Rapid modeling method for complex polymetallic ore resource quantity estimation mineralization domain

Abstract

The invention belongs to the technical field of resource quantity estimation modeling, and particularly relates to a rapid modeling method for a complex polymetallic ore resource quantity estimation mineralization domain. According to the method, multi-metal combination is carried out on a single-metal economic grade combination result, and mixed ore circling is realized by solving a union set; according to the method, numerical data is converted into classified data through binaryzation, numerical modeling is converted into geological modeling, data configuration participating in three-dimensional geological modeling is further simplified, and recognition and extraction of boundary points and implementation of implicit modeling are facilitated. According to the technical route provided by the invention, spatial variability of geological sample data, a mining method and economic and technical feasibility are comprehensively considered, and theoretical and actual requirements on mineralized body modeling are met. Moreover, the method is simple in process, reduces the workload of manual operation, remarkably improves the modeling efficiency, is high in technology repeatability, and is easy to further popularize and use.

Description

Technical Field

[0001] The invention belongs to the technical field of resource estimation modeling, and in particular relates to a rapid modeling method for complex polymetallic ore resource estimation mineralization domain. Background Art

[0002] The core issue of resource estimation is which sample data to use for valuation, and the estimation domain defines the range of sample points involved in resource estimation. The estimation domain consists of one or more mineralized domains, and the morphology of the mineralized domain has a direct impact on the resource estimation results. Therefore, delineating the mineralized domain through three-dimensional geological modeling of sample data is an important prerequisite and basis for carrying out resource estimation.

[0003] At present, implicit modeling based on the rapid reconstruction technology of 3D scattered surface has become an important development direction of 3D geological modeling. Implicit modeling can save a lot of time and avoid the cumbersome procedures from the traditional manual 2D interpretation of the profile to 3D solid modeling, so that geological engineers can focus their main energy on the study of geological laws and the setting of modeling valuation parameters instead of a lot of trivial vectorization work.

[0004] For simple layered or vein ore bodies, implicit modeling is significantly faster than traditional manual modeling. For complex ore bodies (i.e., ore bodies with significant changes in morphology within a short distance), the density of exploration projects and the complexity of spatial configuration will increase accordingly, so the ore body morphology will also be more complex. In this case, the difficulty of both manual modeling and implicit automatic modeling will increase significantly.

[0005] For the delineation of single metal ore bodies, the current three-dimensional mining software provides an economic grade combination (economic composition) method based on drill hole sample data. The economic grade combination of drill hole data not only takes into account the economic feasibility of ore body mining under specific mining technical conditions, but also increases the continuity of the mineralized body, making it easier to delineate and model. The economic grade combination mainly uses data such as industrial boundary grade, minimum mineable thickness determined by mining methods, and interlayer removal thickness to determine the main parameters such as the mineralized domain involved in the statistics, the minimum grade after economic grade combination, and the maximum allowable interlayer length. After the economic grade combination, the original discrete sample data is combined into a relatively continuous mineralized section, so the difficulty of delineation and modeling is also reduced.

[0006] Furthermore, for polymetallic co-existing deposits, the "mixed circle method" of "whoever has enough should circle" should be used to delineate polymetallic ore bodies. Since the distribution and enrichment patterns of different metal elements in the ore deposits are not completely consistent, the complexity of polymetallic ore bodies delineated by the mixed circle method is much greater than that of single metal ore bodies. The correspondence between ore bodies, interlayers, and surrounding rocks (interlayers and surrounding rocks are collectively referred to as "waste rock") between adjacent exploration sections is no longer so direct. Sometimes, there will even be a situation where a specific position of a section is an ore body, while the corresponding position of another section is interlayers or surrounding rocks, or the morphology of the ore body itself changes significantly. This makes it difficult to verify the model when using traditional 2D section lines to generate 3D entities, or the surface model constructed by implicit modeling does not conform to general geological laws. However, the current popular 3D mining software does not provide the operating procedures for mixed circle mining of polymetallic ore bodies, nor does it provide specific methods for how to further simplify the morphology of complex polymetallic ore bodies to facilitate the smooth completion of 3D modeling.

