CN112731527A - Multi-attribute research-based method and device for enhancing characteristics of broken solution - Google Patents
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Abstract
The application discloses a method and a device for enhancing characteristics of an interrupted solution based on multi-attribute research. The method comprises the following steps: calculating a plurality of texture attributes of the disconnected solution signal profile; calculating a plurality of structure tensor attributes of the solution signal profile; selecting a plurality of preferred attributes from the plurality of texture attributes and the plurality of structure tensor attributes; and performing attribute dimensionality reduction clustering according to the plurality of preferred attributes to obtain a final solution clustering profile. According to the technical scheme of the application, the abnormal boundary of the broken solution can be effectively highlighted, the stratum structure is effectively eliminated to a certain extent, the abnormality is focused, the theory is rigorous and reliable, and the operation flow is simple and practical.
Description
Technical Field
The invention belongs to the field of seismic post-stack data signal processing, and particularly relates to a method and a device for enhancing a characteristic of an interrupted solution based on multi-attribute research.
Background
The carbonate rock stratum such as the covering zone of the Ordovician in the northward region is subjected to multi-stage structural deformation and karst action to form various irregular fracture-cave bodies along a large corrosion fracture zone, and the theoretical concept of solution-breaking trap is firstly proposed by medium petrochemical tower river oil field scientific research technicians, and the specific meaning refers to that the compact carbonate rock or low-pore and low-permeability carbonate rock stratum is subjected to multi-stage structural extrusion, the fracture zone with the development scale along a deep fracture zone is subjected to multi-stage karst water seepage along fracture or local hydrothermal upwelling to cause the fracture in the fracture zone and the corrosion and transformation of cracks to form plate-shaped corrosion-dissolving holes and cave reservoirs, and the trap type formed by covering layer plugging of marlite, mudstone and the like and lateral shielding of compact limestone is called as "solution-breaking trap". The trap is a special oil-gas reservoir 'broken solution oil (gas) reservoir' formed after oil (gas) migrates along a deep fracture (mainly vertical) in the later period and is filled into the reservoir. After the discovery of the oil field in northward of 2016, the discovery that a fracture zone is not only an oil and gas dredging channel, but also a favorable space for reservoir formation is realized, and according to the characteristic that the common burial depth in the northward of the same north exceeds 7000 meters, the concept of the oil reservoir with the ultra-deep solution breaking is provided, and the research on the solution breaking is the most important thing.
The characteristics of the solution are better identified, the geological conditions of the relevant areas can be deeply known, and support is provided for the exploration and development of the relevant areas.
Disclosure of Invention
In view of the above, the present application provides a method for enhancing a characteristic of an immiscible liquid based on multi-attribute research. The application also provides a corresponding device.
According to an aspect of the present application, there is provided a method for enhancing a characteristic of an immiscible liquid based on a multi-attribute study, the method including: calculating a plurality of texture attributes of the disconnected solution signal profile; calculating a plurality of structure tensor attributes of the solution signal profile; selecting a plurality of preferred attributes from the plurality of texture attributes and the plurality of structure tensor attributes; and performing attribute dimensionality reduction clustering according to the plurality of preferred attributes to obtain a final solution clustering profile.
In one possible embodiment, the solution signal profile is a two-dimensional empirical mode decomposition (bemd) of an original solution seismic profile.
In one possible embodiment, the calculating the plurality of texture attributes of the disconnected solution signal profile includes: and calculating the entropy property, the energy property and the contrast property of the cut solution signal section according to the gray level co-occurrence matrix of the cut solution signal section.
In one possible embodiment, the calculating the plurality of structure tensor properties of the solution signal profile includes: calculating the gradient vector direction derivative of each point of the three-dimensional seismic data body; reconstructing a gradient structure tensor according to the vector direction derivative; adopting different Gaussian window parameters to carry out smooth test on each component of the gradient structure tensor, and selecting an optimal result according to the noise immunity of the obtained section result and the salient degree of the boundary extraction; and calculating a plurality of eigenvalues of the gradient structure tensor according to the optimal result, wherein the eigenvalues are used as a plurality of structure tensor attributes.
