Methods for analyzing mineral species in ores

Abstract

To provide an ore mineral species analysis method for analyzing the mineral species of ore with higher accuracy.SOLUTION: A method includes: a first step of photographing ore with an optical microscope to obtain an optical microscope image including image information in a pixel unit; a second step of binarizing the optical microscope image to extract a mineral particle area corresponding to mineral particles; a third step of, for pixels to be analyzed in the mineral particle area, generating pixel distribution data in which the values of image information of the pixels are plotted in a space having the image information as variables; a fourth step of comparing a peak position of the density of pixels in the distribution data with a mineral determination area of the mineral species to determine the mineral species included in the pixels to be analyzed; and a fifth step of comparing the image information of the pixels to be analyzed with a mineral identification area of the mineral species determined in the fourth step to identify if the pixels to be analyzed are the mineral species determined in the fourth step.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a method for analyzing mineral species of ores.

Background Art

[0002] Among the ores mined from mines, various minerals are included, from those with high utility to those with low utility. As an example of a method for analyzing mineral particles contained in such ores, a method for analyzing mineral species using an optical microscope has been conventionally known. In the method for analyzing mineral species using an optical microscope, generally, based on the information obtained by the optical microscope, mineral particles are analyzed by quantification by comparison with indices or quantification by the point counting method.

[0003] Also, as an example of a method for analyzing mineral particles contained in ores, MLA: Mineral Liberation Analyzer is known. MLA can automatically analyze minerals based on SEM-EDS.

[0004] For example, Japanese Unexamined Patent Application Publication No. 2019-174473 (Patent Document 1) discloses a method for analyzing slag that enables analysis of the concentration of matte particles present in slag by a simpler method by observing the slag obtained by melting raw materials with a digital microscope in non-ferrous metal smelting.

[0005] Also, as a method for preparing an observation sample for MLA analysis, Japanese Unexamined Patent Application Publication No. 2016-050918 (Patent Document 2) discloses a method for suppressing the bias in the state of existence of minerals caused by the difference in specific gravity of ore particles and observing by applying a predetermined embedding method to mineral powder particles to prepare a sample.

Prior Art Documents

Patent Documents

[0006]

Patent Document 1

[0007] However, conventional mineral species analysis methods using optical microscopes have a narrow measurement range, making quantitative analysis of minerals difficult. Furthermore, in conventional MLA analysis methods, for example, if mineral C, which has an intermediate composition between mineral A and mineral B, theoretically exists, the results may show the apparent presence of mineral C at the boundary between mineral A and mineral B, even though mineral C is not actually present. This leads to a problem where the mineral proportion is misjudged.

[0008] This invention was completed in view of the above-mentioned problems, and in one embodiment, aims to provide a method for analyzing the mineral species of ore that can analyze the mineral species in ore with higher accuracy. [Means for solving the problem]

[0009] As a result of various diligent studies to solve the above problems, the inventors have found that the problems can be solved by utilizing the high qualitative ability of optical microscopes to analyze minerals, using an optical microscope for mineral analysis, programming identification through image analysis, and performing quantitative analysis. Specifically, by utilizing the automatic control of a digital microscope to perform wide-field observation and analyzing based on the observed colors, mineral species can be identified. The present invention was completed based on the above findings and is illustrated below.