[0007] In summary, although implicit modeling based on 3D mining software can avoid time-consuming profile geological interpretation and significantly improve modeling efficiency, it is unable to efficiently and accurately estimate the mineralization domain of complex polymetallic mineral resources. Summary of the invention

[0008] In view of the problems existing in the prior art, the purpose of the present invention is to simplify complex geological problems according to the geological laws of mineralization distribution and the basic principles of resource estimation, so as to meet the requirements of resource estimation for model accuracy and precision while ensuring modeling efficiency. The present invention provides a rapid modeling method for the mineralized domain of complex polymetallic ore resource estimation. The core technical route is: through the economic grade combination of single metal sample data, and the merging (Merging) of multiple single metal economic grade combinations, to obtain a relatively continuous and complete mineralized section, and to binarize the mineralized domain and the non-mineralized domain, so as to achieve reasonable simplification of complex ore body morphology, and on this basis, use implicit modeling technology to finally complete the efficient and accurate modeling of the mineralized domain for resource estimation. The present invention specifically includes the following contents:

[0009] A rapid modeling method for complex polymetallic mineral resource estimation mineralization domain, comprising the following steps:

[0010] S1, generate a drilling database: establish a data form of the hole mouth, inclinometer, and sample files, import the data form into the three-dimensional mining software, and generate a drilling database; wherein the header of the sample file includes the project number, the starting position from, the ending position to, C n0 Data, the C n0is the original sample data of metal n, that is, the mass content of metal n in the original sample, wherein n represents a single metal in the polymetallic ore, and the type of n is an integer ≥ 2; the starting position and the ending position refer to the starting position and the ending position of the sample;

[0011] S2, economic grade combination of single metal raw sample data: according to the industrial cut-off grade C of metal n nE The minimum mineable thickness and the thickness of the interlayer stone removal are determined by the mining method, and then the minimum grade and the maximum allowable interlayer stone length parameters after the economic grade combination are determined. The economic grade combination of the original sample data of the single metal n is realized in the three-dimensional mining software, which is recorded as C n1 ; Identify mineralized sections with grades greater than the industrial cut-off;

[0012] S3, merging and binarization of mineralized sections after multiple single metal economic grade combinations: merging the mineralized sections after each single metal economic grade combination, assigning the merged mineralized sections to "Ore" and the non-mineralized sections to "Waste";

[0013] S4, generate mineralization domain model: extract the boundary points of "ore" and "waste"; perform three-dimensional surface rapid reconstruction based on the boundary points to generate the mineralization domain model;

[0014] S5, determine the final estimation domain: Based on the mineralization domain model, conduct boundary analysis to determine the final estimation domain, and then conduct resource estimation.

[0015] Specifically, the three-dimensional mining software is a geological modeling and grade estimation software with implicit modeling functions.

[0016] Specifically, the three-dimensional mining software described in S1 is Micromine software, Leapfrog software, or Datamine software.

[0017] Specifically, the merging and binarization of the mineralized sections after the combination of multiple single metal economic grades in S3 is achieved through the assignment function of the three-dimensional mining software, and the algorithm is:

[0018] If (if) C n1 ≥C nE ,

[0019] Then assign the merged result to "Ore".

[0020] Otherwise (else), the merged result is assigned to "waste".

[0021] Alternatively, the merging and binarization of the mineralized sections after the combination of multiple single metal economic grades described in S3 is performed by using the if function in Excel, and then the results are imported into the three-dimensional mining software, and the specific method is as follows:

[0022] Category=IF(OR(C n1 ≥C nE ), "ore", "waste"),

[0023] The result of screening out the merged sample segment is assigned as “Ore”, and the remaining sample segments are assigned as “Waste”.

