Method and system for delineating mineralization centers in porphyry-skarn deposits based on short-wave infrared spectroscopy

Abstract

The present invention provides a method and system for delineating mineralization centers in porphyry-skarn deposits based on short-wave infrared spectroscopy, and relates to the technical field of mineralization centers of ore deposits. The present invention combines short-wave infrared spectral data with time series analysis, introduces time series analysis and dynamic mineralization probability distribution models, and combines the hydrothermal activity temperature calculated by oxygen isotope fractionation effect to construct a dynamic mineralization potential prediction framework, which can dynamically simulate the mineralization process and generate a prediction of the spatiotemporal distribution of target mineral mineralization centers. By introducing the time dimension, combining the mineralization stage division with oxygen isotope temperature inversion, and combining the three-dimensional geological model and dynamic prediction technology, the dynamic changes of the mineralization environment can be more accurately reflected, and the prediction accuracy can be improved. Thereby effectively optimizing resource allocation, reducing unnecessary exploration operations, and providing accurate guidance and scientific basis for mining exploration.

Description

Technical Field

[0001] The invention relates to the technical field of prediction of mineralization centers of ore deposits, and in particular to a method and a system for delineating mineralization centers in porphyry-skarn ore deposits based on short-wave infrared spectroscopy. Background Art

[0002] In the process of mineral exploration, accurately delineating the mineralization center is an important part of improving the efficiency of resource development. Traditional methods mostly rely on static analysis of mineralized zones, usually obtaining mineral distribution information through geological surveys and drilling. However, this method often faces problems such as insufficient sample quantity, unreasonable sampling intervals, and data redundancy, which makes it difficult to accurately locate the mineralization center. In addition, the mineralization process is a dynamic geological evolution process. The traditional model lacks consideration of the time dimension, making the prediction results inaccurate in time and difficult to reflect the dynamic changes in the mineralization environment. In this case, how to dynamically predict the distribution of mineralization centers through efficient technical means combined with time and space information has become a technical problem that needs to be solved in mining exploration.

[0003] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0004] The object of the present invention is to provide a method and system for delineating mineralization centers in porphyry-skarn deposits based on short-wave infrared spectroscopy, so as to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] The method for delineating the mineralization center in porphyry-skarn deposits based on short-wave infrared spectroscopy includes the following steps:

[0007] S1: Set up several groups of sample collection points in the target mining area to collect samples, use short-wave infrared spectroscopy to measure the spectral data of the samples, process the spectral data and generate a parameter matrix;

[0008] S2: Based on the coordinate information and spectral data of the target mining area, a three-dimensional spatial model is constructed using geological modeling tools, and the corresponding mineralization probability distribution function is generated;

[0009] S3: Divide the mineralization process of the target mineral according to time, conduct time series analysis on the mineralization probability distribution function, and construct a mineralization potential prediction model;

[0010] S4: Perform dynamic prediction based on the mineralization potential prediction model to generate a prediction of the spatiotemporal distribution of the target mineral mineralization center.

[0011] Preferably, when collecting samples, a segmented comparison cataloging method is adopted, which includes mineralization segmentation, alteration segmentation, lithology segmentation, color segmentation, mineral combination segmentation, lithology comparison, alteration zone comparison, alteration mineral combination comparison, mineralization degree comparison, and mineralization length comparison.

[0012] Preferably, during the sample collection process, the alteration significance of the target mining area is analyzed, and different sampling intervals are set according to the analysis results;

[0013] When using short-wave infrared spectroscopy to measure the spectral data of samples, each group of samples needs to be cleaned and dried before measurement, and three different points are set as test points, and the spectral data of the three test points are averaged as the spectral data of the group of samples.

[0014] Preferably, the logic of performing data processing on the spectral data to generate the parameter matrix is:

[0015] Use a short-wave infrared spectrometer to measure each group of samples and record the characteristic peaks of the target minerals;

[0016] Each group of samples was measured at least three times, and the average was taken as the final measurement result;

[0017] The rows of the parameter matrix are set to sample numbers and the columns are set to target mineral numbers.

