Method for judging skarn copper metallogenic potential based on calcite cathodoluminescence characteristics

Abstract

The application discloses a method for judging skarn copper metallogenic potential based on calcite cathodoluminescence characteristics, and belongs to the technical field of mineral exploration, which comprises the following steps: imaging of ore-forming and non-ore-forming calcite samples in skarn, mapping of the color picture of cathodoluminescence from a three-dimensional color space to a one-dimensional brightness space, gathering of pixels with different brightness of the gray-scale picture to the most suitable gray-scale interval, normalization of the gray-scale interval to 0-1, and distinguishing of different gray-scale value groups by different colors; selection of points on the original sample according to the grouped picture, acquisition of trace element data of each gray-scale group, establishment of a gray-scale, element and Cu mutual relationship expression, and finally feedback to the gray-scale value interval to acquire the Cu metallogenic potential of different gray-scale intervals; the application can efficiently, accurately and intuitively reflect the Cu content range in different luminescence intervals and the corresponding luminescence reasons in actual production, and can significantly reduce the judgment cost.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mineral exploration, and particularly relates to a method for judging skarn copper metallogenic potential based on cathodoluminescence characteristics of calcite. BACKGROUND

[0002] As a very common gangue mineral in hydrothermal deposits, calcite is widely distributed in skarn metallogenic systems. Past research mainly focused on its macro significance as a host rock or altered mineral, and the fine metallogenic information contained in it was not fully explored. In fact, the crystallization and growth of hydrothermal calcite are strictly controlled by the physical and chemical conditions of the ore-forming fluid (such as temperature, pH value, Eh value, fluid composition and evolution process), and these information are recorded in the form of crystal structure defects, trace element content and distribution, and can be directly presented by cathodoluminescence (CL) technology.

[0003] Cathodoluminescence technology can reveal the growth zoning, composition variation and defect structure inside the mineral which are invisible to the naked eye. Previous studies have shown that calcite related to mineralization, due to the fluid rich in specific metal ions (such as Cu, Mn, Fe, REE, etc.) during its formation and the complex water-rock reaction and phase separation process, its cathodoluminescence image often shows unique characteristics, such as specific zoning structure (such as rhythmic zoning, complex oscillation zoning), uneven luminescence intensity and color zoning, and specific luminescence behavior related to the precipitation stage of sulfide. These characteristics are systematically different from calcite formed in barren or barren hydrothermal systems.

[0004] However, current research in this field is mostly limited to qualitative description or case analysis, lacking a systematic and quantitative set of criteria and methods to effectively correlate and predict the cathodoluminescence micro-characteristics of calcite with the macro-regional copper metallogenic potential. It is impossible to quickly determine the cathodoluminescence reason of calcite and the copper content to determine its metallogenic potential just by observing the CL image.

[0005] Currently, to understand the composition of different regions of calcite cathodoluminescence, one can only rely on laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), electron probe microanalysis (EPMA), scanning electron microscopy (SEM), etc. Among them:

[0006] The principle of laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) is that a high-energy laser beam is focused on the sample surface to gasify the micro-area material into aerosol, which is sent into the inductively coupled plasma (ICP) by the carrier gas (usually helium) for complete ionization, and finally the ions are separated and detected by mass spectrometry (MS) according to the mass-to-charge ratio, realizing element and isotope analysis.

[0007] The main limitations of LA-ICP-MS are: (1) the laser will leave micron-level ablation pits on the sample surface, causing permanent and irreversible damage to the sample; (2) the experimental period is long and the cost is high.

[0008] The principle of EPMA is that a focused high-energy electron beam bombards a micro area of the sample to excite the inner layer electrons of the sample atoms to generate characteristic X-rays. By measuring the wavelength (wave spectrometer WDS) and intensity of these X-rays and comparing them with the standard, the accurate quantification of elements is realized.

