Mineral identification method based on Monte Carlo simulation

Abstract

The invention discloses a mineral identification method based on Monte Carlo simulation, which comprises the following steps: S100, placing a prepared sample on a sample table of a scanning electron microscope, controlling the sample table to move, and splicing a light microscope image or a BSE image; s200, selecting a target area in the spliced light microscope image or BSE image; s300, scanning the target area by using a scanning electron microscope and an energy disperse spectroscopy to obtain a mineral distribution diagram of the sample; wherein the EDS parameter corresponding to the energy disperse spectroscopy comprises a scanning step diameter, and the scanning step diameter is determined by adopting a Monte Carlo simulation method to determine the horizontal width of the element characteristic X-ray of the mineral in the sample; and determining the scanning step diameter according to the horizontal width of the characteristic X-ray. Through the composition, the recognition accuracy and efficiency of the complex minerals in the rock sample are effectively improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of microanalysis of mineral materials, and particularly relates to a mineral identification method based on Monte Carlo simulation. BACKGROUND

[0002] Quick and accurate identification of minerals in rocks is of great significance for revealing the genesis of rocks, geological evolution and thermal evolution. Taking the study of the moon as an example, the chemical composition, crystallographic characteristics and isotopic chemical characteristics of lunar minerals can effectively reveal the origin, thermal history and spatio-temporal evolution process of the moon. Taking the study of rare earth deposits on earth as an example, how to identify the types, chemical composition and key metal occurrence of complex rare earth minerals is crucial for revealing the ore-forming mechanism, ore-forming evolution history of rare earth deposits and the utilization of corresponding mineral resources.

[0003] In recent years, based on the backscattered electron (BSE) image and energy dispersive spectrometer (EDS) technology of scanning electron microscope (SEM, hereinafter referred to as scanning electron microscope), a method for automatically identifying minerals has been developed. Referring to Figure 1 The principle of the method is roughly as follows: the scanning electron microscope bombards the sample with accelerated electron beams, generating signals such as backscattered electrons and X-rays; the built-in detector receives the signals to form a backscattered image of the mineral; the mineral identification software distinguishes the mineral grain boundaries in the sample by means of the contrast difference of the backscattered image of the mineral; the mineral identification software controls the energy dispersive spectrometer to receive characteristic X-ray signals and makes it scan on the sample surface according to the preset modes such as point mode, line mode, dot array mode, point-by-point high-resolution mode, etc.; finally, based on the BSE image and EDS composition information, the types and two-dimensional distribution state of the minerals in the sample are obtained.

[0004] Currently commercialized mineral identification software mainly includes Mineral Liberation Analysis (MLA), Quantitative Evaluation of Minerals by Scanning electron microscopy (QEMSCAN), Advanced Mineral Identification and Characterization System (AMICS), Tescan Integrated Mineral Analyzer (TIMA) and Maps Mineralogy, etc. These mineral identification software can realize the visualization of the distribution and particle size of mineral grains on the scale of microns to centimeters. However, for minerals with diversified sizes and relatively complex composition, the above mineral identification methods have obvious deficiencies in identification accuracy and speed.

[0005] Specifically, the above mineral identification software is based on EDS spectral peak to identify minerals, but the important analysis conditions therein, such as the setting of the scanning step length therein, mainly depend on the experience of the operator, lack of constraints and standards, and generally lead to the inability of the identification mechanism to balance accuracy and efficiency. For example, but not limited to: the scanning step length is too small, which will reduce the mineral identification speed; the EDS step length is too large, which will miss smaller minerals in the scanning process.