[0010] [1] A method for analyzing the mineral species of ore, The first step involves photographing the aforementioned ore with an optical microscope to obtain an optical microscope image containing image information at the pixel level, A second step involves binarizing the optical microscope image to extract the mineral particle region corresponding to the mineral particle, A third step involves creating pixel distribution data by plotting the image information values ​​of each pixel in a space where the image information is used as a variable, for the pixels to be analyzed in the mineral particle region, A fourth step involves comparing the peak position of the pixel density in the distribution data with the mineral identification region for each mineral species, and determining the mineral species included in the analysis target. The fifth step includes comparing the image information of each pixel to be analyzed with the mineral identification region of the mineral species determined in the fourth step, and identifying whether each pixel to be analyzed is of the mineral species determined in the fourth step. A method for analyzing the mineral species of ore, wherein the mineral determination region and the mineral identification region are regions set in space using the image information as a variable. [2] The image information used in the third to fifth steps is selected from (R, G, B, H, S, V, L data), as described in [1] for the mineral species analysis method of ore. [3] The image information used in the third to fifth steps is S and V data, as described in [2] for the mineral species analysis method of ore. [4] The method for analyzing the mineral species of ore according to any one of [1] to [3], wherein the setting of the mineral determination region is performed by determining the peak position of each of a plurality of mineral particle regions that have been identified as belonging to a specific mineral species, and setting the mineral determination region of the specific mineral species to include the plurality of peak positions obtained from the plurality of mineral particle regions. [5] The mineral identification region is set so as not to overlap between different mineral species, as described in any one of items [1] to [4] for the mineral species analysis method of ore. [6] The mineral identification region is set by setting the mineral identification region for a specific mineral species to include the distribution range of the pixel data of the image information of a plurality of mineral particle regions that have been identified as being of a specific mineral species, as described in any one of items [1] to [5]. [7] Furthermore, after the fifth step, a step of calculating the ratio for each mineral species based on the area ratio of each identified mineral species and the density of the mineral species is included, and the method for analyzing the mineral species of ore according to any one of [1] to [6].

Advantages of the Invention

[0011] According to an embodiment of the present invention, a method for analyzing the mineral species of ore that can analyze the mineral species in ore with higher accuracy can be provided.

Brief Description of the Drawings

[0012] [Figure 1] In one embodiment of the present invention, it is a diagram explaining a procedure for obtaining an optical microscope image of a mineral particle region. [Figure 2] In one embodiment of the present invention, it is a diagram showing an example of the S-V plot distribution of different mineral species. [Figure 3] In one embodiment of the present invention, it is a diagram showing a mineral determination region determined for an ore actually containing mineral particles of both Ccp and Py. [Figure 4] In one embodiment of the present invention, it is a diagram showing a mineral determination region and a mineral identification region determined for an ore actually containing mineral particles of both Ccp and Py. [Figure 5] In one embodiment of the present invention, it is a flowchart showing a procedure for analyzing an ore sample using a mineral determination region and a mineral identification region. [Figure 6] It is a diagram showing a mineral determination region and a mineral identification region of an ore sample of an example of the present invention. [Figure 7] It is a diagram showing an image when an ore sample is analyzed by MLA.

Modes for Carrying Out the Invention

[0013] Next, embodiments for carrying out the present invention will be described in detail with reference to the drawings. It should be understood that the present invention is not limited to the following embodiments, and design changes, improvements, etc. can be appropriately made based on the ordinary knowledge of those skilled in the art without departing from the gist of the present invention.

[0014] (1. Ore) In the mineral species analysis method of ore according to the embodiment of the present invention, the mineral to be analyzed is not particularly limited as long as it is a mineral of ore that can be determined by color, and any mineral can be analyzed. In particular, copper ore is easy to identify by the color of the mineral species, so the present invention can be suitably used for the analysis of the mineral species of copper ore. Examples of copper ore include various minerals contained in copper concentrate (for example, Chalcopyrite, Chalcocite, Covellite, Bornite, Pyrite, Molybdenite).

[0015] The mineral particles contained in the ore are preferably subjected to pretreatment to form a smooth observation surface for observation by the optical microscope image described later. The specific pretreatment method is not limited, but for example, a method of embedding the mineral particles in a resin material to produce a sample-embedded resin in which the mineral particles are fixed in the resin material can be mentioned.

[0016] To embed the mineral particles in the resin material, a method can be adopted in which the mineral particles are put into a container together with the liquid resin material, the mineral particles and the liquid resin material in the container are stirred, and then the liquid resin material is cured. When stirring, it is preferable to rotate the container containing the mineral particles and the liquid resin material in the revolving direction opposite to the rotation while rotating it with a rotary-revolving stirrer. Thereby, the aggregation of particles can be effectively suppressed, the dispersibility of the granular sample in the resin material can be effectively improved, and a representative composition without segregation can be obtained.

