Mineral resource quantity estimation analysis method and system

Abstract

The invention relates to the technical field of geological exploration, in particular to a mineral resource quantity estimation analysis method and system, and the method comprises the following steps: obtaining a multi-position ore sample, comparing the surface spectrum data of the ore with a known mineral database, recognizing the chemical components of the ore, evaluating the quality of the ore sample, and combining the sampling position and depth to obtain the mineral resource quantity. And obtaining mineral resource distribution data. According to the method, comprehensive and accurate mineral product distribution information is provided by identifying chemical components of ores and evaluating the quality of ore samples, a three-dimensional geologic model is constructed by utilizing seismic wave reflection, gravity and electromagnetic measurement data, geologic structure characteristics are extracted, and spatial distribution characteristics of mineral product resources are visually displayed; by analyzing the relationship between various geological structure features and mineral resource distribution, the accuracy of resource quantity estimation is improved, mineral space distribution prediction is performed by using the three-dimensional geological model, and the mineral resource quantity of the target area is accurately predicted by combining mineral quality information and predicting the mineral resource quantity.

Description

Technical Field

[0001] The present invention relates to the technical field of geological exploration, and particularly to a method and system for estimating and analyzing mineral resource reserves. Background Art

[0002] The technical field of geological exploration involves the exploration, evaluation, and development of the earth's surface and underground resources. The core content is to obtain information on the distribution, reserves, and quality of underground resources through geology, geophysics, and geochemistry, providing a scientific basis for resource development. Geological exploration technology involves various methods, including field geological surveys, drilling technology, seismic exploration, electromagnetic measurement, remote sensing technology, and the application of geographic information systems. By combining multiple technical means, a systematic resource exploration system is formed to comprehensively analyze underground resources from different angles and scales, improving the accuracy and efficiency of resource exploration.

[0003] Among them, the method for estimating and analyzing mineral resource reserves refers to a technical method for quantitatively evaluating the reserves of mineral resources through the collection, processing, and analysis of geological data, combined with statistical and computer modeling techniques. It covers the collection and collation of geological data, spatial analysis of resource distribution, construction of reserve calculation models, and uncertainty analysis. Specifically, this method obtains core samples and geological structure data through geological surveys and drilling, further analyzes resource characteristics using geophysical and geochemical technologies, processes the data based on statistical methods, and generates a three-dimensional geological model through computer modeling to complete the estimation of resource reserves.

[0004] In the actual operation of traditional mineral resource reserve estimation techniques, the collection of geological data usually relies on a single technical means, such as drilling or seismic exploration, and fails to fully utilize the complementarity of multi-source data, resulting in insufficient integrity and accuracy of resource distribution information. When constructing a three-dimensional geological model, the dynamic analysis and iterative optimization of geological structure characteristics are ignored, making it difficult to accurately reflect the resource distribution law under complex geological conditions. In areas with complex fault strikes and lithology distributions, existing technologies are difficult to accurately predict resource distribution, affecting the scientific nature of development decisions. These problems limit the overall efficiency and accuracy of resource exploration, increasing development risks and costs. Summary of the Invention

[0005] In order to solve the technical problems existing in the prior art, an embodiment of the present invention provides a method and system for estimating and analyzing mineral resource reserves. The technical solution is as follows:

[0006] In order to achieve the above object, the present invention adopts the following technical solution. A method for estimating and analyzing mineral resource reserves includes the following steps:

[0007] S1: Obtain ore samples from multiple locations. By comparing the surface spectral data of the ore with a known mineral database, identify the chemical composition of the ore, evaluate the quality of the ore samples, and combine the sampling location and depth to obtain mineral resource distribution data;

[0008] S2: Utilize the mineral resource distribution data, combine with the drilling data, seismic wave reflection data, gravity and electromagnetic measurement data in the mining area to construct a three-dimensional geological model, extract geological structure features, including lithology distribution, fault strike, formation thickness, and map the quality information of the ore samples into the model to obtain a geological structure feature distribution map;

[0009] S3: Use the geological structure feature distribution map and the mineral resource distribution data to calculate the correlation probabilities between lithology distribution, fault strike, formation thickness and mineral resource distribution, and iteratively adjust the contribution degrees of various geological structure features to obtain a correlation analysis result;

[0010] S4: Invoke the correlation analysis result, combine with the regional geological structure features in the three-dimensional geological model to predict the spatial distribution of minerals and generate a mineral spatial distribution prediction map;

[0011] S5: According to the mineral spatial distribution prediction map, combine with the quality information of the ore samples to predict the mineral resource quantity in the target area and generate a resource quantity estimation value.

[0012] As a further solution of the present invention, the mineral resource distribution data is specifically an ore chemical composition table, ore quality evaluation data, sampling location and depth information; the geological structure feature distribution map is specifically lithology distribution information, fault strike data, formation thickness information; the correlation analysis result is specifically the correlation probability between lithology distribution and mineral resource distribution, the correlation probability between fault strike and mineral resource distribution, the correlation probability between formation thickness and mineral resource distribution; the mineral spatial distribution prediction map is specifically the mineral distribution law, multi-location mineral composition prediction result, mineral distribution prediction result; the resource quantity estimation value is specifically the mineral distribution density, ore body volume, mineral resource quantity.

[0013] As a further solution of the present invention, the steps of obtaining ore samples from multiple locations, by comparing the surface spectral data of the ore with a known mineral database, identifying the chemical composition of the ore, evaluating the quality of the ore samples, and combining the sampling location and depth to obtain mineral resource distribution data are specifically as follows:

[0014] S101: Obtain ore samples from multiple locations, collect the surface spectral data of the ore samples, calculate the corresponding probabilities of each mineral component by comparing and matching with a known mineral database, record the chemical composition of each ore sample, and generate a sample component analysis result;

[0015] S102: Based on the results of the sample composition analysis, combined with the chemical composition of the ore sample, evaluate the quality of the ore sample and generate ore quality information;

[0016] S103: According to the ore quality information, combined with the sampling location and sampling depth of the ore, generate mineral resource distribution data.

[0017] As a further solution of the present invention, the steps of using the mineral resource distribution data, combined with the drilling data, seismic wave reflection data, gravity and electromagnetic measurement data in the mining area, to construct a three-dimensional geological model, extract geological structure features, including lithology distribution, fault strike, formation thickness, and map the quality information of the ore sample into the model to obtain a geological structure feature distribution map are specifically as follows:

[0018] S201: Use the mineral resource distribution data to construct a three-dimensional geological model by collecting drilling data, seismic wave reflection data, gravity and electromagnetic measurement data in the mining area;

[0019] S202: Call the three-dimensional geological model, extract the geological structure features of multiple locations, including lithology distribution, fault strike, formation thickness information, by analyzing the geometric shape information of multiple locations in the model, and obtain geological structure feature data;

[0020] S203: Use the geological structure feature data, and use the position and depth information of the ore sample to map the quality information of the ore sample into the three-dimensional geological model to obtain a geological structure feature distribution map.

