Mineral identification method based on monte carlo simulation

Abstract

The application discloses a mineral identification method based on Monte Carlo simulation, comprising the following steps: S100, placing a prepared sample on a sample stage of a scanning electron microscope, controlling the sample stage to move, and splicing out a light microscope image or a BSE image; S200, selecting a target area in the spliced light microscope image or BSE image; S300, causing the scanning electron microscope and the energy spectrometer to scan the target area, and obtaining a mineral distribution map of the sample; wherein the EDS parameters corresponding to the energy spectrometer include a scanning step distance, and the scanning step distance is determined in the following manner: adopting a Monte Carlo simulation method, determining the horizontal width of the element characteristic X-ray of the mineral in the sample; and according to the horizontal width of the characteristic X-ray, determining the scanning step distance. Through the constitution, the identification accuracy and efficiency of the complex mineral in the rock sample are effectively improved.

Description

Technical Field

[0001] This application belongs to the field of micro-area microscopic analysis of mineral materials, and in particular relates to a mineral identification method based on Monte Carlo simulation. Background Technology

[0002] Rapid and accurate identification of minerals in rocks is crucial for revealing rock genesis, geological evolution, and thermal evolution. Taking lunar research as an example, the chemical composition, crystallographic characteristics, and isotopic chemical features of lunar minerals can effectively reveal the moon's origin, thermal history, and spatiotemporal evolution. Similarly, in the study of rare earth mineral deposits on Earth, identifying the types, chemical compositions, and key metal occurrences of complex rare earth minerals is essential for revealing the mineralization mechanisms, mineralization evolution history, and corresponding utilization of mineral resources.

[0003] In recent years, methods for automatically identifying minerals have been developed based on backscattered electron (BSE) images and energy dispersive spectroscopy (EDS) technology using scanning electron microscopy (SEM). (Refer to...) Figure 1 The principle of this method is roughly as follows: a scanning electron microscope (SEM) accelerates an electron beam and bombards the sample, generating backscattered electrons and X-rays; a built-in detector receives the signals to form a backscattered image of the mineral; mineral identification software distinguishes the mineral grain boundaries in the sample by using the contrast difference of the backscattered image; the mineral identification software controls an energy dispersive spectrometer (EDS) to receive characteristic X-ray signals and scans the sample surface according to preset modes such as point mode, line mode, lattice mode, and point-by-point high-resolution mode; finally, the mineral type and two-dimensional distribution state in the sample are obtained based on the BSE image and EDS composition information.

[0004] Currently, commercially available 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), TescanIntegrated Mineral Analyzer (TIMA), and Maps Mineralogy. These software programs can visualize the distribution and size of mineral grains at the micrometer to centimeter scale. However, for minerals with diverse sizes and relatively complex compositions, these methods have significant shortcomings in terms of accuracy and speed.

[0005] Specifically, the aforementioned mineral identification software is based on EDS spectral peak identification. However, crucial analytical conditions, such as the scanning step size setting, largely rely on operator experience and lack constraints and standards. This often results in the identification mechanism failing to balance accuracy and efficiency. For example, setting the scanning step size too small will reduce the mineral identification speed; setting the EDS step size too large will cause smaller minerals to be missed during the scanning process.

[0006] Monte Carlo simulation is a computer simulation method that simulates the trajectory of electrons within a sample, providing the spatial distribution of electron scattering. Based on first-principles methods, this simulation determines parameters such as electron trajectory, scattering angle, and energy loss by comprehensively considering elastic and inelastic scattering models and collective effects. Using these parameters and related analytical geometric equations, the entire trajectory of the electron from its entry into the sample surface to its final state can be simulated step-by-step by relating the scattering angle and step size to the continuous positions of the electrons. Currently, Monte Carlo simulation software can simulate the spatial distribution volume of secondary electrons, backscattered electrons, and characteristic X-rays generated by materials under electron beam excitation. Therefore, by using Monte Carlo simulation to obtain the characteristic X-ray volumes of elements in different minerals, and then using this to guide the setting of the scanning step size in mineral identification software, it is hoped that the above problems can be solved. Summary of the Invention

[0007] The technical problem this application aims to solve is to improve the accuracy and / or speed of mineral identification to at least a certain extent based on the Monte Carlo simulation method.

