A cube prediction model prospecting method and system based on three-dimensional modeling

Abstract

The present invention relates to the field of geological exploration technology, and specifically to a prospecting method and system using a cube prediction model based on three-dimensional modeling, comprising the following steps: based on the location information of the exploration point, using electromagnetic imaging equipment, adjusting scanning parameters, collecting reflected electromagnetic waves at multiple locations in the target area, recording electromagnetic response data at multiple frequencies and intensities, and generating electromagnetic characteristic data. In the present invention, by integrating electromagnetic imaging with geological data interpolation technology, underground structures are accurately assessed and mapped, the construction of geological models is optimized, data utilization is improved, and uncertainty and risk in mining are reduced. By analyzing the geometric morphology and texture characteristics of the ore body, underground minerals are detected and their distribution characteristics are identified, effectively guiding mineral mining strategies, optimizing resource allocation, reducing environmental damage and exploration costs, and enhancing the scientific nature and economic benefits of geological exploration through in-depth analysis of geological data and model optimization.

Description

Technical Field

[0001] The present invention relates to the field of geological prospecting technology, and in particular to a 3D model-based cube prediction model prospecting method and system. Background Art

[0002] The field of geological exploration technology aims to apply various principles and methods of geological sciences to detect and evaluate the earth's mineral resources. It uses geological, geophysical and geochemical data to identify geological structures and mineral distribution through surface and underground surveys, determine the location, quantity, quality and economic value of mineral resources, and carry out mining development and management. Combined with the analysis and interpretation of data, it uses drilling, seismic exploration, magnetism, gravity measurement and other methods to conduct resource exploration, which is applied to many aspects of mining and geological research.

[0003] Among them, the cube prediction model prospecting method integrates three-dimensional geological modeling and statistical analysis technology to accurately predict and locate underground mineral resources, optimize the exploration and mining process, and analyze the geological characteristics and ore body conditions of the mining area by constructing a three-dimensional model of underground rock strata and ore bodies, making mineral exploration more intuitive and accurate, improving the accuracy and efficiency of prospecting, combining geological data analysis to improve the efficiency and success rate of mineral resource assessment, helping mining engineers make decisions more accurately, reducing exploration costs, and providing a scientific basis for mining activities.

[0004] Traditional mineral exploration methods are inadequate in dealing with complex geological structures, lack sufficient accuracy when processing batch geological data, and are insufficient in identifying and analyzing deep underground structures, resulting in inaccurate positioning of mineral resources, waste of resources and excessive damage to the environment. They rely on empirical judgment in data processing and model building, and lack data-driven decision support, which increases the uncertainty of exploration decisions, increases exploration costs and time, makes it difficult to fully assess complex underground structures, and affects the efficiency and safety of mineral development. The lack of accuracy in ore body identification leads to waste of resources and environmental risks in the mining process, including unnecessary rock damage and environmental pollution caused by incorrect mining positioning. Summary of the Invention

[0005] In order to solve the technical problem of poor mineral resource positioning capability in the existing technology, the embodiment of the present invention provides a prospecting method and system based on a cube prediction model of three-dimensional modeling. The technical solution is as follows:

[0006] In one aspect, a cube prediction model prospecting method based on three-dimensional modeling is provided, the method comprising:

[0007] S1: Based on the location information of the exploration point, the electromagnetic imaging equipment is used to adjust the scanning parameters to collect reflected electromagnetic waves at multiple locations in the target area, record the electromagnetic response data at various frequencies and intensities, and generate electromagnetic characteristic data;

[0008] S2: Based on the electromagnetic characteristic data, by calculating the spatial relationship between multiple data points and combining data smoothing, predicting electromagnetic response data at multiple locations, filling geological values of multiple spatial points, and obtaining geological data interpolation records;

[0009] S3: using the geological data interpolation records, identifying geotechnical properties at multiple locations by analyzing electromagnetic response data, assessing stratum continuity and detecting fault locations, constructing a geological model and mapping multiple geological feature information to generate a three-dimensional geological structure model;

[0010] S4: Based on the three-dimensional geological structure model, combined with the geometric morphology and texture characteristics of multiple mineral samples, the ore body in the geological model is identified, the ore body position is calibrated, and an ore body morphology identification result is generated;

[0011] S5: Based on the ore body morphology identification results, analyze the distribution of various minerals in the target area, combine geological exploration samples, predict the content and quality of the minerals, estimate the total resource volume of the mining area, and generate mineral resource prediction information.

[0012] As a further solution of the present invention, the electromagnetic characteristic data specifically includes the reflected electromagnetic wave frequency, electromagnetic wave intensity information, and scanning parameter configuration; the geological data interpolation record includes geological characteristic prediction values, spatial relationship calculation results, and data smoothing processing results; the three-dimensional geological structure model specifically refers to stratum continuity analysis information, fault position identification records, and underground structure mapping results; the ore body morphology identification results include mineral geometry, mineral color information, and mineral texture information; and the mineral resource prediction information specifically includes mineral distribution analysis results, mineral content prediction information, and mining area resource estimation data.

