A multi-stage mineralization analysis method for ore deposits based on variational model and related equipment

Abstract

The present application relates to the technical field of mineralization analysis, and provides a multi-stage mineralization analysis method of a ore deposit based on a variational model and related equipment, the method comprising: calculating the mineralization grade of each voxel based on the mineralization grade of all samples, and determining the number of potential mineralization stages of the voxel based on the mineralization grade; constructing geological observation constraints, spatial smoothing constraints and spatial similarity constraints based on the number of potential mineralization stages of all voxels, and constructing a variational model based on the geological observation constraints, spatial smoothing constraints and spatial similarity constraints; converting the geological observation constraints, spatial smoothing constraints and spatial similarity constraints to obtain the final geological observation constraints, final spatial smoothing constraints and final spatial similarity constraints; solving the variational model using the final geological observation constraints, final spatial smoothing constraints and final spatial similarity constraints to obtain the mineralization stage of the voxel and the relative mineralization grade. The method of the present application can improve the accuracy of mineralization analysis.

Description

Technical Field

[0001] The present application relates to the technical field of mineralization analysis, and in particular to a multi-stage mineralization analysis method for a ore deposit based on a variational model and related equipment. Background Art

[0002] Metallogenic system analysis is an important theory to dissect the spatiotemporal structure of ore deposits and reveal the formation mechanism of ore deposits. Since the ore-forming system needs to be considered from the perspective of the development history of regional geology, there is an intrinsic connection between each ore deposit and it constitutes a four-dimensional ore-forming whole. Therefore, it is very important to use the system theory and activity theory to analyze the ore-forming system from a four-dimensional perspective in each period of the geological history of the earth's evolution and each specific geological structural environment for the prediction and exploration of deep mineral resources. However, due to the complexity of ore deposits and formation, the analysis of ore-forming system is still in the qualitative research stage. The ore deposit model obtained mainly describes the universal characteristics of ore deposits and the coupling effect with the geological environment qualitatively, but it is difficult to quantitatively characterize the spatiotemporal structure of ore deposits that comprehensively reflect the ore-forming elements, so as to clarify the mineralization intensity and distribution information of each ore-forming stage. In addition, there is a lack of in-depth research on information depth mining and spatiotemporal synergistic association, which greatly restricts the fine cognition of the spatiotemporal structure of ore deposits, and further affects the subsequent dynamic reconstruction of ore-forming processes and the accuracy of three-dimensional prediction of deep resources. It can be seen that there is an inaccurate mineralization analysis of ore deposits. Summary of the invention

[0003] The present application provides a multi-stage mineralization analysis method for a ore deposit based on a variational model and related equipment, which can solve the problem of inaccurate mineralization analysis of the ore deposit.

[0004] In a first aspect, an embodiment of the present application provides a multi-stage mineralization analysis method for a ore deposit based on a variational model, and the multi-stage mineralization analysis method for a ore deposit includes:

[0005] Divide the target mineral deposit space into multiple voxels, and obtain the sample mineralization grade of multiple sampling points in the target mineralization space;

[0006] Calculate the mineralization grade of each volume based on the mineralization grades of all samples, and determine the number of potential mineralization stages of each volume based on the mineralization grades of all volumes; the number of potential mineralization stages is the number of mineralization stages that a volume may correspond to;

[0007] Based on the number of potential mineralization stages of all voxels, geological observation constraints, spatial smoothness constraints and spatial similarity constraints are constructed, and a variational model is constructed based on the geological observation constraints, spatial smoothness constraints and spatial similarity constraints; the geological observation constraints are used to describe the mineralization effect of the mineralization stage on the voxels adjacent to the sampling point, the spatial smoothness constraints are used to describe the continuity between the mineralization grades of two adjacent voxels corresponding to the same mineralization stage, the spatial similarity constraints are used to describe the similarity between the mineralization grades of two voxels, and the variational model is used to describe the mineralization effect of multiple mineralization stages on the voxels;

[0008] The geological observation constraints are transformed to obtain the final geological observation constraints, the spatial smoothness constraints are transformed to obtain the final spatial smoothness constraints, and the spatial similarity constraints are transformed to obtain the final spatial similarity constraints;

[0009] The variational model is solved using the final geological observation constraints, the final spatial smoothness constraints, and the final spatial similarity constraints to obtain the mineralization stage and relative mineralization grade of each element in the target ore deposit space; the relative mineralization grade is the mineralization grade of the element under the mineralization action of the mineralization stage.

[0010] Optionally, the mineralization grade of each voxel is calculated based on the mineralization grades of all samples, including:

[0011] For each voxel, perform the following steps:

[0012] If there is a sampling point in the volume, the mineralization grade of the sample at the sampling point will be used as the mineralization grade of the volume;

[0013] If there is no sampling point in the volume element, the mineralization grade of the volume element is calculated using the mineralization grade calculation formula;

[0014] The mineralization grade calculation formula is:

[0015] ;

[0016] in, Representation voxel The mineralization grade represents the weight coefficient of the Kriging difference, Indicates Sampling points The sample mineralization grade, Indicates the number of sampling points.

[0017] Optionally, determine the number of potential mineralization stages in each volume based on the mineralization grade of all volumes, including:

[0018] Setting multiple constant thresholds, dividing all voxels using all constant thresholds and the mineralization grades of all voxels, and obtaining multiple mineralized voxels corresponding to each constant threshold;

[0019] For each constant threshold, perform the following steps:

[0020] A mineralized voxel statistical curve is constructed based on all mineralized voxels corresponding to the constant threshold, and multiple slopes of the mineralized voxel statistical curve are calculated, and the number of slopes is used as the number of potential mineralization stages of each voxel corresponding to the constant threshold.

