Silver-copper ore deposit prospecting prediction method, system and storage medium

Abstract

The present invention discloses a silver-copper ore prospecting prediction method and system, comprising: firstly, using gravity detection, magnetic detection, electromagnetic detection and seismic wave detection equipment to collect geophysical data of the target mining area, obtain comprehensive original geological information, then optimize the data through denoising, correction and inversion processing technology, remove interference signals and construct a physical model reflecting the distribution and boundary characteristics of the ore body, then construct a three-dimensional geological model of the ore deposit based on the inversion result, accurately reflect the position relationship of the ore body in space and the change of resource grade, and then use the three-dimensional model combined with the resource reserve estimation method to predict and evaluate the potential of the ore deposit. Based on this, the present invention overcomes the defects of insufficient data accuracy, low model construction complexity and lack of optimization ability of the prediction results in the existing ore prospecting methods, realizes high-precision, high-efficiency and intelligent prediction of silver-copper ore deposits, and provides a scientific basis for the exploration and development of mineral resources.

Description

Technical Field

[0001] The invention belongs to the technical field of prospecting, and in particular relates to a silver-copper deposit prospecting prediction method, system and storage medium. Background Art

[0002] As an important mineral resource, silver-copper deposits have attracted widespread attention due to their economic value. The symbiosis of silver and copper in the deposits makes the detection and exploration of silver-copper deposits more complicated. Traditional methods of prospecting for mineral deposits usually rely on geological exploration, drilling, and chemical analysis. Although these methods can provide a certain basis for the discovery of mineral deposits, their limitations are also very obvious. Traditional methods have defects such as long cycles, high costs, and limited accuracy, especially when facing silver-copper deposits in deep or complex geological environments, the effect is not good.

[0003] With the development of geophysical exploration technology, more and more mineral prospecting work has begun to rely on physical detection methods, such as gravity, magnetism, electromagnetics and seismic wave reflection. However, each of these methods has certain limitations. For example, gravity exploration can only reflect the density distribution of the underground, magnetic exploration can only provide magnetic information of the ore body, and although electromagnetic exploration can reflect the changes in underground conductivity, it is difficult to extract accurate information on the distribution of mineral deposits under complex geological backgrounds. In order to solve these problems, scholars have proposed data fusion and inversion technology to combine the data of multiple physical detection methods for analysis.

[0004] However, most current research still remains at the level of a single detection method, lacking comprehensive analysis and inversion processing of multi-source data, especially for the precise prediction of silver-copper deposits, a special type of deposit. The accuracy and efficiency of deposit prediction are still issues that need to be broken through in the field of mineral exploration. Summary of the invention

[0005] In order to solve the above technical problems, the present invention proposes a silver-copper deposit prospecting prediction method, system and storage medium to solve the problems existing in the above-mentioned prior art.

[0006] In a first aspect, to achieve the above-mentioned object, the present invention provides a method for prospecting and predicting silver-copper deposits, comprising the following steps:

[0007] Collect data from the target mining area to obtain original geological data reflecting the distribution and properties of the ore body;

[0008] Performing preprocessing and inversion processing on the obtained original geological data;

[0009] Based on the inversion processed data, a 3D geological model of the deposit is constructed through 3D modeling technology;

[0010] Based on the three-dimensional geological model, the resource reserves of the mineral deposit are estimated.

[0011] Optionally, the original geological data includes gravity data, magnetic data, electromagnetic data and seismic wave data.

[0012] Optionally, the inversion process includes: determining an inversion model according to different types of data, selecting an inversion algorithm, and finally optimizing the inversion result to obtain inversion processed data, wherein the inversion model includes a linear inversion model and a magnetic susceptibility model.

[0013] Optionally, the process of constructing a 3D geological model of the deposit using 3D modeling technology based on the inversion processed data includes:

[0014] Determine the spatial framework and resolution of the 3D geological model and integrate the inversion processed data;

[0015] After the integration is completed, the boundaries of the ore deposit are optimized based on the second geological data, which includes drilling data and geological profiles.

[0016] Optionally, based on the three-dimensional geological model, the process of estimating the resource reserves of the mineral deposit includes: using the Monte Carlo simulation method to make multi-scenario predictions of ore reserves and evaluate the resource distribution in different grade areas.

[0017] Optionally, based on the 3D geological model, the resource estimation of the deposit also includes the identification of potential areas. The process includes:

[0018] Acquiring spatial information, geological profile information and ore body boundaries in the three-dimensional geological model;

[0019] Identify potential areas based on spatial information, geological profile information and ore body boundaries, as well as geological characteristics, detection data and historical exploration results.

[0020] In a second aspect, the present invention further provides a silver-copper ore deposit prospecting prediction system for implementing a silver-copper ore deposit prospecting prediction method, the system comprising:

[0021] The geological data acquisition module is used to collect data from the target mining area and obtain the original geological data reflecting the distribution and properties of the ore body;

[0022] A data processing and inversion module, used for preprocessing and inversion processing the obtained original geological data;

[0023] A 3D model building module is used to build a 3D geological model of the deposit through 3D modeling technology based on the inversion processed data;

[0024] Potential assessment module, used to estimate the resource reserves of mineral deposits based on 3D geological models.

[0025] Optionally, in the data processing and inversion module, the inversion processing process includes: determining the inversion model according to different types of data, selecting the inversion algorithm, and finally optimizing the inversion result to obtain the inversion processed data, wherein the inversion model includes a linear inversion model and a magnetic susceptibility model.

[0026] In a third aspect, the present invention further provides a computer terminal device, comprising:

[0027] one or more processors;

[0028] A memory, coupled to the processor, for storing one or more programs;

[0029] When the one or more programs are executed by the one or more processors, the one or more processors implement the steps of a silver-copper deposit prospecting prediction method.

[0030] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a silver-copper deposit prospecting prediction method.

[0031] Compared with the prior art, the present invention has the following advantages and technical effects:

[0032] The present invention provides a silver-copper ore prospecting prediction method and system, firstly, using gravity detection, magnetic detection, electromagnetic detection and seismic wave detection equipment to collect geophysical data of the target mining area, obtain comprehensive original geological information, then optimize the data through denoising, correction and inversion processing technology, remove interference signals and build a physical model reflecting the distribution and boundary characteristics of the ore body, then build a three-dimensional geological model of the ore deposit based on the inversion result, accurately reflect the position relationship of the ore body in space and the change of resource grade, then use the three-dimensional model combined with the resource reserve estimation method to predict and evaluate the potential of the ore deposit, identify high-value areas and analyze economic feasibility, finally optimize the prediction results based on the intelligent optimization algorithm, formulate an efficient mining strategy, maximize resource utilization efficiency and reduce environmental impact. Based on this, the present invention overcomes the defects of insufficient data accuracy, low model construction complexity and lack of optimization ability of the prediction results in the existing ore prospecting methods, realizes the high-precision, high-efficiency and intelligent prediction of silver-copper ore deposits, and provides a scientific basis for the exploration and development of mineral resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0034] Figure 1 is a flow chart of a method according to an embodiment of the present invention;

[0035] Figure 2 Schematic diagram of a system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0036] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0037] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0038] First, the technical terms involved in the following embodiments are explained.