[0021] As a further solution of the present invention, the steps of using the geological structure feature distribution map and the mineral resource distribution data to calculate the correlation probability between the lithology distribution, fault strike, formation thickness and the mineral resource distribution, and iteratively adjust the contribution degree of various geological structure features to obtain the correlation analysis result are specifically as follows:

[0022] S301: Based on the mineral resource distribution data and the geological structure feature distribution map, calculate the correlation probability between the lithology distribution, fault strike, formation thickness and the mineral resource distribution, analyze the relationship between the geological features and the resource aggregation trend, and obtain correlation probability data;

[0023] S302: According to the correlation probability data, calculate the contribution degree of each geological structure feature, calculate the influence intensity of each feature on the mineral resource distribution, and obtain feature contribution degree data;

[0024] S303: Based on the feature contribution degree data, iteratively adjust and optimize the contribution degree of various geological structure features to obtain the correlation analysis result.

[0025] As a further solution of the present invention, the formula for calculating the influence intensity of each feature on the distribution of mineral resources is: ; Calculate the contribution degree of each geological structure feature to obtain feature contribution degree data; Among them, represents the contribution degree of the geological structure feature, is the weight of the th geological feature at the th sampling point, is the correlation probability between the th geological feature and the distribution of mineral resources, is the average correlation probability of the th geological feature, is the total number of feature data points, is the index of the data point, is the index of the geological structure feature.

[0029] As a further solution of the present invention, the steps of calling the correlation analysis result, combining the regional geological structure features in the three-dimensional geological model, predicting the spatial distribution of minerals, and generating a predicted map of the spatial distribution of minerals are specifically as follows:

[0030] S401: Based on the correlation analysis result, obtain the regional geological structure feature data in the three-dimensional geological model, combine the geological features and mineral composition information of the target area, establish the basic data of the spatial distribution of mineral composition, and generate preliminary mineral distribution data;

[0031] S402: According to the preliminary mineral distribution data, use the correlation probability between the geological features and the distribution of mineral resources to simulate the spatial distribution of mineral composition under each geological condition, calculate the distribution law of mineral composition, and obtain the mineral distribution law data;

[0032] S403: Based on the mineral distribution law data, predict the spatial distribution of minerals in the target area and generate a predicted map of the spatial distribution of minerals.

[0033] As a further solution of the present invention, the steps of predicting the mineral resource quantity of the target area and generating a resource quantity estimation value by combining the quality information of the ore sample according to the predicted map of the spatial distribution of minerals are specifically as follows:

[0034] S501: Based on the predicted map of the spatial distribution of minerals, calculate the distribution density of minerals in the target area according to the prediction result, and generate mineral distribution density data;

[0035] S502: Calculate the total volume of the ore within the region based on the mineral distribution density data and in combination with the geographical boundary of the target region to obtain the total volume prediction data;

[0036] S503: Utilize the total volume prediction data and in combination with the quality information of the ore samples to predict the mineral resource quantity of the target region and generate an estimated value of the mineral resource quantity.

[0037] As a further solution of the present invention, the specific formula for calculating the total volume of the ore within the region is:

[0038]

[0039] Calculate the total volume to obtain the total volume prediction data of the target region;

[0040] where D k′ is the mineral distribution density of the k'-th unit, A k′ is the area of the k'-th unit, H k′ is the depth of the k'-th unit, N' is the total number of small units within the target region, V represents the total volume of the ore within the target region, which is the sum of the volumes of all the ore within the target region, N' represents the total number of small units into which the target region is divided, and k' represents the number of each small unit.

[0041] On the other hand, a mineral resource quantity estimation and analysis system is provided. This system is applied to the mineral resource quantity estimation and analysis method. This system includes:

[0042] The ore sample analysis module acquires ore samples at multiple locations, collects the surface spectral data of the ore, compares it with the known mineral database, identifies the chemical composition of the ore, evaluates the quality of the ore samples, and in combination with the spatial location and depth of the ore sampling, generates the mineral resource distribution data;

[0043] The geological model construction module constructs a three-dimensional geological model based on the mineral resource distribution data, in combination with the mining area drilling data, seismic wave reflection data, gravity and electromagnetic measurement data, extracts the geological structure characteristics of the target region, and maps the quality information of the ore samples into the model to obtain the geological structure characteristics distribution map;

[0044] The geological structure correlation analysis module calculates the correlation probabilities between various geological structure characteristics and the mineral resource distribution based on the geological structure characteristics distribution map and the mineral resource distribution data, and calculates and iteratively adjusts the contribution degrees of various geological structure characteristics to obtain the correlation analysis result;

[0045] The mineral spatial distribution prediction module calls the correlation analysis result, in combination with the regional geological structure characteristics in the three-dimensional geological model, analyzes the relationship between the geological characteristics and the resource aggregation trend, predicts the spatial distribution of the minerals, and generates the mineral spatial distribution prediction map;

[0046] Based on the predicted map of the mineral spatial distribution, the resource quantity estimation module calculates the volume and mass of the ore within the target area in combination with the quality information of the ore samples, predicts the mineral resource quantity of the target area, and generates an estimated resource quantity value.

[0047] The beneficial effects brought by the technical solution provided by the embodiments of the present invention at least include:

[0048] By identifying the chemical composition of the ore and evaluating the quality of the ore samples, providing comprehensive and accurate mineral distribution information, constructing a three-dimensional geological model using seismic wave reflection, gravity, and electromagnetic measurement data, extracting geological structure features, visually displaying the spatial distribution features of mineral resources, analyzing the relationship between various geological structure features and the distribution of mineral resources, improving the accuracy of resource quantity estimation, predicting the mineral spatial distribution using the three-dimensional geological model, and combining the ore quality information to predict the mineral resource quantity, accurately predicting the mineral resource quantity of the target area. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0050] Figure 1 It is a schematic diagram of the working process of the present invention;

[0051] Figure 2 It is a detailed flowchart of S1 of the present invention;

[0052] Figure 3 It is a detailed flowchart of S2 of the present invention;

[0053] Figure 4 It is a detailed flowchart of S3 of the present invention;

[0054] Figure 5 It is a detailed flowchart of S4 of the present invention;

[0055] Figure 6 It is a detailed flowchart of S5 of the present invention;

[0056] Figure 7 It is a system flowchart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0057] The following will describe the technical solutions in the present invention in conjunction with the drawings.

