Resource reserve modeling and estimation method based on geochemical elements

Abstract

The present invention discloses a method for modeling and estimating resource reserves based on geochemical elements, which relates to the field of resource reserve estimation. The present invention includes: cleaning the geochemical element data; analyzing the symbiotic relationship through element correlation analysis and detecting outliers; calling the corresponding mineral type based on the correlation analysis result to construct a movement condition expression; using Kriging for spatial interpolation to generate an element concentration field and analyzing the ore body trend; generating an algorithm based on the ore body profile contour; performing three-dimensional modeling on the ore body and optimizing the three-dimensional model of the ore body after manual verification; and estimating the ore body reserves by the block method. The present invention uses geochemical element data to obtain mineral reserve estimation, and at the same time automatically performs ore body delineation, reduces manual errors and improves efficiency.

Description

Technical Field

[0001] The present invention relates to the field of resource reserve estimation, and particularly to a method for modeling and estimating resource reserves based on geochemical elements. Background Art

[0002] With the rapid development of the global economy, the demand for mineral resources continues to grow, and resource reserve reports are of great significance for guiding the development of mineral resources and promoting sustainable economic development.

[0003] Existing reserve assessment methods include geological exploration methods, statistical analysis methods, experimental simulation methods, and economic assessment methods. Traditional reserve estimation methods rely on static statistics and empirical formulas, making it difficult to adapt to complex geological conditions, with low efficiency and accuracy. Summary of the Invention

[0004] The present invention provides a method for modeling and estimating resource reserves based on geochemical elements to solve the problems of the prior art.

[0005] In a first aspect, a method for modeling and estimating resource reserves based on geochemical elements includes the following steps:

[0006] Clean the geochemical element data;

[0007] Analyze the symbiotic relationship through element correlation analysis and detect outliers;

[0008] Call the corresponding mineral type based on the correlation analysis result to construct a movement condition expression;

[0009] Use Kriging for spatial interpolation to generate an element concentration field and analyze the ore body trend;

[0010] Generate based on the algorithm for the ore body profile contour;

[0011] Perform three-dimensional modeling on the ore body and optimize the three-dimensional model of the ore body after manual verification;

[0012] Estimate the ore body reserves using the block method.

[0013] Further, the cleaning of the geochemical element data includes: removing outliers, processing missing values, and then normalizing the geochemical element data.

[0014] Further, the analysis of the symbiotic relationship through element correlation analysis and the detection of outliers include:

[0015] Use the Pearson / Spearman coefficient matrix to analyze the element symbiotic relationship, adopt exploratory data analysis for outlier detection, and call the interquartile range method to identify chemical anomalies.

[0016] Further, the method of using Kriging for spatial interpolation to generate an element concentration field and analyzing the ore body strike includes:

[0017] When the effective range in the anisotropic variogram does not change with direction, detect the stratigraphic structure of the mining area, determine whether there is a special stratigraphic structure, and add corresponding structural constraint conditions. The special stratigraphic structures include faults and alteration zones;

[0018] Verify the anisotropic variogram model with added structural constraint conditions, and check whether the interpolation result of the model is distorted. If it is distorted, perform nested processing on the anisotropic variogram in the anisotropic variogram model to obtain the ore body strike changes in multiple directions or non-linearity. Based on the anisotropic Kriging interpolation result, adjust the parameters of the anisotropic variogram. For the anisotropic parameters including the main direction angle, anisotropic ratio, main direction range, and vertical direction range, perform adjustments within the preset value ranges. For the main direction range and vertical direction range, use the sliding window method to adjust the step size. It also includes setting soft boundary constraints for the ore body boundary to obtain the ore body strike.

[0019] Further, for the interpolation result of the distorted anisotropic variogram model, specifically:

[0020] When the strike mutation area of the anisotropic variogram is distorted, add the structural condition corresponding to the fault distance, divide different interpolation domains with the fault distance as the boundary, and perform piecewise anisotropy within different interpolation domains.

[0021] Further, it also includes calling computing resources to fit the anisotropic variogram model multiple times to verify the effectiveness of the anisotropic variogram model. The specific steps are as follows:

[0022] Perform spatial leave-one-out cross-validation,

[0023] Successively remove one sample point, use the remaining data to train the anisotropic variogram model, and predict the value of the removed point;

[0024] Repeat for all sample points and count the prediction errors.

