Method for quickly judging skarn deposit metallogenic prospect based on characteristic parameters of garnet

Abstract

The application discloses a method for rapidly judging skarn deposit metallogenic prospects based on characteristic parameters of garnet, and the method comprises the following steps: collecting existing geological, geophysical, geochemical and remote sensing data in a research area, and delineating a metallogenic favorable section; collecting garnet-containing samples in the metallogenic favorable section in a zonal manner, and describing the lithology, alteration, mineralization characteristics and hand specimen color of each sample; obtaining the contents of REE (La+Ce+Pr+Nd+Sm+Eu+Gd+Tb+Dy+Ho+Er+Tm+Yb+Lu), Ge, Sn, Zn, Al, Zr and In, which are denoted as c(REE), c(Ge), c(Sn), c(Zn), c(Al), c(Zr) and c(In) respectively, and the unit is ppm; and using discriminant factors f1, f2, f3 and f4 to substitute data for calculation and discrimination; if the four discriminant factors are all judged to have good metallogenic potential, it is determined that the metallogenic favorable section has good skarn deposit metallogenic potential; and in other cases, it is judged that the metallogenic potential is poor.

Description

Technical Field

[0001] This invention belongs to the field of mineral exploration technology, specifically relating to a method for rapidly determining the mineralization potential of skarn deposits based on pomegranate seed characteristic parameters. Background Technology

[0002] Mineral exploration studies the geological conditions for the formation and distribution of mineral deposits, the occurrence patterns of mineral deposits, and the characteristics of ore body variations. It involves the effective exploration and rapid evaluation of mineral deposits, focusing on how to determine the quantity and reserves of mineral resources necessary for mining development and mine construction with minimal investment and in the shortest possible time. Skarn deposits are one of the most abundant types of mineral deposits in the Earth's crust. Mineralogically, skarn is defined as a mineral assemblage primarily composed of calcareous and siliceous minerals, such as garnet and pyroxene. Most skarns occur in the contact zone between intermediate-acidic intrusive bodies and carbonate rocks, and their metasomatic processes involve various fluids of magmatic water, metamorphic water, and atmospheric precipitation origin. The most important aspect of mineral exploration and evaluation of skarn deposits is determining the mineralization of the skarn. Often, two types of skarn develop in skarn deposits: one with mineralization and one without. These two types of skarn have similar mineral assemblages and are closely coexisting in space, making them difficult to distinguish. If their mineralization can be quickly and effectively identified, their mineralization potential can be evaluated.

[0003] Traditional methods for assessing the mineralization potential of skarn primarily involve exposing skarn ore bodies through exploration engineering, collecting skarn samples, and sending these samples to a laboratory for chemical analysis to determine the grade of useful metal components. The mineralization quality of the skarn is then judged based on these grades. This method is extremely time-consuming, labor-intensive, and requires extensive coverage of the entire mining area to obtain satisfactory results, as skarn exhibits diverse spatial occurrences, and local mineralization information cannot represent the entire mining area. Traditional methods are no longer sufficient for current mineral exploration needs. Furthermore, the limited number of surface engineering works and boreholes in the early stages of exploration significantly restricts sample collection, negatively impacting the early exploration and evaluation of skarn deposits. Increased numbers of trenches and boreholes in the early stages also hinder environmental protection. Therefore, it is crucial to accurately assess the mineralization potential of the entire mining area without directly analyzing the ore's ore-forming element content and while minimizing environmental damage. This method also provides valuable guidance for the early exploration direction of this type of deposit. Therefore, it is essential to invent a green and efficient method for determining the mineralization potential of skarn deposits.

