Construction method of three-dimensional protozoa geochemical model
The three-dimensional primary halo model addresses the limitations of traditional two-dimensional methods by using geochemical element distributions and regression analysis to provide accurate and objective mineral resource predictions, especially for deep ore bodies.
Patent Information
- Application Number
- CN202411681930.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-22
- Publication Date
- 2024-12-27
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Traditional profile primary halo models provide only two-dimensional geochemical information, lack three-dimensional spatial continuity, and rely heavily on subjective expert judgment, leading to inaccurate and uncertain mineral prediction results, especially for deep ore bodies.
A method for constructing a three-dimensional primary halo geochemical model that identifies and extracts element combinations of leading, near-ore, and tail halos through three-dimensional implicit modeling, using geochemical element distributions to create a comprehensive three-dimensional ore body model and applying regression analysis to predict halo types.
Accurately identifies and extracts geochemical element combinations, providing objective and accurate mineral resource predictions by simplifying parameter calculations and enhancing spatial distribution visualization, especially for deep ore bodies.
Smart Images
Figure CN119206117A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the field of solid mineral exploration data processing, and in particular to a method for constructing a three-dimensional primary halo geochemical model. Background Art
[0002] As a major development direction of quantitative prediction of mineral resources in recent years, the basic method framework of three-dimensional deep quantitative mineral prediction has been established, and it is playing an important role in mineral resource exploration. A large number of studies have shown that the primary halo model can more intuitively reflect the spatial distribution of the primary halo combination. From the geochemical background of the formation of the ore deposit, the element combination characteristics of the ore deposit, and the element combination characteristics of the mineralization halo, it has a more accurate direction for blind mine prediction and also plays an important guiding role in subsequent mineral exploration.
[0003] However, the traditional profile primary halo model has significant limitations and defects. First, the profile primary halo model can only provide two-dimensional geochemical information. This method easily leads to one-sided information and cannot fully reveal the geological structure and mineralization characteristics of the mining area. Secondly, due to the lack of three-dimensional spatial continuity, the calculation of the Grigorian zoning index and regional chemical parameters becomes more complicated and inaccurate, resulting in increased uncertainty in the prediction results. In addition, the application of the profile primary halo model in deep mineral prediction is limited, and it cannot effectively solve the three-dimensional spatial distribution problem of deep ore bodies.
[0004] At the same time, the extraction of leading halos, near-ore halos and tail halos in traditional three-dimensional primary halos mainly relies on the prior knowledge and experience judgment of geological experts on the geological background. This method is not only subjective and uncertain, but may also lead to differences in prediction performance due to the complexity of the geological background. Summary of the invention
[0005] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for constructing a three-dimensional primary halo geochemical model to more accurately identify and extract the element combinations of the leading halo, near-ore halo and tail halo in the three-dimensional primary halo.
[0006] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is: A method for constructing a three-dimensional primary halo geochemical model is provided, comprising: Step S1: Determine the research area of the ore body, collect geochemical elements in the research area, and determine the types of geochemical elements of the research ore body N , according to the collected N The spatial distribution of various geochemical elements is used to construct a three-dimensional ore body model; Step S2: Determine sample labels according to the distribution of geochemical elements on the three-dimensional ore body model, and construct front edge samples, near-ore samples and tailings samples respectively; Step S3: extracting characteristic combinations related to the ore halo type from the front edge samples, near-ore samples and tailing samples, and obtaining the front edge halo, near-ore halo and tail halo element combinations in the three-dimensional primary halo; Step S4: Based on the element combination of the leading edge halo, the near-ore halo and the tail halo, the superposition visualization of the element combination of the primary halo is realized through the three-dimensional implicit modeling method to construct a three-dimensional primary halo model.