[0018] Preferably, the mineralization probability distribution function is expressed as:

[0019] ;

[0020] In the formula Indicates The mineralization probability of a point, Indicates The integrated spectral intensity of the point, Indicates The distance function between the point and the known alteration zone, Indicates The coordinate information of the point, Indicates the index of the sample collection point, , Represents the total number of collection points, , All represent preset model parameters;

[0021] Among them The integrated spectral intensity of a point is calculated as:

[0022] ;

[0023] In the formula , Respectively represent Click the The weight coefficient and characteristic peak value of the target minerals, Indicates the index of the target mineral, , is the total number of target minerals;

[0024] No. The distance function between a point and a known alteration zone is calculated as:

[0025] ;

[0026] In the formula Indicates The distance between the point and the known alteration zone, Indicates Point preset mineralization grade.

[0027] Preferably, the stages of the mineralization process of the target mineral divided according to time include:

[0028] High-temperature porphyry stage: The target minerals in this stage are chlorite and potassium feldspar, and the characteristic peak of chlorite is greater than ;

[0029] Medium-temperature skarn stage: The target minerals in this stage are serpentine and diopside, and the serpentine characteristic peak is greater than ;

[0030] Low temperature degradation stage: The target minerals in this stage are muscovite and sericite, and the characteristic peak of muscovite is greater than .

[0031] Preferably, the logic of conducting time series analysis on the mineralization probability distribution function and constructing a mineralization potential prediction model is as follows:

[0032] The oxygen isotope fractionation effect is used to calculate the temperature of hydrothermal activity during the mineralization process, and the hydrothermal temperature corresponding to different times is deduced;

[0033] The hydrothermal activity intensity function is constructed through temperature inversion calculation, and the hydrothermal activity intensity function is combined with the mineralization probability distribution function, and the time dimension is introduced to construct the dynamic mineralization probability distribution function;

[0034] Based on the dynamic mineralization probability distribution function and combined with time series data, a mineralization potential prediction model is constructed.

[0035] Preferably, the hydrothermal activity intensity function is calculated as follows:

[0036] ;

[0037] In the formula Indicates about time The hydrothermal activity intensity function is , Respectively, in time The hydrothermal activity temperature and the optimal mineralization temperature at that time, Indicates the standard deviation of temperature fluctuation range;

[0038] The dynamic mineralization probability distribution function is expressed as:

[0039] ;

[0040] In the formula Indicates Point in time The probability of mineralization at

[0041] The mineralization potential prediction model is expressed as:

[0042] ;

[0043] In the formula Indicates about time The mineralization potential prediction model of , Represent the lower and upper limits of the time interval respectively.

[0044] A system for delineating mineralization centers in porphyry-skarn deposits based on short-wave infrared spectroscopy, wherein the system for delineating mineralization centers in porphyry-skarn deposits based on short-wave infrared spectroscopy is used to execute the above-mentioned method for delineating mineralization centers in porphyry-skarn deposits based on short-wave infrared spectroscopy, specifically comprising:

[0045] A data acquisition module, wherein the data acquisition module is used to measure the spectral data of the sample, perform data processing on the spectral data and generate a parameter matrix;

[0046] A simulation analysis module, which is used to construct a three-dimensional space model and generate a corresponding mineralization probability distribution function;

[0047] A time series analysis module, which is used to perform time series analysis on the mineralization probability distribution function and construct a mineralization potential prediction model;