[0017] The particle size of mineral particles ranges from, for example, 1 μm to 700 μm, typically from 20 μm to 200 μm, and is usually fairly evenly distributed and uniform. While particle size analyzers can measure particle sizes from, for example, 0.243 μm to 2000 μm, the particle size of mineral particles, as described above, is usually uniformly distributed within this range.

[0018] Various resin materials can be used to embed and fix mineral particles, as long as they can be kept in a liquid state when added to the container and stirred, and can then be cured. Examples include epoxy resins, acrylic resins, and phenolic resins, among which epoxy resins, being thermosetting resins, are preferred.

[0019] The ratio of mineral particles to the liquid resin material placed in the container is preferably 100% to 300% by volume. More preferably, it is 200% to 300% by volume. This means that if the ratio of mineral particles is 100% or more by volume, particle aggregation can be suppressed, and if the ratio of mineral particles is 300% or less by volume, the mineral particles can be sufficiently solidified, suppressing the phenomenon of breakage during surface polishing when the measurement surface is exposed.

[0020] In the above-described pretreatment method, mineral particles can be sufficiently dispersed in the resin material, so it is not necessary to add graphite or the like. Of course, graphite may be added. For further details of this pretreatment method, please refer to Japanese Patent Publication No. 7018804.

[0021] (2. Optical microscope image) In this embodiment, for a sample to be analyzed that contains mineral particles with multiple mineral species, first, an optical microscope image of the ore is taken to obtain an optical microscope image containing image information at the pixel level (first step). The type of optical microscope used to obtain the optical microscope image is not particularly limited, but preferably, an optical microscope equipped with a digital camera can be used.

[0022] The magnification and field of view of the optical microscope image can be appropriately set depending on the complexity of the mineral being analyzed and the required accuracy. However, from the viewpoint of improving the accuracy of mineral analysis as described later, it is preferable to set the conditions so that the size of one pixel (square) of the optical microscope image is 1 μm or less. This is because if the size of one pixel is 1 μm or less, the accuracy of the analysis of the mineral species corresponding to that pixel is high, and the error in the calculation of the quantitative determination of the mineral species for the entire analysis target is small. From this viewpoint, it is more preferable that the size of one pixel is 0.9 μm or less, even more preferable that it is 0.8 μm or less, even more preferable that it is 0.7 μm or less, even more preferable that it is 0.6 μm or less, and even more preferable that it is 0.5 μm or less. The lower limit of the size of one pixel can be set under appropriate conditions depending on the tissue and size of the object being observed.

[0023] (3. Extraction of mineral particle regions) Next, the optical microscope image of the object to be analyzed is binarized, and the bright areas corresponding to the mineral particles are extracted as mineral particle regions (second step). As mentioned above, since the mineral particles to be analyzed have undergone pretreatment, resins, graphites, etc., are present in the optical microscope image. To exclude these resins and graphites, the optical microscope image is binarized. This is because resins and graphites do not reflect much light and become dark areas when binarized. If the size of the optical microscope image is large, the image data may be divided into sizes that can be analyzed and processed. The number of mineral particle regions may vary depending on the particle size of the mineral particles, the size of the optical microscope image, etc.

[0024] Furthermore, the binarization process only requires that the optical microscope image be converted to a grayscale image, provided that the mineral particles to be analyzed can be appropriately extracted; there are no specific restrictions on the conditions. For example, regarding the brightness of the optical microscope image, 0 represents black and 255 represents white, and 128 is used as the threshold. By replacing the brightness of pixels with a value less than 128 with 0 and the brightness of pixels with a value of 128 or more with 255, binarization can be performed. Alternatively, the color image may be converted to grayscale before the binarization process.

[0025] (4. Processing of image information) Next, the image information of the optical microscope image before binarization is converted to a different color format as needed. For example, if the optical microscope image before binarization consists of RGB data, the RGB data can be converted to HSV data or HSL data. RGB stands for Red, Green, and Blue, which is an additive color space that reproduces a wide range of colors by mixing the three primary colors. HSV stands for Hue, Saturation, and Value / Brightness, which are components of color space. HSL stands for Hue, Saturation, and Lightness, which are components of color space.