[0025] Further, it also includes calling computing resources to fit the anisotropic variogram model multiple times to verify the effectiveness of the anisotropic variogram model. The specific steps are as follows:

[0026] When the data distribution is uneven or there are obvious spatial partitions, divide the mineral area into spatial blocks; successively use each spatial block as the validation set and the remaining spatial blocks as the training set to calculate the overall error.

[0027] Further, the specific steps of performing 3D modeling on the ore body and optimizing the 3D ore body model after manual verification also include:

[0028] For each anisotropic variogram model after verifying its validity, select the anisotropic variogram model with the smallest error within multiple fitting results to perform Kriging interpolation to generate the exploration line profile of the ore body. An algorithm is used to automatically generate the 3D model of the ore body. The Douglas-Peucker algorithm is executed. When the distortion occurs in the region with sudden change in the trend of the anisotropic variogram, the Ramer algorithm is combined with topological constraints to optimize and automatically generate the 3D model of the ore body.

[0029] Furthermore, the block method is used to estimate the ore body reserves, which specifically includes:

[0030] Construct the core model of the dynamic block method, and design multi-dimensional weight coefficients according to geological confidence, data timeliness, and spatial correlation;

[0031] Wg : Geological weight, based on the exploration grid density and sampling density,

[0032] Wt : Time decay weight, Wt = e −λΔt , where λ is the decay coefficient and Δt is the time elapsed since data collection,

[0033] Ws : Spatial similarity weight, using inverse distance weighting (IDW) or Kriging interpolation to calculate the influence degree of adjacent blocks,

[0034] Dynamic comprehensive weight formula:

[0035] Wi = α ⋅ Wg + β ⋅ Wt + γ ⋅ Ws , where α + β + γ =1,

[0036] Then, add the dynamic comprehensive weight to the traditional block method to obtain a reserve calculation model for estimating reserves;

[0037] Construct a real-time error feedback mechanism, and adjust the weights through a PID controller based on the feedback results to the reserve calculation model.

[0038] Furthermore, the construction of the real-time error feedback mechanism specifically includes:

[0039] Set online monitoring indicators, including local grade variation coefficient, volume estimation residual, and weight oscillation amplitude;

[0040] Execute the feedback control strategy. When each monitoring indicator is greater than the preset threshold range, trigger resampling and adjust the dynamic weights simultaneously.

[0041] The resource reserve modeling and estimation method based on geochemical elements provided by the present invention uses geochemical element data to obtain mineral reserve estimation, and at the same time automatically performs ore body delineation, reducing manual errors and improving efficiency;

[0042] By adopting a calibration model, the accuracy of the model is improved. Conditional formulas are executed corresponding to the ore layer conditions, and combined with the ore layer conditions, this is Kriging interpolation, reducing the calculation amount and false alarm rate. Brief Description of the Drawings

[0043] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, form a part of the present invention, and do not limit the embodiments of the present invention. In the drawings:

[0044] Figure 1 It is a flowchart of the resource reserve modeling and estimation method based on geochemical elements provided by an exemplary embodiment of the present invention. Detailed Embodiment

[0045] Here, the exemplary embodiments will be described in detail, and the examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. On the contrary, they are only examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0046] The concept of the present invention is based on the combination of geochemistry, remote sensing, and big data technologies. Through geochemical anomaly delineation and three-dimensional modeling technologies, data collection, sampling to modeling are realized, improving efficiency. At the same time, real-time data is collected by a rapid on-site analyzer for geochemical elements during the process from sampling to modeling, and the efficiency is improved. However, due to factors such as complex terrain, the accuracy is low; the present invention considers verifying the analysis results of geochemical elements based on the compositional relationships of different mineral elements and the symbiotic relationships of minerals, analyzes the mineral types and regions of the target area, adopts corresponding motion condition expressions, generates a model for analyzing geochemical element data, generates a three-dimensional view based on the model, and gives and exports the human-computer interaction results.

