Mine simulation analysis method for mineral exploration

Abstract

The invention relates to the field of mineral exploration, in particular to a mine simulation analysis method for mineral exploration. The method comprises the following steps: establishing a mineral resource concentration distribution model by combining geological exploration data, ore body distribution characteristics and a spatial effect, and predicting mineral resource concentration; based on the mineral resource concentration, a self-adaptive adjustment mechanism is introduced, and the adjustment amount of the mineral resource concentration is calculated; based on the mineral resource concentration adjustment amount, adjusting the mineral resource concentration, and introducing a dynamic adjustment factor proportionality coefficient to predict mineral resource reserves; and updating the dynamic adjustment factor proportionality coefficient, and continuing to predict the mineral resource reserves. The problems that a traditional mineral resource concentration estimation method neglects the spatial variability of mining area resource concentration, and accurate prediction is difficult to provide are solved; a self-adaptive adjustment mechanism is lacked, so that the accuracy of mineral resource concentration and reserve prediction is influenced; and the dynamic fluctuation of the mineral resource concentration is not fully considered, so that the resource reserve estimation result is lack of reliability.

Description

Technical Field

[0001] The present invention relates to the field of mineral exploration, and in particular to a mine simulation analysis method for mineral exploration. Background Art

[0002] The exploration and assessment of mineral resources have always been core tasks in the mining industry, especially in the early stages of a mining operation. Accurately predicting mineral resource concentrations and assessing mineral reserves are key factors in determining the feasibility of mineral development and the cost of mining. With the continuous advancement of geological exploration technology and the increasing diversity of geological data collection methods, the exploration, analysis, and assessment of mineral resources are gradually becoming data-driven and intelligent. Methods for estimating mineral resource concentrations in mining areas no longer rely solely on traditional ore body characteristics and expert experience. Instead, they incorporate geological exploration data and other methods, improving the accuracy of mining resource assessments and the ability to predict spatial distribution. Furthermore, the introduction of dynamic monitoring and real-time data updating technologies allows for the timely adjustment of mineral resource concentration predictions based on new geological exploration data to address complex and uncertain geological environments.

[0003] However, these technologies still face some key challenges that need to be addressed: First, they ignore the spatial variability of resource concentrations within mining areas, resulting in a lack of precision and representativeness in mineral resource concentration estimates for different regions. This is particularly true in the early stages of mining exploration, when geological characteristics and ore body distribution are unclear and traditional methods struggle to provide accurate predictions. Second, they lack an adaptive adjustment mechanism, making it impossible to adjust the mineral resource concentration distribution model in real time. This results in new geological exploration data failing to effectively influence the prediction results, thereby affecting the accuracy of mineral resource concentration and reserve predictions. Furthermore, they fail to fully account for the dynamic fluctuations in mineral resource concentrations and incorporate uncertainty factors, resulting in a lack of reliability in mineral resource reserve estimates. Therefore, new algorithms and processing technologies are urgently needed to improve these approaches. Summary of the Invention

[0004] The present invention provides a mine simulation analysis method for mineral exploration to solve the problems that traditional mineral resource concentration estimation methods ignore the spatial variability of resource concentration in mining areas, resulting in a lack of accuracy and representativeness in the mineral resource concentration estimation results of different regions. In particular, in the initial exploration stage of a mining area, the geological characteristics and ore body distribution are unclear, and traditional methods find it difficult to provide accurate predictions; the lack of an adaptive adjustment mechanism makes it impossible to adjust the mineral resource concentration distribution model in real time, resulting in the new geological exploration data failing to effectively affect the prediction results, thereby affecting the accuracy of mineral resource concentration and reserve predictions; the failure to fully consider the dynamic fluctuations of mineral resource concentration and fail to incorporate uncertainty factors, resulting in a lack of reliability in the resource reserve estimation results.

[0005] The present invention provides a mine simulation analysis method for mineral exploration, which specifically includes the following technical solutions: A mine simulation analysis method for mineral exploration comprises the following steps: S1: Establish a mineral resource concentration distribution model based on geological exploration data, ore body distribution characteristics and spatial effects; and predict mineral resource concentration based on the mineral resource concentration distribution model. S2: Based on the mineral resource concentration, an adaptive adjustment mechanism is introduced to calculate the mineral resource concentration adjustment amount; based on the mineral resource concentration adjustment amount, the mineral resource concentration is adjusted to obtain the adjusted mineral resource concentration; S3: Based on the adjusted mineral resource concentration, the dynamic adjustment factor proportional coefficient is introduced to predict the mineral resource reserves; based on the mineral resource concentration adjustment amount, the dynamic adjustment factor proportional coefficient is updated, and the updated dynamic adjustment factor proportional coefficient is applied to the next mineral resource reserve prediction.

