A prediction model for multi-center porphyry-hydrothermal mineralization system in a composite magmatic arc environment

Abstract

The invention discloses a prediction model for a multi-center porphyry-hydrothermal mineralization system in a composite magmatic arc environment, relates to the technical field of geological exploration, and comprises the following steps: step one: setting a mineralization prediction range area, setting a plurality of different collection points within the mineralization prediction range area, obtaining the ore distribution density, ore thickness, and ore alteration intensity of each collection point, and obtaining the richness coefficient of each collection point; performing element analysis on the ore by demarcating the mineralization prediction range area, performing sampling solubility analysis on underground river water, summarizing the content value of the ore data and the solubility value of the underground river water sample data to obtain the element coefficient of each collection point, performing weighted average normalization of the element coefficient and the richness coefficient to obtain the prediction value of each collection point, thereby facilitating the prediction of the underground mineralization value of each collection point, and then averaging the prediction values ​​of all the collection points to obtain the sample reference coefficient value, thereby facilitating the understanding of the underground mineralization value of the mineralization prediction range area.

Description

Technical Field

[0001] The invention relates to the technical field of geological exploration, in particular to a prediction model for a multi-center porphyry-hydrothermal mineralization system in a composite magmatic arc environment. Background Art

[0002] A magmatic arc refers to a series of magmatic activity zones caused by the collision or subduction of crustal plates. These activity zones are usually formed at the edge of an oceanic plate subducting under a continental plate or another oceanic plate. Porphyry is an intrusive rock formed during the cooling and consolidation of magma. It usually has a certain proportion of large crystals distributed in a finer-grained matrix. In mineralization, the presence of porphyry bodies is often associated with important mineralization events, especially the mineralization of metals such as copper, gold, and molybdenum. Hydrothermal fluids refer to hot fluids associated with magma. These hot fluids deposit minerals when flowing in the crust, forming various types of hydrothermal deposits.

[0003] During the geological exploration of mineral deposits, rich mineral resources may be generated through porphyry hydrothermal fluids in magmatic arc areas. Mineralized gold, silver and iron are extremely valuable resources. However, due to the high cost and long time of mining, it is necessary to fully understand the situation in the underground mineral deposits before mining to avoid the situation where only very few high-quality metal ores can be mined at a huge cost. In order to fully understand the quantity and distribution of underground mineral resources before mining, we propose a prediction model for multi-center porphyry-hydrothermal mineralization system in a composite magmatic arc environment. Summary of the invention

[0004] 1. Technical issues to be solved

[0005] In view of the shortcomings of the prior art, the present invention provides a prediction model for a multi-center porphyry-hydrothermal mineralization system in a composite magmatic arc environment.

[0006] (II) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: A prediction model for multi-center porphyry-hydrothermal mineralization system in a composite magmatic arc environment, comprising the following steps:

[0008] Step 1: Set the mineralization prediction range, set up multiple different collection points within the mineralization prediction range, obtain the ore distribution density, ore thickness, ore alteration intensity of each collection point, and obtain the richness coefficient of each collection point;

[0009] Step 2: Obtain ore samples from each collection point, perform elemental analysis on the ore samples from each collection point, obtain the total content ratio of gold, silver and iron in the ore of each collection point, mark it as the content value, perform solubility analysis on underground river water from each collection point, obtain the content ratio of gold, silver, iron and silicon in water samples from different locations, and obtain the solubility value of each collection point through the sum of the content ratio of gold, silver and iron in the water sample and the silicon ratio;

[0010] Step 3: The element coefficient of each collection point is obtained by weighted summing of the content value and solubility value of each collection point, and the predicted value of each collection point is obtained by weighted average of the richness coefficient and element coefficient of each collection point. The predicted values ​​of all collection points are averaged to obtain the sample reference coefficient;