[0021] Optionally, all constant thresholds and all mineralization grades are used to divide all voxels to obtain multiple mineralized voxels corresponding to each constant threshold, including:

[0022] By formula:

[0023] ;

[0024] Get the Multiple mineralized voxels corresponding to a constant threshold ;

[0025] in, Representation voxel The mineralization grade Indicates A constant threshold, , represents the number of constant thresholds, , Both represent fractal dimensions greater than zero.

[0026] Optionally, the geological observation constraints are:

[0027] ;

[0028] in, represents the value constrained by geological observations, Representation voxel The mineralization grade Indicates Mineralization stage for sampling points Mineralization, sampling point Voxel Sampling points within the preset range around, Representation voxel The number of potential mineralization stages, Indicated in Sampling points at each mineralization stage the grade of mineralization produced;

[0029] The spatial smoothness constraint is:

[0030] ;

[0031] in, represents the value of the spatial smoothness constraint, Indicates The mineralization stage The mineralization of It indicates the grade of mineralization produced by mineralization;

[0032] The spatial similarity constraints are:

[0033] ;

[0034] in, represents the value of the spatial similarity constraint, represents the KL divergence operator, represents the approximate probability estimate, Representation and Voxel The same mineralization stage of the body The mineralization grade produced by the mineralization in each mineralization stage.

[0035] Optionally, the variational model is:

[0036] ;

[0037] in, Representation voxel The variational expression of , , Both represent weight values.

[0038] Optionally, the final geological observation constraint is:

[0039] ;

[0040] in, Indicates the first mineralization stage for the sampling point The mineralization of Indicates the second mineralization stage for the sampling point The mineralization of Indicates Mineralization stage for sampling points mineralization;

[0041] The final spatial smoothness constraint is:

[0042] ;

[0043] ;

[0044] in, represents the number of voxels, Indicates The set of neighboring voxels of an individual voxel, Indicates Individual element The mineralization intensity under the influence of mineralization in each mineralization stage, Indicates Individual element The mineralization intensity under the influence of mineralization in each mineralization stage, Indicates the mineralization grade produced by mineralization. Indicates The mineralization of the first stage is affected by the mineralization of the Weight terms related to individual metaspace positions:

[0045] ;

[0046] in, Represents the statistical expectation value of the mineralization intensity distribution.

[0047] Optionally, the final spatial similarity constraint is:

[0048] ;

[0049] ;

[0050] in, , , represents the number of Monte Carlo sampling, represents the sampling points obtained by Monte Carlo random sampling, represents the probability distribution with spatial position constraints, represents the spatial position weight, Indicated in The mineralization intensity of each mineralization stage, represents the number of sampling points for kernel density estimation, Representation voxel The spatial distance between the elements known to be affected by a certain mineralization stage.

[0051] In a second aspect, the embodiment of the present application provides a multi-stage mineralization analysis device for a ore deposit based on a variational model, comprising:

[0052] A partitioning module divides the target mineral deposit space into multiple voxels and obtains the sample mineralization grade of multiple sampling points in the target mineralization space;

[0053] A calculation module, which calculates the mineralization grade of each voxel based on the mineralization grades of all samples, and determines the number of potential mineralization stages of each voxel based on the mineralization grades of all voxels; the number of potential mineralization stages is the number of mineralization stages that the voxel may correspond to;

[0054] The construction module constructs geological observation constraints, spatial smoothness constraints and spatial similarity constraints based on the number of potential mineralization stages of all voxels, and constructs a variational model based on the geological observation constraints, spatial smoothness constraints and spatial similarity constraints; the geological observation constraints are used to describe the mineralization effect of the mineralization stage on the voxels adjacent to the sampling point, the spatial smoothness constraints are used to describe the continuity between the mineralization grades of two adjacent voxels corresponding to the same mineralization stage, the spatial similarity constraints are used to describe the similarity between the mineralization grades of two voxels, and the variational model is used to describe the mineralization effect of multiple mineralization stages on the voxels;

[0055] A conversion module converts the geological observation constraints to obtain the final geological observation constraints, converts the spatial smoothness constraints to obtain the final spatial smoothness constraints, and converts the spatial similarity constraints to obtain the final spatial similarity constraints;

[0056] The solution module uses the final geological observation constraints, the final spatial smoothness constraints, and the final spatial similarity constraints to solve the variational model to obtain the mineralization stage and relative mineralization grade of each element in the target ore deposit space; the relative mineralization grade is the mineralization grade of the element under the mineralization action of the mineralization stage.

[0057] In a third aspect, an embodiment of the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned multi-stage mineralization analysis method of a ore deposit based on a variational model when executing the above-mentioned computer program.

[0058] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, it implements the above-mentioned multi-stage mineralization analysis method of a ore deposit based on a variational model.