[0058] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0059] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0060] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0061] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0062] See also Figure 1 The present invention provides a technical solution, a method for estimating and analyzing mineral resources, comprising the following steps:

[0063] S1: Obtain ore samples from multiple locations, identify the chemical composition of the ore by comparing the surface spectrum data of the ore with the known mineral database, evaluate the quality of the ore samples, and obtain mineral resource distribution data based on the sampling location and depth;

[0064] S2: Using mineral resource distribution data, combined with mining area drilling data, seismic wave reflection data, gravity and electromagnetic measurement data, a three-dimensional geological model is constructed to extract geological structural characteristics, including lithology distribution, fault orientation, and stratum thickness. The quality information of ore samples is mapped into the model to obtain a distribution map of geological structural characteristics.

[0065] S3: Using the geological structure feature distribution map and mineral resource distribution data, calculate the correlation probability between lithology distribution, fault trend, stratum thickness and mineral resource distribution, and iteratively adjust the contribution of various geological structure features to obtain correlation analysis results;

[0066] S4: Call the correlation analysis results, combine the regional geological structure characteristics in the three-dimensional geological model, predict the spatial distribution of minerals, and generate a mineral spatial distribution prediction map;

[0067] S5: According to the predicted map of the mineral spatial distribution and combined with the quality information of the ore samples, predict the mineral resources volume in the target area and generate the estimated value of the resources volume.

[0068] The mineral resources distribution data specifically includes the ore chemical composition table, the ore quality evaluation data, the sampling position and depth information. The geological structure feature distribution map specifically includes the lithology distribution information, the fault strike data, and the formation thickness information. The correlation analysis results specifically include the correlation probability between the lithology distribution and the mineral resources distribution, the correlation probability between the fault strike and the mineral resources distribution, and the correlation probability between the formation thickness and the mineral resources distribution. The predicted map of the mineral spatial distribution specifically includes the mineral distribution law, the predicted results of the mineral compositions at multiple positions, and the predicted results of the mineral distribution. The estimated value of the resources volume specifically includes the mineral distribution density, the mineral volume, and the mineral resources volume.

[0069] Please refer to Figure 2 , obtain ore samples at multiple positions. By comparing the surface spectral data of the ore with the known mineral database, identify the chemical composition of the ore, evaluate the quality of the ore samples. Combining the sampling position and depth, the steps to obtain the mineral resources distribution data are specifically as follows: S101: Obtain ore samples at multiple positions, collect the surface spectral data of the ore samples. By comparing and matching with the known mineral database, calculate the corresponding probability of each mineral composition, record the chemical composition of each ore sample, and generate the sample composition analysis result; In the spectral data analysis of the ore samples, first, it is necessary to collect the surface spectral data of the ore to obtain the reflection spectrum and the absorption spectrum. The reflection spectral data can reveal the surface optical properties of the minerals, while the absorption spectral data helps to determine the composition and molecular structure of the minerals. The collected data needs to be compared with the spectral characteristics in the mineral database to identify the mineral compositions contained in the samples. In the specific operation process, first extract the spectral data of the ore samples, and then match them with the spectral data of the known minerals in the database. The matching process is achieved by calculating the correlation coefficient. Usually, the Pearson correlation coefficient is used to measure the similarity between the sample spectrum and the database spectrum. The calculation formula is: ; where is the Pearson correlation coefficient, representing the similarity between the sample spectrum and the database spectrum, is the th spectral data point of the sample, is the th spectral data point of the database mineral, is the mean value of the sample spectral data, It is the mean of the spectral data in the database. By calculating the correlation coefficient, the matching degree between the sample spectrum and each mineral in the database can be obtained. If the spectral matching degree of a certain mineral is high, the content of this mineral component in the sample can be obtained. Through this process, the matching probability of each mineral component is finally calculated, and the chemical composition analysis result of the sample is generated. For example, if the spectral similarity between the sample spectrum and the spectrum of mineral A is 0.85, the component probability of this mineral in the sample is 85%.

[0074] S102: Based on the sample composition analysis result, combined with the chemical composition of the ore sample, evaluate the quality of the ore sample, and generate ore quality information;

[0075] The evaluation of ore quality depends on the chemical composition of the ore sample, especially the relative content of mineral components. For a certain mineral component, such as iron ore, a content threshold is usually set. Ores exceeding this threshold are rated as high-quality, and ores below this threshold are rated as low-quality. To quantify this process, a quality scoring formula can be used to calculate the comprehensive quality score of the ore based on the percentage value of the mineral component. The formula is:

[0076]

[0077] Among them, Q is the ore quality score, C is the percentage content of a certain mineral component in the ore sample, and T is the quality threshold of this mineral component. For example, if the content of the iron ore component in the sample is 65%, and the set quality threshold is 60%, the quality score can be calculated by substituting into the formula. Suppose the iron ore content in the sample is 65% (i.e., C = 65), and the set threshold is 60% (i.e., T = 60), then the calculated ore quality score is:

[0078]

[0079] According to the scoring standard, if Q > 100, the ore is rated as high-quality; if Q < 100, the ore quality is low. In this example, the quality score of the ore is 108.33, indicating that the ore belongs to high-quality ore.

[0080] S103: According to the ore quality information, combined with the sampling location and sampling depth of the ore, generate mineral resource distribution data;

[0081] In the process of generating the distribution of ore resources, the location information and depth information of sampling points play a crucial role. The ore quality varies with the sampling location and depth. Therefore, by recording ore samples at different locations and depths, the distribution of mineral resources can be understood more accurately. In the specific operation process, first, the sampling location and depth information of ore samples need to be collected. Then, based on the ore quality data at the sampling location, the overall distribution of resources is evaluated by the method of weighted average. The calculation formula of weighted average is as follows:

[0082]

[0083] Where W is the weighted average quality of the ore, C i is the ore quality score of the i-th sampling point, and D i is the sampling depth of the i-th sampling point. Through this formula, the influence of depth on ore quality can be considered, and the weighted average quality of ore at different depths can be calculated. For example, assume that the ore quality score of the first sampling point is 90 and the depth is 20 meters; the ore quality score of the second sampling point is 80 and the depth is 50 meters. Substitute into

[0084] Through weighted average calculation, the weighted average quality of the ore resources is obtained as 82.86, indicating the average quality of the ore resources at different sampling depths. This data can be further used to generate the distribution map of mineral resources to help evaluate the overall quality and distribution trend of mineral resources.