[0006] Preferably, the S1 specifically includes: The mineral resource concentration distribution model divides the mineral resource concentration into two parts: expected resource concentration and random fluctuation. It combines spatial distance, weighting coefficient and standard normal random variable to predict the mineral resource concentration of each area. The mineral resource concentration calculation formula is as follows: , in, It is The concentration of mineral resources in each region; is the regional index variable within the mining area; It is Expected resource concentration in each region; It is The weighting coefficient of each region reflects the The importance of the region; It is an exponential operation; It is The spatial distance between each area and the central area of ​​the mining area; It is The standard deviation of mineral resource concentration in each region; It is The standard normal random variable of the mineral resource concentration in the region is represented by Random fluctuation factors in a region.

[0007] Preferably, the S2 specifically includes: In the process of implementing the adaptive adjustment mechanism, the adjustment amount of mineral resource concentration is obtained by analyzing the error between the actual mineral resource concentration and the predicted mineral resource concentration.

[0008] Preferably, the S2 specifically includes: A nonlinear adjustment mechanism is introduced to combine the mineral resource concentration adjustment amount with the mineral resource concentration, and a weighted adjustment is performed through a logarithmic function to obtain the adjusted mineral resource concentration.

[0009] Preferably, the S3 specifically includes: Based on the adjusted mineral resource concentration, combined with spatial distribution information and spatial scale, and introducing the dynamic adjustment factor proportional coefficient and spatial attenuation coefficient, the mineral resource reserves are predicted; the mineral resource reserve prediction formula is as follows: , in, It is Mineral resource reserves in each region; is the regional index variable within the mining area; It is the spatial scale of a region; is the total number of regions; is the regional index variable within the mining area, which can be compared with The values ​​are the same; It is The weighting coefficient of the adjusted mineral resource concentration in each region; It is Adjusted Mineral Resource concentrations for each region; It is Region and The spatial distance between two regions is used to represent the spatial distribution information between the two regions; It is The standard deviation of mineral resource concentration in each region; It is Dynamic adjustment factor proportional coefficient of the mineral resource concentration adjustment amount of each region; is the spatial attenuation coefficient; It is The amount of adjustment for the mineral resource concentration in a region.

[0010] Preferably, the S3 specifically includes: Based on the adjustment amount of mineral resource concentration, an adjustment coefficient is introduced to update the dynamic adjustment factor proportional coefficient.

[0011] Preferably, the S3 specifically includes: Integrate the mineral resource reserves of each area of ​​the mining area, and display the resource distribution within the mining area through visualization means to intuitively understand the concentrated areas and distribution characteristics of the mining area resources.

[0012] The beneficial effects of the technical solution of the present invention are: 1. The present invention uses a spatial-random fluctuation resource concentration estimation method, combined with geological exploration data, ore body distribution characteristics and expert experience, to establish a mineral resource concentration distribution model for different areas within the mining area, effectively improving the prediction accuracy of mineral resource concentration. The mineral resource concentration is decomposed into two parts: expected resource concentration and random fluctuation. It fully considers geological uncertainty and natural fluctuation factors, so that a more accurate prediction of mineral resource concentration can be obtained in the initial exploration stage of the mining area, avoiding the neglect of the spatial variability of mineral resource concentration in traditional methods.

[0013] 2. The present invention introduces an adaptive adjustment mechanism, which can optimize the mineral resource concentration distribution model in real time as the geological exploration data is updated. By analyzing the error between the actual mineral resource concentration and the predicted mineral resource concentration, the mineral resource concentration is adjusted, reducing the defect of the traditional method that cannot effectively adjust the prediction error, and can improve the accuracy of mineral resource assessment. It can also adapt to changes in geological exploration data at different stages, ensuring that the predicted mineral resource concentration is always at a high level of accuracy.

[0014] 3. By introducing the dynamic adjustment factor proportional coefficient and the spatial attenuation coefficient, the present invention can fully consider the spatial relationship between regions and the fluctuation of resource concentration when predicting mineral resource reserves, further optimize the estimation of mineral resource reserves, and effectively solve the problem of inaccurate mineral resource reserve prediction under dynamic changing conditions by traditional methods, thereby improving the accuracy and reliability of mineral resource reserve prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flow chart of a mine simulation analysis method for mineral exploration according to the present invention. DETAILED DESCRIPTION

[0016] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0017] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0018] The following describes in detail a specific scheme of a mine simulation analysis method for mineral exploration provided by the present invention with reference to the accompanying drawings.