[0024] Alternatively, the merging and binarization of the mineralized sections after the combination of multiple single metal economic grades in S3 is achieved by an iterative assignment method, the specific method being:

[0025] In 3D mining software or Excel,

[0026] First, assign a value to condition 1 and select "Metal 1" for expansion sorting. 11 ≥C 1E The sample segment is assigned the value of "ore";

[0027] Then assign a value to condition 2, select "Metal 2" for extended sorting, and for those that meet C 21 ≥C 2E The sample segment is assigned the value of "ore";

[0028] Repeat the above steps to assign "ore" to "metal n";

[0029] Finally, all sample segments are expanded and sorted, and all blank cells that have not been assigned a value twice are uniformly assigned the value of "waste".

[0030] It should be noted that the above technical process can be optimized according to the actual complexity of the polymetallic ore body. If the ore body is relatively simple in shape and has few intercalated stones, it is not necessary to carry out a single metal economic grade combination (obtain C n1 ), directly using the original sample point data (i.e. C n0 ), using step S3 to carry out mixed circle mining can obtain a relatively complete ore body shape, and three-dimensional modeling can be completed based on this, then step S2 can be omitted, and modeling and valuation can be completed through steps S1→S3→S4→S5.

[0031] Beneficial effects of the present invention:

[0032] (1) The present invention proposes a rapid modeling method for estimating the mineralized domain of complex polymetallic ore resources, and provides a specific operation method for delineating mineralized bodies using the "mixed circle method" that is not provided by current three-dimensional mining software. Other similar mixed circle methods can also be created and derived based on the present invention.

[0033] (2) The present invention innovatively combines the results of single-metal economic grade combinations into multiple metals, and realizes mixed mining by seeking a "union". This not only increases the continuity of polymetallic mineralization and reduces the difficulty of modeling, but also provides a new idea for delineating mineralized bodies using the polymetallic mixed mining method.

[0034] (3) The present invention converts numerical data into category data through binarization, and also converts numerical modeling into a geological modeling problem, further simplifying the data configuration involved in three-dimensional geological modeling, making it easier to identify and extract boundary points and implement implicit modeling.

[0035] (4) The technical route proposed by the present invention comprehensively considers the spatial variability of geological sample data, mining methods and economic and technical feasibility, and meets the theoretical and practical requirements for mineralized body modeling. In addition, the method has a simple process, reduces the workload of manual operations, significantly improves the modeling efficiency, and has strong repeatability, which is easy to further promote and use. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 is the original sample data of Pb in Example 1;

[0037] Figure 2 is the original sample data of Zn in Example 1;

[0038] Figure 3 is the Ag original sample data in Example 1;

[0039] Figure 4 The binarization result after merging the original sample data of Pb-Zn-Ag in Example 1;

[0040] Figure 5 The economic grade combination data of Pb in Example 1;

[0041] Figure 6 The economic grade combination data of Zn in Example 1;

[0042] Figure 7 Ag economic grade combination data in Example 1;

[0043] Figure 8 is the binarization result of the Pb economic grade combination data in Example 1;

[0044] Fig. 9is the binarization result of the Zn economic grade combination data in Example 1;

[0045] Fig.10 is the binarization result of the Ag economic grade combination data in Example 1;

[0046] Fig.11 It is the binarization result after the Pb-Zn-Ag economic grade combination data in Example 1 are merged;

[0047] Fig.12 The economic grade combination data of Pb in Example 1 (cross-section);

[0048] Fig.13 The economic grade combination data of Zn in Example 1 (cross-section);

[0049] Fig.14 The economic grade combination data of Ag in Example 1 (cross-section);

[0050] Fig.15 The binarization result (cross-section) of the Pb economic grade combination data in Example 1;

[0051] Fig.16 The binarization result (cross-section) of the Zn economic grade combination data in Example 1;

[0052] Fig.17 The binarization result (cross-section) of the Ag economic grade combination data in Example 1;

[0053] Fig.18 The binary result (cross-section) of the Pb-Zn-Ag economic grade combination data after merging in Example 1;

