GNSS regional earth surface mass migration monitoring method, system, equipment and medium

Abstract

The invention discloses a GNSS (Global Navigation Satellite System) regional earth surface mass migration monitoring method, system and equipment and a medium. Acquiring a coordinate time sequence of the GNSS base station as an observation value of earth surface mass migration; based on the coordinate time sequence, dynamically estimating a dynamic noise estimation result of each time period in the coordinate time sequence, and constructing a time-varying weight matrix; and constructing a basic function between the observation value and the regional earth surface mass migration, constructing a regional earth surface mass migration estimation model in combination with a time-varying weight matrix, and calculating to obtain a regional earth surface mass migration estimation result and outputting the regional earth surface mass migration estimation result. According to the method, dynamic noise estimation is carried out on the earth surface mass migration observation value, various noise parameters are introduced to serve as weight basis, the estimation model is constructed to solve the migration result, and weighting processing is carried out on different base station data, so that the noise influence is remarkably reduced, and the accuracy and stability of deformation estimation are greatly improved; non-structural interference signals are suppressed, and the recognition capability of various geophysical signals is enhanced.

Description

Technical Field

[0001] This invention relates to the field of surface deformation monitoring, and in particular to a GNSS regional surface quality migration monitoring method, system, computing device, and computer storage medium. Background Technology

[0002] Surface mass migration is the response of the solid Earth to changes in loads caused by hydrology, atmosphere, and ocean, and is closely related to various changes in the space environment during the evolution of the Earth system. Monitoring surface mass migration and deformation not only serves the purpose of locating and describing the Earth's surface morphology in geodesy, but also provides scientific support for better understanding the evolutionary mechanisms of the Earth's space environment, such as continental glacial melting and retreat, terrestrial water storage distribution, surface subsidence, sea-level rise, and extreme weather. It is an important component of modern Earth science research on global change and sustainable development issues such as resource and environmental changes and geological disasters facing humanity.

[0003] GNSS can monitor changes in surface mass migration and deformation at different temporal and spatial scales with millimeter-level precision. In particular, the time series of GNSS reference station elevation coordinates significantly reflects the characteristics of surface mass migration caused by hydrological and atmospheric loads. Continuous observation data from GNSS reference stations includes two types of information: one is the GNSS reference station coordinate time series, and the other is information based on the noise characteristics of GNSS reference station observations. The GNSS reference station coordinate time series records the three-dimensional coordinate information of the GNSS reference station over time (usually observations at daily, weekly, or monthly scales). GNSS coordinate time series directly reflect geophysical processes such as crustal deformation and are fundamental data for tectonic, seismic, and hydrological studies. With the continuous increase in the scale of GNSS reference station networks and the accumulation of observational data, such as the "China Tectonic Environment Monitoring Network" (referred to as the "Land State Network") which has accumulated observational data from 260 reference stations for over ten years, and the United States with over 2000 reference stations including observation networks such as PBO, these long-term, high-precision GNSS observations provide important data sources for the study of surface mass migration.

[0004] Existing research has utilized time-series coordinate data from GNSS reference stations to invert and obtain regional surface quality migration results. Through verification and comparison with GRACE data, hydrological models, and ground observations, it has been confirmed that GNSS can invert regional surface quality migration changes. However, existing methods all focus on directly utilizing GNSS observation data for inversion, lacking sufficient consideration of the full utilization of GNSS reference station observation noise characteristics when inverting surface quality migration. Therefore, this invention proposes a GNSS regional surface quality migration monitoring method based on dynamic noise weighting to further improve the reliability of GNSS regional surface quality migration monitoring. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a GNSS regional surface quality migration monitoring method, a corresponding GNSS regional surface quality migration monitoring system, computing equipment, and computer storage medium.

[0006] According to one aspect of the present invention, a method for monitoring surface quality migration in a GNSS area is provided, the method comprising:

[0007] Obtain the time series of GNSS reference station coordinates as observations of GNSS surface quality migration;

[0008] Based on the coordinate time series, the dynamic noise estimation results of each time period in the coordinate time series are dynamically estimated, and a time-varying weight matrix is ​​constructed.

