CN116644646B - Method, device, equipment and storage medium for mesoscopic reconstruction of rock - Google Patents
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Abstract
The application relates to the field of rock reconstruction, and provides a method, a device, equipment and a storage medium for mesoscopic reconstruction of rock. The method comprises the following steps: determining a random field with spatial correlation according to the spatial correlation parameter of the rock grains; determining the number of lattices corresponding to the random field, and determining seeds of the voronoi diagram in the random field and weights corresponding to the seeds according to the number of lattices and the random field; and according to the seeds and the corresponding weights, carrying out lattice division on the geometric domain, updating the seed positions, the weights and the lattices according to the centroid positions of the lattices, the positions of the seeds and the random fields, and generating the expected rock mesoscopic lattice structure through multiple iterations. The reconstructed crystal lattice presents the grain size information and also comprises the spatial correlation of grains, so that the mechanical property of the rock can be predicted more accurately, and the mechanical strength, the structural stability and the like of the rock can be determined.
Description
Technical Field
The present application relates to the field of rock reconstruction, and in particular, to a method, apparatus, device and storage medium for mesoscopic reconstruction of rock.
Background
Rock is a heterogeneous material whose microscopic cell parameters are not uniform. Due to the non-uniformity of the microscopic units, the rock starts to be destroyed along the grain boundary surface units in the bearing process, and the crystal units are continuously destroyed, so that macroscopic damage of the rock mass is finally caused, the mechanical properties of the rock are deteriorated, or the rock is broken and fails.
The Voronoi diagram (fully called Voronoi diagram in english) can be used to generate the grain structure of the rock, but the grain size distribution and the spatial correlation of grains cannot be accurately represented, which is disadvantageous for accurately predicting the mechanical properties of the rock, including determining the mechanical properties of the rock, the stability of the structure, and the like.
Disclosure of Invention
In view of this, embodiments of the present application provide a method, an apparatus, a device, and a storage medium for mesoscopic reconstruction of a rock, so as to solve the problems in the prior art that grain size distribution and spatial correlation of grains cannot be accurately presented, which is not beneficial to accurately predicting mechanical properties of the rock, and determining mechanical properties and structural stability of the rock.
A first aspect of embodiments of the present application provides a method of mesoscopic reconstruction of rock, the method comprising:
determining a random field with spatial correlation according to the spatial correlation parameter of the rock grains;
determining the number of lattices corresponding to the random field, and determining seeds of the voronoi diagram in the random field and weights corresponding to the seeds according to the number of lattices and the random field;
and according to the seeds and the corresponding weights, carrying out lattice division on the geometric domain, updating the seed positions, the weights and the lattices according to the centroid positions of the lattices, the positions of the seeds and the random fields, and generating the expected rock mesoscopic lattice structure through multiple iterations.
With reference to the first aspect, in a first possible implementation manner of the first aspect, updating a seed position, a weight, and the lattice according to a centroid position of the lattice, a position of the seed, and a random field, and generating a desired rock micro-lattice structure through multiple iterations includes:
generating a preliminary lattice according to the seeds, calculating the position of the mass center of the lattice, and moving the seeds to the position of the mass center of the lattice;
recalculating the corresponding seed weight according to the current centroid position and the random field, and updating the lattice;
and determining the centroid position of the updated crystal lattice again, moving the seeds to the updated centroid position, calculating the weight, and iteratively updating the crystal lattice according to the seeds and the weight after the movement position until the preset iteration requirement is met, so as to generate the expected rock mesoscopic crystal lattice structure.
With reference to the first possible implementation manner of the first aspect, in a second possible implementation manner of the first aspect, the preset iteration requirement includes a centroid of the current updated lattice, a distance between the centroid and a seed used for updating the current lattice is smaller than a predetermined distance threshold, or the iteration number satisfies a predetermined number of times requirement.
With reference to the first aspect, in a third possible implementation manner of the first aspect, determining the random field with spatial correlation according to the spatial correlation parameter of the rock grain includes:
acquiring a surface area with a preset size, and discretizing the surface area into a plurality of grids;
and determining the value of the corresponding random field at the centroid of the grid according to the spatial correlation parameter of the rock grains to obtain the random field with spatial correlation.
