Method for searching for silver ore based on pyrite electronic configuration parameters

Abstract

The application discloses a method for searching silver ore based on pyrite electronic configuration parameters, relates to the technical field of geological prospecting and mineral exploration, and comprises the following steps: S1, collecting pyrite samples in a region to be measured; S2, measuring electronic configuration parameters of the pyrite; S3, calculating silver mineralization potential indexes of each ore sample through the electronic configuration parameters; S4, establishing an electronic configuration parameter-silver grade quantitative relationship model by using the silver mineralization potential indexes; and S5, predicting silver mineralization potential of an unknown region and delineating a target area. The application can significantly improve the prospecting efficiency and success rate.

Description

Technical Field

[0001] This invention relates to the field of geological prospecting and mineral exploration technology, specifically a method for finding silver deposits based on electronic configuration parameters of pyrite. Background Technology

[0002] As an important precious metal, silver exploration has long faced challenges. Traditional silver-finding methods mainly rely on geological mapping, geochemical anomalies, and geophysical exploration, but these methods are often not direct or accurate enough for deep, concealed, or highly variable ore bodies.

[0003] Pyrite (FeS2) is the most common metallic sulfide in hydrothermal deposits and is often associated with silver deposits. In current techniques, geologists typically use pyrite as a macroscopic type mineral for prospecting, making empirical judgments based on its macroscopic characteristics such as crystal shape, grain size, and content, or the content of major and trace elements. However, these macroscopic or major element characteristics are greatly influenced by later geological processes, have many interpretations, and cannot accurately reveal the intrinsic relationship between the physicochemical conditions at the time of its formation and silver enrichment, resulting in limited prediction success rates and high drilling verification risks. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for finding silver deposits based on the electronic configuration parameters of pyrite. By measuring multiple key electronic configuration parameters of pyrite samples, calculating their silver mineralization potential index, and establishing a quantitative relationship model between electronic configuration parameters and silver grade, it is possible to quickly determine whether there is industrially valuable silver mineralization in the target area, significantly improving the efficiency and success rate of mineral exploration.

[0005] The objective of this invention is achieved through the following technical solution: a method for finding silver deposits based on pyrite electronic configuration parameters, the method comprising the following steps: S1. Collect pyrite samples from the area to be tested: Systematically collect single pyrite mineral or pyrite-rich ore samples from the surface, trenches, boreholes or tunnels of the target exploration area (such as hydrothermal vein type, skarn type or volcanic rock type sulfide mining area). The samples must be fresh and unweathered, and each sample must weigh no less than 50g.

[0006] S2. Determine the electronic configuration parameters of pyrite: Determine the electronic configuration parameters of each ore sample, including the Fermi level shift, effective electron spin density, effective electron mass, and electronic density of states at the Fermi level.

[0007] 1. Fermi level shift (Unit: eV) The work function of the ore sample surface was measured using ultraviolet photoelectron spectroscopy (UPS) or Kelvin probe force microscopy (KPFM). Through formula Calculate the Fermi level shift relative to standard pyrite (silver-free, ideal stoichiometry). , The work function of standard pyrite. Let be the work function of the ore sample, where Take 4.95 eV.

[0008] 2. Effective electron spin density (Unit: spins / nm³) The spin concentration of the ore sample was measured using electron paramagnetic resonance (EPR) or a vibrating sample magnetometer (VSM), and the effective electron spin density was obtained by dividing by the sample volume. .

[0009] 3. Effective electron mass (with the mass of free electrons) (in units) The effective mass of the conduction band bottom electrons is calculated using angle-resolved photoelectron spectroscopy (ARPES) or based on the low-temperature Hall effect. , This represents the second-order change in the total electron energy E as a function of the wave vector. It represents the square of the change in electron crystal momentum along a specific measurement direction in the reciprocal space of the crystal. is the reduced Planck constant.

[0010] 4. The value of the electronic density of states at the Fermi level (Unit: states / eV·cell).

[0011] The electronic density of states at the Fermi level is obtained by fitting X-ray photoelectron spectroscopy valence band spectrum (XPS-VB) with density functional theory (DFT), or directly by calculating the crystal structure of the ore sample using first-principles calculation software (such as VASP).

[0012] S3. Calculate the silver mineralization potential index of each ore sample by substituting the electronic configuration parameters into the following formula. :

[0013] in, The silver mineralization potential index (dimensionless) characterizes the relative silver mineralization intensity at the location of the ore sample. This represents the measured Fermi level shift (in eV). The effective electron spin density (unit: spins / nm³). For the effective mass of electrons (in) (in units) This represents the electronic density of states at the Fermi level (unit: states / eV·cell). =0.10eV, which is the normalization constant; =1.0×1021spins / nm3, which is the normalization constant; , , This is a regional empirical coefficient, obtained by performing nonlinear least squares fitting on the formula using pyrite standards from 3-5 boreholes of known ore bodies in the region, with silver chemical analysis results (Ag grade, g / t) as the target variable. The initial recommended value is: = 0.85, =0.5, = 0.12; exp() is the natural exponential function.