[0006] Monte Carlo simulation is a computer simulation method for simulating the trajectory of electrons in the sample, which can provide the state of the spatial distribution of electron scattering. This simulation method is based on the first principle method, and on the basis of comprehensively considering the elastic scattering model and the model of the collective effect of inelastic scattering, the parameters such as electron trajectory, scattering angle and energy loss are determined. Based on these parameters and related analysis geometric equations, by associating the scattering angle and step length with the continuous position of the electron, the entire trajectory of the electron from the surface of the sample into the final state can be simulated step by step. At present, the Monte Carlo simulation software can simulate the spatial distribution volume of secondary electrons, backscattered electrons and characteristic X-rays generated by the material under electron beam excitation. In this way, by using Monte Carlo simulation to obtain the characteristic X-ray volume of elements in different minerals, and then using it to guide the setting of the scanning step length of the mineral identification software, the above problems can be solved. SUMMARY

[0007] The technical problem to be solved by the present application is to at least improve the mineral identification accuracy and / or identification speed to some extent based on the Monte Carlo simulation method.

[0008] Therefore, the present application provides a mineral identification method based on Monte Carlo simulation, which comprises: S100, placing the prepared sample on the sample stage of the scanning electron microscope, controlling the movement of the sample stage, and splicing out a large-area light microscope image or BSE image; S200, selecting a target area in the spliced light microscope image or BSE image; S300, making the scanning electron microscope and the energy spectrometer scan the target area to obtain a mineral distribution map of the sample; wherein the EDS parameters corresponding to the energy spectrometer include a scanning step length, and the determination method of the scanning step length comprises: using the Monte Carlo simulation method to determine the horizontal width of the element characteristic X-ray of the mineral in the sample; and determining the scanning step length according to the horizontal width of the characteristic X-ray.

[0009] By using the Monte Carlo simulation method, the horizontal width of the X-ray is determined. On this basis, based on the horizontal width of the characteristic X-ray which is more consistent with the SEM-EDS experiment, the scanning step length in the mineral identification method is determined.

[0010] For the above method, in a possible implementation, the "determining the horizontal width of the characteristic X-ray of the element of the mineral in the sample by using the Monte Carlo simulation method" comprises: obtaining a scattering volume image of the X-ray of the element of the mineral in the sample under electron beam excitation by using the Monte Carlo simulation method; performing image processing on the scattering volume image of the X-ray to obtain a scattering volume image of the characteristic X-ray; and determining the horizontal width of the characteristic X-ray according to the scattering volume image of the characteristic X-ray.

[0011] For the above method, in a possible implementation, the "performing image processing on the scattering volume image of the X-ray to obtain a scattering volume image of the characteristic X-ray" comprises:

[0012] performing processing on the scattering volume image of the X-ray by using a hard threshold filtering algorithm to obtain a scattering volume image of the characteristic X-ray.

[0013] For the above method, in a possible implementation, the sample contains at least one mineral, and the "determining the scanning step according to the horizontal width of the characteristic X-ray" comprises: determining the scanning step capable of identifying the target mineral according to the Monte Carlo simulation result of the target mineral to be identified; wherein the target mineral is a mineral selected from the at least one mineral.

[0014] For the above method, in a possible implementation, the "determining the scanning step according to the horizontal width of the characteristic X-ray" comprises: the scanning step is less than or equal to the horizontal width of the characteristic X-ray.

[0015] For the above method, in a possible implementation, the scanning step is the horizontal width of the characteristic X-ray.

[0016] For the above method, in a possible implementation, the scanning step is 1 / 2-2 / 3 of the horizontal width of the characteristic X-ray.

[0017] For the above method, in a possible implementation, the acceleration voltage used in the Monte Carlo simulation method is 15 KV, 20 KV or 25 KV.

[0018] For the above method, in a possible implementation, the Monte Carlo simulation method is performed by using a Monte Carlo simulation software, and the mineral identification method is performed by using a mineral identification software, wherein the Monte Carlo simulation software and the mineral identification software are independently set; or the Monte Carlo simulation software is embedded in the mineral identification software.

