Mineral reserve analysis method and system based on multi-parameter clustering, storage medium and equipment

Abstract

The invention discloses a mineral reserve analysis method and system based on multi-parameter clustering, a storage medium and equipment, and belongs to the field of electrical data clustering analysis, and the method comprises the following steps: S1, obtaining the distribution of a plurality of parameters representing the electrical properties of rocks and ores through a geophysical inversion method; s2, extracting different parameters and recording corresponding positions to obtain data sets corresponding to all the parameters; s3, according to the data groups corresponding to all the parameters, carrying out mineral trap pre-division on the to-be-detected area; s4, clustering analysis is carried out in combination with the data sets corresponding to different parameters, iteration revision is carried out on the pre-division result of the mineral reservoir entrainment, and final mineral reservoir entrainment division based on multiple parameters is obtained after iteration is finished; and S5, estimating mineral reserves according to the final mineral trap division. According to the method, the mineral traps are divided by comprehensively utilizing various parameters which are relatively sensitive to electrical parameter distribution of rocks and ores, so that the mineral traps are divided more accurately, and meanwhile, the judgment accuracy and the reserve estimation precision are improved through clustering.

Description

Technical Field

[0001] The present invention relates to the field of electrical data cluster analysis, and in particular to a multi-parameter clustering mineral reserve analysis method, system, storage medium and equipment. Background Art

[0002] With the implementation of a new round of national prospecting strategies, mineral resource exploration and mineral reserve analysis have also ushered in a new round of technological boom. Geophysical, geochemical and geological modeling methods are usually used before mining. Among them, geophysical methods play a vital role in deep mineral exploration and reserve assessment. Among traditional geophysical methods, direct current or electromagnetic detection has the advantage of being more sensitive to differences in the electrical structure of minerals than other methods. Generally speaking, the resistivity distribution map of the inversion results of direct current or electromagnetic method (hereinafter collectively referred to as electromagnetic method) can be used to enclose the scope of the mineral deposit, and then combined with other geological data to conduct a comprehensive analysis and evaluation of the mineral reserves.

[0003] Existing mineral resource reserve analysis methods are based on geophysical inversion results, and current geophysical inversion methods are mainly single-parameter inversion or imaging, which makes it difficult to accurately restore the distribution of minerals in complex structures. For example, when using magnetotelluric methods to detect mineral distribution, electrodes and magnetic sensors are usually used to collect data on the surface of the target area, and magnetotelluric inversion calculations are performed using electromagnetic signals on the surface to obtain a resistivity model diagram that fits the data. Then, combined with geological background data, comprehensive geological modeling is performed on the resistivity distribution, the scope of the mineral reservoir is calibrated, and finally, the mineral resource reserves are analyzed and evaluated.

[0004] Traditional magnetotelluric inversion resistivity imaging only uses a single resistivity parameter to delineate mineral closures. However, the actual underground geological structure is relatively complex, and the resistivity of some strata may not be accurate, resulting in inaccurate delineation of mineral closures and insufficient accuracy in the subsequent estimation of mineral resource reserves. Summary of the invention

[0005] The purpose of the present invention is to overcome the technical problems existing in the prior art, and to use resistivity and other parameters that are sensitive to the distribution of rock and ore electrical parameters to comprehensively divide the mineral closure. At the same time, clustering algorithms are used to perform cluster analysis on multiple parameters to further distinguish between mineral deposits and surrounding rocks, thereby improving the accuracy of judgment and subsequent reserve estimation.

[0006] The objective of the present invention is achieved through the following technical solutions: In a first aspect, a multi-parameter clustering mineral reserve analysis method is provided, comprising the following steps: S1. Using geophysical inversion methods, we can obtain the distribution of multiple parameters that characterize the electrical properties of rocks and minerals; S2, extract different parameters and record corresponding positions to obtain data sets corresponding to all parameters; S3, pre-dividing the area to be detected into mineral closures according to the data groups corresponding to all parameters; S4, performing cluster analysis on the data groups corresponding to different parameters, iteratively revising the results of the preliminary division of the mineral closure, and obtaining the final mineral closure division based on multiple parameters after the iteration; S5. Estimate mineral reserves based on the final mineral closure division.