[0009] Based on the observed values, a fundamental function relating the observed values ​​to the migration of regional surface quality is constructed. Combined with a time-varying weight matrix, a regional surface quality migration estimation model is constructed, and the estimation results of regional surface quality migration are obtained by solving the model.

[0010] Output the estimated results of regional surface quality migration obtained from the solution.

[0011] In the above scheme, obtaining the time series of GNSS reference station coordinates as observations of GNSS surface quality migration further includes:

[0012] 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.

[0013] In the above scheme, the time series of the vertical coordinates of the GNSS reference station is as follows:

[0014] O i,j i∈[1,t0],j∈[1,n0]

[0015] G GNSS (t), t∈[1,t0]

[0016] Where t0 is the number of times each reference station collects observations of GNSS surface quality migration; n0 is the number of GNSS reference stations; O i,j G represents the GNSS observation value of the j-th reference station at the i-th time; GNSS This is the time series of vertical coordinates of the GNSS reference station; t is time.

[0017] In the above scheme, the step of dynamically estimating the dynamic noise estimation results for each time period in the coordinate time series based on the coordinate time series and constructing a time-varying weight matrix further includes:

[0018] 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.

[0019] Based on the dynamic noise estimation results, the observation error covariance matrix is ​​generated;

[0020] Invert the observation error covariance matrix to generate the time-varying weight matrix of the observations.

[0021] The method described above further includes:

[0022] 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;

[0023] Within a sliding window, the variances of the white noise, random walk noise, and flicker noise components are calculated using maximum likelihood estimation.

[0024]

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

[0026] 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.

[0027]

[0028] Where Q(t) is 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;

[0029] Invert the observation error covariance matrix to generate the time-varying weight matrix of the observations.

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

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

[0032] In the above scheme, the step of constructing a fundamental function based on observed values ​​and regional surface quality migration, combining a time-varying weight matrix to construct a regional surface quality migration estimation model, and solving for the regional surface quality migration estimation result further includes:

[0033] Establish a fundamental function relating observed values ​​to regional surface quality migration;

[0034] 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.

[0035] 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.

[0036] The method described above further includes:

[0037] The fundamental functional relationship between observed values ​​and regional surface quality migration is as follows:

[0038] G(t)=Fm(t)+ε(t)

[0039] Where G(t) is the observed value; Fm(t) is the regional surface mass migration; ε(t) is the noise error; F is the load Green's function; and m is the mass distribution vector.

[0040] The regional surface quality migration estimation model is a time-varying weighted least squares / regularized model.

[0041]

[0042] in, The result represents the estimation of regional surface quality migration; λ is the regularization parameter; L is the Laplacian operator;

[0043] The weighted regularized least squares solution obtained based on the regional surface quality migration estimation model is:

[0044]

[0045] The solution is used as the estimate of regional surface quality migration.

[0046] According to another aspect of the present invention, a GNSS regional surface quality 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 used to acquire the time series of GNSS reference station coordinates as observations of GNSS surface quality migration.

[0048] 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;

[0049] 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.

[0050] The output module is used to output the estimated results of regional surface quality migration obtained from the solution.

[0051] According to another aspect of the present invention, a computing device is provided, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus;

[0052] The memory is used to store at least one executable instruction that causes the processor to perform an operation corresponding to the above-described GNSS regional surface quality migration monitoring method.

[0053] According to another aspect of the present invention, a computer storage medium is provided, the storage medium storing at least one executable instruction, the executable instruction causing a processor to perform an operation corresponding to the above-described GNSS regional surface quality migration monitoring method.