[0009] The main limitations of EPMA are: (1) due to the high background of X-ray signal, the detection lower limit is usually only 100-300 ppm, which is not suitable for analyzing trace elements (such as rare earth elements and ore-forming indicator elements) with a content lower than this value; (2) the analysis speed is slow: especially when using a wave spectrometer (WDS), each element and each point needs to be measured, which takes a long time;

[0010] The principle of SEM is that a high-energy electron beam performs raster scanning on the sample surface to excite multiple signals. The most commonly used are secondary electrons (used to observe the surface topography) and backscattered electrons (used to observe the composition difference, the higher the atomic number). An energy spectrometer (EDS) is usually equipped for element qualitative or semi-quantitative analysis.

[0011] The main limitations of SEM are: (1) the EDS quantitative analysis precision is low; (2) the typical detection limit of EDS is between 0.1% and 1%, and microelements cannot be analyzed; (3) non-conductive samples need to be coated with a layer of conductive film (such as gold and carbon), which is more troublesome to prepare and may interfere with the observation and analysis of the original composition of the surface.

[0012] The above methods have the following shortcomings in the present stage of discrimination of skarn copper mineralization potential:

[0013] (1) the analysis cost and threshold of calcite are high;

[0014] (2) the process is complex, the analysis period is long, and it is difficult to apply quickly. SUMMARY

[0015] The purpose of the present application is to provide a method for discriminating skarn copper mineralization potential based on the cathodoluminescence characteristics of calcite, in order to solve the above problems.

[0016] In order to achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0017] A method for discriminating skarn copper mineralization potential based on the cathodoluminescence characteristics of calcite, comprising the following steps:

[0018] (1) take skarn ore-forming and non-ore-forming calcite samples and perform imaging; then map the cathodoluminescence color picture from three-dimensional color space to one-dimensional brightness space;

[0019] (2) divide the gray scale interval according to the cathodoluminescence color, normalize to the range of 0-1, obtain the gray value, and identify each group with different colors;

[0020] (3) select points on the original sample according to the grouped picture, and then obtain the trace element data of each gray scale group;

[0021] (4) statistically analyze the trace element data, clarify the influence of the molar ratio of Mn / Fe on the cathodoluminescence intensity, establish the interactive relationship model of the molar ratio of Mn / Fe, the gray value and the Cu molar content, and correlate it to the gray scale interval;

[0022] (5) output the Cu molar content range of each gray scale interval and the cathodoluminescence reason analysis.

[0023] The cathodoluminescence of calcite is affected by various reasons. In the present application, according to the analysis and observation of laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) data, it is found that the cathodoluminescence of calcite is affected by the molar ratio of Mn / Fe. A large range of calcite with Mn / Fe ratio below 0.35 basically does not emit light, and calcite with Mn / Fe ratio above 0.35 emits light or strong light. The principle is that Mn 2+ Fe acts as an excitation agent to promote the cathodoluminescence of calcite 2+ Fe acts as a quencher to inhibit the cathodoluminescence of calcite, and the Fe content of calcite that does not emit light is between (3000-4000) ppm. The inventors have proved through a large amount of theoretical research and a large number of experiments that by correlating the molar ratio of Mn / Fe, the gray value and the Cu element content, the Cu mineralization potential can be judged by the gray scale interval.

[0024] As a preferred technical solution,

[0025] In step (1), a micro cathode instrument is used for imaging, which is commercially available, such as YINUO-CL micro cathode instrument;

[0026] The method for mapping the cathodoluminescence color picture from three-dimensional color space to one-dimensional brightness space is through a Python gray processing program. The Python gray processing program, which can be understood by those skilled in the art, belongs to the prior known technology.

[0027] As a preferred technical solution,

[0028] In step (2), the specific method of the obtained gray scale interval is: through at least ten different styles of cathodoluminescence pictures, performing gray processing, comparing the obtained gray scale pictures with the cathodoluminescence pictures, classifying the pixels in the same brightness in the gray scale pictures into a class, and dividing the gray scale value interval, and using different colors to represent the gray scale groups respectively.