[0004] The mineral assemblage of skarn deposits is not only a key factor in identifying and defining skarn, but also crucial for studying the origin of ore-forming materials and determining the economic value of a deposit. Skarn mineral mapping can be conducted in the field, and its broad alteration halos are indicative of potential ore bodies. Garnet is the most representative mineral in skarn deposits, widely distributed and diverse in color, and is also one of the earliest formed hydrothermal alteration minerals. Studies show that garnets occurring near the periphery typically appear dark reddish-brown, while those occurring further away become light brown, and garnets near the marble front are light yellowish-green. This zoning pattern can serve as an important exploration marker, although some skarn deposits do not exhibit obvious zoning. Furthermore, individual descriptions of color differ, and the variety of garnet colors is vast, with some colors being very similar and difficult to distinguish. Judging mineral potential solely based on garnet color will lead to significant errors. Summary of the Invention

[0005] The REE and trace elements in garnet change with hydrothermal activity. The fractionation of REEs in garnet can be caused by various factors, such as changes in temperature and pressure, and different substitution mechanisms of chemically related elements within the garnet. Changes in trace elements are mainly controlled by variations in the physicochemical conditions of the ore-forming fluids; for example, changes in pH affect the Eu value, and changes in oxygen fugacity affect the Sn and U values. The purpose of this invention is to establish effective exploration markers based on the changes in trace elements (chemical properties) in garnet from skarn deposits, to quickly determine the mineralization potential of skarn deposits, and to achieve environmentally friendly, low-cost, and efficient exploration.

[0006] To achieve the above objectives, the following technical solution is adopted:

[0007] A method for rapidly determining the mineralization potential of skarn deposits based on garnet characteristic parameters includes the following steps:

[0008] (1) Collect existing geological, geophysical, geochemical and remote sensing data in the study area and delineate favorable mineralization areas;

[0009] (2) Collect garnet-bearing samples in favorable mineralization zones, and describe the lithology, alteration, mineralization characteristics, and hand specimen color of each garnet.

[0010] (3) Select garnet samples with complete crystal form and undeveloped zoning, perform chemical analysis, and define the contents of trace elements REE (La+Ce+Pr+Nd+Sm+Eu+Gd+Tb+Dy+Ho+Er+Tm+Yb+Lu), Ge, Sn, Zn, Al, Zr and In as c(REE), c(Ge), c(Sn), c(Zn), c(Al), c(Zr) and c(In), with the unit being ppm;

[0011] (4) Substitute c(Ge) into the formula f1=1.4614*c(Ge)-0.0719 to calculate the discrimination factor f1. When c(REE)>f1, it is judged as having poor mineralization potential, and vice versa.

[0012] Substitute c(Al) into the formula f2 = 0.0188 * c(Al) + 98.97 to calculate the discrimination factor f2. When c(Sn) > f2, the mineralization potential is considered to be good; otherwise, the mineralization potential is considered to be poor.

[0013] Substitute c(Zn) into the formula f3 = 0.0793 * c(Zr) + 2.7077 to calculate the discriminant factor f3. When c(In) > f3, the mineralization potential is considered to be good; otherwise, the mineralization potential is considered to be poor.

[0014] Substitute c(Zn) into the formula f4 = -55.847 * c(Zn) + 2261.7 to calculate the discriminant factor f4. When c(Sn) > f4, the mineralization potential is considered to be good; otherwise, the mineralization potential is considered to be poor.

[0015] If all four discrimination factors indicate good mineralization potential, then the skarn deposit in the favorable mineralization area is judged to have good mineralization potential; otherwise, it is judged to have poor mineralization potential.

[0016] According to the above scheme, step 2 of the sampling process includes recording the borehole number and borehole depth, taking photos of the field samples, making detailed field records for each sampling location, and having no fewer than five samples.

[0017] According to the above scheme, step 3 selects the most representative garnet samples, including:

[0018] The collected samples were ground into laser in-situ targets, and the corresponding garnet characteristics were observed under a microscope. The mineral assemblage and the optical morphological characteristics of the garnet were recorded in detail. Garnet samples with complete crystal shape and undeveloped zoning were selected based on the microscopic results.

[0019] According to the above scheme, step 3, chemical analysis, includes:

[0020] In-situ micro-area elemental analysis was performed using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) to obtain recorded data for each test point.