[0014] S4. Establish a quantitative relationship model between electronic configuration parameters and silver grade using the silver mineralization potential index: Substitute the electronic configuration parameters obtained in step S2 into the formula in step S3 to calculate the grade of each sample. The value was obtained by simultaneously performing chemical analysis (such as atomic absorption spectroscopy or ICP-MS) on powder taken from the same sample to determine the true silver grade. Linear regression was performed using the silver mineralization potential index of at least 20 representative ore samples:

[0015] Where k and b are regression coefficients, determined by the least squares method, this electronic configuration parameter-silver grade quantitative relationship model is the core quantitative interpretation model of this method.

[0016] S5. Prediction of Silver Mineralization Potential in Unknown Areas and Target Area Delineation: Repeat steps S2-S3 on the newly collected ore samples to calculate the silver mineralization potential index. Substituting the values ​​into the electronic configuration parameter-silver grade quantitative relationship model, the predicted silver grade value is obtained. Based on the predicted silver grade value, the mineral exploration potential level is classified according to the following criteria: Areas with ≥50g / t (i.e., 50g of Ag per ton) are considered high-potential primary target areas, suitable for deploying intensified exploration projects; 10≤ <50g / t is considered a medium-potential secondary target area, and a large-scale detailed survey is recommended; Areas with a density of <10g / t are considered low-potential Level 3 targets and exploration can be postponed.

[0017] The beneficial effects of this invention are: 1. Directness and Quantification: This invention establishes a direct functional relationship between the electronic configuration scale (or electronic configuration parameters) of pyrite (or ore sample) and silver mineralization, overcoming the shortcomings of traditional geochemical indicators (such as Ag, As, Sb content) being affected by later alteration and surface leaching.

[0018] 2. High sensitivity: Fermi level shift and effective electron spin density substitution for trace amounts of silver (Ag) + Replace Fe² + It is extremely sensitive (or enters the interstitial spaces of the crystal lattice) and can detect silver enrichment trends at the ppm level or even lower.

[0019] 3. High applicability: the coefficients in the formula , , It can be quickly calibrated using a small number of known ore body samples, and is suitable for personalized modeling of different metallogenic belts (such as the Qinling, Greater Khingan, and Andes metallogenic belts).

[0020] 4. High efficiency: The total time for electronic parameter measurement of a single sample (UPS+EPR+Hall effect) is about 4 hours, which is much shorter than the pretreatment and testing cycle of traditional geochemical exploration samples. Attached Figure Description

[0021] Figure 1 This is an overall flowchart of the present invention; Figure 2 for Figure 1 Enlarged view of the upper part; Figure 3 for Figure 1 Enlarged view of the middle section; Figure 4 for Figure 1 The lower part is an enlarged view. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0023] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0024] like Figures 1 to 4 The process is shown below.

[0025] Example 1: A hydrothermal silver-lead-zinc mine Study Area Overview: This mining area is located on the northern edge of the North China Craton, and several veins with silver grades ranging from 15 to 210 g / t are known to exist. Preliminary exploration indicates that pyrite is the main silver-bearing mineral.

[0026] Sample collection: Fresh pyrite samples were collected from the tunnels at 50m intervals along three exploration profiles with a length of 2km along the strike, resulting in a total of 48 samples. Of these, 30 were randomly selected as modeling samples and 18 were selected as validation samples.

[0027] Parameter measurement: The surface work function was measured using KPFM (Bruker Dimension Icon), with measurements taken at 5 points for each sample and the average taken. The Fermi level shift was then calculated. .

[0028] The effective spin density of electrons was determined using EPR (Bruker EMXplus) at 100 K in the X-band. .

[0029] The effective mass of the electron was determined using a Hall effect testing system (Lake Shore 8404) at 10 K and a magnetic field of 0.5 T. .

[0030] The valence band spectrum was obtained using XPS (Thermo Scientific K-Alpha), and the electronic density of states at the Fermi level was obtained by DFT fitting. .

[0031] Coefficient calibration: The formula was fitted using the measured silver grade (chemical analysis) of the modeling sample to obtain the coefficients. =0.82, =0.48, = 0.13. Establish a regression model: = 2.31× + 1.2.

[0032] Verification results: The average relative error between the predicted and measured grades of the 18 verification samples was only 8.6%. Among them, four primary target areas were delineated (predicted grade > 50 g / t), and subsequent drilling verification revealed industrial silver ore bodies (actual grade 52~187 g / t) in all of them. Compared with traditional soil geochemical anomalies (Ag anomaly lower limit 10 g / t), the method of this invention reduces the amount of invalid verification drilling work by 60%.

