Rare metal mineralization potential prediction method, system, equipment and medium

Abstract

The invention relates to a rare metal mineralization potential prediction method, system and device and a medium. The prediction method comprises the following steps: acquiring tourmaline distribution data of a target area; based on the tourmaline distribution data, carrying out microscopic lithofacies analysis on the tourmaline sample, and identifying the cause type and girdle structure characteristics of the tourmaline sample; detecting major elements and trace elements of the tourmaline sample according to the cause type and the girdle structure characteristics of the tourmaline sample to obtain major element data and trace element data; based on the ratio of FeOT to MgO in the main element data and the contents of Nb and Sn in the trace element data, combining the delta 11B value of the B isotope of the tourmaline sample, and establishing a magma-hydrothermal evolution model; on the basis of a magma-hydrothermal evolution model, determining a characteristic element threshold value of the metallogenic potential of the rare metal; and determining a favorable prospecting area of the target area based on the tourmaline distribution data and the characteristic element threshold. According to the method, effective prediction of the rare metal prospecting favorable area can be realized.

Description

Technical Field

[0001] The present application relates to the technical field of rare metal ore deposit exploration, and in particular to a method, system, equipment and medium for predicting rare metal mineralization potential. Background Art

[0002] With the growing global demand for rare metals, finding new economically viable mineral deposits has become an important task in geological prospecting, especially in modern high-tech industries, where the demand for rare metals such as niobium, tantalum, and rare earth elements is becoming more and more urgent. However, the existing exploration technology for rare metal deposits still faces many challenges, mainly relying on traditional geophysical exploration methods such as electrical, gravity, and magnetic methods. However, these methods are less effective in areas with complex terrain or strong interference from surrounding rocks, especially when looking for concealed deposits. The resolution is significantly insufficient. In addition, traditional rock geochemical analysis methods are usually based only on the chemical composition of the whole rock, ignoring the role of key minerals such as tourmaline in mineralogical analysis.

[0003] Tourmaline is a common boron-containing mineral with different chemical compositions and isotopic characteristics in different geological environments. Existing studies have shown that the composition of tourmaline can reflect the magmatic-hydrothermal evolution process and is closely related to the enrichment of rare metals. However, existing studies have mostly focused on the analysis of the genesis of tourmaline, and its chemical composition and isotopic characteristics have not yet been systematically applied to the prospecting of rare metal deposits. Although tourmaline is widely present in rare metal deposits, there is still a lack of quantitative prospecting models based on tourmaline composition, especially how to predict mineralization potential, which still lacks in-depth research and practical application.

[0004] Therefore, how to effectively utilize the chemical composition and isotopic characteristics of tourmaline to provide a more scientific and efficient prospecting method for rare metal deposits is still a technical problem that needs to be solved urgently. Summary of the invention

[0005] In order to achieve effective prediction of favorable areas for rare metal prospecting, the present application provides a method, system, equipment and medium for predicting rare metal mineralization potential.

[0006] In the first aspect, the present application provides a method for predicting the mineralization potential of rare metals, which adopts the following technical solution: A method for predicting rare metal mineralization potential, the method comprising: Acquire tourmaline distribution data of the target area; wherein the tourmaline distribution data includes the occurrence type and spatial distribution information of tourmaline samples in granite, the contact zone between rock mass and surrounding rock, and the surrounding rock; Based on the tourmaline distribution data, microlithographic analysis is performed on the tourmaline sample to identify the genetic type and zonal structural characteristics of the tourmaline sample; According to the genetic type and zoning structure characteristics of the tourmaline sample, the major elements and trace elements of the tourmaline sample are detected to obtain major element data and trace element data; Based on the ratio of FeO T to MgO in the major element data and the contents of Nb and Sn in the trace element data, combined with the B isotope δ 11 B value of the tourmaline sample, a magma-hydrothermal evolution model is established; Based on the magma-hydrothermal evolution model, the characteristic element thresholds of the rare metal metallogenic potential are determined; Based on the tourmaline distribution data and the characteristic element thresholds, the favorable ore prospecting areas in the target area are determined.

[0007] By adopting the above technical solutions, the system integrates the mineralogy, geochemistry and isotope analysis of tourmaline, combines the microscopic petrography to identify the multi-stage growth structure characteristics, constructs a magma-hydrothermal evolution path based on the FeO T / MgO ratio, the enrichment trends of Nb and Sn elements and the B isotope Rayleigh fractionation model, and sets the characteristic element thresholds as quantitative indicators of metallogenic potential, realizing the effective prediction of favorable rare metal ore prospecting areas. Compared with the traditional exploration methods, this method uses tourmaline as a direct ore prospecting indicator mineral, combines geological survey, geochemical analysis and quantitative model simulation, and realizes the effective positioning of rare metal ore bodies.

[0008] Optionally, the genetic type and zoning structure characteristics of the tourmaline sample are obtained by observing the color, crystal form and mineral symbiotic relationship of tourmaline under a polarized light microscope, combined with backscattered electron imaging technology.

[0009] Optionally, the major element data includes SiO2, TiO2, Al2O3, FeO T , MnO, MgO, CaO, Na2O, K2O, F and Cl, and the major element data is obtained by electron probe detection; the trace element data includes Li, Zn, Ga, Sr, Nb, Sn, La, Ce, and the trace element data and the B isotope δ 11 B value of the tourmaline sample are obtained by laser ablation inductively coupled plasma mass spectrometry.

[0010] Optionally, the steps of establishing the magma-hydrothermal evolution model include: Based on the ratio of FeO T to MgO in the major element data, the magma-hydrothermal evolution stages of the tourmaline sample are divided to generate stage classification results; According to the stage classification results and the B isotope δ 11 B value, the B isotope fractionation coefficient α and the melt / fluid residue ratio f are determined; Based on the FeO T / MgO ratio, the Nb and Sn contents in the trace element data, and the fractionation coefficient α and the residual ratio f, a three-dimensional model of the magmatic-hydrothermal evolution path is constructed; The three-dimensional model is compared and verified with the field geological exploration data, and the fractionation coefficient α and the Nb / Sn threshold are adjusted to output the calibrated magmatic-hydrothermal evolution model.