[0021] According to the above scheme, step 3 also includes processing the chemical analysis data using data processing software, including:

[0022] ① Data import: Batch import the elemental analysis records obtained from the in-situ micro-area test points of each garnet sample into the ICPMSDataCal software;

[0023] ② Data interpretation: obtain the micro-area elemental integral curve of the sample at each observation point, and adjust the start and end times of the integral curve at each observation point one by one according to the principle of ensuring the flattest and widest signal range of the selected elemental integral curve.

[0024] ③ Data filtering: Remove invalid data based on abnormal peaks in the element integral curves;

[0025] ④ Data export: The data of each single-point micro-area that has been interpreted and filtered will be exported in batches as a CSV file.

[0026] According to the above scheme, the discriminant factors f1, f2, f3, and f4 in step 4 are obtained as follows:

[0027] (1) Garnet samples were collected in areas where the known skarn deposits have good mineralization potential and areas where the known skarn deposits have poor mineralization potential.

[0028] (2) Select garnet samples with complete crystal form and undeveloped zoning for chemical analysis. Define the contents of trace elements REE (La+Ce+Pr+Nd+Sm+Eu+Gd+Tb+Dy+Ho+Er+Tm+Yb+Lu), Ge, Sn, Zn, Al, Zr and In as c(REE), c(Ge), c(Sn), c(Zn), c(Al), c(Zr) and c(In), with the unit being ppm;

[0029] (3) Calculate the discriminant factor;

[0030] Plotting c(Ge) as the x-axis and c(REE) as the y-axis, the boundary line between good and poor mineralization potential is obtained based on the plotting range and fitted, and the discriminant factor f1 is calculated: f1=1.4614*c(Ge)-0.0719;

[0031] Plotting c(Al) as the abscissa and c(Sn) as the ordinate, the boundary line between good and poor mineralization potential is obtained based on the plotting range and fitted, and the discriminant factor f2 is calculated: f2=0.0188*c(Al)+98.97;

[0032] Plotting is performed with c(Zr) as the abscissa and c(In) as the ordinate; the boundary line between good and poor mineralization potential is obtained based on the plotting range and fitted, and the discriminant factor f3 is calculated: f3=0.0793*c(Zr)+2.7077;

[0033] Plotting c(Zn) as the abscissa and c(Sn) as the ordinate; obtaining the boundary line between good and poor mineralization potential based on the plotting range and fitting it to calculate the discriminant factor f4: f4=-55.847*c(Zn)+2261.7.

[0034] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0035] This invention utilizes REE (La+Ce+Pr+Nd+Sm+Eu+Gd+Tb+Dy+Ho+Er+Tm+Yb+Lu), Ge, Sn, Zn, Zr, In, and the major and trace element Al from garnet. These elements are highly sensitive to changes in temperature, redox conditions, and pH value. Furthermore, the invention creatively proposes an optimal discrimination range for these elements, within which an accurate evaluation of the mineralization potential of skarn deposits can be made.

[0036] This invention proposes using garnet as a distinguishing characteristic mineral. Based on the changes in the geochemical composition of garnet, the mineralization potential of skarn deposits can be quickly determined, achieving environmentally friendly, low-consumption, and efficient mineral exploration. Attached Figure Description

[0037] Figure 1 : Mineralization potential discrimination diagram of skarn deposit in specific implementation method.

[0038] Figure 2 : Geological map of the study area in the specific implementation method. Detailed Implementation

[0039] The following embodiments further illustrate the technical solution of the present invention, but are not intended to limit the scope of protection of the present invention.

[0040] The specific implementation provides a process for obtaining discriminant factors f1, f2, f3, and f4 from skarn deposits with known mineralization potential:

[0041] (1) Garnet samples were collected in areas where the known skarn deposits have good mineralization potential and areas where the known skarn deposits have poor mineralization potential.