[0059] The above solution of the present application has the following beneficial effects:

[0060] In an embodiment of the present application, the target ore deposit space is divided into multiple voxels, and the sample mineralization grades of multiple sampling points in the target mineralization space are obtained, and then the mineralization grade of each voxel is calculated based on all the sample mineralization grades, and the number of potential mineralization stages of each voxel is determined based on the mineralization grades of all the voxels, and then based on the number of potential mineralization stages of all the voxels, geological observation constraints, spatial smoothness constraints and spatial similarity constraints are constructed, and a variational model is constructed based on the geological observation constraints, spatial smoothness constraints and spatial similarity constraints, and then the geological observation constraints are transformed to obtain the final geological observation constraints, the spatial smoothness constraints are transformed to obtain the final spatial smoothness constraints, the spatial similarity constraints are transformed to obtain the final spatial similarity constraints, and finally the final geological observation constraints, the final spatial smoothness constraints and the final spatial similarity constraints are used to solve the variational model to obtain the relative mineralization grade of each voxel in the target ore deposit space in each mineralization stage. Among them, a variational model is constructed based on the mineralization grade of all voxels, taking into account the mutual influence of voxels in time and space, as well as the effect of mineralization stage on voxels, to make up for the limitations of inference of the spatiotemporal structure of ore deposits caused by the lack of deep information, and to achieve a detailed understanding and comprehensive characterization of the spatiotemporal structure of ore deposits. The variational model is solved to obtain the relative mineralization grade of voxels in each mineralization stage, thereby improving the accuracy of mineralization analysis.

[0061] Other beneficial effects of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0063] Figure 1 A flow chart of a multi-stage mineralization analysis method for a ore deposit based on a variational model provided in one embodiment of the present application;

[0064] Figure 2 A schematic diagram of a multi-fractal filtering modeling result provided in an embodiment of the present application;

[0065] Figure 3 A schematic diagram of the structure of a multi-stage mineralization analysis device for a ore deposit based on a variational model provided in one embodiment of the present application;

[0066] Figure 4 A schematic diagram of the structure of a terminal device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0067] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0068] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0069] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0070] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0071] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0072] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0073] In response to the problem of inaccurate mineralization analysis of ore deposits, an embodiment of the present application provides a multi-stage mineralization analysis method for ore deposits based on a variational model. The multi-stage mineralization analysis method for ore deposits divides the target ore deposit space into multiple voxels and obtains the sample mineralization grades of multiple sampling points in the target mineralization space. The mineralization grade of each voxel is then calculated based on all sample mineralization grades, and the number of potential mineralization stages of each voxel is determined based on the mineralization grades of all voxels. Based on the number of potential mineralization stages of all voxels, geological observation constraints, spatial smoothing constraints and spatial similarity constraints are constructed, and a variational model is constructed based on the geological observation constraints, spatial smoothing constraints and spatial similarity constraints. The geological observation constraints are then transformed to obtain the final geological observation constraints, the spatial smoothing constraints are transformed to obtain the final spatial smoothing constraints, the spatial similarity constraints are transformed to obtain the final spatial similarity constraints, and finally the final geological observation constraints, the final spatial smoothing constraints and the final spatial similarity constraints are used to solve the variational model to obtain the relative mineralization grade of each voxel in the target ore deposit space at each mineralization stage. Among them, a variational model is constructed based on the mineralization grade of all voxels, taking into account the mutual influence of voxels in time and space, as well as the effect of mineralization stage on voxels, to make up for the limitations of inference of the spatiotemporal structure of ore deposits caused by the lack of deep information, and to achieve a detailed understanding and comprehensive characterization of the spatiotemporal structure of ore deposits. The variational model is solved to obtain the relative mineralization grade of voxels in each mineralization stage, thereby improving the accuracy of mineralization analysis.

[0074] Next, the technical terms used in this application are explained.

[0075] Mineralization grade refers to the content of useful components or useful minerals in ore per unit volume or unit weight.

[0076] The mineralization stage refers to a shorter mineralization process further divided within the mineralization period, which is often closely related to the evolution of hydrothermal fluids, the staged pulsation of structural fractures, and the intermittent hydrothermal activities related thereto.

[0077] Mineralization refers to the process in which mineral-bearing fluids rise, migrate, and precipitate to favorable mineralization sites during geological activities, filling rock pores and forming minerals. It is related to the permeability, porosity, and connectivity of the rock.

[0078] Next, the multi-stage mineralization analysis method of ore deposits based on variational model provided in this application is exemplified.

[0079] like Figure 1 As shown, the multi-stage mineralization analysis method of a ore deposit based on a variational model provided in this application includes the following steps:

[0080] Step 11, dividing the target mineral deposit space into multiple voxels, and obtaining the sample mineralization grade of multiple sampling points in the target mineralization space.

[0081] The above target ore deposit space is the space where the ore deposit that needs to be subjected to multi-stage mineralization analysis is located. The sample mineralization grade is the mineralization grade of the sampling point.

[0082] In some embodiments of the present application, a three-dimensional simulation model can be performed on the target ore deposit space using simulation software such as Matlab, and the model can be divided into multiple voxels. The sample mineralization grade of multiple sampling points can be obtained by geological drilling or the like.

[0083] Step 12, calculating the mineralization grade of each volume based on the mineralization grades of all samples, and determining the number of potential mineralization stages of each volume based on the mineralization grades of all volumes.

[0084] The above number of potential mineralization stages is the number of mineralization stages that the voxel may correspond to.

[0085] In some embodiments of the present application, the above steps of calculating the mineralization grade of each voxel based on the mineralization grades of all samples, and determining the number of potential mineralization stages of each voxel based on the mineralization grades of all voxels are specifically as follows:

[0086] In the first step, for each voxel, perform the following steps:

[0087] If there is a sampling point in the volume, the mineralization grade of the sample at the sampling point will be taken as the mineralization grade of the volume.