[0019] For the above method, in a possible implementation, the S300 comprises:

[0020] The scanning electron microscope and the energy spectrometer are used to scan the target area with BSE parameters and EDS parameters set in the mineral identification software, to obtain the mineral distribution map of the sample; wherein the BSE parameters include image resolution and BSE residence time, and the EDS parameters further include EDS residence time. BRIEF DESCRIPTION OF DRAWINGS

[0021] The present application is described below with reference to the accompanying drawings. In the drawings:

[0022] Figure 1 A schematic diagram showing the principle of the mineral identification method;

[0023] Figure 2 A schematic diagram showing the flow of the mineral identification method based on Monte Carlo simulation according to an embodiment of the present application;

[0024] Figure 3 Monte Carlo simulation results of ilmenite, pyroxene, iron mica, meteoric pyrite and plagioclase are shown;

[0025] Figure 4 Monte Carlo simulation results of zirconolite, zircon, olivine and apatite are shown;

[0026] Figure 5 EDS line scanning analysis results of pyroxene, ilmenite, plagioclase and meteoric pyrite are shown;

[0027] Figure 6 A mineral distribution map of the recognized lunar basaltic rock fragment (hereinafter referred to as rock fragment) is shown, wherein:

[0028] (1) a-c are BSE images of the rock fragment;

[0029] (2) d-f are mineral distribution maps (two-dimensional) when the scanning step is 1 μm;

[0030] (3) g is a mineral distribution map in the rock fragment in (a) when the scanning step is 3 μm;

[0031] (4) (h, i) are enlarged schematic diagrams of the white boxes in (d, g), respectively;

[0032] (5) j is the area distribution percentage of different minerals in the rock fragment;

[0033] Figure 7 Monte Carlo simulation results of main minerals in a rare earth ore are shown;

[0034] Figure 8 Mineral distribution maps of each mineral in the recognized rare earth ore are shown.

[0035] LIST OF REFERENCE NUMERALS

[0036] 1, scatter volume image of characteristic X-rays;

[0037] 2. Total volume image of electron scattering;

[0038] 101. Characteristic X-ray region;

[0039] 102. Continuous X-ray region;

[0040] 103. Fluorescent X-ray region. DETAILED DESCRIPTION

[0041] The preferred embodiments of the present application will be described below with reference to the accompanying drawings. Those skilled in the art will understand that the embodiments are used to explain the technical principles of the present application, and are not intended to limit the protection scope of the present application. As the sample in the present application can be a mineral, it can also be a ceramic material, a metal material, an artifact fragment, etc. with similar shape, structure, etc. to the rock.

[0042] It should be noted that in the description of the present application, the terms indicating the direction or positional relationship of "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the direction or positional relationship shown in the drawings, which is merely for the convenience of description, and does not indicate or imply that the device or element must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation of the present application. In addition, the terms "first" and "second" are only for the purpose of description, and cannot be understood as indicating or implying relative importance.

[0043] In addition, it should also be noted that in the description of the present application, unless otherwise explicitly specified and limited, the terms "mounting", "setting", "connecting" should be understood broadly, for example, it can be fixedly connected, or detachably connected, or integrally connected; it can be directly connected, or indirectly connected through an intermediate medium, or the internal communication of two elements. Those skilled in the art can understand the specific meaning of the above terms in the present application according to the specific circumstances.

[0044] In addition, in order to better illustrate the present application, a large number of specific details are given in the specific embodiments below, and those skilled in the art should understand that the present application can also be implemented without some specific details. In some examples, the principles of Monte Carlo simulation method, mineral identification software, SEM, BSE, EDS, etc. which are well known to those skilled in the art are not described in detail, in order to highlight the main idea of the present application.

[0045] Mainly referring to Figure 2 In one possible embodiment, the present application provides a mineral identification method based on Monte Carlo simulation, which mainly comprises the following steps:

[0046] S100, placing the prepared sample on the sample stage of the scanning electron microscope, controlling the movement of the sample stage, and splicing out a large-area light microscope image or BSE image.

[0047] S200, selecting a target region of interest in the spliced light microscope image or BSE image.

[0048] S300, causing the scanning electron microscope and the energy spectrometer to scan the target region with fine BSE parameters and EDS parameters set in the mineral software, respectively, to obtain a mineral distribution map of the sample.