[0026] Next, two or more image data (data such as R, G, B, H, S, V, and L) are selected to be used for the analysis of mineral species. Considering a space where the two or more selected image data are variables (dimensional axes), each pixel within the mineral particle region can be plotted at a specific location in this space based on the value of its image data (hereinafter also referred to as pixel data). In the mineral species analysis described later, the mineral species of each pixel is analyzed based on its position in this space.

[0027] The image information used for analyzing mineral species can be any combination of two or more image data (such as R, G, B, H, S, V, and L). For example, if the light source used for optical microscope images is white light, the S and V values ​​of HSV data may be used. The following explanation will use the case where the S and V values ​​of HSV data are selected as the image information used for analyzing mineral species as an example. In the following, the specific value of S will be referred to as the S value, and the specific value of V will be referred to as the V value. The combination of the S value and the V value will be referred to as SV data.

[0028] When SV data from multiple pixels is plotted in a two-dimensional space (hereinafter also called SV space) with S and V as variables (dimensional axes), pixels of the same mineral species tend to concentrate within a certain range in the SV space (this plot of SV data from multiple pixels in the SV space is called an SV plot). Different mineral species will have different ranges of pixel concentration. Therefore, by looking at the range in which a pixel of interest is located, it is possible to identify which mineral species that pixel corresponds to.

[0029] For example, Figure 2 shows a graph plotting the SV data for the mineral species Chalcopyrite (abbreviated as "Ccp") and Pyrite (abbreviated as "Py"). In the SV plot, it can be clearly seen that the regions where the SV data for the two species are densely concentrated are different.

[0030] (5. Comparative Technologies) Here, we will describe a comparative technique that employs a method different from that of this embodiment. When attempting to identify whether a pixel is Ccp or Py from SV data, one can first consider dividing the region where Ccp and Py SV data are distributed into two, assigning Ccp to one region and Py to the other. By identifying the pixels of SV data in the former region as Ccp and the pixels of SV data in the latter region as Py, it becomes possible to some extent to identify the mineral species for each pixel.

[0031] However, depending on the mineral species, the distribution of SV data may overlap even for different mineral species. As shown in Figure 2, the distribution of SV data for Ccp and Py is not completely separate, and the extent of the distribution (i.e., the area where SV data can exist) partially overlaps. Therefore, even if only Py is present and Ccp is not, if a portion of the SV data of a Py pixel exists in the area assigned to Ccp, that pixel will be identified as Ccp, impairing the accuracy of identification. Consequently, simply dividing the area where SV data is distributed to identify mineral species has limitations in accuracy.

[0032] (6. Determination of mineral species) Therefore, the inventors focused on the differences in the peak positions of the image information distribution. That is, although the distribution of image information for each mineral species often overlaps, the peak positions of each distribution (i.e., the positions where pixel data is concentrated) are different and do not overlap with other mineral species. By utilizing this characteristic, more accurate determination is possible.

[0033] Furthermore, if only one mineral species exists in the population of pixels from which the peak position of the pixel data distribution was determined, only one peak position will appear. On the other hand, if multiple mineral species exist, multiple peak positions corresponding to those mineral species will appear. In this way, by determining the peak position, it is possible to identify the mineral species included in the population of those pixels, and prevent some pixels from being mistakenly identified as other mineral species when only one mineral species actually exists, as in the Py and Ccp example mentioned above.

[0034] To determine the peak position of the pixel data distribution, pixel distribution data is created by plotting the image information values ​​of each pixel in a space where the image information is the variable (dimensional axis) for the pixels to be analyzed in the mineral particle region (third step). The pixels to be analyzed are at least a part of the mineral particle region in question. Then, image information of the peak position of the pixel density is obtained based on this distribution data. Note that the set of pixels from which the peak position is determined may be all pixels contained in one mineral particle region, or it may be a part of the pixels within one mineral particle region. Alternatively, pixels from multiple mineral particle regions may be added together to form a single set. Here, we will explain the case where the peak position is determined using all pixels contained in one mineral particle region as the set of pixels as an example.

