Mine field zoning method based on regional uranium resources

Abstract

The invention provides a mining field zoning method based on regional uranium resources. The invention relates to a utilization method for carrying out resource integration on regional uranium ore of a uranium field based on a spatial clustering method, which is specifically realized by adopting a two-step clustering method, the first step is based on a three-dimensional block model of an ore deposit, uranium resource clustering in each ore deposit is carried out, class clusters are formed, and the number of the class clusters is far smaller than that of the block model; and 2, on the basis of uranium resource clustering in each ore deposit, carrying out ore deposit clustering in the ore field range based on the spatial distribution of the ore deposits to realize ore field zoning. After the method is adopted to partition the mine field, full utilization of mine field resources can be achieved in the subsequent uranium mine mining process, the number of'dull ores' can be reduced, and after regional uranium resources are integrated, the automation and intelligence level of the mining process, subsequent waste treatment and the like can be improved, the mining cost can be reduced, and environmental protection is facilitated.

Description

Technical Field

[0001] The invention relates to the technical field of uranium mining, in particular to a method for zoning a mine field based on regional uranium resources. Background Art

[0002] my country's hard rock regions are rich in uranium resources, and the resources are reliable. The main characteristics of resource occurrence are that the deposits are multiple, small, and scattered, with small and medium-sized deposits as the main ones and large deposits accounting for a small proportion. There are thousands of ore bodies in the ore field, with complex ore body shapes, different sizes of ore deposits, and different numbers and spatial distribution characteristics of ore bodies in each ore deposit.

[0003] At present, regional uranium resource mining adopts the traditional mining model of independent, scattered and small-scale mining, which has problems such as low utilization rate of mineral resources, high mining cost and low level of automation and intelligence. Specifically, many "idle mines" are formed, making it difficult to form uranium resource aggregation and integration and increase production and expand capacity; each ore deposit has an independent mining model, the resource mining methods in the region are chaotic, there are multiple development and transportation systems, repeated investment in engineering construction, and high production costs, which restrict the improvement of the economic benefits of mines; it is not conducive to the centralization and minimization of waste, and brings many unfavorable factors to the environmental protection of ore deposit development. Summary of the invention

[0004] The purpose of the present invention is to provide a method for zoning a mine field based on regional uranium resources. The method integrates uranium resources within the mine field according to the resource domain endowment conditions, realizes the concentration of uranium resources in the region, optimizes the allocation of uranium resources, and lays the foundation for the overall planning of the mine field.

[0005] The present invention is achieved in that: The method for zoning a mine field based on regional uranium resources provided by the present invention comprises the following steps: S1: Construct a three-dimensional block model of all ore bodies in the uranium resource-based ore field; S2: extracting the three-dimensional block model attribute information of each ore body in the ore field as input data; S3: Determine the adaptation parameters of each ore deposit through the density-based clustering algorithm DBSCAN, which includes the neighborhood radius and the minimum number of points within the neighborhood radius to become a core object; S4: The density-based clustering algorithm DBSCAN is used to cluster the ore bodies in each ore deposit to obtain the clustering results of the ore bodies in the ore deposit; S5: Taking the clustering results of the ore bodies in each ore deposit in step S4 as input data, clustering the ore deposits in the ore field through the hierarchical clustering algorithm HAC, and finally realizing the ore field zoning based on regional uranium resources.

[0006] Preferably, step S2 specifically comprises: using mining software to extract the three-dimensional spatial coordinates, grade, and size of the three-dimensional block model of each ore body of the ore deposit as input data.

[0007] Preferably, step S3 comprises the following steps: S301: Selecting the three-dimensional space coordinates and the grade of the three-dimensional block model to form four-dimensional attribute information data, and determining the k value in the k nearest neighbor algorithm according to the four-dimensional attribute information data; S302: Calculate the distance from each three-dimensional block model to the kth closest point to it, sort these distances from large to small and draw a k-distance graph; S303: Find the distance of the location where the change trend changes sharply from the k-distance graph, and use it as the value of the neighborhood radius; S304: Set the minimum number of points within the neighborhood radius that become a core object to be k+1; S305: Traverse all the ore deposits in the ore field according to the above steps, and determine the adaptation parameters of each ore deposit in the ore field based on the DBSCAN algorithm.

[0008] More preferably, in step S301, the value of k is: k=2×dimension-1.

[0009] Preferably, step S4 comprises the following steps: S401: Bring the adaptation parameters of each ore deposit into the DBSCAN algorithm for solution, and obtain clusters after clustering based on the three-dimensional block model; S402: Determine the central spatial coordinates, average grade, number of blocks included, and resource weight attribute indicators of each ore body clustering cluster of each ore deposit, where the resource weight is the sum of the product of the volume of each block model included in the cluster and the corresponding block grade.