[0048] A comprehensive processing module is used to perform dynamic prediction based on the mineralization potential prediction model to generate a prediction of the spatiotemporal distribution of the target mineral mineralization center.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] The present invention combines short-wave infrared spectral data with time series analysis, introduces time series analysis and dynamic mineralization probability distribution model, and combines the hydrothermal activity temperature calculated by oxygen isotope fractionation effect to construct a dynamic mineralization potential prediction framework, which can dynamically simulate the mineralization process and generate the spatiotemporal distribution prediction of the target mineral mineralization center. By introducing the time dimension, combining the mineralization stage division with oxygen isotope temperature inversion, and combining the three-dimensional geological model and dynamic prediction technology, it can more accurately reflect the dynamic changes of the mineralization environment and improve the prediction accuracy. Thereby effectively optimizing resource allocation, reducing unnecessary exploration operations, and providing accurate guidance and scientific basis for mining exploration. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0052] Figure 2 It is a schematic diagram of the module structure of the present invention. DETAILED DESCRIPTION

[0053] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0054] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0055] Example:

[0056] See also Figure 1~Figure 2 , the present invention provides a technical solution:

[0057] The method for delineating the mineralization center in porphyry-skarn deposits based on short-wave infrared spectroscopy includes the following steps:

[0058] S1: Set up several groups of sample collection points in the target mining area to collect samples, use short-wave infrared spectroscopy to measure the spectral data of the samples, process the spectral data and generate a parameter matrix.

[0059] When collecting samples, a segmented comparison and cataloging method is used, including mineralization segmentation, alteration segmentation, lithology segmentation, color segmentation, mineral combination segmentation, lithology comparison, alteration zone comparison, altered mineral combination comparison, mineralization degree comparison, and mineralization length comparison. At the same time, the principles of full-hole control, high-density coverage, encryption at characteristic locations, and appropriate sustainability must be followed. This segmented comparison and cataloging method can comprehensively analyze the geological characteristics of all aspects of the mining area and provide comprehensive data support for the subsequent delineation of the mineralization center.

[0060] During the sample collection process, the alteration significance of the target mining area is analyzed, and different sampling intervals are set according to the analysis results. The specific settings are:

[0061] In areas with low alteration, such as distal rock mass or marble, the sampling interval is set to 10-15 m;

[0062] In areas with a high degree of alteration, such as skarnization, degenerative alteration, and potashization, the sampling interval is set to 1-3 m;

[0063] The total number of samples collected is at least 5 groups;

[0064] When using short-wave infrared spectroscopy to measure the spectral data of samples, each group of samples needs to be cleaned and dried before measurement, and three different points are set as test points, and the spectral data of the three test points are averaged as the spectral data of the group of samples. And it is necessary to select fresh and representative samples for probe grinding. Carry out microscopic observation and scanning electron microscope analysis to determine the combination, structure and interlacing relationship of each mineral. According to field and microscopic observations, the mineralization stage and period are clarified, and the mineral generation order is divided.

[0065] Specifically, the spectral instrument used for the test can be a Terraspec portable short-wave infrared spectrometer, and when testing samples, the core samples need to be cleaned and dried to avoid interference caused by moisture or impurities on the surface of the mineral. Before testing, the instrument usually needs to be calibrated, and the instrument's spectrum average is set to 200 and the reference white is set to 400 for optimization and reference white operations. At this time, the instrument's spectral line is very straight, and the sample test can be carried out when there is almost no noise interference. During the test, in order to ensure the quality of the data, the instrument is optimized and the reference white is measured every 15 minutes. For all test data, TSG8.0 software is first used for automatic interpretation; then, it is manually checked and inspected one by one, and the type of mineral is finally determined.

[0066] In this step, high-quality spectral data and comprehensive geological feature records provide a solid foundation for subsequent 3D modeling and mineralization probability distribution, and the use of accurate mineral combinations and mineralization stage divisions can improve the accuracy of subsequent modeling and predictive analysis. This scientific sampling strategy also ensures the effective use of resources and avoids blind sampling and data redundancy.

[0067] S2: Based on the coordinate information and spectral data of the target mining area, a three-dimensional spatial model is constructed using geological modeling tools, and the corresponding mineralization probability distribution function is generated.

