Resource reserve modeling estimation method based on geochemical elements

Abstract

The invention discloses a resource reserve modeling estimation method based on geochemical elements, and relates to the field of resource reserve estimation, and the method comprises the steps: carrying out the data cleaning of geochemical element data; analyzing a symbiotic relationship through element correlation and detecting an abnormal value; calling a corresponding mineral product type to construct a motion condition expression based on a correlation analysis result; performing spatial interpolation by adopting Kriging to generate an element concentration field, and analyzing the trend of the ore body; carrying out algorithm generation 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 adopting a block method. According to the method, mineral reserve estimation is obtained by using geochemical element data, meanwhile, ore body delineation is automatically executed, manual errors are reduced, and efficiency is improved.

Description

Technical Field

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

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

[0003] Existing reserve assessment methods include geological exploration, statistical analysis, experimental simulation and economic assessment. Traditional reserve estimation methods rely on static statistics and empirical formulas, which are difficult to adapt to complex geological conditions, and are inefficient and have low accuracy. Summary of the invention

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

[0005] First, the resource reserve modeling estimation method based on geochemical elements includes the following steps: 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 for spatial interpolation to generate element concentration fields and analyze the ore body trend; Based on the algorithmic generation of the ore body profile; Perform 3D modeling of the ore body and optimize the 3D model of the ore body after manual verification; The block method is used to estimate the ore reserves.

[0006] Furthermore, the data cleaning of the geochemical element data includes: removing outliers, processing missing values, and then standardizing the geochemical element data.

[0007] Furthermore, 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.

[0008] Furthermore, the use of Kriging for spatial interpolation to generate element concentration fields and analyze the ore body trend includes: When the effective range in the anisotropic variation function does not change with direction, the stratigraphic structure of the mining area is detected to determine whether special stratigraphic structures appear and to add corresponding structural constraints. Special stratigraphic structures include faults and alteration zones. Verify the anisotropic variogram model with added structural constraints to see if the interpolation result of the model is distorted. If so, nest the anisotropic variogram in the anisotropic variogram model to obtain multi-directional anisotropic or nonlinear changes in the strike of the ore body. Adjust the parameters of the anisotropic variogram based on the anisotropic Kriging interpolation results. Adjust the anisotropic parameters including the direction angle, anisotropy ratio, main direction range, and vertical direction range within a preset value range. Use the sliding window method to adjust the step size for the main direction range and vertical direction range. Also include setting soft boundaries to constrain the ore body boundaries to obtain the strike of the ore body.

[0009] Furthermore, 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.

[0010] Furthermore, 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.

[0011] Furthermore, 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.

[0012] Furthermore, the performing of three-dimensional modeling on the ore body and optimizing the three-dimensional model of the ore body after manual verification further specifically includes: For the anisotropic variogram model after verification of its effectiveness, the anisotropic variogram model with the smallest error in the multiple fitting results is selected to perform Kriging interpolation to generate the exploration line profile of the ore body. The algorithm is used to automatically generate a three-dimensional model of the ore body and the Douglas-Peucker algorithm is executed. When the anisotropic variogram is distorted in the mutation area, the Ramer algorithm combined with topological constraint optimization is used to automatically generate a three-dimensional model of the ore body.

[0013] Furthermore, 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; Wg : 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, Ws : Spatial similarity weight, using inverse distance weighting (IDW) or Kriging interpolation to calculate the influence of adjacent blocks, Dynamic comprehensive weight formula: Wi = α ⋅ Wg + β ⋅ Wt + γ ⋅ Ws ,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.

[0014] Furthermore, the construction of a 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.

[0015] 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 automatically performs ore body delineation, thereby reducing manual errors and improving efficiency; The verification model is used to improve the accuracy of the model, and the conditional formula is executed according to the ore layer conditions. Kriging interpolation is performed in conjunction with the ore layer conditions to reduce the amount of calculation and false alarm rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of the present invention, and do not constitute a limitation of the embodiments of the present invention. In the drawings: Figure 1 A flow chart of a resource reserve modeling estimation method based on geochemical elements provided as an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0017] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0018] The present invention is based on the combination of geochemistry, remote sensing and big data technology. Through geochemical anomaly delineation and three-dimensional modeling technology, data collection, sampling to modeling is realized to improve efficiency. At the same time, the on-site rapid analyzer of geochemical elements collects real-time data, and the process to modeling improves efficiency, but the accuracy is low due to factors such as complex terrain. The present invention considers verifying the analysis results of geochemical elements based on the composition relationship of different mineral elements and the symbiotic relationship of minerals, analyzes the mineral type and area of ​​the target area, adopts the corresponding motion condition expression, generates a model for geochemical element data analysis, generates a three-dimensional view based on the model, and exports human-computer interaction results.

[0019] 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.

[0020] The specific application scenario of the present invention is ore reserve estimation.

[0021] 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.

[0022] 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.

[0023] 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: A1. Perform data cleaning on geochemical element data, including: removing outliers, processing missing values, and then standardizing the geochemical element data; 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; A3. Based on the correlation analysis results, call the corresponding mineral type to construct the movement condition expression. 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; Verify the anisotropic variogram model with added structural constraints, and verify whether the interpolation result of the model is distorted. If distorted, nest the anisotropic variogram in the anisotropic variogram model to obtain multi-directional anisotropic or nonlinear changes in the ore body's strike direction. Adjust the parameters of the anisotropic variogram based on the anisotropic kriging interpolation results, and adjust the anisotropic parameters including the direction angle, anisotropy ratio, main direction range, and vertical direction range within the preset value range. For the main direction range and vertical direction range, the sliding window method is used to adjust the step size, and it also includes setting soft boundaries to constrain the ore body boundary to obtain the ore body strike direction. 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.