[0011] Step 4: Build a reference database through historical mineral deposits, determine the mineral deposit that is most similar to the mineralization prediction range in the reference database, mark it as a reference mineral deposit and obtain a similarity value, obtain the historical reference coefficient by multiplying the similarity value of the reference mineral deposit and the mining recovery rate, and obtain the recovery coefficient by the sample reference coefficient and the historical reference coefficient;

[0012] Step 5: Use GIS to establish a three-dimensional structural model of the ore deposit within the mineralization prediction range through satellite remote sensing data, ore distribution density and ore thickness at each collection point, mark the position of each collection point in the three-dimensional model of the ore deposit, mark the predicted value at each collection point, and mark the recovery coefficient in the three-dimensional model of the ore deposit.

[0013] Preferably, the calculation method of the richness coefficient is as follows:

[0014]

[0015] Among them, Fs represents the ore richness coefficient, Md represents the ore distribution density, Hd represents the ore thickness, and Sq represents the ore alteration intensity, Hd>0, Md>0, Sq>0.

[0016] Preferably, the ore samples at each collection point are obtained by:

[0017] Step 1: Drill underground at each collection point until the drill bit enters the ore bed, and take samples from the highest point of the ore bed downwards. Sample the ore every 0.5 meters until the drilling depth exceeds the depth of the ore bed, and obtain multiple samples at each collection point;

[0018] Step 2: Define ten alteration grades and ten fold grades from low to high, mark the corresponding alteration grade and fold grade for each ore sample according to the alteration intensity and fold degree on the surface of each ore sample, and obtain the forming grade of each collected sample by weighted summation of the alteration grade and fold grade of each collected sample;

[0019] Step 3: Select the sample with the highest forming grade from multiple samples at each collection point as the ore sample.

[0020] Preferably, the predicted mineralization range is set as follows:

[0021] Step 1: Divide the field area into several grids, use a proton magnetometer to obtain the magnetic magnitude in each grid, set a preset threshold for the magnetic fluctuation range, and mark the grid areas where the magnetic force is not within the preset threshold for the magnetic fluctuation range, and record them as magnetic anomaly grids;

[0022] Step 2: Use gravimeters to obtain gravity in the magnetic anomaly grid area, set a preset threshold for the gravity fluctuation range, and mark the grid area where the gravity is not within the preset threshold for the gravity fluctuation range, and record it as the magnetic gravity anomaly area;

[0023] Step 3: Connect the center points of all the magnetic gravity anomaly areas on the edge to obtain the mineralization prediction range area.

[0024] Preferably, the ore at each collection point is sampled for elemental analysis, specifically:

[0025] Step 1: Set the sampling standard and sample the ore at each collection point according to the sampling standard;

[0026] Step 2: After cleaning and drying the samples, grind them into powders, add an equal amount of flux to each sample powder and melt them at high temperature to make glass slides. Three glass slides are made from the samples at each collection point.

[0027] Step 3: Determine the total content of gold, silver and iron in each glass slide by XRF, take the average of the three measurement results at the same collection point and mark them as the content value.

[0028] Preferably, the underground river water at each collection point is sampled for solubility analysis, specifically:

[0029] Step 1: Set the sampling standard, extract ten samples from the underground river water at each collection point according to the sampling standard, obtain the pH value, conductivity, and dissolved oxygen of the samples, and normalize the pH value, conductivity, and dissolved oxygen in each sample to obtain the standard value;

[0030] Step 2: Set a preset threshold value for the standard value, select the sample with the largest standard value among the ten samples at each collection point as the optimal sample, and compare the standard value of the optimal sample with the preset threshold value for the standard value;

[0031] Step 3: If the standard value of the optimal sample is greater than or equal to the standard preset threshold, the optimal sample is used as the analysis object of the collection point; if the standard value of the optimal sample is less than the standard preset threshold, the collection point is resampled;

[0032] Step 4: Use I CP-MS to determine the content ratio of gold, silver, iron and silicon in the analysis object at each sampling point, and obtain the dissolution value of each collection point by the content ratio of gold, silver, iron and silicon in the analysis object.