[0039] Embodiment 1

[0040] like Figure 1 As shown, this embodiment provides a silver-copper deposit prospecting prediction method, comprising:

[0041] Collect data from the target mining area to obtain original geological data reflecting the distribution and properties of the ore body;

[0042] Performing preprocessing and inversion processing on the obtained original geological data;

[0043] Based on the inversion processed data, a 3D geological model of the deposit is constructed through 3D modeling technology;

[0044] Based on the three-dimensional geological model, the resource reserves of the mineral deposit are estimated.

[0045] As an implementation method of this embodiment, in the process of mineral deposit detection, the original geological data is first obtained by geophysical detection means (such as gravity, magnetic, electromagnetic and seismic wave data acquisition). These data provide basic information for subsequent mineral deposit exploration, including the distribution of ore bodies, changes in ore grades, etc. The quality of the data largely determines the accuracy of the subsequent model, so accurate detection data is the key to the whole process. After collection, these data are stored and ready for further processing and analysis.

[0046] The collected geophysical data often contain noise and uncertainty, so these data need to be denoised, corrected and inverted. The denoised data can more clearly reveal the potential boundaries and structural characteristics of the ore body. The inversion process converts the geophysical signal into a geological model of the underground ore body by using physical models (such as gravity inversion and magnetic inversion). For example, in the process of processing, after removing noise using Kalman filtering and wavelet transform, detailed spatial distribution and depth information of the ore body area are obtained. These optimized data provide more accurate input for the subsequent three-dimensional model construction. The denoised and inverted data are used to construct a three-dimensional geological model of the ore deposit. At this time, based on the optimized data, the geometric shape and ore body boundary of the ore deposit are constructed using spatial interpolation and three-dimensional modeling techniques (such as finite element method, Boolean operation, etc.). This model not only shows the volume of the ore body, but also describes the distribution and spatial variation of the ore grade. For example, based on the inversion data in the previous step, a three-dimensional model of the ore body boundary and ore grade distribution is constructed, showing the main area and potential distribution of the ore body.

[0047] Furthermore, the obtained three-dimensional model and ore deposit distribution data are used to predict and evaluate the potential of the ore deposit, and the potential of the ore deposit is quantitatively evaluated by calculating the ore deposit volume, ore grade and its economic value. This process uses numerical simulation, regression analysis or machine learning algorithms to evaluate the resource volume, mining feasibility and economic value of the ore body. For example, based on the constructed three-dimensional model, the Monte Carlo simulation method was used to estimate the ore reserves of different grade areas, and the copper reserves of the ore deposit were predicted to be 75,000 tons, which further provided the feasibility analysis results of mining.

[0048] According to the results of the ore deposit potential prediction, as well as the three-dimensional model and ore body data established in the previous steps, the entire exploration process and mining strategy are optimized using intelligent optimization algorithms. This step includes further data denoising, selection of mining methods, maximization of resource recovery rate, etc. Optimization algorithms (such as particle swarm optimization algorithms, genetic algorithms, etc.) select the optimal mining sequence and ore body utilization plan through simulation calculations of different mining strategies and conditions to maximize economic benefits and resource recovery rate. For example, in the process of intelligent optimization, the genetic algorithm is used to optimize the mining path, reduce transportation distance and cost, and improve resource recovery efficiency, and finally obtain the optimal mining strategy.

[0049] As an implementation method in this embodiment, the original geological data includes gravity data, magnetic data, electromagnetic data and seismic wave data.

[0050] As an optional implementation in this embodiment, geophysical exploration data collection is the first step in mineral deposit exploration, which mainly collects physical characteristic data of underground mineral deposits by using a variety of geophysical methods (such as gravity measurement, magnetic measurement, electromagnetic exploration, seismic wave reflection, etc.). Different geophysical methods provide information such as density, magnetism, conductivity, and sound wave propagation speed of underground ore bodies through different physical principles, providing a basis for subsequent data processing and inversion.

[0051] As an additional implementation method in this embodiment, the geophysical exploration data collection process includes the following main steps:

[0052] Exploration area selection and design: Before conducting geophysical exploration, a reasonable exploration area must be selected first. The scope and depth of exploration should be determined based on the geological background, ore deposit characteristics and known ore deposit distribution. The exploration design should take into account the scale of the exploration target and the possible burial depth of the ore deposit, and plan the selection of exploration methods and the density of data collection.

[0053] Gravity measurement: Gravity exploration mainly infers the existence of underground ore bodies by measuring the density distribution of underground rock formations. During the acquisition process, the gravity instrument measures the ground gravity value point by point along the planned survey line, and the possible location of the ore body is obtained through gravity anomaly analysis. In this process, it is necessary to take into account the interference of external factors such as ground undulations and climate change on the measurement, and take appropriate correction measures.

[0054] Magnetic measurement: Magnetic measurement is used to detect the magnetic difference between underground ore bodies and their surrounding rock formations. During the exploration process, a magnetometer is carried along the survey line for continuous measurement. When conducting magnetic measurement, attention should be paid to the geological background of the mining area, such as the magnetic strength of the rock, the content of magnetic minerals in the ore deposit, and other factors, in order to more accurately determine the location of the ore body.

[0055] Electromagnetic exploration: Electromagnetic exploration uses the principle that electric current or electromagnetic waves are reflected when they propagate underground and encounter rock layers with different conductivity to detect the distribution of underground ore bodies. During the electromagnetic exploration process, the detection instrument will emit electromagnetic waves and infer the distribution of underground conductivity by receiving reflected signals. Generally, there is a large difference in conductivity between silver and copper deposits and the surrounding rock layers, and electromagnetic exploration can well identify ore bodies.

[0056] Seismic wave reflection exploration: Seismic wave reflection method analyzes the underground geological structure by transmitting seismic waves into the ground and receiving their reflected waves. The depth, shape and size of the underground ore body can be inferred by the propagation time and intensity of the reflected waves. This method is suitable for the detection of deep ore bodies, especially under complex geological conditions, and can provide accurate underground mineral deposit information.

