Ore prospecting prediction method, device and system based on three-dimensional geologic model
By selecting an appropriate search range for each block unit in the three-dimensional geological model for Kriging interpolation, the problem of low prediction accuracy in traditional methods is solved, and higher accuracy mineral resource prediction is achieved.
Patent Information
- Application Number
- CN202511718920.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-21
- Publication Date
- 2026-02-24
AI Technical Summary
When traditional Kriging interpolation uses a fixed search distance in a three-dimensional geological model for mineral resource prediction, the prediction accuracy is reduced, and it cannot effectively capture the spatial structure information of ore grade data.
By selecting an appropriate search range for each block unit in the three-dimensional geological model, grade interpolation is performed using the Kriging interpolation method. This includes obtaining the search range parameter set for block units at faults and non-fault locations, and adjusting the search range to avoid false underestimation and calculation distortion.
It improves the accuracy of mineral resource quantity prediction, ensures the accuracy and reliability of mineral resource distribution, and avoids the introduction of false data and calculation distortion due to stratigraphic fault zones.
Smart Images
Figure CN121559631A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of mineral resource prediction technology, specifically to mineral exploration prediction methods, devices, and systems based on three-dimensional geological models. Background Technology
[0002] With the development of technologies such as big data, artificial intelligence, and industrial internet, three-dimensional geological models constructed based on multi-source heterogeneous geoscience big data such as geological topographic maps, exploration profiles, and borehole data are increasingly widely used in the mining industry, especially in mineral resource reserve prediction and mine design.
[0003] Among existing mineral exploration prediction methods, Kriging interpolation is one of the important technical means to estimate mineral resources. However, traditional Kriging interpolation methods usually use a fixed search distance to select sample points for interpolation. Since the spatial distribution of mineral resources in strata is often non-uniform, when Kriging interpolation algorithms with a fixed search distance are used to predict ore grade data in three-dimensional geological models, the prediction accuracy will be reduced due to the uniform search range, which will affect the subsequent mining of mineral resources. Summary of the Invention
[0004] In view of the above, it is necessary to provide a mineral exploration prediction method, device, and system based on a three-dimensional geological model. Compared with traditional mineral exploration prediction methods based on three-dimensional geological models, this method improves the accuracy of grade value interpolation calculation for unsampled areas in the three-dimensional geological model of the mineral area to be predicted by selecting an appropriate search range for each block unit in the model. This improves the prediction accuracy when predicting the mineral resource quantity of the mineral area. In a first aspect, embodiments of this application provide a mineral exploration prediction method based on a three-dimensional geological model, the method comprising the following steps: Obtain a three-dimensional geological model of the mining area to be predicted, which includes fault entity models and ore body entity models; The geological space in the 3D geological model is divided into block units. Each block unit not belonging to the ore body entity model is statistically analyzed. By analyzing the positional relationship between the block units not belonging to the ore body entity model and the fault grid nodes in the fault entity model, the set of block units at fault locations and the set of block units at non-fault locations are obtained. By analyzing the spatial distribution of fault grid nodes within the nearest neighbor range of each block unit in the set of block units at fault locations, the search range parameter set of each block unit in the set of block units at fault locations is obtained. By analyzing the uniform distribution of the grade values of the ore body entity model within the nearest neighbor range of each block unit in the set of block units at non-fault locations, the grade uniformity of each block unit in the set of block units at non-fault locations is obtained. Then, the search radius of each block unit in the set of block units at non-fault locations is obtained, thus obtaining the search range parameter set of each block unit in the set of block units at non-fault locations. The grade values of each block unit that does not belong to the ore body entity model are interpolated using the Kriging interpolation method based on the obtained search range parameter set. The distribution of mineral resources in different grade areas in the mining area to be predicted is then predicted based on the interpolation results.
[0005] In one embodiment, the process of obtaining the block element set at the fault and the block element set at the non-fault is as follows: The coordinates of all triangular meshes in all fault entity models in the 3D geological model are divided into different fault mesh node coordinate sets according to the different fault entity models they belong to, and the minimum bounding box of each fault mesh node coordinate set is obtained. For block elements that do not belong to the ore body entity model, all block elements within the smallest bounding box are grouped into a block element set at the fault, and all block elements not within the smallest bounding box are grouped into a block element set at the non-fault location.