[0088] If there is no sampling point in the volume element, the mineralization grade of the volume element is calculated using the mineralization grade calculation formula.

[0089] The mineralization grade calculation formula is:

[0090] ;

[0091] in, Representation voxel The mineralization grade represents the weight coefficient of the Kriging difference, Indicates Sampling points The sample mineralization grade, Indicates the number of sampling points.

[0092] It should be noted that setting When taking the value of , it is necessary to satisfy:

[0093] ;

[0094] ;

[0095] in, represents the interpolation expected value, ensuring that the estimator is unbiased, is the estimated equation, Represents the conditional extreme value of the estimated variance, which minimizes the estimated variance. Here, the Lagrange multiplier method is used to make the partial derivative of the above formula equal to 0, so as to determine The value of is taken, and then the mineralization grade of the element is calculated using the above mineralization grade calculation formula.

[0096] If there are multiple sampling points in the voxel, a sampling point is randomly selected to participate in the above calculation.

[0097] The second step is to set multiple constant thresholds, and use all constant thresholds and the mineralization grades of all voxels to divide all voxels to obtain multiple mineralized voxels corresponding to each constant threshold.

[0098] The above constant thresholds are preset values. Specifically, the formula is:

[0099] ;

[0100] Get the Multiple mineralized voxels corresponding to a constant threshold .

[0101] in, Representation voxel The mineralization grade Indicates A constant threshold, , represents the number of constant thresholds, , Both represent fractal dimensions greater than zero.

[0102] It should be noted that the multiple mineralized voxels corresponding to the constant threshold are the mineralized voxels corresponding to the mineralized grade within the unit interval with the constant threshold as the upper bound. For example, if the multiple constant thresholds are 1, 2, and 3, then the multiple mineralized voxels corresponding to the constant threshold 2 are the multiple voxels with the mineralized grade within the interval [1, 2].

[0103] In the third step, for each constant threshold, perform the following steps:

[0104] A mineralized voxel statistical curve is constructed based on all mineralized voxels corresponding to the constant threshold, and multiple slopes of the mineralized voxel statistical curve are calculated, and the number of slopes is used as the number of potential mineralization stages of each mineralized voxel corresponding to the constant threshold. The number of potential mineralization stages is the number of mineralization stages that the voxel may correspond to.

[0105] It should be noted that when constructing the mineralized voxel statistical curve, the horizontal axis is used as the constant threshold and the vertical axis is the number of mineralized voxels. In the interval corresponding to each constant threshold, the mineralized voxels with different mineralization grade values ​​are statistically analyzed in detail, so that in the mineralized voxel statistical curve, the horizontal axis interval corresponding to a constant threshold has different values ​​on the vertical axis. The multi-fractal filtering modeling method can be used to determine the number of potential mineralization stages experienced by the entire mineralization area. The subsequent variational model is constructed and solved based on this number of stages, and the mineralization stage corresponding to each voxel in the mineralization area and the mineralization received in each stage are calculated. According to the mineralization intensity of the target ore deposit, appropriate multiple constant thresholds are selected to form the threshold interval, and multi-fractal modeling is performed. Here, Gaussian distribution can be used to fit the comprehensive intensity of multi-stage mineralization in three-dimensional geological space, and the corresponding variance of the Gaussian distribution is used to determine the threshold. The interval of multi-fractal modeling is set based on this increment, and the discrete unit volume located in different grade intervals is counted, and the corresponding log-log graph is plotted. The slope is observed to determine the number of potential mineralization stages for each voxel.

[0106] It is worth mentioning that since only the mineralization grade of the sampling points is obtained, it has spatial non-uniformity and irregularity, and there are few sample points, it is difficult to count the mineralization grade of the entire ore deposit. Therefore, the mineralization grade of all elements is obtained based on the sampling mineralization grade of the sampling points, which improves the continuity of the mineralization grade of the ore deposit and ensures that the mineralization grade is second-order stable in space.

[0107] By determining the number of potential mineralization stages in this step, we can preliminarily analyze how many mineralization stages a voxel may be affected by.

[0108] The above steps are illustrated below with reference to a specific example.

[0109] The results of multi-fractal filtering modeling are as follows Figure 2 As shown in the figure, the horizontal axis represents the value logC after log calculation of the constant threshold, and the vertical axis represents the value logV after log calculation of the number of voxels at different grade thresholds. The number of slopes of the curve in the interval corresponding to each constant threshold is counted, and the number of slopes is the number of potential mineralization stages of each mineralized voxel corresponding to the constant threshold.

[0110] Step 13, based on the number of potential mineralization stages of all voxels, construct geological observation constraints, spatial smoothness constraints and spatial similarity constraints, and construct a variational model based on the geological observation constraints, spatial smoothness constraints and spatial similarity constraints.

[0111] The above geological observation constraints are used to describe the mineralization effect of the mineralization stage on the voxels adjacent to the sampling point. The spatial smoothness constraint is used to describe the continuity between the mineralization grades of two adjacent voxels corresponding to the same mineralization stage. The spatial similarity constraint is used to describe the similarity between the mineralization grades of two voxels. The variational model is used to describe the mineralization effect of multiple mineralization stages on the voxels.