[0057] Specifically, taking a certain actual silver-copper mining area as an example, assuming that the mining area has been preliminarily geologically explored and some known data of the ore deposit has been obtained. In order to further confirm the scale and location of the ore body, the exploration team decided to use a variety of geophysical exploration methods such as gravity, magnetism, electromagnetic and seismic wave reflection for comprehensive analysis.

[0058] Gravity measurement: In the first phase of exploration, the exploration team used gravity instruments to conduct intensive measurements of the mining area. The measurement results showed that the gravity value had a significant abnormal change. The specific data are as follows:

[0059] The gravity value in the measurement area varies within the range of ±0.8 mGal (milliGal) on the ground, and a negative anomaly of 1.2 mGal appears in a larger area.

[0060] Through further analysis, the team found that the negative anomaly area was located in the central part of the mining area, at a depth of about 600 meters.

[0061] It is preliminarily speculated that there may be high-density ore bodies in this area. In order to confirm the distribution of ore deposits in this area, other detection methods will be used to verify it in the future.

[0062] Magnetic measurement: Based on the gravity measurement, the team continued to conduct magnetic measurement, and the magnetometer measured along the survey line. The magnetic data results are as follows:

[0063] The magnetic value in the center of the mining area is about -300nT (nanotesla), which is 100nT lower than the surrounding normal area.

[0064] According to the known mineral deposit information in the mining area, the content of magnetic minerals in this area is relatively high, and it is speculated that it may be related to the magnetic minerals in the silver-copper deposits.

[0065] Through comprehensive analysis, the team further verified that the central area of ​​the mining area is the possible location of the ore body, and the magnetic anomalies in this area are consistent with the known ore bodies.

[0066] Electromagnetic exploration: During the electromagnetic exploration phase, the exploration team used a ground electromagnetic exploration instrument (EM-31) to collect data. The electromagnetic data showed strong conductivity anomalies, and the specific data are as follows:

[0067] Measurement data showed that the conductivity value in this area reached 4.5S / m, which was significantly higher than the surrounding normal value (about 2.0S / m).

[0068] The central area of ​​the conductivity anomaly is highly consistent with the gravity anomaly and magnetic anomaly areas, and it is speculated that this area may be an enriched area of ​​the ore body.

[0069] Because silver-copper deposits have strong conductivity, electromagnetic exploration methods effectively reveal the possible distribution range of the ore body.

[0070] Seismic wave reflection exploration: In order to further confirm the location and depth of the ore body, the exploration team also used seismic wave reflection exploration methods. The reflection wave data was collected by seismic instruments, and the results showed:

[0071] Reflection wave data showed that there was an obvious reflection wave anomaly about 600 meters underground in the mining area.

[0072] The travel time of the reflected waves indicated that the depth of the anomaly was about 550 to 600 meters and its shape was curved, which was consistent with previous geological exploration data.

[0073] By analyzing the velocity and intensity of the reflected waves, the boundary shape and scale of the ore body were preliminarily confirmed.

[0074] The comprehensive analysis of these geophysical exploration data provides strong support for the precise positioning of the deposit. Based on the results of multiple detection methods such as gravity, magnetism, electromagnetics and seismic wave reflection, the central area of ​​the mining area was confirmed as a possible silver-copper deposit distribution area, and the ore body depth is about 600 meters. Through these detection data, the exploration team can effectively narrow the exploration scope of the deposit and improve the efficiency and accuracy of subsequent exploration work.

[0075] As an implementation method in this embodiment, the inversion processing process includes: determining the inversion model according to different types of data, selecting the inversion algorithm, and finally optimizing the inversion result to obtain the inversion processed data, wherein the inversion model includes a linear inversion model and a magnetic susceptibility model.

[0076] As an optional implementation method in this embodiment, data processing and inversion are key steps to convert the acquired geophysical exploration data into underground mineral deposit characteristics. Through inversion technology, detailed information of underground ore bodies, such as the depth, shape, and scale of the ore bodies, can be extracted from multiple detection data. Common inversion methods include gravity data inversion, magnetic data inversion, electromagnetic data inversion, etc., and the inversion process relies on accurate mathematical models and algorithms. The core goal of this step is to restore the true distribution of underground mineral deposits from the collected geophysical data through accurate mathematical models and inversion algorithms, so as to provide data support for the potential assessment and mining of mineral deposits.

[0077] As an additional implementation method in this embodiment, the process of processing and inverting the acquired geophysical data mainly includes the following important steps:

[0078] Data preprocessing: Data preprocessing is the first step in the inversion process, which mainly includes denoising, interpolation and data standardization. The original geophysical data is often affected by the external environment, such as climate, topography, instrument errors, etc., so these data need to be corrected and denoised. Common denoising methods include median filtering, low-pass filtering, etc. These methods can effectively remove random noise caused by environmental factors.

[0079] When interpolating data, especially when the intervals between collected data are large, interpolation methods can fill in the blank areas of the data and ensure the continuity of the data. Common interpolation methods include Lagrange interpolation and Kriging interpolation. In the processing process, data standardization is very important because different types of geophysical data (such as gravity and electromagnetic data) have large differences in dimensions and amplitudes. Standardization enables these data to be jointly inverted in the same model.

[0080] Select inversion model: Depending on the different geophysical data, you need to select a suitable inversion model. For gravity data, you can use an inversion model based on density changes; for magnetic data, you can invert based on changes in magnetic susceptibility; electromagnetic data inversion is usually based on underground resistivity models. Selecting a suitable inversion model is the key to ensuring the accuracy of the inversion results.

[0081] As an optional implementation in this embodiment, in the inversion of gravity data, a linear inversion model based on gravity anomalies is often used. This model infers the density body that may exist underground through the relationship with the gravity anomaly of the known ore body. In the inversion of magnetic data, a three-dimensional inversion model of the magnetic field is often used to infer the magnetization intensity and distribution of the underground ore body.

[0082] Application of inversion algorithm: Inversion algorithm is the core of geophysical data inversion. Common inversion algorithms include least squares method, weighted least squares method, genetic algorithm, simulated annealing algorithm, etc. Least squares method is usually used to deal with linear problems, while for complex nonlinear inversion problems, genetic algorithm and simulated annealing algorithm can effectively avoid local minima and find the global optimal solution.

[0083] In practical applications, the inversion algorithm needs to be combined with the existing geological background information to adjust the initial parameters of the model so that the inversion process can converge to the correct solution. In the inversion of gravity data, if the mineral composition information of copper or silver mines is known in the mining area, it can be incorporated into the inversion algorithm as a constraint to improve the accuracy of the results.

[0084] Verification and optimization of inversion results: Inversion results usually face certain errors, so the inversion results need to be optimized through multiple iterations and verifications. In order to verify the reliability of the inversion results, the cross-validation method, external geological exploration data and other auxiliary information can be used to compare and analyze the inversion results. Under some complex geological conditions, the inversion results can also be compared with drilling data and other geological data to further optimize the model parameters and ensure the accuracy of the inversion results.