With reference to the third possible implementation manner of the first aspect, in a fourth possible implementation manner of the first aspect, determining a number of lattices corresponding to the random field includes:
determining an average area of grains of the rock;
and determining the number of lattices included in the random field determined by the area according to the average area and the total area of the area.
With reference to the third possible implementation manner of the first aspect, in a fifth possible implementation manner of the first aspect, determining a value of a corresponding random field at a centroid of the grid according to a spatial correlation parameter of a rock grain, to obtain the random field with spatial correlation includes:
and determining a corresponding random field value at the centroid of the grid by adopting a Kohler decomposition according to the spatial correlation parameter of the rock grains.
With reference to the first aspect, in a sixth possible implementation manner of the first aspect, determining, according to the number of lattices and the spatially related random fields, a seed of a voronoi diagram in the random fields and a weight corresponding to the seed includes:
determining the number of seeds disposed within the random field based on the number of lattices;
and determining the weight corresponding to the seed according to the position of the seed and the random field.
A second aspect of embodiments of the present application provides a device for the mesoscopic reconstruction of rock, the device comprising:
a random field determining unit for determining a random field having a spatial correlation based on the spatial correlation parameter of the rock grains;
a weight determining unit, configured to determine a number of lattices corresponding to the random field, and determine a seed of a voronoi diagram in the random field and a weight corresponding to the seed according to the number of lattices and the random field;
and the lattice generation unit is used for carrying out lattice division on the geometric domain according to the seeds and the corresponding weights, updating the seed positions, the weights and the lattices according to the centroid positions of the lattices, the positions of the seeds and the random fields, and generating the expected rock micro lattice structure through multiple iterations.
A third aspect of the embodiments of the present application provides a device for the microscopic reconstruction of rock, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, which processor, when executing the computer program, implements the steps of the method according to any of the first aspects.
A fourth aspect of the embodiments of the present application provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method according to any one of the first aspects.
Compared with the prior art, the embodiment of the application has the beneficial effects that: according to the embodiment of the application, the random field with spatial correlation is determined through the spatial correlation parameters of the rock grains, the number of lattices corresponding to the random field is determined, and the seeds of the Voronoi diagram in the random field and the weights corresponding to the seeds are determined according to the number of lattices and the spatial correlation of the random field. Dividing the crystal lattice according to the weight of the seeds and the weight of the seeds, and iteratively updating the crystal lattice according to the centroid position of the crystal lattice and the position of the seeds to obtain the crystal lattice after the iteration updating, so as to generate the microscopic crystal lattice of the rock. The reconstructed crystal lattice presents the grain size information and also comprises the spatial correlation of grains, so that the mechanical property of the rock can be predicted more accurately, and the mechanical strength, the structural stability and the like of the rock can be determined.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required for the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of an implementation of a method for mesoscopic reconstruction of rock according to an embodiment of the present application;
FIG. 2 is a schematic diagram of dividing a region into grids according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a spatial correlation equation according to an embodiment of the present application;
FIG. 4 is a schematic diagram of random field distributions with different spatial correlations provided by embodiments of the present application;
FIG. 5 is a schematic diagram of an implementation process for constructing a Voronoi diagram with lattice spatial correlation according to an embodiment of the present application;
FIG. 6 is a schematic diagram of a Voronoi diagram generated based on random scatter provided by embodiments of the present application;
FIG. 7 is a schematic diagram of a comparison of a generic Voronoi diagram with a centroid Voronoi diagram provided by embodiments of the present application;
FIG. 8 is a schematic diagram of a method for adjusting lattice dimensions based on weight according to an embodiment of the present application;
FIG. 9 is a schematic view of a rock lattice structure of different spatial correlations after reconstruction provided in an embodiment of the present application;
FIG. 10 is a schematic view of a rock mesoscopic reconstruction device provided in an embodiment of the present application;
fig. 11 is a schematic view of a rock mesoscopic reconstruction device provided in an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
In order to illustrate the technical solutions described in the present application, the following description is made by specific examples.
Upon mesoscopic reconstruction of the rock, the grain structure of the rock can be generated by voronoi diagram for describing the size information of the grains. However, the particle size of the rock is typically interrelated or interdependent. Such autocorrelation characteristics may have a significant impact on the mechanical and fracture response of the rock. Therefore, the grain structure of the rock is generated by the voronoi diagram, and the grain size and the spatial correlation of the grains cannot be accurately represented, which is not beneficial to accurately predicting the mechanical properties of the rock. For example, the mechanical strength of rock, the structural stability, etc. cannot be accurately determined.