In one possible implementation, a plurality of preferred attributes are selected from the plurality of texture attributes and the plurality of structure tensor attributes based on one or more of: the connectivity of the profile corresponding to the attribute; the boundary definition of the section corresponding to the attribute; the attribute corresponds to the background noise of the profile.
In a possible embodiment, the performing attribute dimension reduction clustering according to the plurality of preferred attributes to obtain a final solution cluster profile includes:
step 401, giving C clusters as initial clustering center profiles;
step 402, in the kth iteration, for each attribute data, calculating the distance from the corresponding attribute data profile to the C cluster center profiles;
step 403, updating a clustering center profile according to the current clustering result;
step 404, if the distance value calculated in step 402 remains unchanged, the iteration is ended; otherwise, go back to step 402 and enter the next iteration.
According to another aspect of the present application, a device for enhancing a characteristic of an interrupted solution based on multi-attribute research is provided, the device comprising: the texture attribute extraction unit is used for calculating a plurality of texture attributes of the solution signal section; the structure tensor attribute extraction unit is used for calculating a plurality of structure tensor attributes of the solution signal section; an attribute preference unit configured to select a plurality of preferred attributes from the plurality of texture attributes and the plurality of structure tensor attributes; and the dimension reduction clustering unit is used for performing attribute dimension reduction clustering according to the plurality of preferred attributes to obtain a final solution clustering section.
In one possible embodiment, the solution signal profile is a two-dimensional empirical mode decomposition (bemd) of an original solution seismic profile.
In one possible implementation, a plurality of preferred attributes are selected from the plurality of texture attributes and the plurality of structure tensor attributes based on one or more of: the connectivity of the profile corresponding to the attribute; the boundary definition of the section corresponding to the attribute; the attribute corresponds to the background noise of the profile.
In a possible implementation manner, the dimension reduction clustering unit is specifically configured to:
step 401, giving C clusters as initial clustering center profiles;
step 402, in the kth iteration, for each attribute data, calculating the distance from the corresponding attribute data profile to the C cluster center profiles;
step 403, updating a clustering center profile according to the current clustering result;
step 404, if the distance value calculated in step 402 remains unchanged, the iteration is ended; otherwise, go back to step 402 and enter the next iteration.
According to the technical scheme of the application, aiming at the problem of identifying the boundary of the broken solution, the research of the characteristic enhancement method of the broken solution based on the multi-attribute research is provided, the abnormity of the boundary of the broken solution can be effectively highlighted, the stratum structure is effectively eliminated to a certain extent, the abnormity is focused, the theory is rigorous and reliable, and the operation flow is simple and practical.
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The foregoing and other objects, features and advantages of the application will be apparent from the following more particular descriptions of exemplary embodiments of the application, as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts throughout the exemplary embodiments of the application.
Fig. 1 shows a flow diagram of a method for enhancing an immiscible fluid feature based on multi-attribute studies according to an embodiment of the present application.
Fig. 2 shows a block diagram of a structure of an apparatus for enhancing a dissolved solution feature based on multi-attribute studies according to an embodiment of the present application.
FIG. 3(a) illustrates a geological model of an exemplary solution; FIG. 3(b) shows an exemplary solution-breaking seismic model.
Fig. 4 shows a comparison of texture properties obtained with some commercial software and with the solution of the present application.
FIG. 5(a) shows λ for the structure tensor properties obtained under different Gaussian kernel parameters1A comparison graph of (A); FIG. 5(b) shows λ for the structure tensor properties obtained under different Gaussian kernel parameters2And λ3A comparative graph of (a).
Fig. 6(a) shows a plurality of solution boundary preferred attributes for multi-attribute clustering, fig. 6(b) shows attribute clustering results, and fig. 6(c) shows overlay graphs of the attribute clustering results and original profiles.