[0010] Preferably, step S5 comprises the following steps: taking the result of clustering of ore bodies within each ore deposit as input data, using the Ward method in the linkage function of the hierarchical clustering algorithm HAC to perform spatial clustering of resources within the ore field, and obtaining the ore deposit aggregation result.

[0011] More preferably, the Ward method is to find the best cluster merging method by minimizing the sum of variances within the clusters, specifically: ① Calculate the ESS of each cluster; ② Enumerate all possible situations where clusters are grouped in pairs, and calculate the increment △ESS of ESS after merging all possible two clusters; ③ Select the two clusters with the smallest increment △ESS to merge; ④ Repeat the above process until the number of clusters is reduced to 1; The calculation formula of ESS is as follows.

[0012] In the formula, Xij represents the jth eigenvalue of the i-th cluster, is the mean of the j-th dimension feature, ρ is the feature dimension, and n is the number of clusters.

[0013] The present invention realizes the resource integration and utilization of regional uranium mines in a uranium ore field based on a spatial clustering method, specifically: a two-step clustering method is adopted to realize the integration and utilization, the first step is to cluster the uranium resources in each ore deposit based on a three-dimensional block model of the ore deposit to form clusters, and the number of clusters is much smaller than the number of block models; the second step is to cluster the ore deposits based on the spatial distribution of the ore deposits within the ore field on the basis of the clustering of uranium resources in each ore deposit to realize the zoning of the ore field.

[0014] After the mine field is zoned by the method of the present invention, the mine field resources can be fully utilized in the subsequent uranium mining process, the number of "idle mines" can be reduced, and after the regional uranium resources are integrated, the automation and intelligence level of the mining process and subsequent waste treatment can be improved, the mining cost can be reduced, and it is beneficial to environmental protection.

[0015] The present invention can realize the reasonable zoning and integration of regional uranium resources, and provide technical support for the unified planning and development of regional uranium resources and the improvement of resource utilization level. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of the method for zoning a mineral field based on regional uranium resources of the present invention.

[0017] Figure 2 It is the k-distance graph of the No. 10 ore deposit in the embodiment of the present invention.

[0018] Figure 3 It is a tree structure diagram formed after spatial clustering of mineral resources in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The method for zoning a mineral field based on regional uranium resources provided by the present invention includes two stages, the first stage is the preliminary aggregation of ore bodies in the mineral deposit, and the second stage is the zoning and clustering of mineral deposits in the mineral field. In the first stage, the ore bodies within each mineral deposit are clustered in the ore body dense area based on the DBSCAN clustering method. The output result of the first stage is used as the input of the second stage, and in the second stage, the mineral field is clustered and partitioned based on the HAC clustering algorithm.

[0020] Combination Figure 1 The steps of the method for zoning a mine field based on regional uranium resources provided by the embodiment of the present invention are as follows: S1: Use mining software (3DMine) to construct a three-dimensional block model of all ore deposits in the uranium resource-based mining field.

[0021] S2: Use mining software to extract the spatial coordinates (x, y, z), grade (u), and size (size_x, size_y, size_z) data of the ore block model of each ore deposit in turn.

[0022] S3: The DBSCAN algorithm (density-based clustering algorithm) is used to determine the adaptation parameters of each mineral deposit. The adaptation parameters of each mineral deposit include the neighborhood radius and the minimum number of points within the neighborhood radius to become a core object.

[0023] Each ore body in the deposit is regarded as a point, and the distance from each point in the deposit to the kth closest point is calculated (k nearest neighbor algorithm). Then, after sorting these distances from large to small, a k-distance graph is drawn. The distance of the location where the trend changes sharply is found from the k-distance graph as the value of the neighborhood radius (Eps). Within the neighborhood radius of a certain point, if there are enough points (at least MinPts, MinPts is recorded as the threshold), this area is considered to be a high-density area, which can be expanded into a cluster, and the cluster is expanded through density-connected points. MinPts is used here to determine whether a point is a core object. Assuming that MinPts is set to 4, then if there are at least 4 points close to the point, the point is a core object. If there are less than 4 points near a point, the point is considered a non-core object.

[0024] According to the characteristics of uranium resources and the block model attribute information, four attributes of three-dimensional spatial coordinates (x, y, z) and grade (u) are selected in the clustering process of the DBSCAN algorithm in this embodiment, that is, the dimension is 4; and the value of k is usually k=2×dimension-1, so k=7.