[0007] Furthermore, the method for constructing a three-dimensional ore body model is: Step S11: According to N Sampling coordinates of the geochemical elements in the study area , sampling coordinates In the three-dimensional coordinate system with the center of the research area as the origin, n Number the sampling point where the corresponding geochemical element was collected; Step S12: Based on the origin of the three-dimensional coordinate system o Coordinates Calculate the coordinate increment of each sampling point; ; Step S13: Determine the sampling point n Whether it is in a horizontal window; ; Among them, any point in the three-dimensional coordinate system a With origin o The vector is , For vector Projection on the horizontal plane, vector The azimuth is The inclination angle is , the tolerance angle is , For vector With vector The angle of sampling point n With origin o The connecting vector is , For vector projection on the horizontal plane; when When the sampling point n The sampling point falls in the horizontal window. n Satisfies the horizontal window classification, otherwise, does not satisfy the horizontal window classification, is the classification angle in the horizontal direction; Step S14: Determine the sampling point n Whether the vertical classification angle is in the vertical window ; ; in, For vector With vector The angle between Recorded as parameter ; when , sampling point n A window that falls in a vertical plane satisfies the vertical window classification; Step S15: When the sampling point n When both vertical window classification and horizontal window classification are satisfied, it indicates that the sampling point n If the sampling point falls within the three-dimensional ore body model, the sampling point is retained; otherwise, the surface sampling point n If the sampling point does not fall within the three-dimensional ore body model, the sampling point is deleted; the vertical plane window and the horizontal window are the classification spaces set based on the three-dimensional ore body model; Step S16: Establish a data set of sampling points in the three-dimensional ore body model based on the reserved sampling points , I is the number of sampling points in the three-dimensional ore body model, and the sampling points are classified according to the step size; ; in, are any two sampling points in the three-dimensional ore body model. i 1. i The categorical attribute value of 2, L i For two sampling points i 1. i The distance between 2, G k is the sum of squares of the cumulative classification attribute value deviations of the sampling points, D k is the distance value of the cumulative classification of the sampling points, k is a classification attribute; Step S17: repeating steps S11-S16, after detecting the direction categories of all sampling points, calculating the variance function value in the classification direction; ; in, The sampling point along the direction The variance function value of is the average value of the classification distance of the sampling points, The number of classifications accumulated for the sampling points; Step S18: Taking the average value for x Axis, variogram value for y Axis summary direction The variation curve on Step S19: After calculating the variation curves in different directions, the boundaries of all variation curves are fitted to make the boundary surfaces smooth, thereby forming a three-dimensional ore body model.
[0008] Furthermore, step S2 is specifically as follows: The front, main body and tail areas of the ore body are determined according to the shape of the three-dimensional ore body model. According to the division of the ore body area, the corresponding geochemical elements collected at the sampling points in the front, main body and tail areas are divided into front samples, near-mine samples and tailings samples, and corresponding sample labels are assigned to the front samples, near-mine samples and tailings samples.
[0009] Further, step S3 includes: Step S31: Using the geochemical elements in the front edge samples, near-mine samples or tailings samples to establish the independent variable matrix for each sample type , the independent variable matrix The elements in it are various geochemical elements; Step S32: Constructing a dependent variable matrix for screening the leading edge halo element combination in the leading edge sample, the near-mine halo element combination in the near-mine sample, or the tail halo element combination in the tailing sample , the dependent variable matrix is a categorical variable for the leading halo, near-ore halo, or tail halo elements; Step S33: The independent variable matrix and the dependent variable matrix Decomposed into the product of the score matrix and the loading matrix, plus the residual matrix; ; in, are the score matrices related to the independent variable matrix and the dependent variable matrix, are the loading matrices related to the independent variable matrix and the dependent variable matrix, are the residual matrices related to the independent variable matrix and the dependent variable matrix respectively; Step S34: Establish a regression model to predict the classification results; ; in, is the random error matrix, is the regression coefficient matrix of the regression model; the regression model establishes the independent variable matrix and the dependent variable matrix the relationship between; Step S35: The independent variable matrix Input into the regression model to calculate the predicted values of each geochemical element classification in the front edge sample, near-mine sample or tailings sample; ; in, is the output combination of leading edge halo, near-mine halo or tail halo elements, is the regression coefficient vector calculated according to the regression model.