[0006] In one embodiment, obtaining the search range parameter set for each block element in the block element set at the fault location includes: The maximum and minimum search radii were obtained by using cross-validation to calculate the grade values of block units that do not belong to the ore body entity model using kriging interpolation. For any block unit in the set of block units at the fault, all grid node coordinates that are located in a spherical space centered on the centroid coordinates of the arbitrary block unit and with the maximum search radius are selected from the coordinate set of all fault grid nodes in the three-dimensional geological model. Then, ellipsoid fitting is performed on all the selected grid node coordinates, and the three axis lengths and three rotation angles of the fitted ellipsoid are used to form the search range parameter set of the arbitrary block unit.
[0007] In one embodiment, the process of obtaining the grade uniformity is as follows: For any block unit in the set of block units at non-fault locations, select all ore body entity models from all ore body entity models in the three-dimensional geological model whose centroid coordinates are located in a spherical space centered on the centroid coordinates of the selected block unit and with the minimum search radius. Evaluate the uniformity of the normalized values of the grade values of all selected ore body entity models, and use the evaluation result of the uniformity as the grade uniformity of the selected block unit.
[0008] In one embodiment, obtaining the search radius of each block element in the set of block elements at non-fault locations includes: Calculate the difference between the maximum search radius and the minimum search radius; Calculate the product of the normalized value of the grade uniformity of each block unit in the non-fault block unit set and the difference value; By combining the product with the minimum search radius, the search radius of each block element in the block element set at the non-fault location is obtained.
[0009] In one embodiment, the search radius of each block element in the non-fault block element set is the sum of the product rounded to the nearest integer and the minimum search radius.
[0010] In one embodiment, the search range parameter set for each block element in the non-fault block element set is specifically: the values of the three axis lengths in the search range parameter set are equal to the search radius of each block element in the non-fault block element set, and the values of the three rotation angles are all 0.
[0011] In one embodiment, during the process of interpolating the grade values of each block unit that does not belong to the ore body entity model, the three axis lengths and three rotation angles of the search range parameter set corresponding to each block unit that does not belong to the ore body entity model are used as the grid size and rotation angle values of the three directions of the variant ellipsoid used in the Kriging interpolation method when estimating the grade value of each block unit.
[0012] Secondly, embodiments of this application also provide a mineral exploration prediction device based on a three-dimensional geological model, the device comprising: The 3D geological model building module is used to obtain a 3D geological model of the mining area to be predicted, which includes fault entity models and ore body entity models. The search range acquisition module is used to divide the geological space in the 3D geological model into block units, and to count each block unit that does not belong to the ore body entity model. Based on the positional relationship between the block units not belonging to the ore body entity model and the fault grid nodes in the fault entity model, it obtains the block unit set at the fault location and the block unit set at the non-fault location. Based on the spatial distribution of fault grid nodes within the nearest neighbor range of each block unit in the fault location block unit set, it obtains the search range parameter set for each block unit in the fault location block unit set. Based on the uniform distribution of the grade values of the ore body entity model within the nearest neighbor range of each block unit in the non-fault location block unit set, it obtains the grade uniformity of each block unit in the non-fault location block unit set, and then obtains the search radius of each block unit in the non-fault location block unit set, thus obtaining the search range parameter set for each block unit in the non-fault location block unit set. The grade value interpolation calculation module is used to interpolate the grade values of each block unit that does not belong to the ore body entity model using the Kriging interpolation method based on the obtained search range parameter set. The mineral exploration prediction module is used to predict the distribution of mineral resources in different grade areas of the mining area to be predicted based on the interpolation calculation results.
[0013] Thirdly, embodiments of this application also provide a mineral exploration prediction system based on a three-dimensional geological model, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any of the above-described mineral exploration prediction methods based on a three-dimensional geological model.
