A mine simulation analysis method for mineral exploration

Abstract

The present invention relates to the field of mineral exploration, and particularly to a mine simulation analysis method for mineral exploration. The content includes: combining geological exploration data, ore body distribution characteristics and spatial effects to establish a mineral resource concentration distribution model to predict the mineral resource concentration; introducing an adaptive adjustment mechanism based on the mineral resource concentration to calculate the mineral resource concentration adjustment amount; adjusting the mineral resource concentration based on the mineral resource concentration adjustment amount, and introducing a dynamic adjustment factor proportionality coefficient to predict the mineral resource reserve; updating the dynamic adjustment factor proportionality coefficient and continuing to predict the mineral resource reserve. It solves the problems that the traditional mineral resource concentration estimation method ignores the spatial variability of the resource concentration in the mining area and is difficult to provide accurate prediction; lacks an adaptive adjustment mechanism, affecting the accuracy of the prediction of mineral resource concentration and reserve; and fails to fully consider the dynamic fluctuation of the mineral resource concentration, resulting in the lack of reliability of the resource reserve estimation result.

Description

Technical Field

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

[0002] The exploration and evaluation of mineral resources have always been the core tasks in the mining field. Especially in the initial stage of a mining area, how to accurately predict the concentration of mineral resources and evaluate the mineral reserves is a key factor determining the feasibility of mineral development and the mining cost. With the continuous progress of geological exploration technologies, the means of geological data collection have become increasingly rich, and the exploration, analysis, and evaluation of mineral resources have gradually developed towards data-driven and intelligent directions. The estimation methods of resource concentration in mining areas no longer solely rely on traditional ore body characteristics and expert experience, but by combining means such as geological exploration data, the accuracy of resource evaluation in mining areas and the prediction ability of spatial distribution have been improved. At the same time, the introduction of dynamic monitoring and real-time data update technologies enables the prediction of mineral resource concentration to be adjusted in a timely manner according to new geological exploration data to cope with complex and uncertain geological environments.

[0003] However, there are still some key problems to be solved in the above technologies: First, the spatial variability of resource concentration in mining areas is ignored, resulting in the lack of accuracy and representativeness of the estimated results of mineral resource concentration in different regions. Especially in the initial exploration stage of a mining area, where geological characteristics and ore body distributions are not clear, traditional methods are difficult to provide accurate predictions. Second, there is a lack of an adaptive adjustment mechanism, and the mineral resource concentration distribution model cannot be adjusted in real time, resulting in new geological exploration data failing to effectively affect the prediction results, thus affecting the accuracy of mineral resource concentration and reserve predictions. In addition, the dynamic fluctuations of mineral resource concentration are not fully considered, and uncertainty factors are not incorporated, resulting in the lack of reliability of the estimated results of mineral resource reserves. Therefore, there is an urgent need to improve through a new algorithm and processing technology. Summary of the Invention

[0004] The present invention provides a mine simulation analysis method for mineral exploration to solve the problems that the traditional estimation methods of mineral resource concentration ignore the spatial variability of resource concentration in mining areas, resulting in the lack of accuracy and representativeness of the estimated results of mineral resource concentration in different regions. Especially in the initial exploration stage of a mining area, where geological characteristics and ore body distributions are not clear, traditional methods are difficult to provide accurate predictions; there is a lack of an adaptive adjustment mechanism, and the mineral resource concentration distribution model cannot be adjusted in real time, resulting in new geological exploration data failing to effectively affect the prediction results, thus affecting the accuracy of mineral resource concentration and reserve predictions; the dynamic fluctuations of mineral resource concentration are not fully considered, and uncertainty factors are not incorporated, resulting in the lack of reliability of the estimated results of resource reserves.