[0008] In view of this, this application provides a mineral identification method based on Monte Carlo simulation, the method comprising: S100, placing the prepared sample on the sample stage of a scanning electron microscope, controlling the movement of the sample stage, and stitching together a large-area light microscopy image or BSE image; S200, selecting a target region in the stitched light microscopy image or BSE image; S300, scanning the target region with the scanning electron microscope and energy dispersive spectroscopy to obtain a mineral distribution map of the sample; wherein, the EDS parameters corresponding to the energy dispersive spectroscopy include the scanning step size, the scanning step size being determined by: using the Monte Carlo simulation method to determine the horizontal width of the elemental characteristic X-rays of the minerals in the sample; and determining the scanning step size based on the horizontal width of the characteristic X-rays.

[0009] The horizontal width of the X-rays was determined using Monte Carlo simulation. Based on this, and using the horizontal width of characteristic X-rays that better matches SEM-EDS experiments, the scanning step size in the mineral identification method was determined.

[0010] In one possible implementation of the above method, the step of "determining the horizontal width of the elemental characteristic X-rays of the minerals in the sample using the Monte Carlo simulation method" includes: using the Monte Carlo simulation method to obtain a scattering volume image of the X-rays of the minerals in the sample under electron beam excitation; performing image processing on the scattering volume image of the X-rays to obtain a scattering volume image of the characteristic X-rays; and determining the horizontal width of the characteristic X-rays based on the scattering volume image of the characteristic X-rays.

[0011] In one possible implementation of the above method, the step of "performing image processing on the X-ray scattering volume image to obtain a characteristic X-ray scattering volume image" includes:

[0012] 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.

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

[0014] In one possible implementation of the above method, the step of "determining the scanning step diameter based on the horizontal width of the characteristic X-ray" includes: the scanning step diameter being less than or equal to the horizontal width of the characteristic X-ray.

[0015] In one possible implementation of the above method, the scanning step diameter is the horizontal width of the characteristic X-ray.

[0016] In one possible implementation of the above method, the scanning step size is 1 / 2 to 2 / 3 of the horizontal width of the characteristic X-ray.

[0017] In one possible implementation of the above method, the accelerating voltage used in the Monte Carlo simulation method is 15kV, 20kV, or 25kV.

[0018] In one possible implementation of the above method, the Monte Carlo simulation method is performed using Monte Carlo simulation software, and the mineral identification method is performed using mineral identification software, wherein 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.

[0019] In one possible implementation of the above method, step S300 includes:

[0020] The scanning electron microscope and energy dispersive spectroscopy (EDS) are used to scan the target area with BSE and EDS parameters set in the mineral identification software, respectively, to obtain a mineral distribution map of the sample; wherein, the BSE parameters include image resolution and BSE residence time, and the EDS parameters also include EDS residence time. Attached Figure Description

[0021] The present application will now be described with reference to the accompanying drawings. In the drawings:

[0022] Figure 1 A schematic diagram illustrating the principle of the mineral identification method is shown;

[0023] Figure 2 This diagram illustrates a flowchart of a mineral identification method based on Monte Carlo simulation according to an embodiment of this application.

[0024] Figure 3 Monte Carlo simulation results for ilmenite, pyroxene, kamacite, troilite, and plagioclase are shown.

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

[0026] Figure 5 The results of EDS line scan analysis of pyroxene, ilmenite, plagioclase and troilite are shown.

[0027] Figure 6 The diagram shows the mineral distribution of the identified lunar basalt fragments (hereinafter referred to as fragments).

[0028] (1) ac is the BSE image of rock cuttings;

[0029] (2) df is a two-dimensional mineral distribution map with a scanning step size of 1 μm;

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

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

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

[0033] Figure 7 The results of Monte Carlo simulations of the main minerals in rare earth minerals are shown.

[0034] Figure 8 The diagram shows the mineral distribution of each mineral in the identified rare earth ore.

[0035] List of reference numerals in the attached diagram:

[0036] 1. Scattering volume image of characteristic X-rays;

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

[0038] 101. Characteristic X-ray region;

[0039] 102. Continuous X-ray region;

[0040] 103. Fluorescent X-ray region. Detailed Implementation

[0041] Preferred embodiments of this application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are used to explain the technical principles of this application and are not intended to limit the scope of protection of this application. For example, the mineral corresponding to the sample in this application can be a geological rock, or it can be a ceramic material, metallic material, cultural relic fragment, etc., with a shape and structure similar to rock.

[0042] It should be noted that in the description of this application, terms such as "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. These are used merely for ease of description and do not indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation; therefore, they should not be construed as limitations on this application. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0043] Furthermore, it should be noted that, in the description of this application, unless otherwise expressly specified and limited, the terms "installation," "setup," and "connection" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a direct connection, an indirect connection through an intermediate medium, or a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0044] Furthermore, to better illustrate this application, numerous specific details are provided in the following detailed embodiments. Those skilled in the art should understand that this application can still be implemented without certain specific details. In some instances, the principles of Monte Carlo simulation methods, 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 points of this application.