[0068] The logic of data processing for spectral data to generate parameter matrix is:

[0069] Use a short-wave infrared spectrometer to measure each group of samples and record the characteristic peaks of the target minerals;

[0070] Each group of samples was measured at least three times, and the average was taken as the final measurement result;

[0071] The rows of the parameter matrix are set to sample numbers and the columns are set to target mineral numbers.

[0072] The mineralization probability distribution function is expressed as:

[0073] ;

[0074] In the formula Indicates The mineralization probability of a point, Indicates The integrated spectral intensity of the point, Indicates The distance function between the point and the known alteration zone, Indicates The coordinate information of the point, Indicates the index of the sample collection point, , Represents the total number of collection points, , All represent preset model parameters, which can be flexibly adjusted according to the actual geological conditions of the mining area and the collected data to adapt to different ore deposit types and exploration needs.

[0075] Among them The integrated spectral intensity of a point is calculated as:

[0076] ;

[0077] In the formula , Respectively represent Click the The weight coefficient and characteristic peak value of the target minerals, Indicates the index of the target mineral, , The total number of target minerals and the integrated spectral intensity combine the spectral features of multiple minerals to provide a direct indication of the degree of mineralization.

[0078] No. The distance function between a point and a known alteration zone is calculated as:

[0079] ;

[0080] In the formula Indicates The distance between the point and the known alteration zone, Indicates The preset mineralization grade of the point, the distance function is used to take the known alteration zone as a reference, and the relationship between the spatial position and the alteration zone is combined to enhance the spatial correlation of the model.

[0081] Specifically, the known alteration zone can be used to systematically observe the drill core, record the distribution, color, structure, combination relationship of the altered minerals and the spatial relationship with the mineralization characteristics, and infer the location and scale of the alteration zone through the change pattern of common alteration minerals in the core (such as chlorite, quartz, muscovite, amphibole, etc.). It can also be determined through samples collected at various points in the target mining area. If the collected samples are found to include target minerals such as chlorite, muscovite, serpentine, and sericite through short-wave infrared spectroscopy, the spatial location of the alteration zone can be preliminarily delineated based on the composition distribution of these target minerals and the sampling spatial location (coordinates and depth) of the samples. As for the first As for the mineralization grade of a point, there are many ways to determine it. For example, the samples collected from the sample collection points near the point can be analyzed and inferred, and a model can be established using geochemical data. The grade of the sample collection point can be predicted by statistical methods (such as the Kriging method). The density and magnetic anomalies of the ore body can be combined with the geological model to indirectly infer the grade of the mineralized area. The spectral characteristic peaks of specific minerals (such as chlorite, muscovite, serpentine, etc.) can reflect the enrichment of minerals. Combined with known geological conditions, a relationship model between spectral characteristics and mineralization grade can be established to infer the mineralization grade of the ore body. These technical means are all existing technologies, so they will not be elaborated here.

[0082] In this step, by constructing a three-dimensional spatial model and a mineralization probability distribution function, a reliable data foundation is provided, which provides an important reference for subsequent mining area development and decision-making. It also combines multiple data sources and analysis models to make the prediction results closer to reality, improve the accuracy and practicality of the mineralization center delineation, and at the same time, by accurately locating areas with a high probability of mineralization, it can effectively optimize resource allocation, reduce unnecessary drilling and sampling, and improve the overall efficiency of exploration.

[0083] S3: The mineralization process of the target mineral is divided according to time, and the mineralization probability distribution function is analyzed in time series to construct a mineralization potential prediction model.

[0084] The mineralization process of the target mineral is divided into the following stages according to time:

[0085] High-temperature porphyry stage: The target minerals in this stage are chlorite and potassium feldspar, and the characteristic peak of chlorite is greater than ;

[0086] Medium-temperature skarn stage: The target minerals in this stage are serpentine and diopside, and the serpentine characteristic peak is greater than ;

[0087] Low temperature degradation stage: The target minerals in this stage are muscovite and sericite, and the characteristic peak of muscovite is greater than .