[0005] A mine simulation analysis method for mineral exploration according to the present invention specifically includes the following technical solutions:

[0006] A mine simulation analysis method for mineral exploration, comprising the following steps:

[0007] S1: Combine geological exploration data, ore body distribution characteristics and spatial effects to establish a mineral resource concentration distribution model; Based on the mineral resource concentration distribution model, predict the mineral resource concentration;

[0008] S2: Based on the mineral resource concentration, introduce an adaptive adjustment mechanism to calculate the mineral resource concentration adjustment amount; Based on the mineral resource concentration adjustment amount, adjust the mineral resource concentration to obtain the adjusted mineral resource concentration;

[0009] S3: Based on the adjusted mineral resource concentration, introduce a dynamic adjustment factor proportionality coefficient to predict the mineral resource reserves; Based on the mineral resource concentration adjustment amount, update the dynamic adjustment factor proportionality coefficient and apply the updated dynamic adjustment factor proportionality coefficient to the next mineral resource reserve prediction.

[0010] Preferably, the S1 specifically includes:

[0011] The mineral resource concentration distribution model divides the mineral resource concentration into an expected resource concentration and a random fluctuation. Combining the spatial distance, weighting coefficient and standard normal random variable, predict the mineral resource concentration of each region; The mineral resource concentration calculation formula is as follows:

[0012] ,

[0013] wherein, is the mineral resource concentration of the th region; is the regional index variable in the mining area; is the expected resource concentration of the th region; is the weighting coefficient of the th region, reflecting the importance of the th region; is the exponential operation; is the th region's spatial distance from the central region of the mining area; is the th region's standard deviation of the mineral resource concentration; is the th region's standard normal random variable of the mineral resource concentration, representing the random fluctuation factor of the th region.

[0014] Preferably, the S2 specifically includes:

[0015] In the process of implementing the adaptive adjustment mechanism, the adjustment amount of the mineral resource concentration is obtained by analyzing the error between the actual mineral resource concentration and the predicted mineral resource concentration.

[0016] Preferably, the S2 specifically includes:

[0017] Introduce a non-linear adjustment mechanism, combine the adjustment amount of the mineral resource concentration with the mineral resource concentration, and perform weighted adjustment through a logarithmic function to obtain the adjusted mineral resource concentration.

[0018] Preferably, the S3 specifically includes:

[0019] Based on the adjusted mineral resource concentration, combine the spatial distribution information and the spatial scale, and introduce the dynamic adjustment factor proportional coefficient and the spatial attenuation coefficient to predict the mineral resource reserves; the mineral resource reserve prediction formula is as follows:

[0020] ,

[0021] Where, is the mineral resource reserve of the th area; is the regional index variable in the mining area; is the th area's spatial scale; is the total number of areas; is the regional index variable in the mining area, which can be the same as in value; is the weighting coefficient of the adjusted mineral resource concentration of the th area; is the th area's adjusted mineral resource concentration; is the th area and the th area's spatial distance, which is used to represent the spatial distribution information between the two areas; is the th area's standard deviation of the mineral resource concentration; is the th area's dynamic adjustment factor proportional coefficient of the mineral resource concentration adjustment amount; is the spatial attenuation coefficient; is the th area's mineral resource concentration adjustment amount.

[0022] Preferably, the S3 specifically includes:

[0023] Based on the mineral resource concentration adjustment amount, introduce an adjustment coefficient to update the dynamic adjustment factor proportional coefficient.

[0024] Preferably, step S3 specifically includes:

[0025] Integrate the mineral resource reserves in each area of 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 resources in the mining area.

[0026] The beneficial effects of the technical solution of the present invention are as follows:

[0027] 1. Through the spatial-stochastic fluctuation resource concentration estimation method, combined with geological exploration data, ore body distribution characteristics and expert experience, the present invention establishes a mineral resource concentration distribution model for different areas in the mining area, effectively improving the prediction accuracy of mineral resource concentration. The mineral resource concentration is decomposed into the expected resource concentration and stochastic fluctuation, fully considering 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.

