Method for judging skarn copper mineralization potential based on calcite cathode luminescence characteristics

Abstract

The invention discloses a method for judging mineralization potential of skarn copper based on cathode luminescence characteristics of calcite, which belongs to the technical field of mineral exploration and comprises the following steps of: imaging mineralization and non-mineralization calcite samples in skarn, mapping a cathode luminescence color picture from a three-dimensional color space to a one-dimensional brightness space, and calculating the brightness of the cathode luminescence color picture; pixels with different brightness of the gray scale picture are gathered to the most appropriate gray scale interval, the gray scale intervals are normalized to 0-1, and different gray scale value groups are distinguished through different colors; selecting points on an original sample according to grouped pictures, obtaining trace element data of each gray scale group, establishing a correlation expression of gray scales, elements and Cu, and finally feeding back the correlation expression to gray scale value intervals to obtain Cu mineralization potentials of different gray scale intervals; according to the method, Cu content ranges and corresponding luminescence reasons in different luminescence intervals can be efficiently, accurately and intuitively reflected in actual production, and the judgment cost can be remarkably reduced.

Description

Technical Field

[0001] This invention relates to the field of mineral exploration technology, and in particular to a method for determining the copper mineralization potential of skarn based on the cathodoluminescence characteristics of calcite. Background Technology

[0002] Calcite, a very common gangue mineral in hydrothermal deposits, is widely distributed in skarn mineralization systems. Past research has primarily focused on its macroscopic significance as a host rock or alteration mineral, neglecting to explore the finer mineralization information it contains. In fact, the crystallization and growth of hydrothermal calcite are strictly controlled by the physicochemical conditions of the ore-forming fluids (such as temperature, pH, Eh value, fluid composition, and evolutionary processes). This information is "recorded" by its crystal structure defects, trace element content, and distribution patterns, and can be visually presented through cathodoluminescence (CL) technology.

[0003] Cathodoluminescence technology can reveal growth zoning, compositional variations, and defect structures within minerals that are invisible to the naked eye. Existing research indicates that calcite associated with mineralization often exhibits unique characteristics in its cathodoluminescence images due to the fluids rich in specific metal ions (such as Cu, Mn, Fe, and REE) during its formation and the complex water-rock reactions and phase separation processes. These characteristics include specific zoning structures (such as rhythmic zoning and complex oscillating zoning), non-uniform luminescence intensity and color zoning, and specific luminescent behavior associated with sulfide precipitation stages. These features systematically differ from calcite formed in low-ore or mineral-free hydrothermal systems.

[0004] However, current research in this field largely remains at the level of qualitative description or case analysis, lacking a systematic and quantitative set of criteria and methods to effectively correlate and predict the cathodoluminescence microscopic characteristics of calcite with the copper mineralization potential of macroscopic regions. It is impossible to quickly determine the cause of cathodoluminescence and Cu content in calcite and assess its mineralization potential solely by observing CL images.

[0005] Currently, understanding the composition of different regions in the cathodoluminescence of calcite relies solely on laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), electron probe microanalysis (EPMA), and scanning electron microscopy (SEM). Among these methods: The principle of laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) is as follows: a high-energy laser beam is focused on the sample surface, causing the micro-area material to vaporize into an aerosol, which is then sent into an inductively coupled plasma (ICP) by a carrier gas (usually helium) to be completely ionized. Finally, mass spectrometry (MS) is used to separate and detect ions according to their mass-to-charge ratio, thereby achieving elemental and isotope analysis.

[0006] The main limitations of LA-ICP-MS are: (1) the laser will leave micron-sized etch pits on the sample surface, causing permanent and irreversible damage to the sample; (2) the experiment cycle is long and expensive.

[0007] The principle of electron probe microanalysis (EPMA) is as follows: a focused high-energy electron beam bombards a micro-region of the sample, exciting the inner-shell electrons of the sample atoms to generate characteristic X-rays. By measuring the wavelength (WDS) and intensity of these X-rays and comparing them with standards, precise elemental quantification can be achieved.

