A GNSS regional ground surface quality migration monitoring method, system, device and medium

Abstract

The application discloses a GNSS regional ground surface mass migration monitoring method, system, device and medium. A GNSS reference station coordinate time sequence is acquired as an observation value of ground surface mass migration; based on the coordinate time sequence, dynamic noise estimation results of each period in the coordinate time sequence are dynamically estimated, and a time-varying weight matrix is constructed; a basic function between the observation value and regional ground surface mass migration is constructed, a regional ground surface mass migration estimation model is constructed in combination with the time-varying weight matrix, and a regional ground surface mass migration estimation result is obtained and output. Through the application, dynamic noise estimation is performed on the ground surface mass migration observation value, parameters of various noises are introduced as weight bases, an estimation model is constructed, a migration result is obtained, different reference station data are processed by weighting, noise influence is significantly reduced, the accuracy and stability of deformation estimation are greatly improved, non-structural interference signals are inhibited, and the identification ability of various geophysical signals is enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of ground surface deformation monitoring, and particularly relates to a GNSS regional ground surface mass migration monitoring method, system, computing device and computer storage medium. BACKGROUND

[0002] Ground surface mass migration is the response of solid earth to the load changes caused by hydrology, atmosphere and ocean, and is closely related to various spatial environmental changes in the evolution process of the earth system. Ground surface mass migration deformation monitoring not only serves the positioning and description of the earth's surface morphology in geodesy, but also provides scientific support for human better understanding of the evolution mechanism of the earth's spatial environment such as terrestrial water storage distribution, land subsidence, sea level rise and extreme weather. It is an important part of modern earth science research on global change and sustainable development issues such as resource and environmental change and geological disasters faced by human beings.

[0003] GNSS can monitor the changes of ground surface mass migration deformation in different time and space scales with millimeter-level accuracy, especially the coordinate time series of GNSS reference stations in the height direction significantly reflect the characteristics of regional ground surface mass migration caused by hydrology, atmosphere load and the like. GNSS reference station continuous observation data includes two types of information: one is GNSS reference station coordinate time series, and the other is GNSS reference station observation noise characteristic information. GNSS reference station coordinate time series records the three-dimensional coordinate information of GNSS reference station changing with time (usually daily, weekly or monthly observation values). GNSS coordinate time series directly reflects the geophysical processes such as crustal deformation, and is the basic data for the study of structure, earthquake, hydrology and the like. With the continuous increase of GNSS reference station network scale and the continuous accumulation of observation data, such as the "China Tectonic Environmental Monitoring Network" (referred to as "Land State Network") which has accumulated observation data of 260 reference stations for more than ten years, and the reference stations of more than 2000 in the United States including PBO observation network, these long-time and high-precision GNSS observations provide an important data source for the study of ground surface mass migration.

[0004] The existing research results use GNSS reference station coordinate time series data to obtain regional ground surface mass migration results, and through the verification and comparison with GRACE data, hydrological model, ground observation and the like, it is determined that GNSS can invert the regional ground surface mass migration changes. However, the existing methods are focused on directly using GNSS observation data for inversion, and there is still a lack of full use of GNSS reference station observation noise characteristic information in the inversion of ground surface mass migration. Therefore, the present application proposes a GNSS regional ground surface mass migration monitoring method based on dynamic noise weighting to further improve the reliability of GNSS regional ground surface mass migration monitoring. SUMMARY

[0005] To solve the above technical problems, the application provides a GNSS regional ground surface mass migration monitoring method and a corresponding GNSS regional ground surface mass migration monitoring system, a computing device and a computer storage medium.

[0006] According to one aspect of the application, a GNSS regional ground surface mass migration monitoring method is provided, which comprises:

[0007] Obtaining a GNSS reference station coordinate time sequence as an observation value of GNSS ground surface mass migration;

[0008] Based on the coordinate time sequence, dynamically estimating a dynamic noise estimation result of each period in the coordinate time sequence, and constructing a time-varying weight matrix;

[0009] Based on the observation value, constructing a basic function between the observation value and the regional ground surface mass migration, combining the time-varying weight matrix, constructing a regional ground surface mass migration estimation model, and solving to obtain a regional ground surface mass migration estimation result;

[0010] Outputting the solved regional ground surface mass migration estimation result.

[0011] In the above scheme, the GNSS reference station coordinate time sequence is obtained as the observation value of the GNSS ground surface mass migration, which further comprises:

[0012] Obtaining a GNSS reference station coordinate time sequence; wherein the GNSS reference station coordinate time sequence is a GNSS reference station vertical direction coordinate time sequence.