[0047] Among them, anisotropy refers to the differences in spatial correlation in different directions. Since ore bodies usually extend along the tectonic direction or the sedimentary direction, errors occur in the modeling of the anisotropic variogram, resulting in unclear ore body strike and inability to accurately reflect the spatial distribution of the ore body, resulting in large errors. Therefore, it is necessary to analyze the stratigraphic structure of the ore body and eliminate the influence of faults and alteration zones. At the same time, adjust the direction angle and scale factor of the variogram. While the ore body strikes along the main direction of the variogram, set the anisotropic parameters and simultaneously verify the effectiveness of the anisotropic variogram model. At the same time, filtering and data cleaning are used to solve the problems of noise and uneven sampling. Different processing methods are preset in the anisotropic variogram model for different ore body types. For example, the anisotropic variogram processing methods for layered and massive sulfide deposits are different, which will not be repeated here.

[0048] The specific application scenario of the present invention is ore reserve estimation.

[0049] The resource reserve modeling estimation method based on geochemical elements provided by the present invention is intended to solve the above technical problems of the prior art.

[0050] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present invention will be described below in conjunction with the accompanying drawings.

[0051] Example 1: This example uses a resource reserve modeling estimation method based on geochemical elements, such as Figure 1 As shown, the following steps are included:

[0052] A1. Perform data cleaning on geochemical element data, including: removing outliers, processing missing values, and then standardizing the geochemical element data;

[0053] A2. Analyze symbiotic relationships and detect outliers through element correlation, including: use Pearson / Spearman coefficient matrix to analyze element symbiotic relationships, use exploratory data analysis for outlier detection, and call the interquartile range method to identify chemical anomalies;

[0054] A3. Based on the correlation analysis results, call the corresponding mineral type to construct the movement condition expression.

[0055] A4. Use Kriging to perform spatial interpolation to generate element concentration fields and analyze the direction of the ore body, including: when the effective range in the anisotropic variogram does not change with direction, detect the stratigraphic structure of the mining area, determine whether special stratigraphic structures appear, and add corresponding structural constraints. Special stratigraphic structures include faults and alteration zones;

[0056] Verify the anisotropic variogram model with additional structural constraints, and check whether the interpolation result of the model is distorted. If it is distorted, perform nested processing on the anisotropic variogram in the anisotropic variogram model to obtain the ore body strike changes in multiple directions or non-linearly. Adjust the parameters of the anisotropic variogram based on the anisotropic Kriging interpolation result, and make adjustments within the preset value ranges for the anisotropic parameters including the main direction angle, anisotropic ratio, main direction range, and vertical direction range. For the main direction range and vertical direction range, use the sliding window method to adjust the step size. It also includes setting soft boundary constraints for the ore body boundary to obtain the ore body strike. For the interpolation result of the distorted anisotropic variogram model, specifically:

[0057] When the strike mutation area of the anisotropic variogram is distorted, add the structural conditions corresponding to the fault distance, divide different interpolation domains with the fault distance as the boundary, and perform segmented anisotropy within different interpolation domains.

[0058] It also includes calling computing resources to fit the anisotropic variogram model multiple times to verify the effectiveness of the anisotropic variogram model, specifically including the following steps:

[0059] Perform spatial leave-one-out cross-validation,

[0060] Successively remove one sample point, use the remaining data to train the anisotropic variogram model, and predict the value of the removed point;

[0061] Repeat for all sample points and count the prediction error.

[0062] The following method can also be used to verify the effectiveness of the anisotropic variogram model. When the data distribution is uneven or there are obvious spatial partitions, divide the mineral area into spatial blocks; successively use each spatial block as the validation set and the remaining spatial blocks as the training set to calculate the overall error.

[0063] A5. Generate based on the algorithm for the ore body profile outline,

[0064] A6. Perform 3D modeling on the ore body and optimize the 3D ore body model after manual verification, including: for the anisotropic variogram model after verifying the effectiveness, select the anisotropic variogram model with the smallest error within the multiple fitting results to perform Kriging interpolation to generate the exploration line profile of the ore body, use the algorithm to automatically generate the 3D ore body model, perform the Douglas-Peucker algorithm, and when the strike mutation area of the anisotropic variogram is distorted, use the Ramer algorithm combined with topological constraints to optimize the automatically generated 3D ore body model.