[0008] The main limitations of EPMA are: (1) Due to the high background of X-ray signals, its detection limit is usually only 100-300 ppm, which is not suitable for analyzing trace elements with contents below this value (such as rare earth elements and mineralization indicator elements); (2) The analysis speed is slow: especially when using a wave spectrometer (WDS), it is necessary to measure each element and each point, which takes a long time. The principle of scanning electron microscopy (SEM) is as follows: a high-energy electron beam performs a grating scan on the sample surface, exciting and generating various signals. The most commonly used are secondary electrons (used to observe surface morphology) and backscattered electrons (used to observe compositional differences, with brighter signals indicating higher atomic numbers). It is usually equipped with an energy dispersive spectroscopy (EDS) instrument for qualitative or semi-quantitative elemental analysis.

[0009] The main limitations of SEM are: (1) EDS quantitative analysis has low precision; (2) The typical detection limit of EDS is between 0.1% and 1%, which cannot analyze trace elements; (3) Non-conductive samples need to be coated with a conductive film (such as gold or carbon), which is more troublesome to prepare and may interfere with the observation and analysis of the original surface components.

[0010] Based on the above methods, the current method for determining the mineralization potential of calcite Cu has the following drawbacks: (1) The cost and threshold of quartz analysis are relatively high; (2) The process is complex and the analysis cycle is long, making it difficult to apply quickly. Summary of the Invention

[0011] The purpose of this invention is to provide a method for determining the copper mineralization potential of skarn based on the cathodoluminescence characteristics of calcite, so as to solve the above-mentioned problems.

[0012] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for determining the copper mineralization potential of skarn based on the cathodoluminescence characteristics of calcite includes the following steps: (1) Take skarn mineralized and non-mineralized calcite samples and image them; then map the cathodoluminescent color images from the three-dimensional color space to the one-dimensional brightness space; (2) Divide the grayscale range according to the color of the cathode emission, normalize it to the range of 0 to 1, obtain the grayscale value, and identify each group with different colors; (3) Select points on the original sample based on the grouped images, and then obtain the trace element data of each gray group; (4) Statistically analyze the trace element data, clarify the influence of the molar ratio of Mn / Fe on the cathodic luminescence intensity, establish an interaction model of the molar ratio of Mn / Fe, gray value and Cu molar content, and associate it with the gray range; (5) Output the range of Cu molar content in each grayscale range and analyze the reasons for cathodic luminescence.

[0013] Calcite cathodoluminescence is affected by various factors. In this invention, based on the analysis and observation of laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) data, it was found that calcite cathodoluminescence is influenced by the Mn / Fe molar ratio. Generally, calcite with an Mn / Fe ratio below 0.35 does not emit light, while calcite with a ratio above 0.35 emits light or strong light. The principle behind this is that Mn... 2+ Fe acts as an activator to promote luminescence at the calcite cathode. 2+ As a quenching agent to suppress cathodic luminescence of calcite, the Fe content of calcite that does not emit light is between 3000 and 4000 ppm. The inventors have demonstrated through extensive theoretical research and numerous experiments that by correlating the Mn / Fe molar ratio, gray value, and Cu element content, the mineralization potential of Cu can be determined by the gray value range.

[0014] As a preferred technical solution In step (1), imaging is performed using a micro-area cathode instrument, which is commercially available, such as the YINUO-CL micro-area cathode instrument. The method for mapping a cathode-emitting color image from a three-dimensional color space to a one-dimensional brightness space is performed using a Python grayscale processing program. This Python grayscale processing program is understood by those skilled in the art and is considered prior art.

[0015] As a preferred technical solution In step (2), the specific method for obtaining the grayscale range is as follows: perform grayscale processing on at least ten different styles of cathodic emission images, compare the obtained grayscale images with the cathodic emission images, classify the pixels with the same brightness in the grayscale images into one category, divide the grayscale value range, and use different colors to represent these grayscale groups respectively.

[0016] More preferably, the grayscale grouping is as follows: black: 0.00-0.10, gray: 0.10-0.27, red: 0.27-0.65, purple: 0.65-0.9, white: 0.9-1.

[0017] As a preferred technical solution In step (3), the method for obtaining trace element data for each grayscale group is by laser ablation inductively coupled plasma mass spectrometry, i.e., LA-ICP-MS.