[0033] Preferably, the calculation method of the solubility value is as follows:

[0034]

[0035] Among them, Rj represents the solubility value, Jh represents the proportion of gold, Yh represents the proportion of silver, Th represents the proportion of iron, Gh represents the proportion of silicon, Jh≥0, Yh≥0, Th≥0, Gh>0.

[0036] Preferably, step 4 specifically includes the following steps:

[0037] Step 1: Obtain the climate and environment type, geological plate type, and mining recovery rate of historical mineral deposits to establish a reference database;

[0038] Step 2: Obtain the environmental type and geological plate type of the mineralization prediction range, calculate the proximity between the mineralization prediction range and each historical mineral deposit using the Euclidean distance algorithm based on the climate environment type and geological plate type of the mineral deposit, obtain the similarity value based on the proximity between the mineralization prediction range and each historical mineral deposit, and select the historical mineral deposit with the highest similarity value as the reference mineral deposit;

[0039] Step 3: Obtain the historical reference coefficient by multiplying the similarity value of the reference deposit and the mining recovery rate, and obtain the recovery coefficient by combining the sample reference coefficient and the historical reference coefficient.

[0040] Preferably, the recovery coefficient is calculated as follows:

[0041]

[0042] Among them, Dy represents the recovery coefficient, Yx represents the sample reference coefficient, Lx represents the historical reference coefficient, u1 and u2 are weights, 1>u1>0, 1>u2>0.

[0043] Preferably, the similarity value is calculated as follows:

[0044]

[0045] Among them, Xs represents the similarity value, and Uj represents the proximity distance between the mineralization prediction range area and the historical deposit.

[0046] (III) Beneficial effects

[0047] The present invention provides a prediction model for a multi-center porphyry-hydrothermal mineralization system in a composite magmatic arc environment, which has the following beneficial effects:

[0048] (1) In this plan, the mineralization prediction range is delineated and multiple sampling points are determined within the mineralization range, so as to facilitate understanding of the underground conditions at different locations within the mineralization range. Ore sampling is performed at each sampling point, so as to facilitate understanding of the distribution of underground mineral deposits. Then, elemental analysis is performed on the ore, and solubility analysis is performed on the underground river water under the sampling point. The element coefficient of each sampling point is obtained based on the content value of the ore data and the solubility value of the groundwater sample data. The predicted value of each sampling point is obtained by weighted average normalization of the element coefficient and the richness coefficient, so as to facilitate prediction of the underground mineralization value of each sampling point. Then, the predicted values ​​of all the sampling points are averaged to obtain the sample reference coefficient value, so as to facilitate understanding of the underground mineralization value of the mineralization prediction range.

[0049] (2) In this scheme, a reference database is constructed based on the historical data of historical mineral deposits, and the historical mineral deposits closest to the mineralization prediction range are found among the numerous historical mineral deposits. The historical reference coefficient is obtained by multiplying the similarity value of the reference mineral deposit and the mining recovery rate. The recovery coefficient is then obtained by combining the sample reference coefficient and the historical reference coefficient. This facilitates the comprehensive judgment of the mineralization value of the underground in the mineralization prediction range by combining the historical data with the sampling conditions underground in the mineralization prediction range, thereby facilitating a more accurate prediction of the mineralization value of the underground in the mineralization prediction range.

