxide-apatite deposits indicate a combined formation by igneous and magmatic-hydrothermal processes. Geochim. Cosmochim. Acta 171, 15–38. doi:10.1016/j.gca.2015.08.010 Kontonikas-Charos, A., Ciobanu, C.L., Cook, N.J., 2014. Albitization and redistribution of REE and Y in IOCG systems: Insights from Moonta-Wallaroo, Yorke Peninsula, South Australia. Lithos 208, 178–201. doi:10.1016/j.lithos.2014.09.001 Landtwing, M.R., Pettke, T., 2005. Relationships between SEM-cathodoluminescence response and trace-element composition of hydrothermal vein quartz. Am. Mineral. 90, 122–131. Li, W.C., Wang, J.H., He, Z.H., Dou, S., 2016. Formation of Au-polymetallic ore deposits in alkaline porphyries

at

Beiya,

Yunnan,

Southwest

China. Ore

Geol.

Rev.

73,

241–252.

doi:10.1016/j.oregeorev.2015.05.003 Liu, B., Liu, H., Zhang, C., Mao, Z., Zhou, Y., Huang, H., He, Z., Su, G., 2015. Geochemistry and geochronology of porphyries from the Beiya gold-polymetallic orefield, western Yunnan, China. Ore Geol. Rev. 69, 360–379. doi:10.1016/j.oregeorev.2015.03.004 Lockington, J.A., Cook, N.J., Ciobanu, C.L., 2014. Trace and minor elements in sphalerite from metamorphosed

sulphide

deposits. 20

Mineral.

Petrol.

108,

873–890.

doi:10.1007/s00710-014-0346-2 Lu, Y. jun, Kerrich, R., Cawood, P.A., McCuaig, T.C., Hart, C.J.R., Li, Z. xiang, Hou, Z. qian, Bagas, L., 2012. Zircon SHRIMP U-Pb geochronology of potassic felsic intrusions in western Yunnan, SW China: Constraints on the relationship of magmatism to the Jinsha suture. Gondwana Res. 22, 737–747. doi:10.1016/j.gr.2011.11.016 Metcalfe, I., 2013. Gondwana dispersion and Asian accretion: Tectonic and palaeogeographic evolution of eastern Tethys. J. Asian Earth Sci. doi:10.1016/j.jseaes.2012.12.020 Nadeau, O., 2015. Economic geology: Ore metals beneath volcanoes. Nat. Geosci. 8, 168–170. doi:10.1038/ngeo2379 Nadoll, P., 2011. Geochemistry of magnetite from hydrothermal ore deposits and host rocks. PhD Thesis 313. doi:10.2113/econgeo.107.6.1275 Nadoll, P., Angerer, T., Mauk, J.L., French, D., Walshe, J., 2014a. The chemistry of hydrothermal magnetite: A review. Ore Geol. Rev. 61, 1–32. doi:10.1016/j.oregeorev.2013.12.013 Nadoll, P., Mauk, J.L., Hayes, T.S., Koenig, A.E., Box, S.E., 2012. Geochemistry of magnetite from hydrothermal ore deposits and host rocks of the mesoproterozoic Belt Supergroup, United States. Econ. Geol. 107, 1275–1292. doi:10.2113/econgeo.107.6.1275 Nadoll, P., Mauk, J.L., Leveille, R.A., Koenig, A.E., 2014b. Geochemistry of magnetite from porphyry Cu and skarn deposits in the southwestern United States. Miner. Depos. 50, 1–23. doi:10.1007/s00126-014-0539-y Paquette, J., Reeder, R.J., 1995. Relationship between surface structure, growth mechanism, and trace element incorporation in calcite. Geochim. Cosmochim. Acta 59, 735–749. Park, C., Shin, H.S., Yi, K., Cho, H., Kim, Y., 2015. Quantitative mapping of trace elements in agate using LA-ICP-MS. J. Anal. Sci. Technol. 61, 37. doi:10.1186/s40543-015-0078-x Paton, C., Hellstrom, J., Paul, B., Woodhead, J., Hergt, J., 2011. Iolite: Freeware for the visualisation and processing of mass spectrometric data. J. Anal. At. Spectrom. 26, 2508–2518. Paul, B., Woodhead, J.D., Paton, C., Hergt, J.M., Hellstrom, J., Norris, C.A., 2014. Towards a method for quantitative LA‐ICP‐MS imaging of multi‐phase assemblages: Mineral identification and analysis correction procedures. Geostand. Geoanalytical Res. 38, 253–263. Petrelli, M., Morgavi, D., Vetere,