[0085] Please refer to Figure 3 for the specific steps of constructing a three-dimensional geological model using the mineral resource distribution data, combining the drilling data, seismic wave reflection data, gravity and electromagnetic measurement data in the mining area, extracting geological structure features, including lithology distribution, fault strike, formation thickness, and mapping the quality information of ore samples into the model to obtain the geological structure feature distribution map:

[0086] S201: Using the mineral resource distribution data, construct a three-dimensional geological model by collecting the drilling data, seismic wave reflection data, gravity and electromagnetic measurement data in the mining area;

[0087] In the process of constructing a 3D geological model, it is first necessary to collect drilling data, seismic wave reflection data, gravity and electromagnetic measurement data of the mining area. These data are the basis for constructing the geological model. Drilling data provides information on rock samples at different underground levels. Seismic wave reflection data can reflect the geometry of underground rock layers. Gravity and electromagnetic data help identify the distribution of underground ore veins. In this process, these different types of data are first integrated, and the data is transformed into a 3D model through spatial data interpolation technology. The processing of drilling data usually involves layer-by-layer analysis of lithological information at different depths and division of rock layers based on the collected lithological data. At the same time, by analyzing the propagation time and reflection intensity of seismic waves, the density and wave velocity of the medium at different underground levels can be reflected, thereby identifying the physical properties of different rock layers. Then, through the analysis of gravity and electromagnetic data, the distribution of underground minerals can be inferred, especially electromagnetic measurement can help identify the existence and distribution range of ore veins. Finally, these data are comprehensively analyzed and transformed into a 3D geological model to ensure that the model can accurately reflect the distribution of various underground rock layers and ore veins. For example, the drilling data of a certain mining area shows that there is a copper ore vein at a depth of 100 meters to 150 meters. Through seismic wave reflection data, the extension direction of the ore vein can be confirmed, and combined with gravity data, the boundary position of the ore vein can be confirmed, and finally a 3D geological model of the mining area is constructed. In the processing of seismic wave reflection data, the velocity-depth formula is as follows:

[0088]

[0089] Among them, v is the wave velocity of the underground medium, d is the propagation distance of the wave, and t is the propagation time of the wave.

[0090] Assume that the propagation distance of the seismic wave in the underground medium is 1000 meters and the propagation time of the wave is 5 seconds. The wave velocity calculation is:

[0091]

[0092] Based on this wave velocity and combined with other data, the characteristics of the underground rock layers in this area can be further inferred.

[0093] S202: Invoke the 3D geological model. By analyzing the geometric shape information at multiple positions in the model, extract the geological structure characteristics at multiple positions, including lithology distribution, fault strike, and formation thickness information, to obtain geological structure characteristic data;

[0094] Through the constructed 3D geological model, the geometric shape information at multiple positions in the model can be analyzed, and geological structure features such as lithology distribution, fault strike, and formation thickness can be extracted therefrom. These geological structure features are of great significance for evaluating the mineral resources in the mining area. First of all, in the model, by analyzing the geometric shapes at different positions, the lithology distribution can be determined. The lithology distribution refers to the spatial distribution of different minerals or rocks in the geological body, and usually the lithology changes of the rock strata are described by calibrating the lithology features at different depths and positions. For example, if obvious lithology changes (such as the boundary between sandstone layer and shale layer) are found in the geological model of a certain area, it can be determined that this area may be the location of the fault zone; then, by analyzing the fault strike, the fracture surfaces generated during the crustal movement can be identified. These fracture surfaces may affect the distribution of ore veins and help determine the extension trend of the ore veins; and the extraction of formation thickness information is usually carried out by comparing the rock stratum data at different depths and calculating the thickness of each layer of rock. For example, if in the model of a certain area, the thickness of the sandstone layer is 50 meters, followed by a 20-meter-thick clay layer, and then a 30-meter-thick limestone layer above, the thickness information of these strata will be used for further mineral resource evaluation. The formation thickness calculation formula is:

[0095] h = d2 - d1;

[0096] where h is the formation thickness, and d2 and d1 are the depths of the upper and lower parts of the formation respectively.

[0097] Assume that the depth of the sandstone layer is 100 meters and the depth of the clay layer is 150 meters, then the thickness of the sandstone layer is:

[0098] h = 150 - 100 = 50m;

[0099] In this way, the thickness of the sandstone layer can be obtained, and the thicknesses of different rock strata can be further analyzed to extract the lithology distribution characteristics.

[0100] S203: Using the geological structure feature data, and using the position and depth information of the ore samples, map the quality information of the ore samples into the 3D geological model to obtain the geological structure feature distribution map;

[0101] Mapping the quality information of ore samples into a 3D geological model can combine the location and depth information of ore samples with geological structure feature data, and finally generate a geological structure feature distribution map. In this process, first, the specific location and depth information of ore samples need to be obtained, which are usually provided by the coordinates of sampling points and sampling depths in the mining area. The mineral quality of each sample, such as the content of minerals like iron ore and copper ore in the ore, can be obtained through experimental analysis. Then, the quality data of each ore sample is combined with the geological structure feature data at the corresponding location in the 3D geological model. According to the matching relationship between the location and depth of the sample, the quality information of the ore is accurately mapped into the 3D geological model. For example, assume that at a certain location, the iron ore content of an ore sample is 70%, and this location is at a depth of 100 meters. After matching with the geological structure features in the 3D geological model, the iron ore quality information of this ore sample can be mapped into the model at this depth to show the quality of the ore in this area. Finally, by integrating the quality information of all samples, a geological structure feature distribution map is generated. The map not only shows information such as lithology and fault strike, but also marks the quality changes of ores in different regions, which provides strong data support for the development and mining of mineral resources in the mining area. The formula for mapping the quality information of ore samples into a 3D model is:

[0102]

[0103] Among them, P is the quality information of the ore sample in the 3D geological model, and C s is the content of mineral components in the ore sample, and C m is the reference mineral component at this location.

[0104] Assume that the iron ore component content of an ore sample is 70%, and the reference mineral component at this location is 60%. Then the ore quality mapping at this location is:

[0105]

[0106] Therefore, the ore quality at this location is 116.67, indicating that the ore quality in this area is higher than the reference level, and a corresponding geological structure feature distribution map is generated.

[0107] Please refer to Figure 4 , and using the geological structure feature distribution map and mineral resource distribution data, calculate the correlation probabilities between lithology distribution, fault strike, formation thickness and mineral resource distribution, and iteratively adjust the contribution degrees of various geological structure features to obtain the steps of the correlation analysis results as follows: S301: Based on the mineral resource distribution data and the geological structure feature distribution map, calculate the correlation probabilities between lithology distribution, fault strike, formation thickness and mineral resource distribution, analyze the relationship between geological features and resource aggregation trends, and obtain correlation probability data; Based on the mineral resource distribution data and the geological structure feature distribution map, it is first necessary to calculate the correlation probabilities between the lithology distribution, fault strike, formation thickness, and mineral resource distribution. In actual operation, the role of these features in mineral resource aggregation can be determined by calculating the correlation between each geological feature and the mineral resources. First, extract the lithology distribution data, fault strike, and formation thickness information, which can be obtained through drilling and seismic wave reflection data; then, obtain the mineral resource distribution data, which mainly comes from ore sampling points and the distribution of known ore veins; then, evaluate the correlation between the lithology distribution, fault strike, formation thickness, and mineral resource distribution by calculating the correlation coefficient, such as the Pearson correlation coefficient. The specific formula is: ; where, is the correlation coefficient, is the value of the lithology distribution, is the value of the mineral resource distribution, and are the means of the lithology distribution and the mineral resource distribution respectively, is the total number of data points. By calculating the correlation between these data, the correlation probability between them can be obtained. If the correlation between the lithology distribution and the mineral resource distribution is high (for example, , it indicates that the lithology distribution plays an important role in the aggregation of mineral resources; similarly, a high correlation between the fault strike and the mineral resource distribution can also provide a hint about the extension direction of the ore vein.

