Quantitative simulation method for ore-forming element enrichment during metamorphism-anatexis

Abstract

The present invention discloses a quantitative simulation method for the enrichment of ore-forming elements in the metamorphic-deep melting process, relates to the field of mineral exploration technology, and includes the following steps: collecting geochemical data of important ore-bearing surrounding rocks, whole rocks of ore-bearing rock bodies, and main ore-bearing element minerals, and performing in-situ single mineral analysis; using thermodynamic simulation software to simulate the melting process of metamorphic rocks under different temperature, pressure and water content conditions, and analyzing the distribution behavior of ore-forming elements in mineral-melts. The present invention provides detailed quantitative data on the migration and enrichment of ore-forming elements by simulating the whole process from source melting to magma evolution, including the distribution coefficient of elements in different mineral phases, the distribution behavior between melt and fluid, and the dynamic process changing with temperature and pressure. By performing simulation, the type of ore-forming elements enriched under specific geological conditions and the degree of enrichment can be predicted, and the efficiency of prospecting can be significantly improved.

Description

Technical Field

[0001] The invention relates to the technical field of mineral exploration, and in particular to a quantitative simulation method for the enrichment of ore-forming elements in a metamorphic-anatexilic process. Background Art

[0002] Metamorphism refers to the process in which the mineral composition, structure and texture of rocks in the earth's crust change under changes in physical and chemical conditions such as temperature and pressure. Deep melting is an advanced stage of metamorphism, which is manifested as partial or complete melting of rocks to form melts. During the metamorphism-deep melting process, ore-forming elements such as lithium, beryllium, gold, and silver tend to be enriched to form economically valuable deposits. The evolution of the earth's crust is accompanied by geological events such as plate tectonic movements, magmatic activities, and metamorphism. These events provide conditions for the enrichment of ore-forming elements. As an important link in the evolution of the earth's crust, the metamorphism-deep melting process has an important influence on the enrichment of ore-forming elements and the formation of ore deposits. By quantitatively simulating the enrichment process of ore-forming elements in the metamorphism-deep melting process, the mineralization mechanism can be revealed, providing a theoretical basis for finding new mineral resources. Quantitative simulation methods can help scientists predict the location, scale and grade of potential deposits, and provide important references for mineral exploration and development.

[0003] Traditional rock geochemical analysis methods have obvious limitations and shortcomings when dealing with the multi-stage migration and enrichment process of ore-forming elements in the source area, from pre-enrichment, partial melting to fractional crystallization and fluid activity. It is difficult to obtain continuous sample data, resulting in missing information in key evolutionary stages. At the same time, the multi-solution nature of whole-rock geochemical results also makes the judgment of the behavior of ore-forming elements remain at a qualitative level, making it difficult to conduct in-depth quantitative analysis. Therefore, a quantitative simulation method for the enrichment of ore-forming elements in the metamorphic-deep melting process is proposed. Summary of the invention

[0004] The purpose of the present invention is to provide a quantitative simulation method for the enrichment of ore-forming elements in the metamorphic-deep melting process to solve the problems raised in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] The quantitative simulation method for the enrichment of ore-forming elements in the metamorphic-anatexilic process includes the following steps:

[0007] Step 1: Collect geochemical data of important ore-bearing surrounding rocks, whole rocks of ore-bearing rock bodies, and major ore-forming element minerals (such as biotite, titanite, ilmenite, etc.), conduct in-situ single mineral analysis, and obtain micro-area geochemical composition information and the distribution of ore-forming elements in minerals;

[0008] Step 2: Use thermodynamic simulation software (Perple_X) to simulate the melting process of metamorphic rocks under different temperature, pressure and water content conditions, analyze the distribution behavior of ore-forming elements in mineral-melt, compare with the initial melt, and identify the key factors controlling the enrichment and migration of ore-forming elements during partial melting;

[0009] Step 3: Use Melts software to simulate the separation and crystallization of the initial melt simulated by the thermodynamic simulation software, and identify the details of the enrichment and migration of ore-forming elements during the magma separation and crystallization process by analogy with the ore-forming rock mass;

[0010] Step 4: Use the method of combining the law of mass action (LMA) inversion and Gibbs energy minimization (GEM) forward modeling to simulate the behavior of ore-forming elements migrating from the melt to the fluid during the exsolution of ore-forming fluids in the late stage of magma evolution, and analyze the details of the final mineralization process of the ore-forming elements;

[0011] Step 5: Compare and analyze the simulation results with the actual geological data, ore deposit characteristics and existing geological models to verify the accuracy of the simulation, and analyze the micro-regional geochemical composition of the ore-bearing surrounding rock, the whole rock of the ore-bearing rock body and the main ore-forming element minerals based on the simulation results;

[0012] Step 6: Quantitatively analyze the multi-stage migration and enrichment process of ore-forming elements and their controlling factors during the process from source melting to magma evolution. Based on the quantitative simulation results, explain the ore-forming mechanism and output an analysis report, including the source of ore-forming materials and element pre-enrichment mechanism.