[0011] By adopting the above implementation manner, a magmatic-hydrothermal evolution model of tourmaline is established, providing an innovative tool for the effective exploration of rare metal deposits. Through the comprehensive analysis of data such as the FeO T / MgO ratio, B isotope, trace elements, etc. and the construction of a three-dimensional model, the metallogenic potential areas in the magmatic and hydrothermal stages can be effectively predicted. Finally, through the comparison and verification with the field geological exploration data, the accuracy and reliability of the model are ensured. This technical solution not only improves the accuracy of deposit exploration but also provides a feasible scientific basis and technical support for exploration in related fields.

[0012] Optionally, the formula for determining the melt / fluid residual ratio f is: δ 11 B melt(fluid) =δ 11 B initialmelt（fluid） +(α−1)ln(f); In the above formula, δ 11 B melt(fluid) represents the δ 11 B value in the melt / fluid at different stages of crystallization evolution, and δ 11 B initialmelt（fluid） represents the δ 11 B value in the initial melt / fluid.

[0013] Optionally, the characteristic element thresholds are: Nb content > 10 ppm, Sn content > 100 ppm; The determination of the favorable ore prospecting area is based on the following conditions: the occurrence type of tourmaline is vein or cystoid and meets the characteristic element thresholds.

[0014] In a second aspect, the present application provides a rare metal metallogenic potential prediction system, adopting the following technical solution: A rare metal metallogenic potential prediction system, the prediction system includes: A data acquisition module for acquiring the tourmaline distribution data of the target area; wherein, the tourmaline distribution data includes the occurrence type and spatial distribution information of tourmaline samples in granite, the contact zone between the rock mass and the surrounding rock, and the surrounding rock; An identification module, configured to perform microscopic petrographic analysis on the tourmaline sample based on the tourmaline distribution data, and identify the genetic type and zonation structure characteristics of the tourmaline sample; An element detection module, configured to detect major elements and trace elements of the tourmaline sample according to the zonation structure characteristics, and obtain major element data and trace element data; An evolution model establishment module, configured to, based on the ratio of FeO T to MgO in the major element data and the contents of Nb and Sn in the trace element data, and in combination with the B isotope δ 11 B value of the tourmaline sample, establish a magma-hydrothermal evolution model; A characteristic element threshold determination module, configured to determine the characteristic element threshold of the rare metal metallogenic potential based on the magma-hydrothermal evolution model; A favorable ore prospecting area determination module, configured to determine the favorable ore prospecting area of the target area based on the tourmaline distribution data and the characteristic element threshold.

[0015] Optionally, the evolution model establishment module includes: A stage division unit, configured to divide the magma-hydrothermal evolution stage of the tourmaline sample based on the ratio of FeO T to MgO in the major element data, and generate a stage classification result; A parameter determination unit, configured to determine the B isotope fractionation coefficient α and the melt / fluid residue ratio f according to the stage classification result and the B isotope δ 11 B value; A three-dimensional model construction unit, configured to construct a three-dimensional model of the magma-hydrothermal evolution path based on the FeO T / MgO ratio, the contents of Nb and Sn in the trace element data, and the fractionation coefficient α and the residue ratio f; A model calibration unit, configured to compare and verify the three-dimensional model with the field geological exploration data, adjust the fractionation coefficient α and the Nb / Sn threshold, and output the calibrated magma-hydrothermal evolution model.

[0016] In a third aspect, the present application provides a computer device, adopting the following technical solution: A computer device includes a memory, a processor, and a computer program stored on the memory, and the processor executes the computer program to implement the steps of the method as described in the first aspect.

[0017] In a fourth aspect, the present application provides a computer-readable storage medium, adopting the following technical solution: A computer-readable storage medium stores a computer program that can be loaded and executed by a processor to implement any one of the methods in the first aspect.

[0018] In summary, the present application includes at least one of the following beneficial technical effects: By combining the chemical composition and B isotope characteristics of tourmaline, the key geochemical characteristics during magma-hydrothermal evolution can be effectively identified, and through geological, micro-lithofacies and isotope simulation analysis, the metallogenic potential of geological bodies can be accurately evaluated, and then the favorable ore prospecting areas with further exploration value can be effectively delineated, thereby significantly improving the efficiency and accuracy of mineral exploration. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is the first process schematic diagram of a method for predicting the metallogenic potential of rare metals according to one embodiment of the present application.

[0020] Figure 2 is the second process schematic diagram of a method for predicting the metallogenic potential of rare metals according to one embodiment of the present application.

[0021] Figure 3 are the hand specimen, microscopic photograph and BSE imaging characteristics of disseminated tourmaline in granite according to one embodiment of the present application.

[0022] Figure 4 are the hand specimen, microscopic photograph and BSE imaging characteristics of tourmaline-quartz vesicles at the top and edge of granite according to one embodiment of the present application.

[0023] Figure 5 are the hand specimen, microscopic photograph and BSE imaging characteristics of tourmaline-quartz veins at the edge of the rock mass according to one embodiment of the present application.

[0024] Figure 6 is the B isotope Rayleigh fractionation simulation diagram of different types of tourmaline according to one embodiment of the present application, aiming to show the B isotope evolution characteristics of different tourmaline types during magma-hydrothermal evolution.

[0025] Figure 7 are the box plots of major and trace elements of different types of tourmaline according to one embodiment of the present application, aiming to show the differences and trends in element contents of each type of tourmaline. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] In order to make the objectives, technical solutions and advantages of the present application clearer, the following further describes the present application in detail with reference to the accompanying Figure 1-7 drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0027] The embodiments of the present application disclose a method for predicting the metallogenic potential of rare metals.