[0088] The logic of constructing a mineralization potential prediction model by conducting a time series analysis on the mineralization probability distribution function is as follows:

[0089] The oxygen isotope fractionation effect is used to calculate the temperature of hydrothermal activity during the mineralization process, and the corresponding hydrothermal temperature at different times is derived. Oxygen isotopes represent the relative enrichment of oxygen-18 isotopes and oxygen-16 isotopes between two substances (such as minerals and water, or two minerals). It is used to describe the oxygen isotope fractionation effect between two substances. In geological research, it is often used to analyze hydrothermal mineralization or magmatic processes between minerals and hydrothermal fluids, magma and sediments, or minerals. It can reveal the relationship between temperature and oxygen isotope fractionation of two substances in the mineralization process.

[0090] Specifically, the commonly used oxygen isotope fractionation formula is:

[0091] ;

[0092] in represents the oxygen-18 isotope, , , Both represent the fractionation coefficient of minerals.

[0093] The hydrothermal activity intensity function is constructed through temperature inversion calculation, and the hydrothermal activity intensity function is combined with the mineralization probability distribution function, and the time dimension is introduced to construct the dynamic mineralization probability distribution function;

[0094] Based on the dynamic mineralization probability distribution function and combined with time series data, a mineralization potential prediction model is constructed.

[0095] The calculation method of the hydrothermal activity intensity function is:

[0096] ;

[0097] In the formula Indicates about time The hydrothermal activity intensity function is , Respectively, in time The hydrothermal activity temperature and the optimal mineralization temperature at that time, Representing the standard deviation of the temperature fluctuation range, the hydrothermal activity intensity function can accurately simulate the intensity changes of hydrothermal activity, combine the mineralization probability with the actual dynamic process of hydrothermal activity, and improve the scientific nature of mineralization prediction.

[0098] Different altered minerals are formed at different stages of hydrothermal evolution. By analyzing samples associated with the time series, the hydrothermal temperature corresponding to different times can be deduced. Furthermore, the temperature ranges corresponding to different mineral combinations can be calibrated using mineralogical experimental data (such as water-rock interaction experiments), and then combined with the mineral evolution in time, the temperature ranges corresponding to different mineral combinations can be calibrated. The distribution on the surface of the hydrothermal fluid is used to construct a curve of the hydrothermal temperature changing with time, and the time is obtained by interpolation or fitting. The hydrothermal activity temperature And the corresponding temperature fluctuation range standard deviation .

[0099] The dynamic mineralization probability distribution function is expressed as:

[0100] ;

[0101] In the formula Indicates Point in time The probability of mineralization at that time. Most traditional mineralization probability models are static models that only focus on the mineralization probability distribution in the spatial dimension. However, by introducing the time dimension and combining the formation stage of the mineral and the intensity of hydrothermal activity, it is possible to capture the evolution characteristics of the mineralization process over time, more accurately and dynamically reflect the spatiotemporal changes of the mineralization process, and provide more comprehensive time series information for the prediction of mineralization potential.

[0102] The mineralization potential prediction model is expressed as:

[0103] ;

[0104] In the formula Indicates about time The mineralization potential prediction model of , Respectively represent the lower and upper limits of the time interval. Specifically, the lower limit corresponds to the initial mineralization time, such as the time when magma intrusion or initial hydrothermal activity occurs, and the upper limit corresponds to the end time of the mineralization process, such as the time when hydrothermal activity gradually decays or the mineral precipitation process ends. Dynamic analysis is performed to capture the movement of mineralization centers and changes in mineralization potential. This method provides a complete predictive model framework, including mineralization stage division, hydrothermal activity simulation, dynamic calculation of mineralization probability and potential prediction. It is highly adaptable and applicable to a variety of mineralization types.

