A method for evaluating the mineralization potential based on the elemental composition of magnetite in skarn deposits

Abstract

The present invention discloses a method for evaluating the mineralization potential based on the elemental composition of magnetite in a skarn deposit. The method comprises the following steps: systematically collecting existing geological, geophysical, geochemical and remote sensing data in a study area to delineate a favorable mineralization area; collecting magnetite-containing ore samples in the favorable mineralization area to describe the lithology, alteration and mineralization characteristics of each sample; selecting the most representative magnetite sample for chemical analysis to obtain the average contents of trace elements Ti, Ni, V, K and Al+Si+Mg, which are recorded as c(Ti), c(Ni), c(V), c(K) and c(Al+Si+Mg) in ppm; substituting discriminant factors F1, F2, F3 and F4 into the data for calculation and discrimination; and when all four discriminant factors judge the mineralization potential to be good, the skarn deposit in the favorable mineralization area is judged to have good mineralization potential; and in all other cases, the mineralization potential is judged to be poor.

Description

Technical Field

[0001] The invention belongs to the field of exploration technology, and in particular relates to a method for evaluating the mineralization potential of a skarn deposit. Background Art

[0002] In the fragile ecological environment of the plateau, traditional exploration methods are relatively costly and time-consuming, making it difficult to quickly provide clear exploration directions. Using limited exploration and evaluation techniques, how to predict and evaluate resource potential at the scale of mineral clusters and effectively guide mineral deposit exploration has become a focus of interest for mineral explorationists both domestically and internationally.

[0003] Skarn deposits are often found near the contact zone between intermediate-acidic igneous rocks and carbonates. The ore bodies are complex in occurrence and morphology, with poor continuity, complex mineral compositions, and a wide temperature range of formation. Skarn formation exhibits distinct zoning, with magnetite mostly forming in the late skarn and oxide stages, at higher temperatures. Skarn deposits of varying sizes develop skarns, but the challenge remains to rapidly assess the mineralization potential of a particular deposit (or site) based on its characteristics.

[0004] Traditional evaluation of the mineralization potential of skarn deposits requires large-scale geological mapping, geophysical exploration, geochemical exploration and final drilling verification. It requires the completion of mineral exploration stages such as survey and detailed investigation before the potential of the deposit can be evaluated. However, it has the following disadvantages: the exploration and evaluation cycle is long and the cost is high, which cannot meet the urgent need for rapid exploration and evaluation. Summary of the Invention

[0005] The purpose of the present invention is to provide a new method for evaluating the mineralization potential of skarn deposits. Based on the differences in the main and trace elements of magnetite in skarn deposits, the method organically combines mineral geochemistry and deposit potential evaluation, thereby solving the technical problem of rapid exploration and evaluation of skarn deposits in plateau areas.

[0006] In order to achieve the above purpose, the technical solutions adopted are as follows:

[0007] The method for evaluating the mineralization potential based on the elemental composition of magnetite in skarn deposits includes the following steps:

[0008] (1) Regional data collection and comprehensive analysis

[0009] Systematically collect existing geological, geophysical, geochemical and remote sensing data in the study area to identify favorable mineralization areas;

[0010] (2) Magnetite sample collection

[0011] Collect magnetite-bearing ore samples in zones in favorable mineralization areas and describe the lithology, alteration and mineralization characteristics of each sample;

[0012] (3) Analysis and testing of main and trace elements of samples

[0013] The most representative magnetite samples were selected for chemical analysis to obtain the average contents of trace elements Ti, Ni, V, K and Al+Si+Mg, which were recorded as c(Ti), c(Ni), c(V), c(K), c(Al+Si+Mg) in ppm.

[0014] (4) Evaluation of mineralization potential of ore deposits

[0015] Substitute c(Ni) into F1=-3.1484*c(Ni)+13.301 to calculate the discriminant factor F1. When c(V)>F1, it is judged that the mineralization potential is poor, otherwise it is judged that the mineralization potential is good;

[0016] Compare c(V) with the discriminant factor F2=2. When c(V)>2, it is judged that the mineralization potential is poor, otherwise it is judged that the mineralization potential is good.