[0010] Further, step S4 includes: Based on the corresponding leading edge halo, near-ore halo and tail halo element combinations classified in the leading edge, main body and tail areas, the superposition visualization of each primary halo element combination is realized on the three-dimensional ore body model through three-dimensional implicit modeling to construct a three-dimensional primary halo model.
[0011] The beneficial effects of the present invention are as follows: the present invention is used to more accurately identify and extract the element combination of the leading halo, the near-ore halo and the tail halo in the three-dimensional primary halo, and can effectively extract the feature combination highly correlated with the ore halo type by maximizing the correlation between the independent variable (i.e., the element characteristic) and the dependent variable (i.e., the ore halo type). This method not only overcomes the limitation of the traditional method that relies on the prior knowledge of geological experts, but also can provide more objective and accurate prediction results.
[0012] The geochemical element combination characteristics of different regions in the three-dimensional primary halo can be quantitatively extracted, and the distribution range of the leading halo, near-ore halo and tail halo can be quickly identified based on this. This method not only simplifies the tedious parameter calculation process, but also can quickly display the three-dimensional spatial distribution of deep ore deposits, providing strong support for the rapid prediction and exploration of mineral resources.
[0013] In addition, the three-dimensional primary halo model has significant advantages over the traditional profile primary halo model. First, it can provide more comprehensive and scientific geochemical information, avoiding the one-sidedness and limitations of two-dimensional information. Second, due to the continuity of three-dimensional space, the three-dimensional primary halo model can help more accurately calculate the Grigoriyan zoning index and regional chemical parameters, thereby improving the accuracy and reliability of the prediction results. Finally, the three-dimensional primary halo model can better meet the needs of deep mineral prediction and effectively solve the three-dimensional spatial distribution problem of deep ore bodies. BRIEF DESCRIPTION OF THE DRAWINGS
[0014] Figure 1 Schematic diagram of the three-dimensional primary halo model. DETAILED DESCRIPTION
[0015] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.
[0016] This embodiment takes the three-dimensional primary halo modeling and prediction task of the Zaozigou gold mine in Gansu as an example, and further illustrates the construction method of the present invention by applying the quantitative mineral resource prediction technology of the three-dimensional primary halo model that simulates actual implicit modeling.
[0017] A method for constructing a three-dimensional primary halo geochemical model, comprising: Step S1: Determine the research area of the ore body, collect geochemical elements in the research area, and determine the types of geochemical elements of the research ore body N , according to the collected N The spatial distribution of various geochemical elements is used to construct a three-dimensional ore body model.
[0018] In this embodiment, a three-dimensional ore body model is established for 12 geochemical elements collected in the Zaozigou area of Gansu Province: Au, Ag, Cu, Pb, Zn, As, Sb, Hg, W, Bi, Co, and Mo. According to the boundary grades of industrial standards, Voxler software is used and the modeling method of the three-dimensional ore body model of the present invention is used.