[0013] In the above scheme, the GNSS reference station vertical direction coordinate time sequence is:

[0014] O i,j ,i∈[1,t0],j∈[1,n0]

[0015] G GNSS (t),t∈[1,t0]

[0016] Wherein t0 is the number of observation value collection time of each reference station to the GNSS ground surface mass migration; n0 is the number of GNSS reference stations; O i,j is the GNSS observation value of the jth reference station at the ith time; G GNSS is the GNSS reference station vertical direction coordinate time sequence; t is time.

[0017] In the above scheme, based on the coordinate time sequence, the dynamic noise estimation result of each period in the coordinate time sequence is dynamically estimated, and the time-varying weight matrix is constructed, which further comprises:

[0018] Based on the noise characteristic information in the coordinate time sequence, the noise of each period is dynamically estimated by using a sliding window analysis method, and a dynamic noise estimation result corresponding to each period is obtained;

[0019] According to the dynamic noise estimation result, an observation error covariance matrix is generated;

[0020] The observation error covariance matrix is inverted to generate a time-varying weight matrix of the observation value.

[0021] The method further comprises:

[0022] According to the sliding window analysis method, the GNSS reference station vertical direction coordinate time sequence is segmented according to time to obtain a plurality of sliding windows;

[0023] The variances of white noise, random walk noise and flicker noise components are calculated in the sliding window by maximum likelihood estimation

[0024]

[0025] Wherein, PSD is the noise power spectral density; f is the frequency; σ WN is the white noise term; σ FL is the flicker noise term; σ RW is the random walk noise term;

[0026] The sliding window slides forward, and the dynamic noise estimation is performed on each sliding window until the complete GNSS reference station vertical direction coordinate time sequence is covered, and the time-varying noise variance component is output to generate the observation error covariance matrix

[0027]

[0028] Wherein, Q(t) is the observation error covariance matrix; is the time-varying white noise variance component; is the time-varying flicker noise variance component; is the time-varying random walk noise variance component;

[0029] The observation error covariance matrix is inverted to generate a time-varying weight matrix of the observation value

[0030] P(t)=Q(t) -1

[0031] Wherein, P(t) is the time-varying weight matrix.

[0032] In the above scheme, the method further comprises:

[0033] establishing a basic function between the observation value and the regional surface mass migration;

[0034] According to the basic function, taking the load Green function as a kernel function, combining a time-varying weight matrix, and based on a least square method, a regional surface mass migration estimation model for estimating a noise weight is constructed;

[0035] An optimal regularization parameter is adaptively determined through a generalized cross-validation method, and a regional surface mass migration estimation result is obtained.

[0036] In the above scheme, the method further comprises:

[0037] The basic function relationship between the observation value and the regional surface mass migration is

[0038] G(t) = Fm(t) + ε(t)

[0039] Wherein, G(t) is an observation value; Fm(t) is a regional surface mass migration; ε(t) is a noise error; F is a load Green function; and m is a mass distribution vector;

[0040] The regional surface mass migration estimation model is a time-varying weighted least square / regularization model

[0041]

[0042] Wherein, is a regional surface mass migration estimation result; λ is a regularization parameter; and L is a Laplace operator;

[0043] The weighted regularization least square solution obtained based on the regional surface mass migration estimation model is

[0044]

[0045] After the solution, the regional surface mass migration estimation result is obtained.

[0046] According to another aspect of the present application, a GNSS regional surface mass migration monitoring system is provided, comprising: an acquisition module, a weight determination module, a calculation module and an output module; wherein,

[0047] The acquisition module is configured to acquire a GNSS reference station coordinate time series as an observation value of GNSS surface mass migration;

[0048] The weight determination module is configured to dynamically estimate a dynamic noise estimation result of each period in the coordinate time series based on the coordinate time series, and construct a time-varying weight matrix;

[0049] The computing module is configured to construct a basic function between the observation value and the regional surface mass migration based on the observation value, construct a regional surface mass migration estimation model by combining a time-varying weight matrix, and obtain a regional surface mass migration estimation result by solving.

[0050] The output module is configured to output the obtained regional surface mass migration estimation result.

[0051] According to another aspect of the present application, a computing device is provided, which comprises a processor, a memory, a communication interface and a communication bus, and the processor, the memory and the communication interface complete communication with each other through the communication bus.