[0050] (3) In this plan, satellite remote sensing data, ore distribution density and ore thickness at each collection point are used to establish a three-dimensional structural model of the ore deposit within the mineralization prediction range using GIS. The position of each collection point is marked, the predicted value is marked at each collection point, and the recovery coefficient is marked in the three-dimensional model of the ore deposit. By constructing a three-dimensional structural model, it is easy to clearly understand the ore deposit structure. By marking the collection points, predicted values ​​and recovery coefficients, it is easy to understand the distribution of the mineralization value of underground ore deposits in the mineralization prediction range, which in turn facilitates the formulation of a mining plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 The present invention is a flowchart of a method for predicting a multi-center porphyry-hydrothermal mineralization system in a composite magmatic arc environment. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0053] See also Figure 1 The present invention provides a prediction model for a multi-center porphyry-hydrothermal mineralization system in a composite magmatic arc environment, comprising the following steps:

[0054] Step 1: Set the mineralization prediction range, set up multiple different collection points within the mineralization prediction range, obtain the ore distribution density, ore thickness, ore alteration intensity of each collection point, and obtain the richness coefficient of each collection point;

[0055] Step 2: Obtain ore samples from each collection point, perform elemental analysis on the ore samples from each collection point, obtain the total content ratio of gold, silver and iron in the ore of each collection point, mark it as the content value, perform solubility analysis on underground river water from each collection point, obtain the content ratio of gold, silver, iron and silicon in water samples from different locations, and obtain the solubility value of each collection point through the sum of the content ratio of gold, silver and iron in the water sample and the silicon ratio;

[0056] Step 3: The element coefficient of each collection point is obtained by weighted summing of the content value and solubility value of each collection point, and the predicted value of each collection point is obtained by weighted average of the richness coefficient and element coefficient of each collection point. The predicted values ​​of all collection points are averaged to obtain the sample reference coefficient;

[0057] Step 4: Build a reference database through historical mineral deposits, determine the mineral deposit that is most similar to the mineralization prediction range in the reference database, mark it as a reference mineral deposit and obtain a similarity value, obtain the historical reference coefficient by multiplying the similarity value of the reference mineral deposit and the mining recovery rate, and obtain the recovery coefficient by the sample reference coefficient and the historical reference coefficient;

[0058] Step 5: Use GIS to establish a three-dimensional structural model of the ore deposit within the mineralization prediction range based on satellite remote sensing data, ore distribution density and ore thickness at each collection point, mark the location of each collection point in the three-dimensional model of the ore deposit, mark the predicted value at each collection point, and mark the recovery coefficient in the three-dimensional model of the ore deposit;

[0059] The calculation method of the richness coefficient is as follows:

[0060]

[0061] Among them, Fs represents the ore richness coefficient, Md represents the ore distribution density, Hd represents the ore thickness, Sq represents the ore alteration intensity, Hd>0, Md>0, Sq>0;

[0062] The calculation method of the solubility value is as follows:

[0063]

[0064] Wherein, Rj represents the solubility value, Jh represents the percentage of gold, Yh represents the percentage of silver, Th represents the percentage of iron, Gh represents the percentage of silicon, Jh≥0, Yh≥0, Th≥0, Gh>0;

[0065] The calculation method of recovery coefficient is as follows:

[0066]

[0067] Among them, Dy represents the recovery coefficient, Yx represents the sample reference coefficient, Lx represents the historical reference coefficient, u1 and u2 are weights, 1>u1>0, 1>u2>0.

[0068] In this embodiment, by demarcating the mineralization prediction range area, multiple collection points are determined within the mineralization range area, so as to facilitate understanding the underground conditions at different locations within the mineralization range area, and by sampling ore at each collection point, the richness coefficient of each collection point is obtained through the ore distribution density, ore thickness, and ore alteration intensity, so as to facilitate understanding the distribution of underground mineral deposits, and then by performing elemental analysis on the ore, the content value is obtained through the total content ratio of the three elements of gold, silver, and iron in the ore of each collection point, and then the underground river water under the collection point is sampled for solubility analysis, and the dissolution value of each collection point is obtained through the sum of the gold, silver, and iron content ratios in the water sample and the silicon ratio, and then the content value of the ore data and the dissolution value of the groundwater sample data are summarized to obtain the element coefficient of each collection point, and the weighted average normalization of the element coefficient and the richness coefficient is comprehensively obtained to obtain the predicted value of each collection point, so as to facilitate prediction of the underground mineralization value of each collection point, and then the predicted values ​​of all the collection points are averaged to obtain the sample reference coefficient value, so as to facilitate understanding of the underground mineralization value of the mineralization prediction range area;