[0019] The method of constructing a three-dimensional ore body model is: Step S11: According to N Sampling coordinates of the geochemical elements in the study area , sampling coordinates In the three-dimensional coordinate system with the center of the research area as the origin, n Number the sampling point where the corresponding geochemical element was collected; Step S12: Based on the origin of the three-dimensional coordinate system o Coordinates Calculate the coordinate increment of each sampling point; ; Step S13: Determine the sampling point n Whether it is in a horizontal window; ; Among them, any point in the three-dimensional coordinate system a With origin o The vector is , For vector Projection on the horizontal plane, vector The azimuth is The inclination angle is , the tolerance angle is , For vector With vector The angle of sampling point n With origin o The connecting vector is , For vector projection on the horizontal plane; when When the sampling point n The sampling point falls in the horizontal window. n Satisfies the horizontal window classification, otherwise, does not satisfy the horizontal window classification, is the classification angle in the horizontal direction; Step S14: Determine the sampling point n Whether the vertical classification angle is in the vertical window ; ; in, For vector With vector The angle between Recorded as parameter ; when , sampling point n A window that falls in a vertical plane satisfies the vertical window classification; Step S15: When the sampling point n When both vertical window classification and horizontal window classification are satisfied, it indicates that the sampling point n If the sampling point falls within the three-dimensional ore body model, the sampling point is retained; otherwise, the surface sampling point n If the sampling point does not fall within the three-dimensional ore body model, the sampling point is deleted; the vertical plane window and the horizontal window are the classification spaces set based on the three-dimensional ore body model; Step S16: Establish a data set of sampling points in the three-dimensional ore body model based on the reserved sampling points , I is the number of sampling points in the three-dimensional ore body model, and the sampling points are classified according to the step size; ; in, are any two sampling points in the three-dimensional ore body model. i 1. i The categorical attribute value of 2, L i For two sampling points i 1.i The distance between 2, G k is the sum of squares of the cumulative classification attribute value deviations of the sampling points, D k is the distance value of the cumulative classification of the sampling points, k is a classification attribute; Step S17: repeating steps S11-S16, after detecting the direction categories of all sampling points, calculating the variance function value in the classification direction; ; in, The sampling point along the direction The variance function value of is the average value of the classification distance of the sampling points, The number of classifications accumulated for the sampling points; Step S18: Taking the average value for x Axis, variogram value for y Axis summary direction The variation curve on Step S19: After calculating the variation curves in different directions, the boundaries of all variation curves are fitted to make the boundary surfaces smooth, thereby forming a three-dimensional ore body model.
[0020] Step S2: Determine sample labels based on the distribution of geochemical elements on the three-dimensional ore body model, and construct front edge samples, near-ore samples, and tailings samples respectively. Step S2 is specifically as follows: The front, main body and tail areas of the ore body are determined according to the shape of the three-dimensional ore body model. According to the division of the ore body area, the corresponding geochemical elements collected at the sampling points in the front, main body and tail areas are divided into front samples, near-mine samples and tailings samples, and corresponding sample labels are assigned to the front samples, near-mine samples and tailings samples.
[0021] Step S3: extracting characteristic combinations related to the ore halo type from the leading edge samples, near-ore samples and tailing samples to obtain the leading edge halo, near-ore halo and tail halo element combinations in the three-dimensional primary halo.
[0022] Step S3 specifically includes: Step S31: Using the geochemical elements in the front edge samples, near-mine samples or tailings samples to establish the independent variable matrix for each sample type , the independent variable matrix The elements in it are various geochemical elements; Step S32: Constructing a dependent variable matrix for screening the leading edge halo element combination in the leading edge sample, the near-mine halo element combination in the near-mine sample, or the tail halo element combination in the tailing sample , the dependent variable matrix is a categorical variable for the leading halo, near-ore halo, or tail halo elements; Step S33: The independent variable matrix and the dependent variable matrix Decomposed into the product of the score matrix and the loading matrix, plus the residual matrix; ; in, are the score matrices related to the independent variable matrix and the dependent variable matrix, are the loading matrices related to the independent variable matrix and the dependent variable matrix, are the residual matrices related to the independent variable matrix and the dependent variable matrix respectively; Step S34: Establish a regression model to predict the classification results; ; in, is the random error matrix, is the regression coefficient matrix of the regression model; the regression model establishes the independent variable matrix and the dependent variable matrix the relationship between; Step S35: The independent variable matrix Input into the regression model to calculate the predicted values of each geochemical element classification in the front edge sample, near-mine sample or tailings sample; ; in, is the output combination of leading edge halo, near-mine halo or tail halo elements, is the regression coefficient vector calculated according to the regression model.
[0023] Step S4: Based on the element combination of the leading edge halo, the near-ore halo and the tail halo, the superposition visualization of the element combination of the primary halo is realized through the three-dimensional implicit modeling method to construct a three-dimensional primary halo model. Step S4 specifically includes: Based on the corresponding leading edge halo, near-ore halo and tail halo element combinations classified in the leading edge, main body and tail areas, the superposition visualization of each primary halo element combination is realized on the three-dimensional ore body model through three-dimensional implicit modeling to construct a three-dimensional primary halo model.