[0013] As a further solution of the present invention, based on the location information of the exploration point, electromagnetic imaging equipment is used to adjust the scanning parameters to collect reflected electromagnetic waves at multiple locations in the target area, record electromagnetic response data at multiple frequencies and intensities, and generate electromagnetic characteristic data in the following steps:

[0014] S101: Based on the exploration point location information and the scanning depth, the scanning parameters of the electromagnetic imaging device are adjusted, including frequency and power adjustment, and a set parameter record is generated;

[0015] S102: Using the set parameter records, performing electromagnetic wave scanning at the exploration point, collecting electromagnetic response data of the target location, including signals at multiple frequencies and intensities, to obtain an electromagnetic response data set;

[0016] S103: Based on the electromagnetic response data set, preprocessing is performed on the collected electromagnetic data, including signal enhancement, noise filtering and data formatting, to generate electromagnetic characteristic data.

[0017] As a further solution of the present invention, based on the electromagnetic characteristic data, the spatial relationship between multiple data points is calculated, combined with data smoothing, and electromagnetic response data of multiple locations is predicted, and the geological values of multiple spatial points are filled to obtain geological data interpolation records. Specifically, the steps are as follows:

[0018] S201: Calculating distances and correlations between multiple exploration points based on the electromagnetic characteristic data, analyzing spatial distribution of the data, and creating a spatial relationship diagram of the exploration points;

[0019] S202: using the spatial relationship diagram of the exploration points and data interpolation, predicting electromagnetic response data of multiple uncollected locations, filling in data blank areas, optimizing the continuity of the exploration data, and generating a feature prediction data set;

[0020] S203: Based on the feature prediction data set, combined with data smoothing, outliers and noise are eliminated, the availability and accuracy of the data are optimized, and a geological data interpolation record is obtained.

[0021] As a further solution of the present invention, the specific formula for predicting the electromagnetic response data of multiple uncollected locations is:

[0022]

[0023] Among them, Z(s0) represents the predicted electromagnetic response value of the target position, Z(s i ) represents the electromagnetic response value of a known exploration point, λ i represents the weight coefficient calculated based on spatial autocorrelation, s0 represents the predicted position, s i represents the location of the adjacent exploration points, n represents the number of exploration points involved in the calculation, and i represents the weight coefficient and the index of the exploration point.

[0024] As a further embodiment of the present invention, the steps of using the geological data interpolation records to analyze the electromagnetic response data, identify the geotechnical characteristics of multiple locations, evaluate the continuity of the strata and detect the locations of faults, construct a geological model and map multiple geological feature information to generate a three-dimensional geological structure model are as follows:

[0025] S301: Analyzing geotechnical characteristics at multiple locations using the geological data interpolation records and electromagnetic response data, identifying stratum continuity and geological boundaries at multiple locations in the target area, and constructing a basic stratigraphic framework;

[0026] S302: Based on the basic stratigraphic framework, adjusting stratigraphic layers, including adjusting stratigraphic thickness and ductility, and generating stratigraphic structure adjustment records;

[0027] S303: Based on the stratigraphic structure adjustment record, the geotechnical characteristic information of multiple locations is mapped into the geological model, including lithology, mineral content and stratigraphic age, to generate a three-dimensional geological structure model.

[0028] As a further embodiment of the present invention, the steps of identifying the ore body in the geological model, calibrating the ore body position, and generating the ore body morphology recognition result are as follows:

[0029] S401: Analyze the geometric morphology and texture characteristics of multiple mineral samples according to the three-dimensional geological structure model to generate mineral sample analysis results;

[0030] S402: Based on the mineral sample analysis results, by comparing with the geological three-dimensional model, detecting and identifying the underground ore body in the target area, and generating an underground mineral detection record;

[0031] S403: Using the underground mineral detection record, record and identify the location information of multiple identified ore bodies to generate an ore body morphology recognition result.

[0032] As a further embodiment of the present invention, based on the ore body morphology identification results, the distribution of various minerals in the target area is analyzed, and combined with geological exploration samples, the content and quality of the minerals are predicted, the total resource volume of the mining area is estimated, and the steps of generating mineral resource prediction information are specifically as follows:

[0033] S501: Based on the ore body morphology recognition result, analyze the spatial distribution of minerals in the target area, combine the geological characteristics of the target location, including rock hardness, evaluate the mining difficulty, and generate mineral distribution information;

[0034] S502: Based on the mineral distribution information, collect ore samples from multiple locations, analyze the chemical composition of the ore, including metal content and impurity level, evaluate the ore quality, and generate an ore quality analysis record;

[0035] S503: Based on the ore quality analysis records, the total amount of mineable minerals in the mining area is analyzed, the economic benefits of mineral mining in the target area are calculated, and mineral resource prediction information is generated.

