A skarn-type polymetallic ore deposit prospecting method and system based on three-dimensional modeling

Abstract

The present invention provides a skarn-type polymetallic ore prospecting method and system based on three-dimensional modeling, which relates to the field of geological exploration, including: obtaining ore deposit data in the area where the ore deposit is located, preprocessing the data of the mined skarn-type polymetallic ore deposit; selecting a multi-scale convolutional neural network as a deep learning model for training to predict the location of the ore deposit in the unmined area; generating a geological characteristic coefficient according to the predicted geological characteristics of the unmined area; generating a mineral characteristic coefficient through the mineral characteristic data of the three-dimensional model; comparing the mineral characteristic coefficient with the preset weight to determine whether there is a tungsten-tin ore; generating a mineral type judgment coefficient according to the content of the mineral elements in the sample; generating a comprehensive prediction value through the geological characteristic coefficient and the mineral characteristic coefficient, and judging the type of the ore deposit based on the comprehensive prediction value and the type judgment coefficient. The method improves the accuracy and efficiency of ore deposit prediction.

Description

Technical Field

[0001] The present invention relates to the field of geological prospecting technology, and in particular to a skarn-type polymetallic ore deposit prospecting method and system based on three-dimensional modeling. Background Art

[0002] In the past few decades, geological surveys have been one of the main pillars of energy development, but traditional methods of mineral exploration have been unable to meet the requirements of efficiency and accuracy, especially for complex skarn-type polymetallic deposits. Traditional methods face many challenges in data processing, accuracy, and resource input. Existing technologies usually rely on two-dimensional geological maps for exploration, which limits the comprehensive understanding of the spatial structure of the deposit and leads to insufficient accuracy of exploration results.

[0003] In the prior art, the publication number CN118363087B discloses a skarn-type iron-copper-gold polymetallic ore exploration technology method and system, which constructs a reference model based on the prior information of the target ore deposit; performs three-dimensional physical property inversion of gravity and magnetism with the reference model as a constraint condition, obtains the correspondence between lithology and physical properties and performs lithology mapping, establishes a two-dimensional to three-dimensional model, calculates the connection degree of the prospecting unit from the three-dimensional model, and finds the location of the ore target area based on the connection degree. However, it limits the comprehensive understanding of the spatial structure of the ore deposit, resulting in insufficient accuracy of the exploration results. In addition, in data processing, advanced data processing technologies and models are not fully utilized, and the distribution of ore deposits in unmined areas cannot be effectively predicted. Therefore, there is a need for an advanced exploration method based on three-dimensional modeling that can more accurately process and analyze ore deposit data, improve the accuracy and efficiency of ore deposit prediction, and thus better guide the rational development and utilization of mineral resources.

[0004] 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

[0005] The purpose of the present invention is to provide a skarn-type polymetallic ore deposit prospecting method and system based on three-dimensional modeling to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A skarn-type polymetallic ore deposit prospecting method based on three-dimensional modeling, the specific steps include:

[0008] Step 1: Obtain the deposit data of the area where the tungsten-tin ore and skarn-type polymetallic deposits are located. The deposit data includes geological characteristic data and mineral characteristic data. The data is divided into mined areas and unmined areas, a three-dimensional geological model is constructed, the sampling area is determined, and the geological characteristic coefficient is generated according to the content data of tungsten ore, tin ore and skarn in the mined area;

[0009] Step 2: Build a deep learning model, use the geological characteristic data of the mined area as the training set, and the geological characteristic coefficients of the mined area as labels to train the deep learning model; input the geological characteristic data of the unmined area into the trained deep learning model to obtain the predicted geological characteristic coefficients of the unmined area;

[0010] Step 3: According to the mineral characteristic data in the ore deposit data, calculate the skewness value and the kurtosis value, generate the mineral characteristic coefficient, compare the mineral characteristic coefficient with the preset weight, and determine whether there is tungsten-tin ore;

[0011] Step 4: Generate a mineral type judgment coefficient based on the content of silicon, aluminum, sulfur, and iron in the mineral characteristic data; generate a comprehensive prediction value through the geological characteristic coefficient and the mineral characteristic coefficient, and judge the type of the ore deposit based on the comprehensive prediction value and the mineral type judgment coefficient.