[0024] After analysis, the leading halo element combination of this embodiment is: As-Sb-Hg; the near-ore halo element combination is: Au-Ag-Cu-Pb-Zn; the tail halo element combination is: W-Bi-Co-Mo; the three-dimensional primary halo model is as follows Figure 1As shown, the vertical direction is the depth direction. As the depth increases, the anomaly weakens. At a depth of more than 1500m, there is an obvious superposition of leading edge halo, near-ore halo and tail halo, indicating that there is still potential for mineral exploration in the deep. Combining these two aspects, it can be inferred that there are still hidden ore bodies in the deep, and the scale should be large.
[0025] The present invention is used to more accurately identify and extract the element combination of the leading halo, near-ore halo and tail halo in the three-dimensional primary halo, and can effectively extract the feature combination that is highly correlated with the ore halo type by maximizing the correlation between the independent variable (i.e., element characteristics) and the dependent variable (i.e., ore halo type). This method not only overcomes the limitation of the traditional method that relies on the prior knowledge of geological experts, but also can provide more objective and accurate prediction results.
[0026] The geochemical element combination characteristics of different regions in the three-dimensional primary halo can be quantitatively extracted, and the distribution range of the leading halo, near-ore halo and tail halo can be quickly identified based on this. This method not only simplifies the tedious parameter calculation process, but also can quickly display the three-dimensional spatial distribution of deep ore deposits, providing strong support for the rapid prediction and exploration of mineral resources.
[0027] In addition, the three-dimensional primary halo model has significant advantages over the traditional profile primary halo model. First, it can provide more comprehensive and scientific geochemical information, avoiding the one-sidedness and limitations of two-dimensional information. Second, due to the continuity of three-dimensional space, the three-dimensional primary halo model can help more accurately calculate the Grigoriyan zoning index and regional chemical parameters, thereby improving the accuracy and reliability of the prediction results. Finally, the three-dimensional primary halo model can better meet the needs of deep mineral prediction and effectively solve the three-dimensional spatial distribution problem of deep ore bodies.
Claims
1. A method for constructing a three-dimensional primary halo geochemical model, characterized in that: include: Step S1: Determine the research area of the ore body, collect geochemical elements in the research area, and determine the types of geochemical elements of the research ore body N , according to the collected N The spatial distribution of various geochemical elements is used to construct a three-dimensional ore body model; Step S2: Determine sample labels according to the distribution of geochemical elements on the three-dimensional ore body model, and construct front edge samples, near-ore samples and tailings samples respectively; Step S3: extracting characteristic combinations related to the ore halo type from the front edge samples, near-ore samples and tailing samples, and obtaining the front edge halo, near-ore halo and tail halo element combinations in the three-dimensional primary halo; Step S4: Based on the element combination of the leading edge halo, the near-ore halo and the tail halo, the superposition visualization of the element combination of the primary halo is realized through the three-dimensional implicit modeling method to construct a three-dimensional primary halo model.