[0019] Refer to the attached Figure 1, which shows a flow chart of a mine simulation analysis method for mineral exploration provided by one embodiment of the present invention, the method comprising the following steps: S1: Establish a mineral resource concentration distribution model based on geological exploration data, ore body distribution characteristics and spatial effects; and predict mineral resource concentration based on the mineral resource concentration distribution model. In the early stages of mineral resource exploration, a mineral resource concentration distribution model is established for each region using the spatial-random fluctuation resource concentration estimation method. Expert experience is used to determine the appropriate spatial scale and regional division criteria based on the actual conditions of the mining area, such as geological exploration data, ore body distribution characteristics, topography, and other factors. This is used to divide the regions to ensure that the resource concentration in each region is representative and reliable. The spatial-random fluctuation resource concentration estimation method is a mineral resource concentration prediction method that combines geological exploration data, ore body distribution characteristics and spatial effects; by constructing a mineral resource concentration distribution model, the mineral resource concentration of each area is predicted; The mineral resource concentration distribution model divides mineral resource concentration into two parts: expected resource concentration and random fluctuation. The expected resource concentration reflects the theoretical average resource concentration estimated based on geological characteristics and expert experience, while the random fluctuation takes into account the influence of geological uncertainty and natural fluctuations. Combining spatial distance, weighting coefficients and standard normal random variables, it can effectively describe the spatial variability and random fluctuations of mineral resource concentration between different regions, thereby providing more accurate and reliable mineral resource concentration predictions, which is particularly suitable for resource assessment in the early exploration stage of mining areas. The specific calculation formula for mineral resource concentration is as follows: , in, It is The mineral resource concentration of a region reflects the richness of mineral resources in the region and is a predicted value; is the regional index variable within the mining area; It is The expected resource concentration of a region is estimated based on geological exploration data, mineral distribution characteristics and expert experience in the specific implementation scenario. It represents the theoretical average value of the mineral resource concentration in the region taking into account the geological characteristics and distribution of mineral deposits, that is, the expected mineral resource concentration in the absence of interference from random fluctuations; It is The weighting coefficient of each region reflects the The importance of each area is determined based on the expert experience method based on the geological characteristics, geological exploration data, ore body type and other factors in the specific implementation scenario; It is an exponential operation; It is The spatial distance between each area and the central area of ​​the mining area is used to reflect the impact of spatial distribution on the concentration of mineral resources. The central area of ​​the mining area is determined by calculating the geometric center of the known exploration points; It is The standard deviation of the mineral resource concentration in each region indicates the The degree of fluctuation of mineral resource concentration in a region is obtained by statistical analysis of existing geological exploration data (such as drilling data, sample analysis results, etc.); It is The standard normal random variable of the mineral resource concentration in the region is generated by computer, with an expected value of 0 and a standard deviation of 1, indicating that The random fluctuation factor of each region is used to simulate the random changes in mineral resource concentration due to natural factors, unknown geological factors or uncertainties.

[0020] S2: Based on the mineral resource concentration, an adaptive adjustment mechanism is introduced to calculate the mineral resource concentration adjustment amount; based on the mineral resource concentration adjustment amount, the mineral resource concentration is adjusted to obtain the adjusted mineral resource concentration; Based on mineral resource concentration, an adaptive adjustment mechanism is introduced to further optimize mineral resource concentration. The adaptive adjustment mechanism can adjust the mineral resource concentration distribution model in real time as geological exploration data is continuously updated. By analyzing the error between the actual mineral resource concentration and the predicted mineral resource concentration, the mineral resource concentration is adjusted to reduce the prediction error and gradually improve the prediction accuracy. The calculation formula for the mineral resource concentration adjustment is: , in, It is The mineral resource concentration adjustment amount of each region represents the adjustment value calculated by the adaptive adjustment mechanism, which is intended to adjust the mineral resource concentration distribution model in the first The mineral resource concentration of each region can be estimated to reduce prediction errors and improve the accuracy of mineral resource concentration prediction; represents the regional index variable within the mining area; It is the adjustment coefficient, which is used to control the adjustment amount of mineral resource concentration and is set according to the specific implementation scenario; Indicates the The actual mineral resource concentration in a region is derived from actual geological exploration data, such as drilling data and sample analysis; It is The concentration of mineral resources in each region; It is The prediction error of each region; Is the balance coefficient, which is used to further adjust the mineral resource concentration adjustment amount to control the nonlinear relationship between the predicted mineral resource concentration and the prediction error. It is set according to the specific implementation scenario; applying the mineral resource concentration adjustment amount to the mineral resource concentration distribution model to adjust the mineral resource concentration to obtain an adjusted mineral resource concentration; The adjusted mineral resource concentration calculation formula is as follows: , in, It is Adjusted Mineral Resource concentrations for each region; It is The concentration of mineral resources in the region, namely Mineral resource concentration in each region before adjustment; It is The amount of mineral resource concentration adjustment for each region; is the balance coefficient, which is used to control the amplitude of the logarithmic adjustment to ensure that the adjusted mineral resource concentration can reflect the influence of geological exploration data while avoiding over-adjustment. The specific value of the balance coefficient is set according to expert experience. The mineral resource concentration adjustment formula introduces a nonlinear adjustment mechanism, combining the mineral resource concentration adjustment amount with the mineral resource concentration and performing weighted adjustment through a logarithmic function. The nonlinear adjustment mechanism can dynamically adjust the mineral resource concentration according to different levels of mineral resource concentration, avoiding simple linear addition and subtraction. The adjusted mineral resource concentration can more accurately reflect the distribution of mineral resources within the mining area, reducing the discrepancy between predicted and actual mineral resource concentrations. As geological exploration data is continuously updated, the mineral resource concentration distribution model can be continuously optimized. Each time new geological exploration data is introduced, subsequent adjustments are made based on the adjusted mineral resource concentration through an adaptive adjustment mechanism, allowing the mineral resource concentration distribution model to continuously adapt to new geological information and improve the accuracy of mineral resource assessments in the mining area.