In order to solve the above problems, an embodiment of the present application proposes a method for mesoscopic reconstruction of rock, as shown in fig. 1, the method comprising:
in S101, random fields having spatial correlation are determined from the spatial correlation parameters of the rock grains.
In the embodiment of the present application, a region of a preset size may be determined first. The area may be used to describe the mesostructure of the grains of the rock. After a face region for describing the microstructure of the crystal grains of the rock is determined, the face region may be subjected to a gridding process to obtain a plurality of grids included in the face region. For example, the area may be divided into a plurality of grids by dividing the area by dividing lines in the manner shown in fig. 2.
After dividing the area into a plurality of grids, determining the value of the space correlation random field corresponding to the centroid of each grid through a Cholesky decomposition method, thereby obtaining the space correlation random field. Alternatively, a direct generation method may be used, and the specified spatial correlation may be combined to directly and randomly take values according to the normal distribution of the random variables.
Random field is a variable defined on an n-dimensional euclidean space Ω, where x e Ω represents a point in space. Considering a set of n positions, the random field is quantized to a vector of random variables Z, i.e. z= [ Z ] 1 ,Z 2 ,...,Z n ]. Each argument is denoted as Z (x i ) Where i=1, 2,..n, is the index and position of the variable. In the case of a gaussian distributed stationary random field, the random variable Z i Obeying distribution
Z i ＝N(μ,σ) (1)
Where N (μ, σ) represents a Gaussian distribution, where the mean is μ and the variance is σ.
Furthermore, as shown in FIG. 3, the variable Z i And Z j The spatial correlation between them can be used as a correlation functionIs described wherein deltax represents the relative distance between positions i and j. For example, an exponential function that can be employed in the present application defines the spatial correlation between two variables, specifically expressed as:
wherein,representing the spatial correlation angle of position i (Zi) and position j (Zj), +.>Representing the rotation equation, deltax x And Deltax y Representing the spatial distance, θ, of the positions i and j in the x-direction and y-direction, respectively x And theta y The correlation attenuation coefficients in the x, y directions are shown.
In order to construct a set of random variables Z with spatial correlation, the application can use a Kertuer decomposition method to construct the random variables Z with spatial correlation by the following formula:
Z＝LU(3)
wherein U is a vector array with independent random distribution characteristics, and the array length is consistent with the number of random fields Z planned to be constructed. L is the lower triangular decomposition matrix of covariance matrix C, and the lower triangular decomposition matrix and the variance matrix satisfy c=ll T The covariance matrix C is an n-order symmetric matrix.
Let U be an n-order vector array, i.e., u= [ U ] 1 ,u 2 ,……u n-1 ,u n ]The covariance matrix C is an n×n symmetric matrix, and its calculation formula is:
wherein,and->Representing independent variable u i And u j Spatial distance in x, y direction, respectively, and C i,j ＝C j,i 。/>Representing spatially related angles, +.>Representing the rotation equation, deltax x And Deltax y Representing the spatial distance, θ, of the positions i and j in the x, y direction, respectively x And theta y The correlation attenuation coefficients in the x, y directions are shown.
According to formulas (1) - (4), random field distributions having spatial correlation in specific directions can be conveniently generated. Such as that shown in fig. 4, is a schematic diagram of a random field having spatial correlation of different directions and different attenuation coefficients. From left to right, the distribution diagrams of the random field of the spatial correlation with the attenuation coefficient of 10, the direction of the random field of the spatial correlation with the attenuation coefficient of 10, 0 degree, the random field of the spatial correlation with the attenuation coefficient of 200, the direction of the random field of the spatial correlation with the attenuation coefficient of 45 degrees, the random field of the spatial correlation with the attenuation coefficient of 200, the direction of the random field with the spatial correlation with the attenuation coefficient of 90 degrees, and the random field of the spatial correlation with the attenuation coefficient of 200 are respectively represented from top to bottom.
As shown in fig. 5 (a), the area with a predetermined size can calculate the value of the space-dependent random field corresponding to the centroid of each grid by using the kohlrabi decomposition method, so as to obtain a space-dependent random field.