[0013] A further improvement of the technical solution of the present invention is that in step 1, the process of in-situ single mineral analysis is:

[0014] Step 101, collecting representative important ore-bearing surrounding rocks, whole rocks of ore-bearing rock masses, and single mineral samples of main ore-forming element minerals from different geological units in the study area, wherein the geological units include metamorphic rocks, mixed rocks, and ore-bearing rock masses, and the main ore-forming element minerals include biotite, titanite, and ilmenite. When sampling, the representativeness and integrity of the samples must be ensured to avoid contamination and confusion;

[0015] Step 102, pre-processing the collected samples by washing, drying, cutting and grinding, and preparing for in-situ single mineral analysis;

[0016] Step 103, observing the single mineral sample under a polarizing microscope, analyzing the morphology, structure and distribution of the mineral, identifying and locating the mineral area that needs to be analyzed for in-situ single mineral analysis, and marking the selected mineral area to facilitate subsequent in-situ single mineral analysis;

[0017] Step 104, using a high-precision electron probe micro-analyzer to prepare for in-situ single mineral analysis, adjusting the voltage, current, and laser frequency parameters of the electron probe micro-analyzer according to the type and properties of the mineral to ensure the accuracy and reliability of the analysis results, performing in-situ analysis on the selected mineral area, and collecting geochemical data on the content, distribution, and isotope ratio of the ore-forming elements;

[0018] Step 105, recording the analysis results and analysis conditions, including the type, content, isotope ratio of the ore-forming elements and the instrument parameters during the analysis, and sorting and classifying the collected geochemical data, establishing a data table to record the geochemical data, so as to facilitate subsequent data processing and analysis;

[0019] Step 106, standardize the obtained geochemical data to eliminate the deviation in the analysis process, use geological mapping software to draw element distribution maps and isotope ratio maps of minerals, analyze the distribution characteristics of ore-forming elements in different minerals, and intuitively display the distribution of ore-forming elements in minerals.

[0020] A further improvement of the technical solution of the present invention is that in step 2, the analysis process of the distribution behavior of ore-forming elements in mineral-melt is:

[0021] Step 201, collecting the whole rock composition data of the target metamorphic rock, including major elements, trace elements and the content of existing volatiles, and establishing a mineral and melt thermodynamic database;

[0022] Step 202, determining the simulated temperature, pressure and water content conditions according to the geological background and experimental data, and selecting the chemical composition of the metamorphic rock that matches the actual geological conditions;

[0023] Step 203, according to the composition of the metamorphic rock, the corresponding pressure-temperature (PT) phase diagram is constructed using Perple_X software combined with a thermodynamic model to analyze the stability of minerals and melts under different temperature and pressure conditions, and different water content conditions are set in the thermodynamic model to simulate the melting behavior of the metamorphic rock under different fluid environments;

[0024] Step 204, starting simulation calculation, the Perple_X software calculates the mineral phase equilibrium and melting reaction under given conditions according to the input conditions and the data in the thermodynamic database, and analyzes the simulation results to obtain the mineral-melt distribution coefficient and the chemical composition of the melt;

[0025] Step 205, analyze the distribution behavior of ore-forming elements in different mineral phases, compare the simulated melt composition with the initial melt composition, analyze the differences in enrichment and migration of ore-forming elements, identify the key mineral phases and chemical components that affect the distribution of ore-forming elements, and use the mineral-melt distribution coefficient to analyze the preference of ore-forming elements in distribution between minerals and melts, and then analyze the impact of changes in temperature, pressure and water content on the distribution behavior of ore-forming elements.

[0026] A further improvement of the technical solution of the present invention is that the calculation expression of the mineral-melt partition coefficient is:

[0027] ;

[0028] in, is the mineral-melt partition coefficient, which represents the distribution coefficient of ore-forming element i between mineral and melt, and provides a thermodynamic model to quantify the distribution of elements between mineral and melt. is the melting point temperature, is the reference equilibrium constant associated with a specific mineral j and ore-forming element i, is the heat of formation of the ore-forming element i, which is a function of pressure P and temperature T, where T is the temperature and R is the universal gas constant. is the reference pressure, which is a standard pressure, P is the simulated pressure, n is the number of mineral phases, The value range of is from 0 to positive infinity. The closer the value is to 0, the lower the concentration of the ore-forming element in the melt and the higher the concentration in the mineral. The larger the value, the higher the concentration of the ore-forming element in the melt and the lower the concentration in the mineral.

[0029] A further improvement of the technical solution of the present invention is that in step 3, the process of separation crystallization simulation is:

[0030] Step 301, obtaining the chemical composition of the initial melt from the thermodynamic simulation software (Perple_X), and determining the simulated temperature and pressure conditions, as well as the initial state of the magma;

[0031] Step 302, inputting the chemical composition of the initial melt into the Melts software, setting the simulated pressure-temperature (PT) path, and selecting a thermodynamic model for simulation calculation;

[0032] Step 303, start the Melts software. During the simulation process, the Melts software calculates the crystallization of minerals and the evolution of melts at different temperatures and pressures to simulate the separation and crystallization process of magma, obtains the melt composition and the composition of crystallized minerals at each stage of the simulation process, and records the concentration of ore-forming elements in the melt and each crystallized mineral;

[0033] Step 304, through the simulation results of Melts software, the crystallization sequence of minerals during the magma cooling process is obtained, which helps to understand the stage characteristics of magma evolution, analyze the distribution behavior of ore-forming elements in different mineral phases, identify the enrichment areas and migration paths of ore-forming elements in the simulation results by analogy with ore-forming rock bodies, and determine the main carriers of the mineral phases that enrich the ore-forming elements.