[0021] S3: Based on the adjusted mineral resource concentration, the dynamic adjustment factor proportional coefficient is introduced to predict the mineral resource reserves; based on the mineral resource concentration adjustment amount, the dynamic adjustment factor proportional coefficient is updated, and the updated dynamic adjustment factor proportional coefficient is applied to the next mineral resource reserve prediction.

[0022] Based on the adjusted mineral resource concentration, combined with the spatial distribution information and spatial scale, the mineral resource reserves are predicted to obtain the predicted mineral resource reserves value. The spatial distribution information refers to the geographical location relationship between the various regions within the mining area, which is described by the spatial distance between the regions to reflect the distribution characteristics of mineral resources between different regions; the mineral resource reserves are an estimate of the mineral resource reserves in a certain area within the mining area, which is predicted based on the adjusted mineral resource concentration, spatial distribution information and spatial scale; The formula for predicting mineral resource reserves is as follows: , in, It is The mineral resource reserves of a region are predicted values, indicating the Estimated reserves of mineral resources in the region; is the regional index variable within the mining area; It is The spatial scale of the region, The area or volume of each region is set according to the specific implementation scenario; The total number of regions, indicating how many different regions are divided within the mining area, is set according to the specific implementation scenario; is the regional index variable within the mining area, which can be compared with The values ​​are the same; It is The weighted coefficient of the mineral resource concentration after adjustment in each region is used to adjust the Region to The degree of influence on the prediction of mineral resources reserves in a region is set according to the expert experience method based on the geological characteristics, geological exploration data, ore body type and other factors in the specific implementation scenario; It is The adjusted mineral resource concentrations for each region are derived from the mineral resource concentration distribution model; It is Region and The spatial distance between two regions is used to represent the spatial distribution information between two regions. It is based on the geographical coordinates of the mining area, by calculating the geometric center coordinates of each region and the Euclidean distance between the geometric centers of two regions. It is The standard deviation of the mineral resource concentration in each region indicates the The degree of fluctuation in mineral resource concentration in a region; It is The dynamic adjustment factor proportional coefficient of the mineral resource concentration adjustment amount in each region is used to adjust the impact of the mineral resource concentration adjustment amount on the mineral resource reserve prediction, which is obtained through experimental tuning; is the spatial attenuation coefficient, which represents the effect of spatial distance on the adjusted mineral resource concentration; It is The amount of adjustment for the mineral resource concentration in a region.

[0023] The dynamic adjustment factor ratio coefficient will be updated in real time based on the current actual geological exploration data, and the updated dynamic adjustment factor ratio coefficient will be applied to the next mineral resource reserve forecast. By continuously optimizing and adjusting the dynamic adjustment factor ratio coefficient, it can better adapt to changes in geological exploration data, thereby improving the accuracy and reliability of mineral resource reserve forecasts; The update formula of the dynamic adjustment factor proportional coefficient is as follows: , in, It is The updated dynamic adjustment factor proportional coefficient of the mineral resource concentration adjustment amount of each region; It is the dynamic adjustment factor proportional coefficient of the mineral resource concentration adjustment amount in the current mineral resource reserve prediction formula; and are adjustment coefficients, used to control The degree of impact and The degree of influence is set according to the expert experience method; is a sign function.