[0085] The optimization of inversion results is not only the adjustment of the inversion model, but also the adjustment of algorithm parameters. Optimizing algorithm parameters such as the number of iterations and step length can make the inversion results more consistent with the distribution law of actual ore deposits.

[0086] Specifically, the following is an inversion example of an actual silver-copper mining area, which combines gravity data, magnetic data, and electromagnetic data to obtain a three-dimensional model of the deposit through data processing and inversion.

[0087] First, data preprocessing is performed: During the exploration of a silver-copper mine, the collection results of gravity data showed obvious gravity anomalies. The specific data are as follows:

[0088] The initial collected gravity values ​​have a significant negative gravity anomaly area near the mining area, with an amplitude of -0.9mGal. To avoid external noise interference, the gravity data was processed by median filtering to remove random noise caused by climate change. The interpolation results of the pre-processed data show that the gravity anomaly in the central area of ​​the mining area is -1.1mGal, which is significantly different from the gravity anomaly in the surrounding areas.

[0089] In the magnetic data processing, the raw data was standardized to avoid the amplitude difference between different measurement points. The standardized and filtered data showed that the magnetic anomaly area in the mining area showed a strip distribution with a north-northeast-south-southwest trend. The denoising and interpolation results of the magnetic data were consistent with the location of the mineralized belt of the known silver-copper deposit.

[0090] Select the inversion model: For the gravity data of the mining area, the exploration team selected a model based on linear inversion for inversion, assuming that the density of the ore body is significantly different from that of the surrounding rock formations. The goal of gravity inversion is to infer the underground density distribution through gravity anomaly data. The inversion of magnetic data uses a three-dimensional inversion method based on the magnetic susceptibility model, assuming that the magnetic minerals in the ore body are relatively abundant, and the distribution of magnetic anomalies can reflect the location and shape of the ore body. The inversion of electromagnetic data uses a resistivity inversion model, assuming that the silver-copper deposit has a high conductivity, and the inversion goal is to find areas of conductivity anomaly.

[0091] Application of inversion algorithm: In the process of data inversion, the team used genetic algorithm to deal with nonlinear inversion problems. The algorithm can search for the optimal solution in multidimensional space and avoid the interference of local minimum values. After several iterations and optimizations, a set of optimal inversion parameters was finally obtained, including the distribution of density, magnetic susceptibility and conductivity. For example, the wavelet transform algorithm was used to perform layered inversion on seismic wave data to accurately identify the depth and thickness distribution of the ore body.

[0092] Through the inversion of genetic algorithms, the density distribution of the ore deposit obtained by gravity data inversion shows that there is a density of up to 2.8g / cm in the area with a depth of about 500 meters in the mining area. 3 The ore body is curved, which is in line with geological expectations. The results of magnetic data inversion show that the magnetic susceptibility of the ore body is high, reaching 10-4SI (International Magnetization Unit), indicating that there may be a large number of magnetic minerals enriched in the mining area.

[0093] Finally, the inversion results were verified and optimized: the inversion results were compared with the existing drilling data, and the results showed that the inversion model can better predict the location and size of the ore body. After comparing the drilling data, the depth and morphology of the ore deposit in the inversion results are highly consistent with the actual exploration results. Further optimization showed that by adding boundary conditions in the inversion (such as morphological constraints of the ore body), the accuracy of the inversion results can be further improved.

[0094] Through these comprehensive analyses, the deposit boundaries and ore body morphology of the silver-copper mining area were finally confirmed, and basic data support was provided for the next step of deposit potential assessment and mining.

[0095] As an implementation method in this embodiment, the process of constructing a three-dimensional geological model of a mineral deposit by three-dimensional modeling technology based on the inversion processed data includes:

[0096] Determine the spatial framework and resolution of the 3D geological model and integrate the inversion processed data;

[0097] After the integration is completed, the boundaries of the ore deposit are optimized based on the second geological data, which includes drilling data and geological profiles.

[0098] As an optional implementation method in this embodiment, the construction of a three-dimensional model of a mineral deposit is a key step after the inversion of geophysical detection data. The three-dimensional spatial model of the mineral deposit is constructed by integrating the inversion results obtained by different detection methods (such as gravity, magnetic, electromagnetic and seismic wave data). The three-dimensional model can fully display the morphology, scale, depth and distribution characteristics of the mineral deposit, and is an important basis for decision-making such as mineral deposit assessment, mining planning, and resource reserve estimation. The process of building a three-dimensional model of a mineral deposit includes the establishment of a model framework, data integration, model correction, and final output. The accuracy of the model directly affects the accuracy of mineral deposit exploration and the efficiency of subsequent work.

[0099] As an additional implementation method in this embodiment, the construction of a three-dimensional model of a mineral deposit generally includes the following key steps:

[0100] Establishment of model framework: In the first step of constructing a 3D model of a mineral deposit, it is necessary to determine the spatial framework and resolution of the model, including vertical resolution and horizontal resolution. According to the scale, depth and complexity of the mineral deposit, select the appropriate grid density and boundary conditions. Generally, for deep mineral deposits, the depth of the model needs to reach hundreds of meters or even thousands of meters. When establishing the framework, it is necessary to reasonably define the scope of the model according to the actual geological background of the mineral deposit to ensure that all possible areas of the ore body can be fully covered.

[0101] When establishing a framework, common three-dimensional grid structures include tetrahedral grids and hexahedral grids. Tetrahedral grids are suitable for modeling deposits in complex geological environments, while hexahedral grids are suitable for relatively regular deposit models.

[0102] Data integration and interpolation: Integrate the inversion data obtained from different geophysical methods, usually involving the splicing of gravity, magnetic, electromagnetic, seismic wave and other data. The data obtained by each geophysical method generally only involves a certain feature of the ore body, such as density, magnetism, conductivity, etc. Therefore, it is necessary to convert them into a unified coordinate system according to the spatial distribution relationship of various types of data and perform interpolation processing. Common interpolation methods include inverse distance weighted (IDW) and Kriging interpolation.

[0103] As an optional implementation, when using gravity inversion data to construct a density distribution model of a mineral deposit, the local inversion data can be smoothed by Kriging interpolation to obtain a continuous density distribution map. For the integration of magnetic data, it may be necessary to combine the boundary conditions of the magnetic characteristics to more accurately reflect the morphology of the ore body.

[0104] Determination and optimization of ore deposit boundaries: After data integration, the boundaries of the ore body need to be determined based on the inversion results and actual geological information. The boundaries of the ore body are not only obtained through physical data inversion, but also should be combined with existing drilling data, geological profiles and other exploration information. By optimizing the boundaries of the ore deposit, unreasonable areas in the model can be eliminated and the accuracy of the ore body boundaries can be enhanced.