[0045] Main reference Figure 2 In one possible implementation, the mineral identification method based on Monte Carlo simulation described in this application mainly includes the following steps:

[0046] S100. Place the prepared sample on the sample stage of the scanning electron microscope, control the movement of the sample stage, and stitch together a large-area light microscopy image or BSE image.

[0047] S200. Select the target region of interest in the stitched optical microscopy image or BSE image.

[0048] S300: The scanning electron microscope and energy dispersive spectroscopy (EDS) are used to scan the target area with the fine BSE and EDS parameters set in the mineral software, respectively, to obtain the mineral distribution map of the sample.

[0049] The BSE parameters include image resolution and BSE dwell time, while the EDS parameters include scan step size and EDS dwell time. In this application, the scan step size is determined using a Monte Carlo simulation method, such as the following methods:

[0050] S1. Using Monte Carlo simulation software, the horizontal width of the elemental characteristic X-rays in the sample is determined using the Monte Carlo simulation method (hereinafter referred to as the simulation method). Specifically, this step involves: using the simulation method to obtain the X-ray scattering volume image of the mineral elements in the sample under electron beam excitation; processing the X-ray scattering volume image to obtain the scattering volume image of the characteristic X-rays; and determining the horizontal width of the characteristic X-rays based on the scattering volume image. For example, the horizontal width of the characteristic X-rays can be determined by measuring the width of the scattering volume image of the characteristic X-rays.

[0051] The accelerating voltage used in the simulation method is 15kV, 20kV, or 25kV (e.g., 20kV). The accelerating voltage used in the simulation method should be consistent with the accelerating voltage used for mineral identification in a scanning electron microscope (SEM) to ensure the effectiveness of the guidance. The EDS exit angle selected in the simulation method is the same as the exit angle of the energy dispersive spectrometer installed in the SEM. Furthermore, when simulating different minerals, priority should be given to simulating electron trajectories of major elements commonly found in the minerals (the total number of electron trajectories should exceed 1*10^6). 5 The simulation software and mineral identification software can be set up independently, or the simulation software can be embedded in the mineral identification software.

[0052] The elemental X-rays excited by the electron beam from the mineral elements in the sample include characteristic X-rays, continuous X-rays, and fluorescent X-rays. Image processing techniques such as hard thresholding filtering are used to process the simulation results, including noise reduction, such as stripping fluorescent and continuous X-rays and retaining only the characteristic X-rays (by plotting a scattering volume image of the characteristic X-rays), thus facilitating the determination (measurement) of the horizontal width of the characteristic X-rays.

[0053] The principle of a hard thresholding algorithm is as follows: Calculate the proportion of X-ray pixels to the total number of electron-scattering pixels (e.g., as the threshold ε). Scan each X-ray pixel in the image one by one using a rectangle or square. If the proportion of X-ray pixels in this region is greater than the threshold ε, it is considered a characteristic X-ray region (marked as 1); otherwise, it is considered a non-characteristic X-ray region (marked as 0). Based on the markings 0 and 1, draw a scattering volume image of the characteristic X-rays, thereby measuring the horizontal width of the characteristic X-rays.

[0054] S2. Determine the scanning step size based on the horizontal width of the characteristic X-ray.

[0055] If the scan step size is less than or equal to the horizontal width of the characteristic X-ray. For example, the scan step size can be the horizontal width of the characteristic X-ray or 1 / 2 to 2 / 3 of the horizontal width of the characteristic X-ray.

[0056] For example, the horizontal width of the characteristic X-rays of an element in a certain mineral is determined to be 5 μm. Setting the scan step size to 5 μm balances accuracy and efficiency. However, in some cases, a 5 μm spacing may cross grains smaller than 5 μm. Therefore, choosing a value smaller than 5 μm (such as 1 / 2 to 2 / 3 of 5 μm) as a reference for the scan step size can improve the accuracy of identifying small minerals.

[0057] For example, samples often contain multiple minerals, each with different grain sizes and different volumetric X-ray patterns when excited. Therefore, to identify minerals of varying grain sizes, the scan step size is typically set based on the mineral with the smallest horizontal width of its characteristic X-rays. This results in a smaller scan step size, allowing for the identification of both coarse and fine-grained minerals, thus improving accuracy. If fine-grained minerals are ignored and only coarse-grained minerals are identified, the scan step size is set based on the maximum horizontal width of the characteristic X-rays of these minerals, balancing identification efficiency and accuracy.