[0029] Further preferably, the gray scale groups are: black: 0.00-0.10, gray: 0.10-0.27, red: 0.27-0.65, purple: 0.65-0.9, and white: 0.9-1.

[0030] As a preferred technical solution,

[0031] In step (3), the method for obtaining the trace element data of each gray scale group is through a laser ablation inductively coupled plasma mass spectrometry experiment, i.e., LA-ICP-MS.

[0032] As a preferred technical solution,

[0033] In step (4), the method for establishing the mathematical expression of the Mn / Fe molar ratio, the gray scale value, and the Cu molar content is:

[0034] First, the trace data is cleaned to leave the elements and metal elements Cu that affect cathodoluminescence, and the data outliers are removed (wherein the outliers refer to: the metal element content is abnormally high compared to other data points, more than 20 times the average value), the gray scale value obtained in step (2) is added to the data, and then the molar ratio of Mn to Fe is calculated and added to the data, to establish a multiple regression model mathematical expression with an interaction term based on the molar ratio of Mn to Fe, the gray scale value, and the Cu molar content.

[0035] As a further preferred technical solution, the gray scale value is a range, and the conversion method is: taking the middle value of the gray scale value as a fixed value for calculation.

[0036] As a further preferred technical solution,

[0037] The multiple regression model mathematical expression with an interaction term based on the molar ratio of Mn to Fe, the gray scale value, and the Cu molar content is:

[0038] Cu%=8.4377-15.3568×hd+7.5338×(Mn / Fe)+14.7916×hd×(Mn / Fe),

[0039] Wherein, Cu% is the molar percentage content of Cu, hd is the gray scale value, and Mn / Fe is the molar ratio of Mn to Fe.

[0040] The application can establish a method for revealing the Cu metallogenic potential in the area of different colors and intensities of calcite cathodoluminescence by analyzing the characteristics of calcite cathodoluminescence.

[0041] The application adopts cathodoluminescence technology, laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), python programming and machine learning methods, establishes a relatively simple mathematical model and calculation method, and uses the machine learning method to establish a multivariate cross-band relationship.

[0042] Compared with the prior art, the application has the following advantages:

[0043] (1) objectivity and high efficiency: based on image gray processing and machine learning model, the application realizes semi-automatic discrimination from cathodoluminescence image to copper metallogenic potential, reduces the dependence on subjective experience, and has high objectivity; compared with traditional point-by-point microanalysis, the application can quickly scan the whole sample and grade the potential, without a large amount of data calculation, and the analysis efficiency is improved by about 70%;

[0044] (2) significantly reduce the discrimination cost: in order to obtain comprehensive composition information, the traditional method (such as LA-ICP-MS) needs to arrange 10-15 analysis points on the sample, and the cost of single point analysis is high; and the method of the application only needs to select a small number of verification points (usually only 5-10 points) in the representative interval, so as to establish a reliable correlation model; under the premise of ensuring the discrimination accuracy, the total analysis cost of single sample can be reduced by 50%-80%;

[0045] (3) simplify the process and shorten the analysis period: the application integrates image processing and quantitative model, simplifies the complex microanalysis requirement to the interpretation of cathodoluminescence image and a small amount of key point verification, so that the whole analysis process is shortened from several days required by the traditional method to several hours (the main time-consuming is in CL imaging and a small amount of LA-ICP-MS verification), which is convenient for rapid application in preliminary evaluation of field exploration;

[0046] (4) directly reveal the metallogenic information: the application directly reveals the metallogenic information by the corresponding relationship between the gray interval and the Cu content range, as well as the quantitative model of Mn / Fe ratio, gray value and Cu content, which converts the cathodoluminescence characteristics difficult to distinguish by naked eye into an intuitive metallogenic potential map, and provides a powerful decision support tool for geologists. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 The gray scale image obtained in the embodiment is shown in the figure;