[0017] Substitute c(K) into F3=0.0437*c(K)+0.4093 to calculate the discriminant factor F3. When c(V)>F3, it is judged that the mineralization potential is poor, otherwise it is judged that the mineralization potential is good.

[0018] Substitute c(Ti) into F4=-115.11*c(Ti)+34361 to calculate the discriminant factor F4. When c(Al+Si+Mg)>F4, it is judged that the mineralization potential is poor, otherwise it is judged that the mineralization potential is good.

[0019] When all four discriminant factors judge that the mineralization potential is good, the skarn deposit in the mineralization-favorable area is judged to have good mineralization potential; in other cases, the mineralization potential is judged to be poor.

[0020] According to the above plan, the sampling process in step 2 includes recording the borehole number and borehole depth, taking field photos, and making detailed field records for each sampling location; the number of samples is not less than five.

[0021] According to the above scheme, the most representative magnetite samples selected in step 3 include:

[0022] The collected samples were ground into laser in-situ targets, and their corresponding magnetite characteristics were observed under a microscope. The mineral combination and the morphology of the magnetite were recorded in detail, and the most representative magnetite samples were selected based on the microscopic results.

[0023] According to the above scheme, step 3 chemical analysis includes:

[0024] Laser ablation inductively coupled plasma mass spectrometry was used to perform in-situ micro-area elemental analysis and obtain recorded data for each test point.

[0025] According to the above scheme, step 3 also includes using data processing software to process the recorded data obtained from the chemical analysis, including:

[0026] ① Data import: import the elemental analysis record data obtained from the in-situ micro-area test points of each magnetite sample into the ICPMSDataCal software in batches;

[0027] ② Data interpretation: obtain the micro-area element integral curve of the sample at each observation point. According to the principle of ensuring the flattest and widest signal range of the selected element integral curve, adjust the start and end time of the integral curve of each observation point one by one;

[0028] ③Data screening: remove invalid data based on abnormal peaks in the element integral curve;

[0029] ④ Data export: export the interpreted and filtered data of each single-point micro-area into a batch file in CSV format. According to the above scheme, the discriminant factors F1, F2, F3, and F4 in step 4 are obtained as follows:

[0030] (1) Collect magnetite-bearing ore samples in areas with good and poor skarn deposits;

[0031] (2) Select the most representative magnetite sample and perform chemical analysis to obtain the average content of trace elements Ti, Ni, V, K and Al+Si+Mg, which are recorded as c(Ti), c(Ni), c(V), c(K), c(Al+Si+Mg), in ppm;

[0032] (3) Calculate the discriminant factor F1

[0033] The sampling point data were mapped with c(Ni) as the abscissa and c(V) as the ordinate, and the dividing line between good and poor mineralization potential was fitted. The discriminant factor F1 was calculated as: F1 = -3.1484*c(Ni) + 13.301;

[0034] The sampling point data were projected with c(Ti) as the abscissa and c(V) as the ordinate, and the dividing line between good and poor mineralization potential was fitted to calculate the discriminant factor F2: F2 = 2;

[0035] The sampling point data were mapped with c(K) as the abscissa and c(V) as the ordinate, and the dividing line between good and poor mineralization potential was fitted. The discriminant factor F3 was calculated as: F3 = 0.0437*c(K) + 0.4093;

[0036] The sampling point data were projected with c(Ti) as the abscissa and c(Al+Si+Mg) as the ordinate; the dividing line between good and poor mineralization potential was fitted, and the discriminant factor F4 was calculated: F4=-115.11*c(Ti)+34361.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] Magnetite is a mineral widely found in magma and hydrothermal fluids. Its formation is influenced not only by crystallographic factors but also by changes in physical and chemical conditions, with temperature being one of the primary factors controlling magnetite composition. This innovative method uses magnetite as a characteristic mineral for rapid differentiation of skarns with high and low mineralization potential based on variations in magnetite's major and trace elements. This method represents an economical, green, and efficient new prospecting technology that can shorten exploration and evaluation cycles, reduce exploration costs, and improve efficiency, meeting the urgent needs of mining rights holders for rapid exploration and evaluation.