[0036] As a further solution of the present invention, the specific formula for analyzing the total amount of mineable minerals in the mining area is:

[0037]

[0038] Among them, R represents the total recoverable resources of the mining area, V i Represents the volume estimate of the i-th ore body, D i represents the density of the corresponding ore body, E i represents the extraction rate, n represents the total number of ore bodies considered in the mining area, and i is the index.

[0039] On the other hand, a cube prediction model prospecting system based on three-dimensional modeling is provided, which is applied to a cube prediction model prospecting method based on three-dimensional modeling, and the system includes:

[0040] The electromagnetic data acquisition module sets the parameters of the electromagnetic imaging equipment, including scanning depth and frequency, based on the location information of the exploration points, performs electromagnetic wave scanning, records the electromagnetic response data of multiple exploration points, and generates electromagnetic characteristic data;

[0041] The data interpolation analysis module calculates the spatial relationship between data points based on the electromagnetic characteristic data, combines data smoothing and data interpolation, fills in geological data gaps, and generates geological data interpolation records;

[0042] The three-dimensional modeling module analyzes the geotechnical characteristics of multiple locations based on the interpolation records of the geological data, evaluates the continuity of the strata and detects the locations of faults, and constructs a three-dimensional geological structure model in combination with the geological feature information;

[0043] The ore body analysis module analyzes the geometric shape and texture characteristics of the ore body based on the three-dimensional geological structure model and combined with mineral sample data, detects the location of multiple ore bodies in the target area, predicts the content and quality of the mineral, calculates the total resource volume of the mining area, and generates mineral resource prediction information.

[0044] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0045] By integrating electromagnetic imaging with geological data interpolation technology, we can accurately evaluate and map underground structures, optimize the construction of geological models, improve data utilization, and reduce uncertainty and risks in mining. By analyzing the geometric morphology and texture characteristics of ore bodies, we can detect underground minerals and identify their distribution characteristics, effectively guide mineral mining strategies, optimize resource allocation, reduce environmental damage and exploration costs, and enhance the scientific nature and economic benefits of geological exploration through in-depth analysis of geological data and model optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0047] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0048] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0049] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0050] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0051] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0052] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0053] Figure 7 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0054] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0055] 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 an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0056] In the embodiments of the present invention, the terms "image" and "picture" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same. The terms "of," "corresponding," and "corresponding" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same.

[0057] 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.

[0058] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0059] The embodiment of the present invention provides a prospecting method based on a cube prediction model of three-dimensional modeling, such as Figure 1The flowchart of the prospecting method of the cube prediction model based on three-dimensional modeling is shown. The processing flow of the method may include the following steps:

[0060] S1: Based on the location information of the exploration point, the electromagnetic imaging equipment is used to adjust the scanning parameters to collect reflected electromagnetic waves at multiple locations in the target area, record the electromagnetic response data at various frequencies and intensities, and generate electromagnetic characteristic data;

[0061] S2: Based on electromagnetic characteristic data, by calculating the spatial relationship between multiple data points and combining data smoothing, the electromagnetic response data of multiple locations are predicted, and the geological values of multiple spatial points are filled to obtain geological data interpolation records;

[0062] S3: Using geological data interpolation records, by analyzing electromagnetic response data, identifying geotechnical properties at multiple locations, assessing stratum continuity and detecting fault locations, constructing a geological model and mapping multiple geological feature information to generate a three-dimensional geological structure model;

[0063] S4: Based on the three-dimensional geological structure model, combined with the geometric morphology and texture characteristics of multiple mineral samples, the ore bodies in the geological model are identified, the ore body positions are calibrated, and the ore body morphology identification results are generated;

[0064] S5: Based on the results of ore body morphology identification, analyze the distribution of various minerals in the target area, combine geological exploration samples, predict the content and quality of minerals, estimate the total resource volume of the mining area, and generate mineral resource prediction information.

[0065] The electromagnetic characteristic data specifically include the reflected electromagnetic wave frequency, electromagnetic wave intensity information, and scanning parameter configuration. The geological data interpolation records include geological characteristic prediction values, spatial relationship calculation results, and data smoothing processing results. The three-dimensional geological structure model specifically refers to the stratigraphic continuity analysis information, fault position identification records, and underground structure mapping results. The ore body morphology identification results include mineral geometry, mineral color information, and mineral texture information. The mineral resource prediction information specifically includes mineral distribution analysis results, mineral content prediction information, and mining area resource estimation data.