[0048] Figure 2 The color grouping result map according to geology and mineralogy in the embodiment is shown in the figure;

[0049] Figure 3 Fig. 1-6 is a schematic diagram of the first-6 points selected from pictures of skarn samples of different drill holes and depths in a certain plateau area;

[0050] Figure 4 Fig. 6-9 is a schematic diagram of the 6-9 points selected from pictures of skarn samples of different drill holes and depths in a certain plateau area;

[0051] Figure 5 Fig. 10 is a discrimination result diagram of skarn samples of different drill holes and depths in a certain plateau area (this diagram is a sample whose cathodoluminescence of calcite is red due to Mn and Fe content, and shows gray and red in the gray scale interval).

[0052] Figure 6 Fig. 11 is a discrimination result diagram of skarn samples of different drill holes and depths in a certain plateau area (this diagram is a sample whose cathodoluminescence of calcite is yellow due to rare earth elements, and is purple in the gray scale interval). DETAILED DESCRIPTION

[0053] In order to explain the technical content, the purpose and the effect of the present application in detail, the content of the present application will be further explained in combination with specific examples below, but the content of the present application is far more than the following examples.

[0054] It should be noted that the materials and instruments used in the following examples are commercially available unless otherwise specified.

[0055] Example 1

[0056] A method for discriminating copper mineralization potential based on the cathodoluminescence characteristics of calcite, which comprises the following steps in turn:

[0057] (1) Taking ore-forming and non-ore-forming calcite samples, imaging by YINUO-CL micro-cathode instrument; mapping the cathodoluminescence color picture from three-dimensional color space to one-dimensional brightness space by Python gray processing program;

[0058] The color picture will have some errors due to the influence of various external environments during shooting. In this embodiment, the picture brightness is unified when the gray scale image is obtained, so as to eliminate errors to the greatest extent. The gray scale image is as shown in Figure 1

[0059] (2) Dividing the gray scale interval according to the color of cathodoluminescence, normalizing to the range of 0-1, and marking each group with different colors;

[0060] The range of 0-1 is because there are 256 gray levels of gray value in the computer, which is converted to 0-1 for convenience of calculation. In this embodiment, the gray scale grouping is as shown in Figure 2 Figure 2

[0061] ​​​Group 0: metal ore (black) gray scale range: 0.00-0.10; RGB color: (0, 0, 0); pixel number: 6378137 (32.1%); geological characteristics: metal ore or high Fe quenching area Group 1: Mn / Fe low ratio phase (gray) gray scale range: 0.10-0.27; RGB color: (127, 127, 127); pixel number: 8424702 (42.5%); geological characteristics: Mn / Fe ratio less than 0.35, Fe content 3000-4000 ppm / 35 μm, Cu content 3.03-3.77 ppm / 35 μm, medium Cu mineralization potential; there are a large number of purple zone intervals, indicating that the Mn / Fe ratio is mostly between 0.4-0.8 affected by rare earth elements; Cu mineralization potential is small. Group 2: Mn / Fe high ratio phase (red) gray scale range: 0.27-0.65; RGB color: (255, 0, 0); pixel number: 4985839 (25.1%); geological characteristics: Mn / Fe ratio greater than 0.35, Cu content 6.75-8.65 ppm / 35 μm, Fe content less than 3000 ppm / 35 μm; Cu mineralization potential is large. Group 3: medium Mn / Fe ratio phase (purple) gray scale range: 0.65-0.90; RGB color: (127, 0, 127); pixel number: 49066 (0.2%); geological characteristics: Mn / Fe ratio 0.6-0.8, Fe content 300-500 ppm / 35 μm, REE content 0.13-0.18 ppm / 35 μm, Cu mineralization potential is small. Group 4: sample abnormal area (white) gray scale range: 0.90-1.00; RGB color: (255, 255, 255); pixel number: 7376 (0.0%); geological characteristics: may be sample preparation damage, uneven surface or special mineral phase;

[0062] Black ((0.00-0.10), gray (0.10-0.27), red (0.27-0.65), purple (0.65-0.90), white (0.90-1).