[0054] According to the technical solution provided by this invention, the coordinate time series of GNSS reference stations is obtained as the observed values ​​of GNSS surface quality migration; based on the coordinate time series, the dynamic noise estimation results of each time period in the coordinate time series are dynamically estimated, and a time-varying weight matrix is ​​constructed; based on the observed values, a fundamental function between the observed values ​​and regional surface quality migration is constructed, and combined with the time-varying weight matrix, a regional surface quality migration estimation model is constructed, and the regional surface quality migration estimation result is obtained by solving the model; the obtained regional surface quality migration estimation result is output. Based on the technical solution provided by this invention, the three-dimensional coordinate information of the GNSS reference station changing over time is accurately recorded by acquiring the time series of GNSS reference station coordinates as observation values. Using the noise characteristic information in the GNSS reference station coordinate time series, maximum likelihood estimation is performed on white noise, flicker noise, and random walk noise respectively through a sliding window analysis method to determine the variance of each noise component. This allows for accurate estimation of each type of noise. Repeated estimation is performed through a sliding window to cover the complete sequence, constructing an error covariance matrix, and then obtaining weight matrices for each type of noise. Subsequently, using the load Green's function as the kernel function and combining it with the time-varying weight matrix, the GNSS observations are dynamically weighted to construct a regional surface quality migration estimation model that considers various noise errors, and the corresponding regional surface quality migration estimation results are calculated. By combining noise characteristics, a crucial supplement was provided to the time series coordinates of GNSS reference stations, significantly reducing the impact of random noise and systematic errors on the inversion estimation results. This improved the estimation accuracy of crustal movement parameters, enhancing the accuracy and stability of surface mass migration deformation estimation, reducing misjudgments of surface mass migration trends, and effectively suppressing non-tectonic interference signals such as instrument drift and environmental changes based on the dynamic noise weighting method. This also improved the ability to extract real geological responses caused by hydrological load, glacier changes, and atmospheric load, enhanced the identification of the physical mechanisms of crustal deformation, and provided quantitative support for multi-source data fusion.

[0055] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0056] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0057] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0058] Figure 1A flowchart illustrating a GNSS regional surface quality migration monitoring method according to an embodiment of the present invention is shown.

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

[0060] Figure 3 A flowchart illustrating a method for estimating regional surface quality migration according to an embodiment of the present invention is shown.

[0061] Figure 4 A structural block diagram of a GNSS regional surface quality migration monitoring system according to an embodiment of the present invention is shown;

[0062] Figure 5 A schematic diagram of the structure of a computing device according to an embodiment of the present invention is shown. Detailed Implementation

[0063] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0064] Figure 1 A flowchart illustrating a GNSS regional surface quality migration monitoring method according to an embodiment of the present invention is shown. The method includes the following steps:

[0065] Step S101: Obtain the time series of GNSS reference station coordinates as the observation values ​​for GNSS surface quality migration.

[0066] Specifically, the GNSS reference station coordinate time series is the 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 as it changes over time, typically observations at daily, weekly, or monthly scales. The noise characteristics of GNSS reference station observations are quantified by residual analysis of the time series, quantifying its random error composition, including types such as white noise, flicker noise, and red noise, and their amplitude parameters, such as noise spectral density and power index.

[0068] GNSS coordinate time series directly reflect geophysical processes such as crustal deformation and serve as fundamental data for tectonic, seismic, and hydrological studies. Meanwhile, observation noise characteristics are a key supplement for understanding, modeling, and optimizing these series, helping to improve interpretation accuracy, identify interference factors, and provide quantitative support for multi-source data fusion.

[0069] Preferably, the time series of the vertical coordinates of the GNSS reference station is as follows:

[0070] O i,j i∈[1,t0],j∈[1,n0]

[0071] G GNSS (t), t∈[1,t0]

[0072] Where t0 is the number of times each reference station collects observations of GNSS surface quality migration; n0 is the number of GNSS reference stations; O i,j G represents the GNSS observation value of the j-th reference station at the i-th time; GNSS This is the time series of vertical coordinates of the GNSS reference station; t is time.

[0073] Step S102: Based on the coordinate time series, dynamically estimate the dynamic noise estimation results for each time period in the coordinate time series, and construct a time-varying weight matrix.

[0074] Step S103: Based on the observed values, construct the fundamental function between the observed values ​​and the regional surface quality migration, combine it with the time-varying weight matrix to construct the regional surface quality migration estimation model, and solve to obtain the regional surface quality migration estimation result.

[0075] Step S104: Output the calculated regional surface quality migration estimation results.