[0024] Finally, the mineral resource reserves of various areas in the mining area are integrated, and the resource distribution within the mining area is displayed through visualization means (such as GIS system or 3D visualization software), which helps to intuitively understand the concentration areas and distribution characteristics of mining resources, facilitates the subsequent simulation of the impact of different mining plans on mining resources, optimizes the resource utilization strategy of the mining area, and determines the most appropriate mining area and mining method, thereby ensuring the efficient utilization and sustainable development of mineral resources.

[0025] In summary, a mine simulation analysis method for mineral exploration has been completed.

[0026] The order in which the embodiments of the invention are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0027] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0028] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A mine simulation analysis method for mineral exploration, characterized in that: The following steps are involved: S1: Establish a mineral resource concentration distribution model based on geological exploration data, ore body distribution characteristics and spatial effects; predict mineral resource concentration based on the mineral resource concentration distribution model; S2: Based on the mineral resource concentration, an adaptive adjustment mechanism is introduced to calculate the mineral resource concentration adjustment amount; based on the mineral resource concentration adjustment amount, the mineral resource concentration is adjusted to obtain the adjusted mineral resource concentration; S3: Based on the adjusted mineral resource concentration, the dynamic adjustment factor proportional coefficient is introduced to predict the mineral resource reserves; based on the mineral resource concentration adjustment amount, the dynamic adjustment factor proportional coefficient is updated, and the updated dynamic adjustment factor proportional coefficient is applied to the next mineral resource reserve prediction.

2. A mine simulation analysis method for mineral exploration according to claim 1, characterized in that: The S1 specifically includes: The mineral resource concentration distribution model divides the mineral resource concentration into two parts: expected resource concentration and random fluctuation. It combines spatial distance, weighting coefficient and standard normal random variable to predict the mineral resource concentration of each area. The mineral resource concentration calculation formula is as follows: ， in, It is The concentration of mineral resources in each region; is the regional index variable within the mining area; It is Expected resource concentration in each region; It is The weighting coefficient of the region reflects the The importance of the region; It is an index operation; It is The spatial distance between each area and the central area of ​​the mining area; It is The standard deviation of mineral resource concentration in each region; It is The standard normal random variable of the mineral resource concentration in the region is Random fluctuation factors in a region.

3. A mine simulation analysis method for mineral exploration according to claim 1, characterized in that: The S2 specifically includes: In the process of implementing the adaptive adjustment mechanism, the adjustment amount of mineral resource concentration is obtained by analyzing the error between the actual mineral resource concentration and the predicted mineral resource concentration.

4. A mine simulation analysis method for mineral exploration according to claim 3, characterized in that: The S2 specifically includes: A nonlinear adjustment mechanism is introduced to combine the mineral resource concentration adjustment amount with the mineral resource concentration, and a weighted adjustment is performed through a logarithmic function to obtain the adjusted mineral resource concentration.

5. A mine simulation analysis method for mineral exploration according to claim 1, characterized in that: The S3 specifically includes: Based on the adjusted mineral resource concentration, combined with spatial distribution information and spatial scale, and introducing the dynamic adjustment factor proportional coefficient and spatial attenuation coefficient, the mineral resource reserves are predicted; the mineral resource reserve prediction formula is as follows: ， in, It is Mineral resource reserves in each region; is the regional index variable within the mining area; It is The spatial scale of a region; is the total number of regions; is the regional index variable within the mining area and can be compared with The values ​​are the same; It is The weighting factor of the adjusted mineral resource concentration in each region; It is Adjusted Mineral Resource concentrations for each region; It is Region and The spatial distance between two regions is used to represent the spatial distribution information between two regions; It is The standard deviation of mineral resource concentration in each region; It is Dynamic adjustment factor ratio coefficient of mineral resource concentration adjustment in each region; is the spatial attenuation coefficient; It is The amount of adjustment for the mineral resource concentration in a region.

6. A mine simulation analysis method for mineral exploration according to claim 5, characterized in that: The S3 specifically includes: Based on the adjustment amount of mineral resource concentration, an adjustment coefficient is introduced to update the dynamic adjustment factor proportional coefficient.

7. A mine simulation analysis method for mineral exploration according to claim 6, characterized in that: The S3 specifically includes: Integrate the mineral resource reserves of each area in the mining area, and display the resource distribution in the mining area through visualization means to intuitively understand the concentrated areas and distribution characteristics of the mining area resources.

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