[0035] The method for identifying peak positions will be explained using SV data as an example. Distribution data of pixels is obtained by plotting the SV data of each pixel in the mineral particle region in SV space. Using a Gaussian function (normal distribution) as the kernel function, with S and V values ​​as explanatory variables and the number of pixels as the dependent variable, kernel density estimation is performed by setting the bandwidth appropriately, and the S and V values ​​of the peak positions of the pixel density can be obtained, respectively. However, the method for identifying peak positions is not particularly limited; for example, it is also possible to draw a circle in SV space and set the center of the circle that contains the largest number of pixels as the peak position.

[0036] Once the peak position is determined, the next step is to identify the mineral species. Specifically, the peak position of the pixel density in the distribution data is compared with a pre-defined mineral identification region corresponding to each mineral species to determine the mineral species contained in the mineral particle region being analyzed (fourth step). The mineral identification region is the range set in the spatial area of ​​the image information to identify the mineral species based on the peak position described above. In the identification of mineral species for each pixel using the mineral identification region described later, only the mineral identification region of the mineral species determined by comparison with this mineral identification region is used. In other words, this mineral species determination identifies the mineral species of the mineral identification region used in the later identification of mineral species for each pixel, from among the mineral identification regions of multiple mineral species.

[0037] The mineral identification region is defined using multiple mineral particle regions that are known in advance to be a specific mineral species (e.g., Ccp). Specifically, the peak positions of each of these multiple mineral particle regions are determined by the method described above, and the mineral identification region for that specific mineral species is defined to include the multiple peak positions obtained from these multiple mineral particle regions. The mineral identification region is typically a range enclosed by a certain closed shape. This closed shape can be appropriately defined depending on the distribution characteristics of the peak positions and the required accuracy, and may be, for example, circular, elliptical, or rectangular. The shape, position, and size of this closed shape are set to enclose the aforementioned peak positions of each mineral species. However, the peak positions do not necessarily have to be at the geometric center of the mineral identification region.

[0038] Figure 3 shows the mineral identification regions determined for ore containing both Ccp and Py mineral particles. While Figure 2, mentioned earlier, plots the SV data of each pixel contained within a single mineral particle region, Figure 3 plots the peak positions of multiple mineral particle regions after identifying them according to the procedure described above. That is, in Figure 3, each data point represents the peak position calculated for one mineral particle region. Each mineral identification region (shaded area) encloses these peak positions, but is set so as not to overlap with one another.

[0039] As shown in Figure 3, even for the same mineral species, the peak positions of each mineral particle region are not exactly the same, and a distribution with a certain degree of spread can be observed. Therefore, in order to exclude mineral particle regions that cannot be determined, it is necessary to cover these spreads of distribution when setting up a mineral determination region. Although the characteristics of the spread of distribution differ for each mineral species, when setting up a mineral determination region, it is not necessary to plot the peak positions of the SV plots of all mineral particle regions contained in the mineral sample. It is sufficient to plot the peak positions of a sufficient number of mineral particle regions that ensure representativeness in showing the distribution of peak positions for each mineral species. This sufficient number can be set as needed, but for example, it can be set within the range of 30 to 1000.

[0040] Thus, in the SV plot, there may be overlaps in the overall distribution of pixel data for each mineral species (Figure 2), but there is no overlap in the distribution of peak positions between different mineral species (Figure 3). By setting the mineral identification region to cover the distribution of peak positions for each mineral species while avoiding overlaps between different mineral species, it becomes possible to identify mineral species based on peak positions, thus avoiding unidentifiable or overlapping identifications.

[0041] The example in Figure 3 shows Ccp and Py, but the mineral identification area can be set in a similar manner for mineral species other than Ccp or Py. Note that the combination and frequency of mineral species differ from mine to mine, so the mineral identification area may be set for each mine, or it may be set broadly to cover the differences between mines.

[0042] (7. Identification of mineral species) Next, in order to identify the mineral species for each pixel, the image information of each pixel in the mineral particle region is compared with a pre-set mineral identification region for the mineral species determined using the mineral determination region, in a space where the aforementioned image information is used as a variable, and it is identified whether each pixel is a mineral species determined using the mineral determination region (fifth step). The mineral determination region is a region in the image information space set by the method described later.