[0105] In this step, through the automated mineralization stage division and model construction, it can adapt to different types of deposits (such as porphyry, skarn, etc.), and by introducing time series analysis, the mineralization prediction is upgraded from static analysis to dynamic analysis, which can better simulate the dynamic changes of the mineralization environment, thereby improving the prediction accuracy. The final mineralization potential prediction model can quantify the mineralization potential in different time intervals, determine the key mineralization stages and high-potential areas, so as to better predict the mineralization center, provide accurate guidance for mining exploration, and reduce unnecessary exploration scope and resource waste.

[0106] S4: Perform dynamic prediction based on the mineralization potential prediction model to generate a prediction of the spatiotemporal distribution of the target mineral mineralization center.

[0107] In summary, the present invention can dynamically simulate the mineralization process and generate a prediction of the spatiotemporal distribution of the target mineral mineralization center by combining short-wave infrared spectral data with time series analysis. By introducing the time dimension and combining the mineralization stage division with oxygen isotope temperature inversion, the dynamic changes of the mineralization environment can be more accurately reflected and the prediction accuracy can be improved. This effectively optimizes resource allocation, reduces unnecessary exploration operations, and provides precise guidance and scientific basis for mining exploration.

[0108] The present invention also provides a system for delineating mineralization centers in porphyry-skarn deposits based on short-wave infrared spectroscopy, which is used to execute the above-mentioned method for delineating mineralization centers in porphyry-skarn deposits based on short-wave infrared spectroscopy, specifically comprising:

[0109] A data acquisition module, wherein the data acquisition module is used to measure the spectral data of the sample, perform data processing on the spectral data and generate a parameter matrix;

[0110] A simulation analysis module, which is used to construct a three-dimensional space model and generate a corresponding mineralization probability distribution function;

[0111] A time series analysis module, which is used to perform time series analysis on the mineralization probability distribution function and construct a mineralization potential prediction model;

[0112] A comprehensive processing module is used to perform dynamic prediction based on the mineralization potential prediction model to generate a prediction of the spatiotemporal distribution of the target mineral mineralization center.

[0113] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0114] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0115] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0116] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the 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.

Claims

1. A method for delineating mineralization centers in porphyry-skarn deposits based on short-wave infrared spectroscopy, characterized in that: The specific steps include: S1: Set up several groups of sample collection points in the target mining area to collect samples, use short-wave infrared spectroscopy to measure the spectral data of the samples, process the spectral data and generate a parameter matrix; S2: Based on the coordinate information and spectral data of the target mining area, a three-dimensional spatial model is constructed using geological modeling tools, and the corresponding mineralization probability distribution function is generated; S3: Divide the mineralization process of the target mineral according to time, conduct time series analysis on the mineralization probability distribution function, and construct a mineralization potential prediction model; The logic of constructing a mineralization potential prediction model by conducting a time series analysis on the mineralization probability distribution function is as follows: The oxygen isotope fractionation effect is used to calculate the temperature of hydrothermal activity during the mineralization process, and the hydrothermal temperature corresponding to different times is deduced; The hydrothermal activity intensity function is constructed through temperature inversion calculation, and the hydrothermal activity intensity function is combined with the mineralization probability distribution function, and the time dimension is introduced to construct the dynamic mineralization probability distribution function; Based on the dynamic mineralization probability distribution function and combined with time series data, a mineralization potential prediction model is constructed; The calculation method of the hydrothermal activity intensity function is: In the formula Indicates about time The hydrothermal activity intensity function is , Respectively expressed in time The hydrothermal activity temperature and the optimal mineralization temperature at that time, Indicates the standard deviation of temperature fluctuation range; The dynamic mineralization probability distribution function is expressed as: In the formula Indicates Point in time The probability of mineralization at The mineralization potential prediction model is expressed as: In the formula Indicates about time The mineralization potential prediction model of , Respectively represent the lower and upper limits of the time interval; S4: Perform dynamic prediction based on the mineralization potential prediction model to generate a prediction of the spatiotemporal distribution of the target mineral mineralization center.