[0028] Reference Figure 1 , a method for predicting the metallogenic potential of rare metals, the prediction method includes: Step S101, obtaining the tourmaline distribution data of the target area; Among them, the tourmaline distribution data includes the occurrence types and spatial distribution information of tourmaline samples in granite, the contact zone between the rock mass and the surrounding rock, and the surrounding rock; Specifically, obtaining the distribution data of tourmaline samples in the target area is the basis of the entire prediction method. Tourmaline is an indicator mineral of granitic pegmatite-type rare metal deposits, and its distribution data can reveal the metallogenic potential of this area. By investigating the occurrence types and spatial distribution of tourmaline samples in granite, the contact zone between the rock mass and the surrounding rock, and the surrounding rock, researchers can initially understand the distribution characteristics of tourmaline in the area and judge which areas have higher metallogenic potential.

[0029] Exemplarily, assume that a large number of tourmaline minerals are found in a certain area, and these minerals are concentrated near the contact zone between granite and the surrounding rock, and the types of tourmaline are diverse. This provides effective data support for subsequent microscopical petrography analysis and metallogenic potential prediction. By obtaining the tourmaline distribution data, the areas with the potential for rare metal mineralization can be effectively delineated, providing guidance for subsequent detailed analysis and enhancing the efficiency and pertinence of prospecting.

[0030] Step S102, based on the tourmaline distribution data, performing microscopical petrography analysis on the tourmaline samples to identify the genetic types and zonation structure characteristics of the tourmaline samples; Among them, microscopical petrography analysis can help identify the different components and formation processes of tourmaline minerals. By observing the zonation structure of tourmaline, different formation stages can be judged. The zonation structure characteristics include oscillatory zonation, compositional mutation zonation, and dissolution structures, which can reveal the different evolutionary processes experienced by tourmaline during the magmatic-hydrothermal evolution process.

[0031] Exemplarily, through microscopic observation of tourmaline samples, it is found that some tourmaline samples have a typical core-edge structure, indicating that these tourmalines have two-stage growth, while the edges of some samples show dissolution structures, which may be related to fluid action. Through microscopical petrography analysis, the genetic types and evolutionary processes of tourmaline can be effectively identified, providing an important basis for subsequent geochemical analysis and metallogenic potential prediction.

[0032] Step S103, according to the genetic types and zonation structure characteristics of the tourmaline samples, performing major element and trace element detections on the tourmaline samples to obtain major element data and trace element data; Among them, the major element data includes but is not limited to SiO2, TiO2, Al2O3, FeO T, MnO, MgO, CaO, Na2O, K2O, F, Cl, etc., and trace element data includes but is not limited to Li, Zn, Ga, Sr, Nb, Sn, La, Ce, etc.

[0033] Specifically, the contents of major elements and trace elements are the core indicators of the chemical characteristics of tourmaline, which can reflect the element migration and enrichment during the magmatic-hydrothermal process. By detecting tourmaline samples with an electron probe microanalyzer (EPMA) and a laser ablation inductively coupled plasma mass spectrometer (LA-ICP-MS), SiO2, TiO2, Al2O3, FeO T , MnO, MgO, CaO, Na2O, K2O, F, Cl and other major elements and data of trace elements such as Li, Zn, Ga, Sr, Nb, Sn, La, Ce, etc.

[0034] Exemplarily, in some tourmaline samples, the FeO T / MgO ratio is relatively high, and the contents of Nb and Sn exceed specific thresholds, which means that there may be a high potential for rare metal mineralization in this area. By obtaining the data of major elements and trace elements of tourmaline samples, the chemical composition characteristics of tourmaline can be further determined, which is closely related to the formation of rare metal deposits, enhancing the prediction accuracy of mineralization potential.

[0035] Step S104, based on the FeO T in the major element data and the ratio of MgO, as well as the contents of Nb and Sn in the trace element data, combined with the B isotope δ 11 B value of the tourmaline sample, establish a magmatic-hydrothermal evolution model; Among them, the chemical composition and isotope characteristics of tourmaline are important records of the magmatic-hydrothermal evolution process. By combining the major element, trace element data and B isotope δ 11 B value of tourmaline samples to establish a magmatic-hydrothermal evolution model, the evolution process of tourmaline from the magmatic stage to the hydrothermal stage can be effectively simulated, further revealing the mineralization process.

[0036] Exemplarily, during the simulation process, it is found that the δ 11 B values of different types of tourmaline are consistent with the evolution of the simulation curve, indicating that its crystallization evolution degree is increasing. By establishing a magmatic-hydrothermal evolution model, it can help clarify the evolution path of the tourmaline mineralization process, and then predict the distribution characteristics of rare metal deposits, improving the prediction accuracy and credibility.

[0037] Step S105, based on the magmatic-hydrothermal evolution model, determine the characteristic element thresholds of rare metal mineralization potential; Among them, based on the magma-hydrothermal evolution model, the contents of major and trace elements in different types of tourmaline are analyzed. Combining the changing trends of the contents of these elements and the δ¹¹B value of B isotope, the characteristic element thresholds closely related to the rare metal metallogenic potential are determined through statistical and empirical data. These thresholds are usually the minimum values of element contents and are used to identify potential high-metallogenic-potential areas.

[0038] Step S106: Determine the favorable ore prospecting areas in the target area based on the tourmaline distribution data and the characteristic element thresholds.

[0039] Among them, by combining the tourmaline distribution data, micro-lithofacies analysis results, element thresholds, and magma-hydrothermal evolution model of the target area, potential favorable ore prospecting areas can be effectively delimited.

[0040] Exemplarily, based on the tourmaline composition analysis and characteristic element thresholds, combined with the prediction of the magma-hydrothermal model, a favorable ore prospecting area with a diameter range of 200 meters is finally determined. Two vein-shaped ore bodies are found in this area. Through this method, rare metal ore bodies can be accurately located, the cost of blind exploration can be reduced, and the efficient utilization of resources and exploration efficiency can be ensured.