[0012] Furthermore, the skarn-type polymetallic mineral deposit prospecting and exploration method based on three-dimensional modeling is characterized in that the geological characteristic data include: lithological characteristic data, stratigraphic characteristic data, and the mineral characteristic data include: data on the content of tungsten, tin, antimony, molybdenum, silicon, aluminum, sulfur, and iron in soil and core samples.

[0013] Furthermore, the skarn-type polymetallic ore prospecting method based on three-dimensional modeling is characterized in that the logic of setting the data set and label of the mined area data is:

[0014] The entire target area is divided into several sub-areas, each sub-area is represented by a sample, and each sample contains all the geological characteristic data of the sub-area. All geological characteristic data are set as a data set, including lithological characteristic data and stratigraphic characteristic data; the data set is divided into a training set and a validation set with a ratio of 7:3.

[0015] Furthermore, the skarn-type polymetallic ore prospecting and exploration method based on three-dimensional modeling is characterized in that the calculation formula of the geological characteristic coefficient is:

[0016]

[0017] in, is the geological characteristic coefficient, For the The mineral content of each mined area, including data on the content of tungsten, tin and skarn; For tungsten ore, For tin ore, For skarn, For the The preset weights of each mineral are: , and are greater than 0, and .

[0018] Furthermore, the formula for calculating the skewness and kurtosis values ​​based on the data of tungsten, tin, antimony and molybdenum content in soil and core samples is:

[0019]

[0020]

[0021] in, is the skewness value of the element, is the kurtosis value of the element, is the index number of the sample, ,in is the total number of samples, For the The element content data of each sample, is the mean, is the standard deviation.

[0022] Furthermore, the calculation formula for generating the mineral characteristic coefficient is:

[0023]

[0024] in, is the mineral characteristic coefficient, and are the weighting coefficients of tungsten and tin and antimony and molybdenum respectively, where and are greater than 0, and .

[0025] Furthermore, the calculation formula of the mineral type determination coefficient is:

[0026]

[0027] in, , , , They are the contents of silicon, aluminum, sulfur and iron in the unmined areas respectively.

[0028] Furthermore, the comprehensive prediction value calculation formula is:

[0029]

[0030] in, is the comprehensive prediction value, and Adjust the parameters for the preset model, where and are greater than 0, and .

[0031] Furthermore, based on the comprehensive prediction value and type judgment coefficient, the logic for judging the type of mineral deposit is as follows:

[0032] Preliminary judgment based on comprehensive prediction value, preset threshold and threshold ,like , then the deposit is low-grade or has no economic value; if , then the deposit is low-grade or has no economic value; if , then the deposit is low-grade or has no economic value;

[0033] Refine categories based on category judgment coefficients and preset thresholds and threshold ,like , then it is judged to be a quartz vein type tungsten-tin ore; if , it is judged to be a silicate tungsten-tin ore; if , it is judged to be sulfide-type tungsten-tin ore.

[0034] The present invention also provides a skarn-type polymetallic ore deposit prospecting and exploration method and system based on three-dimensional modeling. The distribution method is obtained by executing the above-mentioned skarn-type polymetallic ore deposit prospecting and exploration method. The specific modules include:

[0035] Acquisition module: Obtain the deposit data of the area where the tungsten-tin ore and skarn-type polymetallic deposits are located. The deposit data includes geological characteristic data and mineral characteristic data. The data is divided into mined areas and unmined areas, a three-dimensional geological model is constructed, the sampling area is determined, and the geological characteristic coefficient is generated according to the content data of tungsten ore, tin ore and skarn in the mined area;

[0036] Training module: Build a deep learning model, use the geological characteristic data of the mined area as the training set, and the geological characteristic coefficients of the mined area as labels to train the deep learning model; input the geological characteristic data of the unmined area into the trained deep learning model to obtain the predicted geological characteristic coefficients of the unmined area;