[0065] A7. Estimate the ore body reserves using the block method, including:

[0066] Build the core model of the dynamic block section method, and design multi-dimensional weight coefficients according to geological confidence, data timeliness, and spatial correlation;

[0067] Wg : Geological weight, based on exploration grid density and sampling density,

[0068] Wt : Time decay weight, Wt = e −λΔt , where λ is the decay coefficient and Δt is the time since data collection,

[0069] Ws

[0070] : Spatial similarity weight, calculate the influence degree of adjacent blocks using inverse distance weighting (IDW) or Kriging interpolation,

[0071] Wi α = Wg ⋅ β + Wt ⋅ γ + Ws ⋅ α , where β + γ + Wg = 1. When the borehole spacing ≤ 50m, Wg = 1.0. When the spacing > 100m, = 0.7

[0072] Then add the dynamic comprehensive weight to the traditional block section method to obtain a reserve calculation model for estimating reserves;

[0073] The formula of the traditional block section method is upgraded to:

[0074] Vi : Block volume (calculated by 3D Delaunay triangulation),

[0075] ρi : Bulk density (including the fracture porosity correction coefficient , where ϕ is the porosity),

[0076] Ci : Dynamic grade (weighted mean of the sliding window),

[0077] Build a real-time error feedback mechanism and set online monitoring indicators, including the local grade variation coefficient, volume estimation residual, and weight oscillation amplitude; among them, the calculation method of the local grade variation coefficient is:

[0078]

[0079] ​Among them, the threshold setting is that CV > 0.3 triggers resampling, and the volume estimation residual: Δ V = ∥ V measured - V model ∥, where the threshold setting is Δ V > 5%, and the weight oscillation amplitude: Δ W = ∥ Wt − Wt −1 ∥, and the threshold setting is Δ W > 0.2.

[0080] Execute the feedback control strategy. When each monitoring index is greater than the preset threshold range, while triggering resampling, adjust the dynamic weight based on the feedback result to the reserve calculation model and adjust the weight through the PID controller.

[0081] The software for setting the PID weight value is as follows:

[0082] class PIDController:

[0083] def __init__(self, Kp, Ki, Kd):

[0084] self.Kp, self.Ki, self.Kd = Kp, Ki, Kd

[0085] self.integral = 0

[0086] self.prev_error = 0

[0087] def update(self, error, dt):

[0088] self.integral += error * dt

[0089] derivative = (error - self.prev_error) / dt

[0090] output = self.Kp * error + self.Ki * self.integral + self.Kd * derivative

[0091] self.prev_error = error

[0092] return output

[0093] For example, adjust the time decay coefficient λ:

[0094] pid = PIDController(0.8, 0.05, 0.1)

[0095] error = current_CV - target_CV

[0096] lambda_adjustment = pid.update(error, delta_time)

[0097] In several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0098] The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical modules, that is, they can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0099] In addition, the functional modules in various embodiments of the present invention can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module. The above integrated modules can be implemented in the form of hardware, or in the form of a combination of hardware and software functional modules.

[0100] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods or systems. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects.

[0101] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover a non-exclusive inclusion, so that a process, method, commodity or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, commodity or device. Without further limitation, the element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, commodity or device including the element.

[0102] The above are only embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, various modifications and variations can be made to the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the scope of the claims of the present invention.

[0103] Other embodiments of the present invention will be readily apparent to those skilled in the art after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include known common general knowledge or conventional technical means in the technical field not disclosed by the present invention. The specification and examples are only illustrative, and the true scope and spirit of the present invention are pointed out by the claims above.

[0104] It should be understood that the present invention is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is only limited by the appended claims.