[0034] A further improvement of the technical solution of the present invention is that in step 4, the analysis process of the details of the final mineralization process of the ore-forming elements is as follows:

[0035] Step 401, collect whole-rock chemical composition data of the late stage of magma evolution, obtain chemical analysis data of fluid inclusions, determine the initial composition of the fluid, and use the law of mass action (LMA) inversion method to establish an initial model of the magma-fluid system based on the existing geochemical data;

[0036] Step 402, using Gibbs Energy Minimization (GEM) software to perform forward simulation, calculate the composition and phase state of the fluid under specific pressure-temperature (PT) conditions, simulate the exsolution process of the fluid, and track the migration of ore-forming elements from magma to fluid;

[0037] Step 403, using the mass action law algorithm, according to the known melt composition and fluid composition data, inversely infer the change of the concentration of ore-forming elements in the melt during the exsolution of the ore-forming fluid, and by solving the mass balance and mass action equations, obtain the equilibrium concentration of the ore-forming elements in the melt and the fluid at different time points. Based on the inversion result of the mass action law, the Gibbs energy minimization algorithm is used to perform forward simulation, set the initial melt and fluid components, and the temperature-pressure conditions during the exsolution of the fluid, and calculate the melt-fluid element distribution coefficient during the exsolution of the fluid and the enrichment and migration law of the ore-forming elements in the fluid through the Gibbs energy minimization algorithm, and perform iterative calculations until the equilibrium state of minimizing the Gibbs free energy is reached;

[0038] Step 404, analyze the concentration change curves of the ore-forming elements in the melt and the fluid in the simulation results, as well as the enrichment and migration rules of the ore-forming elements in the fluid, identify the migration paths and enrichment areas of the ore-forming elements in the fluid, and interpret the mineralization process of the ore-forming elements in the late stage of magma evolution based on the simulation results, and analyze the enrichment mechanism and mineralization of the ore-forming elements.

[0039] A further improvement of the technical solution of the present invention is that the calculation expression of the melt-fluid element distribution coefficient is:

[0040] ;

[0041] in, is the melt-fluid element partition coefficient, is the distribution coefficient of ore-forming element i between melt and fluid, is the reference value between ore-forming element i and fluid component k, is the activation energy between ore-forming element i and fluid component k, R is the universal gas constant, is the absolute temperature, is the activity coefficient of fluid component k, is the partial pressure of the ore-forming element i in the fluid, is the total pressure in the melt, is the mole fraction of ore-forming element i in the melt, m is the number of components in the fluid, The value range of is from 0 to positive infinity. The value of is close to 0, indicating that the ore-forming elements are easily enriched in the melt. The larger the value, the easier it is for the ore-forming elements to be enriched in the fluid.

[0042] A further improvement of the technical solution of the present invention is that in step 5, the analysis process of the micro-region geochemical composition of the ore-bearing surrounding rock, the whole rock of the ore-bearing rock mass and the main ore-forming element minerals is:

[0043] Step 501, collect relevant actual geological data, including but not limited to drilling data, outcrop samples, geochemical exploration data, etc., obtain mineral deposit characteristic information, such as spatial distribution of ore bodies, ore types, mineralization stages, etc., traverse existing geological models, including mineral deposit genesis, mineralization process and mineralization control factors, etc., and extract relevant output data from thermodynamic simulation software;

[0044] Step 502, comparing the simulated melt-fluid system composition, temperature-pressure conditions, mineral-melt partition coefficient, and melt-fluid element partition coefficient parameters with the actual sample analysis results to analyze the consistency between the simulated predicted mineralization process and the actual ore deposit characteristics;

[0045] Step 503, using regression analysis method to obtain the coefficient of determination, quantify the similarity between the simulation results and the actual data, perform sensitivity tests on key parameters in the simulation, and evaluate the impact of changes in key parameters on the simulation results;

[0046] Step 504, conduct micro-area geochemical analysis on the ore-bearing surrounding rock, the whole rock of the ore-bearing rock mass and the main ore-forming element minerals, analyze the micro-area geochemical composition, determine the spatial distribution, enrichment characteristics and mineral carriers of the ore-forming elements, identify the main ore-bearing minerals and secondary ore-bearing minerals of the ore-forming elements, and based on the comparison results of the simulation and actual data, propose the genetic explanation of the ore deposit and analyze the migration, enrichment and precipitation mechanism of the ore-forming elements.

[0047] A further improvement of the technical solution of the present invention is that the calculation expression of the determination coefficient is:

[0048] ;

[0049] in, Is the coefficient of determination, an important indicator in regression analysis, used to quantify the degree of fit of the model to the data. is the u-th actual observation value, is the u-th simulated prediction value, is the mean of the observations, N is the number of data points, The value range is from 0 to 1. The closer it is to 1, the more similar the simulation results are to the actual data.

[0050] A further improvement of the technical solution of the present invention is that in step 6, the process of explaining the mineralization mechanism and outputting the analysis report is as follows:

[0051] Step 601, collecting geochemical data of rocks in the source area, including the contents of major elements and trace elements, and obtaining rock samples at different stages of magma evolution, such as intrusive rocks, volcanic rocks, ores, etc.;

[0052] Step 602, using thermodynamic simulation software (Perple_X) to simulate the source region melting and magma evolution process, and simulate the mineral phase change, element distribution and melt composition under different temperature-pressure conditions;

[0053] Step 603, tracking the migration path and enrichment area of ​​ore-forming elements during the simulation process, analyzing the change of the distribution coefficient of elements between minerals and melts, and identifying the main controlling factors affecting the migration and enrichment of ore-forming elements, such as temperature, pressure, oxygen fugacity, water content, etc., and analyzing the influence of magma composition, mineral combination and structure on element migration;

[0054] Step 604, based on the simulation results, interpret the source of minerals, such as partial melting of crustal or mantle materials, and then analyze the element pre-enrichment mechanism, such as early metasomatism, magma mixing, crystal differentiation, etc.;

[0055] Step 605, constructing a geological model of the evolution from source melting to magma, including the migration and enrichment process of ore-forming elements, combining the geological model with geophysical, geochemical and geological observation data, and performing sensitivity analysis on key parameters in the model to evaluate the impact of parameter changes on the ore-forming process;

[0056] Step 606, integrate the simulation results, experimental data and geological observations, propose a comprehensive explanation of the mineralization mechanism, write a detailed analysis report, including methods, results, models and conclusions, and archive the simulation data, experimental data and analysis results for subsequent research and comparative analysis.