[0037] Mineral detection module: Calculate the skewness and kurtosis values ​​according to the mineral characteristic data in the ore deposit data, generate the mineral characteristic coefficient, compare the mineral characteristic coefficient with the preset weight, and determine whether there is tungsten-tin ore;

[0038] Mineral type judgment module: Generate mineral type judgment coefficients based on the content of silicon, aluminum, sulfur and iron in the mineral characteristic data; generate comprehensive prediction values ​​through geological characteristic coefficients and mineral characteristic coefficients, and judge the type of ore deposit based on the comprehensive prediction values ​​and mineral type judgment coefficients.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] The present invention significantly improves the exploration accuracy and efficiency of tungsten-tin ore and skarn-type polymetallic deposits by introducing deep learning technology based on three-dimensional modeling. Specifically, the method first systematically processes the deposit data, and carefully marks the data of mined areas and unmined areas, providing a solid data foundation for subsequent analysis. Through the deep learning model training of multi-scale convolutional neural networks, the system can accurately predict the distribution of deposits in unmined areas, making full use of the geological characteristics and mineral characteristic information in historical data, greatly improving the efficiency and accuracy of resource exploration. In addition, the generated geological characteristic coefficients are compared with the mineral characteristic coefficients, which further enhances the ability to judge the type of deposit and effectively reduces the risk of misjudgment caused by insufficient information in traditional exploration methods. Compared with the prior art, this scheme provides a more forward-looking solution for the rational development of mineral resources, which not only improves the reliability of exploration results, but also promotes technological progress in the mining field. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0042] Figure 2 It is a schematic diagram of the overall system structure of the present invention. DETAILED DESCRIPTION

[0043] 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.

[0044] 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.

[0045] Example:

[0046] See also Figure 1 , the present invention provides a technical solution:

[0047] A skarn-type polymetallic ore deposit prospecting method based on three-dimensional modeling, the specific steps include:

[0048] Step 1: Obtain the deposit data of the area where the tungsten-tin ore and skarn-type polymetallic deposits are located. The deposit data includes geological characteristic data and mineral characteristic data. The data is divided into mined areas and unmined areas, a three-dimensional geological model is constructed, the sampling area is determined, and the geological characteristic coefficient is generated according to the content data of tungsten ore, tin ore and skarn in the mined area;

[0049] Use drones and automated sampling equipment to collect high-precision data in the target area. The collected data include: lithology data: distribution of volcanic rocks, sedimentary rocks, metamorphic rocks, etc., stratigraphic data: distribution and sequence of different strata, and data on the content of tungsten, tin, antimony, molybdenum, silicon, aluminum, sulfur, and iron in soil and core samples.

[0050] Import the above collected data into Gocad to construct a three-dimensional geological model of the target area. Mark the specific locations of existing tungsten-tin mines and skarn-type polymetallic deposits, divide the entire target area into several sub-areas, each sub-area is represented by a sample, and each sample contains all the characteristic data of the sub-area. Set all characteristic data as a data set, including geological characteristic coefficients: lithological characteristic data, stratigraphic characteristic data; the preprocessed data set is divided into a training set and a validation set, with a ratio of 7:3; according to the known mineral exploration data, each sub-area is labeled with the labels of tungsten, tin, and skarn-type polymetallic deposits.

[0051] Step 2: Build a deep learning model, use the geological characteristic data of the mined area as the training set, and the geological characteristic coefficients of the mined area as labels to train the deep learning model; input the geological characteristic data of the unmined area into the trained deep learning model to obtain the predicted geological characteristic coefficients of the unmined area;

[0052] A multi-scale convolutional neural network is selected as a deep learning model, which can process geological features of different scales and improve the ability to identify complex geological structures. A time series analysis module is introduced into the multi-scale convolutional neural network to analyze the time series characteristics in the geological evolution process and enhance the timeliness and robustness of the model.

