A method and device for dividing crustal blocks

Abstract

The present invention discloses a method and device for dividing crustal blocks, the method comprising: obtaining navigation satellite data of several stations within a preset crustal range; determining a first clustering result according to the navigation satellite data, and dividing the crustal range into a plurality of first blocks according to the first clustering result; calculating the Euler vector of each of the first blocks according to the navigation satellite data; calculating the fitting velocity residual of each of the stations on each of the first blocks based on the navigation satellite data of each of the stations and the Euler vector of each of the first blocks; correcting the first clustering result according to the fitting velocity residual of each of the stations on each of the first blocks to obtain a second clustering result, and dividing the crustal range into a plurality of second blocks according to the second clustering result. The present invention can realize unsupervised crustal block division of active blocks.

Description

Technical Field

[0001] The invention relates to the field of crust division, and in particular to a method and equipment for dividing crust blocks. Background Art

[0002] In the prior art, the traditional method of dividing crustal blocks is mainly based on geological data, spatial distribution data of historical earthquakes and human subjective experience. For example, the division is based on the study of the distribution of active faults and the horizontal differential movement characteristics of active blocks, or based on the study of the distribution characteristics of active faults and the comprehensive spatial distribution of historical earthquakes based on human experience.

[0003] It can be seen that in the prior art, the division of crustal blocks requires reliance on human supervision, which is difficult to apply to the division and study of active blocks, and is not conducive to further research on tectonic movements and seismic activity. Summary of the invention

[0004] In order to solve the above technical problems, the embodiments of the present invention provide a crustal block division method and device, which can realize unsupervised crustal block division of active blocks without human participation.

[0005] In order to achieve the above object, an embodiment of the present invention provides a method for dividing crustal blocks, comprising:

[0006] Acquire navigation satellite data of a plurality of stations within a preset crustal range; wherein the navigation satellite data includes speed and longitude and latitude;

[0007] Determining a first clustering result according to the navigation satellite data, and dividing the crust range into a plurality of first blocks according to the first clustering result;

[0008] Calculating the Euler vector of each of the first blocks according to the navigation satellite data;

[0009] Calculating the fitting velocity residual of each of the measuring stations on each of the first blocks based on the navigation satellite data of each of the measuring stations and the Euler vector of each of the first blocks;

[0010] The first clustering result is corrected according to the fitting velocity residual of each of the measuring stations on each of the first blocks to obtain a second clustering result, and the crust range is divided into a plurality of second blocks according to the second clustering result.

[0011] Furthermore, determining a first clustering result based on the navigation satellite data and dividing the crustal range into a plurality of first blocks based on the first clustering result specifically includes: using a hierarchical clustering algorithm to perform clustering analysis processing based on the speeds of all measuring stations to obtain a first clustering result.

[0012] Further, the calculating the Euler vector of each first block according to the navigation satellite data specifically includes: substituting the navigation satellite data of all the measuring stations in each first block into the following formula to obtain a plurality of first equations, and solving the plurality of first equations together to obtain the Euler vector of each first block;

[0013]

[0014]

[0015] Where R is the radius of the earth, V e is the eastward component of the velocity of the station, V n is the north component of the velocity of the station, is the longitude of the station, λ is the latitude of the station, Φ is the latitude of the Euler pole, Λ is the longitude of the Euler pole, ω x is the component of the Euler vector in the x direction, ω y is the component of the Euler vector in the y direction, ω z is the component of the Euler vector in the z direction.

[0016] Furthermore, the calculation of the fitting velocity residual of each measuring station on each first block based on the navigation satellite data of each measuring station and the Euler vector of each first block specifically includes: calculating the fitting velocity of each measuring station on each first block based on the navigation satellite data of each measuring station and the Euler vector of each first block; calculating the fitting velocity residual of each measuring station on each first block based on the fitting velocity of each measuring station on each first block and the velocity of each measuring station.

[0017] Furthermore, the calculation formula of the fitting speed residual is as follows:

[0018] ΔV ij =V GNSS,j -V ij

[0019] Where, ΔV ij is the residual of the fitted velocity at the jth station on the first plot of the ith plot, V GNSS,j is the velocity of the jth station, V ij is the fitting speed of the j-th station on the i-th first plot.