[0057] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art:

[0058] The present invention provides a quantitative simulation method for the enrichment of ore-forming elements in the metamorphism-deep melting process. By simulating the entire process from source area melting to magma evolution, detailed quantitative data on the migration and enrichment of ore-forming elements are provided, including the distribution coefficients of elements in different mineral phases, the distribution behavior between melt and fluid, and the dynamic process that changes with temperature and pressure. Through simulation, the types of ore-forming elements enriched under specific geological conditions and the degree of enrichment can be predicted, thereby significantly improving the efficiency of ore prospecting.

[0059] The present invention provides a quantitative simulation method for the enrichment of ore-forming elements in the metamorphism-anatechnism process. By integrating a variety of geological parameters and physicochemical processes, the migration and enrichment paths of ore-forming elements in the metamorphism-anatechnism process are more accurately simulated. Not only the effect of a single factor is considered, but also the interaction and feedback mechanism between multiple factors are comprehensively considered, thereby greatly improving the accuracy and reliability of mineralization prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0061] Figure 1 is a flow chart of the method of the present invention;

[0062] Figure 2 The present invention is a flow chart for analyzing the distribution behavior of ore-forming elements in mineral-melt;

[0063] Figure 3 This is a flow chart showing the details of the final mineralization process of the mineralization elements of the present invention. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] Embodiment 1, as Figure 1 , Figure 2 As shown, the present invention provides a quantitative simulation method for the enrichment of ore-forming elements in the metamorphic-anatexilic process, comprising the following steps:

[0066] Step 1: Collect geochemical data of the whole rock and main ore-forming element minerals (such as biotite, titanite, ilmenite, etc.) of important ore-bearing surrounding rocks and ore-bearing rock bodies, conduct in-situ single mineral analysis, obtain micro-region geochemical composition information and the distribution of ore-forming elements in minerals, and collect representative single mineral samples of the whole rock and main ore-forming element minerals of important ore-bearing surrounding rocks and ore-bearing rock bodies from different geological units in the study area. The geological units include metamorphic rocks, mixed rocks, and ore-bearing rock bodies. The main ore-forming element minerals include biotite, titanite, and ilmenite. The representativeness and integrity of the samples must be ensured during sampling to avoid contamination and confusion. The collected samples must be pre-processed by cleaning, drying, cutting and grinding, and prepared for in-situ single mineral analysis. The single mineral samples must be observed under a polarizing microscope to analyze the morphology, structure and distribution of the minerals, identify and locate the mineral areas that require in-situ single mineral analysis, and mark the selected mineral areas. To facilitate the subsequent in-situ single mineral analysis, use a high-precision electron probe micro-area analyzer to prepare for in-situ single mineral analysis. According to the type and nature of the mineral, adjust the voltage, current, and laser frequency parameters of the electron probe micro-area analyzer to ensure the accuracy and reliability of the analysis results. Perform in-situ analysis on the selected mineral area, collect geochemical data on the content, distribution, and isotope ratio of ore-forming elements, record the analysis results and analysis conditions, including the type, content, isotope ratio, and instrument parameters of the ore-forming elements, and organize and classify the collected geochemical data. Establish a data table to record the geochemical data for subsequent data processing and analysis. Standardize the obtained geochemical data to eliminate deviations in the analysis process. Use geological mapping software to draw element distribution maps and isotope ratio maps of minerals, analyze the distribution characteristics of ore-forming elements in different minerals, and intuitively display the distribution of ore-forming elements in minerals.

[0067] Step 2: Use thermodynamic simulation software (Perple_X) to simulate the melting process of metamorphic rocks under different temperature, pressure and water content conditions, analyze the distribution behavior of ore-forming elements in mineral-melt, compare with the initial melt, identify the key factors controlling the enrichment and migration of ore-forming elements in the partial melting process, collect the whole rock composition data of the target metamorphic rock, including major elements, trace elements and the content of volatiles (such as water, carbon dioxide, etc.), establish a mineral and melt thermodynamic database to ensure that the thermodynamic simulation software (such as Perple_X) used has a complete mineral and melt thermodynamic database, determine the simulated temperature, pressure and water content conditions according to the geological background and experimental data, and select the chemical composition of the metamorphic rock that matches the actual geological conditions. According to the composition of the metamorphic rock, use Perple_X software combined with the thermodynamic model to construct the corresponding The pressure-temperature (PT) phase diagram is used to analyze the stability of minerals and melts under different temperature and pressure conditions. Different water content conditions are set in the thermodynamic model to simulate the melting behavior of metamorphic rocks under different fluid environments. The simulation calculation is started. The Perple_X software calculates the mineral phase equilibrium and melting reaction under given conditions based on the input conditions and the data in the thermodynamic database, and analyzes the simulation results to obtain the mineral-melt distribution coefficient and the chemical composition of the melt, analyze the distribution behavior of ore-forming elements in different mineral phases, compare the melt composition obtained by simulation with the initial melt composition, analyze the differences in enrichment and migration of ore-forming elements, identify the key mineral phases and chemical components that affect the distribution of ore-forming elements, and use the mineral-melt distribution coefficient to analyze the preference of ore-forming elements in distribution between minerals and melts, and then analyze the influence of changes in temperature, pressure and water content on the distribution behavior of ore-forming elements;

[0068] Furthermore, the calculation expression of the mineral-melt partition coefficient is:

[0069] ;