[0039] The present invention creatively proposes the use of Ti, Ni, V, K and the main trace elements Al, Si and Mg in magnetite, and creatively proposes their optimal discrimination range. These elements are relatively sensitive to changes in temperature, water-rock interaction and redox conditions. Within the optimal discrimination range, an accurate evaluation of the mineralization potential of skarn deposits can be made. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 : A discriminant diagram of the mineralization potential of skarn deposits in a specific implementation manner.

[0041] Figure 2 : Sampling geological map of the study area in the specific implementation method. DETAILED DESCRIPTION

[0042] The following examples further illustrate the technical solutions of the present invention but are not intended to limit the scope of protection of the present invention.

[0043] The specific embodiment provides a process for obtaining the discriminant factors F1, F2, F3, and F4 using a known skarn deposit with mineralization potential:

[0044] (1) Collect magnetite-bearing ore samples in areas with good and poor skarn deposits;

[0045] (2) Select the most representative magnetite sample and perform chemical analysis to obtain the average content of trace elements Ti, Ni, V, K and Al+Si+Mg, which are recorded as c(Ti), c(Ni), c(V), c(K), c(Al+Si+Mg), in ppm;

[0046] (3) Calculate the discriminant factors F1, F2, F3, and F4; refer to the attached Figure 1 As shown, a, b, c, and d are the fitting processes of the discriminant factors F1, F2, F3, and F4 respectively;

[0047] The sampling point data were mapped with c(Ni) as the abscissa and c(V) as the ordinate, and the dividing line between good and poor mineralization potential was fitted. The discriminant factor F1 was calculated as: F1 = -3.1484*c(Ni) + 13.301;

[0048] The sampling point data were projected with c(Ti) as the abscissa and c(V) as the ordinate, and the dividing line between good and poor mineralization potential was fitted to calculate the discriminant factor F2: F2 = 2;

[0049] The sampling point data were mapped with c(K) as the abscissa and c(V) as the ordinate, and the dividing line between good and poor mineralization potential was fitted. The discriminant factor F3 was calculated as: F3 = 0.0437*c(K) + 0.4093;

[0050] The sampling point data were projected with c(Ti) as the abscissa and c(Al+Si+Mg) as the ordinate; the dividing line between good and poor mineralization potential was fitted, and the discriminant factor F4 was calculated: F4=-115.11*c(Ti)+34361.

[0051] The specific embodiment also provides a process for identifying skarn deposits of unknown mineralization potential:

[0052] a. Systematically collect existing geological, geophysical, geochemical and remote sensing data in the mining area, and collect magnetite samples from drill holes in two areas. Figure 2 As shown, they are area A and area B respectively.

[0053] b. Field sample collection

[0054] Five drill holes were selected to collect magnetite samples. During the sampling process, the following information was recorded truthfully and in detail, as shown in Table 1.

[0055] Table 1

[0056] Sample number Magnetite morphology Lithology Hand specimen alteration mineralization Zk3503-174.6 self-shaped Actinolite skarn Actinobacteria Galena mineralization and sphalerite mineralization Zk1003-524.7 Pulse Chlorite skarn Chloritization Galena mineralization and sphalerite mineralization Zk0301-85.9 It shape Epidote skarn Epidote Petrochemical No mineralization … … … … …

[0057] c. Sample testing

[0058] The collected samples were ground into laser in-situ targets, and the corresponding magnetite characteristics were observed under a microscope. The mineral assemblage and the morphology of the magnetite (including euhedral or vein-like, etc.) were recorded in detail. The most representative magnetite sample was selected based on the microscopic results and marked with a marker. Laser ablation inductively coupled plasma mass spectrometry (LA-ICPMS) in-situ micro-area element analysis was carried out. The circled area was the location where magnetite was developed. Laser LA-ICPMS in-situ analysis test was carried out on it, and the number of each test point was marked. The in-situ analysis data are shown in Table 2, unit ppm (10 -6 ).