[0105] For complex mineral deposits, different types of inversion results may be needed to define the boundaries of the ore body. Gravity data may reveal the approximate location of the ore body, while magnetic data can further clarify the ore body's morphology, and electromagnetic data can be used to determine the depth distribution of the ore body. Therefore, the optimization of the deposit boundary requires not only spatial comparison, but also repeated iterations to confirm the optimal ore body boundary.

[0106] Visualization and output of 3D models: After the 3D model of the ore deposit is constructed, it needs to be converted into a visual form to facilitate subsequent analysis and decision-making. The visualization of the 3D model of the ore deposit usually adopts the 3D modeling technology in computer graphics and is displayed using professional software such as CAD and GIS. The visualization effect of the model can help geologists and engineers intuitively understand the spatial distribution of the ore body and the potential mining area.

[0107] The output 3D model can include data such as the volume of the ore body, ore grade distribution, and reserve estimation. At the same time, the model can be compared with existing exploration data (such as drilling, well location, geological profile, etc.) to further improve the accuracy of the model. Spatial information includes the volume of the ore body and the distribution of ore grade.

[0108] Specifically, the following is a specific example of the construction of a 3D model of a deposit in a silver-copper mining area, which combines multiple geophysical data such as gravity, magnetism, and seismic wave reflection with actual exploration data.

[0109] Establishment of model framework: In the modeling of a silver-copper deposit in a certain silver-copper mining area, the spatial range and grid density of the model were first determined. The depth of the deposit is approximately between 500 and 1500 meters, so the depth of the model is set to 2000 meters. In order to ensure the accuracy of the deposit boundary, the horizontal resolution of the model is 100 meters and the vertical resolution is 50 meters. The boundary of the deposit model includes the surrounding rock formations and the predicted area of ​​ore body distribution.

[0110] In the central area of ​​the mine, after multiple data evaluations, the framework was further refined, and the boundary of the deposit showed a curved shape that conformed to the known geological structure of the mine. This framework design ensured that the 3D model of the deposit could fully cover all potential areas of the ore body and provide high spatial resolution.

[0111] Data integration and interpolation: After establishing the model framework, the exploration team used Kriging interpolation to integrate data from different exploration methods. In the process of gravity data processing, the original measurement data was relatively sparsely distributed on the surface, so Kriging interpolation was used to smooth the gravity anomaly area and generate a density distribution map. In the process of magnetic data processing, the exploration team created a magnetic distribution map of the deposit by integrating the inverted magnetic anomaly values ​​into the model.

[0112] In the process of integrating electromagnetic data, due to the scarcity of conductivity data, the team used the inverse distance weighted interpolation method (IDW) to supplement the conductivity anomaly area, thereby obtaining a more detailed conductivity distribution map. After integrating these different types of data, a complete three-dimensional mineral deposit model was formed, covering important characteristics of the mineral deposit such as density, magnetism, and conductivity.

[0113] Determination and optimization of the deposit boundary: Through repeated analysis of gravity, magnetic, electromagnetic and seismic wave reflection data, the team successfully determined the boundaries of the deposit. Gravity anomalies indicate that the depth of the deposit is between 500 meters and 1,500 meters, and magnetic anomalies further determine the morphology of the ore body. Electromagnetic inversion results show that there are strong conductivity anomalies in the deep area of ​​the ore body, which supports the existence of the deposit in this depth area.

[0114] During the optimization of the deposit boundary, the exploration team combined the drilling data to modify the shape of the deposit to ensure that the model was consistent with the geological characteristics of the actual deposit. At the northern boundary of the deposit, the geological profile data showed that the boundary of the ore body was steeper, so the inversion model was appropriately adjusted to make the boundary of the deposit more accurate.

[0115] Visualization and output of 3D models: After the 3D ore deposit model was constructed, the team used GIS software to visualize the model. The display of the ore deposit model clearly shows the depth, shape, scale and other information of the ore body. The model also marks potential high-grade ore areas and key mining areas to help decision makers evaluate the economic value of the ore deposit and mining planning.

[0116] Finally, the model outputs include volume estimates of the ore body, ore distribution maps, reserve estimates, etc., and the model is compared with known drilling data and geological profiles. Through these visual data, the team can further optimize the evaluation model of the deposit and provide a reliable basis for the mining and further exploration of the deposit.

[0117] As an implementation method in this embodiment, the process of estimating the resource reserves of a mineral deposit based on a three-dimensional geological model includes: using a Monte Carlo simulation method to make multi-scenario predictions on ore reserves and evaluating the resource distribution in different grade areas.

[0118] As an optional implementation method in this embodiment, the prediction and evaluation of the potential of the ore deposit is the core link of in-depth analysis based on the constructed three-dimensional model of the ore deposit, aiming to estimate the resource potential and development value of the ore deposit. By quantitatively evaluating the ore body characteristics, ore grade, ore body scale, etc. in the three-dimensional model, it is possible to provide information such as the reserves, grade distribution, and mining feasibility of the ore deposit, providing a scientific basis for subsequent mineral resource development and economic evaluation. The process usually includes multiple steps such as identification of potential areas, estimation of resource volume, economic evaluation of the ore body, and mining feasibility analysis.

[0119] As an implementation method in this embodiment, based on the three-dimensional geological model, the resource reserve estimation of the mineral deposit also includes identifying potential areas, and the process includes:

[0120] Acquiring spatial information, geological profile information and ore body boundaries in the three-dimensional geological model;

[0121] Identify potential areas based on spatial information, geological profile information and ore body boundaries, as well as geological characteristics, detection data and historical exploration results.

[0122] As an additional implementation method in this embodiment, the core goal of the prediction and evaluation of mineral deposit potential is to accurately predict the resource potential of the mineral deposit and provide a basis for mineral deposit development and mining. Its main steps include:

[0123] Identification of potential areas: Identification of potential areas is the first step in predicting the potential of a mineral deposit. It usually requires identifying areas that may contain high-grade ore bodies based on the results of a three-dimensional model of the mineral deposit, combined with known geological features, detection data and historical exploration results. This process is usually achieved through spatial analysis, geological profile analysis and superposition of ore body boundaries. The determination of potential areas is an exploration of the deep and marginal areas of the ore body to identify those areas that may not have been fully explored.

[0124] In this process, geologists usually use geological volumes, spatial distribution patterns, and depth limitations of detection methods to analyze potential areas of mineral deposits. In some silver and copper mining areas, known ore bodies are usually concentrated within a certain depth range, and potential areas are usually located in the deep part of the deposit and the outer edge of the ore body.