[0020] Furthermore, the first clustering result is corrected according to the fitting velocity residual of each measuring station on each first block to obtain a second clustering result, and the crust range is divided into multiple second blocks according to the second clustering result, specifically including: for each measuring station, determining whether the first block with the smallest fitting velocity residual is the first block divided by the measuring station according to the first clustering result; if not, re-dividing the measuring station into the first block with the smallest fitting velocity residual to correct the first clustering result.

[0021] Furthermore, after obtaining the second clustering result and before dividing the crustal range into multiple second blocks according to the second clustering result, it also includes: calculating the clustering validity DB index of the second clustering result; determining the clustering classification number corresponding to the second clustering result according to the clustering validity DB index; wherein the clustering classification number is used to determine the number of the second blocks.

[0022] Furthermore, after the first clustering result is corrected according to the fitting velocity residual of each measuring station on each first block to obtain a second clustering result, and the crust range is divided into a plurality of second blocks according to the second clustering result, the method further includes: obtaining a plurality of isolated points in the second clustering result; wherein each isolated point determines a measuring station; for each isolated point, performing an F test according to the following formula, and correcting the division result of the second block according to the result of the F test;

[0023]

[0024] Where F is the result of the F test, n is the number of stations in the second block where the station determined by the isolated point is located, The Euler poles and the residuals after fitting of all the stations in the second block where the station determined by the isolated point is located, The Euler poles and the residual after fitting of all the stations except the isolated point in the second block where the station determined by the isolated point is located.

[0025] Furthermore, after the first clustering result is corrected according to the fitted velocity residuals of each measuring station on each of the first blocks to obtain the second clustering result, and the crust range is divided into multiple second blocks according to the second clustering result, it also includes: calculating the Euler vector of each of the second blocks according to the navigation satellite data, calculating the fitted velocity residuals of each measuring station on each of the second blocks based on the navigation satellite data of each measuring station and the Euler vector of each of the second blocks, and verifying the accuracy of the second clustering result based on the distribution of the fitted velocity residuals and the values ​​of the fitted velocity residuals; or, verifying the accuracy of the second clustering result based on the geological fault data of the crust range.

[0026] An embodiment of the present invention also provides a computer device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the steps of any of the above-mentioned crustal block division methods when executing the computer program.

[0027] In summary, the present invention has the following beneficial effects:

[0028] By adopting the embodiment of the present invention, it is possible to use navigation satellite data with richer data volume, finer accuracy and direct reflection of block characteristics to perform high-precision crustal block division on active blocks; cluster analysis is adopted so as not to be affected by human subjective experience, thereby improving the objective accuracy of the division result; Euler vector is introduced to optimize the clustering result, thereby making the optimized clustering result more consistent with the physical characteristics of the active block; the F test is used to eliminate gross errors, and starting from the geographic space coherence, the divided boundaries are made more coherent and clear. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic flow chart of an embodiment of a crust block division method provided by the present invention;

[0030] Figure 2 It is a schematic diagram of a clustering effectiveness DB index of a crustal block partitioning method provided by the present invention;

[0031] Figure 3 It is a flow chart of a specific embodiment of a crust block division method provided by the present invention. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] See also Figure 1 , is a flow chart of an embodiment of a method for dividing crustal blocks provided by the present invention, the method comprising steps S1 to S5, which are specifically as follows:

[0034] S1, obtaining navigation satellite data of a plurality of stations within a preset crustal range; wherein the navigation satellite data includes speed and longitude and latitude;

[0035] Exemplarily, the navigation satellite data includes GPS data, and the GPS data of the station includes the coordinates of the station (Lon i ,Lat i ,V ei ,V ni ), where i = 1, 2, ..., n, n is the total number of measuring stations; Lon i ,Lat i is the longitude and latitude of the i-th station; V ei ,V ni is the east and north speed of the ith station in the Eurasian frame.

[0036] S2, determining a first clustering result according to the navigation satellite data, and dividing the crust range into a plurality of first blocks according to the first clustering result;

[0037] Preferably, determining a first clustering result based on the navigation satellite data, and dividing the crustal range into a plurality of first blocks based on the first clustering result, specifically includes: using a hierarchical clustering algorithm, performing clustering analysis processing according to the speeds of all measuring stations, and obtaining a first clustering result.