[0042] (2) Select garnet samples with complete crystal form and undeveloped zoning for chemical analysis. Define the contents of trace elements REE (La+Ce+Pr+Nd+Sm+Eu+Gd+Tb+Dy+Ho+Er+Tm+Yb+Lu), Ge, Sn, Zn, Al, Zr and In as c(REE), c(Ge), c(Sn), c(Zn), c(Al), c(Zr) and c(In), with the unit being ppm;

[0043] (3) Calculate the discriminant factors f1, f2, f3, and f4; refer to the appendix. Figure 1As shown, A, B, C, and D represent the fitting processes for discriminant factors f1, f2, f3, and f4, respectively.

[0044] Plotting c(Ge) as the x-axis and c(REE) as the y-axis, the boundary line between good and poor mineralization potential is obtained based on the plotting range and fitted, and the discriminant factor f1 is calculated: f1=1.4614*c(Ge)-0.0719;

[0045] Plotting c(Al) as the abscissa and c(Sn) as the ordinate, the boundary line between good and poor mineralization potential is obtained based on the plotting range and fitted, and the discriminant factor f2 is calculated: f2=0.0188*c(Al)+98.97;

[0046] Plotting is performed with c(Zr) as the abscissa and c(In) as the ordinate; the boundary line between good and poor mineralization potential is obtained based on the plotting range and fitted, and the discriminant factor f3 is calculated: f3=0.0793*c(Zr)+2.7077;

[0047] Plotting c(Zn) as the abscissa and c(Sn) as the ordinate; obtaining the boundary line between good and poor mineralization potential based on the plotting range and fitting it to calculate the discriminant factor f4: f4=-55.847*c(Zn)+2261.7.

[0048] Specific embodiments also provide a process for identifying skarn deposits with unknown mineralization potential:

[0049] a. Existing geological, geophysical, geochemical, and remote sensing data within the mineralization area were systematically collected, and its mineralization potential was comprehensively analyzed. Garnet-bearing samples were collected from boreholes in both areas, as shown in the attached figures. Figure 2 As shown, these are areas A and B, respectively.

[0050] b. Field Sample Collection. Garnet samples were collected from nine boreholes. During the sampling process, the following information was recorded accurately and in detail, as shown in Table 1.

[0051] Table 1

[0052]

[0053] c. Sample Testing. The collected samples were ground into laser in-situ targets, and the corresponding garnet characteristics were observed under a microscope. The mineral assemblage and the optical characteristics of the garnet (including total extinction, anomalous extinction, etc.) were recorded in detail. Based on the microscopic results, garnet samples with intact crystals and undeveloped zoning were selected and marked with a marker. Laser ablation inductively coupled plasma mass spectrometry (LA-ICPMS) in-situ micro-area elemental analysis was performed. The delineated areas were those with garnet development, and LA-ICPMS in-situ analysis was conducted on these areas. Each test point was numbered, and the in-situ analysis data are shown in Table 2, in ppm (10⁻¹⁰). -6 ).

[0054] Table 2

[0055]

[0056]

[0057]

[0058] d. Data Processing. Data processing was performed using ICPMSDataCal software, including data import, data interpretation, and data filtering. The average contents of REE (La+Ce+Pr+Nd+Sm+Eu+Gd+Tb+Dy+Ho+Er+Tm+Yb+Lu), Ge, Sn, Zn, Al, Zr, and In were ultimately obtained and denoted as c(REE), c(Ge), c(Sn), c(Zn), c(Al), c(Zr), and c(In).

[0059] In region A, c(REE) = 11.34, c(Ge) = 23.21, c(Sn) = 5798.93, c(Zn) = 12.05, c(Al) = 10982.83, c(Zr) = 1.54, and c(In) = 34.99. In region B, c(REE) = 57.29, c(Ge) = 7.59, c(Sn) = 111.54, c(Zn) = 4.3, c(Al) = 53148.41, c(Zr) = 27.19, and c(In) = 1.43.

[0060] e. Evaluation of the mineralization potential of area A.

[0061] Substituting the obtained c(Ge) content into f1 = 1.4614 * c(Ge) - 0.0719, the discriminant factor f1 = 33.85 is calculated. When c(REE) > f1, the mineralization potential is judged to be poor, and vice versa.