[0066] See also Figure 2 Based on the location information of the exploration point, the electromagnetic imaging equipment is used to adjust the scanning parameters to collect reflected electromagnetic waves at multiple locations in the target area and record electromagnetic response data at multiple frequencies and intensities. The specific steps for generating electromagnetic characteristic data are as follows:

[0067] S101: Based on the exploration point location information and the scanning depth, the scanning parameters of the electromagnetic imaging device are adjusted, including frequency and power adjustment, and a set parameter record is generated;

[0068] In sub-step S101, the scanning parameters of the electromagnetic imaging equipment are automatically set according to the geographic location information and geological data of the exploration point. The control system of the equipment uses the geological depth information to adjust the scanning frequency and power to optimize the signal penetration capability and reception quality. The frequency setting value depends on the complexity and depth of the expected geological structure. The power adjustment is based on the electromagnetic characteristics of the formation medium and the expected signal attenuation. After the parameters are adjusted, a set parameter record is generated, which includes the frequency setting, power level and other relevant configuration details and is stored in the control unit for subsequent operation and review.

[0069] S102: Using the set parameter recording, performing electromagnetic wave scanning at the exploration point, collecting electromagnetic response data at the target location, including signals at multiple frequencies and intensities, to obtain an electromagnetic response data set;

[0070] In sub-step S102, electromagnetic wave scanning is automatically performed. During the process, electromagnetic waves are emitted at each exploration point according to the set parameters and electromagnetic signals reflected by underground structures are received. The frequency and intensity of the electromagnetic waves change according to the preset parameters to maximize the ability to detect different strata. The electromagnetic response data of each location is automatically collected and organized into an electromagnetic response data set by the system. The data set records the frequency, intensity and reflection timestamp of the signal in detail. The data is then transmitted to the main data processing center for more in-depth analysis.

[0071] S103: Preprocessing the collected electromagnetic data based on the electromagnetic response data set, including signal enhancement, noise filtering, and data formatting, to generate electromagnetic characteristic data;

[0072] In sub-step S103, optimization is performed through preprocessing steps to improve data quality and prepare for subsequent detailed analysis. The process includes signal enhancement, noise filtering and data formatting. Signal enhancement increases the contrast between the signal and background noise by adjusting the dynamic range of the data. Noise filtering uses digital filtering technology to remove signal interference from non-target frequencies. The data formatting step converts all signals into a standardized digital format to ensure data compatibility and processing efficiency in the subsequent analysis stage.

[0073] See also Figure 3 Based on the electromagnetic characteristic data, the spatial relationship between multiple data points is calculated, combined with data smoothing, and the electromagnetic response data of multiple locations are predicted. The geological values of multiple spatial points are filled in to obtain the geological data interpolation record. The specific steps are as follows:

[0074] S201: Based on the electromagnetic characteristic data, calculate the distance and correlation between multiple exploration points, analyze the spatial distribution of the data, and create a spatial relationship diagram of the exploration points;

[0075] In sub-step S201, the geographic coordinates and electromagnetic properties of each exploration point are extracted from the electromagnetic characteristic dataset. Distance calculation formulas, such as Euclidean distance, are applied to determine the physical distance between the exploration points. Correlation analysis uses the Pearson correlation coefficient method to evaluate the interdependence of the electromagnetic data at each point. Based on the distance and correlation analysis results, a spatial relationship diagram of the exploration points is created to visualize the relative position of each exploration point in space and the correlation degree of its electromagnetic properties, providing a basic framework for subsequent data interpolation.

[0076] S202: using the spatial relationship diagram of the exploration points and data interpolation, predicting electromagnetic response data of multiple uncollected locations, filling in data blank areas, optimizing the continuity of the exploration data, and generating a feature prediction data set;

[0077] The specific formula for predicting electromagnetic response data of multiple uncollected locations is:

[0078]

[0079] Among them, Z(s0) represents the predicted electromagnetic response value of the target position, Z(s i ) represents the electromagnetic response value of a known exploration point, λ i represents the weight coefficient calculated based on spatial autocorrelation, s0 represents the predicted position, s i represents the location of the adjacent exploration points, n represents the number of exploration points involved in the calculation, and i represents the weight coefficient and the index of the exploration point.

[0080] formula:

[0081]

[0082] Detailed explanation of the formula and the process of formula calculation and derivation:

[0083] The formula is used to predict the electromagnetic response values of uncollected locations and analyze geological structures and mineral distribution;

[0084] Parameter meaning and setting value:

[0085] Z(s0) is the predicted electromagnetic response value at the target location;

[0086] Z(s i ) represents the electromagnetic response value of the known exploration point. Assume that the electromagnetic response of point s1 is 50mV and that of s2 is 45mV;

[0087] λ i is the weight coefficient, assuming λ1=0.6, λ2=0.4;

[0088] s0 is the predicted position;

[0089] s iis the location of the known exploration point;

[0090] n is the number of exploration points involved in the calculation;

[0091] Substitute the parameters into the formula for calculation:

[0092] Z(s0)=(0.6×50)+(0.4×45)=30+18=48;

[0093] Result 48 shows that the electromagnetic response value at the predicted location s0 is 48 mV. The value reflects the geological and mineral conditions of the site. The calculation process is used to provide a data basis for subsequent geological data analysis and resource assessment.