[0018] As a preferred technical solution In step (4), the method for establishing the mathematical expressions for the Mn / Fe molar ratio, gray value, and Cu molar content is as follows: First, the trace data is cleaned to retain the elements that affect cathode luminescence and the metal element Cu, and outliers are removed (wherein, the outlier refers to the metal element content being abnormally high compared to other data points, more than 20 times the average value). The gray 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. A mathematical expression for a multiple regression model with interaction terms is established based on the Mn / Fe molar ratio, gray value and Cu molar content.

[0019] As a further preferred technical solution, the grayscale value is a range, and the conversion method is: take the median value of the grayscale value as a fixed value for calculation.

[0020] As a further preferred technical solution The mathematical expression for the multiple regression model with interaction terms established using the Mn / Fe molar ratio, gray value, and Cu molar content is as follows: Cu%=8.4377-15.3568×hd+7.5338×(Mn / Fe)+14.7916×hd×(Mn / Fe), Where Cu% is the molar percentage of Cu, hd is the gray value, and Mn / Fe is the molar ratio of Mn to Fe.

[0021] This invention, through the analysis of the cathodic emission characteristics of calcite, can establish a more intuitive method to reveal the mineralization potential of Cu in regions with different colors and intensities of cathodic emission from calcite.

[0022] This invention employs cathodic emission technology, laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), Python programming, and machine learning to establish a relatively simple mathematical model and calculation method, and uses machine learning to establish a multivariate cross-banding relationship.

[0023] Compared with the prior art, the advantages of the present invention are as follows: (1) Objectivity and efficiency: Based on image grayscale processing and machine learning models, this invention realizes semi-automatic discrimination from cathodoluminescence images to copper mineralization potential, reducing reliance on subjective experience and achieving high objectivity; compared with traditional point-by-point micro-analysis, it can quickly perform regional scanning and potential classification of the entire sample without requiring a large amount of data calculation, improving analysis efficiency by about 70%; (2) Significantly reduced discrimination cost: Traditional methods (such as LA-ICP-MS) require 10 to 15 analytical points on the sample to obtain comprehensive component information, resulting in high cost per point analysis; while the method of this invention pre-divides grayscale intervals, requiring only a small number of verification points (usually only 5 to 10 points) in representative intervals to establish a reliable correlation model; while ensuring discrimination accuracy, the overall analysis cost of a single sample can be reduced by 50% to 80%; (3) Simplified process and short analysis cycle: This invention integrates image processing and quantitative model, simplifying the complex micro-analysis requirements to the interpretation of cathodoluminescence images and verification of a small number of key points, reducing the entire analysis process from several days to several hours (mainly time is spent on CL imaging and a small amount of LA-ICP-MS verification), making it easy to quickly apply to the preliminary evaluation of field exploration. (4) Intuitively revealing mineralization information: This invention transforms the cathodic emission characteristics that are difficult to distinguish with the naked eye into an intuitive mineralization potential map by using the clear correspondence between gray range and Cu content range, as well as the quantitative model of Mn / Fe ratio, gray value and Cu content, providing geologists with a powerful decision support tool. Attached Figure Description

[0024] Figure 1 The grayscale image obtained in the example; Figure 2 This is a diagram showing the results of color-grouping based on geological and mineralogical principles in the examples; Figure 3 This is a schematic diagram of the first to sixth points selected from skarn samples taken at different borehole depths in a plateau region. Figure 4 A schematic diagram of points 6-9 selected from skarn samples at different borehole depths in a plateau region; Figure 5 The image shows the discrimination results of skarn samples from different boreholes and depths in a plateau region (this image shows the sample where calcite emits red cathodic emission due to the influence of Mn and Fe content, and appears as gray and red in the grayscale range). Figure 6 This image shows the discrimination results of skarn samples from different boreholes and depths in a plateau region (this image shows a sample where calcite emits yellow cathodic luminescence due to the influence of rare earth elements, and appears purple in the grayscale range). Detailed Implementation

[0025] To explain the technical content, objectives, and effects of the present invention in detail, the following specific embodiments are provided to further illustrate the content of the present invention. However, the content of the present invention is far more than the following examples.

[0026] It should be noted that, unless otherwise specified, all materials and instruments used in the following embodiments are commercially available.