[0059] Table 2

[0060]

[0061]

[0062] d. Data processing: Data processing was performed using ICPMS DataCal software, which includes three steps: data import, data interpretation, and data screening. The average contents of the main and trace elements Ti, Ni, V, K, and Al+Si+Mg in magnetite were finally obtained, denoted as c(Ti), c(Ni), c(V), c(K), and c(Al+Si+Mg). In Area A, c(Ti) = 48.341, c(Ni) = 1.894, c(V) = 1.081, c(K) = 106.379, and c(Al+Si+Mg) = 11930.329. In Area B, c(Ti) = 133.35, c(Ni) = 3.69, c(V) = 30.45, c(K) = 65.59, and c(Al+Si+Mg) = 23217.140 were obtained.

[0063] Evaluation of the mineralization potential of Area A:

[0064] Substituting c(Ni) into F1=-3.1484*c(Ni)+13.301, the discriminant factor F1=7.340 is calculated. When c(V)>F1, it is judged that the mineralization potential is poor, otherwise it is judged that the mineralization potential is good.

[0065] Compare c(V) with the discriminant factor F2=2. When c(V)>2, it is judged that the mineralization potential is poor, otherwise it is judged that the mineralization potential is good.

[0066] Substituting c(K) into F3=0.0437*c(K)+0.4093, the discriminant factor F3=5.060 is calculated. When c(V)>F3, it is judged that the mineralization potential is poor, otherwise it is judged that the mineralization potential is good.

[0067] Substituting c(Ti) into F4=-115.11*c(Ti)+34361, the discriminant factor F4=28791.150 is calculated. When c(Al+Si+Mg)>F4, it is judged that the mineralization potential is poor, otherwise it is judged that the mineralization potential is good.

[0068] After comparison, the four discriminant factors all judged that the mineralization potential was good, and it was determined that the skarn deposit in area A had good mineralization potential.

[0069] Evaluation of the mineralization potential of Area B:

[0070] Substituting c(Ni) into F1=-3.1484*c(Ni)+13.301, the discriminant factor F1=1.675 is calculated. When c(V)>F1, it is judged that the mineralization potential is poor, otherwise it is judged that the mineralization potential is good.

[0071] Compare c(V) with the discriminant factor F2=2. When c(V)>2, it is judged that the mineralization potential is poor, otherwise it is judged that the mineralization potential is good.

[0072] Substituting c(K) into F3=0.0437*c(K)+0.4093, the discriminant factor F3=3.276 is calculated. When c(V)>F3, it is judged that the mineralization potential is poor, otherwise it is judged that the mineralization potential is good.

[0073] Substituting c(Ti) into F4=-115.11*c(Ti)+34361, the discriminant factor F4=19011.572 is calculated. When c(Al+Si+Mg)>F4, it is judged that the mineralization potential is poor, otherwise it is judged that the mineralization potential is good.

[0074] After comparison, the four discriminant factors all judged that the mineralization potential was poor, and it was determined that the mineralization potential of the skarn deposit in area B was poor.

[0075] The calculation results of the discrimination factors F1, F2, F3 and F4 show that the mineralization potential of area A is greater than that of area B, which is consistent with the actual field survey results, further proving the effectiveness of the new method for mineralization potential evaluation based on the mineral chemistry of magnetite in skarn deposits proposed this time.