[0014] This application has at least the following beneficial effects: This application analyzes the distribution of stratigraphic fault zones in a three-dimensional geological model of the mining area to be predicted. The block units in the three-dimensional geological model are divided into two parts. Based on the distribution of stratigraphic fault zones within the vicinity of the block units located at stratigraphic fault zones, a search range parameter set is obtained. Based on the obtained search range parameter set, the search range for each block unit located at a stratigraphic fault zone in the three-dimensional geological model is adjusted when performing grade interpolation calculations using the Kriging interpolation method. This effectively avoids the situation where the grade value of the block units located at stratigraphic fault zones in the three-dimensional geological model of the mining area to be predicted is falsely lowered due to the introduction of grade values from ore body samples not located at fault zones during the subsequent grade interpolation calculations. Furthermore, by analyzing the uniform distribution of grade values of ore body samples in the vicinity of block units not located in stratigraphic fault zones in the three-dimensional geological model of the mining area to be predicted, grade uniformity is constructed, and a search range parameter set for block units is obtained based on grade uniformity. The search range parameter set is used to adjust the search range of block units when performing grade value interpolation calculations using the Kriging interpolation method in the subsequent process. This avoids situations where the Kriging interpolation method cannot fully capture the spatial structure information of the grade values of ore body samples in the local geological space where the block unit is located due to an excessively small search range, and avoids situations where the grade values of distant ore body samples are introduced due to an excessively large search range, resulting in distorted grade value calculations. Furthermore, based on the obtained search range parameter set, the search range for each block unit in the three-dimensional geological model is adjusted when using Kriging interpolation to calculate the grade value. Based on the three-dimensional geological model obtained after grade value interpolation, the distribution of ore resources in the target mining area is predicted. Compared with the traditional Kriging interpolation method that uses a fixed search distance, this method can select an appropriate search range for each block unit in the three-dimensional geological model of the target mining area, thereby improving the accuracy of grade value interpolation calculation for unsampled areas in the three-dimensional geological model of the target mining area, and thus improving the prediction accuracy when predicting the mineral resource quantity of the target mining area. Attached Figure Description
[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0016] Figure 1 A flowchart illustrating the steps of a mineral exploration prediction method based on a three-dimensional geological model, provided in one embodiment of this application; Figure 2 This is a schematic diagram illustrating the process of obtaining the search range parameter set. Detailed Implementation
[0017] In the description of the embodiments in this application, the words "exemplary," "or," and "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary," "or," and "for example" is intended to present the relevant concepts in a specific manner.
[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. It should be understood that, unless otherwise stated, " / " in this application means "or".
[0019] It should also be noted that the terms "first" and "second" in this application are used to distinguish similar objects, rather than to describe a specific order or sequence.
[0020] The following description, in conjunction with the accompanying drawings, details the specific scheme of the mineral exploration prediction method, device, and system based on a three-dimensional geological model provided in this application.
[0021] Please see Figure 1 The diagram illustrates a flowchart of a mineral exploration prediction method based on a three-dimensional geological model according to an embodiment of this application. The method includes the following steps: Step 1: Obtain a three-dimensional geological model of the mining area to be predicted, which includes fault entity models and ore body entity models.
[0022] Big data on geology of the mining area to be predicted is collected, including contour lines from geological maps, geological profiles of exploration lines, borehole data, ore grade data, and lithological data. Based on the collected big data, a geological database of the mining area to be predicted is constructed in Surpac software, and a three-dimensional geological model of the mining area to be predicted is constructed using Surpac software. The three-dimensional geological model consists of a surface model, a stratigraphic entity model, a fault entity model, and an ore body entity model. The grid cell type used in the three-dimensional geological model is triangular grid cell. The attributes of the ore body entity model include centroid coordinates and grade values.
[0023] A uniform grid algorithm is used to divide the geological space in a 3D geological model into multiple block units of equal size. The size of the cubic grid in the uniform grid algorithm is determined by the size of the minimum bounding box of the ore body entity model in the 3D geological model. Then, from all the divided block units, all block units that do not belong to the ore body entity model of the 3D geological model are selected and formed into a block unit set A. This set A is used for subsequent interpolation calculations of the grade values of the block units in block unit set A, which is then used for mineral exploration prediction in the target mining area. Boolean operations are used to calculate the intersection between the block unit and the ore body entity model. If the intersection is not empty, the block unit is determined to belong to the ore body entity model. Boolean operations are a well-known technique and will not be elaborated upon in this application. The uniform grid algorithm and the acquisition of the minimum bounding box are also well-known techniques and will not be elaborated upon in this application.
[0024] Step 2: Divide the geological space in the 3D geological model into block units, and count the block units that do not belong to the ore body entity model, thereby obtaining the search range parameter set for each block unit that does not belong to the ore body entity model.