[0112] Specifically, the geological observation constraints are:

[0113] ;

[0114] in, represents the value constrained by geological observations, Representation voxel The mineralization grade Indicates Mineralization stage for sampling points Mineralization, sampling point Voxel Sampling points within the preset range around, Representation voxel The number of potential mineralization stages, Indicated in Sampling points at each mineralization stage The grade of mineralization produced.

[0115] It should be noted that the above preset range is determined according to the distribution of sampling points. When setting, it is considered that there is at least one sampling point around each voxel within the preset range. If there are multiple sampling points, a sampling point is randomly selected for the above calculation. The specific mineralization stage corresponding to each mineralization stage can be determined by manual analysis, such as obtaining multiple mineralization stages corresponding to the sampling points adjacent to the voxel through manual analysis, and selecting The mineralization stages are involved in the above calculations. The value of can be obtained through geological qualitative analysis (such as ore structure, genesis, mineral symbiosis and output characteristics, mineral element enrichment mechanism, mineralogy characteristics under rock and ore microscope, vein type, generation sequence and other factors). This constraint characterization determines that the volume elements within a certain range of the sampling point affected by the mineralization stage are also affected by the mineralization stage.

[0116] The spatial smoothness constraint is:

[0117] ;

[0118] in, represents the value of the spatial smoothness constraint, Indicates The mineralization stage The mineralization of Indicates the mineralization grade produced by mineralization.

[0119] It should be noted that the spatial smoothness constraint characterizes that the mineralization of adjacent elements at the same mineralization stage has a certain continuity.

[0120] The spatial similarity constraints are:

[0121] ;

[0122] in, represents the value of the spatial similarity constraint, Represents the KL divergence operator, which is used for the The data of each mineralization stage are estimated as a whole, and the similarity between the estimated probability distribution and the overall probability distribution of the voxels in the determined period is measured. represents the approximate probability estimate, Representation and Voxel The same mineralization stage of the body The mineralization grade produced by the mineralization in each mineralization stage.

[0123] It should be noted that for ore bodies that are relatively far away from each other, if they are affected by the same mineralization stage, the two have similar mineralization grades to a certain extent.

[0124] The variational model is:

[0125] ;

[0126] in, Representation voxel The variational expression of , , Both represent weight values.

[0127] It is worth mentioning that the variational model is constructed based on the mineralization grade of all elements, which takes into account the mutual influence of elements in time and space, as well as the effect of mineralization stage on the elements, making up for the limitations of inference of the spatiotemporal structure of ore deposits caused by the lack of deep information, and realizing the detailed understanding and comprehensive characterization of the spatiotemporal structure of ore deposits.

[0128] Step 14, transforming the geological observation constraints to obtain the final geological observation constraints, transforming the spatial smoothness constraints to obtain the final spatial smoothness constraints, and transforming the spatial similarity constraints to obtain the final spatial similarity constraints.

[0129] Specifically, the final geological observation constraints are:

[0130] ;

[0131] in, Indicates the first mineralization stage for the sampling point The mineralization of Indicates the second mineralization stage for the sampling point The mineralization of Indicates Mineralization stage for sampling points Mineralization of the The mineralization stage corresponding to the element has been determined through geological observation data.

[0132] It should be noted that since the mineralization at different mineralization stages has a coupling effect on the volume element, that is, the grade of the volume element is not the sum of the mineralization at each mineralization stage, it is difficult to simulate and solve the functional expression of the mineralization at each mineralization stage. Here, the problem of time-space decomposition of the superimposed characteristics of multi-stage mineralization of the ore deposit is transformed into solving the mineralization grade of the volume element affected by different mineralization stages. Therefore, for the geological observation constraints, it can be transformed into:

[0133] ;

[0134] Right now:

[0135] ;

[0136] For spatial smoothness constraints Each of , which can be written as an objective function term related to the spatial position, that is, the final spatial smoothness constraint is:

[0137] ;

[0138] ;

[0139] in, represents the number of voxels, Indicates The set of neighboring voxels of an individual voxel, Indicates Individual element The mineralization intensity under the influence of mineralization in each mineralization stage, Indicates Individual element The mineralization intensity under the influence of mineralization in each mineralization stage, Indicates the mineralization grade produced by mineralization. Indicates The mineralization of the first stage is affected by the mineralization of the Weight terms related to individual metaspace positions:

[0140] ;

[0141] in, Represents the statistical expectation value of the mineralization intensity distribution.

[0142] In addition, when the volume element samples of a certain mineralization stage are missing in the geological observation information, or the effect of this stage is obviously superimposed on the characteristics of other stages, the Gaussian-like distribution can be used to construct the variation function of its spatial influencing factor :

[0143] ;

[0144] in, is the constructed variance function factor, which satisfies In the process of temporal and spatial decomposition of the superimposed characteristics of multi-stage mineralization in actual ore deposits, it can be considered that the surrounding areas of units with superimposed characteristics of this stage have similar mineralization stage effects.

[0145] The final spatial similarity constraint is:

[0146] ;

[0147] ;

[0148] in, , , represents the number of Monte Carlo sampling, represents the sampling points obtained by Monte Carlo random sampling, represents the probability distribution with spatial position constraints, represents the spatial position weight, Indicated in The mineralization intensity of each mineralization stage, represents the number of sampling points for kernel density estimation, Representation voxel The spatial distance between the elements known to be affected by a certain mineralization stage.