[0049] The BSE parameters include image resolution, BSE residence time, etc., and the EDS parameters include scanning step, EDS residence time, etc. In the present application, the scanning step is determined according to the Monte Carlo simulation method, and the determination method includes:

[0050] S1, using a Monte Carlo simulation software to determine the horizontal width of the characteristic X-ray of the element of the mineral in the sample by the Monte Carlo simulation method (hereinafter referred to as the simulation method). The specific steps are as follows: using the simulation method to obtain the scattering volume image of the X-ray of the element of the mineral in the sample under electron beam excitation; performing image processing on the scattering volume image of the X-ray to obtain the scattering volume image of the characteristic X-ray; and determining the horizontal width of the characteristic X-ray according to the scattering volume image of the characteristic X-ray. For example, the horizontal width of the characteristic X-ray is determined by measuring the width of the scattering volume image of the characteristic X-ray.

[0051] In the simulation method, the acceleration voltage is 15 kV, 20 kV or 25 kV (for example, 20 kV), and the acceleration voltage used in the simulation method should be consistent with the acceleration voltage when the mineral is identified in the scanning electron microscope, so as to ensure the effectiveness of the guidance. In the simulation method, the EDS exit angle is the same as the exit angle of the energy spectrometer installed in the scanning electron microscope. In addition, when simulating different minerals, the main elements commonly contained in the minerals are preferentially selected to simulate the electron trajectory (the total number of electron trajectories should be more than 1*10 5 The simulation software and the mineral identification software can be independently set or the simulation software can be embedded in the mineral identification software.

[0052] The element X-ray of the mineral element of the sample excited under the action of the electron beam includes the characteristic X-ray, the continuous X-ray and the fluorescent X-ray. The simulation results are processed by image processing techniques such as hard threshold filtering algorithm, such as stripping the fluorescent X-ray and the continuous X-ray, and only the characteristic X-ray is retained (the scattering volume image of the characteristic X-ray is drawn), so as to conveniently determine (measure) the horizontal width of the characteristic X-ray.

[0053] The principle of the hard threshold filtering algorithm is as follows: the proportion of the X-ray pixel in the total pixel of the electron scattering is calculated (e.g., as a threshold value ε). The X-ray pixel in the image is scanned by a rectangle or a square. If the proportion of the X-ray pixel in the region is greater than the threshold value ε, it is considered as a characteristic X-ray region (marked as 1), otherwise, it is considered as a non-characteristic X-ray region (marked as 0). According to the marked 0 and 1, a scatter volume image of the characteristic X-ray is drawn, so as to measure the horizontal width of the characteristic X-ray.

[0054] S2, determining a scanning step according to the horizontal width of the characteristic X-ray.

[0055] For example, the scanning step is equal to the horizontal width of the characteristic X-ray or the scanning step is 1 / 2-2 / 3 of the horizontal width of the characteristic X-ray.

[0056] For example, the horizontal width of the characteristic X-ray of an element in a certain mineral is determined to be 5 μm. In the case where the scanning step is set to 5 μm, the recognition accuracy and efficiency can be considered. However, in some cases, the interval of 5 μm may just cross the crystal grain smaller than 5 μm. Therefore, a value smaller than 5 μm (e.g., 1 / 2-2 / 3 of 5 μm) can be selected as a reference of the scanning step, which can improve the accuracy of recognizing the small mineral.

[0057] For another example, the sample usually contains multiple minerals, each mineral has different particle size, and the volume image of the characteristic X-ray excited by each mineral is different. Therefore, if each mineral with different particle size is to be recognized, usually the mineral with the smallest horizontal width of the characteristic X-ray is referred to, and the simulated horizontal width value of the characteristic X-ray is used as the scanning step. In this way, the scanning step is small, and the minerals with different particle sizes can be recognized, which improves the recognition accuracy. If the fine-grained mineral is ignored and only the coarse-grained mineral is recognized, the scanning step can be set according to the maximum horizontal width value of the characteristic X-ray of these minerals, which can consider the recognition efficiency and accuracy.

[0058] In this way, the present application introduces the simulation method, and the scanning step in the SEM-EDS experiment is guided by the horizontal width of the characteristic X-ray determined by the image processing technology of the simulation result.