[0038] S3, calculating the Euler vector of each of the first blocks according to the navigation satellite data;

[0039] Preferably, the calculating the Euler vector of each first block according to the navigation satellite data specifically comprises: substituting the navigation satellite data of all the measuring stations in each first block into the following formula to obtain a plurality of first equations, and solving the plurality of first equations together to obtain the Euler vector of each first block;

[0040]

[0041]

[0042] Where R is the radius of the earth, V e is the eastward component of the velocity of the station, V n is the north component of the velocity of the station, is the longitude of the station, λ is the latitude of the station, Φ is the latitude of the Euler pole, Λ is the longitude of the Euler pole, ω x is the component of the Euler vector in the x direction, ω y is the component of the Euler vector in the y direction, ω z is the component of the Euler vector in the z direction.

[0043] It can be understood that the Euler vector is introduced in the present application, starting from the physical properties of the active block itself, so that the clustering results are more consistent with the physical properties of the active block.

[0044] S4, calculating the fitting velocity residual of each of the measuring stations on each of the first blocks based on the navigation satellite data of each of the measuring stations and the Euler vector of each of the first blocks;

[0045] Preferably, the calculating the fitting velocity residual of each measuring station on each first block based on the navigation satellite data of each measuring station and the Euler vector of each first block specifically includes: calculating the fitting velocity of each measuring station on each first block based on the navigation satellite data of each measuring station and the Euler vector of each first block; calculating the fitting velocity residual of each measuring station on each first block based on the fitting velocity of each measuring station on each first block and the velocity of each measuring station.

[0046] As an improvement of the above solution, the calculation formula of the fitting speed residual is as follows:

[0047] ΔV ij =V GNSS,j -V ij

[0048] Where, ΔV ij is the residual of the fitted velocity at the jth station on the first plot of the ith plot, V GNSS,j is the velocity of the jth station, V ij is the fitting speed of the j-th station on the i-th first plot.

[0049] S5, according to the fitting velocity residual of each measuring station on each first block, the first clustering result is corrected to obtain a second clustering result, and according to the second clustering result, the crust range is divided into a plurality of second blocks.

[0050] Preferably, the first clustering result is corrected according to the fitting velocity residual of each measuring station on each first block to obtain a second clustering result, and the crust range is divided into multiple second blocks according to the second clustering result, specifically including: for each measuring station, judging whether the first block with the smallest fitting velocity residual is the first block divided by this measuring station according to the first clustering result; if not, re-dividing this measuring station into the first block with the smallest fitting velocity residual to correct the first clustering result.

[0051] Preferably, after obtaining the second clustering result and before dividing the crust range into multiple second blocks according to the second clustering result, it also includes: calculating the clustering validity DB index of the second clustering result; determining the clustering classification number corresponding to the second clustering result according to the clustering validity DB index; wherein the clustering classification number is used to determine the number of second blocks.

[0052] It should be noted that the present application uses the DB indicator to evaluate the effectiveness of clustering. The DB indicator estimates the similarity of different stations in the same class by describing the distance from each station in the same class to the cluster center of this class in the clustering result, and uses the distance between the cluster centers of different classes in the clustering result to represent the difference between classes. The calculation formula of the DB indicator is as follows:

[0053]

[0054] Where k is the number of clusters, w i Represents class C i The average distance from all GPS points to their cluster centers, w j Represents class C j The average distance from all GPS points to their cluster centers, C ij Represents class C i and C j The distance between cluster centers.

[0055] It should be noted that the number of cluster classifications is selected based on the minimum value of the cluster validity DB indicator. Figure 2 When the number of categories k = 4, the DB index reaches a low value first, so the crust is divided into 4 categories, i.e., 4 second blocks. Although the DB index breaks through the low value of k = 4 when k = 6, 7, and 8 and reaches a new low value, it may be due to the limited GPS data and the uneven distribution of the actual stations, as well as the irreversible characteristics of hierarchical clustering.