[0052] The memory is configured to store at least one executable instruction, and the executable instruction causes the processor to perform operations corresponding to the GNSS regional surface mass migration monitoring method.

[0053] According to another aspect of the present application, a computer storage medium is provided, and the storage medium stores at least one executable instruction, and the executable instruction causes the processor to perform operations corresponding to the GNSS regional surface mass migration monitoring method.

[0054] According to the technical scheme provided in the application, the coordinate time sequence of the GNSS reference station is obtained as the observation value of the GNSS surface mass migration, the dynamic noise estimation result of each period in the coordinate time sequence is dynamically estimated based on the coordinate time sequence, the time-varying weight matrix is constructed, the basic function between the observation value and the regional surface mass migration is constructed based on the observation value, the regional surface mass migration estimation model is constructed by combining the time-varying weight matrix, the regional surface mass migration estimation result is obtained by solving, and the regional surface mass migration estimation result obtained by solving is output. According to the technical scheme provided in the application, the three-dimensional coordinate information of the GNSS reference station changing with time is accurately recorded by obtaining the coordinate time sequence of the GNSS reference station as the observation value, the noise characteristic information in the coordinate time sequence of the GNSS reference station is utilized, the maximum likelihood estimation of white noise, flicker noise and random walk noise is obtained by the sliding window analysis method, the variance of each type of noise component is determined, each type of noise is accurately estimated, the complete sequence is covered by repeated estimation by the sliding window, the error covariance matrix is constructed, and then the weight matrix for each type of noise is obtained. Then, the load Green function is taken as the kernel function, the GNSS observation is dynamically weighted by combining the time-varying weight matrix, the regional surface mass migration estimation model considering various noise errors is constructed, and the corresponding regional surface mass migration estimation result is calculated. By combining the noise characteristics, the GNSS reference station coordinate time sequence is supplemented, the influence of random noise and systematic error on the inversion estimation result is greatly reduced, the estimation accuracy of the crustal movement parameter is improved, the accuracy and stability of the surface mass migration deformation estimation are improved, the misjudgment of the surface mass migration trend is reduced, the instrument drift, environmental change and other non-tectonic interference signals are effectively suppressed based on the dynamic noise weighting method, the extraction ability of the real geological response caused by hydrological load, glacier change and atmospheric load is improved, the identification of the crustal deformation physical mechanism is enhanced, and quantitative support is provided for multi-source data fusion.

[0055] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application will be realized and attained by the structure particularly pointed out in the written description and claims hereof.

[0056] The technical scheme of the present application will be further described in detail below by means of the drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0057] The accompanying drawings are included to provide a further understanding of the present application, and constitute a part of the specification, illustrate embodiments of the present application and are used to explain the present application, and do not constitute a limitation on the present application. In the drawings:

[0058] Figure 1A flowchart of a GNSS regional crustal mass migration monitoring method according to an embodiment of the application is shown;

[0059] Figure 2 A flowchart of a time-varying weight matrix generation method based on dynamic noise estimation according to an embodiment of the application is shown;

[0060] Figure 3 A flowchart of a regional crustal mass migration estimation method according to an embodiment of the application is shown;

[0061] Figure 4 A structural block diagram of a GNSS regional crustal mass migration monitoring system according to an embodiment of the application is shown;

[0062] Figure 5 A structural diagram of a computing device according to an embodiment of the application is shown. DETAILED DESCRIPTION

[0063] The preferred embodiments of the application are described below in detail with reference to the accompanying drawings, and it should be understood that the preferred embodiments described herein are only used to explain and illustrate the application, and are not used to limit the application.

[0064] Figure 1 A flowchart of a GNSS regional crustal mass migration monitoring method according to an embodiment of the application is shown, which comprises the following steps:

[0065] In step S101, a GNSS reference station coordinate time series is obtained as an observation value of GNSS crustal mass migration.

[0066] Specifically, the GNSS reference station coordinate time series is a GNSS reference station vertical direction coordinate time series.

[0067] Preferably, the GNSS reference station coordinate time series records the three-dimensional coordinate information of the GNSS reference station changing with time, and is usually an observation value of daily, weekly or monthly scale. The GNSS reference station observation noise characteristics are quantified by residual analysis of the time series, including white noise, flicker noise, red noise and other types and their amplitude parameters, such as noise spectral density, power index, etc.

[0068] The GNSS coordinate time series directly reflects the geophysical processes such as crustal deformation, and is the basic data for the study of structure, earthquake, hydrology, etc.; and the observation noise characteristic information is a key supplement for understanding, modeling and optimizing these sequences, which helps to improve the interpretation accuracy, identify interference factors, and provide quantitative support for multi-source data fusion.