[0070] in, is the mineral-melt partition coefficient, which represents the distribution coefficient of ore-forming element i between mineral and melt, and provides a thermodynamic model to quantify the distribution of elements between mineral and melt. is the melting point temperature, is the reference equilibrium constant associated with a specific mineral j and ore-forming element i, is the heat of formation of the ore-forming element i, which is a function of pressure P and temperature T, where T is the temperature and R is the universal gas constant. is the reference pressure, which is a standard pressure, P is the simulated pressure, n is the number of mineral phases, The value range of is from 0 to positive infinity. The closer the value is to 0, the lower the concentration of the ore-forming element in the melt and the higher the concentration in the mineral. The larger the value, the higher the concentration of the ore-forming element in the melt and the lower the concentration in the mineral. As the temperature T approaches the melting point, , tends to the limit value, reflecting the distribution behavior of ore-forming elements under near-molten conditions. As the pressure P increases, the ratio in the radical decreases, resulting in a distribution coefficient decreases, indicating that at higher pressures, ore-forming elements are more likely to enter the mineral phase. Considering the effects of temperature and pressure on the heat of formation of elements, Consider the effect of pressure on the partition coefficient, reflecting the changes in the distribution of elements between minerals and melts at different pressures;

[0071] Step 3: Use Melts software to simulate the separation and crystallization of the initial melt simulated by the thermodynamic simulation software. By analogy with the mineralized rock mass, identify the details of the enrichment and migration of mineralizing elements in the process of magma separation and crystallization. Obtain the chemical composition of the initial melt from the thermodynamic simulation software (Perple_X), determine the simulated temperature and pressure conditions, and the initial state of the magma, input the chemical composition of the initial melt into the Melts software, set the simulated pressure-temperature (PT) path, select the thermodynamic model for simulation calculation, and start the Melts software. During the simulation, the Melts software The software calculates the crystallization of minerals and the evolution of melts at different temperatures and pressures to simulate the separation and crystallization process of magma, obtains the melt composition and the composition of crystallized minerals at each stage of the simulation process, and records the concentration of ore-forming elements in the melt and each crystallized mineral. Through the simulation results of Melts software, the crystallization sequence of minerals in the process of magma cooling is obtained, which is helpful to understand the stage characteristics of magma evolution, analyze the distribution behavior of ore-forming elements in different mineral phases, identify the enrichment area and migration path of ore-forming elements in the simulation results and determine the main carrier of the mineral phase enriched with ore-forming elements by analogy with ore-forming rock bodies;

[0072] Step 4: Use the method of combining the law of mass action (LMA) inversion and Gibbs energy minimization (GEM) forward modeling to simulate the behavior of ore-forming elements migrating from the melt to the fluid during the exsolution of ore-forming fluids in the late stage of magma evolution, and analyze the details of the final mineralization process of the ore-forming elements;

[0073] Step 5: Compare and analyze the simulation results with the actual geological data, ore deposit characteristics and existing geological models to verify the accuracy of the simulation, and analyze the micro-regional geochemical composition of the ore-bearing surrounding rock, the whole rock of the ore-bearing rock body and the main ore-forming element minerals based on the simulation results;

[0074] Step 6: Quantitatively analyze the multi-stage migration and enrichment process of ore-forming elements and their controlling factors during the process from source melting to magma evolution. Based on the quantitative simulation results, explain the ore-forming mechanism and output an analysis report, including the source of ore-forming materials and element pre-enrichment mechanism.

[0075] Embodiment 2, as Figure 3 As shown, based on Example 1, the present invention provides a technical solution: Preferably, in step 4, the analysis process of the details of the final mineralization process of the ore-forming elements is:

[0076] Collect whole-rock chemical composition data of the late stage of magma evolution, obtain chemical analysis data of fluid inclusions, determine the initial composition of the fluid, and use the law of mass action (LMA) inversion method to establish an initial model of the magma-fluid system based on the existing geochemical data. Use Gibbs energy minimization (GEM) software for forward simulation to calculate the composition and phase state of the fluid under specific pressure-temperature (PT) conditions, simulate the exsolution process of the fluid, track the migration of ore-forming elements from magma to fluid, and use the law of mass action algorithm to reverse the change in the concentration of ore-forming elements in the melt during the exsolution of the ore-forming fluid based on the known melt composition and fluid composition data. By solving the mass balance and mass action equations, the average concentration of ore-forming elements in the melt and fluid at different time points is obtained. Balance concentration. On the basis of the inversion results of the law of mass action, forward simulation is performed using the Gibbs energy minimization algorithm. The initial melt and fluid components, as well as the temperature-pressure conditions during fluid exsolution are set. The melt-fluid element distribution coefficient and the enrichment and migration law of ore-forming elements in the fluid are calculated using the Gibbs energy minimization algorithm during fluid exsolution. Iterative calculation is performed until the equilibrium state of minimization of Gibbs free energy is reached. The concentration change curves of ore-forming elements in the melt and fluid in the simulation results, as well as the enrichment and migration law of ore-forming elements in the fluid are analyzed. The migration path and enrichment area of ​​ore-forming elements in the fluid are identified. Based on the simulation results, the mineralization process of ore-forming elements in the late stage of magma evolution is explained, and the enrichment mechanism and mineralization of ore-forming elements are analyzed.