[0054] Fig.19 It is the implicit modeling result after extracting the ore-waste rock contact point based on the binary data in Example 1;

[0055] Fig. 20 It is the implicit modeling result based on the binary data in Example 1 (cross-section diagram);

[0056] Fig.21 Comparison between the binarized data and the implicit modeling results in Example 1;

[0057] Fig. 22 This is the boundary analysis based on the mineralized domain in Example 1;

[0058] Fig.23 The Pb grade estimation results in Example 1 are compared with the 10m equal length combined sample data;

[0059] Fig.24The Zn grade estimation results in Example 1 are compared with the data of the 10m equal length combined sample;

[0060] Fig.25 The Ag grade estimation results in Example 1 are compared with the data of the 10m equal length combined sample;

[0061] Fig.26 The merging and binarization method based on the three-dimensional mining software (Leapfrog) in Example 1;

[0062] Fig. 27 The merging and binarization method based on the if function in Excel in Example 1;

[0063] Figure 28 (a)-(i) is a merging and binarization method based on the Excel iteration method in Example 1, wherein:

[0064] Figure 28(a) is a screenshot of the page for sorting the Ag economic grade combination data;

[0065] Figure 28(b) is a screenshot of the page where the data with Ag≥40 is assigned the value “ore”;

[0066] Figure 28(c) is a screenshot of the page for sorting Pb economic grade combination data;

[0067] Figure 28(d) is a screenshot of the page that assigns the data with Pb≥0.3 to “ore”;

[0068] Figure 28(e) is a screenshot of the page for sorting Zn economic grade combination data;

[0069] Figure 28(f) is a screenshot of the page that assigns the data with Zn≥0.5 to “ore”;

[0070] Figure 28(g) is a screenshot of the page for finding unassigned blank areas;

[0071] Figure 28(h) is a screenshot of the page assigned the value "waste";

[0072] Figure 28(i) is a screenshot of the completed binarization page. DETAILED DESCRIPTION

[0073] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. The embodiments shown below do not limit the invention content described in the claims. In addition, the entire contents of the structures represented by the following embodiments are not limited to those necessary as solutions to the invention described in the claims.

[0074] A rapid modeling method for complex polymetallic mineral resource estimation mineralization domain, comprising the following steps:

[0075] S1, generate a drilling database: create a data form (such as an Excel data form) of the borehole, inclinometer, and sample files, import the data form into the three-dimensional mining software, and generate a drilling database; wherein the header of the sample file includes the project number, the starting position from and the ending position to of the sample, and the C n0 Data, the C n0 is the original sample data of metal n, wherein n represents a single metal in a polymetallic ore, and the type of n is an integer ≥ 2; the three-dimensional mining software is a geological modeling and grade estimation software with implicit modeling function, preferably Micromine software, Leapfrog software, or Datamine software.

[0076] S2, economic grade combination of single metal raw sample data: according to the industrial cut-off grade C of metal n nE The minimum mineable thickness and the thickness of the interlayer stone removal are determined by the mining method, and then the minimum grade and the maximum allowable interlayer stone length parameters after the economic grade combination are determined. The economic grade combination of the original sample data of the single metal n is realized in the three-dimensional mining software, which is recorded as C n1 ; Identify mineralized sections with grades greater than the industrial cut-off;

[0077] Specifically, this step can be achieved through the following three methods:

[0078] The first method is realized through the assignment function of the three-dimensional mining software, and its algorithm is:

[0079] If (if) C n1 ≥C nE ,

[0080] Then assign the merged result to "Ore".

[0081] Otherwise (else), the merged result is assigned to "waste".

[0082] Alternatively, the second method is to use the if function in Excel to merge and binarize, and then import the results into the 3D mining software. The specific method is as follows:

[0083] Category=IF(OR(C n1 ≥C nE ), "ore", "waste"),

[0084] The result of screening out the merged sample segment is assigned as “Ore”, and the remaining sample segments are assigned as “Waste”.