[0028] 2. The present invention introduces an adaptive adjustment mechanism, which can optimize the mineral resource concentration distribution model in real time with the update of geological exploration data. 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 that the prediction error cannot be effectively adjusted in traditional methods, improving the accuracy of mineral resource assessment, and being able to adapt to the changes in geological exploration data at different stages to ensure that the predicted mineral resource concentration is always at a high-precision level.

[0029] 3. By introducing the dynamic adjustment factor proportional coefficient and spatial attenuation coefficient, the present invention can fully consider the spatial relationship between regions and resource concentration fluctuations when predicting mineral resource reserves, further optimizing the estimation of mineral resource reserves, effectively solving the problem of inaccurate prediction of mineral resource reserves in traditional methods under dynamic change conditions, and improving the accuracy and reliability of mineral resource reserve prediction. Brief Description of the Drawings

[0030] Figure 1 It is a flowchart of a mine simulation analysis method for mineral exploration according to the present invention. Detailed Embodiment

[0031] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, 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 a 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 those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0032] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this invention belongs.

[0033] The following specifically describes the specific solution of a mine simulation analysis method for mineral exploration provided by the present invention in conjunction with the accompanying drawings.

[0034] Refer to the attached Figure 1 , which shows a flow chart of a mine simulation analysis method for mineral exploration provided by an embodiment of the present invention. The method includes the following steps:

[0035] S1: Combine geological exploration data, ore body distribution characteristics, and spatial effects to establish a mineral resource concentration distribution model; based on the mineral resource concentration distribution model, predict the mineral resource concentration;

[0036] In the initial stage of mineral resource exploration, a mineral resource concentration distribution model is established for each area through a spatial-stochastic fluctuation resource concentration estimation method. Using expert experience, according to the actual situation of the mining area, such as combining geological exploration data, ore body distribution characteristics, topography, and other factors, determine appropriate spatial scales and regional division criteria to divide the area to ensure that the resource concentration of each area is representative and reliable;

[0037] The spatial-stochastic 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, predict the mineral resource concentration of each area;

[0038] The mineral resource concentration distribution model divides the mineral resource concentration into two parts: expected resource concentration and stochastic fluctuation. Among them, the expected resource concentration reflects the theoretical average resource concentration estimated based on geological characteristics and expert experience method, and the stochastic fluctuation takes into account the influence of geological uncertainty and natural fluctuation. Combining spatial distance, weighting coefficient, and standard normal random variable, it can effectively describe the spatial variability and stochastic fluctuation of mineral resource concentration between different areas, thus providing a more accurate and reliable prediction of mineral resource concentration, which is particularly suitable for resource assessment in the initial exploration stage of the mining area;

[0039] The specific formula for calculating the mineral resource concentration is as follows:

[0040] ,

[0041] Where, is the mineral resource concentration of the th area, reflecting the richness of mineral resources in the area, which is a predicted value; is the regional index variable in the mining area; is the The expected resource concentration of a region, estimated based on geological exploration data, mineral distribution characteristics, and expert experience method in a specific implementation scenario, represents the theoretical average of the mineral resource concentration in the region considering geological characteristics and ore deposit distribution, that is, the expected mineral resource concentration without the interference of random fluctuation factors; is the weighting coefficient of the th region, reflecting the importance degree of the th region, and is set according to expert experience method based on factors such as geological characteristics, geological exploration data, and ore body type in a specific implementation scenario; is the spatial distance between the th region and the central region of the mining area, used to reflect the influence of spatial distribution on the mineral resource concentration, and the central region of the mining area is determined by calculating the geometric center of known exploration points; is the standard deviation of the mineral resource concentration of the th region, indicating the fluctuation degree of the mineral resource concentration of the th region, and is obtained by statistical analysis of existing geological exploration data (such as borehole data, sample analysis results, etc.); is the standard normal random variable of the mineral resource concentration of the th region, generated by computer, with an expected value of 0 and a standard deviation of 1, representing the random fluctuation factor of the