[0069] A reference database is constructed based on the historical data of historical mineral deposits, and the historical mineral deposit closest to the mineralization prediction range is found among many historical mineral deposits. The historical reference coefficient is obtained by multiplying the similarity value of the reference mineral deposit and the mining recovery rate, and then the recovery coefficient is obtained by combining the sample reference coefficient and the historical reference coefficient, so as to facilitate the comprehensive judgment of the mineralization value of the underground in the mineralization prediction range by combining the historical data with the underground sampling conditions in the mineralization prediction range, and then facilitate a more accurate prediction of the mineralization value of the underground in the mineralization prediction range;

[0070] Through satellite remote sensing data, ore distribution density and ore thickness at each collection point, GIS is used to establish a three-dimensional structural model of the ore deposit within the mineralization prediction range, mark the position of each collection point, mark the predicted value at each collection point, and mark the recovery coefficient in the three-dimensional model of the ore deposit. By constructing a three-dimensional structural model, it is easy to clearly understand the ore deposit structure, and by marking the collection points, predicted values ​​and recovery coefficients, it is easy to understand the distribution of the mineralization value of underground ore deposits in the mineralization prediction range, which in turn facilitates the formulation of mining plans.

[0071] The predicted mineralization range is set as follows:

[0072] Step 1: Divide the field area into several grids, use a proton magnetometer to obtain the magnetic magnitude in each grid, set a preset threshold for the magnetic fluctuation range, and mark the grid areas where the magnetic force is not within the preset threshold for the magnetic fluctuation range, and record them as magnetic anomaly grids;

[0073] Step 2: Use gravimeters to obtain gravity in the magnetic anomaly grid area, set a preset threshold for the gravity fluctuation range, and mark the grid area where the gravity is not within the preset threshold for the gravity fluctuation range, and record it as the magnetic gravity anomaly area;

[0074] Step 3: Connect the center points of all the magnetic gravity anomaly areas on the edge to obtain the mineralization prediction range area.

[0075] In this embodiment, when determining the predicted mineralization range, the regional grid is divided and magnetic and gravity monitoring is performed within the regional grid, so as to predict whether there are mineral deposits underground through the tiny magnetic and gravity changes of metal elements in the underground mineral deposits, and the predicted mineralization range is determined based on the screening and division of the grid.

[0076] The ore samples from each collection point were analyzed for elements, specifically:

[0077] Step 1: Set the sampling standard and sample the ore at each collection point according to the sampling standard;

[0078] Step 2: After cleaning and drying the samples, grind them into powders, add an equal amount of flux to each sample powder and melt them at high temperature to make glass slides. Three glass slides are made from the samples at each collection point.

[0079] Step 3: Determine the total content of gold, silver and iron in each glass slide by XRF, take the average of the three measurement results at the same collection point and mark them as the content value.

[0080] In this embodiment, the ore is sampled and processed into glass slides, and then the glass slides are subjected to elemental analysis using XRF technology. Three glass slides are made from samples at each collection point and the three measurement results are averaged. This helps reduce the risk of inaccurate test results due to omissions in the elemental analysis process, and further helps improve the accuracy of mineralization predictions.