2. The method for delineating the mineralization center in a porphyry-skarn deposit based on short-wave infrared spectroscopy according to claim 1, characterized in that: When collecting samples, a segmented comparison cataloging method is adopted, which includes mineralization segmentation, alteration segmentation, lithology segmentation, color segmentation, mineral combination segmentation, lithology comparison, alteration zone comparison, altered mineral combination comparison, mineralization degree comparison, and mineralization length comparison.

3. The method for delineating the mineralization center in a porphyry-skarn deposit based on short-wave infrared spectroscopy according to claim 2, characterized in that: During the sample collection process, the alteration significance of the target mining area is analyzed, and different sampling intervals are set according to the analysis results; The total number of samples collected is at least 5 groups; When using short-wave infrared spectroscopy to measure the spectral data of samples, each group of samples needs to be cleaned and dried before measurement, and three different points are set as test points, and the spectral data of the three test points are averaged as the spectral data of the group of samples.

4. The method for delineating the mineralization center in a porphyry-skarn deposit based on short-wave infrared spectroscopy according to claim 1, characterized in that: The logic of data processing for spectral data to generate parameter matrix is: Use a short-wave infrared spectrometer to measure each group of samples and record the characteristic peaks of the target minerals; Each group of samples was measured at least three times, and the average was taken as the final measurement result; The rows of the parameter matrix are set to sample numbers and the columns are set to target mineral numbers.

5. The method for delineating the mineralization center in porphyry-skarn deposits based on short-wave infrared spectroscopy according to claim 1, characterized in that: The mineralization probability distribution function is expressed as: In the formula Indicates The mineralization probability of each sample collection point is Indicates The comprehensive spectral intensity of each sample collection point, Indicates The distance function between the sample collection point and the known alteration zone is Indicates The coordinate information of the sample collection points, represents the index of the sample collection point, , Represents the total number of collection points, , All represent preset model parameters; Among them The integrated spectral intensity of a point is calculated as: In the formula , Respectively represent Click the The weight coefficient and characteristic peak value of the target minerals, Indicates the index of the target mineral, , is the total number of target minerals; No. The distance function between a point and a known alteration zone is calculated as: In the formula Indicates The distance between the point and the known alteration zone, Indicates Point preset mineralization grade.

6. The method for delineating the mineralization center in a porphyry-skarn deposit based on short-wave infrared spectroscopy according to claim 1, characterized in that: The mineralization process of the target mineral is divided into the following stages according to time: High-temperature porphyry stage: The target minerals in this stage are chlorite and potassium feldspar, and the characteristic peak of chlorite is greater than ; Medium-temperature skarn stage: The target minerals in this stage are serpentine and diopside, and the serpentine characteristic peak is greater than ; Low temperature degradation stage: The target minerals in this stage are muscovite and sericite, and the characteristic peak of muscovite is greater than .

7. A system for delineating mineralization centers in porphyry-skarn deposits based on short-wave infrared spectroscopy, characterized by: The system for delineating mineralization centers in porphyry-skarn deposits based on short-wave infrared spectroscopy is used to execute the method for delineating mineralization centers in porphyry-skarn deposits based on short-wave infrared spectroscopy as claimed in any one of claims 1 to 6, specifically comprising: A data acquisition module, wherein the data acquisition module is used to measure the spectral data of the sample, perform data processing on the spectral data and generate a parameter matrix; A simulation analysis module, which is used to construct a three-dimensional space model and generate a corresponding mineralization probability distribution function; A time series analysis module, which is used to perform time series analysis on the mineralization probability distribution function and construct a mineralization potential prediction model; A comprehensive processing module is used to perform dynamic prediction based on the mineralization potential prediction model to generate a prediction of the spatiotemporal distribution of the target mineral mineralization center.

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