[0024] 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.

[0025] The following method can also be used to verify the validity of the anisotropic variogram model: 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.

[0026] A5. Based on the algorithmic generation of the ore body profile, A6. Perform three-dimensional modeling of 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, execute the Douglas-Peucker algorithm, and 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.

[0027] A7. Use the block method to estimate the ore reserves, 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; Wg : 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, Ws : Spatial similarity weight, using inverse distance weighting (IDW) or Kriging interpolation to calculate the influence of adjacent blocks, Dynamic comprehensive weight formula: Wi = α ⋅ Wg + β ⋅ Wt + γ ⋅ Ws ,in α + β + γ =1, when the drilling distance is ≤50m Wg =1.0, when the distance is >100m Wg =0.7 Then, dynamic comprehensive weights are added to the traditional block method to obtain a reserve calculation model to estimate reserves; The traditional block method formula is upgraded to: Vi : segment volume (computed via 3D Delaunay triangulation), ρi : Weight (including crack correction factor , ϕ is the porosity), Ci : Dynamic grade (sliding window weighted mean), A real-time error feedback mechanism is established, and online monitoring indicators are set, including local grade variation coefficient, volume estimation residual and weight oscillation amplitude; among which, the local grade variation coefficient is calculated as follows: Among them, the threshold setting CV>0.3 triggers resampling, and the volume estimation residual is: Δ V =∥ V Measured − V Model ∥, where the threshold is set to Δ V >5%, weight fluctuation range: Δ W =∥ Wt − Wt −1∥, threshold setting Δ W >0.2.

[0028] 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 based on the feedback result to the reserve calculation model through the PID controller to adjust the weight.

[0029] The PID weight value setting software is as follows: class PIDController: def __init__(self, Kp, Ki, Kd): self.Kp, self.Ki, self.Kd = Kp, Ki, Kd self.integral = 0 self.prev_error = 0 def update(self, error, dt): self.integral += error * dt derivative = (error - self.prev_error) / dt output = self.Kp*error + self.Ki*self.integral + self.Kd*derivative self.prev_error = error return output For example, to adjust the time decay coefficient λ: pid = PIDController(0.8, 0.05, 0.1) error = current_CV - target_CV lambda_adjustment = pid.update(error, delta_time) In the 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 only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0030] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0031] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0032] Those skilled in the art will appreciate that the embodiments of the present invention may be provided as methods or systems. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects.

[0033] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0034] The above are only embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

[0035] Those skilled in the art will readily appreciate other embodiments of the present invention 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 common knowledge or customary techniques in the art that are not disclosed by the present invention. The description and examples are to be considered exemplary only, and the true scope and spirit of the present invention is indicated by the claims above.

[0036] It should be understood that the present invention is not limited to the exact construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only 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 for spatial interpolation to generate element concentration fields and analyze the ore body trend; Based on the algorithmic generation of the ore body profile; Perform 3D modeling of the ore body and optimize the 3D model of the ore body after manual verification; 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 method of using Kriging to perform spatial interpolation to generate element concentration fields and analyze the ore body trend includes: When the effective range in the anisotropic variation function does not change with direction, the stratigraphic structure of the mining area is detected to determine whether special stratigraphic structures appear and to add corresponding structural constraints. Special stratigraphic structures include faults and alteration zones. Verify the anisotropic variogram model with added structural constraints to see if the interpolation result of the model is distorted. If so, nest the anisotropic variogram in the anisotropic variogram model to obtain multi-directional anisotropic or nonlinear changes in the strike of the ore body. Adjust the parameters of the anisotropic variogram based on the anisotropic Kriging interpolation results. Adjust the anisotropic parameters including the direction angle, anisotropy ratio, main direction range, and vertical direction range within a preset value range. Use the sliding window method to adjust the step size for the main direction range and vertical direction range. Also include setting soft boundaries to constrain the ore body boundaries to obtain the strike of the ore body.

5. The resource reserve modeling estimation method based on geochemical elements according to claim 4 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.

6. The resource reserve modeling estimation method based on geochemical elements according to claim 4, 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.

7. The resource reserve modeling estimation method based on geochemical elements according to claim 4, 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.

8. The resource reserve modeling estimation method based on geochemical elements according to claim 6 or 7, characterized in that: The performing of three-dimensional modeling on the ore body and optimizing the three-dimensional model of the ore body after manual verification also specifically includes: For the anisotropic variogram model after verification of its effectiveness, the anisotropic variogram model with the smallest error in the multiple fitting results is selected to perform Kriging interpolation to generate the exploration line profile of the ore body. The algorithm is used to automatically generate a three-dimensional model of the ore body and the Douglas-Peucker algorithm is executed. When the anisotropic variogram is distorted in the mutation area, the Ramer algorithm combined with topological constraint optimization is used to automatically generate a three-dimensional model of the ore body.

9. The resource reserve modeling estimation method based on geochemical elements according to claim 8, 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.

10. The resource reserve modeling estimation method based on geochemical elements according to claim 9, characterized in that: The real-time error feedback mechanism is constructed, specifically including: 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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