[0149] It should be noted that the KL divergence is used to estimate the entire ore deposit space, and the overall probability estimate of the discrete unit that has been clearly affected by the mineralization is compared with the approximate probability distribution of the discrete unit with multi-stage superposition characteristics. It is believed that the volume elements affected by the same period of mineralization have similar grades, so as to decompose the multi-stage superposition characteristics of mineralization in space in time and space. Therefore, it can be expressed as:

[0150] ;

[0151] in, Indicates that The collection of all the elements that acted in the mineralization stage, represents the probability distribution of mineralization intensity of the voxel, Representation and Voxel The same mineralization stage of the body, due to is a known probability distribution, so it can be converted to:

[0152] ;

[0153] in, is a constant (this term can be ignored when minimizing). However, it should be noted that since the influence of the mineralization enrichment center on the volume element is related to the spatial position and distance, when the volume element Voxel When the spatial distance is large, the above correlation measure needs to be added with constraints related to the spatial position to ensure that only the voxels within a certain range Voxel The mineralization grades are similar. Therefore, the kernel density estimation algorithm is used to express the two probability distributions of the above formula:

[0154] ;

[0155] in, and is the weight term related to the unit space position, and is the number of sampling points for kernel density estimation, and Characterizes the spatial distance between the sampling point and the known point. Since the similarity relationship between the unknown point and the known point is inversely proportional to the structured spatial distance, the spatial position weight term can be characterized as:

[0156] ;

[0157] ;

[0158] And the known point position coefficients must satisfy the normalization conditions, namely:

[0159] ;

[0160] Therefore, the above KL divergence algorithm can be simplified as a probability distribution with spatial position constraints: and , the above formula can be simplified to:

[0161] ;

[0162] in,

[0163] ;

[0164] ;

[0165] Since the above kernel density estimation is based on random variables The probability distribution of is estimated to obtain the real random variable The value of is obtained by Monte Carlo random sampling. For a known voxel set under geological exploration constraints, the probability distribution of random sampling is a known constant value, so the above formula can be simplified to:

[0166] ;

[0167] in, is the number of samples for Monte Carlo sampling, is the spatial random variable sample sampled by Monte Carlo. The above formula can be further expanded to obtain the final spatial similarity constraint.

[0168] Step 15, using the final geological observation constraint, the final spatial smoothness constraint, and the final spatial similarity constraint to solve the variational model, and obtain the mineralization stage and relative mineralization grade of each element in the target ore deposit space.

[0169] The above relative mineralization grade is the mineralization grade of the volume element under the mineralization effect of the mineralization stage. The mineralization stage of the volume element obtained by solving is the mineralization stage that has the main mineralization effect on the volume element.

[0170] In some embodiments of the present application, the variational model can be solved using a constrained optimization algorithm, a simulated annealing algorithm, or other solving algorithms using the final geological observation constraints, the final spatial smoothness constraints, and the final spatial similarity constraints to obtain a solution result that describes the mineralization stage and relative mineralization grade of each voxel in the target ore deposit space.

[0171] It should be noted that step 12 only determines the number of mineralization stages that the volume element may correspond to. By solving the variational model in this step, the mineralization stage that has a major effect on the volume element is obtained.

[0172] For example, after obtaining the mineralization stage corresponding to each voxel, it can be visualized to quantitatively analyze the spatiotemporal evolution characteristics of the mineralization intensity of the ore deposit in different mineralization stages.

[0173] It should be noted that after obtaining the mineralization stage and relative mineralization grade of each element in the target ore deposit space, it can be applied to other analyses of the ore deposit, such as predicting the mineralization stage and mineralization grade of the elements of the ore deposit at future times.

[0174] It is worth mentioning that a variational model is constructed based on the mineralization grade of all voxels, which takes into account the mutual influence of voxels in time and space, as well as the effect of mineralization stage on voxels, to make up for the limitations of inference of spatiotemporal structure of ore deposits caused by the lack of deep information, and realizes the detailed cognition and comprehensive characterization of the spatiotemporal structure of ore deposits. The variational model is solved to obtain the relative mineralization grade of voxels in each mineralization stage, thereby improving the accuracy of mineralization analysis.

[0175] The following is an exemplary description of the multi-stage mineralization analysis device for a ore deposit based on a variational model provided in the present application.

[0176] like Figure 3 As shown, the embodiment of the present application provides a multi-stage mineralization analysis device for a ore deposit based on a variational model. The multi-stage mineralization analysis device 300 for a ore deposit based on a variational model includes:

[0177] A division module 301 divides the target mineral deposit space into a plurality of voxels and obtains the sample mineralization grades of a plurality of sampling points in the target mineralization space;

[0178] A calculation module 302 calculates the mineralization grade of each voxel based on the mineralization grades of all samples, and determines the number of potential mineralization stages of each voxel based on the mineralization grades of all voxels; the number of potential mineralization stages is the number of mineralization stages that the voxel may correspond to;

[0179] Building module 303, based on the number of potential mineralization stages of all voxels, constructing geological observation constraints, spatial smoothness constraints and spatial similarity constraints, and constructing a variational model based on the geological observation constraints, spatial smoothness constraints and spatial similarity constraints; the geological observation constraints are used to describe the mineralization effect of the mineralization stage on the voxels adjacent to the sampling point, the spatial smoothness constraints are used to describe the continuity between the mineralization grades of two adjacent voxels corresponding to the same mineralization stage, and the spatial similarity constraints are used to describe the similarity between the mineralization grades of two voxels, and the variational model is used to describe the mineralization effect of multiple mineralization stages on the voxels;

[0180] The conversion module 304 converts the geological observation constraint to obtain the final geological observation constraint, converts the spatial smoothness constraint to obtain the final spatial smoothness constraint, and converts the spatial similarity constraint to obtain the final spatial similarity constraint;

[0181] The solution module 305 uses the final geological observation constraints, the final spatial smoothness constraints, and the final spatial similarity constraints to solve the variational model to obtain the mineralization stage and relative mineralization grade of each element in the target ore deposit space, where the relative mineralization grade is the mineralization grade of the element under the mineralization action of the mineralization stage.