[0059] Example 1

[0060] In the present embodiment, the sample is a resin target sample of lunar basaltic rock debris. The sample is prepared by embedding the debris in resin, mechanically polishing the resin target using 1500#, 3000# and 5000# SiC sandpaper, and then polishing the resin target using 1 μm, 0.25 μm and 0.1 μm diamond polishing paste in sequence until the surface is smooth and free of obvious scratches. Finally, a carbon film of about 8 nm thick is sprayed on the surface of the resin target to ensure that the debris is conductive.

[0061] The mineral identification method based on Monte Carlo simulation is used to identify the minerals in the sample.

[0062] As in S100 described above, the prepared resin target sample is fixed on the sample stage of the scanning electron microscope, the acceleration voltage and beam current of the scanning electron microscope are turned on, and the backscattered image is adjusted to be clear. Then, the interface of the mineral identification software is switched to and the sample stage is controlled to move.

[0063] As in S200 described above, the target area can be set as a square, rectangular, circular or other area. By adjusting the magnification of the SEM, the mineral identification software automatically divides the target area into multiple sub-areas. For example, the target area is divided into multiple small squares. The BSE parameters for fine scanning can be set in the mineral identification software. For example, the image resolution is 1024*1024 and the BSE residence time is 3 μs.

[0064] As in S300 described above, the mineral identification software obtains the BSE images and EDS element compositions of the minerals frame by frame, and finally presents a large-area mineral distribution map.

[0065] The process of determining the EDS scanning path in the EDS parameters based on the simulation method will be described below.

[0066] In the Monte Carlo simulation software, by setting the acceleration voltage, EDS exit angle, element composition of the target mineral, mineral density and number of electron trajectories, the overall volume image of electron scattering and the scattering volume image of X-ray under electron beam excitation of common major elements (such as Fe, Ca, Zr, Cu, etc.) in the mineral are simulated.

[0067] Referring to Figure 3 and 4 , the overall volume image of electron scattering and the scattering volume image of excited element X-ray in ilmenite (Fe-kα), pyroxene (Fe-kα), iron mica (Fe-kα), meteoric pyrite (Fe-kα), plagioclase (Ca-kα), clinozircon (Zr-Lα), zircon (Zr-Lα), olivine (Fe-kα) and apatite (Ca-kα) under different acceleration voltages are determined by the simulation method. The simulation results are shown in Figure 4For example, the scattering volume image 1 (red) of the characteristic X-rays of ilmenite is smaller than the total volume image 2 (blue) of the electron scattering, and the intensity of the characteristic X-ray region 101 is much higher than the intensity of the continuous X-ray region 102 and the fluorescent X-ray region 103.

[0068] The image processing techniques such as the hard threshold filtering algorithm are used to exclude the proportion of the continuous X-ray region and the fluorescent X-ray region, separate the characteristic X-ray region, and measure the horizontal width of the characteristic X-ray region. In this example, the horizontal width of the characteristic X-rays of the major elements of the iron taenite, ilmenite, meteoric pyrite, olivine, pyroxene, apatite, plagioclase, clinazosite and zircon is ~2.25, ~3.3, ~3.33, ~3.9, ~4.41, ~4.7, ~5.43, ~0.6 (clinazosite and zircon) μm, respectively.

[0069] Reference Figure 5 In order to further evaluate the accuracy of the Monte Carlo simulation results, the applicants performed the EDS element line scanning analysis on the main minerals (pyroxene, ilmenite, feldspar, meteoric pyrite) in the rock debris. As shown in b of Figure 5 The EDS element line scanning is performed along the arrow (from left to right) in the pyroxene, and the starting point is 0 μm and the ending point is 5.9 μm (ending at the interface of the pyroxene-ilmenite), and the scanning step is set to 0.5 μm. The experimental results show that with the increase of the scanning distance, the contents of magnesium, silicon and calcium continuously decrease, and the contents of titanium and iron significantly increase. At the position of ~3.75 μm, the content of titanium exceeds the normal content of titanium in the pyroxene (measured by the electron probe analyzer as ~0.69-1.1 wt%). This indicates that the characteristic X-rays generated by the electron beam in the pyroxene have spread to the adjacent ilmenite. It can be seen that the action radius of the characteristic X-rays in the pyroxene is 2.15 μm (5.9-3.75), and the corresponding lateral width is 4.3 μm (2.15*2). Further EDS element line scanning results show that the lateral width of the characteristic X-rays in the plagioclase, ilmenite and meteoric pyrite is 5.36, 3.22 and 3.18 μm, respectively.