Detailed Description
Preferred embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While the preferred embodiments of the present application are shown in the drawings, it should be understood that the present application may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Please refer to fig. 1. Fig. 1 shows a flow diagram of a method for enhancing an immiscible fluid feature based on multi-attribute studies according to an embodiment of the present application. As shown, the method includes the following steps.
Step 1, calculating a plurality of texture attributes of the disconnected solution signal profile.
In a possible embodiment, step 1 specifically includes: and calculating the entropy property, the energy property and the contrast property of the cut solution signal section according to the gray level co-occurrence matrix of the cut solution signal section.
For a seismic section described by gray scale, the probability of occurrence of any two data with a statistical distance δ along a certain direction meeting a certain condition is:
pij＝∑{g(x,y)＝i,g(x+Δx,y+Δy)＝j},i,j＝0,1,...,L-1
wherein L is the number of gray levels of the seismic section; x, y are coordinates of the profile data; g (x, y) is the grey value at (x, y);
pijconstituting a gray level co-occurrence matrix. The gray level co-occurrence matrix counts the profile information. In order to intuitively obtain the section gray level, namely the change condition of data, the texture attribute value derived from the gray level co-occurrence matrix can be calculated from different angles, different directions and different gray levels.
The Energy attribute Energy may be calculated according to:
the energy attribute can measure the uniformity of texture gray scale change and can reflect the uniformity degree of gray scale distribution and the thickness of texture.
The Entropy attribute, Encopy, may be calculated according to the following equation:
the entropy property may measure the randomness of the texture. When all values in the gray level co-occurrence matrix are equal, the entropy attribute obtains the maximum value; conversely, if the values in the gray level co-occurrence matrix are very non-uniform, the entropy attribute takes a smaller value.
The Contrast attribute Contrast may be calculated according to the following formula:
the contrast attribute is the moment of inertia near the principal diagonal of the gray level co-occurrence matrix, which can measure the distribution and local variation of matrix values, reflecting the definition and the shade of the texture.
And 2, calculating a plurality of structure tensor attributes of the section of the solution signal.
In a possible embodiment, step 2 specifically comprises: calculating the gradient vector direction derivative of each point of the three-dimensional seismic data body; reconstructing a gradient structure tensor according to the vector direction derivative; adopting different Gaussian window parameters to carry out smooth test on each component of the gradient structure tensor, and selecting an optimal result according to the noise immunity of the obtained section result and the salient degree of the boundary extraction; and calculating a plurality of eigenvalues of the gradient structure tensor according to the optimal result, wherein the eigenvalues are used as a plurality of structure tensor attributes.
The gradient vector direction derivative for each point of the three-dimensional seismic data volume can be calculated according to the following formula:
wherein u (x, y, z) is a three-dimensional seismic amplitude function, x, y, z are the line, the trace position and the two-way travel time of a three-dimensional seismic voxel respectively, g is an amplitude gradient field, and g1, g2 and g3 are the derivatives along the directions of x, y and z respectively.
The gradient structure tensor T can be reconstructed according to:
each component of the gradient structure tensor can be tested for smoothness using different gaussian window parameters according to,
wherein G is a Gaussian kernel function, σTIs a scale functionNumber of
Selecting an optimal result according to the noise immunity of the obtained profile result and the salient degree of the boundary extraction:
|Tυ-λυ|＝0
where λ and ν are the eigenvalues and eigenvectors, respectively. Since the matrix T is a real symmetric matrix, λ is satisfied1≥λ2≥λ3>0. According to the property of the real symmetric matrix, the three eigenvectors v1, v2 and v3 are orthogonal in pairs. The above formula can be solved to obtain three eigenvalues λ of λ1、λ2And λ3As three structure tensor attributes.
And 3, selecting a plurality of preferable attributes from the texture attributes and the structure tensor attributes.
Each attribute corresponds to a profile and a person skilled in the art can select preferred attributes from these empirically. In one possible implementation, a plurality of preferred attributes may be selected from the plurality of texture attributes and the plurality of structure tensor attributes based on one or more of:
the connectivity of the profile corresponding to the attribute;
the boundary definition of the section corresponding to the attribute;
the attribute corresponds to the background noise of the profile.