[0027] Example 1 A method for determining copper mineralization potential based on cathodoluminescence characteristics of calcite includes the following steps: (1) Take mineralized and non-mineralized calcite samples and image them using a YINUO-CL micro-area cathode ray tube; use Python grayscale processing program to map the cathode luminescence color images from the three-dimensional color space to the one-dimensional brightness space; The color images may contain errors due to various external environmental factors during shooting. In this embodiment, when acquiring grayscale images, the image brightness is standardized to minimize these errors. The grayscale image is as follows: Figure 1 As shown; (2) Divide the grayscale range according to the color of the cathode emission, normalize it to the range of 0 to 1, and use different colors to identify each group; The range of 0 to 1 is because there are 256 gray levels in a computer, and the values ​​are converted to 0 to 1 for easier calculation. In this embodiment, the gray level grouping is as follows: Figure 2 As shown, Figure 2 middle: Group 0: Metallic ore (black) Grayscale range: 0.00-0.10; RGB color: (0, 0, 0); Number of pixels: 6,378,137 (32.1%); Geological characteristics: Metallic ore or high-Fe quenched area. Group 1: Low Mn / Fe ratio phase (gray) Grayscale range: 0.10-0.27; RGB color: (127, 127, 127); Number of pixels: 8,424,702 (42.5%); Geological characteristics: Mn / Fe ratio less than 0.35, Fe content 3000-4000ppm / 35μm, Cu content 3.03-3.77ppm / 35μm, medium Cu mineralization potential; The presence of a large number of purple areas indicates that the Mn / Fe ratio is mostly between 0.4 and 0.8 due to the influence of rare earth elements; low Cu mineralization potential. Group 2: High Mn / Fe ratio phase (red); grayscale range: 0.27-0.65; RGB color: (255, 0, 0); number of pixels: 4,985,839 (25.1%); geological characteristics: Mn / Fe ratio greater than 0.35, Cu content 6.75-8.65 ppm / 35μm, Fe content less than 3000 ppm / 35μm; high Cu mineralization potential. Group 3: Medium Mn / Fe ratio phase (purple); grayscale range: 0.65-0.90; RGB color: (127, 0, 127); number of pixels: 49,066 (0.2%); geological characteristics: Mn / Fe ratio 0.6-0.8, Fe content 300-500 ppm / 35μm, REE content 0.13-0.18 ppm / 35μm; low Cu mineralization potential. Group 4: Sample anomaly area (white) Grayscale range: 0.90-1.00; RGB color: (255, 255, 255); Number of pixels: 7376 (0.0%); Geological characteristics: may be due to sample preparation damage, uneven surface, or special mineral phases; Black (0.00-0.10), Gray (0.10-0.27), Red (0.27-0.65), Purple (0.65-0.90), White (0.90-1).

[0028] (3) Select points on the original sample based on the grouped images, and obtain the trace element data of each gray group by LA-ICP-MS experiment; The LA-ICP-MS was performed using a NewWave Research 193nm ArF excimer laser ablation system coupled with an Agilent 8900 quadrupole inductively coupled plasma mass spectrometer (ICP-MS). Specifically, a deep ultraviolet beam generated by the excimer laser generator was focused onto the sample surface through a homogenizing optical path. The laser beam diameter was 35µm, the frequency was 6Hz, and the energy density was 3.5J / cm². 2 Helium is used as the carrier gas and argon as the compensating gas during laser ablation to adjust the sensitivity. (4) Statistically analyze the trace element data, clarify the influence of the Mn / Fe ratio on the cathodic luminescence intensity, establish an interaction model of the Mn / Fe molar ratio, gray value and Cu molar content, and link it to the gray range; (5) Output the Cu content range of each grayscale interval and analyze the reasons for cathodic luminescence.