[0085] Or, the third method is to implement it by iterative assignment method, the specific method is:

[0086] In 3D mining software or Excel,

[0087] First, assign a value to condition 1 and select "Metal 1" for expansion sorting. 11 ≥C 1E The sample segment is assigned the value of "ore";

[0088] Then assign a value to condition 2, select "Metal 2" for extended sorting, and for those that meet C 21 ≥C 2E The sample segment is assigned the value of "ore";

[0089] Repeat the above steps to assign "ore" to "metal n";

[0090] Finally, all sample segments are expanded and sorted, and all blank cells that have not been assigned a value twice are uniformly assigned the value of "waste".

[0091] S3, merging and binarization of mineralized sections after multiple single metal economic grade combinations: merge the mineralized sections after each single metal economic grade combination, assign the merged mineralized sections to "Ore", and assign the non-mineralized sections to "Waste".

[0092] S4, generate a mineralization domain model: extract the boundary points of "ore" and "waste"; perform rapid three-dimensional surface reconstruction based on the boundary points to generate a mineralization domain model.

[0093] S5, determine the final estimation domain: Based on the mineralization domain model, conduct boundary analysis to determine the final estimation domain, and then conduct resource estimation.

[0094] Example 1

[0095] The deposit in this example is located in the Eastern Province of Mongolia. It is a volcanic hydrothermal breccia-type Pb-Zn-Ag polymetallic deposit controlled by faults. The polymetallic ore body strikes NW340°, dips SW, is nearly upright, and has a cylindrical three-dimensional shape as a whole. The exploration type of the deposit is Class II, and the basic exploration engineering spacing is 100m×100m. The ore body is stored at an elevation of 1090~420m, and is designed for underground mining, with a middle section elevation of 60m. The polymetallic mineralization is controlled by volcanic hydrothermal breccia, showing a network vein and branching composite characteristics in plane and section. Production exploration revealed that the morphology of the mineralized bodies in adjacent sections varies greatly. The upper part of the ore body has a high degree of control, with an overall area of ​​50×50m from 825 to 765m, and some sections are encrypted to 20~25×25m during production exploration. The spacing of the middle section below 765m is relatively large, most of which are 100×100m, and a small number of projects are 50×50m. The depth and edge of the deposit are not completely controlled, and there is still a certain potential for prospecting. 906 drill holes of various types have been constructed in the deposit, with a cumulative footage of 102,645m and 440,828 samples analyzed.

[0096] Traditional 3D modeling methods are more applicable to layered or vein-like deposits. The 2D profile geological interpretation of these deposits and the connection of the boundaries of mineralized bodies in different profiles are relatively simple. However, for the cylindrical ore body (containing a large amount of intercalated stone) controlled by volcanic breccia involved in this embodiment, the traditional modeling method seems helpless. According to the distribution characteristics of mineralization in three-dimensional space, the mine divides the entire ore deposit into multiple vein-like ore bodies to realize the 3D geological modeling of the ore deposit. However, this treatment method destroys the geological and statistical continuity of mineralization, and increases the uncertainty of mine production exploration, engineering excavation and grade control.

[0097] This embodiment realizes the rapid and accurate delineation of complex polymetallic mineralization bodies based on the new method created by the present invention. The specific implementation process is as follows.

[0098] S1, create an Excel data form for the hole (Collar), inclinometer (Survey), and sample (Assay) files, import them into the 3D mining software, and generate a drilling database. The header of the sample file should include the project number (HoleID), the starting position of the sample (from, in m), the ending position (to, in m), the sample number (Sample), the sample length (Length), Pb (C Pb0 , wt.%)、Zn(C Zn0 , wt. %) and Ag(C Ag0 , g / t) and other fields. The sample length of the original sample data is 0.2 to 9.8 m, with an average of 1.6 m. Drilling project distribution and Pb (C Pb0 )、Zn(C Zn0 )、Ag(C Zn0 ) Mineralization characteristics are shown in Figure 1-3 It can be seen that the original sample data varies greatly in the well direction (drilling direction) and between adjacent boreholes, making it almost impossible to directly interpret the geological structure or connect the profile ore bodies. Pb0 )、Zn(C Zn0 )、Ag(C Zn0 ) The original sample data is merged and binarized (step S3 is executed), and the results are shown in Figure 4 The continuity of this result is significantly improved compared with the original sample data, but there are still a lot of intercalated rocks in the well, which affects the interpretation of the ore body of a single drill hole. Therefore, it is necessary to further carry out economic grade combination of the original sample data according to step S2.