[0063] (3) According to the grouping picture, points are selected on the original sample, and microelement data of each gray scale group is obtained through LA-ICP-MS experiment;

[0064] The LA-ICP-MS is combined with Agilent 8900 quadrupole inductively coupled plasma mass spectrometer (ICP-MS) using NewWaveResearch 193 nm ArF excimer laser ablation system. The specific method is as follows: the deep ultraviolet light beam generated by the excimer laser generator is focused on the sample surface through the homogenization light path, the laser beam spot diameter is 35 µm, the frequency is 6 Hz, the energy density is 3.5 J / cm 2Helium was used as carrier gas and argon as compensation gas to adjust the sensitivity during the laser ablation process.

[0065] (4) Statistical analysis of trace element data, clear Mn / Fe ratio on the intensity of cathodoluminescence, establish the relationship model of Mn / Fe molar ratio, gray value and Cu molar content, and related to the gray interval;

[0066] (5) Output of Cu content range and cathodoluminescence reason analysis in each gray interval.

[0067] Discrimination examples:

[0068] In order to have a clearer effect, take some skarn samples from different drill holes and depths in a plateau area for cathodoluminescence experiment, and then obtain color images of different gray value intervals according to the gray program, select points according to different color intervals, and then perform laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) experiment, and the selected points and data are as shown in Figure 3 and Figure 4 and Table 1 as follows:

[0069] Table 1 Experimental data table

[0070] Sample No. Mn (ppm) Fe (ppm) Cu (ppm) Mn / Fe (ppm) ZK1519-364.24-1 1334.010219 1163.592561 6.797770682 1.146458188 ZK1519 364.24-2 1184.755567 616.8117222 5.636227073 1.92077343 ZK1519-364.24-3 1334.010219 1163.592561 6.797770682 1.146458188 ZK1519-364.24-4 1297.352643 471.4990976 6.746633265 2.751548519 ZK1519-364.24-5 1163.05783 5279.229108 4.440088825 0.22030827 ZK1519-364.24-6 2294.673433 11163.37407 4.548745857 0.205553753 ZK4102-708.65-6 284.1432253 628.4107257 0.157626208 0.452161642 ZK4102-708.65-7 250.8632632 380.5918313 0 0.659139904 ZK4102-708.65-8 257.1238715 416.3618741 0.413617043 0.61754903 ZK4102-708.65-9 252.5965827 376.5590981 0.237526947 0.670801964

[0071] According to the results obtained by the above method, the following results are obtained:

[0072] The selected points correspond to red, purple and gray, and the results of the first sample are consistent with the data in Table 1. Since the second sample has a large number of purple intervals, the Mn and Fe contents are low in the gray interval of 0.10-0.27, and the Mn / Fe ratio is between 0.4 and 0.8. The measured samples are consistent with the data of laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) experiment.

[0073] According to the output results in Figure 5 and Figure 6 , the following results are obtained:

[0074] Group 0: metallic ore (black),

[0075] Gray range: 0.00-0.10; RGB color: (0, 0, 0); Pixel number: 266321 (1.3%); Geological characteristics: metallic ore or high Fe quenching area.

[0076] Group 1: Mn / Fe low ratio phase (gray),

[0077] Gray scale range: 0.10-0.27; RGB color: (127, 127, 127); Pixel number: 12919630 (65.1%); Geological characteristics: Mn / Fe ratio less than 0.35, Fe content 3000-4000 ppm / 35 μm, Cu content 3.03-3.77 ppm / 35 μm, Cu metallogenic potential moderate;

[0078] Purple zone interval appears in large quantities, which indicates that the Mn / Fe ratio is mostly between 0.4-0.8 due to the influence of rare earth elements; Cu metallogenic potential is small.