[0029] Discrimination example: To demonstrate the results more clearly, cathodoluminescence experiments were conducted on skarn samples from different boreholes and depths in a plateau region. Color images of different grayscale ranges were then obtained using a grayscale program. Based on the different color ranges, points were selected, and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) experiments were performed. The selected points and data are shown below. Figure 3 and Figure 4 And as shown in Table 1 below: Table 1 Experimental Data Sample number Mn (ppm) Fe (ppm) Cu (ppm) Mn / Fe (ppm) ZK1519-364.24-1 1334.010219 1163.592561 6.797770682 1.146458188 ZK1519364.24-2 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 The results obtained using the above method are as follows: The selected points correspond to red, purple, and gray. The results obtained for the first sample are consistent with the data in Table 1. Since the purple range appears more frequently in the second sample, the Mn and Fe contents are low in the gray range of 0.10 to 0.27, and the Mn / Fe ratio is between 0.4 and 0.8. All the measured samples are consistent with the experimental data of laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS).

[0030] according to Figure 5 and Figure 6 The output results are as follows: Group 0: Metallic ore (black), Grayscale range: 0.00~0.10; RGB color: (0, 0, 0); Number of pixels: 266321 (1.3%); Geological characteristics: metallic ore or high-Fe quenched area.

[0031] Group 1: Low Mn / Fe ratio phase (gray), Grayscale range: 0.10~0.27; RGB color: (127, 127, 127); Number of pixels: 12,919,630 (65.1%); Geological characteristics: Mn / Fe ratio less than 0.35, Fe content 3000~4000ppm / 35μm, Cu content 3.03~3.77ppm / 35μm, Cu mineralization potential is moderate; The presence of a large number of purple areas indicates that the Mn / Fe ratio is mostly between 0.4 and 0.8 due to the influence of rare earth elements; Cu has little mineralization potential.

[0032] Group 2: High Mn / Fe ratio phase (red), Grayscale range: 0.27~0.65; RGB color: (255, 0, 0); Number of pixels: 4302804 (21.7%); Geological characteristics: Mn / Fe ratio greater than 0.35, Cu content 6.75~8.65ppm / 35μm, Fe content less than 3000ppm / 35μm; Cu mineralization potential is high.

[0033] Group 3: Phase with medium Mn / Fe ratio (purple), Grayscale range: 0.65-0.90; RGB color: (127, 0, 127); Number of pixels: 2,355,691 (11.9%); Geological characteristics: Mn / Fe ratio 0.6-0.8, Fe content 300-500ppm / 35μm, REE content 0.13-0.18ppm / 35μm, low Cu mineralization potential.

[0034] Group 4: Abnormal area of ​​the sample (white), Grayscale range: 0.90~1.00; RGB colors: (255, 255, 255); Number of pixels: 674 (0.0%); Geological characteristics: may be due to sample preparation damage, uneven surface, or special mineral phases.

[0035] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

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) Take skarn ore-forming and non-ore-forming calcite samples and perform imaging; then map the cathodoluminescence color picture from a three-dimensional color space to a one-dimensional brightness space; (2) Divide the gray scale interval according to the cathodoluminescence color, normalize to the range of 0-1, obtain the gray scale value, and identify each group with different colors; (3) Select points on the original sample according to the grouped picture, and then obtain the trace element data of each gray scale group; (4) Statistically analyze the trace element data, determine 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 scale value and the Cu molar content, and correlate it to the gray scale interval; (5) Output the Cu molar content range of each gray scale interval and the cathodoluminescence reason analysis.

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

3. The method of claim 1, wherein in step (2), the specific method of obtaining the gray scale interval is as follows: at least ten different styles of cathodoluminescence pictures are obtained, 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 as follows: 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 of obtaining the trace element data of each gray scale group is by using a laser ablation inductively coupled plasma mass spectrometry experiment.

6. The method of claim 1, wherein in step (4), the method of establishing the interactive relationship model expression of the molar ratio of Mn / Fe, the gray scale value and the Cu molar content is as follows: first, data cleaning is performed on the trace data, the elements affecting cathodoluminescence and the metal element Cu are retained, and data outliers are removed; 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; a multiple regression model mathematical expression with interaction terms is established with Cu, the gray scale value and the molar ratio of Mn to Fe. 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.

8. The method of claim 6, wherein the multiple regression model mathematical expression with interaction terms of the molar ratio of Mn / Fe, the gray scale value and the Cu molar content is as follows: 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 to Fe. ​ ​ ​ 7. The method of claim 6, wherein, ​ ​ ​ ​ ​

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