[0042] S2: Based on the mineral resource concentration, introduce an adaptive adjustment mechanism to calculate the mineral resource concentration adjustment amount; based on the mineral resource concentration adjustment amount, adjust the mineral resource concentration to obtain the adjusted mineral resource concentration;

[0043] Based on the mineral resource concentration, introduce an adaptive adjustment mechanism to further optimize the mineral resource concentration. The adaptive adjustment mechanism can continuously adjust the mineral resource concentration distribution model in real time as the geological exploration data is continuously updated. By analyzing the error between the actual mineral resource concentration and the predicted mineral resource concentration, adjust the mineral resource concentration to reduce the prediction error and gradually improve the prediction accuracy;

[0044] The calculation formula for the mineral resource concentration adjustment amount is:

[0045] ,

[0046] where, is the The adjustment amount of mineral resource concentration in a region, representing the adjusted value calculated through the adaptive adjustment mechanism, aims to adjust the mineral resource concentration in the th region of the mineral resource concentration distribution model to reduce the prediction error and improve the accuracy of mineral resource concentration prediction; Represents the regional index variable within the mining area; Is the adjustment coefficient used to control the magnitude of the adjustment amount of mineral resource concentration, which is set according to the specific implementation scenario; Represents the actual mineral resource concentration in the th region, which is sourced from actual geological exploration data, such as borehole data, sample analysis, etc.; Is the mineral resource concentration in the th region; Is the prediction error in the th region; Is the balance coefficient used to further adjust the adjustment amount of mineral resource concentration to control the non-linear relationship between the predicted mineral resource concentration and the prediction error, which is set according to the specific implementation scenario;

[0047] Apply the adjustment amount of mineral resource concentration to the mineral resource concentration distribution model to adjust the mineral resource concentration and obtain the adjusted mineral resource concentration;

[0048] The formula for the adjusted mineral resource concentration is as follows:

[0049] ,

[0050] Among them, Is the adjusted mineral resource concentration in the th region; Is the mineral resource concentration in the th region, that is, the mineral resource concentration before adjustment in the th region; Is the mineral resource concentration adjustment amount in the th region; Is the balance coefficient used to control the amplitude of logarithmic adjustment, ensuring 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 the expert experience method; The mineral resource concentration adjustment formula introduces a non-linear adjustment mechanism, combines the adjustment amount of mineral resource concentration with the mineral resource concentration, and performs weighted adjustment through the logarithmic function; the non-linear adjustment mechanism can dynamically adjust the mineral resource concentration according to different levels of mineral resource concentration, avoiding simple linear addition and subtraction;

[0051] The mineral resource concentration adjustment formula introduces a non-linear adjustment mechanism, combines the adjustment amount of mineral resource concentration with the mineral resource concentration, and performs weighted adjustment through the logarithmic function; the non-linear adjustment mechanism can dynamically adjust the mineral resource concentration according to different levels of mineral resource concentration, avoiding simple linear addition and subtraction;

[0052] The adjusted mineral resource concentration can more accurately reflect the distribution of mineral resources in the mining area, reducing the difference between the predicted and actual mineral resource concentrations. With the continuous update of geological exploration data, 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, enabling the mineral resource concentration distribution model to continuously adapt to new geological information and improving the accuracy of mineral resource assessment in the mining area.

[0053] S3: Based on the adjusted mineral resource concentration, introduce the proportional coefficient of the dynamic adjustment factor to predict the mineral resource reserves; based on the adjustment amount of the mineral resource concentration, update the proportional coefficient of the dynamic adjustment factor and apply the updated proportional coefficient of the dynamic adjustment factor to the next prediction of mineral resource reserves.

[0054] Based on the adjusted mineral resource concentration, combined with the spatial distribution information and spatial scale, predict the mineral resource reserves to obtain the predicted value of the mineral resource reserves. The spatial distribution information refers to the geographical location relationship of each area in the mining area, described by the spatial distance between areas to reflect the distribution characteristics of mineral resources between different areas; the mineral resource reserves are the estimation of the mineral resource reserves in a certain area of the mining area, predicted based on the adjusted mineral resource concentration, spatial distribution information and spatial scale.