F.P., Perugini, D., 2016. Elemental imaging and

petro-volcanological applications of an improved laser ablation inductively coupled quadrupole plasma mass spectrometry. Period. di Mineral. doi 10. Raimondo, T., Payne, J., Wade, B., Lanari, P., Clark, C., Hand, M., 2017. Trace element mapping by LA-ICP-MS: assessing geochemical mobility in garnet. Contrib. to Mineral. Petrol. 172, 17. 21

doi:10.1007/s00410-017-1339-z Righter, K., Leeman, W.P., Hervig, R.L., 2006. Partitioning of Ni, Co and V between spinel-structured oxides and silicate melts: Importance of spinel composition. Chem. Geol. 227, 1–25. Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., Tinevez, J.-Y., White, D.J., Hartenstein, V., Eliceiri, K., Tomancak, P., Cardona, A., 2012. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682. doi:10.1038/nmeth.2019 Sharrad, K.A., McKinnon-Matthews, J., Cook, N.J., Ciobanu, C.L., Hand, M., 2014. The basil cu-co deposit, eastern arunta region, northern territory, australia: A metamorphosed volcanic-hosted massive sulphide deposit. Ore Geol. Rev. 56, 141–158. doi:10.1016/j.oregeorev.2013.08.008 Smithies, R.H., Champion, D.C., 2000. The Archaean high-Mg diorite suite: links to tonalite–trondhjemite–granodiorite magmatism and implications for early Archaean crustal growth. J. Petrol. 41, 1653–1671. Ubide, T., McKenna, C.A., Chew, D.M., Kamber, B.S., 2015. High-resolution LA-ICP-MS trace element mapping of igneous minerals: In search of magma histories. Chem. Geol. 409, 157–168. doi:10.1016/j.chemgeo.2015.05.020 Ulrich, T., Kamber, B.S., Jugo, P.J., Tinkham, D.K., 2009. Imaging Element Distribution Patterns in Minerals By Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry (LA-ICP-ME). Can. Mineral. 47, 1001–1012. doi:10.3749/canmin.47.5.1001 Wang, J.-H., Yin, A., Harrison, T.M.M., Grove, M., Zhang, Y.-Q., Xie, G.-H., 2001. A tectonic model for Cenozoic igneous activities in the eastern Indo–Asian collision zone. Earth Planet. Sci. Lett. 188, 123–133. doi:10.1016/S0012-821X(01)00315-6 Whitney, P.R., 2002. Geology, geochemistry, and mineralogy of the iron mountain skarn, Adirondack Highlands, New York. Xiao, X.N., Yu, X.H., Mo, X.X., Li, Y., Huang, X.K., 2011. Geochemical characteristics of the metallogenesis in the gold-polymetallic desposit in Beiya, western Yunnan Province. Geol. Explor. 47, 170–179. Xu, S.M., 2007. Metallogenic modeling of the Beiya gold deposit in western Yunnan and its relation to the Cenozoic alkali-rich porphries. China University of Geosciences (Beijing). Xu, X.W., Cai, X.P., Song, B.C., Zhang, B.L., Ying, H.L., Xiao, Q. Bin, Wang, J., 2006. Petrologic, chronological and geochemistry characteristics and formation mechanism of alkaline porphyries in the Beiya gold district, western Yunnan. Acta Petrol. Sin. 22, 631–642. 22

Xu, X.W., Cai, X.P., Xiao, Q.B., Peters, S.G., 2007. Porphyry Cu-Au and associated polymetallic Fe-Cu-Au deposits in the Beiya Area, western Yunnan Province, south China. Ore Geol. Rev. 31, 224–246. doi:10.1016/j.oregeorev.2005.03.015 Xue, C.D., Hou, Z. qian, Liu, X., Yang, Z.M., Liu, Y.Q., Hao, B.W., 2008. Petrogenesis and metallogenesis of the Beiya gold-polymetallic ore district, northwestern Yunnan province, China: Responses to the Indo-Asian collisional processes. Acta Petrol. Sin. 24, 457–472. Yin, S., Ma, C., Robinson, P.T., 2017. Textures and high field strength elements in hydrothermal magnetite from a skarn system: Implications for coupled dissolution-reprecipitation reactions. Am. Mineral. 102, 1045–1056. doi:10.2138/am-2017-5913 Zhou, H., Sun, X., Cook, N.J., Lin, H., Fu, Y., Richen, Z., Joël, B., 2017. Nano- to micron-scale particulate gold hosted by magnetite: a product of gold scavenging by bismuth melts. Econ. Geol. 112, 993–1010. Zhou, H., Sun, X., Fu, Y., Lin, H., 2016. Mineralogy and mineral chemistry of Bi-minerals : Constraints on ore genesis of the Beiya giant porphyry-skarn gold deposit , southwestern China. Ore Geol. Rev. 3, 408–424. doi:10.1016/j.oregeorev.2016.06.008 Zhu, Z.-Y., Cook, N.J., Yang, T., Ciobanu, C.L., Zhao, K.-D., Jiang, S.-Y., 2016. Mapping of Sulfur Isotopes and Trace Elements in Sulfides by LA-(MC)-ICP-MS: Potential Analytical Problems, Improvements and Implications. Minerals 6, 110. doi:10.3390/min6040110