[0062] Substituting the obtained c(Al) content into f2=0.0188*c(Al)+98.97, the discriminant factor f2=305.45 is calculated. When c(Sn)>f2, it is judged to have good mineralization potential, otherwise it is judged to have poor mineralization potential.

[0063] Substituting the obtained c(Zr) content into f3=0.0793*c(Zr)+2.7077, the discriminant factor f3=2.83 is calculated. When c(In)>F3, it is judged to have good mineralization potential, otherwise it is judged to have poor mineralization potential.

[0064] Substituting the obtained c(Zn) content into f4=-55.847*c(Zn)+2261.7, the discriminant factor f4=1588.74 is calculated. When c(Sn)>f4, it is judged to have good mineralization potential, otherwise it is judged to have poor mineralization potential.

[0065] Evaluation of the mineralization potential of area B:

[0066] Substituting the obtained c(Ge) content into f1 = 1.4614 * c(Ge) - 0.0719, the discriminant factor f1 = 11.02 is calculated. When c(REE) > f1, the mineralization potential is judged to be poor, and vice versa.

[0067] Substituting the obtained c(Al) content into f2=0.0188*c(Al)+98.97, the discriminant factor f2=1098.16 is calculated. When c(Sn)>f2, it is judged to have good mineralization potential, otherwise it is judged to have poor mineralization potential.

[0068] Substituting the obtained c(Zr) content into f3=0.0793*c(Zr)+2.7077, the discriminant factor f3=4.86 is calculated. When c(In)>F3, it is judged to have good mineralization potential, otherwise it is judged to have poor mineralization potential.

[0069] Substituting the obtained c(Zn) content into f4=-55.847*c(Zn)+2261.7, the discriminant factor f4=2021.55 is calculated. When c(Sn)>f4, it is judged to have good mineralization potential, otherwise it is judged to have poor mineralization potential.

[0070] The calculation results of the discriminant factors f1, f2, f3 and f4 indicate that the mineralization potential of area A is relatively good and that of area B is relatively poor. This is consistent with the actual field survey results and further proves the effectiveness of the new method for evaluating the mineralization potential based on the physicochemical composition of garnet in skarn deposits.

Claims

1. A method for rapidly determining the mineralization potential of skarn deposits based on characteristic parameters of garnet, characterized in that... Includes the following steps: (1) Collect existing geological, geophysical, geochemical and remote sensing data in the study area and delineate favorable mineralization areas; (2) Collect garnet-bearing samples in favorable mineralization zones, and describe the lithology, alteration, mineralization characteristics, and hand specimen color of each sample; (3) Select garnet samples with complete crystal form and undeveloped zoning, perform chemical analysis, and define the contents of trace elements REE, Ge, Sn, Zn, Al, Zr and In as c(REE), c(Ge), c(Sn), c(Zn), c(Al), c(Zr) and c(In), with the unit being ppm; wherein REE is La+Ce+Pr+Nd+Sm+Eu+Gd+Tb+Dy+Ho+Er+Tm+Yb+Lu, and c(REE) is the sum of the contents of La+Ce+Pr+Nd+Sm+Eu+Gd+Tb+Dy+Ho+Er+Tm+Yb+Lu; (4) Substitute c(Ge) into the formula f 1 = 1.4614 * c(Ge) - 0.0719, calculate the discriminant factor. f 1. When c(REE) > f If the value is 1, it is judged as having poor mineralization potential; otherwise, it is judged as having good mineralization potential. Substitute c(Al) into the formula f 2 = 0.0188 * c(Al) + 98.97, calculate the discriminant factor. f 2. When c(Sn) > f If the value is 2, it is judged to have good mineralization potential; otherwise, it is judged to have poor mineralization potential. Substitute c(Zr) into the formula f 3 = 0.0793 * c(Zr) + 2.7077, calculate the discriminant factor. f 3. When c(In) > f If the value is 3, it is judged to have good mineralization potential; otherwise, it is judged to have poor mineralization potential. Substitute c(Zn) into the formula f 4 = -55.847*c(Zn) + 2261.7, calculate the discriminant factor. f 4. When c(Sn) > f If the value is 4, it is judged to have good mineralization potential; otherwise, it is judged to have poor mineralization potential. If all four discrimination factors indicate good mineralization potential, then the skarn deposit in the favorable mineralization area is judged to have good mineralization potential; otherwise, it is judged to have poor mineralization potential.