[0076] According to the GNSS regional surface quality migration monitoring method provided in this embodiment, the coordinate time series of GNSS reference stations is obtained as the observed values ​​of GNSS surface quality migration. Based on the coordinate time series, the dynamic noise estimation results of each time period in the coordinate time series are dynamically estimated, and a time-varying weight matrix is ​​constructed. Based on the observed values, a fundamental function between the observed values ​​and regional surface quality migration is constructed. Combined with the time-varying weight matrix, a regional surface quality migration estimation model is constructed, and the regional surface quality migration estimation result is obtained by solving the model. The obtained regional surface quality migration estimation result is then output. This GNSS regional surface quality migration monitoring method accurately records the three-dimensional coordinate information of the GNSS reference station over time by obtaining the coordinate time series of GNSS reference stations as observed values. Utilizing the noise characteristic information in the GNSS reference station coordinate time series, a weight matrix for each type of noise is obtained through accurate estimation. GNSS observations are then dynamically weighted to construct a regional surface quality migration estimation model that considers various noise errors, and the corresponding regional surface quality migration estimation result is calculated. By combining noise characteristics, a crucial supplement was provided to the time series coordinates of GNSS reference stations, significantly reducing the impact of random noise and systematic errors on the inversion estimation results. This improved the accuracy and stability of surface mass migration deformation estimation. Furthermore, based on the dynamic noise weighting method, non-tectonic interference signals such as instrument drift and environmental changes were effectively suppressed, enhancing the ability to extract real geological responses caused by hydrological load, glacier changes, and atmospheric load. This strengthened the identification of the physical mechanisms of crustal deformation and provided quantitative support for multi-source data fusion.

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

[0078] like Figure 2 As shown, the method includes the following steps:

[0079] Step S201: 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 to obtain the dynamic noise estimation results corresponding to each time period.

[0080] Specifically, based on the sliding window analysis method, the time series of the vertical coordinates of the GNSS reference station is segmented according to time to obtain multiple sliding windows, and the interior of each sliding window is regarded as locally stationary.

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

[0082]

[0083] Where PSD is the noise power spectral density; f is the frequency; σ WN For white noise; σ FL For flicker noise; σ RW This represents the random walk noise term.

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

[0085] Step S202: Generate the observation error covariance matrix based on the dynamic noise estimation results.

[0086] Preferably, based on the dynamic noise estimation results, the time-varying noise variance component is output to generate the observation error covariance matrix.

[0087]

[0088] Where Q(t) is the observation error covariance matrix; The variance component of time-varying white noise; The variance component of the time-varying flicker noise; This represents the variance component of the time-varying random walk noise.

[0089] Step S203: Invert the observation error covariance matrix to generate the time-varying weight matrix of the observations.

[0090] Preferably, the time-varying weight matrix is ​​as follows:

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

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

[0093] Based on the above method, noise characteristic information from the time series of GNSS reference station coordinates is used. A sliding window analysis method is employed to obtain maximum likelihood estimates for white noise, flicker noise, and random walk noise, respectively, determining the variance of each noise component. This allows for accurate estimation of each type of noise. Repeated estimations are performed using a sliding window to cover the entire series, constructing an error covariance matrix. This leads to a weight matrix for each type of noise, accurately reflecting its impact. Furthermore, this weight matrix facilitates the subsequent estimation of land quality migration, allowing the influence of various noise types to be reasonably integrated into the estimation results, making the results more scientific. Moreover, the noise characteristic information provides quantitative support for multi-source data fusion.

[0094] Figure 3 A flowchart illustrating a method for estimating regional surface quality migration according to an embodiment of the present invention is shown.

[0095] like Figure 3 As shown, the method includes the following steps:

[0096] Step S301: Establish the fundamental function between the observed values ​​and the migration of regional surface quality.

[0097] Preferably, the fundamental functional relationship between observed values ​​and regional surface quality migration is as follows:

[0098] G(t)=Fm(t)+ε(t)

[0099] Where G(t) is the observed value; Fm(t) is the regional surface mass migration; ε(t) is the noise error; F is the load Green's function; and m is the mass distribution vector.

[0100] Step S302: Based on the basic function, using the load Green's function as the kernel function and combining it with the time-varying weight matrix, construct a regional surface quality migration estimation model for estimating noise weights using the least squares method.

[0101] Preferably, the regional surface quality migration estimation model is a time-varying weighted least squares / regularized model, i.e.