[0041] In the embodiment of this application, through actual research data, it is determined that the characteristic element thresholds are: Nb content > 10 ppm, Sn content > 100 ppm, and δ 11 B value is higher than -10.0‰; the determination of the favorable ore prospecting area is based on the following conditions: the occurrence type of tourmaline is vein-shaped or cystoid, and it meets the above characteristic element thresholds and the δ 11 B value range.

[0042] Specifically, Nb and Sn, as the marker elements of rare metals, when their contents are enriched in the ore deposit, are usually related to hydrothermal activities and the formation of rare metal deposits. δ 11 The B value is an important indicator during the formation of tourmaline. In the embodiment of this application, the δ 11 B value higher than -10.0‰ indicates that the tourmaline has the characteristics of the hydrothermal stage.

[0043] In addition, the occurrence type of tourmaline (such as vein-shaped or cystoid) is usually related to the magma-hydrothermal evolution stage. Vein-shaped tourmaline usually represents the product of the hydrothermal stage, and cystoid is the product of the magma-hydrothermal transition stage. According to the occurrence type of tourmaline and combined with the characteristic element thresholds (Nb, Sn contents, and δ 11 B value range), favorable ore prospecting areas with further exploration value can be determined.

[0044] Exemplarily, if the occurrence of tourmaline in a certain area is vein-shaped, and the Nb content is 20 ppm, the Sn content is 120 ppm, and δ 11If the B value is -9‰, then this area meets the conditions for a favorable ore prospecting area, indicating that this area has a high metallogenic potential.

[0045] In the above embodiment, the system integrates mineralogy, geochemistry, and isotope analysis of tourmaline, combines microscopic petrography to identify the characteristics of multi-stage growth structures, constructs a magmatic-hydrothermal evolution path based on the FeO T / MgO ratio, the enrichment trends of Nb and Sn elements, and the B isotope Rayleigh fractionation model, and sets the characteristic element threshold as a quantitative index for metallogenic potential, realizing an effective prediction of favorable areas for rare metal ore prospecting. Compared with traditional exploration methods, this method uses tourmaline as a direct ore prospecting indicator mineral, combines geological survey, geochemical analysis, and quantitative model simulation, and improves the exploration efficiency.

[0046] As an embodiment of step S102, the genetic type and zonation structure characteristics of the tourmaline sample are obtained by observing the color, crystal form, and mineral paragenetic relationship of tourmaline under a polarized light microscope and combining backscattered electron imaging technology.

[0047] Among them, the zonation structure characteristics include oscillatory zonation, compositional mutation zonation, and dissolution structure; oscillatory zonation refers to the structure that shows periodic changes inside the mineral crystal due to the influence of external environmental changes (such as temperature, pressure, chemical composition, etc.) during the crystallization process of the mineral; compositional mutation zonation refers to the sudden change in the internal composition of the mineral, usually caused by drastic changes in the external environment (such as temperature, chemical environment, etc.); dissolution structure refers to the damage to the mineral structure caused by the interaction between the mineral surface or inside the crystal and the fluid, which is usually caused by hydrothermal erosion, chemical dissolution, etc., and may be manifested as irregular shapes, pores, or cracks on the mineral surface.

[0048] Specifically, a polarized light microscope is a tool used to observe the color, crystal form, and mineral paragenetic relationship in a mineral sample. By irradiating the sample with a polarized light source, its microscopic structure and material properties can be revealed. Combining backscattered electron imaging (BSE) technology can further analyze the crystal morphology and composition distribution of the mineral. This method is used to obtain the zonation structure characteristics of tourmaline and help determine its origin and evolution process.

[0049] Exemplarily, under a polarized light microscope, it is observed that the tourmaline sample shows obvious zonation structure, and its color gradually changes from brown to blue-green, suggesting that it may be formed by multi-stage growth. Combining with the BSE image, the chemical composition changes of the zonation can be determined, such as the areas rich in Nb and Sn are located at the edge of the zonation.

[0050] As an embodiment of step S103, the major element data is obtained by electron probe detection, and the trace element data and the B isotope δ of the tourmaline sample 11The B value is obtained by detecting with a laser ablation inductively coupled plasma mass spectrometer.

[0051] Among them, an electron probe micro-analyzer (EPMA) is a technique widely used in the analysis of minerals and rocks. It uses an electron beam to excite the surface of a sample to generate X-rays, and then obtains the quantitative composition of each element in the sample by analyzing the energy spectrum of these X-rays. The electron probe can detect major elements in a sample with high precision, such as SiO2, TiO2, Al2O3, FeO T , MnO, MgO, CaO, Na2O, K2O, F, and Cl, etc. Exemplarily, assume that the content of FeO T in a tourmaline sample is detected to be 20% and the content of MgO is 10% by an electron probe. These data can be used to further calculate the FeO T / MgO ratio to assist in judging the magmatic or hydrothermal evolution stage of the sample. Through the high-precision detection of the electron probe, the accuracy and reliability of the major element data are ensured, providing basic support for the division of the magmatic-hydrothermal evolution stage.

[0052] In addition, a laser ablation inductively coupled plasma mass spectrometer (LA-ICP-MS) is a technique commonly used in the analysis of trace elements and isotopes in minerals. A laser beam is used to ablate a small amount of material from the surface of a sample, and then the sample material is excited into ions by a plasma source and enters the mass spectrometer for analysis. In this way, trace elements (such as Li, Nb, Sn, etc.) and B isotopes (such as δ 11 B value) in a tourmaline sample can be effectively determined. Assume that the Nb concentration in a tourmaline sample is detected to be 30 ppm and the Sn concentration is 110 ppm by using the LA-ICP-MS technique, and the δ 11 B value is -12.6‰. These data can be used for the subsequent establishment of an evolution model. By obtaining the trace element and isotope data in a tourmaline sample, reliable data support is provided for the subsequent calculation of the evolution path and the prediction of favorable ore prospecting areas.