[0053] Input the lithology and formation characteristics data of each area in the training set. Use the adaptive optimization algorithm to dynamically adjust the weights and parameters of the model, and use the back propagation algorithm to minimize the error between the output result and the sample label. Use the validation set to evaluate the trained model until the accuracy and precision of the evaluation results meet the set requirements, and output the verified deep learning model.

[0054] Step 3: According to the mineral characteristic data in the ore deposit data, calculate the skewness value and the kurtosis value, generate the mineral characteristic coefficient, compare the mineral characteristic coefficient with the preset weight, and determine whether there is tungsten-tin ore;

[0055] The calculation formula of the geological characteristic coefficient is:

[0056]

[0057] in, is the geological characteristic coefficient, For the The mineral content of each mined area, including data on the content of tungsten, tin and skarn; For tungsten ore, For tin ore, For skarn, For the The preset weights of each mineral are: , and are greater than 0, and . Weight Corresponding to the influence of tungsten ore content, weight Corresponding to the impact of tin ore content, weight The relative size of the weights corresponding to the influence of skarn content determines the relative importance of these three factors in the priority calculation. , and The setting ensures that the tungsten content has a greater impact on the priority, because the tungsten content is usually a more critical factor. Even if the tin content is sufficient during the mineral exploration process, if the tungsten content is too low, its priority may not be very high. Although the tungsten content is more critical, tin cannot be ignored. A large enough tin content can improve the efficiency of geological resolution, so it also has a certain weight, but it is relatively small compared to the tungsten content. The skarn content is an important factor in identifying the geology of the mineral deposit, but compared with tungsten and tin, its economic value is lower, so the weight given is set smaller. The value of can be set to 0.1-0.2, indicating that the importance of skarn content is small but not negligible. The value of can be set to 0.3-0.2, indicating that the importance of tin content is limited but not negligible. The setting is 0.4-0.5, indicating that the tungsten content is more important. This range is selected based on experience and actual operational needs to ensure that in most cases, the formula can reflect a reasonable priority ranking.

[0058] The formula for calculating the skewness and kurtosis values ​​based on the data of tungsten, tin, antimony and molybdenum content in soil and core samples is:

[0059]

[0060]

[0061] in, is the skewness value of the element, is the kurtosis value of the element, is the index number of the sample, ,in is the total number of samples, For the The element content data of each sample, is the mean, is the standard deviation, and the skewness value of the element reflects the symmetry of the distribution. If the skewness value of the element is 0, it means that the distribution is symmetrical; if the skewness value of the element is positive, it means that the distribution is skewed to the right; if the skewness value of the element is negative, it means that the distribution is skewed to the left. The cubic function is used because of the nature of the cubic function, which can amplify the influence of samples greater than the mean and the influence of samples less than the mean. The kurtosis value of the element reflects the sharpness of the distribution. High kurtosis values ​​indicate that the data is highly concentrated near the mean, while low kurtosis values ​​indicate that the data distribution is relatively flat. The fourth power is used because the fourth power function will amplify the influence of extreme values, so that it can effectively describe the thickness of the tail. In order to standardize the kurtosis value of the element, the kurtosis value of the normal distribution is made 0. The kurtosis value of the normal distribution is 3, so 3 is subtracted.

[0062] The calculation formula of the generated mineral characteristic coefficient is:

[0063]