[0069] Preferably, the GNSS reference station vertical direction coordinate time series is:

[0070] O i,j Oij, i∈[1,t0],j∈[1,n0]

[0071] G GNSS G(t),t∈[1,t0]

[0072] Wherein, t0 is the number of observation value collection time of each reference station to GNSS ground mass migration; n0 is the number of GNSS reference station; O i,j Oij is the GNSS observation value of the jth reference station at the ith time; G GNSS G is the GNSS reference station vertical direction coordinate time series; t is time.

[0073] Step S102, based on the coordinate time series, dynamically estimate the dynamic noise estimation result of each period in the coordinate time series, and construct the time-varying weight matrix.

[0074] Step S103, based on the observation value, construct the basic function between the observation value and the regional ground mass migration, combine the time-varying weight matrix, construct the regional ground mass migration estimation model, and solve to obtain the regional ground mass migration estimation result.

[0075] Step S104, output the regional ground mass migration estimation result obtained by solving.

[0076] According to the GNSS regional ground surface mass migration monitoring method provided in the embodiment, the coordinate time sequence of the GNSS reference station is obtained as the observation value of the GNSS ground surface mass migration; based on the coordinate time sequence, the dynamic noise estimation result of each period in the coordinate time sequence is dynamically estimated, and a time-varying weight matrix is constructed; based on the observation value, a basic function between the observation value and the regional ground surface mass migration is constructed, the time-varying weight matrix is combined, a regional ground surface mass migration estimation model is constructed, and a regional ground surface mass migration estimation result is obtained by solving; and the obtained regional ground surface mass migration estimation result is output. Through the GNSS regional ground surface mass migration monitoring method provided in the embodiment, the three-dimensional coordinate information of the GNSS reference station changing with time is accurately recorded by obtaining the coordinate time sequence of the GNSS reference station as the observation value, and the weight matrix for each type of noise is obtained by accurately estimating each type of noise by using the noise characteristic information in the coordinate time sequence of the GNSS reference station, the GNSS observation is dynamically weighted, the regional ground surface mass migration estimation model considering various noise errors is constructed, and the corresponding regional ground surface mass migration estimation result is calculated. Through the combination of noise characteristics, the GNSS reference station coordinate time sequence is supplemented, the influence of random noise and systematic errors on the inversion estimation result is greatly reduced, the accuracy and stability of the ground surface mass migration deformation estimation are improved, the extraction ability of the real geological response caused by hydrological load, glacier change, atmospheric load and the like is effectively inhibited based on the dynamic noise weighting method, the identification of the crust deformation physical mechanism is enhanced, and quantitative support is provided for multi-source data fusion.

[0077] Figure 2 A flowchart of a time-varying weight matrix generation method based on dynamic noise estimation according to an embodiment of the application is shown.

[0078] As shown in Figure 2 , the method comprises the following steps:

[0079] In step S201, based on the noise characteristic information in the coordinate time sequence, the noise of each period is dynamically estimated by using the sliding window analysis method, and the dynamic noise estimation result corresponding to each period is obtained.

[0080] Specifically, according to the sliding window analysis method, the GNSS reference station vertical direction coordinate time sequence is segmented according to time to obtain a plurality of sliding windows, and each sliding window is regarded as locally stationary.

[0081] In the sliding window, the variances of white noise, random walk noise and flicker noise components are calculated by maximum likelihood estimation.

[0082]

[0083] wherein PSD is the noise power spectral density; f is the frequency; σ WN is a white noise term; σ FL is a flicker noise term; σ RW is a random walk noise term.

[0084] The sliding window slides forward, and the dynamic noise estimation is performed for each sliding window until the complete GNSS reference station vertical direction coordinate time series is covered.

[0085] In step S202, the observation error covariance matrix is generated according to the dynamic noise estimation result.

[0086] Preferably, the time-varying noise variance component is output according to the dynamic noise estimation result, and the observation error covariance matrix is generated

[0087]

[0088] wherein Q(t) is the observation error covariance matrix; is a time-varying white noise variance component; is a time-varying flicker noise variance component; is a time-varying random walk noise variance component.

[0089] In step S203, the time-varying weight matrix of the observation value is generated by inverting the observation error covariance matrix.

[0090] Preferably, the time-varying weight matrix is

[0091] P(t) = Q(t) -1

[0092] wherein P(t) is the time-varying weight matrix.