And 4, performing attribute dimensionality reduction clustering according to the plurality of optimal attributes to obtain a final solution clustering section.
In one possible embodiment, step 4 may include the following steps 401 to 404.
Step 401, C initial cluster center profiles are given.
Step 402, in the k-th iteration, for each attribute data, the distance from the corresponding attribute data profile to the C cluster center profiles 3 is calculated.
For any two-dimensional attribute profile data, the distance from the data to the C cluster center profiles can be calculated according to the following formula, and the profile value is classified into the cluster where the center with the shortest distance is located;
wherein x is(i)Represents the attribute profile, μjRepresenting a cluster center profile, c(i)The distance value representing the attribute profile to the cluster center profile, ": "represents the iteration number, and the value of i is ended when the iteration is ended.
Step 403, updating the clustering center profile according to the current clustering result and the following formula:
wherein, mujRepresenting the updated cluster center profile, a "1" indicates a weight of 1.
Step 404, if the distance value iteratively calculated in step 402 remains unchanged, the iteration is ended; otherwise, go back to step 402 and enter the next iteration.
In one possible embodiment, the solution signal profile is a two-dimensional empirical mode decomposition (bemd) of an original solution seismic profile.
In one exemplary embodiment, a two-dimensional empirical mode decomposition (bemd) may be performed on the original fractured-solution seismic section according to the following steps to obtain a feature-enhanced fractured-solution signal section.
In the first step, the extreme value of the broken solution data profile can be calculated: and taking the mean envelope.
The two-dimensional post-stack data plane is assumed to contain at least one maximum value point and one minimum value point, or the whole two-dimensional plane has no extreme value point but can have one maximum value point and one minimum value point after one-order or several-order operation. The feature scale may be defined by a scale of the distance between the extreme points.
The maximum value E of the envelope surface of the post-stack seismic section can be calculatedMAX(x, y) and a minimum value EMIN(x, y), taking their mean value as the mean envelope of the original signal, namely:
in the second step, the envelope mean value is subtracted from the original signal to obtain a first-order imf (intrinsic mode function) component of the original signal f (x, y), and a screening criterion SD of each layer is established.
D1(x,y)＝f(x,y)-E1(x,y)
For D (x, y) a criterion is set for each layer of screening, namely:
generally, SD is less than or equal to 0.3.
Can define
C1(x,y)＝D1 k(x,y)
I.e. the first order IMF component of the original signal.
And thirdly, separating imf components from the original data to obtain a remainder, and repeating iteration by using the remainder as a new signal until the residual quantity is a monotonic function or a constant.
Handle C1(x, y) separating the remainder R from the original data1(x, y), i.e. R1(x,y)＝f(x,y)-C1(x,y)。
And fourthly, taking the remainder as a new signal, repeating the process until the residual quantity is a monotonic function or a constant, and finally decomposing the original signal into:
as described above, the obtained signal profile of the dissolved-state signal can be obtained
And (5) performing attribute extraction, and performing dimensionality reduction clustering according to the preferred attribute to obtain a final solution clustering profile.
According to the technical scheme of the application, aiming at the problem of identifying the boundary of the broken solution, the research of the characteristic enhancement method of the broken solution based on the multi-attribute research is provided, the abnormity of the boundary of the broken solution can be effectively highlighted, the stratum structure is effectively eliminated to a certain extent, the abnormity is focused, the theory is rigorous and reliable, and the operation flow is simple and practical.
Fig. 2 shows a block diagram of a structure of an apparatus for enhancing a dissolved solution feature based on multi-attribute studies according to an embodiment of the present application. As shown, the apparatus includes a texture attribute extraction unit 21, a texture attribute extraction unit 22, an attribute preference unit 23, and a dimension reduction clustering unit 24.
The texture property extraction unit 21 is configured to calculate a plurality of texture properties of the cut solution signal profile.