[0182] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0183] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0184] like Figure 4 As shown, an embodiment of the present application provides a terminal device. The terminal device D10 of this embodiment includes: at least one processor D100 ( Figure 4 Only one processor is shown in the figure), a memory D101, and a computer program D102 stored in the memory D101 and executable on the at least one processor D100, wherein the processor D100 implements the steps of any of the above-mentioned method embodiments when executing the computer program D102.

[0185] Specifically, when the processor D100 executes the computer program D102, it divides the target ore deposit space into multiple voxels and obtains the sample mineralization grades of multiple sampling points in the target mineralization space, then calculates the mineralization grade of each voxel based on all the sample mineralization grades, and determines the number of potential mineralization stages of each voxel based on the mineralization grades of all the voxels, and then constructs geological observation constraints, spatial smoothing constraints and spatial similarity constraints based on the number of potential mineralization stages of all the voxels, and constructs a variational model based on the geological observation constraints, spatial smoothing constraints and spatial similarity constraints, then transforms the geological observation constraints to obtain the final geological observation constraints, transforms the spatial smoothing constraints to obtain the final spatial smoothing constraints, transforms the spatial similarity constraints to obtain the final spatial similarity constraints, and finally uses the final geological observation constraints, the final spatial smoothing constraints and the final spatial similarity constraints to solve the variational model to obtain the relative mineralization grade of each voxel in the target ore deposit space at each mineralization stage. Among them, a variational model is constructed based on the mineralization grade of all voxels, taking into account the mutual influence of voxels in time and space, as well as the effect of mineralization stage on voxels, to make up for the limitations of inference of the spatiotemporal structure of ore deposits caused by the lack of deep information, and to achieve a detailed understanding and comprehensive characterization of the spatiotemporal structure of ore deposits. The variational model is solved to obtain the relative mineralization grade of voxels in each mineralization stage, thereby improving the accuracy of mineralization analysis.

[0186] The processor D100 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0187] In some embodiments, the memory D101 may be an internal storage unit of the terminal device D10, such as a hard disk or memory of the terminal device D10. In other embodiments, the memory D101 may also be an external storage device of the terminal device D10, such as a plug-in hard disk, a smart memory card (SMC, SmartMedia Card), a secure digital (SD, Secure Digital) card, a flash card (Flash Card), etc. equipped on the terminal device D10. Further, the memory D101 may also include both an internal storage unit of the terminal device D10 and an external storage device. The memory D101 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program, etc. The memory D101 may also be used to temporarily store data that has been output or is to be output.

[0188] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0189] An embodiment of the present application provides a computer program product. When the computer program product runs on a terminal device, the terminal device can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0190] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the multi-stage mineralization analysis method device / terminal device based on the variational model, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0191] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0192] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0193] The above is a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles described in the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A multi-stage mineralization analysis method for a ore deposit based on a variational model, characterized in that: include: Divide the target mineral deposit space into multiple voxels, and obtain the sample mineralization grade of multiple sampling points in the target mineralization space; Calculate the mineralization grade of each of the voxels based on the mineralization grades of all samples, and determine the number of potential mineralization stages of each of the voxels based on the mineralization grades of all the voxels; the number of potential mineralization stages is the number of mineralization stages that the voxels may correspond to; Based on the number of potential mineralization stages of all voxels, geological observation constraints, spatial smoothness constraints and spatial similarity constraints are constructed, and a variational model is constructed based on the geological observation constraints, spatial smoothness constraints and spatial similarity constraints; the geological observation constraints are used to describe the mineralization effect of the mineralization stage on the voxels adjacent to the sampling point, the spatial smoothness constraints are used to describe the continuity between the mineralization grades of two adjacent voxels corresponding to the same mineralization stage, the spatial similarity constraints are used to describe the similarity between the mineralization grades of two voxels, and the variational model is used to describe the mineralization effect of multiple mineralization stages on the voxels; The geological observation constraint is converted to obtain a final geological observation constraint, the spatial smoothness constraint is converted to obtain a final spatial smoothness constraint, and the spatial similarity constraint is converted to obtain a final spatial similarity constraint; The variational model is solved using the final geological observation constraints, the final spatial smoothness constraints, and the final spatial similarity constraints to obtain the mineralization stage and relative mineralization grade of each voxel in the target ore deposit space; the relative mineralization grade is the mineralization grade of the voxel under the mineralization action of the mineralization stage.

2. The multi-stage mineralization analysis method of a ore deposit according to claim 1, characterized in that: The calculating the mineralization grade of each of the volume elements based on the mineralization grades of all samples comprises: For each of the voxels, perform the following steps: If there is a sampling point in the volume element, the mineralization grade of the sample at the sampling point is used as the mineralization grade of the volume element; If the volume element does not have a sampling point, the mineralization grade of the volume element is calculated using a mineralization grade calculation formula; The mineralization grade calculation formula is: ； in, Representation voxel The mineralization grade represents the weight coefficient of the Kriging difference, Indicates Sampling points The sample mineralization grade, Indicates the number of sampling points.