[0108] Assume that the data of the lithology distribution is , and the data of the mineral resource distribution is . By calculating the Pearson correlation coefficient, we get: ; ; Next, calculate the product sum and the sum of squares of each part: ; ; ; Finally, calculate the Pearson correlation coefficient: ; This result indicates that there is a very high positive correlation between the lithology distribution and the mineral resource distribution, and the correlation coefficient is 0.993.

[0119] S302: According to the correlation probability data, calculate the contribution degree of each geological structure feature, calculate the influence intensity of each feature on the mineral resource distribution, and obtain the feature contribution degree data; The formula for calculating the influence intensity of each feature on the distribution of mineral resources is as follows: ; Calculate the contribution degree of each geological structure feature and obtain the feature contribution degree data; Among them, represents the contribution degree of the geological structure feature, is the weight of the th geological feature at the th sampling point, is the th geological feature and the is the average correlation probability of the th geological feature, is the total number of feature data points, is the index of the data point, is the index of the geological structure feature.

[0120] ; Detailed explanation of the formula and the derivation process of the formula calculation: The formula is used to calculate the contribution degree of the geological structure feature, indicating the influence degree of a certain geological structure feature on the distribution of mineral resources, and the result is used to evaluate the relative importance of each geological structure feature in the distribution of mineral resources.

[0121] Meaning and setting values of parameters: : The contribution degree of the geological structure feature k, reflecting the influence degree of this feature on the distribution of mineral resources; : The weight of the th geological feature at the th sampling point, reflecting the relative importance of the geological feature of this sampling point in the calculation of the contribution degree; : The correlation probability between the th geological feature and the distribution of mineral resources, indicating the correlation between the geological feature of the th sampling point and the distribution of mineral resources; : The average correlation probability of the th geological feature, reflecting the average correlation between this geological feature and the distribution of mineral resources at all sampling points; : The total number of feature data points, indicating the number of geological feature data samplings; Assume the number of sampling points , the weight of the lithological feature of the first sampling point , and the correlation probability with the distribution of mineral resources , the weight of the lithological characteristics of the second sampling point , the correlation probability with the distribution of mineral resources , the weight of the lithological characteristics of the tenth sampling point , the correlation probability with the distribution of mineral resources , the average correlation probability ; Substitute the parameters into the formula for calculation: ; ; ; ; ; The result 0.64 indicates that the contribution of lithological characteristics to the distribution of mineral resources is relatively significant, and lithological characteristics play an important role in the process of resource aggregation.

[0138] S303: Based on the feature contribution data, iteratively adjust and optimize the contribution of various geological structure features to obtain the correlation analysis result;

[0139] Finally, based on the feature contribution data, iteratively adjust and optimize the contribution of various geological structure features to obtain the correlation analysis result. First, by analyzing the contribution data of different geological features, determine the importance of each feature to mineral resources and make adjustments according to the actual situation. For example, if the contribution of a certain geological feature is too low (e.g., less than 0.5), the calculation result can be optimized by increasing the weight of this feature, or the influence of this feature can be enhanced by introducing more relevant data. Then, repeatedly perform iterative calculations and adjustments until the contributions of all features tend to be balanced and all important geological features can be fully reflected in the resource distribution analysis. Finally, through these adjustments, an optimized correlation analysis result can be obtained, which can provide a more accurate prediction for the development of mineral resources.

[0140] Suppose after one iteration adjustment, the weight of lithology distribution increases to w1 = 0.5, the weight of fault strike remains w2 = 0.3, and the weight of formation thickness is adjusted to w3 = 0.2, then the new contribution is calculated as:

[0141]

[0142] The optimized contribution is 0.845, indicating that the influence of lithology distribution on the distribution of mineral resources has been enhanced, and the finally generated correlation analysis result can more accurately predict the distribution trend of mineral resources.

[0143] Please refer to Figure 5, call the correlation analysis results, and combine with the regional geological structure characteristics in the 3D geological model to predict the spatial distribution of minerals. The specific steps for generating the mineral spatial distribution prediction map are as follows:

[0144] S401: Based on the correlation analysis results, obtain the regional geological structure characteristic data in the 3D geological model, combine with the geological characteristics and mineral composition information of the target area, establish the basic data of the spatial distribution of mineral composition, and generate the preliminary mineral distribution data;

[0145] Based on the correlation analysis results, first obtain the regional geological structure characteristic data in the 3D geological model. These data are usually obtained through information such as lithology distribution, fault strike, and formation thickness. Through these geological characteristics, basic data for the spatial distribution of mineral composition can be provided. To establish the basic data of the spatial distribution of mineral composition, it is necessary to combine the geological characteristics of the target area with the mineral composition information. First, collect and organize the 3D geological model data of the target area. These data include the mineral composition data of each area and the related geological characteristic data; then, according to the geological characteristics of each area, use spatial interpolation technology to map the mineral composition data into the 3D space, so as to obtain the basic data of the spatial distribution of mineral composition. For example, assume that the mineral composition data of the target area is 60% copper ore content, 25% iron ore content, and 15% bauxite content. According to the lithology distribution information of this area, through spatial interpolation method, the mineral distribution information of each position is obtained, and finally the preliminary mineral distribution data is generated.

[0146] One of the methods for spatial interpolation calculation is Kriging interpolation, and the formula is as follows:

[0147]

[0148] Among them, Z(kx) is the mineral composition value of the target point kx, Z0 is the mean value of the overall mineral composition, λ Cj is the weighting coefficient, Z(kx Cj ) is the mineral composition value of the known sampling point kx Cj , and CN is the number of known sampling points. Through Kriging interpolation, the mineral composition can be extrapolated from the sampling points to the entire area to generate a spatial distribution map.

[0149] Assume that the copper ore content of the known sampling point kx1 is 70%, and the copper ore content of kx2 is 50%, and the weighting coefficients are 0.6 and 0.4 respectively. Then the copper ore composition of the target point is:

[0150] Z(kx) = 60+(0.6·(70 - 60)+0.4·(50 - 60)) = 60 +

[0151] (0.6·10 + 0.4·(-10)) = 60 + 6 - 4 = 62;

[0152] Therefore, the copper ore composition at the target point is 62%, and this result can be used to further draw the mineral distribution map to assist in the decision-making of mineral resource development.