[0043] The mineral identification region encompasses the corresponding mineral determination region and is wider than the corresponding mineral determination region. The mineral identification region is set by defining the mineral identification region for a specific mineral species so that it includes the distribution range of pixel data for multiple mineral particle regions that are known in advance to be that particular mineral species. As a concrete example, as shown in Figure 4, each mineral identification region is set so as to cover the distribution range of the SV data for Ccp and Py respectively (shaded areas in the upper right and lower right figures).

[0044] As mentioned above, even for different mineral species, there is overlap in the distribution of pixel data, resulting in overlapping mineral identification regions. However, since the mineral species of the mineral particle is determined first based on the mineral determination region, it is determined which mineral species' mineral identification region to use. Specifically, even if some of the SV data is distributed in the overlapping region of the Ccp and Py SV plots described above, if the peak position of the SV plot is observed only in the Py mineral determination region, it can be determined in advance that the mineral particle contains only Py. In this case, the Py mineral identification region is used for identifying the mineral species for each pixel, and the Ccp mineral identification region is not used. Furthermore, by setting the Py mineral identification region to a wide range that includes the overlapping region of the Ccp and Py SV plots, all pixels can be correctly recognized as Py, avoiding the misidentification as Ccp as in the comparison technique described above. Note that, as can be seen in the lower left of Figure 4, not all SV data within a mineral particle region is necessarily included in the mineral determination region, but if it is included in the mineral identification region, these pixel data can identify the corresponding mineral species.

[0045] Furthermore, even when narrowing down mineral species using mineral identification regions, it is possible that in rare cases, the mineral identification regions may partially overlap depending on the mineral species. For example, in the above example, if the peak position of the SV plot is observed in both the Ccp and Py mineral identification regions, identification will be performed using both the Ccp and Py mineral identification regions. Therefore, it is necessary to decide how to handle the pixel data contained within the overlapping region. Arbitrary rules can be established as needed for handling such pixel data. For example, the following methods can be considered. (1) Of the multiple mineral species identified using the mineral identification area, identification is first performed using the mineral identification area of ​​one mineral species, and then using the mineral identification areas of the other mineral species. The order in which each mineral identification area is used can be determined arbitrarily. In this case, pixel data within areas where the mineral identification areas overlap will be identified as the mineral species of the last mineral identification area used, and will not be identified as any other mineral species. (2) The distance between the pixel data in the overlapping mineral identification regions and the peak position of the mineral identification region for each mineral species is calculated, and the mineral species corresponding to the peak position with the shorter distance is identified. (3) Modify the shape of the mineral identification area to avoid overlap. When modifying, it is ideal to include as much as possible the distribution range of the pixel data for the mineral species corresponding to each mineral identification area.

[0046] However, in practice, if the mineral species are narrowed down in advance using the mineral identification region, even if overlap occurs, the pixel data contained within the overlapping region is only a small part of the total data, so arbitrarily deciding how to handle it has little impact on the accuracy of the analysis.

[0047] Since the combination and frequency of mineral species vary from mine to mine, the mineral identification area may be set for each mine, or it may be set to be broader to cover the differences between mines.

[0048] Furthermore, a single mineral particle region may contain two or more mineral species. In such cases, two or more peak positions should be observed within two or more mineral identification regions. Therefore, if the mineral species corresponding to the mineral identification region containing each peak position is known, the mineral species contained in that mineral particle region can be determined.

[0049] (8. Analysis Procedure) The significance of the mineral determination region and the mineral identification region has been explained above. Below, we will show the procedure for analyzing an ore sample using the mineral determination region and the mineral identification region in the analysis method of this embodiment (Figure 5). In this embodiment, determination means identifying the type of mineral species contained in the mineral particle region, and identification means determining, pixel by pixel, whether or not it corresponds to the determined mineral species based on the image information of the mineral particle region.

[0050] First, a single mineral particle region is selected for mineral identification and determination (S101). The mineral particle region is extracted by binarizing the aforementioned optical microscope image.