[0077] Furthermore, the calculation expression of the melt-fluid element partition coefficient is:

[0078] ;

[0079] in, is the melt-fluid element partition coefficient, is the distribution coefficient of ore-forming element i between melt and fluid, is the reference value between ore-forming element i and fluid component k, is the activation energy between ore-forming element i and fluid component k, R is the universal gas constant, is the absolute temperature, is the activity coefficient of fluid component k, is the partial pressure of the ore-forming element i in the fluid, is the total pressure in the melt, is the mole fraction of ore-forming element i in the melt, m is the number of components in the fluid, The value range of is from 0 to positive infinity. The value of is close to 0, indicating that the ore-forming elements are easily enriched in the melt. The larger the value, the easier it is for the ore-forming elements to be enriched in the fluid. As the temperature increases, the exponential term decrease, resulting in decreases, indicating that high temperature is conducive to the enrichment of elements in the melt. As the activity coefficient of the fluid component k increases increase, The increase in activity coefficient is conducive to the enrichment of elements in the fluid. represents the partial pressure of the ore-forming element i in the fluid, and its increase will lead to decreases, indicating that the increase of partial pressure is conducive to the enrichment of elements in the melt;

[0080] In step 5, the analysis process of the micro-region geochemical composition of the ore-bearing surrounding rock, the whole rock of the ore-bearing rock mass and the main ore-forming element minerals is as follows:

[0081] Collect relevant actual geological data, including but not limited to drilling data, outcrop samples, geochemical exploration data, etc., obtain mineral deposit characteristic information, such as the spatial distribution of ore bodies, ore types, mineralization stages, etc., traverse existing geological models, including ore deposit genesis, mineralization process and mineralization control factors, etc., and extract relevant output data from thermodynamic simulation software, compare the simulated melt-fluid system composition, temperature-pressure conditions, mineral-melt distribution coefficient, melt-fluid element distribution coefficient parameters with the actual sample analysis results, and analyze the consistency between the simulated predicted mineralization process and the actual ore deposit characteristics The determination coefficient is obtained by regression analysis, the similarity between the simulation results and the actual data is quantified, the sensitivity test of the key parameters in the simulation is carried out, the impact of the change of key parameters on the simulation results is evaluated, and the micro-geochemical analysis of the ore-bearing surrounding rock, the whole rock of the ore-bearing rock mass and the main ore-forming element minerals is carried out. The micro-geochemical composition is analyzed to determine the spatial distribution, enrichment characteristics and mineral carriers of the ore-forming elements, identify the main and secondary ore-forming minerals of the ore-forming elements, and propose the genetic explanation of the ore deposit based on the comparison results of the simulation and actual data, and analyze the migration, enrichment and precipitation mechanism of the ore-forming elements;

[0082] Furthermore, the calculation expression of the coefficient of determination is:

[0083] ;

[0084] in, Is the coefficient of determination, an important indicator in regression analysis, used to quantify the degree of fit of the model to the data. is the u-th actual observation value, is the u-th simulated prediction value, is the mean of the observations, N is the number of data points, The value range is from 0 to 1. When it is close to 1, it means that the simulation results are very close to the actual data. The closer it is to 1, the more similar the simulation results are to the actual data, and the model fit is very high. When it is close to 0, it means that the simulation results are less consistent with the actual data. The greater the difference between the simulation results and the actual data, the lower the model fit;

[0085] In step 6, the process of interpreting the mineralization mechanism and outputting the analysis report is as follows:

[0086] Collect geochemical data of source rocks, including the contents of major and trace elements, obtain rock samples at different stages of magma evolution, such as intrusive rocks, volcanic rocks, ores, etc. Use thermodynamic simulation software (Perple_X) to simulate source melting and magma evolution, and simulate mineral phase changes, element distribution and melt composition under different temperature-pressure conditions, track the migration path and enrichment area of ​​ore-forming elements during the simulation, analyze the changes in the distribution coefficient of elements between minerals and melts, and identify the main controlling factors affecting the migration and enrichment of ore-forming elements, such as temperature, pressure, oxygen fugacity, water content, etc., analyze the influence of magma composition, mineral combination and structure on element migration, and according to the simulation results, Explain the sources of mineralizing materials, such as partial melting of crustal or mantle materials, and then analyze the element pre-enrichment mechanism, such as early metasomatism, magma mixing, crystal differentiation, etc., construct a geological model of the evolution from source melting to magma, including the migration and enrichment process of ore-forming elements, combine the geological model with geophysical, geochemical and geological observation data, and conduct sensitivity analysis on key parameters in the model, evaluate the impact of parameter changes on the mineralization process, integrate simulation results, experimental data and geological observations, propose a comprehensive explanation of the mineralization mechanism, write a detailed analysis report, including methods, results, models and conclusions, and archive the simulation data, experimental data and analysis results for subsequent research and comparative analysis.

[0087] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A quantitative simulation method for the enrichment of ore-forming elements during metamorphism-anatexis, characterized by: The following steps are involved: Step 1: Collect geochemical data of important ore-bearing surrounding rocks, ore-bearing rock bodies and main ore-forming element minerals, and conduct in-situ single mineral analysis; Step 2: Use thermodynamic simulation software to simulate the melting process of metamorphic rocks under different temperature, pressure and water content conditions, and analyze the distribution behavior of ore-forming elements in minerals and melts; Step 3: Use Melts software to simulate the separation and crystallization of the initial melt simulated by the thermodynamic simulation software, and identify the details of the enrichment and migration of ore-forming elements during the magma separation and crystallization process by analogy with the ore-forming rock mass; Step 4: Use the method of combining the inversion of the law of mass action and the forward modeling of Gibbs energy minimization to simulate the behavior of ore-forming elements migrating from the melt to the fluid during the exsolution of ore-forming fluids in the late stage of magma evolution, and analyze the details of the final mineralization process of the ore-forming elements; Step 5: Compare and analyze the simulation results with the actual geological data, ore deposit characteristics and existing geological models to verify the accuracy of the simulation, and analyze the micro-regional geochemical composition of the ore-bearing surrounding rock, the whole rock of the ore-bearing rock body and the main ore-forming element minerals based on the simulation results; Step 6: Quantitatively analyze the multi-stage migration and enrichment process of ore-forming elements and their controlling factors during the process from source melting to magma evolution. Based on the quantitative simulation results, explain the ore-forming mechanism and output an analysis report.