[0007] In some embodiments, the geophysical inversion method includes magnetotelluric inversion based on resistivity parameters and transient electromagnetic inversion based on induced polarization effect.

[0008] In some embodiments, the plurality of parameters includes resistivity and polarizability.

[0009] In some embodiments, step S2 specifically includes: Using the resistivity P (ρ) and polarizability distribution P (η), the two parameters are extracted and stored in ρ and η, and the corresponding position and depth of each parameter are recorded at the same time, that is, the data group ρ[x, z, ρ] corresponding to the resistivity and the data group η[x, z, η] corresponding to the polarizability are obtained, where x represents the x-axis and z represents the depth.

[0010] In some embodiments, the mineral closure pre-division includes: The area to be explored is divided into four central clusters: mineral area, pore area, boundary area and surrounding rock area, which are marked as A, B, C and D respectively. The initial values ​​of the centers, A0, B0, C0 and D0, are set based on the experience of previous mineral data.

[0011] In some embodiments, the step S4 uses a Kmeans clustering algorithm to perform cluster analysis.

[0012] In some embodiments, step S4 specifically includes the following steps: S41, calculate the distances between all data points in ρ[x, z, ρ] and η[x, z, η] and A0, B0, C0, D0; S42, divide the data points in ρ[x, z, ρ] and η[x, z, η] into four major regions according to the shortest distance, denoted as ρ0=[ρ A0 , B0 , C0 , D0 ] and η0=[η A0 , η B0 , η C0 , η D0 ]; S43. Calculate the root mean square error between each central cluster and the central point, denoted as J i , the formula is as follows:

[0013] Among them, J i is the root mean square error of the Kmeans clustering partition of the i-th iteration, f(x i ) is ρ i and η i , that is, corresponding to A i-1 , B i-1 , C i-1 , D i-1 All resistivity and polarizability distribution data within the four clusters; S44, when the root mean square error and J i When the minimum value is met or the number of iterations reaches the upper limit, jump to S45, otherwise go to S41 and update A0, B0, C0, D0 to A i , B i , C i , D i , execute in a loop until the condition is met and proceed to S45; S45, the root mean square error and J i The minimum value of A i , B i , C i , D i The final central cluster combination is determined and the final mineral closure division is obtained.

[0014] In a second aspect, a multi-parameter clustering mineral reserve analysis system is provided, comprising: A parameter distribution acquisition module is used to obtain the distribution of multiple parameters characterizing the electrical properties of rocks and ores by using a geophysical inversion method; The parameter extraction module is used to extract different parameters and record the corresponding positions to obtain the data groups corresponding to all parameters; A mineral closure pre-division module is used to pre-divide the mineral closure of the area to be detected according to the data groups corresponding to all parameters; A multi-parameter clustering module is used to perform cluster analysis in combination with data groups corresponding to different parameters, iteratively revise the results of the mineral closure pre-division, and obtain the final mineral closure division based on multiple parameters after the iteration; A mineral reserve calculation module is used to estimate the mineral reserves according to the final mineral closure division.

[0015] In a third aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the multi-parameter clustering mineral reserve analysis method is implemented.

[0016] In a fourth aspect, an electronic device is provided, comprising a memory and a processor, wherein the memory stores computer instructions executable on the processor, and the processor executes the multi-parameter clustering mineral reserve analysis method when executing the computer instructions.

[0017] It should be further explained that the technical features corresponding to the above options can be combined or replaced with each other to form a new technical solution if there is no conflict.