[0025] Step 2.1: Divide the geological space in the 3D geological model into block units, and count each block unit that does not belong to the ore body entity model. By the positional relationship between the block units that do not belong to the ore body entity model and the fault grid nodes in the fault entity model, obtain the block unit set at the fault and the block unit set at the non-fault location. By the spatial distribution of the fault grid nodes within the nearest neighbor range of each block unit in the block unit set at the fault location, obtain the search range parameter set of each block unit in the block unit set at the fault location.
[0026] Generally, the formation of mineral resources in strata is influenced by fault structures within the strata. For example, iron, nickel, and copper deposits are often distributed along rift axes. This is because fault activity in strata provides magma ascent channels and storage space, bringing a large amount of ore-forming materials and heat to the mineralization process. For instance, in some porphyry copper deposits, magma intrudes into the shallow crust along fault channels, forming magma chambers near the faults. With the evolution of the magma and the migration of hydrothermal fluids, large-scale copper ore bodies are formed in and around the fault zones. Therefore, to avoid the false underestimation of grade values in the subsequent interpolation calculation of the grade values of block units located in the fault zones of the predicted mining area due to the introduction of grade values of block units not located in the fault zones, the following processing is performed.
[0027] The coordinates of each triangular mesh in each fault entity model of the three-dimensional geological model are obtained. Based on the different fault entity models to which the mesh node coordinates belong, all the obtained mesh node coordinates are divided into multiple sets and denoted as the set of each fault mesh node coordinate of the three-dimensional geological model. At the same time, the centroid coordinates of each block unit in the block unit set A are obtained.
[0028] The coordinates of all grid nodes in the coordinate set of each fault grid node in the three-dimensional geological model are used as inputs to the minimum bounding box algorithm. The minimum bounding box of each fault grid node coordinate set is output to represent the minimum bounding geological space of each fault zone in the three-dimensional geological model of the mining area to be predicted. The minimum bounding box algorithm is a well-known technology and will not be described in detail in this application.
[0029] Furthermore, based on whether the centroid coordinates of each block unit in the block unit set A are within the minimum bounding box of any fault grid node coordinate set in the three-dimensional geological model, all block units in the block unit set A are divided into two parts: specifically, all block units in the block unit set A that are within the minimum bounding box form the fault block unit set, which is used to characterize the set of all block units located at fault zones in the strata of the three-dimensional geological model of the mining area to be predicted; and all block units in the block unit set A that are not within the minimum bounding box form the non-fault block unit set.
[0030] Furthermore, the maximum and minimum search radii are obtained by using cross-validation to calculate the grade values of the block units in the block unit set A using Kriging interpolation, and are denoted as the maximum search radius R1 and minimum search radius R2 of the three-dimensional geological model, respectively. In the Kriging interpolation, a spherical search space is used, that is, a spherical model is used as the variation function, and the grid size of the three directions of the variation ellipsoid used is equal. Cross-validation is a well-known technique and will not be described in detail in this application.
[0031] Furthermore, taking any block unit a1 in the set of block units at the fault as an example, all grid node coordinates within a spherical space centered on the centroid coordinates of block unit a1 and with a maximum search radius R1 are selected from the coordinate set of all fault grid nodes in the 3D geological model. Then, an ellipsoidal fitting least squares method is used to fit all the selected grid node coordinates. Specifically, the centroid coordinates of block unit a1 are used as the center coordinates of the ellipsoid in the ellipsoidal fitting least squares method. The set of the three axis lengths and three rotation angles of the fitted ellipsoid is used as the search range parameter set for block unit a1, which is used to determine the subsequent Kriging interpolation method. The values of the grid size and rotation angles in the three directions of the modified ellipsoid used are specifically as follows: the three axis lengths and three rotation angles from the search range parameter set are used as the values of the grid size and rotation angles in the three directions of the modified ellipsoid used in the Kriging interpolation method. This is to ensure that the grade values of the ore body samples continuously distributed along the fault direction of the fault zone where block unit a1 is located are all included in the calculation range, thereby more accurately estimating the grade value of block unit a1 and avoiding the inclusion of the grade values of ore body samples in the strata on both sides of the fault zone, as the grade values of ore body samples in the strata on both sides of the fault zone would cause the grade value of block unit a1 to be falsely lowered. Among them, the three rotation angles obtained by fitting refer to the rotation angles in the X, Y, and Z axes of the three-dimensional geological model of the mining area to be predicted. The ellipsoid fitting least squares method is a well-known technique and will not be described in detail in this application.