Claims

1. A resource reserve modeling and estimation method based on geochemical elements, characterized in that: The steps include: Perform data cleaning on geochemical element data; Analyze symbiotic relationships and detect outliers through element correlation; Based on the correlation analysis results, the corresponding mineral type is called to construct the movement condition expression; Kriging is used to perform spatial interpolation to generate element concentration fields and analyze the ore body trend, including: when the effective range in the anisotropic variogram does not change with the direction, the stratigraphic structure of the mining area is detected to determine whether special stratigraphic structures appear and to add corresponding structural constraints, and the special stratigraphic structures include faults and alteration zones; the anisotropic variogram model with added structural constraints is verified to verify whether the interpolation result of the model is distorted. If distorted, the anisotropic variogram in the anisotropic variogram model is nested to obtain multi-directional anisotropic or nonlinear ore body trend changes, and the parameters of the anisotropic variogram are adjusted based on the anisotropic Kriging interpolation results. The anisotropic parameters including the direction angle, anisotropy ratio, main direction range, and vertical direction range are adjusted within a preset value range. The sliding window method is used to adjust the step size for the main direction range and the vertical direction range, and the soft boundary is set to constrain the ore body boundary to obtain the ore body trend; Based on the algorithmic generation of the ore body profile; Perform three-dimensional modeling on the ore body and optimize the three-dimensional model of the ore body after manual verification, including: for the anisotropic variogram model after verification of validity, select the anisotropic variogram model with the smallest error in multiple fitting results to perform Kriging interpolation to generate the exploration line profile of the ore body, use the algorithm to automatically generate the three-dimensional model of the ore body, and execute the Douglas-Peucker algorithm. When the anisotropic variogram is distorted in the mutation area, use the Ramer algorithm combined with topological constraint optimization to automatically generate the three-dimensional model of the ore body; The block method is used to estimate the ore reserves.

2. The resource reserve modeling estimation method based on geochemical elements according to claim 1 is characterized in that: The data cleaning of the geochemical element data includes: removing outliers, processing missing values, and then standardizing the geochemical element data.

3. The resource reserve modeling estimation method based on geochemical elements according to claim 2 is characterized in that: The method of analyzing the symbiotic relationship through element correlation and detecting outliers includes: The Pearson / Spearman coefficient matrix was used to analyze the elemental symbiosis, exploratory data analysis was used for outlier detection, and the interquartile range method was called to identify chemical anomalies.

4. The resource reserve modeling and estimation method based on geochemical elements according to claim 3 is characterized in that: The interpolation results of the distorted anisotropic variogram model are as follows: When the anisotropic variogram is distorted in the mutation area, the structural condition corresponding to the fault distance is added, and different interpolation domains are divided based on the fault distance, and piecewise anisotropy is performed in different interpolation domains.

5. The resource reserve modeling estimation method based on geochemical elements according to claim 3 is characterized in that: It also includes calling computing resources to fit the anisotropic variogram model multiple times to verify the validity of the anisotropic variogram model, which specifically includes the following steps: Perform space leave-one-out cross-validation, Remove one sample point at a time, use the remaining data to train the anisotropic variogram model, and predict the value of the removed point; Repeat for all sample points and calculate the prediction error.

6. The resource reserve modeling and estimation method based on geochemical elements according to claim 1, characterized in that: It also includes calling computing resources to fit the anisotropic variogram model multiple times to verify the validity of the anisotropic variogram model, which specifically includes the following steps: When the data distribution is uneven or there is an obvious spatial partition, the mineral area is divided into spatial blocks; each spatial block is used as a validation set in turn, and the remaining spatial blocks are used as training sets to calculate the overall error.

7. The resource reserve modeling estimation method based on geochemical elements according to claim 6, characterized in that: The block method is used to estimate the ore reserves, specifically including: Construct the core model of the dynamic block method and design multi-dimensional weight coefficients based on geological confidence, data timeliness and spatial correlation; W : Geological weight, based on exploration grid and sampling density, Wt : time decay weight, Wt = e −λΔt , λ is the attenuation coefficient, Δt is the time since data collection, W : Spatial similarity weight, using inverse distance weighting or Kriging interpolation to calculate the influence of adjacent blocks, Dynamic comprehensive weight formula: Wi = α ⋅ W + β ⋅ Wt + γ ⋅ W ,in α + β + γ =1, Then, dynamic comprehensive weights are added to the traditional block method to obtain a reserve calculation model to estimate reserves; A real-time error feedback mechanism is constructed, and the weights are adjusted through a PID controller based on the feedback results to the reserve calculation model.

8. The resource reserve modeling estimation method based on geochemical elements according to claim 7, characterized in that: The construction of the real-time error feedback mechanism specifically includes: Set up online monitoring indicators, including local grade variation coefficient, volume estimation residual and weight oscillation amplitude; The feedback control strategy is executed. When each monitoring indicator is greater than the preset threshold range, resampling is triggered and the dynamic weight is adjusted.

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