[0053] Referring to Figure 2 , as an implementation manner of step S104, the steps of establishing a magmatic-hydrothermal evolution model include: Step S201, based on the ratio of FeO T to MgO in the major element data, divide the magmatic-hydrothermal evolution stage of the tourmaline sample to generate a stage classification result; Among them, first, the major element data of the tourmaline sample need to be obtained, especially the contents of FeO T (iron oxide) and MgO (magnesium oxide). By calculating the ratio of the two (FeO T / (MgO), the magmatic-hydrothermal evolution stage of the sample can be determined, providing important initial information for the subsequent evolution model.

[0054] Step S202: According to the stage classification result and the B isotope δ 11 B value, determine the B isotope fractionation coefficient α and the melt / fluid residue ratio f; Among them, the B isotope (δ 11 B value) is used to further infer the evolution characteristics of the tourmaline sample. According to different evolution stages (magmatic stage or hydrothermal stage), the B isotope fractionation coefficients α between the magmatic melt and tourmaline, and between the hydrothermal fluid and tourmaline are different and are affected by temperature.

[0055] Exemplarily, assume that for a certain tourmaline sample in the magmatic stage, its δ 11 B value is -12.6‰. Then, based on the fractionation coefficient α = 0.98 and f ≈ 0.8, the evolution path of the sample is further deduced. Through the analysis of the B isotope, more accurate fractionation and residue ratio data for the evolution process of the tourmaline sample are provided, which helps to distinguish the crystallization evolution degree of minerals at different stages.

[0056] In the embodiment of the present application, the formula for determining the melt / fluid residue ratio f is: δ 11 B melt(fluid) =δ 11 B initialmelt（fluid） +(α−1)ln(f); In the above formula, δ 11 B melt(fluid) represents the δ 11 B value in the melt / fluid at different crystallization evolution stages, and δ 11 B initialmelt（fluid） represents the δ 11 B value in the initial melt / fluid.

[0057] Among them, this formula is a formula for calculating the B isotope in the melt or fluid stage based on the fractionation coefficient α, the initial B isotope value, and the melt / fluid residue ratio f. This formula indicates that as the melt or fluid evolves, the δ 11 B value of the B isotope will change with the change of the fractionation coefficient α. (α−1)ln(f) reflects the degree of change of the B isotope in the melt or fluid stage, and the ln(f) term represents the contribution of the melt / fluid residue ratio to the change of the B isotope.

[0058] Through this formula, when the initial B isotope value and the fractionation coefficient are known, the change of the B isotope of tourmaline at different evolution stages can be deduced, and then the detailed information of the magmatic-hydrothermal evolution process can be inferred.

[0059] Step S203: Based on FeOT Construct a three-dimensional model of the magmatic-hydrothermal evolution path by using the FeO / MgO ratio, the contents of Nb and Sn in trace element data, and the fractionation coefficient α and the residual ratio f. T Among them, combine the FeO T / MgO ratio (representing the degree of magmatic-hydrothermal evolution), the contents of Nb and Sn (reflecting the enrichment degree of rare metals), and the fractionation coefficient α and the residual ratio f to construct a three-dimensional model of magmatic-hydrothermal evolution. Among them, the X-axis of the three-dimensional model represents the FeO 11 / MgO ratio, depicting the evolution trend from magma to hydrothermal fluid; the Y-axis represents the δ

[0060] Exemplarily, assume that in the magmatic stage, the FeO T / MgO ratio is 5, the δ 11 B value is -12.6‰, Nb is 5 ppm, and Sn is 10 ppm. In the hydrothermal stage, the FeO T / MgO ratio rises to 20, the δ 11 B value is -8‰, Nb is 35 ppm, and Sn is 80 ppm. By mapping these data into three-dimensional space, the evolution path from the magmatic stage to the hydrothermal stage can be clearly shown, providing a spatial view for subsequent prediction of metallogenic potential and improving the prediction accuracy.

[0061] Step S204: Compare and verify the three-dimensional model with the field geological exploration data, adjust the fractionation coefficient α and the Nb / Sn threshold, and output the calibrated magmatic-hydrothermal evolution model.

[0062] Among them, the field geological exploration data includes the spatial positions and element enrichment characteristics of known rock / ore bodies. Compare the obtained three-dimensional model with the field geological exploration data to verify the accuracy of the model. For example, whether the actual ore body position overlaps with the predicted high-potential area. If the prediction deviation exceeds a certain range (such as 20%), then it is necessary to calibrate the fractionation coefficient α, the initial δ 11 B value, and the Nb / Sn threshold to optimize the model prediction results.

[0063] Exemplarily, assume that in actual exploration, the FeO T / MgO ratio in a certain area is 15, the Nb concentration is 70 ppm, and the δ 11 B value is -9‰, which is consistent with the predicted high-potential hydrothermal area of the model. By comparing with the actual data, if there is a deviation, adjust the model parameters such as the α value and the Sn threshold to optimize the final model.

[0064] In the above embodiments, a magmatic-hydrothermal evolution model of tourmaline is established, providing an innovative tool for the effective exploration of rare metal deposits. By comprehensively analyzing data such as the FeO T / MgO ratio, B isotope, and trace elements, and constructing a three-dimensional model, it is possible to effectively predict the metallogenic potential areas in the magmatic and hydrothermal stages. Finally, through comparison and verification with field geological exploration data, the accuracy and reliability of the model are ensured. This technical solution not only improves the accuracy of deposit exploration but also provides a feasible scientific basis and technical support for exploration in related fields.