[0032] Following the method for obtaining the search range parameter set of block element a1, the search range parameter set of each block element in the block element set at the fault is obtained.
[0033] Step 2.2: By analyzing the uniform distribution of grade values of ore body entity models in the neighborhood of each block unit in the non-fault block unit set, the grade uniformity of each block unit in the non-fault block unit set is obtained. Then, the search radius of each block unit in the non-fault block unit set is obtained, thus obtaining the search range parameter set of each block unit in the non-fault block unit set.
[0034] Since the core advantage of Kriging interpolation lies in its utilization of the spatial correlation between data points and interpolation points in space, naturally incorporating the inherent spatial structure information of the data, when interpolating the grade value of any block unit in the three-dimensional geological model of the mining area to be predicted, if the grade values of the nearby ore body samples have a relatively uniform distribution, then the block unit should use a larger search range to allow Kriging interpolation to more comprehensively capture the spatial structure of the grade values of the nearby ore body samples, thereby improving the stability and representativeness of the grade value calculation for the block unit. Conversely, when the grade values of the ore body samples near the block unit show enriched data changes, then the block unit should use a smaller search range to allow Kriging interpolation to better capture the local grade value data changes, avoiding oversmoothing or calculation distortion caused by introducing distant ore body samples.
[0035] Based on the above analysis, the normalized grade values of each ore body entity model in the 3D geological model are first obtained and recorded as the normalized grade value of each ore body entity model to avoid the influence of different numerical scales on subsequent analysis. Then, taking any block unit a2 in the set of block units in non-fault locations as an example, all ore body entity models whose centroid coordinates are located in a spherical space centered on the centroid coordinates of block unit a2 and with a minimum search radius R2 are selected from all ore body entity models in the 3D geological model. The uniformity of the normalized grade values of all selected ore body entity models is evaluated, and the evaluation result of the uniformity is used as the grade uniformity of block unit a2. This is used to evaluate the uniformity of the grade distribution of ore body samples near block unit a2. The larger the calculated grade uniformity, the more uniform the distribution of grade values of ore body samples near block unit a2. Therefore, the search range used when using Kriging interpolation to interpolate the grade value of block unit a2 should be larger.
[0036] In this embodiment, the Min-Max normalization method is used to obtain the normalized result of the grade value of each ore body entity model. The Min-Max normalization method is a well-known technology and will not be described in detail in this application.
[0037] In this embodiment, the Nearest Neighbor Distance (NND) method is used to evaluate the uniformity of the normalized grade values of all selected orebody entity models. Specifically, the empirical distribution function of the normalized grade values of all selected orebody entity models is first calculated. The one-sample Kolmogorov-Smirnov test is used to calculate the test statistic D between the empirical distribution function and the theoretical distribution function of uniform distribution. The smaller the calculated test statistic D, the more uniform the empirical distribution function follows. The reciprocal of the sum of the test statistic D and a preset value greater than 0 is used as the grade uniformity of block unit a2. The purpose of the preset value greater than 0 is to avoid the denominator being 0. At the same time, to avoid the preset value greater than 0 affecting the calculation result of grade uniformity, the preset value greater than 0 should be a very small positive number. In this embodiment, the preset value greater than 0 is 0.01. The implementer can set the specific value of the preset value greater than 0 according to the actual situation. The nearest neighbor distance method, its empirical distribution function calculation, and the Kolmogorov-Smirnov test are all well-known techniques and will not be elaborated upon in this application. As another implementation method, based on the ability to achieve a uniform distribution of normalized grade values for all selected orebody entity models, the implementer may adopt other existing feasible techniques, and this application does not impose any special restrictions.
[0038] Furthermore, the grade uniformity of all block elements in the non-fault block element set is normalized, and the normalized result of the grade uniformity of each block element in the non-fault block element set is recorded as the search range adjustment coefficient of each block element. The larger the calculated search range adjustment coefficient, the larger the search range of the block element in the subsequent Kriging interpolation method.
[0039] In this embodiment, the Min-Max normalization method is used to normalize the grade uniformity of all block units in the non-fault block unit set.