[0033] Example 2: Evaluation of Associated Silver in a Volcanic Copper Mine Study Area Overview: A volcanic copper deposit with medium copper grade but highly variable associated silver content, requiring rapid evaluation of its associated silver potential.

[0034] Sample collection: 35 samples were collected from both sides of the pyrite veinlets in the existing borehole core.

[0035] Parameter determination and calculation: Same as the parameter determination and coefficient calibration in Example 1. Because the known effective electron spin density of silver-bearing pyrite in this area is generally low, after calibration... = 0.95, = 0.7, = 0.08.

[0036] Results: The predicted silver grades were calculated, with five samples showing predicted values ​​>30 g / t. Chemical analysis confirmed the actual silver grades to be 34.2, 41.5, 28.7, 56.3, and 33.8 g / t, respectively, showing good agreement. Based on this, a new silver-rich area was delineated in the mining area, adding approximately 120 tons of silver resources.

[0037] The above description is merely an embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the concept described herein through the above teachings or related technologies or knowledge. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. A method for finding silver deposits based on the electronic configuration parameters of pyrite, characterized in that: Includes the following steps: S1. Collect pyrite samples from the area to be tested: Collect single pyrite mineral samples or ore samples rich in pyrite. S2. Determine the electronic configuration parameters of pyrite: Determine the electronic configuration parameters of each ore sample, including the Fermi level shift, effective electron spin density, effective electron mass, and electronic density of states at the Fermi level. S3. Calculate the silver mineralization potential index of each ore sample using electronic configuration parameters; S4. Establish a quantitative relationship model between electronic configuration parameters and silver grade using the silver mineralization potential index; S5. Prediction of Silver Mineralization Potential in Unknown Areas and Target Area Delineation: Repeat steps S2 to S3 with newly collected ore samples, and substitute the calculated silver mineralization potential index into the electronic configuration parameter-silver grade quantitative relationship model to obtain the predicted silver grade value.

2. The method for finding silver deposits based on pyrite electronic configuration parameters according to claim 1, characterized in that: In step S1, pyrite single mineral or pyrite-rich ore samples are collected from the surface, trench, borehole or tunnel of the target exploration area.

3. The method for finding silver deposits based on pyrite electronic configuration parameters according to claim 2, characterized in that: The target exploration area is a hydrothermal vein type, skarn type, or volcanic rock type sulfide mineral area.

4. The method for finding silver deposits based on pyrite electronic configuration parameters according to claim 1, characterized in that: In step S2, the work function of the ore sample surface is first measured. Through formula Calculate the Fermi level offset relative to standard pyrite. , The work function of standard pyrite. Let be the work function of the ore sample.

5. The method for finding silver deposits based on pyrite electronic configuration parameters according to claim 4, characterized in that: The effective electron spin density is obtained by measuring the spin concentration of an ore sample and dividing it by the sample volume. .

6. The method for finding silver deposits based on pyrite electronic configuration parameters according to claim 4 or 5, characterized in that: The effective mass of electrons is the effective mass of electrons at the bottom of the conduction band. , The effective electron mass is given by the value of the electronic density of states at the Fermi level, which is calculated from the crystal structure of the ore sample. This represents the second-order change in the total electron energy E as a function of the wave vector. This represents the square of the change in electron crystal momentum along the measurement direction in the reciprocal space of the crystal. is the reduced Planck constant.

7. The method for finding silver deposits based on pyrite electronic configuration parameters according to claim 1, characterized in that: In step S3, the silver mineralization potential index of each ore sample is calculated using the following formula: in, The silver mineralization potential index characterizes the relative intensity of silver mineralization at the location of the ore sample. This represents the measured Fermi level shift. For the effective mass of electrons; This represents the electronic density of states at the Fermi level. , All are normalized constants; , , Here, exp() represents the regional empirical coefficient, and exp() is the natural exponential function. This represents the effective spin density of electrons.

8. The method for finding silver deposits based on pyrite electronic configuration parameters according to claim 1, characterized in that: In step S4, linear regression is performed using the silver mineralization potential index of at least 20 representative ore samples: Where k and b are regression coefficients, determined by the least squares method. This is an index representing the potential for silver mineralization.

9. The method for finding silver deposits based on pyrite electronic configuration parameters according to claim 1, characterized in that: In step S5, the mineral exploration potential level can be classified according to the predicted silver grade value.

10. The method for finding silver deposits based on pyrite electronic configuration parameters according to claim 9, characterized in that: Based on the predicted silver grade, the mineral exploration potential is classified according to the following criteria: ≥50g / t represents a high-potential primary target area, 10≤ <50g / t represents a medium-potential secondary target area. <10g / t represents a low-potential level 3 target area.

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