[0081] The solubility analysis of underground river water at each collection point is carried out as follows:

[0082] Step 1: Set the sampling standard, extract ten samples from the underground river water at each collection point according to the sampling standard, obtain the pH value, conductivity, and dissolved oxygen of the samples, and normalize the pH value, conductivity, and dissolved oxygen in each sample to obtain the standard value;

[0083] Step 2: Set a preset threshold value for the standard value, select the sample with the largest standard value among the ten samples at each collection point as the optimal sample, and compare the standard value of the optimal sample with the preset threshold value for the standard value;

[0084] Step 3: If the standard value of the optimal sample is greater than or equal to the standard preset threshold, the optimal sample is used as the analysis object of the collection point; if the standard value of the optimal sample is less than the standard preset threshold, the collection point is resampled;

[0085] Step 4: Use I CP-MS to determine the content ratio of gold, silver, iron and silicon in the analysis object at each sampling point, and obtain the dissolution value of each collection point by the content ratio of gold, silver, iron and silicon in the analysis object.

[0086] In this embodiment, before using I CP-MS technology to detect water samples, the samples are screened to avoid selecting reasonable normal samples, which is beneficial to avoid the impact of inaccurate sample selection on the detection results, and further beneficial to improving the accuracy of mineralization prediction.

[0087] Step 4 specifically includes the following steps:

[0088] Step 1: Obtain the climate and environment type, geological plate type, and mining recovery rate of historical mineral deposits to establish a reference database;

[0089] Step 2: Obtain the environmental type and geological plate type of the mineralization prediction range, calculate the proximity between the mineralization prediction range and each historical mineral deposit using the Euclidean distance algorithm based on the climate environment type and geological plate type of the mineral deposit, obtain the similarity value based on the proximity between the mineralization prediction range and each historical mineral deposit, and select the historical mineral deposit with the highest similarity value as the reference mineral deposit;

[0090] Step 3: The historical reference coefficient is obtained by multiplying the similarity value of the reference deposit and the mining recovery rate, and the recovery coefficient is obtained by the sample reference coefficient and the historical reference coefficient;

[0091] The similarity value is calculated as:

[0092]

[0093] Among them, Xs represents the similarity value, and Uj represents the proximity distance between the mineralization prediction range area and the historical deposit.

[0094] In this embodiment, the historical mineral deposit most similar to the mineralization prediction range is determined by the climate environment type and the geological plate type, and then the historical reference coefficient is obtained by the mining recovery rate and similarity value of the reference mineral deposit, and finally the recovery coefficient is obtained by the sample reference coefficient and the historical reference coefficient, so as to facilitate more accurate prediction of the underground mineralization situation in the mineralization prediction range;

[0095] The preset threshold in this scheme can be obtained through weight analysis method, and the weight can be obtained through hierarchical analysis method.

[0096] Obtain ore samples from each collection point, specifically:

[0097] Step 1: Drill underground at each collection point until the drill bit enters the ore bed, and take samples from the highest point of the ore bed downwards. Sample the ore every 0.5 meters until the drilling depth exceeds the depth of the ore bed, and obtain multiple samples at each collection point;

[0098] Step 2: Define ten alteration grades and ten fold grades from low to high, mark the corresponding alteration grade and fold grade for each ore sample according to the alteration intensity and fold degree on the surface of each ore sample, and obtain the forming grade of each collected sample by weighted summation of the alteration grade and fold grade of each collected sample;

[0099] Step 3: Select the sample with the highest forming grade from multiple samples at each collection point as the ore sample.

[0100] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill 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 in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0101] In this embodiment, sampling is performed at different depths of the ore deposit location at the collection point, and then the surface alteration intensity and wrinkle degree of multiple collected samples are comprehensively analyzed to define the molding grade of each collected sample, and the collected sample with the highest molding grade is selected from the multiple collected samples at each collection point as the ore sample. Since the porphyry-hydrothermal cooling and molding at the bottom of the ore deposit mostly takes a corrugated bowl shape, and valuable ores are accumulated on the surface of the ore deposit, it is difficult to ensure that the samples at each collection point are all ores on the surface of the ore deposit by directly sampling according to the depth. Obtaining ore samples through this scheme facilitates the accuracy of subsequent elemental analysis of samples from multiple collection points, which is beneficial to promoting the accuracy of content values. At the same time, after constructing the three-dimensional structural model of the ore deposit, the depth of the ore sample can be inferred through the obtained ore sample, so as to facilitate the understanding of the undulating shape of the underground ore deposit surface, and then facilitate the correction of the constructed three-dimensional structural model of the ore deposit according to the undulating shape of the ore deposit surface.