[0018] Compared with the prior art, the present invention has the following beneficial effects: The present invention comprehensively utilizes a variety of parameters that are sensitive to the distribution of electrical parameters of rocks and ores to divide mineral closures, and specifically uses a variety of electrical parameters when using geophysical methods for inversion interpretation. Using traditional geophysical inversion methods to obtain the distribution of a variety of electrical parameters can provide more electrical structure information, reduce the multi-solution nature of the single parameter inversion model, and make the mineral closure division more accurate. At the same time, by clustering and analyzing a variety of electrical parameters, it is also possible to further distinguish between mineral deposits and surrounding rocks, thereby improving the accuracy of judgment and the accuracy of subsequent estimated reserves. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A flow chart of a multi-parameter clustering mineral reserve analysis method shown in an embodiment of the present invention; Figure 2 The resistivity-polarizability dual parameter mineral reserve analysis flow chart shown in the embodiment of the present invention; Figure 3 The resistivity-polarizability dual parameter Kmeans clustering analysis flow chart shown in the embodiment of the present invention; Figure 4 Schematic diagram of resistivity-polarizability dual parameter clustering shown in an embodiment of the present invention; Figure 5 This is a schematic diagram of the mineral closure division shown in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The technical solution of the present invention is clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the accompanying drawings can be arranged and designed in various configurations. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0021] It should be noted that the defects existing in the solutions in the above-mentioned prior art are the results obtained by the inventor after practice and careful research. Therefore, the discovery process of the above-mentioned problems and the solutions proposed in the embodiments of the present application for the above-mentioned problems below should all be the contributions made by the inventor to the present application in the process of invention and creation, and should not be understood as technical contents known to technical personnel in this field.

[0022] In view of the technical problems pointed out in the background technology, the embodiments provided by the present invention are as follows: Example

[0023] Reference Figure 1 In an exemplary embodiment, a multi-parameter clustering mineral reserve analysis method is provided, comprising the following steps: S1. Using geophysical inversion methods, we can obtain the distribution of multiple parameters that characterize the electrical properties of rocks and minerals; S2, extract different parameters and record corresponding positions to obtain data sets corresponding to all parameters; S3, pre-dividing the area to be detected into mineral closures according to the data groups corresponding to all parameters; S4, performing cluster analysis on the data groups corresponding to different parameters, iteratively revising the results of the preliminary division of the mineral closure, and obtaining the final mineral closure division based on multiple parameters after the iteration; S5. Estimate mineral reserves based on the final mineral closure division.

[0024] Specifically, the principle of this method is: a variety of electrical parameters (resistivity, polarizability, etc.) are used in the inversion interpretation using geophysical methods. The distribution of different parameters is obtained by using methods such as magnetotelluric inversion based on resistivity parameters and transient electromagnetic inversion based on induced polarization effect, respectively, using traditional geophysical inversion methods. The use of other parameters besides resistivity (such as polarizability, etc.) can provide more electrical structure information, reduce the multi-solution of the single parameter inversion model, and further improve the inversion accuracy. The division of mineral closures is made more accurate. At the same time, by clustering and analyzing a variety of electrical parameters, the difference between mineral deposits and surrounding rocks can be further divided, which improves the accuracy of judgment and the accuracy of subsequent estimated reserves. Example

[0025] Based on the inventive concept of Example 1, this example uses resistivity and polarizability as two parameters to analyze mineral reserves. Figure 2 The specific steps of this method are as follows:

S01

[0026]

S02

[0027]

S03

[0028] Among them, J i is the root mean square error of the Kmeans clustering partition of the i-th iteration, and f(xi) is ρ i and η i , that is, corresponding to A i-1 , B i-1 , C i-1 , D i-1 All resistivity and polarizability distribution data within the four clusters; Step 6: When the RMS error and J iWhen the minimum value is met or the number of iterations reaches the upper limit of the initial setting, the clustering result corresponding to the minimum value (or the minimum value within the upper limit of the number of iterations) is the best clustering division, and it also corresponds to the four most accurate regional division methods. At this time, jump to step 7. When the root mean square error and does not meet the above conditions, go to step 3 and update A0, B0, C0, and D0 to A. i , B i , C i , D i , execute the loop until the condition is met and go to step 7; Step 7: Substitute the RMS error and J i The minimum value of A i , B i , C i , D i The final center cluster combination is determined and the clustering algorithm ends.

[0029]

S04

[0030] [S05] Estimate the mineral resource reserves based on the mineral reserve range obtained in step S04.