[0093] According to the above method, the noise characteristic information in the GNSS reference station coordinate time series is utilized, the maximum likelihood estimation is obtained for the white noise, the flicker noise and the random walk noise respectively by the sliding window analysis method, the variance of each type of noise component is determined, each type of noise is accurately estimated, the complete sequence is covered by repeated estimation through the sliding window, the error covariance matrix is constructed, and then the weight matrix for each type of noise is obtained, thereby accurately reflecting the influence of each type of noise, and facilitating subsequent ground surface mass transfer estimation, wherein the influence of each type of noise is reasonably integrated into the result of the ground surface mass transfer estimation by using the weight matrix, so that the result is more scientific. And the noise characteristic information provides quantitative support for multi-source data fusion.

[0094] Figure 3 Fig. 1 shows a flowchart of a regional ground surface mass transfer estimation method according to an embodiment of the present application;

[0095] AsFigure 3 The method comprises the following steps:

[0096] Step S301, establishing a basic function between the observation value and the regional surface mass transfer.

[0097] Preferably, the basic function relationship between the observation value and the regional surface mass transfer is

[0098] G(t) = Fm(t) + ε(t)

[0099] Wherein, G(t) is the observation value; Fm(t) is the regional surface mass transfer; ε(t) is the noise error; F is the load Green function; and m is the mass distribution vector.

[0100] Step S302, constructing a regional surface mass transfer estimation model for estimating the noise weight based on the least square method according to the basic function, taking the load Green function as the kernel function, and combining the time-varying weight matrix.

[0101] Preferably, the regional surface mass transfer estimation model is a time-varying weighted least square / regularization model, that is,

[0102]

[0103] Wherein, is the estimation result of the regional surface mass transfer; λ is the regularization parameter; and L is the Laplace operator.

[0104] Step S303, adaptively determining the optimal regularization parameter through the generalized cross-validation method, and obtaining the estimation result of the regional surface mass transfer.

[0105] Preferably, the weighted regularization least square solution obtained based on the regional surface mass transfer estimation model is

[0106]

[0107] After being solved, it is taken as the estimation result of the regional surface mass transfer.

[0108] Further, based on the above formula, in order to solve the value of , the regularization parameter λ needs to be solved. Therefore, the following function needs to be constructed through the generalized cross-validation method (Generalized Cross-Validation, GCV):

[0109]

[0110] Wherein, N is the total number of observation values; A(λ) is the influence matrix; trace[A(λ)] is the trace of A(λ), that is, the sum of the diagonal elements of the matrix; is the predicted value corresponding to the observation value G.

[0111] And in the formula, A (λ) = F (F T PF+λ 2 L T L) -1 F T P,

[0112] By selecting the value of λ in a certain range of values, the value of λ that minimizes GCV is determined as the final regularization parameter for the regularized least squares, denoted as λ opt .

[0113] Thus, by

[0114]

[0115] The regional surface mass transfer estimation result is calculated.

[0116] According to the method, the load Green function is used as the kernel function, the GNSS observation is dynamically weighted by combining the time-varying weight matrix, the regional surface mass transfer estimation model considering various noise errors is constructed, the corresponding regional surface mass transfer estimation result is calculated by combining the noise characteristics, the weight matrix for the noise is introduced, the key supplement is provided for the GNSS reference station coordinate time series, the influence of random noise and systematic errors on the inversion estimation result is greatly reduced, and the accuracy and stability of the model for the regional surface mass transfer estimation are improved; the efficiency of the model calculation is improved by the generalized cross-validation method, the deviation existing in the data division is avoided, and the complexity of the regional surface mass transfer estimation model is balanced. In addition, the method further reduces the misjudgment of the surface mass transfer trend, and effectively suppresses the instrument drift, environmental change and other non-tectonic interference signals based on the dynamic noise weighting method, improves the extraction ability of the real geological response caused by the hydrological load, glacial change, atmospheric load and the like, enhances the identification of the physical mechanism of the crustal deformation, and provides quantitative support for multi-source data fusion.

[0117] Figure 4 A structural block diagram of a GNSS regional surface mass transfer monitoring system according to one embodiment of the application is shown;

[0118] As Figure 4 shown, the system includes an acquisition module 401, a weight determination module 402, a calculation module 403 and an output module 404; wherein,

[0119] The acquisition module 401 is configured to acquire the GNSS reference station coordinate time series as the observation value of the GNSS surface mass transfer.