[0064] in, is the mineral characteristic coefficient, The calculation takes into account the skewness and kurtosis values ​​of four mineral elements: tungsten, tin, antimony and molybdenum, reflecting the comprehensive characteristics of the deposit, especially the dominant role of tungsten and tin. The mineral characteristic coefficient can provide a quantitative basis for the evaluation of the deposit, help geological prospectors to formulate more effective exploration strategies, and improve the efficiency and economic benefits of resource development. and are the weighting coefficients of tungsten and tin and antimony and molybdenum respectively, where and are greater than 0, and , weight Corresponding to the influence of skewness value of tungsten and tin elements, weight The relative size of the weights corresponding to the influence of the skewness values ​​of the antimony and molybdenum elements determines the relative importance of these two factors in the priority calculation. and The setting ensures that the skewness values ​​of tungsten and tin elements have a greater impact on the priority, because the skewness values ​​of tungsten and tin elements are usually more critical factors. During the mineral exploration process, even if the skewness values ​​of antimony and molybdenum elements are sufficient, if the skewness values ​​of tungsten and tin elements are too small, their priority may not be very high. Although the skewness values ​​of tungsten and tin elements are more critical, the skewness values ​​of antimony and molybdenum elements cannot be ignored. A large enough skewness value of antimony and molybdenum elements can improve the efficiency of mineral resolution. Therefore, there is also a certain weight, but it is relatively small. The value of can be set to 1.0-2.0, indicating that the importance of the skewness values ​​of antimony and molybdenum elements is limited but not negligible. The setting is 2.0-3.0, indicating that the skewness values ​​of tungsten and tin are more important. This range was chosen based on experience and practical operational needs to ensure that in most cases, the formula reflects a reasonable priority order.

[0065] Step 4: Compare the mineral characteristic coefficient with the preset weight to determine whether there is tungsten-tin ore, and generate a mineral type judgment coefficient based on the content of silicon, aluminum, sulfur, and iron in the sample; generate a comprehensive prediction value through the geological characteristic coefficient and the mineral characteristic coefficient, and judge the type of the deposit based on the comprehensive prediction value and the type judgment coefficient.

[0066] Mineral Characteristic Factor With the preset weight threshold For comparison, based on experience and geological characteristics, The value can be set to 2.0-3.0. , it is predicted that there is no tungsten-tin ore in the area, otherwise it is judged that there is tungsten-tin ore.

[0067] The calculation formula of the mineral type determination coefficient is:

[0068]

[0069] in, , , , They are the contents of silicon, aluminum, sulfur and iron in the unmined areas respectively.

[0070] Elements such as silicon and aluminum are often associated with silicate minerals in skarn-type deposits. The silicon and aluminum content can help identify and differentiate between different types of silicate minerals, such as feldspar and mica, which are important components of skarn.

[0071] Sulfur and iron are the main components of many sulfide minerals. Iron often forms pyrite, pyrrhotite and other minerals with sulfur. These sulfide minerals are also very common in skarn-type deposits.

[0072] The comprehensive prediction value calculation formula is:

[0073]

[0074] in, It is a comprehensive prediction value used to reflect the potential value and enrichment of a mineral deposit. It is obtained by combining the mineral characteristic coefficient and the geological characteristic coefficient. Its purpose is to evaluate the economic value or development potential of a mineral deposit. It can help geological prospectors to select exploration areas more scientifically, thereby increasing the discovery rate of mineral resources, saving time and costs. emphasize The secondary impact of the element content may increase the impact of the comprehensive prediction. and It has a linear effect on the comprehensive prediction value. or When the comprehensive prediction value increases It usually increases as well, which indicates that the improvement of the deposit characteristic quantity and mineral characteristic coefficient is positively correlated with the comprehensive prediction value, reflecting the enhancement of the deposit potential. and Adjust the parameters for the preset model, where and are greater than 0, and , weight Corresponding to the influence of mineral characteristic coefficient, weight The relative size of the weights corresponding to the influence of the square term of the geological characteristic coefficient determines the relative importance of these two factors in the priority calculation. and The setting ensures that the mineral characteristic coefficient has a greater impact on the priority, because the mineral characteristic coefficient is usually a more critical factor. During the exploration of a mineral deposit, even if the geological characteristic coefficient is sufficient, if the mineral characteristic coefficient is too small, its priority may not be very high. Although the mineral characteristic coefficient is more critical, the geological characteristic coefficient cannot be ignored. A large enough geological characteristic coefficient can improve the exploration efficiency. Therefore, there is also a certain weight, but it is relatively small. The value of can be set to 0.5-3.0, indicating that the importance of the geological characteristic coefficient is limited but cannot be ignored. The range is set between 5.0 and 10.0, indicating a higher importance of the mineral characteristics coefficient. This range was chosen based on experience and practical operational needs to ensure that in most cases the formula reflects a reasonable priority ranking.