[0099] S2, the economic grade combination of the original sample data. Refer to the cut-off grade of general industrial indicators to determine C PbE =0.3%, C ZnE =0.5%, C AgE =40g / t, that is, Pb(C Pb0 )、Zn(C Zn0 )、Ag(C Zn0 ) are respectively greater than or equal to 0.3%, 0.5% and 40g / t. Only the original sample data will participate in the economic grade combination. Above the 945m level of the ore deposit, the medium-deep hole segmented rock drilling stage mine room method is mainly adopted, with a middle section height of 60m and a segment height of 11m; below 945m, the large-diameter deep hole stage empty field and subsequent filling mining method is mainly adopted, with a middle section height of 60m. Combined with the basic sample length average of 1.6m, the minimum mineable thickness and the thickness of the interlayer stone removal can be set between 1.5~1.6m and 10~11m. The larger the value, the better the continuity of the combined borehole grade, and the easier it is to delineate the mineralized body, but at the same time, the introduction of interlayer stones will increase, and the depletion rate of the mining area will increase. The invention selects twice the basic sample length, that is, the minimum mineralized body combination length (minimum mineable thickness) is 3m, and the interlayer stone removal thickness and the maximum allowable continuous length of interlayer stones are both 3m. In the three-dimensional mining software, the above parameters are set to respectively calculate Pb(C Pb0 )、Zn(C Zn0 )、Ag(C Zn0 ) The economic grade combination of the original sample data is carried out, and the combined results are recorded as C Pb1 , C Zn1 , C Ag1 After economic grade combination, Pb(C Pb1 )、Zn(C Zn1 )、Ag(C Ag1 ) Mineralization distribution see Figure 5-7 , Figure 12-14 The corresponding binarization results are shown in Figure 8-10 , Figure 15-17 .

[0100] S3, merge and binarize multiple single metal economic grade combinations. Pb1 )、Zn(C Zn1 )、Ag(C Ag1 ) is greater than the industrial cut-off grade (C PbE =0.3%, C ZnE =0.5%, C AgE =40g / t), the mineralized sections of each metal are merged, and the merged mineralized section is assigned to "Ore" and the non-mineralized section is assigned to "Waste" (the method of merging and binarization is detailed in Figure 26 to Figure 2 8), the results of merging and binarization are shown in Fig.11 and Fig.18 .

[0101] S4, extract the boundary points of the above-mentioned "ore" and "waste"; perform rapid three-dimensional surface reconstruction based on the boundary points to generate a mineralization domain model ( Figure 19-20 ).from Fig.21 It can be seen that the implicit modeling results based on binary data can include the vast majority of mineralized sections, indicating that this technical method is accurate, efficient and feasible.

[0102] S5, based on the above mineralization domain model, boundary analysis was carried out. The boundary analysis was carried out using the original data 10m equal length combined sample data. The boundary analysis results showed that the Pb, Zn and Ag grades inside and outside the mineralization domain boundary showed significant differences, which is a typical hard boundary feature ( Fig. 22 ). Therefore, the mineralized area delineated is the final estimated area. The resource estimation uses 10m long sample data, and the valuation method is the inverse power method of distance. The comparison between the valuation block model results and the 10m long sample data is shown in Figure 23-25 It can be seen that the valuation results are generally consistent with the sample data, and the local verification effect is good.