[0056] Preferably, after the first clustering result is corrected according to the fitting velocity residual of each measuring station on each first block to obtain the second clustering result, and the crust range is divided into a plurality of second blocks according to the second clustering result, the method further includes: obtaining a plurality of isolated points in the second clustering result; wherein each isolated point determines a measuring station; for each isolated point, performing an F test according to the following formula, and correcting the division result of the second block according to the result of the F test;

[0057]

[0058] Where F is the result of the F test, n is the number of stations in the second block where the station determined by the isolated point is located, The Euler poles and the residuals after fitting of all the stations in the second block where the station determined by the isolated point is located, The Euler poles and the residual after fitting of all the stations except the isolated point in the second block where the station determined by the isolated point is located.

[0059] It should be noted that:

[0060] ①F test is used to determine whether a station belongs to a certain block, mainly to judge whether a station is compatible with other stations in the block and whether there are significant differences;

[0061] ② Calculate the isolated points in a block (i.e. the Euler poles of the common motion of all stations and the residuals after fitting), (i.e., the Euler poles of other stations moving together with the block except the station and the residual after fitting), and the F statistic value (i.e., the F test result) is calculated;

[0062] ③ Look up the table at a confidence level of 95%, that is, when the significance P is 0.05, if F is less than the F table, then the isolated point has no significant difference with other stations in the block, then the isolated point is a misjudged point, and the independent point belongs to the block;

[0063] ④ Look up the table at a confidence level of 95%, that is, with a significance level of P of 0.05. If F is greater than the F table, then there is a significant difference between the isolated point and other stations in the block, and the isolated point does not belong to the block.

[0064] It is understandable that by using the F test to eliminate gross errors, the boundaries are made more coherent and clear based on the consistency of geographic space.

[0065] Preferably, after the first clustering result is corrected according to the fitted velocity residuals of each measuring station on each first block to obtain the second clustering result, and the crust range is divided into multiple second blocks according to the second clustering result, it also includes: calculating the Euler vector of each second block according to the navigation satellite data, calculating the fitted velocity residuals of each measuring station on each second block based on the navigation satellite data of each measuring station and the Euler vector of each second block, and verifying the accuracy of the second clustering result based on the distribution of the fitted velocity residuals and the values ​​of the fitted velocity residuals; or, verifying the accuracy of the second clustering result based on the geological fault data of the crust range.

[0066] It should be noted that when the value of the fitted velocity residual is small and the distribution of the fitted velocity residual conforms to the normal distribution, it means that the fitting effect is good and the accuracy of the second clustering result is high.

[0067] Example 1, see Figure 3 :

[0068] 1. Input the refined GPS station data (including the coordinates of each GPS station (Lon i ,Lat i ,V ei ,V ni ), where i = 1, 2, ..., n, n is the total number of measuring stations; Lon i ,Lat i is the longitude and latitude of the i-th station; V ei ,V ni is the east and north speed of the ith station in the Eurasian frame).

[0069] 2. Use the hierarchical clustering algorithm (hierarchical clustering method of Euclidean distance sum of squares and centroid distance method) to cluster the GPS speed (east direction, north direction) and obtain the first clustering result;

[0070] 3. Using a clustering algorithm that considers geographic spatial coherence and the consistency of the physical motion vector of the same block, based on the first clustering result, recalculate the classification results of the station to obtain the second clustering result. The specific steps are shown in steps 4) to 12):

[0071] 4) According to the clustering results, calculate the Euler vectors of different blocks;

[0072]

[0073]

[0074] R is the radius of the earth, take 6378km, V e ,Vn are the east and north velocities of the measuring station, respectively. λ is the latitude and longitude of the measuring point, Φ, Λ are the latitude and longitude of the Euler pole, ω x ω y ω z are the components of the Euler vector in the x, y, and z directions.

[0075] 5) According to the Euler vector of each block and the longitude and latitude of the station, the fitting speed of the station on each block is calculated. It is worth noting that the fitting speed is also calculated according to the formula in 4).

[0076] 6) Calculate the residual between the true velocity of the measuring station and the fitted velocity on each block;

[0077] ΔV=V GNSS -V i

[0078] ΔV is the fitting velocity residual, V GNSS is the true speed of the station (calculated by GPS), V i is the fitting velocity of the measuring station on each block, and i is the i-th block.