[0040] Furthermore, taking block element a2 as an example, the search radius of block element a2 is calculated. This radius is used to obtain the search range parameter set for block element a2. The search range parameter set consists of three axis lengths and three rotation angles. This parameter set is used to adjust the search range of block element a2 in the Kriging interpolation method used for grade value interpolation calculation. The expression for the search radius of block element a2 is as follows: In the formula, R2 represents the search radius of block element a2; R3 represents the minimum search radius of the 3D geological model; R4 represents the absolute value of the difference between the maximum and minimum search radii of the 3D geological model. This represents the search range adjustment coefficient for block unit a2; This represents the rounding function.
[0041] Furthermore, the search range parameter set of block element a2 is obtained, wherein the values of the three axis lengths in the search range parameter set are all equal to the search radius of block element a2, and the values of the three rotation angles in the search range parameter set are all 0. Then, the search space in the Kriging interpolation method used by block element a2 in the subsequent grade value interpolation calculation is a sphere search space with the search radius r as the radius.
[0042] According to the method for obtaining the search range parameter set of block element a2, the search range parameter set of each block element in the block element set at non-fault locations is obtained.
[0043] Step 2.3: Obtain the search range parameter set for each block unit that does not belong to the ore body entity model.
[0044] Since the block element set at the fault and the block element set at the non-fault are obtained by dividing the block elements in block element set A, the search range parameter set for each block element in block element set A is obtained by using the search range parameter set of the block elements in the fault and non-fault block element sets. A schematic diagram of the process for obtaining the search range parameter set is shown below. Figure 2 As shown.
[0045] Step 3: Using the Kriging interpolation method, the grade values of each block unit that does not belong to the ore body entity model are interpolated based on the obtained search range parameter set. The distribution of mineral resources in different grade areas in the mining area to be predicted is then predicted based on the interpolation results.
[0046] If any block unit belongs to the ore body entity model, the grade value of the ore body entity model is assigned to the grade value of the block unit, and each block unit belonging to the ore body entity model is taken as each sample point, and each block unit in the block unit set A is taken as each point to be estimated.
[0047] Based on the grade values of all sample points in the 3D geological model, Kriging interpolation is used to interpolate the grade value of each point to be estimated in the 3D geological model. Specifically, the three axis lengths and three rotation angles of the search range parameter set for each block element in block element set A are used as the values of the grid size and rotation angles of the three directions of the variant ellipsoid used in the Kriging interpolation method to calculate the grade value of the point to be estimated corresponding to each block element. This yields the interpolated 3D geological model, at which point the grade values of all block elements in the 3D geological model are known. The Kriging interpolation method is a well-known technique and will not be described further in this application.
[0048] Based on the grade values of all block units in the interpolated 3D geological model, the Monte Carlo simulation method is used to predict ore reserves in the 3D geological model in multiple scenarios and assess the distribution of mineral resources in areas with different grades. The use of the Monte Carlo simulation method for multi-scenario prediction of ore reserves is a well-known technique and will not be elaborated upon in this application.
[0049] Based on the same inventive concept as the above method, embodiments of this application also provide a mineral exploration prediction device based on a three-dimensional geological model, including: The 3D geological model building module is used to obtain a 3D geological model of the mining area to be predicted, which includes fault entity models and ore body entity models. The search range acquisition module is used to divide the geological space in the 3D geological model into block units, and to count each block unit that does not belong to the ore body entity model. Based on the positional relationship between the block units not belonging to the ore body entity model and the fault grid nodes in the fault entity model, it obtains the block unit set at the fault location and the block unit set at the non-fault location. Based on the spatial distribution of fault grid nodes within the nearest neighbor range of each block unit in the fault location block unit set, it obtains the search range parameter set for each block unit in the fault location block unit set. Based on the uniform distribution of the grade values of the ore body entity model within the nearest neighbor range of each block unit in the non-fault location block unit set, it obtains the grade uniformity of each block unit in the non-fault location block unit set, and then obtains the search radius of each block unit in the non-fault location block unit set, thus obtaining the search range parameter set for each block unit in the non-fault location block unit set. The grade value interpolation calculation module is used to interpolate the grade values of each block unit that does not belong to the ore body entity model using the Kriging interpolation method based on the obtained search range parameter set. The mineral exploration prediction module is used to predict the distribution of mineral resources in different grade areas of the mining area to be predicted based on the interpolation calculation results.