[0075] According to the comprehensive prediction value and type judgment coefficient, the logic for judging the type of mineral deposit is as follows:

[0076] Preliminary judgment based on comprehensive prediction value, preset threshold and threshold ,like , then the deposit is low-grade or has no economic value; if , then the deposit is low-grade or has no economic value; if , then the deposit is low-grade or has no economic value;

[0077] Refine categories based on category judgment coefficients and preset thresholds and threshold ,like , then it is judged to be a quartz vein type tungsten-tin ore; if , it is judged to be a silicate tungsten-tin ore; if , it is judged to be sulfide-type tungsten-tin ore.

[0078] Types of deposits

[0079] See also Figure 2 The present invention also provides a skarn-type polymetallic ore deposit prospecting and exploration system based on three-dimensional modeling. The distribution method is obtained by executing the above-mentioned skarn-type polymetallic ore deposit prospecting and exploration method. The specific modules include:

[0080] Acquisition module: Obtain the deposit data of the area where the tungsten-tin ore and skarn-type polymetallic deposits are located. The deposit data includes geological characteristic data and mineral characteristic data. The data is divided into mined areas and unmined areas, a three-dimensional geological model is constructed, the sampling area is determined, and the geological characteristic coefficient is generated according to the content data of tungsten ore, tin ore and skarn in the mined area;

[0081] Training module: Build a deep learning model, use the geological characteristic data of the mined area as the training set, and the geological characteristic coefficients of the mined area as labels to train the deep learning model; input the geological characteristic data of the unmined area into the trained deep learning model to obtain the predicted geological characteristic coefficients of the unmined area;

[0082] Mineral detection module: Calculate the skewness and kurtosis values ​​according to the mineral characteristic data in the ore deposit data, generate the mineral characteristic coefficient, compare the mineral characteristic coefficient with the preset weight, and determine whether there is tungsten-tin ore;

[0083] Mineral type judgment module: Generate mineral type judgment coefficients based on the content of silicon, aluminum, sulfur and iron in the mineral characteristic data; generate comprehensive prediction values ​​through geological characteristic coefficients and mineral characteristic coefficients, and judge the type of ore deposit based on the comprehensive prediction values ​​and mineral type judgment coefficients.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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 skarn-type polymetallic ore deposit prospecting method based on three-dimensional modeling, characterized in that: The specific steps include: Step 1: Obtain the deposit data of the area where the tungsten-tin ore and skarn-type polymetallic deposits are located. The deposit data includes geological characteristic data and mineral characteristic data. The data is divided into mined areas and unmined areas, a three-dimensional geological model is constructed, the sampling area is determined, and the geological characteristic coefficient is generated according to the content data of tungsten ore, tin ore and skarn in the mined area; Step 2: Build a deep learning model, use the geological characteristic data of the mined area as the training set, and the geological characteristic coefficients of the mined area as labels to train the deep learning model; input the geological characteristic data of the unmined area into the trained deep learning model to obtain the predicted geological characteristic coefficients of the unmined area; Step 3: According to the mineral characteristic data in the ore deposit data, calculate the skewness value and the kurtosis value, generate the mineral characteristic coefficient, compare the mineral characteristic coefficient with the preset weight, and determine whether there is tungsten-tin ore; The formula for calculating the skewness and kurtosis values ​​is: in, is the skewness value of the element, is the kurtosis value of the element, is the index number of the sample, ,in is the total number of samples, For the The element content data of each sample, is the mean, is the standard deviation; Step 4: Generate mineral type judgment coefficients based on the contents of silicon, aluminum, sulfur and iron in the mineral characteristic data; generate comprehensive prediction values ​​through geological characteristic coefficients and mineral characteristic coefficients, and judge the type of ore deposit based on the comprehensive prediction values ​​and mineral type judgment coefficients; The calculation formula of the generated mineral characteristic coefficient is: in, is the mineral characteristic coefficient, and are the weighting coefficients of tungsten and tin and antimony and molybdenum respectively, where and are greater than 0, and .