[0153] S402: According to the preliminary mineral distribution data, using the correlation probability between geological features and the distribution of mineral resources, simulate the spatial distribution of mineral composition under each geological condition, calculate the distribution law of mineral composition, and obtain the mineral distribution law data;

[0154] Based on the preliminary mineral distribution data, the next step is to simulate the spatial distribution of mineral composition under each geological condition using the correlation probability between geological features and the distribution of mineral resources. First, based on the known correlation probability between geological features and the distribution of mineral resources, calculate the correlation data to simulate the spatial distribution of minerals. This process can be achieved through a probability distribution function. For example, a Gaussian distribution can be used to simulate the spatial distribution of minerals. In specific operations, first combine the correlation probability data of each geological condition with the mineral composition, and according to the known distribution law, simulate the change of mineral composition through the Gaussian distribution function, and then obtain the spatial distribution law of mineral composition. For example, assume that the distribution probability of copper ore under a certain geological condition is 70% and the distribution probability of iron ore is 20%. Based on these probabilities, the spatial distribution of mineral composition in this area can be calculated using the probability distribution function. The probability density function formula of the Gaussian distribution is:

[0155]

[0156] where f(gx) is the probability density at point gx, μ is the mean value (i.e., the average value of mineral composition), and σ is the standard deviation, which reflects the distribution width of mineral composition.

[0157] Assume that the mean value of copper ore under a certain geological condition is 60% and the standard deviation is 10%. Then the probability density of copper ore composition at different points gx is calculated as:

[0158]

[0159] For the target point gx = 65, substitute it into the formula to calculate the probability density:

[0160]

[0161] This calculation result indicates that the probability density of copper ore composition at the target point gx = 65 is 0.0376. In this way, the probability simulation of the spatial distribution of mineral composition can be carried out to further analyze the distribution law of minerals.

[0162] S403: Based on the mineral distribution law data, predict the spatial distribution of minerals in the target area and generate a predicted map of the spatial distribution of minerals;

[0163] Based on the data of the mineral distribution law, the next step is to predict the spatial distribution of minerals in the target area and generate a prediction map of the spatial distribution of minerals. In this process, first, according to the simulated mineral distribution law, combined with the three-dimensional geological model data of the target area, spatial prediction is carried out. At this time, using the known mineral distribution data and the characteristics in the geological model, through spatial interpolation and correlation analysis, the mineral distribution at the unsampled positions in the target area is predicted. In specific operations, according to the aforementioned mineral distribution law and spatial interpolation results, a prediction map of the spatial distribution of minerals will be generated, which shows the mineral composition probability and distribution characteristics of each position in the target area. Through this process, more accurate spatial data support can be provided for the development of mineral resources.

[0164] The prediction of the spatial distribution of minerals can be calculated by the interpolation method and combined with the probability distribution for the final prediction. The specific formula is as follows:

[0165]

[0166] Among them, P(cx) is the predicted value of the mineral composition at the target point cx, λ Kj is the weighting coefficient, f(cx Kj ) is the value of the mineral distribution law at the known sampling point cx Kj , and KN is the number of known sampling points. By performing prediction calculations for each point in the target area, a prediction map of the spatial distribution of minerals is finally obtained.

[0167] Suppose there are three sampling points in the target area, and their copper ore compositions are 65%, 55%, and 70% respectively, and the weight coefficients are 0.4, 0.3, and 0.3 respectively. Then the predicted value of the copper ore composition at the target point is:

[0168] P(cx) = (0.4 × 65) + (0.3 × 55) + (0.3 × 70)

[0169] = 26 + 16.5 + 21 = 63.5;

[0170] This predicted value is 63.5%, showing the predicted result of the copper ore composition at this point in the target area.

[0171] Please refer to Figure 6 , according to the prediction map of the spatial distribution of minerals, combined with the quality information of the ore samples, the steps to predict the mineral resource volume in the target area and generate the estimated resource volume value are specifically as follows:

[0172] S501: Based on the prediction map of the spatial distribution of minerals, calculate the distribution density of minerals in the target area according to the prediction results and generate mineral distribution density data;

[0173] Based on the mineral spatial distribution prediction map, it is first necessary to calculate the distribution density of minerals in the target area. The mineral distribution density refers to the amount of minerals distributed in a unit volume or unit area within a specific area. To calculate the mineral distribution density, it is first necessary to obtain the mineral composition values of each area through the spatial distribution prediction map, and these composition values can be expressed as the concentration or content of minerals; then, according to the spatial division of the target area, the area is divided into several small units, and the mineral composition value within each unit is the mineral distribution density of that unit. For example, assume that the target area is divided into 100 small units, and the mineral content data within each unit has been calculated through the spatial distribution prediction map. For example, the copper ore content of a certain unit is 70%, the iron ore content is 20%, and the bauxite content is 10%. Then, the distribution density of these minerals is the corresponding mineral distribution values within each unit, and finally, the mineral distribution density data within the target area is calculated.

[0174] The calculation formula for the mineral distribution density is:

[0175]

[0176] Among them, D is the mineral distribution density, and C Mk is the mineral composition value (such as copper ore content) in the Mk-th unit in the area, MN is the total number of units in the area, and A is the total area or volume of the target area.

[0177] Assume that the target area is divided into 10 small units, and the copper ore composition contents within each unit are 70%, 60%, 80%, 65%, 75%, 85%, 90%, 80%, 70%, and 60% respectively. The total area of the target area is 1000 square meters, then the mineral distribution density is calculated as:

[0178]

[0179] This indicates that the copper ore distribution density in the target area is 0.735 kilograms per square meter.

[0180] S502: According to the mineral distribution density data, combined with the geographical boundary of the target area, calculate the total volume of the ore within the area to obtain the total volume prediction data;

[0181] The specific formula for calculating the total volume of the ore within the area is:

[0182]

[0183] Calculate the total volume to obtain the total volume prediction data of the target area;

[0184] Among them, D k′ is the mineral distribution density of the k'-th unit, A k′ is the area of the k'-th unit, and H k′is the depth of the k'-th unit, N' is the total number of small units in the target area, V represents the total volume of ore in the target area, which is the sum of the volumes of all ores in the target area, N' represents the total number of small units into which the target area is divided, and k' represents the number of each small unit.