[0050] Based on the same inventive concept as the above methods, this application also provides a mineral exploration prediction system based on a three-dimensional geological model, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described mineral exploration prediction methods based on a three-dimensional geological model.
[0051] In summary, this application analyzes the distribution of stratigraphic fault zones in the three-dimensional geological model of the mining area to be predicted, divides the block units in the three-dimensional geological model into two parts, and obtains a search range parameter set based on the distribution of stratigraphic fault zones in the vicinity of the block units located at stratigraphic fault zones. Based on the obtained search range parameter set, the search range of each block unit located at stratigraphic fault zones in the three-dimensional geological model is adjusted when performing grade interpolation calculations using the Kriging interpolation method. This effectively avoids the situation where the grade value of the block units located at stratigraphic fault zones in the three-dimensional geological model of the mining area to be predicted is falsely lowered due to the introduction of grade values of ore body samples not located at fault zones when interpolating the grade values. Furthermore, by analyzing the uniform distribution of grade values of ore body samples in the vicinity of block units not located in stratigraphic fault zones in the three-dimensional geological model of the mining area to be predicted, grade uniformity is constructed, and a search range parameter set for block units is obtained based on grade uniformity. The search range parameter set is used to adjust the search range of block units when performing grade value interpolation calculations using the Kriging interpolation method in the subsequent process. This avoids situations where the Kriging interpolation method cannot fully capture the spatial structure information of the grade values of ore body samples in the local geological space where the block unit is located due to an excessively small search range, and avoids situations where the grade values of distant ore body samples are introduced due to an excessively large search range, resulting in distorted grade value calculations. Furthermore, based on the obtained search range parameter set, the search range for each block unit in the three-dimensional geological model is adjusted when using Kriging interpolation to calculate the grade value. Based on the three-dimensional geological model obtained after grade value interpolation, the distribution of ore resources in the target mining area is predicted. Compared with the traditional Kriging interpolation method that uses a fixed search distance, this method can select an appropriate search range for each block unit in the three-dimensional geological model of the target mining area, thereby improving the accuracy of grade value interpolation calculation for unsampled areas in the three-dimensional geological model of the target mining area, and thus improving the prediction accuracy when predicting the mineral resource quantity of the target mining area.
[0052] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.
[0053] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from its essential characteristics. Therefore, the embodiments described above should be considered exemplary and non-limiting in all respects.
Claims
1. A mineral exploration prediction method based on a three-dimensional geological model, characterized in that, The method includes the following steps: Obtain a three-dimensional geological model of the mining area to be predicted, which includes fault entity models and ore body entity models; The geological space in the 3D geological model is divided into block units. Each block unit not belonging to the ore body entity model is statistically analyzed. By analyzing the positional relationship between the block units not belonging to the ore body entity model and the fault grid nodes in the fault entity model, the set of block units at fault locations and the set of block units at non-fault locations are obtained. By analyzing the spatial distribution of fault grid nodes within the nearest neighbor range of each block unit in the set of block units at fault locations, the search range parameter set of each block unit in the set of block units at fault locations is obtained. By analyzing the uniform distribution of the grade values of the ore body entity model within the nearest neighbor range of each block unit in the set of block units at non-fault locations, the grade uniformity of each block unit in the set of block units at non-fault locations is obtained. Then, the search radius of each block unit in the set of block units at non-fault locations is obtained, thus obtaining the search range parameter set of each block unit in the set of block units at non-fault locations. The grade values of each block unit that does not belong to the ore body entity model are interpolated using the Kriging interpolation method based on the obtained search range parameter set. The distribution of mineral resources in different grade areas in the mining area to be predicted is then predicted based on the interpolation results.
2. The mineral exploration prediction method based on a three-dimensional geological model as described in claim 1, characterized in that, The process of obtaining the set of block elements at the fault and the set of block elements at non-fault locations is as follows: The coordinates of all triangular meshes in all fault entity models in the 3D geological model are divided into different fault mesh node coordinate sets according to the different fault entity models they belong to, and the minimum bounding box of each fault mesh node coordinate set is obtained. For block elements that do not belong to the ore body entity model, all block elements within the smallest bounding box are grouped into a block element set at the fault, and all block elements not within the smallest bounding box are grouped into a block element set at the non-fault location.