[0031] In this embodiment, the resistivity-polarizability dual parameter Kmeans clustering algorithm is used for comprehensive analysis and classification. By extracting the resistivity and polarizability parameters in the geophysical inversion, the area to be detected is divided into four areas: mineral area (A), pore area (B), boundary area (C) and surrounding rock area (D). The initial center point (A0, B0, C0, D0) is set, and the extracted resistivity and polarizability parameters are clustered using the Kmeans clustering algorithm to minimize the Cartesian distance between each data point and the center point of the area to which it belongs, such as Figure 4 After the classification is completed, the root mean square error of all points is calculated. If the root mean square error and the error reach the minimum value, the classification is completed. Otherwise, the center point (A) is revised. i , B i , C i , D i ) and repeatedly iterate until the root mean square error and the iteration are completed. The final cluster center combination is the optimal resistivity-polarizability clustering method, such as Figure 5 As shown, it was finally divided into the mineral deposit area (A i ), pore area (B i ), boundary area (C i ) and surrounding rock area (D i ) four areas. Example

[0032] Based on the same inventive concept as that of Example 1, a multi-parameter clustering mineral reserve analysis system is provided, comprising: A parameter distribution acquisition module is used to obtain the distribution of multiple parameters characterizing the electrical properties of rocks and ores by using a geophysical inversion method; The parameter extraction module is used to extract different parameters and record the corresponding positions to obtain the data groups corresponding to all parameters; A mineral closure pre-division module is used to pre-divide the mineral closure of the area to be detected according to the data groups corresponding to all parameters; A multi-parameter clustering module is used to perform cluster analysis in combination with data groups corresponding to different parameters, iteratively revise the results of the mineral closure pre-division, and obtain the final mineral closure division based on multiple parameters after the iteration; A mineral reserve calculation module is used to estimate the mineral reserves according to the final mineral closure division. Example

[0033] Based on the same inventive concept as Example 1, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the multi-parameter clustering mineral reserve analysis method provided in the embodiment of the present invention is implemented. Based on such an understanding, the technical solution of this embodiment is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk. Example

[0034] Based on the same inventive concept as Example 1, an electronic device is provided, including a memory and a processor, wherein the memory stores computer instructions that can be executed on the processor, and when the processor executes the computer instructions, the multi-parameter clustering mineral reserve analysis method provided in the embodiment of the present invention is executed.

[0035] The processor may be a single-core or multi-core central processing unit or a specific integrated circuit, or one or more integrated circuits configured to implement the present invention.

[0036] Embodiments of the subject matter and functional operations described in this specification may be implemented in: tangibly embodied computer software or firmware, computer hardware including the structures disclosed in this specification and their structural equivalents, or a combination of one or more of them. Embodiments of the subject matter described in this specification may be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible, non-transitory program carrier to be executed by a data processing device or to control the operation of the data processing device. Alternatively or additionally, the program instructions may be encoded on an artificially generated propagated signal, such as a machine-generated electrical, optical, or electromagnetic signal, which is generated to encode and transmit information to a suitable receiver device for execution by the data processing device.

[0037] The processes and logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform corresponding functions by operating on input data and generating output. The processes and logic flows can also be performed by special purpose logic circuits, such as FPGAs (field programmable gate arrays) or ASICs (application-specific integrated circuits), and the apparatus can also be implemented as special purpose logic circuits.

[0038] Processors suitable for executing computer programs include, for example, general and / or special microprocessors, or any other type of central processing unit. Typically, the central processing unit will receive instructions and data from a read-only memory and / or a random access memory. The basic components of a computer include a central processing unit for implementing or executing instructions and one or more memory devices for storing instructions and data. Typically, the computer will also include one or more large-capacity storage devices for storing data, such as magnetic disks, magneto-optical disks, or optical disks, or the computer will be operably coupled to this large-capacity storage device to receive data from it or to transmit data to it, or both. However, the computer does not necessarily have such a device. In addition, the computer can be embedded in another device, such as a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device such as a universal serial bus (USB) flash drive, just to name a few.

[0039] It should be understood that each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0040] The above specific implementation methods are detailed descriptions of the present invention. It cannot be determined that the specific implementation methods of the present invention are limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions and substitutions can be made without departing from the concept of the present invention, which should be regarded as belonging to the protection scope of the present invention.