[0120] Specifically, the acquisition module 401 is further used for,

[0121] acquiring a GNSS reference station coordinate time sequence; wherein the GNSS reference station coordinate time sequence is a GNSS reference station vertical direction coordinate time sequence.

[0122] The GNSS reference station vertical direction coordinate time sequence is:

[0123] O i,j , i ∈ [1, t0], j ∈ [1, n0]

[0124] G GNSS (t), t ∈ [1, t0]

[0125] wherein t0 is the number of collection time of each reference station for GNSS ground mass migration observation value; n0 is the number of GNSS reference stations; O i,j is the GNSS observation value of the jth reference station at the ith time; G GNSS is the GNSS reference station vertical direction coordinate time sequence; t is time.

[0126] The weight determination module 402 is used for dynamically estimating the dynamic noise estimation result of each period in the coordinate time sequence based on the coordinate time sequence, and constructing a time-varying weight matrix.

[0127] Specifically, the weight determination module 402 is further used for,

[0128] based on the noise characteristic information in the coordinate time sequence, dynamically estimating the noise of each period by using a sliding window analysis method, to obtain the dynamic noise estimation result corresponding to each period;

[0129] According to the dynamic noise estimation result, an observation error covariance matrix is generated;

[0130] The observation error covariance matrix is inverted to generate a time-varying weight matrix of the observation value.

[0131] Specifically, the weight determination module 402 is further used for,

[0132] According to the sliding window analysis method, the GNSS reference station vertical direction coordinate time sequence is segmented according to time to obtain a plurality of sliding windows; (each sliding window is regarded as locally stationary)

[0133] The variances of white noise, random walk noise and flicker noise components are calculated in the sliding window by maximum likelihood estimation

[0134]

[0135] wherein PSD is the noise power spectral density; f is the frequency; σ WN is a white noise term; σ FL is a flicker noise term; σ RW is a random walk noise term;

[0136] The sliding window slides forward, and the dynamic noise estimation is performed for each sliding window until the complete GNSS reference station vertical direction coordinate time series is covered, and the time-varying noise variance component is output, and the observation error covariance matrix is generated

[0137]

[0138] wherein Q(t) is the observation error covariance matrix; is a time-varying white noise variance component; is a time-varying flicker noise variance component; is a time-varying random walk noise variance component;

[0139] The observation error covariance matrix is inverted to generate the time-varying weight matrix of the observation value

[0140] P(t) = Q(t) -1

[0141] wherein P(t) is the time-varying weight matrix.

[0142] The calculation module 403 is configured to construct a basic function between the observation value and the regional surface mass transfer based on the observation value, construct a regional surface mass transfer estimation model by combining the time-varying weight matrix, and obtain a regional surface mass transfer estimation result.

[0143] Specifically, the calculation module 403 is further configured to,

[0144] establish a basic function between the observation value and the regional surface mass transfer;

[0145] establish a basic function between the observation value and the regional surface mass transfer;

[0146] The optimal regularization parameter is adaptively determined by a generalized cross-validation method, and a regional surface mass transfer estimation result is obtained.

[0147] Preferably, the calculation module 403 is further configured to,

[0148] The basic function relationship between the observation value and the regional surface mass transfer is

[0149] G(t) = Fm(t) + ε(t)

[0150] Wherein, G(t) is an observation value function; Fm(t) is a regional surface mass transfer function; ε(t) is a noise error function; F is a load Green function; m is a mass distribution vector;

[0151] The regional surface mass transfer estimation model is a time-varying weighted least square / regularization model

[0152]

[0153] Wherein, is a regional surface mass transfer estimation result; λ is a regularization parameter; L is a Laplace operator;

[0154] The weighted regularization least square solution based on the regional surface mass transfer estimation model is

[0155]

[0156] The solution is taken as the regional surface mass transfer estimation result.

[0157] The output module 404 is used for outputting the regional surface mass transfer estimation result obtained by the solution.