[0025] Generally speaking, MinPts≥the dimension of the data set + 1. In this embodiment, MinPts=k+1; since the value of k is 7, MinPts=8.

[0026] In this embodiment, block models of 21 ore deposits in the mining field are selected, and the DBSCAN algorithm adaptation parameters (domain radius and threshold) of the 21 ore deposit block models are obtained according to the above method, as shown in Table 1 below.

[0027] Table 1 Algorithm adaptation parameters of 21 ore body models in this example like Figure 2 As shown, Figure 2 This is the k-distance graph of deposit No. 10 when k=7. Figure 2 It can be seen that the distance of the inflection point in the k-distance graph is the value of Eps. Figure 2The middle inflection point position: 2, inflection point value: 4.0, so Eps=4.0.

[0028] S4: After obtaining the DBSCAN algorithm adaptation parameters of 21 mineral deposits, the DBSCAN algorithm is used to cluster the ore bodies in the mineral deposits to generate clusters, greatly reducing the number of blocks. After clustering the mineral deposits in turn, the central spatial coordinates of the ore body clusters of each mineral deposit are obtained. , average grade, number of blocks (count), resource weight (weight) and other attribute information, a total of 4076 clusters of varying sizes were obtained. The resource weight (weight) is the sum of the product of the volume of each block model contained in the cluster and the corresponding block grade.

[0029] S5: The clustering results of the ore bodies of each ore deposit in the first stage are used as the input data of the second stage clustering, and the hierarchical clustering algorithm (Hierarchical Agglomerative Clustering, HAC) is used for clustering. The HAC algorithm uses the linkage function to solve the model. linkage() is a function model used to generate a clustering hierarchy. Its input data is preprocessed data. The hierarchical clustering algorithm often depends on the scale of the data. Therefore, before clustering, the data is usually standardized. The output of the hierarchical clustering algorithm is the hierarchical structure of the clustering results.

[0030] The core parameter of the HAC algorithm is the similarity measurement method, which is used to specify the criteria for merging clusters in hierarchical clustering. The Ward method finds the best cluster merging strategy by minimizing the sum of variances within all clusters, which makes it tend to generate spherical clusters of similar size, which can maximize the quality of clustering. Therefore, the HAC algorithm of the present invention chooses to use Ward to make cluster merging judgments. The Ward method minimizes the total variance of each cluster as much as possible, so it will choose to merge the two clusters that minimize the variance within the cluster, and finally generate more uniform clusters.

[0031] At the same time, the present invention selects to use the Euclidean distance measurement method in combination with the Ward similarity measurement method, and the two are combined to calculate the square error distance between clusters.

[0032] Ward's method can minimize the intra-cluster variance. In each step, consider every possible combination of two clusters, and merge the two clusters with the smallest error sum-of-squares criterion (ESS) increment after merging. Here, the error sum-of-squares increment is represented by △ESS. For example, if cluster A and cluster B are merged, the error sum-of-squares increment △ESS is: △ESS=ESS A∪B-(ESS A +ESS B ) ESS A∪B It represents the sum of squared errors after cluster A and cluster B are merged, ESS A and ESS B Represent the sum of squared errors of cluster A and cluster B respectively.

[0033] The optimal control of intra-cluster variance is achieved by minimizing △ESS.

[0034] The calculation formula of ESS is as follows: In the formula, X ij represents the jth eigenvalue of the i-th cluster, is the mean of the j-th dimension feature, ρ is the characteristic dimension. The dimensions selected in this embodiment include the center space coordinates and resource weight, so ρ is 4; n is the number of clusters. ESS is essentially the sum of squares of the Euclidean distances from the cluster to its mean point, reflecting the degree of dispersion of the data in the feature space.

[0035] The Ward method requires that the increment △ESS of ESS after each merger is the smallest. The specific steps are as follows: ① Calculate the ESS of each cluster. Initially, each cluster is a separate class, and the ESS of all clusters is 0.

[0036] ② Group the clusters in pairs and enumerate all possible grouping situations. When the number of clusters is n, there are n×(n-1) / 2 possible pairwise grouping situations. Calculate the increment △ESS of ESS after merging all possible two clusters.

[0037] ③ Select the two clusters with the smallest incremental △ESS to merge, and use the merged cluster as the new cluster.

[0038] ④ Repeat the above process until the number of clusters n is reduced to 1.

[0039] By traversing and calculating the △ESS after merging every two clusters, finding the two clusters with the smallest △ESS after merging, connecting the clusters to form a new cluster, and describing the cluster by the maximum distance required to connect the parts, different clusters are formed at different distances, which can be presented using a dendrogram. In the dendrogram, the y-axis marks the distance of cluster merging, and the deposits are placed along the x-axis to prevent the clusters from mixing.