[0094] S203: Based on the feature prediction data set, combined with data smoothing, outliers and noise are eliminated to optimize data availability and accuracy, and geological data interpolation records are obtained;

[0095] In sub-step S203, data smoothing is performed using moving average and Gaussian smoothing. Data points are adjusted by calculating the weighted average of the point value and its neighborhood, reducing outliers and noise in the data and eliminating random fluctuations and non-representative peaks in the data. After data smoothing, the program further checks the consistency and accuracy of the data set and generates geological data interpolation records. The records describe in detail the electromagnetic characteristics of each location point after processing, providing basic data for subsequent geological modeling and analysis.

[0096] See also Figure 4 , using geological data interpolation records, by analyzing electromagnetic response data, identifying geotechnical characteristics at multiple locations, assessing stratum continuity and detecting fault locations, constructing a geological model and mapping multiple geological feature information, the specific steps for generating a 3D geological structure model are as follows:

[0097] S301: Using geological data interpolation records and electromagnetic response data, analyze geotechnical characteristics at multiple locations, identify stratigraphic continuity and geological boundaries at multiple locations in the target area, and construct a basic stratigraphic framework;

[0098] In sub-step S301, the electromagnetic response data of multiple exploration points are analyzed, and the Bayesian network model is applied to analyze the relationship between the electromagnetic data and the rock and soil properties at each location. The differences in the electromagnetic properties of each rock layer are identified, and the continuity and discontinuity of the strata are determined. The identification of geological boundaries is based on significant changes in the electromagnetic response. The edges of the geological structure are automatically marked through an algorithm. Based on the data, the program constructs a basic stratigraphic framework. The framework details the layout and boundaries of each stratum in the exploration area, providing a preliminary structure for subsequent detailed stratigraphic analysis.

[0099] S302: Based on the basic stratigraphic framework, adjusting the stratigraphic layers, including adjusting the thickness and ductility of the stratigraphic layers, and generating stratigraphic structure adjustment records;

[0100] In sub-step S302, the structure of the stratum is further refined, and the thickness and ductility of the stratum are dynamically adjusted using adaptive grid adjustment technology to accurately reflect the actual situation of the underground structure. During the adjustment process, the parameters of each stratum unit, such as thickness and ductility, are optimized based on the data of the adjacent strata to ensure accurate representation of the stratum structure. All adjustments are recorded in the stratum structure adjustment record, which lists the parameters and results of each adjustment in detail, providing a complete history and basis for the stratum adjustment.

[0101] S303: Based on the stratigraphic structure adjustment record, the geotechnical characteristic information of multiple locations is mapped into the geological model, including lithology, mineral content, and stratigraphic age, to generate a three-dimensional geological structure model;

[0102] In sub-step S303, the geotechnical property information is mapped into the geological model. Using stereo visualization technology, a detailed three-dimensional geological structure model is created based on the physical and chemical properties of the strata, such as lithology, mineral content, and stratigraphic age. The model comprehensively considers the data obtained from the stratigraphic structure adjustment records and the analysis results in the geological data interpolation records, providing a three-dimensional view of the underground structure and laying the foundation for exploration decisions and resource assessment.

[0103] See also Figure 5 Based on the three-dimensional geological structure model, combined with the geometric morphology and texture characteristics of multiple mineral samples, the ore bodies in the geological model are identified, the ore body positions are calibrated, and the steps to generate the ore body morphology recognition results are as follows:

[0104] S401: Analyze the geometric morphology and texture characteristics of multiple mineral samples based on the three-dimensional geological structure model and generate mineral sample analysis results;

[0105] In sub-step S401, morphological analysis and texture analysis techniques are applied to process the mineral sample data extracted from the three-dimensional geological structure model. Morphological analysis uses mathematical morphological operations such as dilation and erosion to identify the geometric characteristics of the mineral sample. Texture analysis evaluates the texture characteristics of the sample through the gray-level co-occurrence matrix. The analysis results are integrated to form the mineral sample analysis results, including the shape description, size, edge characteristics and detailed parameters of the surface texture of each mineral, such as texture contrast and uniformity. The results are recorded and used to compare with the existing mineral database to verify and classify the mineral type.

[0106] S402: Based on the mineral sample analysis results, by comparing with the geological three-dimensional model, detect and identify the underground ore body in the target area and generate an underground mineral detection record;

[0107] In the above content, the mineral sample analysis results are compared with the three-dimensional geological structure model. For each mineral sample, the geometric morphology and texture features are extracted as, and the same type of features are extracted from the corresponding position in the three-dimensional geological model as, using the eigenvalues, the matching degree is calculated using the formula Calculate how well each mineral sample matches the corresponding location in the geological model;

[0108] In the formula, C represents the matching degree, F i Represents the characteristic value of the mineral sample, G i represents the characteristic value of the corresponding position in the geological model, n represents the total number of characteristics,

[0109] Detailed explanation of the formula and the process of formula calculation and derivation:

[0110] Assume that the eigenvalues of the mineral sample in the 3D geological structure model are F = [0.8, 0.6, 0.9], the eigenvalues of the corresponding positions in the geological model are G = [0.75, 0.65, 0.88], and the total number of features is n = 3. Calculate the matching degree C:

[0111] C=(0.8×0.75)+(0.6×0.65)+(0.9×0.88)=0.6+0.39+0.792=1.782;

[0112] The result of 1.782 indicates that the target ore body has a matching degree of 1.782. This value is used to compare with the preset threshold to identify it as an underground ore body. The calculation process is used to identify the location of the ore body and provide data support for subsequent exploration and development activities.