[0065] This application provides a method for predicting the metallogenic potential of rare metals based on the chemical composition and B isotope of tourmaline. By combining field geological surveys and indoor tourmaline geochemical research, it realizes the effective positioning of favorable ore prospecting areas. First, through systematic field geological surveys of the target area, the occurrence differences of tourmaline in granite, contact zones, and surrounding rocks are accurately divided, and the distribution characteristics of different types of tourmaline are identified. Secondly, combined with microfacies analysis, the multi-stage zoning structure of tourmaline is identified, and a quantitative correlation between the FeO T / MgO ratio and hydrothermal evolution is further established to provide a geological background for the metallogenic process. Through the combined technology of EPMA and LA-ICP-MS, the main trace elements and B isotope data of tourmaline are systematically obtained synchronously to reveal the key geochemical characteristics of the magmatic-hydrothermal transition stage, especially the variation laws of the contents of elements such as Nb and Sn and the B isotope values in tourmaline. Based on B isotope simulation, a regional magmatic-hydrothermal evolution model is constructed to reveal the relationship between magmatic and hydrothermal evolution during the crystallization process of tourmaline, providing a theoretical support for subsequent ore prospecting work. Finally, the correlation between the magmatic-hydrothermal evolution degree and favorable ore prospecting areas is established, and the metallogenic potential is evaluated by combining characteristic element thresholds (such as Nb > 10 ppm, Sn > 100 ppm), and finally, favorable ore prospecting areas with high potential are delineated.

[0066] The following is a specific example of the method for predicting the metallogenic potential of rare metals in this application during the actual research process: Currently, the Xinjiang Huoshibulake rare metal granite pluton is a newly discovered Nb-REE mineralized pluton in recent geological survey work. Previous work has shown that the whole-rock niobium content of this pluton mostly exceeds the boundary grade, and multiple Nb and REE ore bodies have developed around the edge of the pluton. The main economic minerals include columbite-tantalite, bastnaesite, and parisite, with great metallogenic potential. Therefore, for this favorable ore prospecting area, the method for predicting the metallogenic potential of rare metals in this application is adopted, and the detailed technical solution is as follows: 1. Obtain the field geological characteristics of the target area: Conduct a route geological survey of the ore-bearing pluton and find that tourmaline is generally developed in the pluton. Refer to Figure 3, further large-scale profile measurements at a scale of 1:500 were carried out at the rock mass boundary, and it was found that tourmaline in the rock mass mainly showed disseminated forms. Among them, a, b, and c are subhedral - anhedral disseminated tourmaline in the granite of the Huoshibulake rock mass; d and e are micrographs of disseminated tourmaline (plane-polarized light), with the core showing brown - yellow pleochroism (Tur-DB) and the edge showing blue - green pleochroism (Tur-DG); f is the backscattered image of disseminated tourmaline, and the orange circles and blue circles with numbers represent the positions of boron isotope analysis and the corresponding δ 11 B value, Tur = tourmaline, Qtz = quartz, Kf = potassium feldspar, Zr = zircon.

[0067] Refer to Figure 4 , a large number of tourmaline - quartz cystoids appeared at the top and edge of the rock mass. a, b, and c are field photos of tourmaline - quartz cystoids in the Huoshibulake rock mass, showing a typical core - mantle - edge structure; d and e are the sieve structures formed by the intergrowth of tourmaline and quartz, with a yellowish - brown core (Tur-OB) and a blue - green edge (Tur-OG) visible, and the orange circles and blue circles with numbers represent the positions of boron isotope analysis and the corresponding δ 11 B value; f is the two - stage growth structure of tourmaline under the backscattered image, Tur = tourmaline, Qtz = quartz.

[0068] Refer to Figure 5 , a large number of tourmaline - quartz veins appeared at the contact zone between the edge of the rock mass and the surrounding rock. a, b, and c are field photos of tourmaline - quartz veins in the Huoshibulake rock mass; d and e are the zoning characteristics of tourmaline in the vein under plane - polarized light, and the orange and blue circles with numbers represent the positions of boron isotope analysis and the corresponding δ 11 B value; f is the tourmaline zoning under the backscattered image, Tur = tourmaline, Qtz = quartz.

[0069] Typical cystoids consist of a columnar tourmaline core, a tourmaline - quartz mantle, and a light - colored edge. The core is mainly composed of anhedral columnar tourmaline clusters and a small amount of quartz, while the mantle is composed of intergrown tourmaline and quartz, and the edge is a fine - grained granite structure with underdeveloped dark minerals. Tourmaline - quartz veins usually range in width from a few millimeters to dozens of centimeters and in length from several meters to hundreds of meters, and the main minerals are tourmaline, quartz, a small amount of mica, and fluorite. For different types of tourmaline, hand specimen observations were carried out and their mineral characteristics and occurrence rules were detailedly recorded, and different types of samples were systematically collected.

[0070] 2. Identification of multi - stage growth information of tourmaline by micro - petrography: For the three types of samples collected in the field, polarized light microscopy and BSE imaging observations were carried out to identify five types of tourmaline, which are respectively: (1) Disseminated tourmaline symbiotic with feldspar and quartz in granite matrix. Under plane polarized light, this type of tourmaline shows a brown - yellowish brown core (HS - DB) and a blue - green rim (HS - DG). (2) Tourmaline in tourmaline - quartz vesicles. Under plane polarized light, it has a brown - yellowish brown core (HS - OB) and a blue - green rim (HS - OG). (3) Tourmaline in tourmaline - quartz veins (HS - V).

[0071] 3. Obtain the major and trace element compositions and B isotope characteristics of tourmaline: Use EPMA to analyze the major element compositions (SiO2, TiO2, Al2O3, FeO T , MnO, MgO, CaO, Na2O, K2O, F, and Cl) of different types of tourmaline, and use LA - ICP - MS to analyze the trace elements (Li, Zn, Ga, Sr, Nb, Sn, La, Ce, etc.) and B isotope compositions of tourmaline. Based on the petrographic and geochemical characteristics of tourmaline, it is determined that the brown core of disseminated tourmaline is magmatic tourmaline crystallized from the residual melt in the late stage of the magmatic stage, the brown tourmaline in vesicles is magmatic tourmaline crystallized from the exsolved boron - rich melt phase, the blue - green accretion rims on the edges of disseminated and vesicular tourmaline are hydrothermal accretion rims formed by exsolved fluids, and vein - type tourmaline is formed during another boron - rich melt / fluid pulse.