[0125] Resource estimation: Resource estimation usually relies on a three-dimensional model of the ore deposit. The resource reserves of the ore deposit are evaluated by quantitatively calculating the ore volume, ore grade distribution, mineral composition and other information in the model. Resource estimation methods include volumetric method, mass method, ore density method and other methods. The choice of specific method is usually determined by factors such as the complexity of the ore deposit, mineability, ore type and ore grade.

[0126] In the process of estimating mineral deposit resources, one of the important technical means is Kriging interpolation, which provides a spatial interpolation scheme for geological estimation by statistically analyzing the spatial data of the ore body. The core idea of ​​this method is to predict the ore grade and reserves of unexplored areas by modeling the spatial correlation of ore samples. The Kriging interpolation method is used to predict the grade distribution of unsampled areas within the ore body based on the sampling point data. Using 3D modeling software and combined with the interpolation results, a 3D model of the ore body is constructed to determine the shape and boundaries of the ore body. Potential assessment uses the Monte Carlo simulation method to perform multi-scenario predictions on ore reserves and evaluate the resource distribution in different grade areas. Resource estimation method:

[0127] Volumetric method: Calculate the volume of the ore body based on a three-dimensional model;

[0128] Density method: Determine the average density of the ore;

[0129] Mass calculation: total amount of ore = volume × density;

[0130] Calculation of metal content: Metal content = total amount of ore × average grade.

[0131] Economic evaluation of ore bodies: Economic evaluation of ore bodies is another important part of the potential prediction and evaluation process. It aims to analyze the economic value of ore deposits based on resource estimation results, ore grade and market price. Economic evaluation not only involves ore reserves, grade and mining costs, but also needs to consider market price fluctuations, feasibility of mining technology and the impact of environmental protection policies.

[0132] In the evaluation process, commonly used economic indicators include payback period, net present value (NPV), internal rate of return (IRR), etc. These indicators help decision makers evaluate the commercial development potential of the deposit and provide a basis for subsequent development decisions. For example, the silver-copper ore grade of a certain deposit is 300g / t silver and 0.8% copper. Based on the market price and mining cost, the development prospects and profitability of the deposit are evaluated.

[0133] Mining feasibility analysis: Mining feasibility analysis is the final step in the evaluation of the potential of a mineral deposit. It evaluates the mining method of the mineral deposit (open pit mining or underground mining) and the technical difficulties and economic risks that may be encountered during the mining process. Mining feasibility analysis mainly includes a comprehensive evaluation of the geometric shape of the mineral deposit, the depth distribution of the ore body, the physical and chemical properties of the ore, and the amount of resources that can be mined.

[0134] As an optional implementation method, in the feasibility analysis of silver and copper mines, factors such as the hardness and brittleness of the ore and the inclination of the ore body need to be considered to assess the difficulty of ore mining. If the ore body is located at a deep depth, underground mining technology may be required, and the mining equipment, transportation system and safety measures need special consideration. In addition, the hydrological conditions of the ore deposit, environmental protection policies and the sustainability of resource utilization are also important components of the feasibility analysis of mining.

[0135] Specifically, the following is a specific example of potential prediction and evaluation of a silver-copper mining area, combining exploration data of actual deposits with a potential prediction model:

[0136] Identification of potential areas: In a silver-copper mining area, the exploration team identified two potential areas through analysis of existing exploration data. The first area is located in the northwest of the deposit, with a depth range of 600-1000 meters; the second area is located in the southeast of the deposit, with a depth range of 1000-1500 meters. In these areas, geological profiles show the possibility of ore body distribution, and gravity anomaly and magnetic anomaly data suggest that there may be underexplored ore bodies in these two areas.

[0137] In the northwest area, a combination of gravity and magnetic data was used to determine the geological characteristics of the potential area. Through spatial interpolation techniques, the model shows the deep extension of the ore body boundary in this area. The potential identification in the southeast area is based on electromagnetic data and seismic wave data, especially the reflection characteristics of seismic waves, indicating that there may be extensions of copper deposits in this area.

[0138] Estimation of resource volume: After the three-dimensional model of the ore deposit was constructed, the team used the Kriging interpolation method to estimate the resource volume of the ore deposit. The estimated resource volume of the ore body in the northwest area is 10 million tons, with a copper grade of 1.2% and a silver grade of 250g / t; the estimated resource volume of the ore body in the southeast area is 8 million tons, with a copper grade of 0.9% and a silver grade of 270g / t. The overall resource volume of the ore deposit is 18 million tons, with a total copper content of 220,000 tons and a total silver content of 4,500 tons.

[0139] These estimation results provide important basis for further exploration of the deposit and lay the foundation for the development evaluation of the deposit.

[0140] Economic evaluation of the ore body: Based on the above resource estimate, the exploration team analyzed the economic evaluation of the deposit. Assuming the market price is: the price of copper is US$6,000 per ton and the price of silver is US$500 per kilogram. Based on this price, the copper resource value of the deposit is US$1.32 billion and the silver resource value is US$225 million. By further calculating the mining cost, transportation cost and equipment investment of the deposit, the net present value (NPV) of the deposit is assessed to be US$530 million and the internal rate of return (IRR) is 18%.

[0141] These economic evaluation results indicate that the deposit has good economic potential and commercial value for mining and development.

[0142] Mining feasibility analysis: Based on the resources and economic evaluation results of the ore deposit, the team further conducted a feasibility analysis of the mining method. The ore body in the northwest area is located from the surface to 1,000 meters deep, suitable for open-pit mining; the ore body in the southeast area is located at a depth of 1,000 to 1,500 meters, suitable for underground mining. The team analyzed the geological environment, physical properties of the ore and the difficulty of mining, and proposed a corresponding mining plan.

[0143] The Northwest area, which is open-pit mining, is expected to be mined using the stripping method at a cost of $10 per ton of ore. The Southeast area, which is underground mining, is expected to be mined using the ramp method at a cost of $25 per ton of ore. The development of the mining plan combines the depth of the deposit, the amount of resources, the difficulty of mining and the economic assessment to ensure the sustainable development of the deposit.

[0144] As an optional implementation method in this embodiment, the intelligent optimization of the mineral deposit detection results is to process and optimize various information such as geological exploration data, mineral deposit models, potential prediction and evaluation results through artificial intelligence (AI) technology, so as to improve the accuracy and efficiency of mineral deposit detection, reduce resource waste, and ensure the scientific nature of exploration decisions. Intelligent optimization technology mainly uses machine learning, optimization algorithms and big data analysis to intelligently adjust and optimize various aspects of mineral deposit detection, including data denoising, feature extraction, model optimization and mining strategy optimization.