[0113] S403: Using underground mineral detection records, record and identify the location information of multiple identified ore bodies to generate an ore body morphology recognition result;

[0114] In sub-step S403, a data set containing ore body detection results is imported. The data describes the geographical location, shape and size of each ore body in detail. The location of each ore body is marked through the spatial analysis module, and a three-dimensional coordinate system is used to ensure the accuracy of the location information. Stereo mapping technology is applied to compare the three-dimensional model of each ore body with the actual geological structure to confirm the geological location. The process includes adjusting the boundaries of the ore body and matching the corresponding stratigraphic and lithological characteristics in the geological model. After the operation is completed, the ore body morphology recognition results are generated. The results are displayed in the form of graphics and data reports, listing the location, estimated volume and associated mineral types of each ore body, providing direct navigation information for subsequent exploration activities, providing key data for mineral development plans and resource assessments, helping to locate and evaluate the mining potential of each ore body, and optimizing the development and utilization of resources.

[0115] See also Figure 6 Based on the results of ore body morphology identification, the distribution of various minerals in the target area is analyzed. Combined with geological exploration samples, the content and quality of the minerals are predicted, and the total resource volume of the mining area is estimated. The specific steps for generating mineral resource prediction information are as follows:

[0116] S501: Based on the ore body morphology identification results, analyze the spatial distribution of minerals in the target area, combine the geological characteristics of the target location, including rock hardness, evaluate the mining difficulty, and generate mineral distribution information;

[0117] In sub-step S501, the results include the geometry and location data of each ore body. The software uses geostatistical methods, including variogram analysis, to evaluate the spatial distribution pattern of minerals in the target area. Combined with the geological characteristic data of each location, such as rock hardness, the software uses a mineral mining difficulty assessment model to estimate the mining difficulty of each ore body, taking into account rock hardness, the degree of fracture development and various mechanical properties, to determine the effort and potential cost required for mining. The analysis results generate mineral distribution information, which records in detail the mineral type, predicted abundance and its distribution status in the underground.

[0118] S502: Based on the mineral distribution information, ore samples are collected from multiple locations, and the ore quality is evaluated by analyzing the chemical composition of the ore, including metal content and impurity level, and an ore quality analysis record is generated;

[0119] In sub-step S502, mass spectrometry and X-ray fluorescence spectroscopy are used to determine the chemical composition of the sample, including a detailed analysis of metal content and impurity levels. The chemical composition data of each sample is analyzed by software to assess the overall quality of the ore. The quality analysis takes into account the economic value of the metal content and the difficulty of removing impurities, and generates an ore quality analysis record. The record provides a decision-making basis for further processing and purification operations.

[0120] S503: Based on the ore quality analysis records, analyze the total amount of mineable minerals in the mining area, calculate the economic benefits of mineral mining in the target area, and generate mineral resource prediction information;

[0121] The specific formula for analyzing the total amount of mineable minerals in a mining area is:

[0122]

[0123] Among them, R represents the total recoverable resources of the mining area, V i Represents the volume estimate of the i-th ore body, D i represents the density of the corresponding ore body, E i represents the extraction rate, n represents the total number of ore bodies considered in the mining area, and i is the index.

[0124] formula:

[0125]

[0126] Detailed explanation of the formula and the process of formula calculation and derivation:

[0127] The formula is used to estimate the total recoverable resources in a mining area;

[0128] Parameter meaning and setting value:

[0129] V i For the volume estimation of the i-th ore body, assume V1 = 200000, V2 = 150000;

[0130] D i is the density of the corresponding ore body, assuming the ore density is 5 tons / cubic meter;

[0131] E i For the extraction rate, assume E1 = 0.8, E2 = 0.75;

[0132] n is the total number of ore bodies considered, which is assumed to be 2;

[0133] Substitute the parameters into the formula for calculation:

[0134] R=(200,000×5×0.8)+(150,000×5×0.75);

[0135] R = (800,000) + (562,500);

[0136] R = 1,362,500;

[0137] The result 1362500 indicates that the estimated recoverable resources in the target area are 1362500 tons. The result is used to provide important information on the potential economic value of the mining area based on actual geological surveys and current technical conditions, helping decision makers to make more accurate resource management and development plans.