The structure tensor attribute extraction unit 22 is configured to calculate a plurality of structure tensor attributes of the solution signal profile.
The attribute preference unit 23 is configured to select a plurality of preferred attributes from the plurality of texture attributes and the plurality of structure tensor attributes.
And the dimension reduction clustering unit 24 is used for performing attribute dimension reduction clustering according to the plurality of preferred attributes to obtain a final solution clustering section.
In one possible embodiment, the solution signal profile is a two-dimensional empirical mode decomposition (bemd) of an original solution seismic profile.
In one possible implementation, a plurality of preferred attributes are selected from the plurality of texture attributes and the plurality of structure tensor attributes based on one or more of: the connectivity of the profile corresponding to the attribute; the boundary definition of the section corresponding to the attribute; the attribute corresponds to the background noise of the profile.
In a possible implementation manner, the dimension reduction clustering unit 24 is specifically configured to:
step 401, giving C clusters as initial clustering center profiles;
step 402, in the kth iteration, for each attribute data, calculating the distance from the corresponding attribute data profile to the C cluster center profiles;
step 403, updating a clustering center profile according to the current clustering result;
step 404, if the distance value calculated in step 402 remains unchanged, the iteration is ended; otherwise, go back to step 402 and enter the next iteration.
According to the technical scheme of the application, aiming at the problem of identifying the boundary of the broken solution, the research of the characteristic enhancement method of the broken solution based on the multi-attribute research is provided, the abnormity of the boundary of the broken solution can be effectively highlighted, the stratum structure is effectively eliminated to a certain extent, the abnormity is focused, the theory is rigorous and reliable, and the operation flow is simple and practical.
Application example
In order to test the application effect of the technical scheme disclosed by the application, a data test is carried out by adopting a solution fracture post-stack data model established on the basis of a north-bound work area, wherein the model has certain banding performance, and a glide fracture seismic section is linear as shown in fig. 3(a) and 3 (b). The blank weak reflectance features are mainly produced at weak fragmentation (relatively uniform within the fragmentation zone) for skid fractures. When the broken degree of the broken zone is strong (non-uniform), the seismic section shows a disordered abnormal reflection characteristic. The research on the earthquake recognition mode of the solution reservoir improves the pertinence of solution recognition and lays a foundation for the effect analysis of an actual work area.
Fig. 4 shows a comparison of texture properties obtained with some commercial software and with the solution of the present application. And contrast display shows that the obtained texture attribute, the contrast attribute, the entropy attribute and the energy attribute can represent the solution on the section, the continuity of the optimized texture attribute is correspondingly enhanced, wherein the contrast attribute is more coherent, and the change of the gray value of the solution is larger.
The structure tensor attribute extraction under different gaussian kernel parameters is performed on the model, and the result is shown in fig. 5(a) and 5 (b). Contrast shows the structure tensor attribute lambda under different Gaussian kernel parameters1And λ2The results of (A) are clearly different3The difference is not great. In addition, λ1The extraction result is relatively poor, and some integral information is relatively rich, but the background noise is relatively serious; lambda [ alpha ]2The effect is better, but a partial blurring phenomenon still exists, the effect is better and is not influenced by parameters, and the effect of 0.5 and 2 is considered to be ideal by comparison.
FIG. 6(a) shows a number of solution boundary preferred attributes for multi-attribute clustering, with the clustering results shown in FIG. 6 (b). According to the scheme, the banding of the solvent abnormal body can be effectively highlighted, and the problems of single attribute limitation and multi-attribute multi-solution are solved through attribute dimension reduction of the combination of the model and actual data. As shown in fig. 6(c), the attribute clustering result and the original profile are superimposed, and the banding clustering result illustrates the rationality of the algorithm. According to the technical scheme of the application, a banded solution breaking multi-attribute clustering result containing a transition zone is formed, and solution breaking abnormity can be effectively highlighted.
In conclusion, the method can effectively highlight the abnormal solution boundary, effectively eliminate the stratum structure to a certain extent, focus the abnormality more, and has rigorous and reliable theory and simple and practical operation flow.