Claims

1. A multi-parameter clustering mineral reserve analysis method, characterized in that: The following steps are involved: S1. Using geophysical inversion methods, we can obtain the distribution of multiple parameters that characterize the electrical properties of rocks and minerals; S2, extract different parameters and record corresponding positions to obtain data sets corresponding to all parameters; S3, pre-dividing the area to be detected into mineral closures according to the data groups corresponding to all parameters; S4, performing cluster analysis on the data groups corresponding to different parameters, iteratively revising the results of the preliminary division of the mineral closure, and obtaining the final mineral closure division based on multiple parameters after the iteration; S5. Estimate mineral reserves based on the final mineral closure division.

2. The mineral reserve analysis method of multi-parameter clustering according to claim 1, characterized in that: The geophysical inversion method includes magnetotelluric inversion based on resistivity parameters and transient electromagnetic inversion based on induced polarization effect.

3. The mineral reserve analysis method of multi-parameter clustering according to claim 1, characterized in that: The plurality of parameters include resistivity and polarizability.

4. The mineral reserve analysis method of multi-parameter clustering according to claim 3 is characterized in that: The step S2 specifically includes: Using the resistivity P (ρ) and polarizability distribution P (η), the two parameters are extracted and stored in ρ and η, and the corresponding position and depth of each parameter are recorded at the same time, that is, the data group ρ[x, z, ρ] corresponding to the resistivity and the data group η[x, z, η] corresponding to the polarizability are obtained, where x represents the x-axis and z represents the depth.

5. The mineral reserve analysis method of multi-parameter clustering according to claim 4 is characterized in that: The mineral closure pre-division includes: The area to be explored is divided into four central clusters: mineral area, pore area, boundary area and surrounding rock area, which are marked as A, B, C and D respectively. The initial values ​​of the centers, A0, B0, C0 and D0, are set based on the experience of previous mineral data.

6. The multi-parameter clustering mineral reserve analysis method according to claim 5, characterized in that: In step S4, Kmeans clustering algorithm is used to perform cluster analysis.

7. The multi-parameter clustering mineral reserve analysis method according to claim 6, characterized in that: The step S4 specifically comprises the following steps: S41, calculate the distances between all data points in ρ[x, z, ρ] and η[x, z, η] and A0, B0, C0, D0; S42, divide the data points in ρ[x, z, ρ] and η[x, z, η] into four major regions according to the shortest distance, denoted as ρ0=[ρ A0 , B0 , C0 , D0 ] and η0=[η A0 , η B0 , η C0 , η D0 ]; S43. Calculate the root mean square error between each central cluster and the central point, denoted as J i , the formula is as follows: in, J i is the root mean square error of the Kmeans clustering partition of the i-th iteration, f(x i ) is ρ i and η i , that is, corresponding to A i-1 , B i-1 , C i-1 , D i-1 All resistivity and polarizability distribution data within the four clusters; S44, when the root mean square error and J i When the minimum value is met or the number of iterations reaches the upper limit, jump to S45, otherwise go to S41 and update A0, B0, C0, D0 to A i , B i , C i , D i , execute in a loop until the condition is met and proceed to S45; S45, the root mean square error and J i The minimum value of A i , B i , C i , D i The final central cluster combination is determined and the final mineral closure division is obtained.

8. A multi-parameter clustering mineral reserve analysis system, characterized in that: include: A parameter distribution acquisition module is used to obtain the distribution of multiple parameters characterizing the electrical properties of rocks and ores by using a geophysical inversion method; The parameter extraction module is used to extract different parameters and record the corresponding positions to obtain the data groups corresponding to all parameters; A mineral closure pre-division module is used to pre-divide the mineral closure of the area to be detected according to the data groups corresponding to all parameters; A multi-parameter clustering module is used to perform cluster analysis in combination with data groups corresponding to different parameters, iteratively revise the results of the mineral closure pre-division, and obtain the final mineral closure division based on multiple parameters after the iteration; A mineral reserve calculation module is used to estimate the mineral reserves according to the final mineral closure division.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the multi-parameter clustering mineral reserve analysis method described in any one of claims 1 to 7 is implemented.

10. An electronic device comprising a memory and a processor, wherein the memory stores computer instructions that can be executed on the processor, wherein: When the processor runs the computer instructions, it executes the multi-parameter clustering mineral reserve analysis method described in any one of claims 1-7.

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