[0079] Group 2: Mn / Fe high ratio phase (red),

[0080] Gray scale range: 0.27-0.65; RGB color: (255, 0, 0); Pixel number: 4302804 (21.7%); Geological characteristics: Mn / Fe ratio greater than 0.35, Cu content 6.75-8.65 ppm / 35 μm, Fe content less than 3000 ppm / 35 μm; Cu metallogenic potential is large.

[0081] Group 3: moderate Mn / Fe ratio phase (purple),

[0082] Gray scale range: 0.65-0.90; RGB color: (127, 0, 127); Pixel number: 2355691 (11.9%); Geological characteristics: Mn / Fe ratio 0.6-0.8, Fe content 300-500 ppm / 35 μm, REE content 0.13-0.18 ppm / 35 μm, Cu metallogenic potential is small.

[0083] Group 4: sample abnormal area (white),

[0084] Gray scale range: 0.90-1.00; RGB color: (255, 255, 255); Pixel number: 674 (0.0%); Geological characteristics: It may be that the sample preparation is damaged, the surface is uneven or special mineral phase.

[0085] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for identifying the potential of skarn copper mineralization based on the characteristics of calcite cathodoluminescence, characterized in that, The method comprises the following steps: (1) taking skarn ore-forming and non-ore-forming calcite samples and imaging; then mapping the cathodoluminescence color picture from a three-dimensional color space to a one-dimensional brightness space; (2) dividing the gray scale interval according to the cathodoluminescence color, normalizing to the range of 0-1 to obtain the gray scale value, and identifying each group with different colors; (3) selecting points on the original sample according to the grouped picture, and then obtaining the trace element data of each gray scale group; (4) statistically analyzing the trace element data, determining the influence of the molar ratio of Mn / Fe on the cathodoluminescence intensity, establishing the interactive relationship model of the molar ratio of Mn / Fe, the gray scale value and the Cu molar content, and correlating to the gray scale interval; (5) outputting the Cu molar content range of each gray scale interval and the cathodoluminescence reason analysis, wherein, In step (4), the method for establishing the interactive relationship model expression of the molar ratio of Mn / Fe, the gray scale value and the Cu molar content is: First, data cleaning is performed on the trace data, leaving the elements and metal element Cu that affect cathodoluminescence and eliminating data outliers, the gray scale value obtained in step (2) is added to the data, and then the molar ratio of Mn and Fe is calculated and added to the data, to establish a multiple regression model mathematical expression with interactive terms based on Cu, gray scale value, and molar ratio of Mn / Fe; The multiple regression model mathematical expression with interactive terms based on the molar ratio of Mn / Fe, the gray scale value and the Cu molar content is: Cu%=8.4377-15.3568×hd+7.5338×(Mn / Fe)+14.7916×hd×(Mn / Fe), wherein Cu% is the molar percentage content of Cu, hd is the gray scale value, and Mn / Fe is the molar ratio of Mn and Fe.

2. The method of claim 1, wherein In step (1), the imaging is performed using a micro cathodoluminescence instrument; The method for mapping the cathodoluminescence color picture from a three-dimensional color space to a one-dimensional brightness space is performed by a Python gray scale processing program.

3. The method of claim 1, wherein In step (2), the specific method for obtaining the gray scale interval is: through at least ten different styles of cathodoluminescence pictures, gray scale processing is performed, the obtained gray scale picture is compared with the cathodoluminescence picture, the same brightness pixels in the gray scale picture are classified into a class, the gray scale value interval is divided, and different colors are used to represent these gray scale groups.

4. The method of claim 3, wherein The gray scale groups are: black: 0.00-0.10, gray: 0.10-0.27, red: 0.27-0.65, purple: 0.65-0.9, and white: 0.9-1.

5. The method of claim 1, wherein In step (3), the method for obtaining the trace element data of each gray scale group is by laser ablation inductively coupled plasma mass spectrometry experiment.

6. The method of claim 1, wherein The gray scale value is a range, and the conversion method is to take the middle value of the gray scale value as a fixed value for calculation.

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