[0185]

[0186] Detailed explanation of the formula and the derivation process of formula calculation:

[0187] The formula is used to calculate the total volume of ore in the target area, and the result is used for further mineral resource estimation;

[0188] Meaning of parameters and set values:

[0189] V is the total volume of ore in the target area, representing the sum of the volumes of ore in the entire area;

[0190] D k′ is the mineral distribution density of the k'-th small unit, representing the mass or concentration of minerals in this small unit;

[0191] A k′ is the area of the k'-th small unit, representing the planar projection area of this small unit in the target area;

[0192] H k′ is the depth of the k'-th small unit, representing the vertical height of this small unit;

[0193] N' is the total number of small units into which the target area is divided, representing the number of units into which the area is divided;

[0194] Assume that the number of small units N' in the target area = 100, and the mineral distribution density D of each small unit k′ = 0.8 kg / m 3 , the area A k′ = 100 m 2 , and the depth H k′ = 50 m;

[0195] Substitute the parameters into the formula for calculation:

[0196]

[0197] 0.8·100·50 = 4000;

[0198]

[0199] V = 3985.6×100 = 398,560 m 3 ;

[0200] Result interpretation:

[0201] The results show that the total volume of ore in the target area is 3,985,60 cubic meters, representing the total amount of ore within the entire target area, which is used to estimate the mineral resources. Further resource assessment is carried out based on the composition and quality information of the minerals.

[0202] S503: Using the total volume prediction data and combining with the quality information of the ore samples, predict the mineral resources in the target area and generate an estimated value of the mineral resources.

[0203] Using the total volume prediction data and combining with the quality information of the ore samples, the next step is to predict the mineral resources in the target area. Mineral resources refer to the mass of ore or the amount of mineral components in the target area. To make the prediction, first, according to the quality information of the samples, for example, the copper ore content is 70%, and combine with the total volume prediction data; then, calculate the mineral resources in the target area based on the content and volume of the minerals. In specific operations, first determine the quantity of various minerals in the area according to the distribution density of the minerals and the total volume of the ore; then, multiply the content of each type of mineral by its corresponding volume to obtain the estimated value of the mineral resources. For example, assuming the quality of the copper ore is 70% and the total volume is 36,750 cubic meters, the resources of the copper ore can be predicted based on this data.

[0204] The calculation formula for mineral resources is:

[0205] RC = V × C;

[0206] Where RC is the mineral resources, V is the total volume of the ore, and C is the component content of the mineral (such as the content of copper ore).

[0207] Assuming the quality of the copper ore is 70% (i.e., C = 0.7) and the total volume of the ore is 36,750 cubic meters, the calculation of the copper ore resources is:

[0208] RC = 36,750 × 0.7 = 25725m 3 ;

[0209] Therefore, the estimated value of the copper ore resources in the target area is 25,725 cubic meters.

[0210] Please refer to Figure 7 , a mineral resources estimation and analysis system. The mineral resources estimation and analysis system is used to execute the above-mentioned mineral resources estimation and analysis method. The system includes:

[0211] The ore sample analysis module obtains ore samples at multiple locations, collects the surface spectral data of the ore, compares it with the known mineral database, identifies the chemical composition of the ore, evaluates the quality of the ore samples, and generates mineral resource distribution data in combination with the spatial location and depth of the ore sampling.

[0212] Based on the mineral resource distribution data, the geological model construction module constructs a three-dimensional geological model by combining the drilling data, seismic wave reflection data, gravity and electromagnetic measurement data in the mining area, extracts the geological structure features of the target area, and maps the quality information of the ore samples into the model to obtain the geological structure feature distribution map;

[0213] According to the geological structure feature distribution map and the mineral resource distribution data, the geological structure correlation analysis module calculates the correlation probabilities between various geological structure features and the mineral resource distribution, and calculates and iteratively adjusts the contribution degrees of various geological structure features to obtain the correlation analysis result;

[0214] The mineral spatial distribution prediction module calls the correlation analysis result, combines the regional geological structure features in the three-dimensional geological model, analyzes the relationship between the geological features and the resource aggregation trend, predicts the spatial distribution of minerals, and generates the mineral spatial distribution prediction map;

[0215] Based on the mineral spatial distribution prediction map and combined with the quality information of the ore samples, the resource volume estimation module calculates the volume and mass of the ore in the target area, predicts the mineral resource volume in the target area, and generates the resource volume estimation value.

[0216] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware, or any other arbitrary combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions according to the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that the computer can access, or a data storage device such as a server or data center that contains one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0217] It should be understood that the term "and / or" in this text is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. Additionally, the character " / " in this text generally indicates an "or" relationship between the preceding and following associated objects, but it may also represent an "and / or" relationship, which can be specifically understood by referring to the context before and after.

[0218] In the present invention, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following items (or a similar expression)" refers to any combination of these items, including any combination of single items (or a single item) or plural items (or multiple items). For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0219] It should be understood that in various embodiments of the present invention, the magnitude of the serial numbers of the above processes does not mean the sequence of execution in terms of priority. The execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0220] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0221] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the devices, apparatuses, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0222] In several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be in an electrical, mechanical, or other forms.

[0223] The unit described as a separation component may or may not be physically separated. The component shown as a unit may or may not be a physical unit, that is, it may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0224] In addition, each functional unit in various embodiments of the present invention may be integrated in a processing unit, may exist separately as individual physical units, or two or more units may be integrated in one unit.

[0225] If the above-mentioned function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0226] As described above, the above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A method for estimating and analyzing mineral resources, characterized in that: The method comprises: Obtain ore samples from multiple locations, identify the chemical composition of the ore by comparing the surface spectrum data of the ore with the known mineral database, evaluate the quality of the ore samples, and obtain mineral resource distribution data based on the sampling location and depth; Using the mineral resource distribution data, combined with mining area drilling data, seismic wave reflection data, gravity and electromagnetic measurement data, a three-dimensional geological model is constructed to extract geological structural characteristics, including lithology distribution, fault orientation, and stratum thickness, and the quality information of ore samples is mapped into the model to obtain a distribution map of geological structural characteristics; Using the geological structure characteristic distribution map and mineral resource distribution data, the correlation probability between lithology distribution, fault trend, stratum thickness and mineral resource distribution is calculated, and the contribution of various geological structure characteristics is iteratively adjusted to obtain correlation analysis results; The correlation analysis results are called, combined with the regional geological structure characteristics in the three-dimensional geological model, to predict the spatial distribution of minerals and generate a mineral spatial distribution prediction map; Based on the mineral spatial distribution prediction map and combined with the quality information of the ore samples, the mineral resources in the target area are predicted to generate a resource estimation value.

2. The method for estimating and analyzing mineral resources according to claim 1, characterized in that: The mineral resource distribution data specifically includes the ore chemical composition table, ore quality assessment data, sampling location and depth information; the geological structure feature distribution map specifically includes lithology distribution information, fault trend data, and stratum thickness information; the correlation analysis results specifically include the probability of association between lithology distribution and mineral resource distribution, the probability of association between fault trend and mineral resource distribution, and the probability of association between stratum thickness and mineral resource distribution; the mineral spatial distribution prediction map specifically includes the mineral distribution law, multi-location mineral composition prediction results, and mineral distribution prediction results; the resource quantity estimation value specifically includes mineral distribution density, mineral volume, and mineral resource quantity.