In S102, determining a number of lattices corresponding to the random field, and determining a seed of the voronoi diagram in the random field and a weight corresponding to the seed according to the number of lattices and the spatially correlated random field.
After a predetermined size of the area is determined, the number of lattices required to fill the area may be estimated based on the average area of the grains determined in advance, in combination with the total area of the area. The crystal lattice is the description of the crystal grains in the area, and one crystal lattice corresponds to one crystal grain. The total area of the area may be divided by the average area of the grains to obtain the number of lattices, i.e., the number of lattices=the total area of the area/the average area of the grains.
After the number of lattices included in the area is determined, as shown in the (b) diagram in fig. 5, a corresponding number of seeds, that is, the same number of seeds as the number of lattices, may be arranged in the area. The positions of the seeds may be set randomly or may be set in a uniformly distributed manner as shown in the (b) diagram of fig. 5. And after the position of the seed is determined, interpolation can be performed according to the position of the seed and by combining the random field of the spatial correlation constructed by the S101, so as to determine the weight corresponding to the seed.
In S103, according to the seeds and the corresponding weights, the lattice of the voronoi diagram is divided for the geometric domain, and according to the centroid position of the lattice, the position of the seeds and the random field, the seed position, the weights and the lattice are updated, and through multiple iterations, the desired rock micro-lattice structure is generated.
After determining the weight of the seeds, the WCVT (centroid weighted voronoi diagram) method can be combined to obtain a lattice divided in the area as shown in the (c) diagram in fig. 5.
Wherein a Voronoi (Voronoi) diagram is a division of space into regions according to distance to a set of points. As shown in the two-dimensional diagram in fig. 6, constructing a Voronoi diagram requires a set of randomly distributed points (i.e., the points in the left diagram of fig. 6), which are referred to as the seeds of the Voronoi diagram. These regions (i.e., C in the right-hand view of FIG. 6 i Is defined by points within a certain distance from the seeds of the Voronoi diagram). The seeds of each Voronoi diagram correspond to the lattice of one Voronoi diagram. Lattice routing of Voronoi diagram of seedAll points closer to this seed than to any other seed, which can be expressed mathematically as:
C i ＝{p∈Pl|d(p,S i )<d(p,S j )} S j ∈S,j≠i (5)
where p represents any point on the plane, d represents the distance between the two points, and S represents the set of all seed points.
A centroid Voronoi diagram is a special type of Voronoi diagram, in contrast to a traditional Voronoi diagram, in which the seed of the lattice is also the centroid of the cell. Fig. 7 is a schematic diagram showing a comparison of a centroid Voronoi diagram with a normal Voronoi diagram, and the lattice of the centroid Voronoi diagram is more uniform in size and distribution in space than the lattice of the normal Voronoi diagram. Nonetheless, the capability to control cell size is lacking in both Voronoi diagrams or centroid Voronoi diagrams. To address this problem, weighted Voronoi diagram mosaicing (WVT) is further used to obtain grains of various sizes.
In weighted Voronoi diagram lattice partitioning, each Voronoi seed is associated with one weight, and each Voronoi lattice consists of all points with the following criteria:
C i ＝{p∈Pl|dw(p,S i ,w i )<dw(p,S j w j )} S j ∈S,j≠i (6)
wherein,w i the weight corresponding to each point i is represented.
As shown in fig. 8, if its corresponding seed is designated to have a greater weight than the adjacent seed, the lattice will have a larger area. With S in FIG. 8 (b) 1 And S in FIG. 8 (c) 2 For example, an increase in weight will force its corresponding Voronoi lattice to expand outward, satisfying equation (6). Thus, WVT provides a method of controlling lattice size by setting the weight of each seed. Also, similar to the definition of a centroid Voronoi diagram, a centroid weighted Voronoi diagram (WCVT) is a weighted Voronoi diagram, but where each Voronoi seed is located at eachThe center of mass of the Voronoi lattice.