[0158] According to the GNSS regional earth surface mass migration monitoring system provided in the embodiment, the GNSS reference station coordinate time sequence is obtained as an observation value to accurately record the three-dimensional coordinate information of the GNSS reference station changing with time, and the noise characteristic information in the GNSS reference station coordinate time sequence is used to obtain the maximum likelihood estimation of white noise, flicker noise and random walk noise respectively by using the sliding window analysis method, to accurately estimate the variances of various noise components, to repeatedly estimate by using the sliding window to cover the complete sequence, to construct the error covariance matrix, and to obtain the weight matrix for various noises. Then, the load Green function is taken as the kernel function, and the GNSS observation is dynamically weighted by combining the time-varying weight matrix, to construct the regional earth surface mass migration estimation model considering various noise errors, to calculate the corresponding regional earth surface mass migration estimation result. By combining the noise characteristics, the GNSS reference station coordinate time sequence is supplemented, the influence of random noise and systematic error on the inversion estimation result is greatly reduced, the estimation accuracy of the crustal movement parameters is improved, the accuracy and stability of the earth surface mass migration deformation estimation are improved, the misjudgment of the earth surface mass migration trend is reduced, and the dynamic noise weighting method is used to effectively suppress the non-tectonic interference signals such as instrument drift and environmental change, to improve the extraction ability of the real geological response caused by hydrological load, glacier change and atmospheric load, to enhance the identification of the crustal deformation physical mechanism, and to provide quantitative support for multi-source data fusion.

[0159] The application further provides a non-volatile computer storage medium, which stores at least one executable instruction, and the executable instruction can execute the GNSS regional earth surface mass migration monitoring method in any method embodiment described above.

[0160] Figure 5 A structural schematic diagram of a computing device according to an embodiment of the application is shown, and the specific implementation of the computing device is not limited in the specific embodiments of the application.

[0161] As Figure 5As shown, the computing device can include a processor 502, a communications interface 504, a memory 506, and a communications bus 508.

[0162] Wherein:

[0163] The processor 502, the communications interface 504, and the memory 506 complete the communication with each other through the communications bus 508.

[0164] The communications interface 504 is configured to communicate with network elements such as clients or other servers.

[0165] The processor 502 is configured to execute the program 510, and specifically can execute the related steps in the above-described GNSS regional ground surface quality migration monitoring method embodiment.

[0166] Specifically, the program 510 can include program codes, and the program codes include computer operation instructions.

[0167] The processor 502 can be a central processing unit CPU, or an application specific integrated circuit ASIC, or one or more integrated circuits configured to implement embodiments of the present application. The one or more processors included in the computing device can be the same type of processor, such as one or more CPUs; or can be different types of processors, such as one or more CPUs and one or more ASICs.

[0168] The memory 506 is configured to store the program 510. The memory 506 can include a high-speed RAM memory, and can also include a non-volatile memory such as at least one disk memory.

[0169] The program 510 can be specifically used to enable the processor 502 to execute a GNSS regional ground surface quality migration monitoring method in any of the above-described method embodiments. The specific implementation of each step in the program 510 can refer to the corresponding description in the corresponding step and unit in the above-described GNSS regional ground surface quality migration monitoring method embodiment, and will not be described here. Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the device and the module described above can refer to the corresponding process description in the foregoing method embodiments, and will not be described here.

[0170] The algorithms and displays presented herein are not inherently related to any particular computer, virtual system, or other apparatus. Various general purpose systems can be used with programs in accordance with the teachings herein, or it can prove convenient to construct more specialized apparatus to perform the required method steps. The required structure for a variety of these systems will be apparent from the description above. In addition, the present application is not intended to be limited to any particular programming language. It will be appreciated that there are many programming languages that can be used to implement the teachings herein, and any specific language can be chosen for use in this application.

[0171] In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the application can be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been described in detail in order to not obscure the understanding of this description.

[0172] Similarly, it is to be understood that the above description is intended to be illustrative and not restrictive. Many embodiments of the application will be apparent to those of skill in the art upon reviewing the above description, and it is therefore contemplated that the claims should be construed in light of the full scope of the disclosure and the claims, and that the scope of the claims should not be limited to the particular examples disclosed. In addition, the described embodiments are to be considered merely exemplary and are not intended to limit the scope of the application to the precise details of these examples. Various modifications and changes can be made thereto by those of ordinary skill in the art, which modifications and changes are to be interpreted as falling within the scope of the appended claims.

[0173] Those skilled in the art will appreciate that the modules in the apparatuses in the embodiments can be adapted and placed in one or more apparatuses other than the embodiments. The modules or units or components in the embodiments can be combined into one module or unit or component, and further can be divided into multiple sub-modules or sub-units or sub-components. Any combination of all the features disclosed in the specification (including the accompanying claims, abstract and drawings), and any method or apparatus so disclosed, can be made unless specifically stated otherwise. Unless explicitly stated otherwise, each feature disclosed in the specification (including the accompanying claims, abstract and drawings) can be replaced by alternative features that serve the same, equivalent or similar purpose.