[0102] 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, that is, they 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.

[0103] 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 prediction model for multi-center porphyry-hydrothermal mineralization system in a composite magmatic arc environment, It is characterized in that The steps include: Step 1: Set the mineralization prediction range, set up multiple different collection points within the mineralization prediction range, obtain the ore distribution density, ore thickness, ore alteration intensity of each collection point, and obtain the richness coefficient of each collection point; Step 2: Obtain ore samples from each collection point, perform elemental analysis on the ore samples from each collection point, obtain the total content ratio of gold, silver and iron in the ore of each collection point, mark it as the content value, perform solubility analysis on underground river water from each collection point, obtain the content ratio of gold, silver, iron and silicon in water samples from different locations, and obtain the solubility value of each collection point through the sum of the content ratio of gold, silver and iron in the water sample and the silicon ratio; Step 3: The element coefficient of each collection point is obtained by weighted summing of the content value and solubility value of each collection point, and the predicted value of each collection point is obtained by weighted average of the richness coefficient and element coefficient of each collection point. The predicted values ​​of all collection points are averaged to obtain the sample reference coefficient; Step 4: Build a reference database through historical mineral deposits, determine the mineral deposit that is most similar to the mineralization prediction range in the reference database, mark it as a reference mineral deposit and obtain a similarity value, obtain the historical reference coefficient by multiplying the similarity value of the reference mineral deposit and the mining recovery rate, and obtain the recovery coefficient by the sample reference coefficient and the historical reference coefficient; Step 5: Use GIS to establish a three-dimensional structural model of the ore deposit within the mineralization prediction range through satellite remote sensing data, ore distribution density and ore thickness at each collection point, mark the position of each collection point in the three-dimensional model of the ore deposit, mark the predicted value at each collection point, and mark the recovery coefficient in the three-dimensional model of the ore deposit.

2. A prediction model for a multi-center porphyry-hydrothermal mineralization system in a composite magmatic arc environment according to claim 1, Features: The calculation method of the richness coefficient is as follows: Among them, Fs represents the ore richness coefficient, Md represents the ore distribution density, Hd represents the ore thickness, and Sq represents the ore alteration intensity, Hd>0, Md>0, Sq>0.

3. The prediction model of a multi-center porphyry-hydrothermal mineralization system in a composite magmatic arc environment according to claim 1, Features: Obtain ore samples from each collection point, specifically: Step 1: Drill underground at each collection point until the drill bit enters the ore bed, and take samples from the highest point of the ore bed downwards. Sample the ore every 0.5 meters until the drilling depth exceeds the depth of the ore bed, and obtain multiple samples at each collection point; Step 2: Define ten alteration grades and ten fold grades from low to high, mark the corresponding alteration grade and fold grade for each ore sample according to the alteration intensity and fold degree on the surface of each ore sample, and obtain the forming grade of each collected sample by weighted summation of the alteration grade and fold grade of each collected sample; Step 3: Select the sample with the highest forming grade from multiple samples at each collection point as the ore sample.

4. The prediction model of a multi-center porphyry-hydrothermal mineralization system in a composite magmatic arc environment according to claim 1, Features: The specific area of ​​predicted mineralization range is set as follows: Step 1: Divide the field area into several grids, use a proton magnetometer to obtain the magnetic magnitude in each grid, set a preset threshold for the magnetic fluctuation range, and mark the grid areas where the magnetic force is not within the preset threshold for the magnetic fluctuation range, and record them as magnetic anomaly grids; Step 2: Use gravimeters to obtain gravity in the magnetic anomaly grid area, set a preset threshold for the gravity fluctuation range, and mark the grid area where the gravity is not within the preset threshold for the gravity fluctuation range, and record it as the magnetic gravity anomaly area; Step 3: Connect the center points of all the magnetic gravity anomaly areas on the edge to obtain the mineralization prediction range.