3. The method for estimating and analyzing mineral resources according to claim 1, characterized in that: The steps for obtaining ore samples at multiple locations, comparing the surface spectrum data of the ore with the known mineral database, identifying the chemical composition of the ore, evaluating the quality of the ore samples, and obtaining mineral resource distribution data based on the sampling location and depth are as follows: Obtain ore samples from multiple locations, collect surface spectral data of ore samples, compare and match with known mineral databases, calculate the corresponding probability of each mineral component, record the chemical composition of each ore sample, and generate sample composition analysis results; Based on the sample component analysis results and in combination with the chemical composition of the ore sample, the quality of the ore sample is evaluated to generate ore quality information; According to the ore quality information, combined with the sampling location and sampling depth of the ore, mineral resource distribution data is generated.

4. The method for estimating and analyzing mineral resources according to claim 1, characterized in that: The steps of constructing a three-dimensional geological model by using the mineral resource distribution data, combining the mining area drilling data, seismic wave reflection data, gravity and electromagnetic measurement data, extracting geological structure characteristics, including lithology distribution, fault direction, and stratum thickness, and mapping the quality information of the ore sample into the model to obtain the geological structure characteristic distribution map are as follows: Using the mineral resource distribution data, a three-dimensional geological model is constructed by collecting mining area drilling data, seismic wave reflection data, gravity and electromagnetic measurement data; Calling the three-dimensional geological model, extracting geological structural features of multiple locations by analyzing geometric shape information of multiple locations in the model, including lithology distribution, fault direction, and stratum thickness information, and obtaining geological structural feature data; By using the geological structure characteristic data and the location and depth information of the ore samples, the quality information of the ore samples is mapped into a three-dimensional geological model to obtain a geological structure characteristic distribution map.

5. The method for estimating and analyzing mineral resources according to claim 1, characterized in that: The steps of calculating the correlation probability between lithology distribution, fault orientation, stratum thickness and mineral resource distribution by using the geological structure characteristic distribution map and mineral resource distribution data, and iteratively adjusting the contribution of various geological structure characteristics to obtain the correlation analysis results are as follows: Based on the mineral resource distribution data and the geological structure characteristic distribution map, calculate the correlation probability between lithology distribution, fault trend, stratum thickness and mineral resource distribution, analyze the relationship between geological characteristics and resource aggregation trend, and obtain correlation probability data; According to the association probability data, the contribution of each geological structure feature is calculated, the influence intensity of each feature on the distribution of mineral resources is calculated, and the feature contribution data is obtained; Based on the feature contribution data, the contribution of various geological structure features is iteratively adjusted and optimized to obtain correlation analysis results.

6. The method for estimating and analyzing mineral resources according to claim 5, characterized in that: The formula for calculating the impact intensity of each characteristic on the distribution of mineral resources is: ； Calculate the contribution of each geological structure feature and obtain feature contribution data; in, Represents the contribution of geological structural characteristics, For the The geological features are The weight of the sampling points, For the The probability of association between various geological features and mineral resource distribution, For the The average association probability of the geological features is is the total number of feature data points, is the index of the data point, An index of geological structural features.

7. The method for estimating and analyzing mineral resources according to claim 1, characterized in that: The steps of calling the correlation analysis results and combining the regional geological structure characteristics in the three-dimensional geological model to predict the spatial distribution of minerals and generate a mineral spatial distribution prediction map are as follows: Based on the correlation analysis results, the regional geological structure characteristic data in the three-dimensional geological model is obtained, and the spatial distribution basic data of the mineral composition is established in combination with the geological characteristics and mineral composition information of the target area to generate preliminary mineral distribution data; Based on the preliminary mineral distribution data, using the correlation probability between geological characteristics and mineral resource distribution, the spatial distribution of mineral components under each geological condition is simulated, the distribution law of mineral components is calculated, and the mineral distribution law data is obtained; Based on the mineral distribution law data, the mineral spatial distribution of the target area is predicted to generate a mineral spatial distribution prediction map.

8. The method for estimating and analyzing mineral resources according to claim 1, characterized in that: According to the mineral spatial distribution prediction map, combined with the quality information of the ore sample, the mineral resources in the target area are predicted, and the steps of generating the estimated resource value are specifically as follows: Based on the mineral spatial distribution prediction map, the distribution density of minerals in the target area is calculated according to the prediction results to generate mineral distribution density data; According to the mineral distribution density data, combined with the geographical boundaries of the target area, the total volume of the ore in the area is calculated to obtain the total volume prediction data; The total volume prediction data is used in combination with the quality information of the ore samples to predict the mineral resources in the target area and generate a mineral resource estimate.

9. The method for estimating and analyzing mineral resources according to claim 1, characterized in that: The specific formula for calculating the total volume of ore in the area is: Calculate the total volume and obtain the total volume prediction data of the target area; Among them, D k′ is the mineral distribution density of the k′th unit, A k′ is the area of ​​the k′th unit, H k′ is the depth of the k′th unit, N′ is the total number of small units in the target area, V represents the total volume of ore in the target area, which is the sum of the volumes of all ore in the target area, N′ represents the total number of small units into which the target area is divided, and k′ represents the number of each small unit.

10. A mineral resource estimation and analysis system, characterized in that: According to the method for estimating and analyzing mineral resources according to any one of claims 1 to 9, the system comprises: The ore sample analysis module obtains ore samples from multiple locations, collects surface spectral data of the ore, and compares it with the known mineral database to identify the chemical composition of the ore, evaluate the quality of the ore samples, and generate mineral resource distribution data based on the spatial location and depth of the ore sampling; The geological model building module builds a three-dimensional geological model based on the mineral resource distribution data, combined with the mining area drilling data, seismic wave reflection data, gravity and electromagnetic measurement data, extracts the geological structure characteristics of the target area, and maps the quality information of the ore sample into the model to obtain a distribution map of geological structure characteristics; The geological structure correlation analysis module calculates the correlation probability between various geological structure characteristics and mineral resource distribution according to the geological structure characteristic distribution map and mineral resource distribution data, and calculates and iteratively adjusts the contribution of various geological structure characteristics to obtain correlation analysis results; The mineral spatial distribution prediction module calls the correlation analysis results, combines the regional geological structure characteristics in the three-dimensional geological model, analyzes the relationship between geological characteristics and resource aggregation trends, predicts the spatial distribution of minerals, and generates a mineral spatial distribution prediction map; The resource estimation module calculates the volume and quality of ore in the target area based on the mineral spatial distribution prediction map and the quality information of the ore samples, predicts the mineral resources in the target area, and generates a resource estimation value.

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