2. The method for constructing a three-dimensional primary halo geochemical model according to claim 1, characterized in that: The method for constructing the three-dimensional ore body model is: Step S11: According to N Sampling coordinates of the geochemical elements in the study area , sampling coordinates In the three-dimensional coordinate system with the center of the research area as the origin, n Number the sampling point where the corresponding geochemical element was collected; Step S12: Based on the origin of the three-dimensional coordinate system o Coordinates Calculate the coordinate increment of each sampling point; ； Step S13: Determine the sampling point n Whether it is in a horizontal window; ； Among them, any point in the three-dimensional coordinate system a With origin o The vector is , For vector Projection on the horizontal plane, vector The azimuth is The inclination angle is , the tolerance angle is , For vector With vector The angle of sampling point n With origin o The connecting vector is , For vector projection on the horizontal plane; when When the sampling point n The sampling point falls in the horizontal window. n Satisfies the horizontal window classification, otherwise, does not satisfy the horizontal window classification, is the classification angle in the horizontal direction; Step S14: Determine the sampling point n Whether the vertical classification angle is in the vertical window ; ； in, For vector With vector The angle between Recorded as parameter ; when , sampling point n A window that falls in a vertical plane satisfies the vertical window classification; Step S15: When the sampling point n When both vertical window classification and horizontal window classification are satisfied, it indicates that the sampling point n If the sampling point falls within the three-dimensional ore body model, the sampling point is retained; otherwise, the surface sampling point n If the sampling point does not fall within the three-dimensional ore body model, the sampling point is deleted; the vertical plane window and the horizontal window are the classification spaces set based on the three-dimensional ore body model; Step S16: Establish a data set of sampling points in the three-dimensional ore body model based on the reserved sampling points , I is the number of sampling points in the three-dimensional ore body model, and the sampling points are classified according to the step size; ； in, are any two sampling points in the three-dimensional ore body model. i 1. i The categorical attribute value of 2, L i For two sampling points i 1. i The distance between 2, G k is the sum of squares of the cumulative classification attribute value deviations of the sampling points, D k is the distance value of the cumulative classification of the sampling points, k is a classification attribute; Step S17: repeating steps S11-S16, after detecting the direction categories of all sampling points, calculating the variance function value in the classification direction; ； in, The sampling point along the direction The variance function value of is the average value of the classification distance of the sampling points, The number of classifications accumulated for the sampling points; Step S18: Taking the average value for x Axis, variogram value for y Axis summary direction The variation curve on Step S19: After calculating the variation curves in different directions, the boundaries of all variation curves are fitted to make the boundary surfaces smooth, thereby forming a three-dimensional ore body model.
3. The method for constructing a three-dimensional primary halo geochemical model according to claim 1, characterized in that: The step S2 is specifically as follows: The front, main body and tail areas of the ore body are determined according to the shape of the three-dimensional ore body model. According to the division of the ore body area, the corresponding geochemical elements collected at the sampling points in the front, main body and tail areas are divided into front samples, near-mine samples and tailings samples, and corresponding sample labels are assigned to the front samples, near-mine samples and tailings samples.
4. The method for constructing a three-dimensional primary halo geochemical model according to claim 3, characterized in that: The step S3 comprises: Step S31: Using the geochemical elements in the front edge samples, near-mine samples or tailings samples to establish the independent variable matrix for each sample type , the independent variable matrix The elements in it are various geochemical elements; Step S32: Constructing a dependent variable matrix for screening the leading edge halo element combination in the leading edge sample, the near-mine halo element combination in the near-mine sample, or the tail halo element combination in the tailing sample , the dependent variable matrix is a categorical variable for the leading halo, near-ore halo, or tail halo elements; Step S33: The independent variable matrix and the dependent variable matrix Decomposed into the product of the score matrix and the loading matrix, plus the residual matrix; ； in, are the score matrices related to the independent variable matrix and the dependent variable matrix, are the loading matrices related to the independent variable matrix and the dependent variable matrix, are the residual matrices related to the independent variable matrix and the dependent variable matrix respectively; Step S34: Establish a regression model to predict the classification results; ； in, is the random error matrix, is the regression coefficient matrix of the regression model; the regression model establishes the independent variable matrix and the dependent variable matrix the relationship between; Step S35: The independent variable matrix Input into the regression model to calculate the predicted values of each geochemical element classification in the front edge sample, near-mine sample or tailings sample; ； in, is the output combination of leading edge halo, near-mine halo or tail halo elements, is the regression coefficient vector calculated according to the regression model.
5. The method for constructing a three-dimensional primary halo geochemical model according to claim 4, characterized in that: The step S4 comprises: Based on the corresponding leading edge halo, near-ore halo and tail halo element combinations classified in the leading edge, main body and tail areas, the superposition visualization of each primary halo element combination is realized on the three-dimensional ore body model through three-dimensional implicit modeling to construct a three-dimensional primary halo model.