[0058] In this way, by introducing a simulation method and applying image processing techniques to the simulation results, this application ensures that the determined horizontal width of the characteristic X-rays can better guide the scanning step size in SEM-EDS experiments.

[0059] Example 1

[0060] In this embodiment, the sample is a resin target sample of rock fragments from lunar basalt. Exemplarily, the sample preparation process includes: embedding the rock fragments in resin to prepare a resin target; mechanically polishing the resin target with 1500#, 3000# and 5000# SiC sandpaper; then polishing it sequentially with 1μm, 0.25μm and 0.1μm diamond polishing paste until the surface is smooth and without obvious scratches; finally, spraying a carbon film of about 8nm thick on the surface of the resin target to ensure that the rock fragments are conductive.

[0061] For the above samples, the mineral identification method based on Monte Carlo simulation of this application is used to identify the minerals in the samples.

[0062] As in the aforementioned S100, after fixing the prepared resin target sample on the sample stage of the scanning electron microscope, turning on the accelerating voltage and beam current of the scanning electron microscope, adjusting the backscattered image to be clear, and then switching to the interface of the mineral identification software and controlling the movement of the sample stage.

[0063] As described in S200 above, the target area can be set as a square, rectangle, circle, or other similar region. By adjusting the magnification of the SEM, the mineral identification software will automatically divide the target area into multiple sub-regions. For example, it can be divided into multiple small squares. BSE parameters for fine scanning can be set in the mineral identification software; for example, the image resolution is 1024*1024, and the BSE dwell time is 3μs.

[0064] As in the aforementioned S300, the mineral identification software obtains the BSE image and EDS elemental composition of the mineral frame by frame, and finally presents a large-area mineral distribution map.

[0065] The following section will introduce the process of determining the EDS scan path in EDS parameters based on simulation methods.

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

[0067] Reference Figure 3 and 4 Through simulation, the total volumetric electron scattering and the scattering volumetric X-rays of excited elements in ilmenite (Fe-kα), pyroxene (Fe-kα), kamacite (Fe-kα), troilite (Fe-kα), plagioclase (Ca-kα), zircon (Zr-Lα), zircon (Zr-Lα), olivine (Fe-kα), and apatite (Ca-kα) were determined under different accelerating voltages. Figure 4Taking ilmenite as an example, the scattering volume image 1 (red) of excited X-rays is smaller than the total volume image 2 (blue) of electron scattering, and the intensity of the characteristic X-ray region 101 is much higher than that of the continuous X-ray region 102 and the fluorescent X-ray region 103.

[0068] Image processing techniques such as hard threshold filtering were used to exclude the proportion of continuous X-ray regions and fluorescent X-ray regions, separating the characteristic X-ray regions and measuring their horizontal widths. In this example, the horizontal widths of the characteristic X-rays of major elements such as kamacite, ilmenite, troilite, olivine, pyroxene, apatite, plagioclase, zircon, and zircon are ~2.25, ~3.3, ~3.33, ~3.9, ~4.41, ~4.7, ~5.43, and ~0.6 (zircon and zircon) μm, respectively.

[0069] Reference Figure 5 To further evaluate the accuracy of the Monte Carlo simulation results, the applicant conducted EDS elemental line scan analysis on the main minerals (pyroxene, ilmenite, feldspar, and troilite) in the rock fragments. Figure 5 As shown in b, the EDS elemental line scan was performed along the arrows (from left to right) in pyroxene, with a starting point of 0 μm and an ending point of 5.9 μm (ending at the pyroxene-ilmenite interface), and a scan step size of 0.5 μm was set. The experimental results show that with increasing scan distance, the contents of magnesium, silicon, and calcium continuously decrease, while the contents of titanium and iron significantly increase. At a position of ~3.75 μm, the titanium content exceeds the normal titanium content in pyroxene (measured by electron probe microanalyzer as ~0.69-1.1 wt%). This indicates that the characteristic X-rays generated by the electron beam in pyroxene have extended to the adjacent ilmenite. Therefore, the radius of influence of the characteristic X-rays in pyroxene is 2.15 μm (5.9-3.75), and the corresponding lateral width is 4.3 μm (2.15*2). Further EDS elemental line scans showed that the lateral widths of the characteristic X-rays in plagioclase, ilmenite, and troilite were 5.36, 3.22, and 3.18 μm, respectively.