Figure Captions Figure 1. A. Tectonic framework of the major geological terranes and sutures of the Eastern Tethyan belt (Metcalfe, 2013). B. Geologic map of the Sanjiang region, showing the locations of the Beiya Fe-Au deposit and its neighboring deposits (Wang et al., 2001). Figure 2. Microphotographs (reflected light) of A. Well-crystallized magnetite with core-rim texture. B. Magnetite replaced garnet with some garnet remnants included in the magnetite. Abbreviations: Mgt: Magnetite; Grt: Garnet. Figure 3. 2D/3D mapping procedure established for LA-ICP-MS (GeoLasPro based: Coherent Ltd.) Figure 4. A, petrography of the ablation mapping area and (B-O) quantitative 2D maps of selected elements (Na, Mg, Al, Si, Ca, Sr Cr Mn, Ti, V, Bi, Co, Sn and Ga). all the maps are shown in log ppm concentration. 23

Figure 5. A, petrography of the ablation mapping area and (B-O) quantitative LA-ICP-MS trace element maps of the magnetite + garnet assemblage, all maps show log ppm abundances. Figure 6. The 3D image stack made up of multiple 2D image layers, all maps show lg ppm abundances. Figure 7. The simulation modeling of the element (Ti, V, Sr and Ca) distribution in the magnetite grains.

Tables Table S1. Experimental conditions on Coherent laser machine and Agilent ICP-MS. Table 1 Experimental conditions ICP-MS system

Agilent 7700x

RF power

1500 W

Plasma Ar flow rate

15 L min-1

Auxiliary Ar flow rate

1 L min-1

Acquisition parameters Acquisition time

200 ms

Acquisition rate

~ 30 kHz

Total spectra per reading

5882

Laser ablation/translation stage system

Coherent Geolas Pro

Wavelength

193 nm

Pulse length

15ns

Method

Matrix

Energy density

8 mJ cm-3

Repeat rate

5 Hz 24

Ablation diameter

10–160 µm

Number of pulses

6

Wait time

0

Delay after ablation

0

Number of columns

1–100

Number of rows

1–100

Distance x-direction

Should be less than the Ablation diameter

Distance y-direction

Should be less than the Ablation diameter

Table S2. The IOGAS setting for mapping Table 2 Iogas grid setting for element attribution map Pre-Grinding Operation

Maximum of cell

Cell size (map units)

1 um

Search Radius (cells)

1–50

Extend grid by search radius

Yes or No

Minimum Smoothing Radius (cells)

1–50

Colouring Operation

Unequal Bins/Equal Bins/Linear Scale

Colour Spectrum

Either

Shading Direction

No shading

Shading Brightness

1–5

No Data Colour

Gray or Transparent

25

North China -G

1

sh

Lhasa

Fig.b III

VI

n ia aj

30°N IV

g u fa

India

ys

s lt

V Qamdo

Yulong Zhangga Mangzong Duoxiasongduo Malasongduo

20°N

Indian Ocean

Garze

A

500 km 90°E

I II II III IV1 IV2 IV3 V

Changning-Menglian Chiang Mai Bangong-Nujiang

VI

Indus-Yarlung-Zangbo

Garze-Litang Ailaoshan Songma Jinshajiang Longmu Tso-Shuanghu

Zaduo-Jinghong

X

Yunxian-Jinggu

Yangxianqiao

Yangtze Craton

Lijiang

County

Metamorphic belt

Shear direction

Beiya

imao

Suture zone

Terrane

30°N

I

do-S

IX

Qam

Jomda-Weixi

Machangqing Kunming

Ail

Weishan

Cu deposit

ao

sh

an

VIII

-R

ed

AS BU M SI

ys

tem

Tongchang

A

IN

B

fau

Habo

CH

Faults

Menglian

IV2

O

Eocene potassic mafic rock

ve rs

II

D IN

Eocene alkaline felsic intrusions

Mojiang

U

Granitoid (T~K)

Ri

lt s

IV2

Au deposit

25°N

Dali

Mo deposit

Volcanic belt(P~T)

110°E

Shangri-la

Arc-volcanic belts VII VIII

100°E

Litang

Zhongza

Suture zones:

o ma -Si

200 km

Yidun Arc

o md

Qa

100

III

1

3

Songpan-Garze

N

30°N

II IV

ina ch do In

m te

ang

South China 2

II

u mas Sibu

ngt

0

rma W.Bu

IX

W.Q ia

arz e

IV

V

Jin

VII

ngta

W.Qiangtang

Yushu

Narigomgma

So n ng gpan

E.Qia

100°E

98°E

96°E

X

Mapping procedure

Laser Ablation

ICP-MS

1

Matrix

Time resolved method

2

auto_coordinat e_attribute.xls

Raw data.csv

Raw data with relative coordinates FIJI 3

IOGAS

4

2D Mapping

3D stack mapping

3D volume modeling

5

6

A

Mgt Core

B

Mgt rim

Cal

Mgt Core Mgt rim

Cal

Mgt Core

(counts) 140 132 125 117 109 101 93 86 78 70 62 55 47 39 31 23 16

Mgt Core

200 μm

200 μm

C

!