2. The quantitative simulation method for the enrichment of ore-forming elements in the metamorphic-anatexilic process according to claim 1, characterized in that: In step 1, the process of in-situ single mineral analysis is: Step 101, collecting representative single mineral samples of important ore-bearing surrounding rocks, whole rocks of ore-bearing rock masses, and main ore-forming element minerals from different geological units in the study area, wherein the geological units include metamorphic rocks, mixed rocks, and ore-bearing rock masses, and the main ore-forming element minerals include biotite, titanite, and ilmenite; Step 102, pre-processing the collected samples by washing, drying, cutting and grinding, and preparing for in-situ single mineral analysis; Step 103, observing the single mineral sample under a polarizing microscope, analyzing the morphology, structure and distribution of the mineral, identifying and locating the mineral area that needs to be subjected to in-situ single mineral analysis, and marking the selected mineral area; Step 104, using an electron probe micro-area analyzer to prepare for in-situ single mineral analysis, adjusting the voltage, current, and laser frequency parameters of the electron probe micro-area analyzer according to the type and properties of the mineral, performing in-situ analysis on the selected mineral area, and collecting geochemical data on the content, distribution, and isotope ratio of the ore-forming elements; Step 105, recording the analysis results and analysis conditions, including the type, content, isotope ratio of the ore-forming elements and the instrument parameters during the analysis, and sorting and classifying the collected geochemical data, and establishing a data table to record the geochemical data; Step 106, standardize the obtained geochemical data to eliminate the deviation in the analysis process, use geological mapping software to draw element distribution maps and isotope ratio maps of minerals, analyze the distribution characteristics of ore-forming elements in different minerals, and intuitively display the distribution of ore-forming elements in minerals.

3. The quantitative simulation method for the enrichment of ore-forming elements in the metamorphic-anatexilic process according to claim 2, characterized in that: In step 2, the analysis process of the distribution behavior of ore-forming elements in mineral-melt is: Step 201, collecting the whole rock composition data of the target metamorphic rock, including major elements, trace elements and the content of existing volatiles, and establishing a mineral and melt thermodynamic database; Step 202, determining the simulated temperature, pressure and water content conditions according to the geological background and experimental data, and selecting the chemical composition of the metamorphic rock that matches the actual geological conditions; Step 203, according to the composition of the metamorphic rock, the corresponding pressure-temperature phase diagram is constructed using Perple_X software combined with a thermodynamic model to analyze the stability of minerals and melts under different temperature and pressure conditions, and different water content conditions are set in the thermodynamic model to simulate the melting behavior of the metamorphic rock under different fluid environments; Step 204, starting simulation calculation, the Perple_X software calculates the mineral phase equilibrium and melting reaction under given conditions according to the input conditions and the data in the thermodynamic database, and analyzes the simulation results to obtain the mineral-melt distribution coefficient and the chemical composition of the melt; Step 205, analyze the distribution behavior of ore-forming elements in different mineral phases, compare the simulated melt composition with the initial melt composition, analyze the differences in enrichment and migration of ore-forming elements, identify the key mineral phases and chemical components that affect the distribution of ore-forming elements, and use the mineral-melt distribution coefficient to analyze the preference of ore-forming elements in distribution between minerals and melts, and then analyze the impact of changes in temperature, pressure and water content on the distribution behavior of ore-forming elements.

4. The quantitative simulation method for the enrichment of ore-forming elements in the metamorphic-anatexilic process according to claim 3, characterized in that: The calculation expression of the mineral-melt partition coefficient is: ； in, is the mineral-melt partition coefficient, which represents the distribution coefficient of ore-forming element i between mineral and melt. is the melting point temperature, is the reference equilibrium constant associated with a specific mineral j and ore-forming element i, is the heat of formation of the ore-forming element i, which is a function of pressure P and temperature T, where T is the temperature and R is the universal gas constant. is the reference pressure, which is a standard pressure, P is the simulated pressure, n is the number of mineral phases, The closer the value is to 0, the lower the concentration of the ore-forming element in the melt and the higher the concentration in the mineral. The larger the value, the higher the concentration of the ore-forming element in the melt and the lower the concentration in the mineral.

5. The quantitative simulation method for the enrichment of ore-forming elements in the metamorphic-anatexilic process according to claim 4, characterized in that: In step 3, the process of separation crystallization simulation is: Step 301, obtaining the chemical composition of the initial melt from thermodynamic simulation software, and determining the simulated temperature and pressure conditions, as well as the initial state of the magma; Step 302, inputting the chemical composition of the initial melt into the Melts software, setting the simulated pressure-temperature path, and selecting a thermodynamic model for simulation calculation; Step 303, start the Melts software. During the simulation process, the Melts software calculates the crystallization of minerals and the evolution of melts at different temperatures and pressures to simulate the separation and crystallization process of magma, obtains the melt composition and the composition of crystallized minerals at each stage of the simulation process, and records the concentration of ore-forming elements in the melt and each crystallized mineral; Step 304, through the simulation results of Melts software, the crystallization order of minerals in the process of magma cooling is obtained, the distribution behavior of ore-forming elements in different mineral phases is analyzed, and by analogy with the ore-forming rock body, the enrichment area and migration path of the ore-forming elements in the simulation results are identified, and the main carrier of the mineral phase enriched with the ore-forming elements is determined.