3. The multi-stage mineralization analysis method of a ore deposit according to claim 1, characterized in that: The method of determining the number of potential mineralization stages of each volume based on the mineralization grade of all the volumes comprises: Setting multiple constant thresholds, dividing all voxels using all constant thresholds and the mineralization grades of all voxels, and obtaining multiple mineralized voxels corresponding to each constant threshold; For each constant threshold, perform the following steps: A mineralized voxel statistical curve is constructed according to all mineralized voxels corresponding to the constant threshold, and multiple slopes of the mineralized voxel statistical curve are calculated, and the number of the slopes is used as the number of potential mineralization stages of each voxel corresponding to the constant threshold.

4. The multi-stage mineralization analysis method of a ore deposit according to claim 3, characterized in that: The method of dividing all voxels by using all constant thresholds and all mineralization grades to obtain a plurality of mineralized voxels corresponding to each constant threshold includes: By formula: ； Get the Multiple mineralized voxels corresponding to a constant threshold ; in, Representation voxel The mineralization grade Indicates the A constant threshold, , represents the number of constant thresholds, , Both represent fractal dimensions greater than zero.

5. The multi-stage mineralization analysis method of a ore deposit according to claim 1, characterized in that: The geological observation constraints are: ； in, represents the value constrained by geological observations, Representation voxel The mineralization grade Indicates Mineralization stage for sampling points The mineralization of the sampling point For the voxel Sampling points within the preset range around, Represents the voxel The number of potential mineralization stages, Indicated in Sampling points at each mineralization stage the grade of mineralization produced; The spatial smoothness constraint is: ； in, represents the value of the spatial smoothness constraint, Indicates The mineralization stage The mineralization of It indicates the grade of mineralization produced by mineralization; The spatial similarity constraint is: ； in, represents the value of the spatial similarity constraint, represents the KL divergence operator, represents the approximate probability estimate, Representation and Voxel The same mineralization stage of the body The mineralization grade produced by the mineralization in each mineralization stage.

6. The multi-stage mineralization analysis method of a ore deposit according to claim 5, characterized in that: The variational model is: ； in, Representation voxel The variational expression of , , Both represent weight values.

7. The multi-stage mineralization analysis method of a ore deposit according to claim 5, characterized in that: The final geological observation constraints are: ； in, Indicates the first mineralization stage for the sampling point The mineralization of Indicates the second mineralization stage for the sampling point The mineralization of Indicates Mineralization stage for sampling points mineralization; The final spatial smoothness constraint is: ； ； in, represents the number of voxels, Indicates The set of neighboring voxels of an individual voxel, Indicates the Individual element The mineralization intensity under the influence of mineralization in each mineralization stage, Indicates Individual element The mineralization intensity under the influence of mineralization in each mineralization stage, Indicates the mineralization grade produced by mineralization. Indicates The mineralization of the first stage is influenced by the mineralization of the Weight terms related to individual metaspace positions: ； in, Represents the statistical expectation of the mineralization intensity distribution; The final spatial similarity constraint is: ； ； ； in, , , represents the number of Monte Carlo sampling, represents the sampling points obtained by Monte Carlo random sampling, represents the probability distribution with spatial position constraints, represents the spatial position weight, Indicated in The mineralization intensity of each mineralization stage, represents the number of sampling points for kernel density estimation, Representation voxel The spatial distance between the elements known to be affected by a certain mineralization stage.

8. A multi-stage mineralization analysis device for a ore deposit based on a variational model, characterized in that: include: A partitioning module divides the target mineral deposit space into multiple voxels and obtains the sample mineralization grade of multiple sampling points in the target mineralization space; A calculation module, which calculates the mineralization grade of each voxel based on the mineralization grades of all samples, and determines the number of potential mineralization stages of each voxel based on the mineralization grades of all voxels; the number of potential mineralization stages is the number of mineralization stages that the voxel may correspond to; The construction module constructs geological observation constraints, spatial smoothness constraints and spatial similarity constraints based on the number of potential mineralization stages of all voxels, and constructs a variational model based on the geological observation constraints, spatial smoothness constraints and spatial similarity constraints; the geological observation constraints are used to describe the mineralization effect of the mineralization stage on the voxels adjacent to the sampling point, the spatial smoothness constraints are used to describe the continuity between the mineralization grades of two adjacent voxels corresponding to the same mineralization stage, the spatial similarity constraints are used to describe the similarity between the mineralization grades of two voxels, and the variational model is used to describe the mineralization effect of multiple mineralization stages on the voxels; A conversion module converts the geological observation constraints to obtain the final geological observation constraints, converts the spatial smoothness constraints to obtain the final spatial smoothness constraints, and converts the spatial similarity constraints to obtain the final spatial similarity constraints; The solution module uses the final geological observation constraints, the final spatial smoothness constraints, and the final spatial similarity constraints to solve the variational model to obtain the mineralization stage and relative mineralization grade of each element in the target ore deposit space; the relative mineralization grade is the mineralization grade of the element under the mineralization action of the mineralization stage.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the multi-stage mineralization analysis method of a ore deposit based on a variational model as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the multi-stage mineralization analysis method of a ore deposit based on a variational model as described in any one of claims 1 to 7 is implemented.

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