[0103] In this embodiment, the original sample data of Pb, Zn and Ag are merged and binarized. Although the continuity of mineralization is increased to a certain extent and the difficulty of mineralization body delineation is reduced, it still cannot meet the requirements of fast and efficient modeling. Therefore, step S2 cannot be omitted. The application results show that by implementing steps S1→S5, the invention can achieve fast, accurate and efficient delineation of the mineralized domain for resource estimation of complex polymetallic deposits, providing an important three-dimensional model basis for the final resource estimation. This method is worthy of further promotion and use.

[0104] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A rapid modeling method for complex polymetallic ore resource estimation mineralization domain, characterized in that: The following steps are involved: S1, generate a drilling database: establish a data form of the hole opening, inclinometer, and sample files, import the data form into the three-dimensional mining software, and generate a drilling database; wherein the header of the sample file includes the project number, the starting position, the ending position, C n0 Data, the C n0 is the original sample data of metal n, where n represents a single metal in a polymetallic ore, and the type of n is an integer ≥ 2; S2, economic grade combination of single metal raw sample data: according to the industrial cut-off grade C of metal n nE The minimum mineable thickness and the thickness of the interlayer stone removal are determined by the mining method, and then the minimum grade and the maximum allowable interlayer stone length parameters after the economic grade combination are determined. The economic grade combination of the original sample data of the single metal n is realized in the three-dimensional mining software, which is recorded as C n1 ; Identify mineralized sections with grades greater than the industrial cut-off; S3, merging and binarization of mineralized sections after multiple single metal economic grade combinations: merging the mineralized sections after each single metal economic grade combination, assigning the merged mineralized sections to "Ore", and assigning the non-mineralized sections to "Waste"; S4, generate mineralization domain model: extract the boundary points of "ore" and "waste"; carry out three-dimensional surface rapid reconstruction based on the boundary points to generate the mineralization domain model; S5, determine the final estimation domain: Based on the mineralization domain model, conduct boundary analysis to determine the final estimation domain, and then conduct resource estimation.

2. A rapid modeling method for complex polymetallic ore resource estimation mineralization domain according to claim 1, characterized in that: The three-dimensional mining software is a geological modeling and grade estimation software with implicit modeling functions.

3. A rapid modeling method for complex polymetallic ore resource estimation mineralization domain according to claim 2, characterized in that: S1 The three-dimensional mining software is Micromine software, Leapfrog software, or Datamine software.

4. The rapid modeling method for complex polymetallic ore resource estimation mineralization domain according to any one of claims 1 to 3, characterized in that: The merging and binarization of the mineralized sections after the combination of multiple single metal economic grades in S3 is achieved through the assignment function of the three-dimensional mining software, and the algorithm is: If C n1 ≥C nE , Then assign the merged result to "Ore". Otherwise, the merged result is assigned to "waste".

5. The rapid modeling method for complex polymetallic ore resource estimation mineralization domain according to any one of claims 1 to 3, characterized in that: The merging and binarization of the mineralized sections after the combination of multiple single metal economic grades in S3 are performed by using the if function in Excel, and then the results are imported into the three-dimensional mining software. The specific method is as follows: Category=IF(OR(C n1 ≥C nE ),"ore","waste"), The result of the filtered and merged sample segment is assigned as "Ore", and the remaining sample segments are assigned as "Waste".

6. The rapid modeling method for complex polymetallic ore resource estimation mineralization domain according to any one of claims 1 to 3, characterized in that: The merging and binarization of the mineralized sections after the combination of multiple single metal economic grades in S3 is achieved by an iterative assignment method, and the specific method is as follows: In 3D mining software or Excel, First, assign a value to condition 1, select "Metal 1" for expansion sorting, and for those that meet C 11 ≥C 1E The sample segment is assigned the value "ore"; Then assign a value to condition 2, select "Metal 2" for extended sorting, and for those that meet C 21 ≥C 2E The sample segment is assigned the value "ore"; Repeat the above steps to assign "ore" to "metal n"; Finally, all sample segments are expanded and sorted, and all blank cells that have not been assigned a value twice are uniformly assigned the value of "waste".

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