[0079] 7) If the minimum velocity residual value of the measuring station and the block corresponding to the measuring station are different from the clustering result, the measuring station is divided into the block to which the minimum velocity residual value belongs;

[0080] 8) Using the corrected block division result as the clustering result;

[0081] 9) Calculate the clustering validity of the clustering results;

[0082] 10) Statistical clustering results are divided into 2-10 clustering effectiveness DB indicators;

[0083] 11) Select the number of clustering categories according to the minimum value of the clustering effectiveness DB indicator;

[0084] 12. Perform F statistical test on isolated points in the clustering results to determine their independence from other stations in the block;

[0085]

[0086] n is the number of selected measuring stations, represents the Euler poles of the common motion of all stations and the residual after fitting, It is the residual after fitting and the Euler pole of other stations moving together except this station.

[0087] Main calculation method:

[0088] ①F statistical test is used to determine whether a GPS station belongs to a certain block, mainly to determine whether a certain station is compatible with other stations in the block and whether there are significant differences. ;

[0089] ② Calculate isolated points in a block (Eulerian poles of all stations moving together and residuals after fitting), (is the Euler pole of other stations of the block moving together except this station and the residual after fitting), and the F statistic is calculated;

[0090] ③ Look up the table at a confidence level of 95%, that is, when the significance P is 0.05, if F is less than the F table, then the independent point has no significant difference with other stations in the block, then the independent point is a misjudged point, and the independent point belongs to the block;

[0091] ④ Look up the table at a confidence level of 95%, that is, when the significance P is 0.05, if F is greater than F table, then the independent point is significantly different from other stations in the block, and the independent point does not belong to the block;

[0092] 13. Clustering results are superimposed with geological fault data for verification and accuracy statistical analysis;

[0093] An embodiment of the present invention also provides a computer device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the steps of any of the above-mentioned crustal block division methods when executing the computer program.

[0094] The computer device of this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a crustal block division program. When the processor executes the computer program, the steps in the above-mentioned crustal block division method embodiments are implemented, such as Figure 1 Steps S1 to S5 are shown.

[0095] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the computer device.

[0096] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The computer device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the schematic diagram is merely an example of a computer device and does not constitute a limitation on the computer device. The computer device may include more or fewer components than shown in the diagram, or may combine certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0097] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the computer device, and uses various interfaces and lines to connect various parts of the entire computer device.

[0098] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the computer device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0099] Wherein, if the module / unit integrated in the computer device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0100] In summary, the present invention has the following beneficial effects:

[0101] By adopting the embodiment of the present invention, it is possible to use navigation satellite data with richer data volume, finer accuracy and direct reflection of block characteristics to perform high-precision crustal block division on active blocks; cluster analysis is adopted so as not to be affected by human subjective experience, thereby improving the objective accuracy of the division result; Euler vector is introduced to optimize the clustering result, thereby making the optimized clustering result more consistent with the physical characteristics of the active block; the F test is used to eliminate gross errors, and starting from the geographic space coherence, the divided boundaries are made more coherent and clear.

[0102] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus a necessary hardware platform, and of course can also be implemented entirely by hardware. Based on such an understanding, all or part of the contribution of the technical solution of the present invention to the background technology can be embodied in the form of a software product, and the computer software product can be stored in a storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present invention or certain parts of the embodiments.