2. The method for rapidly determining the mineralization prospect of skarn deposits based on the characteristic parameters of garnet as described in claim 1, characterized in that... Step 2, the sampling process, includes recording the borehole number and depth, taking photos of the field samples, making detailed field records for each sampling location, and having no fewer than five samples.

3. The method for rapidly determining the mineralization potential of skarn deposits based on the characteristic parameters of garnet as described in claim 1, characterized in that... Step 3: Select the most representative garnet samples, including: The collected samples were ground into laser in-situ targets, and the corresponding garnet characteristics were observed under a microscope. The mineral assemblage and the optical morphological characteristics of the garnet were recorded in detail. Garnet samples with complete crystal shape and undeveloped zoning were selected based on the microscopic results.

4. The method for rapidly determining the mineralization prospect of skarn deposits based on the characteristic parameters of garnet as described in claim 1, characterized in that... Step 3, chemical analysis, includes: In-situ micro-area elemental analysis was performed using laser ablation inductively coupled plasma mass spectrometry (ICP-MS) to obtain recorded data for each test point.

5. The method for rapidly determining the mineralization prospect of skarn deposits based on the characteristic parameters of garnet as described in claim 1, characterized in that... Step 3 also includes processing the chemical analysis data using data processing software, including: ① Data import: Batch import the elemental analysis records obtained from the in-situ micro-area test points of each garnet sample into the ICPMSDataCal software; ② Data interpretation: obtain the micro-area elemental integral curve of the sample at each observation point, and adjust the start and end times of the integral curve at each observation point one by one according to the principle of ensuring the flattest and widest signal range of the selected elemental integral curve. ③ Data filtering: Remove invalid data based on abnormal peaks in the element integral curves; ④ Data export: The data of each single-point micro-area that has been interpreted and filtered will be exported in batches as a CSV file.

6. The method for rapidly determining the mineralization prospect of skarn deposits based on the characteristic parameters of garnet as described in claim 1, characterized in that... Discriminant factor in step 4 f 1. f 2. f 3. f 4. Obtained using the following method: (1) Garnet samples were collected in areas where the known skarn deposits have good mineralization potential and areas where the known skarn deposits have poor mineralization potential. (2) Select the most representative garnet sample, perform chemical analysis, and define the contents of trace elements REE, Ge, Sn, Zn, Al, Zr and In as c(REE), c(Ge), c(Sn), c(Zn), c(Al), c(Zr) and c(In), with the unit being ppm; (3) Calculate the discriminant factor; Plotting c(Ge) as the x-axis and c(REE) as the y-axis, the boundary line between good and poor mineralization potential is obtained based on the plotted area, and then fitted to calculate the discriminant factor. f 1: f 1 = 1.4614 * c(Ge) - 0.0719; Plotting c(Al) as the x-axis and c(Sn) as the y-axis, the boundary line between good and poor mineralization potential is obtained based on the plotted range, and then fitted to calculate the discriminant factor. f 2: f 2 = 0.0188 * c(Al) + 98.97; Plotting Zr as the x-axis and In as the y-axis; obtaining the boundary line between good and poor mineralization potential based on the plotted area and fitting the data to calculate the discriminant factor. f 3: f 3 = 0.0793 * c(Zr) + 2.7077; Plotting c(Zn) as the x-axis and c(Sn) as the y-axis; obtaining the boundary line between good and poor mineralization potential based on the plotted range and fitting the data to calculate the discriminant factor. f 4: f 4 = -55.847*c(Zn) + 2261.7.

Images