[0072] 4. Construct a magmatic - hydrothermal evolution geological model: The δ 11 B range of the five types of tourmaline is from - 12.6‰ to - 5.2‰. Among them, brown - yellowish brown tourmaline has a relatively negative δ 11 B range (- 12.6‰ to - 10.0‰), while the blue - green tourmaline accretion rims have a significantly more positive δ 11 B range (- 10.2‰ to - 4.9‰).

[0073] Refer to Figure 6 , and combine the distribution coefficients between tourmaline and melt as well as between tourmaline and fluid to conduct Rayleigh fractionation simulations of tourmaline B isotopes. Among them, Rayleigh fractionation simulations of B isotopes between (a) melt and magmatic tourmaline and (b) fluid and hydrothermal tourmaline are carried out. The initial δ 11 B values of the melt and fluid are set to - 12.34‰ and - 8.29‰ respectively. The violin plot shows the B isotope composition ranges of different types of tourmaline. The results show that as the δ 11 B value in the residual melt / fluid during tourmaline crystallization gradually increases, the rock mass → tourmaline - quartz vesicles → veins show a continuous evolution process from the magmatic stage to the magmatic - hydrothermal transition stage, and then to the hydrothermal stage.

[0074] 5. Prediction of rare metal metallogenic potential: Based on the above results, refer toFigure 7 Box plots of some major elements (apfu, a - d) and trace elements (ppm, e - k) in tourmaline from the Huoshibulak rock mass. The results show that for major elements, from HS - DB → HS - OB → HS - DG → HS - OG → HS - V, there is a trend of decreasing Fe and Ti contents and increasing Al and X - vac contents; for trace elements, vein - type tourmaline (HS - V) and early brown tourmaline (HS - DB and HS - OB) show higher Sc, Ga, Sr, Sn, and Nb concentrations and lower Li concentration compared with late blue - green tourmaline (HS - DG and HS - OG); for B isotopes, tourmaline of HS - OB, HS - DB, and HS - V types shows a relatively narrow δ 11 B range, which is - 11.8‰ to - 10.2‰, - 12.6‰ to - 10.0‰, and - 11.7‰ to - 10.1‰ respectively. In contrast, the δ 11 B value ranges of blue - green tourmaline of HS - OG and HS - DG types are higher, which are - 9.8‰ to - 7.4‰ (average value is - 8.7‰, n = 32) and - 10.2‰ to - 5.2‰ (average value is - 8.2‰, n = 29) respectively. Based on three indicators of high FeO T / MgO ratio in major elements, Nb > 10 ppm and Sn > 100 ppm in trace elements, and high δ 11 B value, it is determined that the area within 200 meters near HS - V is a favorable prospecting area for Nb and REE. After verification, two vein - type ore bodies have been discovered in this favorable prospecting area.

[0075] The embodiment of the present application also discloses a rare metal metallogenic potential prediction system.

[0076] A rare metal metallogenic potential prediction system, the prediction system includes: A data acquisition module, used to acquire the tourmaline distribution data of the target area; wherein, the tourmaline distribution data includes the occurrence types and spatial distribution information of tourmaline samples in granite, the contact zone between the rock mass and the surrounding rock, and the surrounding rock; An identification module, used to conduct micro - petrographic analysis on tourmaline samples based on the tourmaline distribution data, and identify different genetic types and zonation structure characteristics of tourmaline samples; An element detection module, used to detect major elements and trace elements of tourmaline samples according to the zonation structure characteristics, and obtain major element data and trace element data; An evolution model establishment module, used to establish a magmatic - hydrothermal evolution model based on the FeO T and MgO ratio in major element data and the Nb and Sn contents in trace element data, combined with the B isotope δ 11 B value of tourmaline samples; A characteristic element threshold determination module, configured to determine the characteristic element threshold of the rare metal metallogenic potential based on a magma-hydrothermal evolution model; A favorable ore prospecting area determination module, configured to determine the favorable ore prospecting area of the target area based on the tourmaline distribution data and the characteristic element threshold.

[0077] As an implementation manner of the evolution model establishment module, it includes: A stage division unit, configured to divide the magma-hydrothermal evolution stages of the tourmaline samples based on the ratio of FeO T to MgO in the major element data, and generate a stage classification result; A parameter determination unit, configured to determine the B isotope fractionation coefficient α and the melt / fluid residue ratio f according to the stage classification result and the B isotope δ 11 B value; A three-dimensional model construction unit, configured to construct a three-dimensional model of the magma-hydrothermal evolution path based on the FeO T / MgO ratio, the Nb and Sn contents in the trace element data, and the fractionation coefficient α and the residue ratio f; A model calibration unit, configured to compare and verify the three-dimensional model with the field geological exploration data, adjust the fractionation coefficient α and the Nb / Sn threshold, and output the calibrated magma-hydrothermal evolution model.

[0078] The rare metal metallogenic potential prediction system according to the embodiment of the present application can implement any of the above prediction methods, and the specific working processes of each module in the prediction system can refer to the corresponding processes in the above method embodiments.

[0079] In several embodiments provided in the present application, it should be understood that the provided methods and systems can be implemented in other ways. For example, the system embodiments described above are only illustrative; for example, the division of a certain module is only a logical function division, and there may be other division methods in actual implementation. For example, multiple modules can be combined or integrated into another system, or some features can be ignored or not executed.

[0080] The embodiment of the present application also discloses a computer device.

[0081] The computer device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements a rare metal metallogenic potential prediction method as described above.

[0082] The embodiment of the present application also discloses a computer-readable storage medium.

[0083] A computer-readable storage medium stores a computer program that can be loaded and executed by a processor to perform any one of the rare metal metallogenic potential prediction methods as described above.

[0084] Among them, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, device, or component; the program code contained on the computer-readable medium can be transmitted by any suitable medium, including but not limited to wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0085] It should be noted that in the above embodiments, the descriptions of each embodiment have their own emphases. For parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0086] The above are all preferred embodiments of the present application. The protection scope of the present application is not limited hereby. Any feature disclosed in this specification (including the abstract and drawings), unless specifically stated, can be replaced by other equivalent or similar-purpose alternative features. That is, unless specifically stated, each feature is only an example of a series of equivalent or similar features.