After the lattice of the voronoi diagram is divided according to the seeds and the corresponding weights, as shown in (c) diagram in fig. 5, the divided lattice and the seeds used in dividing the lattice are obtained. As shown in fig. 5 (d), the centroid of the lattice can be determined from the divided lattices, and the distance between the centroid and the seed can be calculated. If the distance is less than a predetermined distance threshold, or the number of iterative updates of the crystal lattice meets a predetermined number of requirements, the iterative updating of the crystal lattice may be ended. If the iteration end requirement is not met, the seeds may be moved to the centroid position of the lattice as shown in fig. 5 (e), and the lattice may be recalculated and updated based on the moved seeds, the centroid of the updated lattice may be determined, and the updated centroid of the lattice may be further compared to whether the distance between the seeds used to calculate the lattice for updating is less than a predetermined distance threshold. If the distance between the seed and the centroid is smaller than a preset distance threshold value or the iteration times meet the preset times requirement, ending the iteration update, otherwise, continuing to calculate the centroid of the lattice according to the updated lattice, and further iteratively comparing the distance between the seed and the centroid or judging whether the iteration times meet the ending iteration calculation requirement.
Through repeated iterative computation, when the requirement of iteration completion is met, a fine crystal lattice corresponding to the rock can be generated. As shown in fig. 9, the rock lattice structures of different spatial correlation features are shown, wherein the corresponding spatial correlation features include lattice structures with grain sizes of 0.5, 0.35, 0.25 and 0.2, attenuation coefficients of 10, 50, 100 and 200, and directions of 0 degrees, 30 degrees, 45 degrees and 90 degrees, respectively.
The reconstructed crystal lattice of the method and the device has the advantages that the crystal grain size information is presented, and meanwhile, the spatial correlation of crystal grains is included, so that the mechanical property of the rock can be predicted more accurately, and the mechanical strength of the rock, the structural stability and the like are determined.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic of each process, and should not limit the implementation process of the embodiment of the present application in any way.
Fig. 10 is a schematic view of a rock micro-reconstruction device according to an embodiment of the present application, where the device includes:
a random field determining unit 1001 for determining a random field having a spatial correlation based on a spatial correlation parameter of the rock grains;
a weight determining unit 1002, configured to determine a number of lattices corresponding to the random field, and determine a seed of a voronoi diagram in the random field and a weight corresponding to the seed according to the number of lattices and the random field;
the lattice generation unit 1003 is configured to perform lattice division of the voronoi diagram on the geometric domain according to the seeds and the corresponding weights, update the seed positions, the weights and the lattices according to the centroid positions of the lattices, the positions of the seeds and the random fields, and generate the desired rock micro-lattice structure through multiple iterations.
The apparatus for the fine reconstruction of rock shown in fig. 10 corresponds to the method for the fine reconstruction of rock shown in fig. 1.
Fig. 11 is a schematic view of a rock mesoscopic reconstruction device provided in an embodiment of the present application. As shown in fig. 11, the fine reconstruction device 11 of the rock of this embodiment includes: a processor 110, a memory 111 and a computer program 112, such as a rock minireconstruction program, stored in said memory 111 and executable on said processor 110. The processor 110, when executing the computer program 112, implements the steps in the embodiments of the mesoscopic reconstruction method for each rock described above. Alternatively, the processor 110, when executing the computer program 112, performs the functions of the modules/units of the apparatus embodiments described above.
By way of example, the computer program 112 may be partitioned into one or more modules/units that are stored in the memory 111 and executed by the processor 110 to complete the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing a specific function for describing the execution of the computer program 112 in the rock mesoscopic reconstruction device 11.
The rock micro-reconstruction device 11 may be a computing device such as a desktop computer, a notebook computer, a palm computer, a cloud server, etc. The microscopic reconstruction device of the rock may include, but is not limited to, a processor 110, a memory 111. It will be appreciated by those skilled in the art that fig. 11 is merely an example of a rock mesoscopic reconstruction device 11 and does not constitute a limitation of the rock mesoscopic reconstruction device 11, and may comprise more or fewer components than shown, or may combine certain components, or different components, e.g. the rock mesoscopic reconstruction device may further comprise input-output devices, network access devices, buses, etc.