Claims

1. A method for evaluating the mineralization potential based on the elemental composition of magnetite in skarn deposits, characterized by The following steps are involved: (1) Systematically collect existing geological, geophysical, geochemical and remote sensing data in the study area to identify favorable mineralization areas; (2) Collect magnetite-bearing ore samples in zones in favorable mineralization areas and describe the lithology, alteration, and mineralization characteristics of each sample; (3) Select the most representative magnetite sample and conduct chemical analysis to obtain the average content of trace elements Ti, Ni, V, K and Al+Si+Mg, which are recorded as c(Ti), c(Ni), c(V), c(K), c(Al+Si+Mg), in ppm; (4) Substitute c(Ni) into F1=-3.1484*c(Ni)+13.301 to calculate the discriminant factor F1. When c(V)>F1, it is judged that the mineralization potential is poor, otherwise it is judged that the mineralization potential is good; Compare c(V) with the discriminant factor F2=2. When c(V)>2, it is judged that the mineralization potential is poor, otherwise it is judged that the mineralization potential is good. Substitute c(K) into F3=0.0437*c(K)+0.4093 to calculate the discriminant factor F3. When c(V)>F3, it is judged that the mineralization potential is poor, otherwise it is judged that the mineralization potential is good. Substitute c(Ti) into F4=-115.11*c(Ti)+34361 to calculate the discriminant factor F4. When c(Al+Si+Mg)>F4, it is judged that the mineralization potential is poor, otherwise it is judged that the mineralization potential is good. When all four discriminant factors judge that the mineralization potential is good, the skarn deposit in the mineralization-favorable area is judged to have good mineralization potential; in other cases, the mineralization potential is judged to be poor.

2. The method for evaluating the mineralization potential of a skarn deposit according to claim 1, wherein Step 2 The sampling process includes recording the borehole number and depth, taking field photos, and making detailed field records for each sampling location; the number of samples should be no less than five.

3. The method for evaluating the mineralization potential of a skarn deposit according to claim 1, wherein Step 3: Select the most representative magnetite samples including: The collected samples were ground into laser in-situ targets, and their corresponding magnetite characteristics were observed under a microscope. The mineral combination and the morphology of the magnetite were recorded in detail, and the most representative magnetite samples were selected based on the microscopic results.

4. The method for evaluating the mineralization potential of a skarn deposit according to claim 1, wherein Step 3 Chemical Analysis includes: Laser ablation inductively coupled plasma mass spectrometry was used to perform in-situ micro-area elemental analysis and obtain recorded data for each test point.

5. The method for evaluating the mineralization potential of a skarn deposit according to claim 1, wherein Step 3 also includes processing the recorded data obtained from the chemical analysis using data processing software, including: ① Data import: import the elemental analysis record data obtained from the in-situ micro-area test points of each magnetite sample into the ICPMSDataCal software in batches; ② Data interpretation: obtain the micro-area element integral curve of the sample at each observation point. According to the principle of ensuring the flattest and widest signal range of the selected element integral curve, adjust the start and end time of the integral curve of each observation point one by one; ③Data screening: remove invalid data based on abnormal peaks in the element integral curve; ④ Data export: export the interpreted and filtered data of each single-point micro-area in batches into a csv format file.

6. The method for evaluating the mineralization potential of a skarn deposit according to claim 1, wherein In step 4, the discriminant factors F1, F2, F3, and F4 are obtained as follows: (1) Collect magnetite-bearing samples in areas with good and poor skarn deposits; (2) Select the most representative magnetite sample and perform chemical analysis to obtain the average content of trace elements Ti, Ni, V, K and Al+Si+Mg, which are recorded as c(Ti), c(Ni), c(V), c(K), c(Al+Si+Mg), in ppm; (3) Calculate the discriminant factor F1 The sampling point data were mapped with c(Ni) as the abscissa and c(V) as the ordinate, and the dividing line between good and poor mineralization potential was fitted. The discriminant factor F1 was calculated as: F1 = -3.1484*c(Ni) + 13.301; The sampling point data were projected with c(Ti) as the abscissa and c(V) as the ordinate, and the dividing line between good and poor mineralization potential was fitted to calculate the discriminant factor F2: F2 = 2; The sampling point data were mapped with c(K) as the abscissa and c(V) as the ordinate, and the dividing line between good and poor mineralization potential was fitted. The discriminant factor F3 was calculated as: F3 = 0.0437*c(K) + 0.4093; The sampling point data were projected with c(Ti) as the abscissa and c(Al+Si+Mg) as the ordinate; the dividing line between good and poor mineralization potential was fitted, and the discriminant factor F4 was calculated: F4=-115.11*c(Ti)+34361.

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