[0051] Next, an SV plot of the mineral particle region is created, and the peak position is calculated using the method described above. Then, the peak position is compared with the mineral identification region set for known mineral species to confirm which mineral identification region it falls into, and the mineral species is determined (S102).

[0052] Once the mineral species is identified, one pixel is selected from the image information of the mineral particle region in order to perform pixel-by-pixel identification (S103).

[0053] Next, the selected pixels are compared against the mineral identification region corresponding to the mineral species determined in S102 to determine whether the pixels correspond to that mineral species (S104). If the pixel data (SV data in the case of an SV plot) is within the mineral identification region, it is identified as corresponding to that mineral species; otherwise, it is not identified as a mineral.

[0054] Next, it is determined whether the above identification has been completed for all pixels in the mineral particle region. If it has not been completed, the process returns to S103, selects the next pixel, and repeats S103 and S104. If it has been completed, the process proceeds to the next step (S105).

[0055] Next, it is determined whether the above determination and identification have been completed for all mineral particle regions. If not, the process returns to S101, the next mineral particle region is selected, and the above procedure is repeated. If completed, the determination and identification are terminated (S106). In this embodiment, the repeated determination and identification procedure is performed on a single mineral particle region as the unit. However, it is also possible to perform the repeated determination and identification procedure on multiple mineral particle regions as the unit, or to divide a single mineral particle region into multiple subdivided regions and perform the repeated determination and identification procedure on each subdivided region as the unit.

[0056] (9. Determination of mineral species (optional)) As mentioned above, since each pixel in each mineral particle region can identify the corresponding mineral species, the same procedure can be applied to the entire optical microscope image to determine the types of minerals and their respective proportions within the entire analyte. Specifically, the pixels of the entire optical microscope image are analyzed using the method described above to calculate the proportion of pixels corresponding to each mineral species. Since mineral particles usually have an isotropic and average distribution, if this is considered to correspond to the proportion of volume of each mineral species, it becomes possible to calculate the mass proportion based on the density of each mineral species.

[0057] When analyzing optical microscope images by dividing them, the divided images may be combined before performing the quantitative analysis described above. In this way, even when image data is divided and processed and then the data is combined later, data consistency is maintained, and this method can be easily applied to processing high-resolution, wide-field images.

[0058] In other embodiments of the present invention, the ore to be analyzed is not limited to copper concentrate, but can be applied to any color-distinguishable sample. Furthermore, when there are multiple parameters, it is possible to perform the analysis in combination with methods such as principal component analysis. [Examples]

[0059] The following examples illustrate the present invention and its advantages, but the present invention is not limited to these examples.

[0060] (1. Setting of mineral identification area and mineral identification area) First, a copper concentrate ore sample was prepared as the sample to be analyzed. Prior compositional analysis had revealed that this copper concentrate ore sample contained Chalcopyrite (Ccp), Chalcocite (Clc), Covellite (Cv), Bornite (Bn), Pyrite (Py), and Molybdenite (Mo).

[0061] Next, in the SV plot diagram, the mineral determination region and mineral identification region were set for each mineral species. Specifically, following the method described above, the peak positions of the individual particles of the known mineral species Ccp, Clc, Cv, Bn, Py, and Mo are shown in Figure 6. Then, for each mineral species, the mineral determination region and mineral identification region were set as shown in Figure 6.

[0062] (2. Analysis of mineral species) For the above ore samples, a sample embedding resin was prepared using the aforementioned method with a rotational and revolutionary agitator. Specifically, the ore samples were placed in a clear cup together with epoxy resin (EpoCure 2 manufactured by Buehler), and then stirred using a rotational and revolutionary agitator (Awatori Rentaro® manufactured by Thinky Co., Ltd.). After that, the epoxy resin was cured in the air.

[0063] Next, an image of approximately 8 mm × 4 mm was captured using an Olympus DSX500 optical microscope. The obtained optical microscope image was binarized, and only the bright areas were extracted as mineral particle regions (Figure 1). The size of one pixel was 0.92 μm.