[0138] See also Figure 7 A cube prediction model prospecting system based on three-dimensional modeling is provided. The cube prediction model prospecting system based on three-dimensional modeling is used to execute the above-mentioned cube prediction model prospecting method based on three-dimensional modeling. The system includes:

[0139] The electromagnetic data acquisition module sets the parameters of the electromagnetic imaging equipment, including scanning depth and frequency, based on the location information of the exploration points, performs electromagnetic wave scanning, records the electromagnetic response data of multiple exploration points, and generates electromagnetic characteristic data;

[0140] The data interpolation analysis module calculates the spatial relationship between data points based on electromagnetic characteristic data, combines data smoothing and data interpolation, fills in geological data gaps, and generates geological data interpolation records;

[0141] The 3D modeling module analyzes geotechnical characteristics at multiple locations based on interpolated geological data records, assesses stratum continuity, detects fault locations, and constructs a 3D geological structure model in combination with geological feature information.

[0142] The ore body analysis module is based on a three-dimensional geological structure model and combines mineral sample data to analyze the geometric shape and texture characteristics of the ore body, detect the location of multiple ore bodies in the target area, predict the content and quality of the mineral, calculate the total resource volume of the mining area, and generate mineral resource prediction information.

[0143] The above embodiments can be implemented in whole or in part through software, hardware (such as circuits), firmware, or any other 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 program are loaded or executed on a computer, the processes or functions described in accordance with 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 device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired method (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, or magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0144] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0145] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0146] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0147] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0148] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0149] In the 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 merely a logical function division. In actual implementation, there may be other division methods, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of the device or unit, which can be electrical, mechanical or other forms.

[0150] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0151] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0152] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0153] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A prospecting method based on a cube prediction model of three-dimensional modeling, characterized in that: The method comprises: Based on the location information of the exploration point, electromagnetic imaging equipment is used to adjust the scanning parameters to collect reflected electromagnetic waves at multiple locations in the target area, record electromagnetic response data at various frequencies and intensities, and generate electromagnetic characteristic data; Based on the electromagnetic characteristic data, the spatial relationship between multiple data points is calculated, combined with data smoothing, to predict the electromagnetic response data of multiple locations, and the geological values of multiple spatial points are filled to obtain geological data interpolation records; Using the geological data interpolation records, by analyzing the electromagnetic response data, identifying geotechnical properties at multiple locations, evaluating the continuity of the strata and detecting the locations of faults, constructing a geological model and mapping multiple geological feature information to generate a three-dimensional geological structure model; The steps of using the geological data interpolation records to analyze the electromagnetic response data of multiple exploration points and applying the Bayesian network model to analyze the relationship between the electromagnetic data and rock and soil properties at each location, identifying the differences in the electromagnetic properties of each layer of rock, evaluating the continuity of the strata and detecting the location of faults, constructing a geological model and mapping multiple geological feature information to generate a three-dimensional geological structure model are as follows: Utilizing the geological data interpolation records, and based on the electromagnetic response data, analyzing geotechnical characteristics at multiple locations, identifying stratigraphic continuity and geological boundaries at multiple locations in the target area, and constructing a basic stratigraphic framework; Adjusting the stratigraphic layers based on the basic stratigraphic framework, including adjusting the thickness and ductility of the stratigraphic layers, and generating stratigraphic structure adjustment records; Based on the stratigraphic structure adjustment record, mapping geotechnical characteristic information at multiple locations into a geological model, including lithology, mineral content, and stratigraphic age, to generate a three-dimensional geological structure model; Based on the three-dimensional geological structure model, combined with the geometric morphology and texture characteristics of multiple mineral samples, the ore body in the geological model is identified, the ore body position is calibrated, and the ore body morphology identification result is generated; Based on the ore body morphology identification results, the distribution of various minerals in the target area is analyzed, and combined with geological exploration samples, the content and quality of the minerals are predicted, the total resource volume of the mining area is estimated, and mineral resource prediction information is generated; Based on the ore body morphology identification results, the distribution of various minerals in the target area is analyzed, and combined with geological exploration samples, the content and quality of the minerals are predicted, the total resource volume of the mining area is estimated, and the steps for generating mineral resource prediction information are as follows: Based on the ore body morphology identification results, the spatial distribution pattern of minerals in the target area is evaluated. In combination with the geological characteristic data of each location, the mining difficulty is evaluated taking into account the rock hardness, degree of fracture development and various mechanical properties, and the mineral distribution information is generated; Based on the mineral distribution information, collect ore samples from multiple locations, analyze the chemical composition of the ore, including metal content and impurity level, using mass spectrometry and X-ray fluorescence spectroscopy, evaluate the ore quality, and generate an ore quality analysis record; Based on the ore quality analysis records, the total amount of mineable minerals in the mining area is analyzed, the economic benefits of mineral mining in the target area are calculated, and mineral resource prediction information is generated.