6. The quantitative simulation method for the enrichment of ore-forming elements in the metamorphic-anatexilic process according to claim 5, characterized in that: In step 4, the analysis process of the details of the final mineralization process of the ore-forming elements is as follows: Step 401, collect whole-rock chemical composition data of the late stage of magma evolution, obtain chemical analysis data of fluid inclusions, determine the initial composition of the fluid, and use the mass action law inversion method to establish an initial model of the magma-fluid system based on the existing geochemical data; Step 402, using Gibbs energy minimization software to perform forward simulation, calculate the composition and phase state of the fluid under specific pressure-temperature conditions, simulate the exsolution process of the fluid, and track the migration of ore-forming elements from magma to fluid; Step 403, using the mass action law algorithm, according to the known melt composition and fluid composition data, inversely infer the change of the concentration of ore-forming elements in the melt during the exsolution of the ore-forming fluid, and by solving the mass balance and mass action equations, obtain the equilibrium concentration of the ore-forming elements in the melt and the fluid at different time points. Based on the inversion result of the mass action law, the Gibbs energy minimization algorithm is used to perform forward simulation, set the initial melt and fluid components, and the temperature-pressure conditions during the exsolution of the fluid, and calculate the melt-fluid element distribution coefficient during the exsolution of the fluid and the enrichment and migration law of the ore-forming elements in the fluid through the Gibbs energy minimization algorithm, and perform iterative calculations until the equilibrium state of minimizing the Gibbs free energy is reached; Step 404, analyze the concentration change curves of the ore-forming elements in the melt and the fluid in the simulation results, as well as the enrichment and migration rules of the ore-forming elements in the fluid, identify the migration paths and enrichment areas of the ore-forming elements in the fluid, and interpret the mineralization process of the ore-forming elements in the late stage of magma evolution based on the simulation results, and analyze the enrichment mechanism and mineralization of the ore-forming elements.

7. The quantitative simulation method for the enrichment of ore-forming elements in the metamorphic-anatexilic process according to claim 6, characterized in that: The calculation expression of the melt-fluid element partition coefficient is: ； in, is the melt-fluid element partition coefficient, is the reference value between ore-forming element i and fluid component k, is the activation energy between ore-forming element i and fluid component k, R is the universal gas constant, is the absolute temperature, is the activity coefficient of fluid component k, is the partial pressure of the ore-forming element i in the fluid, is the total pressure in the melt, is the mole fraction of ore-forming element i in the melt, m is the number of components in the fluid, The value of is close to 0, indicating that the ore-forming elements are easily enriched in the melt. The larger the value, the easier it is for the ore-forming elements to be enriched in the fluid.

8. The quantitative simulation method for the enrichment of ore-forming elements in the metamorphic-anatexilic process according to claim 7, characterized in that: In step 5, the analysis process of the micro-region geochemical composition of the ore-bearing surrounding rock, the whole rock of the ore-bearing rock mass and the main ore-forming element minerals is as follows: Step 501, collect relevant actual geological data, obtain ore deposit characteristic information, traverse the existing geological model, and extract relevant output data from the thermodynamic simulation software; Step 502, comparing the simulated melt-fluid system composition, temperature-pressure conditions, mineral-melt partition coefficient, and melt-fluid element partition coefficient parameters with the actual sample analysis results to analyze the consistency between the simulated predicted mineralization process and the actual ore deposit characteristics; Step 503, using regression analysis method to obtain the coefficient of determination, quantify the similarity between the simulation results and the actual data, perform sensitivity tests on key parameters in the simulation, and evaluate the impact of changes in key parameters on the simulation results; Step 504, conduct micro-area geochemical analysis on the ore-bearing surrounding rock, the whole rock of the ore-bearing rock mass and the main ore-forming element minerals, analyze the micro-area geochemical composition, determine the spatial distribution, enrichment characteristics and mineral carriers of the ore-forming elements, identify the main ore-bearing minerals and secondary ore-bearing minerals of the ore-forming elements, and based on the comparison results of the simulation and actual data, propose the genetic explanation of the ore deposit and analyze the migration, enrichment and precipitation mechanism of the ore-forming elements.

9. The quantitative simulation method for the enrichment of ore-forming elements in the metamorphic-anatexilic process according to claim 8, characterized in that: The calculation expression of the determination coefficient is: ； in, is the coefficient of determination, is the u-th actual observation value, is the u-th simulated prediction value, is the mean of the observations, N is the number of data points, The value range is from 0 to 1. The closer it is to 1, the more similar the simulation results are to the actual data.

10. The quantitative simulation method for the enrichment of ore-forming elements in the metamorphic-anatexilic process according to claim 9, characterized in that: In step 6, the process of explaining the mineralization mechanism and outputting the analysis report is as follows: Step 601, collecting geochemical data of rocks in the source area, including the contents of major elements and trace elements, and obtaining rock samples at different stages of magma evolution; Step 602, using thermodynamic simulation software to simulate the source region melting and magma evolution process, and simulate the mineral phase change, element distribution and melt composition under different temperature-pressure conditions; Step 603, tracking the migration path and enrichment area of ​​ore-forming elements during the simulation process, analyzing the change of the distribution coefficient of elements between minerals and melts, identifying the main controlling factors affecting the migration and enrichment of ore-forming elements, and analyzing the influence of magma composition, mineral combination and structure on element migration; Step 604, interpreting the source of minerals based on the simulation results, and then analyzing the element pre-enrichment mechanism; Step 605, constructing a geological model of the evolution from source melting to magma, including the migration and enrichment process of ore-forming elements, combining the geological model with geophysical, geochemical and geological observation data, and performing sensitivity analysis on key parameters in the model to evaluate the impact of parameter changes on the ore-forming process; Step 606, integrate the simulation results, experimental data and geological observations, propose a comprehensive explanation of the mineralization mechanism, write an analysis report, and archive the simulation data, experimental data and analysis results.

Images