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

[0071] In mineral identification software, the key scan step size is set based on the horizontal width of the characteristic X-rays of elements obtained through simulation methods. For example, zircon-bearing minerals (zircon and zircon) are known to be important dating minerals in rock cuttings. Based on the aforementioned Monte Carlo simulation results, the horizontal width of the characteristic X-rays of element Zr-Lα is approximately 0.6 μm (approximately equal to 1 μm). The minimum adjustable beam size of the ion probe used for Pb-Pb / U-Pb dating is 3 μm; therefore, when screening for zircon-bearing minerals larger than 3 μm, the scan step size should be set to at least 3 μm. When screening for finer-grained minerals in rock cuttings, the minimum scan step size can be set to 1 μm.

[0072] The EDS dwell time in the EDS parameters is typically 5-8 ms. Based on this, with the scan step size set to 3 μm, the following results were obtained: Figure 6 The mineral distribution map shown did not find any valid zirconium-bearing minerals (h). However, when the scan step size was adjusted to 1 μm as guided by Monte Carlo simulation results, zirconium-bearing minerals of ~1.6 μm could be identified (i).

[0073] You can first set the BSE parameters, then perform a Monte Carlo simulation, and finally set the EDS parameters based on the simulation structure. Alternatively, you can perform a Monte Carlo simulation on each mineral in advance, and then set the BSE and EDS parameters in the mineral identification software.

[0074] Example 2

[0075] In this embodiment, the sample is a thin sheet of rare earth ore. The sample preparation method is as follows: the rare earth rock is prepared into a thin sheet, and the thin sheet is polished with SiC sandpaper and diamond polishing paste in sequence until there are no scratches on the surface and the thin sheet sample has a thickness of 40-50μm. Then, a conductive carbon film is sprayed on it.

[0076] The mineral identification method based on Monte Carlo simulation proposed in this application is used to identify minerals in the sample.

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

[0078] In this example, the image resolution is set to 1024*1024, the BSE dwell time is 3μs, the EDS dwell time is 7ms, and the scanning step size will be determined using a simulation method.

[0079] The process of determining the scanning step path includes:

[0080] Reference Figure 7The X-ray scattering volume images of the main minerals in rare earth ores were simulated using a simulation method. Then, image processing techniques such as hard thresholding filtering were used to separate the characteristic X-rays, and the horizontal width of the characteristic X-ray scattering volume images was measured. In this example, the horizontal widths of the characteristic X-rays for the elements in magnetite (Fe-kα), monazite (Ce-Lα), bastnaesite (Ce-Lα), pyrite (Fe-kα), rutile (Ti-kα), barite (Ba-Lα), bastnaesite (Ce-Lα), fluorite (Ca-kα), and calcite (Ca-kα) were determined to be (~2.66, ~3.07, ~3.1, ~2.88, ~3.4, ~3.57, ~3.9, ~4.42, ~5.1) μm, respectively. These horizontal widths of the characteristic X-rays can be used to guide the scanning step size settings described above.

[0081] Since the largest characteristic X-ray horizontal width among the main mineral categories of rare earth minerals is approximately 5 μm, in order to balance accuracy and identification efficiency, the scanning step size in the mineral identification software is set to 5 μm in this example, which yields results such as... Figure 8 The large-scale mineral distribution map of the complex rare earth ore is shown.

[0082] As can be seen, in the preferred embodiment of this application, the introduction of the Monte Carlo simulation method can guide the scanning step size in the EDS parameters. Through image processing techniques, characteristic X-rays that better match EDS analysis are separated from the elemental X-ray scattering volume image of the mineral obtained by the simulation method, providing more accurate guidance (the horizontal width of the characteristic X-rays). This improves the accuracy and efficiency of identifying complex minerals.

[0083] The technical solutions of this application have been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of this application is obviously not limited to these specific embodiments. Without departing from the principles of this application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of this 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 two-dimensional distribution map of minerals in 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; The method of determining the horizontal width of elemental characteristic X-rays of minerals in a sample using Monte Carlo simulation 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.

2. The method according to claim 1, characterized in that, The image processing of the X-ray scattering volume image to obtain a characteristic X-ray scattering volume image 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.

3. The method according to claim 1, characterized in that, The sample contains at least one mineral. 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.

4. The method according to claim 1, characterized in that, 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.

5. The method according to claim 4, characterized in that, The scanning step diameter is the horizontal width of the characteristic X-ray.

6. The method according to claim 4, characterized in that, The scanning step is 1 / 2 to 2 / 3 of the horizontal width of the characteristic X-ray.

7. 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.

8. 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.

9. The mineral identification method based on Monte Carlo simulation according to claim 8, 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 a two-dimensional distribution map of minerals in 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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