[0055] The formula for predicting mineral resource reserves is as follows:

[0056] ,

[0057] Among them, is the mineral resource reserve of the th area, which is the predicted value and represents the estimated reserve of mineral resources in the th area; is the regional index variable in the mining area; is the th area's spatial scale, representing the area or volume of the th area, which is set according to the specific implementation scenario; is the total number of areas, indicating how many different areas are divided in the mining area, which is set according to the specific implementation scenario; is the regional index variable in the mining area, which can have the same value as ; is the weighting coefficient of the adjusted mineral resource concentration of the th area, used to adjust the contribution of the th area to the The influence degree of the mineral resource reserves prediction in a region is set based on expert experience method considering factors such as geological characteristics, geological exploration data, and ore body types in the specific implementation scenario; is the adjusted mineral resource concentration of the th region, which is sourced from the mineral resource concentration distribution model; is the spatial distance between the th region and the th region, used to represent the spatial distribution information between the two regions, and is obtained by calculating the Euclidean distance between the geometric center coordinates of the two regions based on the geographical coordinates of the mining area; is the dynamic adjustment factor proportionality coefficient of the mineral resource concentration adjustment amount of the th region, used to adjust the influence of the mineral resource concentration adjustment amount on the mineral resource reserves prediction, and is obtained through experimental optimization; is the spatial decay coefficient, representing the influence of spatial distance on the adjusted mineral resource concentration;

[0058] The dynamic adjustment factor proportionality coefficient will be updated in real-time according to the current actual geological exploration data, and the updated dynamic adjustment factor proportionality coefficient will be applied to the next mineral resource reserves prediction. By continuously optimizing and adjusting the dynamic adjustment factor proportionality coefficient, it can better adapt to the changes in geological exploration data, thereby improving the accuracy and reliability of the mineral resource reserves prediction;

[0059] The update formula of the dynamic adjustment factor proportionality coefficient is as follows:

[0060] ,

[0061] where, is the updated dynamic adjustment factor proportionality coefficient of the mineral resource concentration adjustment amount of the th region; and are adjustment coefficients, used to control the influence degree of and the influence degree of respectively, and are set according to the expert experience method; is the sign function.

[0062] Finally, integrate the mineral resource reserves in each area of the mining area, and display the resource distribution in the mining area through visualization means (such as GIS system or 3D visualization software), which helps to intuitively understand the concentrated areas and distribution characteristics of the resources in the mining area, facilitates subsequent simulation of the impact of different mining plans on the resources in the mining area, optimizes the resource utilization strategy of the mining area, determines the most suitable mining area and mining method, so as to ensure the efficient utilization and sustainable development of mineral resources.

[0063] In summary, a mine simulation analysis method for mineral exploration is completed.

[0064] The sequence of the invention embodiments is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0065] Each embodiment in this specification is described in a progressive manner. For the same or similar parts between the embodiments, reference can be made to each other. The key point of each embodiment is to illustrate the differences from other embodiments.

[0066] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A mine simulation analysis method for mineral exploration, characterized in that: The following steps are involved: S1: Combine geological exploration data, ore body distribution characteristics and spatial effects to establish a mineral resource concentration distribution model; the mineral resource concentration distribution model divides the mineral resource concentration into two parts: expected resource concentration and random fluctuation, and combines spatial distance, weighting coefficient and standard normal random variable to predict the mineral resource concentration; 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, combined with spatial distribution information and spatial scale, and introducing the dynamic adjustment factor proportional coefficient and spatial attenuation coefficient, predict the mineral resource reserves; based on the mineral resource concentration adjustment amount, update the dynamic adjustment factor proportional coefficient, and apply the updated dynamic adjustment factor proportional coefficient 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 specific calculation formula for the mineral resource concentration 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: The prediction formula for the mineral resource reserves 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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