2. The method for prospecting and exploration of skarn-type polymetallic deposits based on three-dimensional modeling according to claim 1, characterized in that: The geological characteristic data include: lithological characteristic data, stratigraphic characteristic data, and mineral characteristic data include: data on the content of tungsten, tin, antimony, molybdenum, silicon, aluminum, sulfur, and iron in soil and core samples.

3. The method for prospecting and exploration of skarn-type polymetallic deposits based on three-dimensional modeling according to claim 1, characterized in that: The logic for setting the data set and label for the mined area data is: The entire target area is divided into several sub-areas, each sub-area is represented by a sample, each sample contains all geological characteristic data of the sub-area, and all geological characteristic data are set as a data set, including lithology characteristic data and stratigraphic characteristic data; The dataset is divided into a training set and a validation set with a ratio of 7:

3.

4. The method for prospecting and exploration of skarn-type polymetallic deposits based on three-dimensional modeling according to claim 1, characterized in that: The calculation formula of the geological characteristic coefficient is: in, is the geological characteristic coefficient, For the The mineral content of each mined area, including data on the content of tungsten, tin and skarn; For tungsten ore, For tin ore, For skarn, For the The preset weights of each mineral are: , and are greater than 0, and .

5. The method for prospecting and exploration of skarn-type polymetallic deposits based on three-dimensional modeling according to claim 1, characterized in that: The calculation formula of the mineral type determination coefficient is: in, , , , They are the contents of silicon, aluminum, sulfur and iron in the unmined areas respectively.

6. The method for prospecting and exploration of skarn-type polymetallic deposits based on three-dimensional modeling according to claim 4 is characterized in that: The comprehensive prediction value calculation formula is: in, is the comprehensive prediction value, and Adjust the parameters for the preset model, where and are greater than 0, and .

7. The method for prospecting and exploration of skarn-type polymetallic deposits based on three-dimensional modeling according to claim 1, characterized in that: According to the comprehensive prediction value and type judgment coefficient, the logic for judging the type of mineral deposit is as follows: Preliminary judgment based on comprehensive prediction value, preset threshold and threshold ,like , then the deposit is low-grade or has no economic value; if , then the deposit is low-grade or has no economic value; if , then the deposit is low-grade or has no economic value; Refine categories based on category judgment coefficients and preset thresholds and threshold ,like , then it is judged to be a quartz vein type tungsten-tin ore; if , it is judged to be a silicate tungsten-tin ore; if , it is judged to be a sulfide-type tungsten-tin ore.

8. A skarn-type polymetallic ore deposit prospecting and exploration system based on three-dimensional modeling, characterized in that: The exploration system is used to implement the skarn-type polymetallic ore deposit prospecting method according to any one of claims 1 to 7, comprising: The acquisition module is used to obtain the deposit data of the area where the tungsten-tin ore and skarn-type polymetallic deposits are located. The deposit data includes geological characteristic data and mineral characteristic data. The data is divided into mined areas and unmined areas, a three-dimensional geological model is constructed, the sampling area is determined, and the geological characteristic coefficient is generated according to the content data of tungsten ore, tin ore and skarn in the mined area; The training module is used to construct a deep learning model, and the geological characteristic data of the mined area is used as a training set, and the geological characteristic coefficients of the mined area are used as labels to train the deep learning model; the geological characteristic data of the unmined area is input into the trained deep learning model to obtain the predicted geological characteristic coefficients of the unmined area; The mineral detection module is used to calculate the skewness value and the kurtosis value according to the mineral characteristic data in the ore deposit data, generate the mineral characteristic coefficient, compare the mineral characteristic coefficient with the preset weight, and determine whether there is tungsten-tin ore; The mineral type judgment module is used to generate a mineral type judgment coefficient based on the content of silicon, aluminum, sulfur and iron in the mineral characteristic data; generate a comprehensive prediction value through the geological characteristic coefficient and the mineral characteristic coefficient, and judge the type of the ore deposit based on the comprehensive prediction value and the mineral type judgment coefficient.

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