3. The mineral exploration prediction method based on a three-dimensional geological model as described in claim 1, characterized in that, The step of obtaining the search range parameter set for each block element in the block element set at the fault location includes: The maximum and minimum search radii were obtained by using cross-validation to calculate the grade values of block units that do not belong to the ore body entity model using kriging interpolation. For any block unit in the set of block units at the fault, all grid node coordinates that are located in a spherical space centered on the centroid coordinates of the arbitrary block unit and with the maximum search radius are selected from the coordinate set of all fault grid nodes in the three-dimensional geological model. Then, ellipsoid fitting is performed on all the selected grid node coordinates, and the three axis lengths and three rotation angles of the fitted ellipsoid are used to form the search range parameter set of the arbitrary block unit.
4. The mineral exploration prediction method based on a three-dimensional geological model as described in claim 3, characterized in that, The process for obtaining the grade uniformity is as follows: For any block unit in the set of block units at non-fault locations, select all ore body entity models from all ore body entity models in the three-dimensional geological model whose centroid coordinates are located in a spherical space centered on the centroid coordinates of the selected block unit and with the minimum search radius. Evaluate the uniformity of the normalized values of the grade values of all selected ore body entity models, and use the evaluation result of the uniformity as the grade uniformity of the selected block unit.
5. The mineral exploration prediction method based on a three-dimensional geological model as described in claim 3, characterized in that, The process of obtaining the search radius of each block element in the set of block elements at non-fault locations includes: Calculate the difference between the maximum search radius and the minimum search radius; Calculate the product of the normalized value of the grade uniformity of each block unit in the non-fault block unit set and the difference value; By combining the product with the minimum search radius, the search radius of each block element in the block element set at the non-fault location is obtained.
6. The mineral exploration prediction method based on a three-dimensional geological model as described in claim 5, characterized in that, The search radius of each block element in the set of block elements at the non-fault location is the sum of the product rounded to the nearest integer and the minimum search radius.
7. The mineral exploration prediction method based on a three-dimensional geological model as described in claim 1, characterized in that, The search range parameter set for each block element in the non-fault block element set is specifically as follows: the values of the three axis lengths in the search range parameter set are equal to the search radius of each block element in the non-fault block element set, and the values of the three rotation angles are all 0.
8. The mineral exploration prediction method based on a three-dimensional geological model as described in claim 1, characterized in that, In the process of interpolating the grade values of each block unit that does not belong to the ore body entity model, the three axis lengths and three rotation angles of the search range parameter set corresponding to each block unit that does not belong to the ore body entity model are used as the grid size values and rotation angle values of the three directions of the variant ellipsoid used in the Kriging interpolation method when estimating the grade value of each block unit.
9. A mineral exploration prediction device based on a three-dimensional geological model, characterized in that, The device includes: The 3D geological model building module is used to obtain a 3D geological model of the mining area to be predicted, which includes fault entity models and ore body entity models. The search range acquisition module is used to divide the geological space in the 3D geological model into block units, and to count each block unit that does not belong to the ore body entity model. Based on the positional relationship between the block units not belonging to the ore body entity model and the fault grid nodes in the fault entity model, it obtains the block unit set at the fault location and the block unit set at the non-fault location. Based on the spatial distribution of fault grid nodes within the nearest neighbor range of each block unit in the fault location block unit set, it obtains the search range parameter set for each block unit in the fault location block unit set. Based on the uniform distribution of the grade values of the ore body entity model within the nearest neighbor range of each block unit in the non-fault location block unit set, it obtains the grade uniformity of each block unit in the non-fault location block unit set, and then obtains the search radius of each block unit in the non-fault location block unit set, thus obtaining the search range parameter set for each block unit in the non-fault location block unit set. The grade value interpolation calculation module is used to interpolate the grade values of each block unit that does not belong to the ore body entity model using the Kriging interpolation method based on the obtained search range parameter set. The mineral exploration prediction module is used to predict the distribution of mineral resources in different grade areas of the mining area to be predicted based on the interpolation calculation results.
10. A mineral exploration prediction system based on a three-dimensional geological model, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the mineral exploration prediction method based on a three-dimensional geological model as described in any one of claims 1-8.