The present application may be a system, method and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present application.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
Various aspects of the present application are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
Having described embodiments of the present application, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terms used herein were chosen in order to best explain the principles of the embodiments, the practical application, or technical improvements to the techniques in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (10)
1. A method for enhancing features of an exsolution based on multi-attribute studies, the method comprising:
calculating a plurality of texture attributes of the disconnected solution signal profile;
calculating a plurality of structure tensor attributes of the solution signal profile;
selecting a plurality of preferred attributes from the plurality of texture attributes and the plurality of structure tensor attributes;
and performing attribute dimensionality reduction clustering according to the plurality of preferred attributes to obtain a final solution clustering profile.
2. The method of claim 1, wherein the solution signal profile is a two-dimensional empirical mode decomposition (bemd) of an original solution seismic profile.
3. The method of claim 1, wherein said calculating a plurality of texture attributes for the disconnected solvent signal profile comprises:
and calculating the entropy property, the energy property and the contrast property of the cut solution signal section according to the gray level co-occurrence matrix of the cut solution signal section.
4. The method of claim 1, wherein the calculating a plurality of structure tensor properties of an interrupted solution signal profile comprises:
calculating the gradient vector direction derivative of each point of the three-dimensional seismic data body;
reconstructing a gradient structure tensor according to the vector direction derivative;
adopting different Gaussian window parameters to carry out smooth test on each component of the gradient structure tensor, and selecting an optimal result according to the noise immunity of the obtained section result and the salient degree of the boundary extraction;
and calculating a plurality of eigenvalues of the gradient structure tensor according to the optimal result, wherein the eigenvalues are used as a plurality of structure tensor attributes.
5. The method of claim 1, wherein a plurality of preferred attributes are selected from the plurality of texture attributes and the plurality of structure tensor attributes based on one or more of:
the connectivity of the profile corresponding to the attribute;
the boundary definition of the section corresponding to the attribute;
the attribute corresponds to the background noise of the profile.
6. The method according to claim 1, wherein said performing attribute dimensionality reduction clustering according to the plurality of preferred attributes to obtain a final solution cluster profile comprises:
step 401, giving C clusters as initial clustering center profiles;
step 402, in the kth iteration, for each attribute data, calculating the distance from the corresponding attribute data profile to the C cluster center profiles;
step 403, updating a clustering center profile according to the current clustering result;
step 404, if the distance value calculated in step 402 remains unchanged, the iteration is ended; otherwise, go back to step 402 and enter the next iteration.
7. An apparatus for enhancing a characteristic of a dissolved solution based on multi-attribute research, the apparatus comprising:
the texture attribute extraction unit is used for calculating a plurality of texture attributes of the solution signal section;
the structure tensor attribute extraction unit is used for calculating a plurality of structure tensor attributes of the solution signal section;
an attribute preference unit configured to select a plurality of preferred attributes from the plurality of texture attributes and the plurality of structure tensor attributes;
and the dimension reduction clustering unit is used for performing attribute dimension reduction clustering according to the plurality of preferred attributes to obtain a final solution clustering section.
8. The apparatus of claim 7 wherein the solution signal profile is a two-dimensional empirical mode decomposition (bemd) of an original solution seismic profile.
9. The apparatus of claim 7, wherein a plurality of preferred attributes are selected from the plurality of texture attributes and the plurality of structure tensor attributes based on one or more of:
the connectivity of the profile corresponding to the attribute;
the boundary definition of the section corresponding to the attribute;
the attribute corresponds to the background noise of the profile.
10. The apparatus according to claim 7, wherein the dimension reduction clustering unit is specifically configured to:
step 401, giving C clusters as initial clustering center profiles;
step 402, in the kth iteration, for each attribute data, calculating the distance from the corresponding attribute data profile to the C cluster center profiles;
step 403, updating a clustering center profile according to the current clustering result;
step 404, if the distance value calculated in step 402 remains unchanged, the iteration is ended; otherwise, go back to step 402 and enter the next iteration.
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