Claims

1. A method for predicting the metallogenic potential of rare metals, characterized in that, The prediction method includes: Obtaining tourmaline distribution data of the target area; wherein, the tourmaline distribution data includes the occurrence types and spatial distribution information of tourmaline samples in granite, the contact zone between the rock mass and the surrounding rock, and the surrounding rock; Based on the tourmaline distribution data, performing micro-lithofacies analysis on the tourmaline samples to identify the genetic types and zonation structure characteristics of the tourmaline samples; According to the genetic types and zonation structure characteristics of the tourmaline samples, performing major element and trace element detections on the tourmaline samples to obtain major element data and trace element data; Based on the ratio of FeO to MgO in the major element data and the contents of Nb and Sn in the trace element data, combined with the B isotope δ T B value of the tourmaline sample, a magmatic-hydrothermal evolution model is established; 11 ​ Based on the magmatic-hydrothermal evolution model, determining the characteristic element thresholds for rare metal metallogenic potential; Based on the tourmaline distribution data and the characteristic element thresholds, determining the favorable ore prospecting areas in the target area.

2. The prediction method for the metallogenic potential of rare metals according to claim 1, wherein: The genetic types and zonation structure characteristics of the tourmaline samples are obtained by observing the color, crystal form and mineral paragenetic relationship of tourmaline through a polarized light microscope, combined with backscattered electron imaging technology.

3. A method for predicting the metallogenic potential of rare metals according to claim 1, characterized in that: The major element data includes SiO2, TiO2, Al2O3, FeO T , MnO, MgO, CaO, Na2O, K2O, F and Cl, and the major element data is obtained by electron probe detection; the trace element data includes Li, Zn, Ga, Sr, Nb, Sn, La, Ce, and the trace element data and the B isotope δ 11 B value of the tourmaline sample is obtained by laser ablation inductively coupled plasma mass spectrometry.

4. A method for predicting the metallogenic potential of rare metals according to claim 1, characterized in that The steps for establishing the magmatic-hydrothermal evolution model include: Based on the ratio of FeO to MgO in the major element data, the magmatic-hydrothermal evolution stages of tourmaline samples are divided to generate stage classification results; T Based on the ratio of FeO to MgO in the major element data, the magmatic-hydrothermal evolution stages of tourmaline samples are divided to generate stage classification results; Based on the phase classification result and the δ 11 B value, determine the B isotope fractionation coefficient α and the melt / fluid residue ratio f; Based on the FeO T / MgO ratio, the Nb and Sn contents in the trace element data, and the fractionation coefficient α and the residual ratio f, a three-dimensional model of the magmatic-hydrothermal evolution path is constructed; Comparing and validating the three-dimensional model with the field geological exploration data, adjusting the fractionation coefficient α and the Nb / Sn threshold, and outputting the calibrated magmatic-hydrothermal evolution model.

5. A method for predicting the metallogenic potential of rare metals according to claim 4, characterized in that, The formula for determining the melt / fluid residue ratio f is: δ 11 B melt(fluid) =δ 11 B initial melt（fluid） +(α−1)ln(f); In the above formula, δ 11 B melt(fluid) represents δ in the melt / fluid at different stages of crystallization evolution 11 B value, and δ 11 B initial melt（fluid） represents the δ 11 B value in the initial melt / fluid.

6. A method for predicting the metallogenic potential of rare metals according to any one of claims 1 to 5, characterized in that The characteristic element thresholds are: Nb content > 10 ppm, Sn content > 100 ppm; The determination of the favorable ore prospecting areas is based on the following conditions: the occurrence type of tourmaline is vein or cystoid, and it meets the characteristic element thresholds.

7. A prediction system for the metallogenic potential of rare metals, characterized in that, For implementing a rare metal metallogenic potential prediction method according to any one of claims 1 to 6, the prediction system includes: A data acquisition module for obtaining tourmaline distribution data of the target area; wherein, the tourmaline distribution data includes the occurrence types and spatial distribution information of tourmaline samples in granite, the contact zone between the rock mass and the surrounding rock, and the surrounding rock; An identification module for performing micro-lithofacies analysis on the tourmaline samples based on the tourmaline distribution data to identify the genetic types and zonation structure characteristics of the tourmaline samples; An element detection module for performing major element and trace element detections on the tourmaline samples according to the zonation structure characteristics to obtain major element data and trace element data; An evolution model establishment module for establishing a magma-hydrothermal evolution model based on the ratio of FeO to MgO in the major element data and the contents of Nb and Sn in the trace element data, in combination with the B isotope δ T B value of the tourmaline sample; 11 ​ A characteristic element threshold determination module for determining the characteristic element thresholds for rare metal metallogenic potential based on the magmatic-hydrothermal evolution model; A favorable ore prospecting area determination module for determining the favorable ore prospecting areas in the target area based on the tourmaline distribution data and the characteristic element thresholds.

8. A rare metal metallogenic potential prediction system according to claim 7, characterized in that The evolution model establishment module includes: A stage division unit, configured to divide the magmatic-hydrothermal evolution stages of the tourmaline samples based on the ratio of FeO to MgO in the major element data, and generate a stage classification result; T ​ A parameter determination unit for determining the B isotope fractionation coefficient α and the melt / fluid residue ratio f according to the phase classification result and the B isotope δ 11 B value; 3D model construction unit, for constructing a 3D model of the magmatic-hydrothermal evolution path based on the FeO T / MgO ratio, the Nb and Sn contents in the trace element data, and the fractionation coefficient α and the residual ratio f; A model calibration unit for comparing and validating the three-dimensional model with the field geological exploration data, adjusting the fractionation coefficient α and the Nb / Sn threshold, and outputting the calibrated magmatic-hydrothermal evolution model.

9. A computer device, characterized in that: Including a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the program, it implements the method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: Stored with a computer program that can be loaded and executed by a processor to implement the method according to any one of claims 1 to 6.

Images