[0040] Based on the results of the first phase of internal resource clustering in the mining area, the HAC algorithm is used to perform spatial clustering of resources within the mining area to obtain the clustering results of the ore deposits. The tree structure formed by the final clustering is as follows: Figure 3 As shown. Figure 3 It can be seen from the tree diagram that the mineral resources are roughly divided into two parts. Considering the requirements of subsequent production scale and intensive development, the optimal partitioning scheme is to select four clustering areas (as shown by the dotted line in the figure).

[0041] In the embodiment of the present invention, the HAC clustering algorithm is used to form a tree structure, and the 21 ore deposits in the mining field are finally integrated into 4 areas, as shown in Table 2 below.

[0042] Table 2 HAC clustering results Depend on Figure 3 As shown in Table 2, the two-step clustering method of the present invention can eventually realize the partition clustering of uranium resource mines, which lays a foundation for subsequent mining and intelligent processing.

Claims

1. A method for zoning a mine field based on regional uranium resources, characterized in that: The steps include: S1: Construct a three-dimensional block model of all ore bodies in the uranium resource-based ore field; S2: extracting the three-dimensional block model attribute information of each ore body in the ore field as input data; S3: Determine the adaptation parameters of each ore deposit through the density-based clustering algorithm DBSCAN, which includes the neighborhood radius and the minimum number of points within the neighborhood radius to become a core object; S4: The density-based clustering algorithm DBSCAN is used to cluster the ore bodies in each ore deposit to obtain the clustering results of the ore bodies in the ore deposit; S5: Taking the clustering results of the ore bodies in each ore deposit in step S4 as input data, clustering the ore deposits in the ore field through the hierarchical clustering algorithm HAC, and finally realizing the ore field zoning based on regional uranium resources.

2. The method for zoning a mine field based on regional uranium resources according to claim 1, characterized in that: Step S2 specifically includes: using mining software to extract the three-dimensional spatial coordinates, grade, and size of the three-dimensional block model of each ore body of the ore deposit as input data.

3. The method for zoning a mine field based on regional uranium resources according to claim 2, characterized in that: Step S3 includes the following steps: S301: Selecting the three-dimensional space coordinates and the grade of the three-dimensional block model to form four-dimensional attribute information data, and determining the k value in the k nearest neighbor algorithm according to the four-dimensional attribute information data; S302: Calculate the distance from each three-dimensional block model to the kth closest point to it, sort these distances from large to small and draw a k-distance graph; S303: Find the distance of the location where the change trend changes sharply from the k-distance graph, and use it as the value of the neighborhood radius; S304: Set the minimum number of points within the neighborhood radius that become a core object to be k+1; S305: Traverse all the ore deposits in the ore field according to the above steps, and determine the adaptation parameters of each ore deposit in the ore field based on the DBSCAN algorithm.

4. The method for zoning a mine field based on regional uranium resources according to claim 3, characterized in that: In step S301, the value of k is: k=2×dimension-1.

5. The method for zoning a mine field based on regional uranium resources according to claim 1, characterized in that: Step S4 includes the following steps: S401: Bring the adaptation parameters of each ore deposit into the DBSCAN algorithm for solution, and obtain clusters after clustering based on the three-dimensional block model; S402: Determine the central spatial coordinates, average grade, number of blocks included, and resource weight attribute indicators of each ore body clustering cluster of each ore deposit, where the resource weight is the sum of the product of the volume of each block model included in the cluster and the corresponding block grade.

6. The method for zoning a mine field based on regional uranium resources according to claim 1, characterized in that: Step S5 includes the following steps: taking the clustering results of the ore bodies in each ore deposit as input data, using the Ward method in the linkage function of the hierarchical clustering algorithm HAC to perform spatial clustering of uranium resources within the ore field, and obtaining the ore deposit clustering results.

7. The method for zoning a mine field based on regional uranium resources according to claim 6, characterized in that: Ward's method is to find the best cluster merging method by minimizing the sum of variance within the cluster, specifically: ① Calculate the ESS of each cluster; ② Enumerate all possible situations where clusters are grouped in pairs, and calculate the increment △ESS of ESS after merging all possible two clusters; ③ Select the two clusters with the smallest increment △ESS to merge; ④ Repeat the above process until the number of clusters is reduced to 1; The calculation formula of ESS is as follows: In the formula, X ij represents the jth eigenvalue of the i-th cluster, is the mean of the j-th dimension feature, ρ is the feature dimension, and n is the number of clusters.

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