[0103] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for dividing crustal blocks, characterized in that: include: Acquire navigation satellite data of a plurality of stations within a preset crustal range; wherein the navigation satellite data includes speed and longitude and latitude; Determine a first clustering result according to the navigation satellite data, and divide the crust range into a plurality of first blocks according to the first clustering result; Calculating the Euler vector of each of the first blocks according to the navigation satellite data; Calculating the fitting velocity residual of each of the measuring stations on each of the first blocks based on the navigation satellite data of each of the measuring stations and the Euler vector of each of the first blocks; According to the fitting velocity residual of each of the measuring stations on each of the first blocks, the first clustering result is corrected to obtain a second clustering result, and according to the second clustering result, the crust range is divided into a plurality of second blocks; The step of calculating the fitting velocity residual of each measuring station on each first block based on the navigation satellite data of each measuring station and the Euler vector of each first block specifically includes: Calculating the fitting velocity of each of the measuring stations on each of the first blocks based on the navigation satellite data of each of the measuring stations and the Euler vector of each of the first blocks; Calculate the residual of the fitting speed of each measuring station on each first block based on the fitting speed of each measuring station on each first block and the speed of each measuring station; The calculation formula of the fitting speed residual is as follows: ΔV ij =V GNSS,j -V ij Where, ΔV ij is the residual of the fitted velocity at the jth station on the first plot of the ith plot, V GNSS,j is the velocity of the jth station, V ij is the fitting speed of the jth station on the i-th first plot; The first clustering result is corrected according to the fitting velocity residual of each measuring station on each first block to obtain a second clustering result, and the crust range is divided into a plurality of second blocks according to the second clustering result, specifically including: For each of the measuring stations, it is determined whether the first block with the smallest fitting velocity residual is the first block to which the measuring station is divided according to the first clustering result; if not, the measuring station is re-divided into the first block with the smallest fitting velocity residual to correct the first clustering result.

2. The method for dividing crustal blocks according to claim 1, characterized in that: Determining a first clustering result according to the navigation satellite data, and dividing the crust range into a plurality of first blocks according to the first clustering result, specifically includes: A hierarchical clustering algorithm is used to perform cluster analysis based on the speeds of all measuring stations to obtain the first clustering result.

3. The method for dividing crustal blocks according to claim 1, characterized in that: The calculating the Euler vector of each first block according to the navigation satellite data specifically includes: Substituting the navigation satellite data of all the stations in each of the first blocks into the following formulas respectively to obtain a plurality of first equations, and solving the plurality of first equations together to obtain the Euler vector of each of the first blocks; Where R is the radius of the earth, V e is the eastward component of the velocity of the station, V n is the north component of the velocity of the station, is the longitude of the station, λ is the latitude of the station, Φ is the latitude of the Euler pole, Λ is the longitude of the Euler pole, ω x is the component of the Euler vector in the x direction, ω y is the component of the Euler vector in the y direction, ω z is the component of the Euler vector in the z direction.

4. The method for dividing crustal blocks according to claim 1, characterized in that: After obtaining the second clustering result and before dividing the crust range into a plurality of second blocks according to the second clustering result, the method further includes: Calculating a clustering effectiveness DB index of the second clustering result; The cluster classification number corresponding to the second clustering result is determined according to the cluster validity DB indicator; wherein the cluster classification number is used to determine the number of the second blocks.

5. The method for dividing crustal blocks according to claim 1, characterized in that: After the first clustering result is corrected according to the fitting velocity residual of each measuring station on each first block to obtain a second clustering result, and the crust range is divided into a plurality of second blocks according to the second clustering result, the method further includes: Acquire a plurality of isolated points in the second clustering result; wherein each of the isolated points determines a measuring station; For each of the isolated points, an F test is performed using the following formula, and the division result of the second block is corrected according to the result of the F test; Where F is the result of the F test, n is the number of stations in the second block where the station determined by the isolated point is located, The Euler poles and the residuals after fitting of all the stations in the second block where the station determined by the isolated point is located, The Euler poles and the residual after fitting of all the stations except the isolated point in the second block where the station determined by the isolated point is located.

6. The method for dividing crustal blocks according to claim 1, characterized in that: After the first clustering result is corrected according to the fitting velocity residual of each measuring station on each first block to obtain a second clustering result, and the crust range is divided into a plurality of second blocks according to the second clustering result, the method further includes: calculating the Euler vector of each second block according to the navigation satellite data, calculating the fitting velocity residual of each station on each second block based on the navigation satellite data of each station and the Euler vector of each second block, and verifying the accuracy of the second clustering result based on the distribution of the fitting velocity residual and the value of the fitting velocity residual; or, The accuracy of the second clustering result is verified based on the geological fault data in the crustal range.

7. A computer device, characterized in that: The method comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the method for dividing crustal blocks according to any one of claims 1 to 6 is implemented when the processor executes the computer program.

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