2. The 3D model-based cube prediction model prospecting method according to claim 1, characterized in that: The electromagnetic characteristic data specifically includes the reflected electromagnetic wave frequency, electromagnetic wave intensity information, and scanning parameter configuration; the geological data interpolation record includes geological characteristic prediction values, spatial relationship calculation results, and data smoothing processing results; the three-dimensional geological structure model specifically refers to stratum continuity analysis information, fault position identification records, and underground structure mapping results; the ore body morphology identification results include mineral geometry, mineral color information, and mineral texture information; the mineral resource prediction information specifically includes mineral distribution analysis results, mineral content prediction information, and mining area resource estimation data.

3. The 3D model-based cube prediction model prospecting method according to claim 1, characterized in that: Based on the location information of the exploration point, the electromagnetic imaging equipment is used to adjust the scanning parameters to collect reflected electromagnetic waves at multiple locations in the target area and record electromagnetic response data at various frequencies and intensities. The specific steps for generating electromagnetic characteristic data are as follows: Based on the location information of the exploration point and the scanning depth, the scanning parameters of the electromagnetic imaging equipment are adjusted, including frequency and power adjustment, and a record of the set parameters is generated; Using the set parameter records, performing electromagnetic wave scanning at the exploration point, collecting electromagnetic response data at the target location, including signals at multiple frequencies and intensities, to obtain an electromagnetic response data set; Based on the electromagnetic response data set, the collected electromagnetic data is preprocessed, including signal enhancement, noise filtering and data formatting, to generate electromagnetic characteristic data.

4. The 3D model-based cube prediction model prospecting method according to claim 1, characterized in that: Based on the electromagnetic characteristic data, the steps of calculating the spatial relationship between multiple data points, combining data smoothing, predicting the electromagnetic response data of multiple locations, and filling the geological values of multiple spatial points to obtain the geological data interpolation record are specifically as follows: Based on the electromagnetic characteristic data, calculating the distance and correlation between multiple exploration points, analyzing the spatial distribution of the data, and creating a spatial relationship diagram of the exploration points; Using the spatial relationship diagram of the exploration points and data interpolation, electromagnetic response data of multiple uncollected locations are predicted, and blank areas of the data are filled to optimize the continuity of the exploration data and generate a feature prediction data set; Based on the feature prediction data set, combined with data smoothing, outliers and noise are eliminated, the availability and accuracy of the data are optimized, and the geological data interpolation record is obtained.

5. The 3D model-based cube prediction model prospecting method according to claim 4, characterized in that: The specific formula for predicting the electromagnetic response data of multiple uncollected locations is: ； in, represents the predicted electromagnetic response value at the target location, Represents the electromagnetic response value of a known exploration point, represents the weight coefficient calculated based on spatial autocorrelation, represents the predicted position, Represents the location of the adjacent collected exploration points, Represents the number of exploration points involved in the calculation, Represents the weight coefficient and the index of the exploration point.

6. The 3D model-based cube prediction model prospecting method according to claim 1, characterized in that: Based on the three-dimensional geological structure model, combined with the geometric morphology and texture characteristics of multiple mineral samples, the steps of identifying the ore body in the geological model, calibrating the ore body position, and generating the ore body morphology identification result are as follows: Analyzing the geometrical morphology and texture characteristics of various mineral samples according to the three-dimensional geological structure model to generate mineral sample analysis results; Based on the analysis results of the mineral samples, by comparing them with the geological three-dimensional model, the underground ore bodies in the target area are detected and identified, and underground mineral detection records are generated; The underground mineral detection records are used to record and identify the location information of multiple identified ore bodies, and generate ore body morphology identification results.

7. The 3D model-based cube prediction model prospecting method according to claim 1, characterized in that: The specific formula for analyzing the total amount of mineable minerals in the mining area is: ； in, Represents the total recoverable resources of the mining area, Representative Estimation of the volume of the ore body, represents the density of the corresponding ore body, represents the extraction rate, represents the total number of ore bodies considered in the mining area, is the index.

8. A cube prediction model prospecting system based on three-dimensional modeling, characterized in that: The method for prospecting using a cube prediction model based on three-dimensional modeling according to any one of claims 1 to 7, wherein the system comprises: The electromagnetic data acquisition module sets the parameters of the electromagnetic imaging equipment, including scanning depth and frequency, based on the location information of the exploration points, performs electromagnetic wave scanning, records the electromagnetic response data of multiple exploration points, and generates electromagnetic characteristic data; The data interpolation analysis module calculates the spatial relationship between data points based on the electromagnetic characteristic data, combines data smoothing and data interpolation, fills in geological data gaps, and generates geological data interpolation records; The three-dimensional modeling module analyzes the geotechnical characteristics of multiple locations based on the interpolation records of the geological data, evaluates the continuity of the strata and detects the locations of faults, and constructs a three-dimensional geological structure model in combination with the geological feature information; The ore body analysis module analyzes the geometric shape and texture characteristics of the ore body based on the three-dimensional geological structure model and combined with mineral sample data, detects the location of multiple ore bodies in the target area, predicts the content and quality of the mineral, calculates the total resource volume of the mining area, and generates mineral resource prediction information.

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