The processor 110 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 111 may be an internal storage unit of the rock's mesoscopic reconstruction device 11, for example a hard disk or a memory of the rock's mesoscopic reconstruction device 11. The memory 111 may also be an external storage device of the rock micro-reconstruction device 11, such as a plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card) or the like, which is provided on the rock micro-reconstruction device 11. Further, the memory 111 may also comprise both an internal memory unit and an external memory device of the rock mesoscopic reconstruction device 11. The memory 111 is used for storing the computer program as well as other programs and data required for the fine-scale reconstruction device of the rock. The memory 111 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other manners. For example, the apparatus/terminal device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. With such understanding, the present application implements all or part of the flow of the method of the above embodiments, and may also be implemented by hardware associated with computer program instructions, where the computer program may be stored on a computer readable storage medium, where the computer program, when executed by a processor, implements the steps of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium may include content that is subject to appropriate increases and decreases as required by jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is not included as electrical carrier signals and telecommunication signals.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (9)
1. A method of mesoscopic reconstruction of rock, the method comprising:
determining a random field with spatial correlation according to the spatial correlation parameter of the rock grains;
determining the number of lattices corresponding to the random field, and determining seeds of the voronoi diagram in the random field and weights corresponding to the seeds according to the number of lattices and the random field;
according to the seeds and the corresponding weights, carrying out centroid weighted Voronoi diagram lattice division on the geometric domain, updating the seed positions, the weights and the lattices according to the centroid positions of the lattices, the positions of the seeds and random fields, and generating expected rock micro lattice structures through multiple iterations;
the determining the random field with spatial correlation according to the spatial correlation parameter of the rock grains comprises the following steps:
acquiring a surface area with a preset size, and discretizing the surface area into a plurality of grids;
and determining the value of the corresponding random field at the centroid of the grid according to the spatial correlation parameter of the rock grains to obtain the random field with spatial correlation.
2. The method of claim 1, wherein updating the seed location, the weights, and the lattice based on the centroid location of the lattice, the location of the seed, and the random field, generates a desired rock mesoscopic lattice structure through a plurality of iterations, comprising:
generating a preliminary lattice according to the seeds, calculating the position of the mass center of the lattice, and moving the seeds to the position of the mass center of the lattice;
recalculating the corresponding seed weight according to the current centroid position and the random field, and updating the lattice;
and determining the centroid position of the updated crystal lattice again, moving the seeds to the updated centroid position, calculating the weight, and iteratively updating the crystal lattice according to the seeds and the weight after the movement position until the preset iteration requirement is met, so as to generate the expected rock mesoscopic crystal lattice structure.
3. The method of claim 2, wherein the predetermined iteration requirement includes a centroid of the current updated lattice, a distance between the current updated lattice and a seed used to update the current lattice is less than a predetermined distance threshold, or a predetermined number of iterations satisfies a predetermined number of iterations requirement.
4. The method of claim 1, wherein determining the number of lattices to which the random field corresponds comprises:
determining an average area of grains of the rock;
and determining the number of lattices included in the random field determined by the area according to the average area and the total area of the area.
5. The method of claim 1, wherein determining the corresponding random field value at the centroid of the grid based on the spatial correlation parameter of the rock grain, results in a random field having spatial correlation, comprises:
and determining a corresponding random field value at the centroid of the grid by adopting a Kohler decomposition according to the spatial correlation parameter of the rock grains.
6. The method of claim 1, wherein determining the seeds of the voronoi diagram in the random field and the weights corresponding to the seeds based on the number of lattices and the random field comprises:
determining the number of seeds disposed within the random field based on the number of lattices;
and determining the weight corresponding to the seed according to the position of the seed and the space-related random field.
7. A device for the mesoscopic reconstruction of rock, said device comprising:
a random field determining unit for determining a random field having a spatial correlation based on the spatial correlation parameter of the rock grains;
a weight determining unit, configured to determine a number of lattices corresponding to the random field, and determine a seed of a voronoi diagram in the random field and a weight corresponding to the seed according to the number of lattices and the random field;
the lattice generation unit is used for carrying out the lattice division of the centroid weighted Voronoi diagram on the geometric domain according to the seeds and the corresponding weights, updating the positions of the seeds, the weights and the lattices according to the centroid positions of the lattices, the positions of the seeds and random fields, and generating a desired rock micro lattice structure through multiple iterations;
the random field determining unit is used for obtaining a surface area with a preset size and discretizing the surface area into a plurality of grids; and determining the value of the corresponding random field at the centroid of the grid according to the spatial correlation parameter of the rock grains to obtain the random field with spatial correlation.