[0102]

[0103] in, The result represents the estimation of regional surface quality migration; λ is the regularization parameter; and L is the Laplace operator.

[0104] Step S303: The optimal regularization parameter is adaptively determined using the generalized cross-validation method, and the regional surface quality migration estimation result is obtained by solving the problem.

[0105] Preferably, the weighted regularized least squares solution obtained based on the regional surface quality migration estimation model is:

[0106]

[0107] The solution is then used as the result of the regional surface quality migration estimation.

[0108] Furthermore, based on the above formula, in order to solve... To find the value of , we need to determine the regularization parameter λ. Therefore, we need to construct the following function using the Generalized Cross-Validation (GCV) method:

[0109]

[0110] Where N is the total number of observations; A(λ) is the influence matrix; and trace[A(λ)] is the trace of A(λ), which is the sum of the diagonal elements of the matrix. The predicted value is the value corresponding to the observed value G;

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

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

[0113] Therefore, through

[0114]

[0115] The estimated results of regional surface quality migration were obtained through calculation.

[0116] Based on the above method, a regional surface quality migration estimation model considering various noise errors is constructed by dynamically weighting GNSS observations using the load Green's function as the kernel function and combining it with a time-varying weight matrix. By combining noise characteristics, the corresponding regional surface quality migration estimation results are calculated. The introduction of a noise-specific weight matrix provides a crucial supplement to the GNSS reference station coordinate time series, significantly reducing the impact of random noise and systematic errors on the inversion estimation results and improving the model's accuracy and stability for regional surface quality migration estimation. The generalized cross-validation method improves the adaptive determination of optimal regularization parameters, increasing the model's computational efficiency, avoiding biases in data partitioning, and balancing the complexity of the regional surface quality migration estimation model. Furthermore, this method further reduces misjudgments of surface quality migration trends and effectively suppresses non-tectonic interference signals such as instrument drift and environmental changes based on the dynamic noise weighting method. It also improves the extraction capability of real geological responses caused by hydrological load, glacier changes, and atmospheric load, enhances the identification of the physical mechanisms of crustal deformation, and provides quantitative support for multi-source data fusion.

[0117] Figure 4 A structural block diagram of a GNSS regional surface quality migration monitoring system according to an embodiment of the present invention is shown;

[0118] like Figure 4 As 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 used to acquire the time series of GNSS reference station coordinates as observations of GNSS surface quality migration.

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

[0121] 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.

[0122] The time series of the vertical coordinates of the GNSS reference station is as follows:

[0123] O i,j i∈[1,t0],j∈[1,n0]

[0124] G GNSS (t), t∈[1,t0]

[0125] Where t0 is the number of times each reference station collects observations of GNSS surface quality migration; n0 is the number of GNSS reference stations; O i,j G represents the GNSS observation value of the j-th reference station at the i-th time; GNSS This is the time series of vertical coordinates of the GNSS reference station; t is time.

[0126] The weight determination module 402 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.

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

[0128] 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.

[0129] Based on the dynamic noise estimation results, the observation error covariance matrix is ​​generated;

[0130] Invert the observation error covariance matrix to generate the time-varying weight matrix of the observations.

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

[0132] Based on the sliding window analysis method, the time series of the vertical coordinates of the GNSS reference station is segmented according to time to obtain multiple sliding windows; (each sliding window is considered to be locally stationary).

[0133] Within a sliding window, the variances of the white noise, random walk noise, and flicker noise components are calculated using maximum likelihood estimation.

[0134]

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

[0136] 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.

[0137]

[0138] Where Q(t) is 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;

[0139] Invert the observation error covariance matrix to generate the time-varying weight matrix of the observations.

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

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

[0142] The calculation module 403 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.

[0143] Specifically, the computing module 403 is further used for,

[0144] Establish a fundamental function relating observed values ​​to regional surface quality migration;

[0145] 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.

[0146] 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.

[0147] Preferably, the calculation module 403 is further used for,

[0148] The fundamental functional relationship between observed values ​​and regional surface quality migration is as follows:

[0149] G(t)=Fm(t)+ε(t)

[0150] Where G(t) is the observation function; Fm(t) is the regional surface mass migration function; ε(t) is the noise error function; F is the load Green's function; and m is the mass distribution vector.