[0070] It can be seen that the scattering volume image of the characteristic X-rays excited in the real mineral is basically consistent with the Monte Carlo simulation results. Therefore, the results of the Monte Carlo simulation can be used to guide the EDS scanning step of the mineral identification method.

[0071] In the mineral identification software, based on the horizontal width of the characteristic X-ray of the element obtained by the simulation method, the key scanning step is set. Exemplarily, it is known that zirconium-containing minerals (xenotime and zircon) are important dating minerals in the rock debris, and based on the Monte Carlo simulation result described above, the horizontal width of the characteristic X-ray of the element Zr-Lα is about 0.6 μm (about equal to 1 μm). The ion probe beam spot for Pb-Pb / U-Pb dating can be adjusted to 3 μm at most, so when screening zirconium-containing minerals of more than 3 μm, the scanning step is set to at least 3 μm. When it is necessary to screen finer minerals in the rock debris, the scanning step can be set to 1 μm at most.

[0072] In the EDS parameters, the EDS residence time is usually 5-8 ms. Based on this, in the case of setting the scanning step to 3 μm, the EDS residence time is set to 7 ms, and the EDS residence time is set to 5 ms in the case of setting the scanning step to 1 μm. Figure 6 As shown in the mineral distribution diagram, no effective zirconium-containing minerals (h) are searched. When the scanning step is adjusted to 1 μm guided by the Monte Carlo simulation result, the zirconium-containing minerals of about 1.6 μm (i) can be identified.

[0073] As the BSE parameters can be set first, then the Monte Carlo simulation is performed, and finally the EDS parameters are set according to the simulation structure, the Monte Carlo simulation can also be performed in advance for each mineral, and then the BSE parameters and the EDS parameters can be set in the mineral identification software.

[0074] Example 2

[0075] In this embodiment, the sample is a flake sample of a rare earth ore. The sample preparation method is as follows: the rare earth rock is prepared into a flake, and the flake is polished to have no scratches on the surface and a thickness of 40-50 μm by using SiC sandpaper and diamond polishing paste in sequence, and then a conductive carbon film is sprayed.

[0076] The mineral in the sample is identified by using the mineral identification method based on the Monte Carlo simulation.

[0077] The main steps S100-S300 are described above, and will not be described here.

[0078] As in this example, the image resolution set in the mineral identification software is 1024*1024, the BSE residence time is 3 μs, the EDS residence time is 7 ms, and the scanning step is determined by using the simulation method.

[0079] The determination process of the scanning step includes:

[0080] Referring to Figure 7, the X-ray scattering volume images of the main minerals in the rare earth ore are simulated by using the simulation method. Then, the characteristic X-rays are separated by using image processing techniques such as hard threshold filtering algorithm, and the horizontal width of the characteristic X-ray scattering volume images is measured. In this example, the horizontal width of the characteristic X-rays of magnetite (Fe-kα), monazite (Ce-Lα), bastnaesite (Ce-Lα), pyrite (Fe-kα), rutile (Ti-kα), barite (Ba-Lα), fluorcarbonatocalciocerite (Ce-Lα), fluorite (Ca-kα), calcite (Ca-kα) is determined as (~2.66, ~3.07, ~3.1, ~2.88, ~3.4, ~3.57, ~3.9, ~4.42, ~5.1) μm, respectively. The horizontal width of these characteristic X-rays can be used to guide the setting of the scanning step size.

[0081] Since the largest characteristic X-ray horizontal width in the main mineral categories of the rare earth ore is about 5 μm, in order to balance the accuracy and recognition efficiency, in this example, the scanning step size is set to 5 μm in the mineral recognition software, and the large-scale mineral distribution map of the complex rare earth ore as shown in Figure 8 can be obtained.