5. The prediction model of a multi-center porphyry-hydrothermal mineralization system in a composite magmatic arc environment according to claim 1, Features: The ore samples from each collection point were analyzed for elements, specifically: Step 1: Set the sampling standard and sample the ore at each collection point according to the sampling standard; Step 2: After cleaning and drying the samples, grind them into powders, add an equal amount of flux to each sample powder and melt them at high temperature to make glass slides. Three glass slides are made from the samples at each collection point. Step 3: Determine the total content of gold, silver and iron in each glass slide by XRF, take the average of the three measurement results at the same collection point and mark them as the content value.

6. The prediction model of a multi-center porphyry-hydrothermal mineralization system in a composite magmatic arc environment according to claim 1, Features: The solubility analysis of underground river water at each collection point is carried out as follows: Step 1: Set the sampling standard, extract ten samples from the underground river water at each collection point according to the sampling standard, obtain the pH value, conductivity, and dissolved oxygen of the samples, and normalize the pH value, conductivity, and dissolved oxygen in each sample to obtain the standard value; Step 2: Set a preset threshold value for the standard value, select the sample with the largest standard value among the ten samples at each collection point as the optimal sample, and compare the standard value of the optimal sample with the preset threshold value for the standard value; Step 3: If the standard value of the optimal sample is greater than or equal to the standard preset threshold, the optimal sample is used as the analysis object of the collection point; if the standard value of the optimal sample is less than the standard preset threshold, the collection point is resampled; Step 4: Use ICP-MS to determine the proportion of gold, silver, iron and silicon in the analysis object at each sampling point, and obtain the dissolution value of each collection point by the proportion of gold, silver, iron and silicon in the analysis object.

7. The prediction model of a multi-center porphyry-hydrothermal mineralization system in a composite magmatic arc environment according to claim 1, Features: The calculation method of the solubility value is as follows: Among them, Rj represents the solubility value, Jh represents the proportion of gold, Yh represents the proportion of silver, Th represents the proportion of iron, Gh represents the proportion of silicon, Jh≥0, Yh≥0, Th≥0, Gh>0.

8. The prediction model of a multi-center porphyry-hydrothermal mineralization system in a composite magmatic arc environment according to claim 1, Features: Step 4 specifically includes the following steps: Step 1: Obtain the climate and environment type, geological plate type, and mining recovery rate of historical mineral deposits to establish a reference database; Step 2: Obtain the environmental type and geological plate type of the mineralization prediction range, calculate the proximity between the mineralization prediction range and each historical mineral deposit using the Euclidean distance algorithm based on the climate environment type and geological plate type of the mineral deposit, obtain the similarity value based on the proximity between the mineralization prediction range and each historical mineral deposit, and select the historical mineral deposit with the highest similarity value as the reference mineral deposit; Step 3: Obtain the historical reference coefficient by multiplying the similarity value of the reference deposit and the mining recovery rate, and obtain the recovery coefficient by combining the sample reference coefficient and the historical reference coefficient.

9. The prediction model of a multi-center porphyry-hydrothermal mineralization system in a composite magmatic arc environment according to claim 7, Features: The calculation method of recovery coefficient is as follows: Where Dy represents the recovery coefficient, Yx represents the sample reference coefficient, Lx represents the historical reference coefficient, and u 1 and u 2 is the weight, 1>u 1 >0,1>u 2 >0.

10. The prediction model of a multi-center porphyry-hydrothermal mineralization system in a composite magmatic arc environment according to claim 8, Features: The similarity value is calculated as: Among them, Xs represents the similarity value, and Uj represents the proximity distance between the mineralization prediction range area and the historical deposit.

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