[0145] As an additional implementation method in this embodiment, the intelligent optimization of the mineral deposit detection results generally includes the following key steps:

[0146] Data denoising and preprocessing: In mineral deposit exploration, especially when using geophysical methods (such as gravity, magnetism, electromagnetics, seismic waves, etc.) to collect data, noise and errors often occur. These noises may be caused by instrument sensitivity, environmental changes, geological anomalies, etc. The purpose of data denoising and preprocessing is to remove these irrelevant noises through machine learning and signal processing algorithms to improve the accuracy of subsequent analysis.

[0147] Common data denoising methods include:

[0148] Wavelet transform: Use wavelet transform to separate high-frequency noise and low-frequency signals from raw data and retain useful signals.

[0149] Kalman filter: Recursively estimates the noise in a dynamic system and optimizes the measurement data.

[0150] Deep learning autoencoder: Use neural network models to automatically learn low-dimensional representations of data and remove irrelevant information.

[0151] Through these methods, various data in mineral exploration can be clearly characterized without excessive interference, serving as the basis for subsequent analysis.

[0152] Feature extraction and pattern recognition: In the data after noise removal, the geological information of the ore deposit often exists in a complex pattern. Feature extraction and pattern recognition technology can extract potential geological features from the data through automated methods, such as ore body distribution, ore grade changes, etc. Common feature extraction methods include:

[0153] Principal component analysis (PCA): PCA is used to reduce the dimensionality of multidimensional data and extract the information that best reflects the characteristics of the mineral deposit.

[0154] Convolutional Neural Network (CNN): Automatically identify complex geological structures and features through CNN models in deep learning.

[0155] Clustering algorithms (such as K-means, DBSCAN): Divide the deposit area according to the similarity of the data and identify different types of ore bodies.

[0156] The results of feature extraction and pattern recognition can help the exploration team understand the distribution patterns of ore bodies more clearly, and provide more accurate data support for subsequent resource estimation, mining feasibility analysis, etc.

[0157] Optimization algorithms are applied to the optimization of 3D models of mineral deposits: The 3D model of a mineral deposit is not only the basis for mineral deposit exploration, but also plays an important role in resource estimation, economic evaluation and mining feasibility analysis. Traditional 3D models are often based on exploration data and geological experience, but as the amount of exploration data increases, traditional methods may not be able to fully tap the potential of the data, resulting in insufficient model accuracy.

[0158] Intelligent optimization technology optimizes the three-dimensional model through optimization algorithms (such as genetic algorithms, particle swarm optimization algorithms, etc.), updates information such as ore body boundaries, ore body volumes, and ore grades, thereby improving the accuracy of the model. By simulating natural selection and evolutionary processes, the optimization algorithm can find the optimal solution in a complex parameter space, thereby improving the accuracy in resource estimation and ore body prediction.

[0159] Intelligent optimization of mining strategies: Intelligent optimization of mining strategies aims to combine the results of mineral exploration, the three-dimensional model of the mineral deposit, and the results of economic evaluation to provide the best mining plan for mineral deposit development. By using machine learning models (such as reinforcement learning and deep reinforcement learning), various decisions in the mining process can be automatically optimized, including:

[0160] Selection of mining method: Select the appropriate mining method (open-pit mining, underground mining, slope mining, etc.) based on the depth of the ore deposit, ore grade, ore body morphology and other conditions.

[0161] Mining route planning: Optimize the ore transportation route during the mining process and reduce transportation costs.

[0162] Maximize resource utilization: Optimize the order of mining according to the distribution of the ore body to ensure the maximum recovery rate of the ore.

[0163] These intelligent mining strategies can effectively reduce mining costs, reduce resource waste and improve mining efficiency.

[0164] Comprehensive decision support system: The intelligent optimization of mineral exploration results does not only rely on a single optimization algorithm, but also requires a comprehensive decision support system. The system integrates mineral exploration data, model optimization results, economic evaluation, mining strategy and other information, performs global optimization through big data analysis and artificial intelligence technology, and provides a scientific basis for decision-making.

[0165] The system is usually based on a multi-objective optimization algorithm (such as multi-objective particle swarm optimization, genetic algorithm, etc.) to comprehensively optimize multiple objectives (such as mining costs, resource recovery rate, environmental impact, etc.) in order to provide the best decision support for mineral deposit development.

[0166] Specifically, the following is the intelligent optimization process of the detection results of a copper deposit, combining actual data and optimization strategies:

[0167] Data denoising and preprocessing: In a copper mining area, the exploration team collected gravity data, magnetic data, electromagnetic data, and seismic wave data. Preliminary analysis found that there was a lot of noise in the original data, especially in the deep area of ​​the ore deposit, where the noise was more serious. The team used the Kalman filter method to process the data to remove surface and underground noise. Through this process, the outline of the ore body boundary in the denoised data was clearer, and the data quality was significantly improved.

[0168] For example, after filtering the outliers in the gravity data, the gravity anomaly in the western mining area is more obvious, suggesting that the ore body may extend deep underground. The noise in the magnetic data is also effectively removed, making the deep structure of the ore body clearer and facilitating subsequent potential prediction.

[0169] Feature extraction and pattern recognition: The denoised data enters the feature extraction stage. The team used the principal component analysis (PCA) method to extract the most representative geological features from the detection data in multiple dimensions and input these features into the convolutional neural network (CNN) model. The CNN model automatically identifies the distribution characteristics of the ore body, including the boundary of the ore body, grade changes and other information through multi-layer convolution and pooling layer processing.

[0170] Through the training of the CNN model, an accurate ore body distribution map was finally obtained, which shows the spatial distribution of the main ore bodies of the deposit underground, providing important support for the subsequent ore body resource estimation. The model predicts that the copper ore body distribution area of ​​the mining area is about 2000 meters by 1500 meters, and the ore grade is between 0.8% and 1.5%.

[0171] 3D model optimization: After the 3D ore deposit model was constructed, the team optimized the model using a genetic algorithm. The genetic algorithm simulates the selection process in nature and gradually improves the accuracy of the model through multiple generations of evolution. During the optimization process, the algorithm continuously adjusts the boundaries of the ore body, the distribution of ore grades, and the volume of the ore body, thereby accurately estimating the reserves of the ore deposit.

[0172] The optimized 3D model shows that the total resource of the deposit is 4.5 million tons, with a total copper content of 75,000 tons. Compared with the initial model, the optimized model is more accurate in terms of ore body morphology and grade distribution, which is helpful for subsequent resource estimation and mining planning.