8. A rock micro-reconstruction device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any one of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method according to any one of claims 1 to 6.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105917389A (en) * | 2014-03-21 | 2016-08-31 | 圣犹达医疗用品心脏病学部门有限公司 | Methods and systems for generating a multi-dimensional surface model of a geometric structure |
| CN109063275A (en) * | 2018-07-12 | 2018-12-21 | 西安电子科技大学 | The construction method of three-dimensional polycrystalline microstructure material model based on FEAP |
| CN109509251A (en) * | 2018-11-08 | 2019-03-22 | 重庆大学 | Multifactor three-dimensional soil-rock mixture generation method |
| CN110990911A (en) * | 2019-10-29 | 2020-04-10 | 武汉大学 | Rock crystal structure rapid modeling method based on particle cluster substitution |
| CN112507581A (en) * | 2020-11-18 | 2021-03-16 | 华能澜沧江水电股份有限公司 | Rock mesoscopic numerical model generation method and system capable of avoiding malformation computing unit |
| CN112560125A (en) * | 2020-12-10 | 2021-03-26 | 上海联泰科技股份有限公司 | Generation method, generation system and preprocessing system of lattice structure model |
| CN113254673A (en) * | 2021-06-28 | 2021-08-13 | 明品云(北京)数据科技有限公司 | Data management method, system, equipment and medium based on knowledge graph |
| CN114565736A (en) * | 2022-02-21 | 2022-05-31 | 北京航空航天大学 | Controllable nano composite polycrystal modeling method based on weighted Thiessen polygon |
| CN116011302A (en) * | 2023-02-16 | 2023-04-25 | 广西大学 | Continuous-discontinuous numerical model building method considering real microscopic characteristics of rock |
-
2023
- 2023-05-17 CN CN202310557637.XA patent/CN116644646B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105917389A (en) * | 2014-03-21 | 2016-08-31 | 圣犹达医疗用品心脏病学部门有限公司 | Methods and systems for generating a multi-dimensional surface model of a geometric structure |
| CN109063275A (en) * | 2018-07-12 | 2018-12-21 | 西安电子科技大学 | The construction method of three-dimensional polycrystalline microstructure material model based on FEAP |
| CN109509251A (en) * | 2018-11-08 | 2019-03-22 | 重庆大学 | Multifactor three-dimensional soil-rock mixture generation method |
| CN110990911A (en) * | 2019-10-29 | 2020-04-10 | 武汉大学 | Rock crystal structure rapid modeling method based on particle cluster substitution |
| CN112507581A (en) * | 2020-11-18 | 2021-03-16 | 华能澜沧江水电股份有限公司 | Rock mesoscopic numerical model generation method and system capable of avoiding malformation computing unit |
| CN112560125A (en) * | 2020-12-10 | 2021-03-26 | 上海联泰科技股份有限公司 | Generation method, generation system and preprocessing system of lattice structure model |
| CN113254673A (en) * | 2021-06-28 | 2021-08-13 | 明品云(北京)数据科技有限公司 | Data management method, system, equipment and medium based on knowledge graph |
| CN114565736A (en) * | 2022-02-21 | 2022-05-31 | 北京航空航天大学 | Controllable nano composite polycrystal modeling method based on weighted Thiessen polygon |
| CN116011302A (en) * | 2023-02-16 | 2023-04-25 | 广西大学 | Continuous-discontinuous numerical model building method considering real microscopic characteristics of rock |
Non-Patent Citations (6)
| Title |
|---|
| Linchong Huang et al ; .On the optimization of site investigation programs using centroidal Voronoi tessellation and random field theory.Computers and Geotechnics.2019,全文. * |
| 基于Voronoi细观数值模型预测PBX的有效弹性模量;王竟成;罗景润;;含能材料(第07期);全文 * |
| 异质地层稳定渗流场随机多尺度有限元模拟研究;夏艳华;;皖西学院学报(第05期);全文 * |
| 结合特征场和标号场MRF的区域化纹理图像分割;杨圣;;测绘与空间地理信息(第05期);全文 * |
| 考虑空间相关尺度特征的细观力学模型及其应用;唐欣薇;周元德;张楚汉;;岩土力学(第07期);全文 * |
| 采用Voronoi模型研究多孔金属材料准静态力学特性;康锦霞;王志华;赵隆茂;;工程力学(第07期);全文 * |
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