[0064] Next, for each of the extracted mineral particle regions, the image information consisting of RGB data at the pixel level was converted to HSV data. Here, an SV plot was created for each extracted mineral particle region after converting from RGB to HSV. For each SV plot, the distribution density was drawn and the peak position was obtained. The obtained peak positions were compared with the mineral identification regions mentioned above to determine the mineral species contained in each mineral particle region.

[0065] Next, based on the aforementioned mineral identification regions, mineral identification was performed for each pixel on the SV plot. By performing these procedures for all mineral particle regions and stitching the results together, the area proportion of pixels corresponding to each mineral species in the overall optical microscope image could be calculated. Converting the area to a weight ratio using the mineral density yielded the composition shown in Table 1.

[0066] [Table 1]

[0067] For comparison, MLA analysis was performed on the same ore sample. The analysis conditions were as follows: Analytical instrument: Thermo Fisher MLA650F EDS detector: Bruker XFlash Detector 5030, 30mm 2 ×2 bottles Measurement mode: GXMAP (Grain X-ray Map)

[0068] Table 2 shows the compositional results obtained from MLA analysis.

[0069] [Table 2]

[0070] Comparing Table 1 and Table 2, the MLA analysis clearly shows a higher weight ratio of Bornite (Bn). However, it was known beforehand that the ore sample in question contained almost no Bn. In other words, the analysis results based on the analytical method of the present invention are closer to the actual results.

[0071] Figure 7 shows why the weight ratio of Bornite (Bn) is high in MLA analysis. Specifically, at the boundaries between Chalcopyrite (Ccp) and Chalcocite (Clc), and between Pyrite (Py) and Chalcocite (Clc), the two signals can mix and be misinterpreted as a Bornite (Bn) signal. On the other hand, in the analysis method of the present invention, if a peak corresponding to Bn is not detected in the mineral identification region, it will not be identified as Bn, thus the accuracy of mineral species identification is much higher. As a result, compared to MLA analysis, Chalcopyrite (Ccp) decreases, Chalcocite (Clc) increases, Pyrite (Py) increases, and Covellite (Cv) also increases, and these results are considered to be closer to reality.

Claims

1. A method for analyzing the mineral species of ore, The first step involves photographing the aforementioned ore with an optical microscope to obtain an optical microscope image containing image information at the pixel level, A second step involves binarizing the optical microscope image to extract the mineral particle region corresponding to the mineral particle, A third step involves creating pixel distribution data by plotting the image information values ​​of each pixel in a space where the image information is used as a variable, for the pixels to be analyzed in the mineral particle region, A fourth step involves comparing the peak position of the pixel density in the distribution data with the mineral identification region for each mineral species, and determining the mineral species included in the analysis target. The fifth step includes comparing the image information of each pixel to be analyzed with the mineral identification region of the mineral species determined in the fourth step, and identifying whether each pixel to be analyzed is of the mineral species determined in the fourth step. A method for analyzing the mineral species of ore, wherein the mineral determination region and the mineral identification region are regions set in a space with the image information as a variable.

2. The method for analyzing the mineral species of ore according to claim 1, wherein the image information used in the third to fifth steps is selected from (R, G, B, H, S, V, L data).

3. The method for analyzing the mineral species of ore according to claim 2, wherein the image information used in the third to fifth steps is S and V data.

4. The method for analyzing the mineral species of ore according to claim 1 or 2, wherein the setting of the mineral determination region is performed by determining the peak position of each of a plurality of mineral particle regions that have been identified as belonging to a specific mineral species, and setting the mineral determination region for the specific mineral species to include the plurality of peak positions obtained from the plurality of mineral particle regions.

5. The mineral identification region is set so as not to overlap between different mineral species, as described in claim 1 or 2, for the mineral species analysis method of ore.

6. The method for analyzing the mineral species of an ore according to claim 1 or 2, wherein the setting of the mineral identification region is performed by setting the mineral identification region of a specific mineral species to include the distribution range of the pixel data of the image information of a plurality of mineral particle regions that have been identified as being of a specific mineral species.

7. Furthermore, the method for analyzing the mineral species of ore according to claim 1 or 2, further comprising the step of calculating the proportion of each mineral species based on the area proportion and density of each identified mineral species after the fifth step.

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