[0173] Intelligent optimization of mining strategy: In the optimization of mining strategy, the team applied the deep reinforcement learning (DRL) algorithm to intelligently optimize the mining sequence and path planning of the ore deposit based on the resource distribution, ore grade, ore body morphology and other parameters of the ore deposit. According to this optimization strategy, open-pit mining was selected for the western ore body area, while underground mining was adopted for the eastern ore body.

[0174] The optimization of mining paths greatly reduces transportation costs and improves resource recovery rates. For example, the optimized mining path reduces the transportation distance by 20%, thereby saving about 10% of transportation costs. At the same time, the reinforcement learning algorithm also optimizes the mining sequence of the ore body, giving priority to mining high-grade areas, further improving resource recovery rates.

[0175] Integrated Decision Support System: Finally, the team combined all the optimization results and established an integrated decision support system, which can monitor the exploration progress, resource estimation, economic benefit analysis and mining strategy of the deposit in real time. Through big data analysis and artificial intelligence technology, the system provides a global optimization solution to ensure the maximum economic benefit and minimum environmental impact of the deposit development.

[0176] In summary, a silver-copper ore prospecting prediction method provided by an embodiment of the present invention first uses gravity detection, magnetic detection, electromagnetic detection and seismic wave detection equipment to collect geophysical data of the target mining area, obtains comprehensive original geological information, and then optimizes the data through denoising, correction and inversion processing technology, removes interference signals and constructs a physical model reflecting the distribution and boundary characteristics of the ore body, and then constructs a three-dimensional geological model of the ore deposit based on the inversion result, accurately reflects the position relationship of the ore body in space and the change of resource grade, and then uses the three-dimensional model in combination with the resource reserve estimation method to predict and evaluate the potential of the ore deposit, identify high-value areas and analyze economic feasibility, and finally optimizes the prediction results based on the intelligent optimization algorithm, formulates an efficient mining strategy, maximizes resource utilization efficiency and reduces environmental impact. Based on this, the present invention overcomes the defects of insufficient data accuracy, low model construction complexity and lack of optimization ability in the existing ore prospecting method, and realizes the high precision, high efficiency and intelligence of silver-copper ore deposit prediction, which provides a scientific basis for mineral resource exploration and development.

[0177] Embodiment 2

[0178] In this embodiment, a computer terminal device is provided, including:

[0179] one or more processors;

[0180] A memory, coupled to the processor, for storing one or more programs;

[0181] When the one or more programs are executed by the one or more processors, the one or more processors implement the methods in the above embodiments.

[0182] In this embodiment, a computer-readable storage medium is further provided, on which a computer program is stored. When the computer program is executed by a processor, the method in the above embodiment is implemented.

[0183] In this embodiment, an electronic device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the method in the above embodiment.

[0184] The above program can be run in the processor, or it can also be stored in the memory (or computer-readable medium), which includes permanent and non-permanent, removable and non-removable media. Information storage can be achieved by any method or technology. Information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0185] These computer programs can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps of the functions specified in one or more blocks can be implemented by different modules corresponding to different steps.

[0186] like Figure 1 As shown, this embodiment provides such a device or system. The system is called a silver-copper deposit prospecting prediction system, including:

[0187] The geological data acquisition module is used to collect data from the target mining area and obtain the original geological data reflecting the distribution and properties of the ore body;

[0188] A data processing and inversion module, used for preprocessing and inversion processing the obtained original geological data;

[0189] A 3D model building module is used to build a 3D geological model of the deposit through 3D modeling technology based on the inversion processed data;

[0190] Potential assessment module, used to estimate the resource reserves of mineral deposits based on 3D geological models.

[0191] The system or device is used to implement the functions of the method in the above-mentioned embodiment. Each module in the system or device corresponds to each step in the method, which has been explained in the method and will not be repeated here.

[0192] Through the above implementation methods, the defects of insufficient data accuracy, low model construction complexity and lack of optimization ability of prediction results in the mineral deposit prospecting methods in the related technologies are solved, thereby achieving high-precision, high-efficiency and intelligent prediction of silver-copper deposits, and providing a scientific basis for the exploration and development of mineral resources.

[0193] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for prospecting and predicting silver-copper deposits, characterized in that: The following steps are involved: Collect data from the target mining area to obtain original geological data reflecting the distribution and properties of the ore body; Performing preprocessing and inversion processing on the obtained original geological data; Based on the inversion processed data, a 3D geological model of the deposit is constructed through 3D modeling technology; Based on the three-dimensional geological model, the resource reserves of the mineral deposit are estimated.

2. The method according to claim 1, characterized in that The original geological data includes gravity data, magnetic data, electromagnetic data and seismic wave data.

3. The method according to claim 1, characterized in that: The inversion process includes: determining an inversion model according to different types of data, selecting an inversion algorithm, and finally optimizing the inversion result to obtain inversion processed data, wherein the inversion model includes a linear inversion model and a magnetic susceptibility model.

4. The method according to claim 1, characterized in that: Based on the inversion processed data, the process of constructing a 3D geological model of the deposit through 3D modeling technology includes: Determine the spatial framework and resolution of the 3D geological model and integrate the inversion processed data; After the integration is completed, the boundaries of the ore deposit are optimized based on the second geological data, which includes drilling data and geological profiles.

5. The method according to claim 1, characterized in that Based on the three-dimensional geological model, the process of estimating the resource reserves of the mineral deposit includes: using the Monte Carlo simulation method to make multi-scenario predictions of ore reserves and evaluating the resource distribution in different grade areas.

6. The method according to claim 5, characterized in that Based on the 3D geological model, the resource reserve estimation of the deposit also includes the identification of potential areas. The process includes: Acquiring spatial information, geological profile information and ore body boundaries in the three-dimensional geological model; Identify potential areas based on spatial information, geological profile information and ore body boundaries, as well as geological characteristics, detection data and historical exploration results.

7. A silver-copper deposit prospecting prediction system, characterized in that: The system comprises: The geological data acquisition module is used to collect data from the target mining area and obtain the original geological data reflecting the distribution and properties of the ore body; A data processing and inversion module, used for preprocessing and inversion processing the obtained original geological data; A 3D model building module is used to build a 3D geological model of the deposit through 3D modeling technology based on the inversion processed data; Potential assessment module, used to estimate the resource reserves of mineral deposits based on 3D geological models.

8. The system according to claim 7, characterized in that In the data processing and inversion module, the inversion process includes: determining the inversion model according to different types of data, selecting the inversion algorithm, and finally optimizing the inversion result to obtain the inversion processed data, wherein the inversion model includes a linear inversion model and a magnetic susceptibility model.

9. A computer terminal device, characterized in that: include: one or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the various steps of the silver-copper deposit prospecting prediction method as described in any one of claims 1-6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, each step of the silver-copper deposit prospecting and prediction method as described in any one of claims 1 to 6 is implemented.

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