[0082] It can be seen that in the preferred embodiment of the present application, the scanning step size in the EDS parameters can be guided by introducing the Monte Carlo simulation method. By using image processing techniques, the characteristic X-rays more suitable for EDS analysis can be separated from the element X-ray scattering volume images of the minerals obtained by the simulation method, and more accurate guidance (the horizontal width of the characteristic X-rays) can be provided. In this way, the recognition accuracy and efficiency of complex minerals can be improved.

[0083] So far, the technical solution of the present application has been described in combination with the preferred embodiments shown in the drawings, but those skilled in the art can easily understand that the protection scope of the present application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to the related technical features without departing from the principles of the present application, and the technical solutions after these changes or replacements will all fall within the protection scope of the present application.

Claims

1. A mineral identification method based on Monte Carlo simulation, characterized in that, The method includes: S100. Place the prepared sample on the sample stage of the scanning electron microscope, control the movement of the sample stage, and stitch together the light microscope image or BSE image. S200: Select the target region in the stitched optical microscopy image or BSE image; S300: Use a scanning electron microscope and an energy dispersive spectroscopy (EDS) instrument to scan the target area and obtain a mineral distribution map of the sample. Among them, the EDS parameters corresponding to the energy dispersive spectrometer include the scanning step size, which is determined by the following methods: The horizontal width of the elemental characteristic X-rays of minerals in the sample was determined using the Monte Carlo simulation method. The scanning step size is determined based on the horizontal width of the characteristic X-ray.

2. The method according to claim 1, characterized in that, The phrase "using Monte Carlo simulation to determine the horizontal width of elemental characteristic X-rays of minerals in a sample" includes: Monte Carlo simulation was used to obtain the X-ray scattering volume image of mineral elements in the sample under electron beam excitation; Image processing is performed on the scattering volume image of the X-rays to obtain a scattering volume image of the characteristic X-rays; The horizontal width of the characteristic X-ray is determined based on the scattering volume image of the characteristic X-ray.

3. The method according to claim 2, characterized in that, The phrase "performing image processing on the scattering volume image of the X-rays to obtain a scattering volume image of the characteristic X-rays" includes: The scattering volume image of the X-rays is processed using a hard threshold filtering algorithm to obtain a scattering volume image of the characteristic X-rays.

4. The method according to claim 1, characterized in that, The sample contains at least one mineral. The phrase "determining the scanning step size based on the horizontal width of the characteristic X-ray" includes: Based on the Monte Carlo simulation results of the target mineral to be identified, determine the scanning step path that can identify the target mineral; The target mineral is a mineral selected from at least one mineral.

5. The method according to claim 1, characterized in that, The phrase "determining the scanning step size based on the horizontal width of the characteristic X-ray" includes: The scanning step size is less than or equal to the horizontal width of the characteristic X-ray.

6. The method according to claim 5, characterized in that, The scanning step diameter is the horizontal width of the characteristic X-ray.

7. The method according to claim 5, characterized in that, The scanning step is 1 / 2 to 2 / 3 of the horizontal width of the characteristic X-ray.

8. The mineral identification method based on Monte Carlo simulation according to claim 1, characterized in that, The accelerating voltage used in the Monte Carlo simulation method is 15kV, 20kV or 25kV.

9. The method according to claim 1, characterized in that, The Monte Carlo simulation method is performed using Monte Carlo simulation software, and the mineral identification method is performed using mineral identification software. The Monte Carlo simulation software and the mineral identification software are set up independently; or the Monte Carlo simulation software is embedded in the mineral identification software.

10. The mineral identification method based on Monte Carlo simulation according to claim 9, characterized in that, The S300 includes: The scanning electron microscope and energy dispersive spectroscopy were used to scan the target area with the BSE and EDS parameters set in the mineral identification software, respectively, to obtain the mineral distribution map of the sample. The BSE parameters include image resolution and BSE dwell time, and the EDS parameters also include EDS dwell time.

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