[0174] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the claims, any of the claimed embodiments can be used in any combination.

[0175] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components according to the embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0176] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for monitoring surface quality migration in a GNSS area, characterized in that, include: Obtain the time series of GNSS reference station coordinates as observations of GNSS surface quality migration; Based on coordinate time series, the dynamic noise estimation results for each time period in the coordinate time series are dynamically estimated, and a time-varying weight matrix is ​​constructed, including: Based on the noise feature information in the coordinate time series, the noise of each time period is dynamically estimated using the sliding window analysis method, and the dynamic noise estimation results corresponding to each time period are obtained. Based on the dynamic noise estimation results, the observation error covariance matrix is ​​generated; Invert the observation error covariance matrix to generate the time-varying weight matrix of the observations; Based on the sliding window analysis method, the time series of vertical coordinates of the GNSS reference station is segmented according to time to obtain multiple sliding windows; Within a sliding window, the variances of the white noise, random walk noise, and flicker noise components are calculated using maximum likelihood estimation. in, The noise power spectral density; For frequency; This is the white noise term; This is the flicker noise term; This is the random walk noise term; The sliding window moves forward, performing dynamic noise estimation for each window until the complete GNSS reference station vertical coordinate time series is covered. The time-varying noise variance component is output, and the observation error covariance matrix is ​​generated. in, The observation error covariance matrix; The variance component of time-varying white noise; The variance component of the time-varying flicker noise; The variance component of the time-varying random walk noise; Invert the observation error covariance matrix to generate the time-varying weight matrix of the observations. in, It is a time-varying weight matrix; Based on observed values, a fundamental function relating observed values ​​to regional surface quality migration is constructed. Combined with a time-varying weight matrix, a regional surface quality migration estimation model is built, and the estimation results are obtained, including: Establish a fundamental function relating observed values ​​to regional surface quality migration; Based on the fundamental function, using the load Green's function as the kernel function and combining it with the time-varying weight matrix, a regional surface quality migration estimation model for estimating noise weights is constructed using the least squares method. The optimal regularization parameter is adaptively determined using a generalized cross-validation method, and the regional surface quality migration estimation results are obtained by solving the problem. The fundamental functional relationship between observed values ​​and regional surface quality migration is as follows: in, These are the observed values; For regional surface quality migration; This is noise error; For the load Green's function; The mass distribution vector; The regional surface quality migration estimation model is a time-varying weighted least squares / regularized model. in, The results are the estimation results of regional surface quality migration; For regularization parameters; For the Laplace operator; The weighted regularized least squares solution obtained based on the regional surface quality migration estimation model is: ； The solution is used as the estimation result of regional surface quality migration. Output the estimated results of regional surface quality migration obtained from the solution.

2. The method according to claim 1, characterized in that, The acquisition of the GNSS reference station coordinate time series as observations of GNSS surface quality migration further includes: Obtain the time series of GNSS reference station coordinates; where the time series of GNSS reference station coordinates is the time series of GNSS reference station vertical coordinates.

3. The method according to claim 2, characterized in that, The time series of the vertical coordinates of the GNSS reference station is as follows: in, The number of times at which observations of GNSS surface quality migration are collected for each reference station; The number of GNSS reference stations; For the first At the [time]th moment GNSS observations from one base station; Time series of vertical coordinates of GNSS reference station; For time.

4. A GNSS regional surface quality migration monitoring system, used to implement the GNSS regional surface quality migration monitoring method as described in any one of claims 1-3, comprising: The module comprises an acquisition module, a weight determination module, a calculation module, and an output module; among which, The acquisition module is used to acquire the time series of GNSS reference station coordinates as observations of GNSS surface quality migration. The weight determination module is used to dynamically estimate the dynamic noise estimation results of each time period in the coordinate time series based on the coordinate time series, and construct a time-varying weight matrix. The calculation module is used to construct a basic function between the observed values ​​and the regional surface quality migration based on the observed values, and to construct a regional surface quality migration estimation model by combining the time-varying weight matrix, and to obtain the regional surface quality migration estimation result. The output module is used to output the estimated results of regional surface quality migration obtained from the solution.

5. A computing device, comprising: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction, which causes the processor to perform an operation corresponding to the GNSS regional surface quality migration monitoring method as described in any one of claims 1-3.

6. A computer storage medium storing at least one executable instruction that causes a processor to perform an operation corresponding to a GNSS regional surface quality migration monitoring method as described in any one of claims 1-3.

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