[0151] The regional surface quality migration estimation model is a time-varying weighted least squares / regularized model.

[0152]

[0153] in, The result represents the estimation of regional surface quality migration; λ is the regularization parameter; L is the Laplacian operator;

[0154] The weighted regularized least squares solution obtained based on the regional surface quality migration estimation model is:

[0155]

[0156] The solution is used as the estimate of regional surface quality migration.

[0157] The output module 404 is used to output the calculated regional surface quality migration estimation results.

[0158] A GNSS regional surface quality migration monitoring system provided in this embodiment includes: an acquisition module, a weight determination module, a calculation module, and an output module; wherein, the acquisition module is used to acquire the coordinate time series of GNSS reference stations as the observed values ​​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 construct a regional surface quality migration estimation model in combination with the time-varying weight matrix, and solve for the regional surface quality migration estimation result; the output module is used to output the solved regional surface quality migration estimation result. This embodiment provides a GNSS regional surface quality migration monitoring system. It accurately records the three-dimensional coordinate information of the GNSS reference station over time by acquiring the time series coordinates of the GNSS reference station as observation values. Utilizing the noise characteristic information in the GNSS reference station coordinate time series, it performs maximum likelihood estimation for white noise, flicker noise, and random walk noise using a sliding window analysis method to determine the variance of each noise component. This allows for accurate estimation of various noise types. Repeated estimations are performed using a sliding window to cover the entire sequence, constructing an error covariance matrix, and subsequently obtaining weight matrices for each type of noise. Then, using the loading Green's function as the kernel function and combining it with the time-varying weight matrix, the GNSS observations are dynamically weighted to construct a regional surface quality migration estimation model that considers various noise errors, and the corresponding regional surface quality migration estimation results are calculated. By combining noise characteristics, a crucial supplement was provided to the time series coordinates of GNSS reference stations, significantly reducing the impact of random noise and systematic errors on the inversion estimation results. This improved the estimation accuracy of crustal movement parameters, enhancing the accuracy and stability of surface mass migration deformation estimation, reducing misjudgments of surface mass migration trends, and effectively suppressing non-tectonic interference signals such as instrument drift and environmental changes based on the dynamic noise weighting method. This also improved the ability to extract real geological responses caused by hydrological load, glacier changes, and atmospheric load, enhanced the identification of the physical mechanisms of crustal deformation, and provided quantitative support for multi-source data fusion.

[0159] The present invention also provides a non-volatile computer storage medium storing at least one executable instruction that can execute a GNSS regional surface quality migration monitoring method in any of the above method embodiments.

[0160] Figure 5 The diagram illustrates the structure of a computing device according to an embodiment of the present invention. The specific embodiments of the present invention do not limit the specific implementation of the computing device.

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

[0162] in:

[0163] The processor 502, communication interface 504, and memory 506 communicate with each other via communication bus 508.

[0164] Communication interface 504 is used to communicate with other network elements such as clients or other servers.

[0165] The processor 502 is used to execute program 510, which can specifically execute the relevant steps in the above embodiment of the GNSS regional surface quality migration monitoring method.

[0166] Specifically, program 510 may include program code, which includes computer operation instructions.

[0167] Processor 502 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The computing device includes one or more processors, which may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0168] Memory 506 is used to store program 510. Memory 506 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0169] Specifically, program 510 can be used to cause processor 502 to execute a GNSS regional surface quality migration monitoring method in any of the above method embodiments. The specific implementation of each step in program 510 can be found in the corresponding descriptions of the steps and units in the above embodiments of the GNSS regional surface quality migration monitoring method, and will not be repeated here. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the devices and modules described above can be referred to the corresponding process descriptions in the foregoing method embodiments, and will not be repeated here.

[0170] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, this invention is not directed to any particular programming language. It should be understood that the contents of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0171] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0172] Similarly, it should be understood that, in order to streamline this disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof. However, this method of disclosure should not be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of the invention.