D

Ablation area

Ablation area

Grt

Mgt Core

Mgt

Laser spot

Mgt rim 100 um

Grt fragments

500 450 400 350 300 250 200 150 100 50 y_μm

A

Mineral inclusion

Na

Mineral inclusion

B

Mgt core

Mineral inclusion

C

2.38

4.76

1.80

3.04

1.74 1.67

2.85 2.72

1.57 0.24

2.57 1.50

0

Mgt rim

Mg

0

50

Al

Sr

Ti

100 150 200 250 300 350 400 450 500 x_μm

Mineral inclusion

Mineral inclusion

Si

D

Mineral inclusion

Mineral inclusion

F

3.27

4.03

4.79

2.18

3.23

3.12

2.11

3.16

2.94

2.03

3.10

2.83

1.93 0.60

3.02 1.32

2.73 2.18

Cr

G

Mineral inclusion

Mn

H

Mineral inclusion

I

2.51

4.11

4.47

0.82

3.11

3.01

0.55

2.94

2.85

0.38

2.75

2.75

0.24 0

2.51 1.05

2.65 1.02

J

V

K

Bi

L

2.49

1.75

1.500

1.66

1.11 1.01 0.89

0.160

1.51 1.33

M

0.110 0.076 0.037 0.002

0.72

1.07 0.11

Co

Ca

E

0.02

Sn

N

Ga

O

2.79

2.06

1.66

1.61

1.56

1.18

1.55

1.52

1.10

1.49

1.48

1.00

1.40 0.25

1.43 0.90

0.87 0.11

600

Fe

A

Na

B

C

500 400

Grt

300 y_μm

Mgt

5.10

Grt fragments

200

Grt fragments

0

50

150

250

350 450 x_μm

550

Mg

650

Al

Grt fragments

2.15 1.19

Grt fragments

5.22

Grt fragments

2.85

5.13 5.11 5.10

2.67 1.92

2.28 1.15

5.09 3.42

Ti

V

H

Grt fragments

I

3.35

1.54

2.22

1.25

4.83 4.81

2.18 2.15

1.23 1.22

4.81 3.65

2.13 1.50

1.20 0.82

Mn

J

Grt fragments

F

2.52 2.34

4.88

Grt fragments

Si

2.75 2.69

6.00

Co

K

L

3.75

4.16

1.39

2.98

3.25

0.92

3.22 3.21

0.90 0.87

3.21 2.28

0.83 0.53

Grt fragments

2.94 2.91 2.88 2.02

REE

M

0.98 0.94 0.93 0.92 0.62

Bi

N

2.43

Grt fragments

4.62 2.12

3.84

2.87

G

Sr

2.21 2.18

E

4.09

Cr

2.24

4.76 4.70

750

D

Ca

Grt fragments

4.81

100 0

3.47

Grt fragments

O

2.21

1.03

1.80

0.94

1.79 1.79

0.94 0.94

1.79 1.25

0.94 0.64

A

B

1.66 1.51 1.33 1.07 0.11

Ablation downward

Ablation downward

2.49

C

1.75 1.11 1.01 0.89 0.72 0.02

D

3.12 2.94 2.83 2.73 2.18

Ablation downward

Ablation downward

4.79

2.51 0.82 0.55 0.38 0.24 0

A

B

2.49

1.75

1.66

1.11 1.01 0.89 0.72 0.02

1.51 1.33 1.07 0.11

D

C

Ca

4.79

2.51

3.12

0.82

2.94 2.83 2.73 2.18

Sr

0.55 0.38 0.24 0

Highlights The visible element mobility in skarn magnetite is poorly understood.

This is a suitable resolution for 2D/3D quantitative element mapping approach.

Which could be able to reveal the element mobility of magnetite.

These maps provide "see though" technique for the multiple heating events.

26

A

B

2.49

1.75

1.66

1.11 1.01 0.89 0.72 0.02

1.51 1.33 1.07 0.11

D

C

Ca

4.79

2.51

3.12

0.82

2.94 2.83 2.73 2.18

Sr

0.55 0.38 0.24 0