[0173] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves 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 GNSS regional land surface mass transfer monitoring method, characterized in that, The method comprises the following steps: Obtaining a GNSS reference station coordinate time series as an observation value of GNSS ground mass migration; Based on the coordinate time series, dynamically estimating the dynamic noise estimation results of each period in the coordinate time series, and constructing a time-varying weight matrix; Based on the observation value, a basic function between the observation value and the regional ground mass migration is constructed, and a time-varying weight matrix is combined to construct a regional ground mass migration estimation model, and the regional ground mass migration estimation result is solved. The output of the regional ground mass migration estimation result obtained by solving is output.

2. The method of claim 1, wherein, The method further comprises the following steps: Obtaining a GNSS reference station coordinate time series; wherein the GNSS reference station coordinate time series is a GNSS reference station vertical direction coordinate time series.

3. The method of claim 2, wherein, The GNSS reference station vertical direction coordinate time series is: O i,j i∈[1,t0],j∈[1,n0] G GNSS (t), t e [1, t0] wherein t0 is the number of collection time of each reference station's observation value of GNSS ground 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 series; t is time.

4. The method of claim 1, wherein, The method further comprises the following steps: Based on the noise characteristic information in the coordinate time series, the noise of each period is dynamically estimated by using a sliding window analysis method to obtain the dynamic noise estimation result corresponding to each period; According to the dynamic noise estimation result, an observation error covariance matrix is generated; The observation error covariance matrix is inverted to generate a time-varying weight matrix of the observation value.

5. The method of claim 4, wherein, The method further comprises the following steps: According to the sliding window analysis method, the GNSS reference station vertical direction coordinate time series is segmented according to time to obtain a plurality of sliding windows; In the sliding window, the variances of white noise, random walk noise and flicker noise components are calculated by maximum likelihood estimation where 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; The sliding window is slid forward, and the dynamic noise estimation of each sliding window is performed until the complete GNSS reference station vertical direction coordinate time series is covered, the time-varying noise variance component is output, and the observation error covariance matrix is generated where 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; The observation error covariance matrix is inverted to generate a time-varying weight matrix of the observation value P(t) = Q(t) -1 Wherein, P(t) is a time-varying weight matrix.

6. The method of claim 1, wherein, The method further comprises the following steps: A basic function between the observation value and the regional ground mass migration is established; According to the basic function, taking the load Green function as the kernel function and combining the time-varying weight matrix, a regional ground mass migration estimation model for estimating noise weight is constructed based on the least square method; An optimal regularization parameter is adaptively determined by a generalized cross-validation method, and the regional ground mass migration estimation result is solved.

7. The method of claim 6, wherein, The method further comprises the following steps: The basic function relationship between the observation value and the regional ground mass migration is G(t) = Fm(t) + ε(t) Wherein, G(t) is an observation value; Fm(t) is a regional ground mass migration; ε(t) is a noise error; F is a load Green function; m is a mass distribution vector; The regional ground mass migration estimation model is a time-varying weighted least square / regularization model wherein, is the result of the regional surface quality migration estimation; λ is the regularization parameter; L is the Laplacian operator; The weighted regularized least square solution based on the regional surface mass transfer estimation model is The solution is taken as the regional surface mass transfer estimation result.

8. A GNSS regional geoid quality migration monitoring system comprising: The acquisition module, the weight determination module, the calculation module and the output module are used to acquire the GNSS reference station coordinate time series as the observation value of the GNSS surface mass transfer, dynamically estimate the dynamic noise estimation result of each period in the coordinate time series based on the coordinate time series, construct the time-varying weight matrix, construct the basic function between the observation value and the regional surface mass transfer based on the observation value, combine the time-varying weight matrix, construct the regional surface mass transfer estimation model, solve the regional surface mass transfer estimation result, and output the solved regional surface mass transfer estimation result. The processor, the memory, the communication interface and the communication bus are used to complete the communication among each other. The memory is used to store at least one executable instruction, and the executable instruction makes the processor execute the operation corresponding to the GNSS regional surface mass transfer monitoring method in any one of claims 1-7. 10.A computer storage medium, wherein at least one executable instruction is stored in the computer storage medium, and the executable instruction makes a processor execute the operation corresponding to the GNSS regional surface mass transfer monitoring method in any one of claims 1-7. ​ 9. A computing device comprising: ​ ​ ​

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