Precise correction method of GNSS vertical velocity field based on multi-source load modeling

Abstract

This application discloses a precise correction method for the GNSS vertical velocity field based on multi-source load modeling, relating to the fields of geodesy and geophysics. The method includes: acquiring multi-source surface mass load data; constructing a multi-source load dataset; calculating the vertical displacement response of the Earth's surface under point loads based on an elastic Earth model and using Farrell's load Green's function theory, obtaining the load Green's function by solving for the load Love number and summing Legendre series; using the spherical Green's function integration method to calculate the vertical displacement of each grid point on the global grid caused by all mass loads, constructing a global load deformation field, performing time series analysis, and extracting the load correction at the locations of each GNSS station globally; subtracting the load correction from the global GNSS vertical velocity field, while simultaneously subtracting the glacial isostatic adjustment effect, to obtain the corrected GNSS vertical velocity field. This application can obtain accurate and reliable GNSS vertical velocity information.

Description

Technical Field

[0001] This application relates to the fields of geodesy and geophysics, and in particular to a precise correction method for the GNSS vertical velocity field based on multi-source load modeling. Background Technology

[0002] The velocity field observed by the Global Navigation Satellite System (GNSS) directly reflects the displacement field of the solid Earth surface and includes various geophysical signals such as tidal deformation, tectonic movements, and environmental load effects. Seasonal and long-term vertical deformations caused by environmental loads can reach the millimeter to centimeter level. To accurately extract tectonic deformation information reflecting the Earth's internal dynamic processes from the observed velocity field, it is necessary to subtract the load deformation effects caused by the migration of surface mass from the atmosphere, oceans, and terrestrial water.

[0003] Currently, research on surface mass load deformation is mainly conducted through two approaches: one is forward calculation based on meteorological and hydrological observation data combined with physical models; the other is load inversion using Gravity Recovery and Climate Experiment (GRACE) satellite gravity data. Currently, many scholars typically use the second method due to its computational convenience. However, GRACE satellite gravity data only reflects large-scale mass migration and lacks spatial resolution for regional small-scale load sources. Furthermore, its inversion is based on surface load assumptions, which can lead to significant biases in areas dominated by subsurface loads. The Green's function integral method is a classic theory for calculating surface mass load displacement, capable of fully considering load contributions across the entire spectrum. However, the application of this method has long been constrained by the imperfections of global mass change models, and early studies often used single data sources, making it difficult to comprehensively reflect the combined contributions of multiple load sources. In addition, existing research lacks sufficient methods for separating and extracting long-term trend terms of load effects, resulting in residual load influences remaining in the corrected velocity field. Summary of the Invention

[0004] The purpose of this application is to provide a precise correction method for the GNSS vertical velocity field based on multi-source load modeling. This method can systematically correct the GNSS vertical velocity field by integrating multi-source surface load data and calculating load deformation using the Green's function integral method, thereby obtaining more realistic and reliable GNSS vertical velocity information.

[0005] To achieve the above objectives, this application provides the following solution: This application provides a precise correction method for the GNSS vertical velocity field based on multi-source load modeling. The precise correction method for the GNSS vertical velocity field based on multi-source load modeling includes: Acquire multi-source surface mass load data; the multi-source surface mass load data includes: atmospheric load data, marine non-tidal load data, terrestrial hydrological load data, and surface solid load data.

[0006] The multi-source surface quality load data are subjected to spatiotemporal registration, outlier removal, and unit unification processing to construct a multi-source load dataset.

[0007] Based on the elastic Earth model, the vertical displacement response of the Earth's surface under point load is calculated using the Farrell load Green's function theory. The load Green's function is obtained by solving for the load Love number and summing the Legendre series. The elastic Earth model is either the PREM Earth model or a custom Earth layering model.

[0008] Based on the multi-source load dataset and the load Green's function, the vertical displacement of each grid point on the global grid caused by all mass loads is calculated using the spherical Green's function integration method, thus constructing a global load deformation field.

[0009] A time series analysis was performed on the global load deformation field. The least squares method was used to fit the linear trend in the load deformation sequence. At the same time, the annual, semi-annual and ten-year period signals were separated, and the load correction at the location of each GNSS station worldwide was extracted.

[0010] The global GNSS vertical velocity field is subtracted from the load correction at the location of each GNSS station worldwide, and the glacier isostatic adjustment effect is also deducted to obtain the GNSS vertical velocity field after deducting the load effect, thus completing the precise correction. The global GNSS vertical velocity field is a velocity field obtained by preprocessing and linear trend estimation based on the coordinate time series data of continuous global GNSS observation stations.

[0011] Optionally, the atmospheric load data includes surface pressure data provided by the European Centre for Medium-Range Weather Forecasts (ERA5) reanalysis data.

[0012] The ocean non-tidal load data includes: AVISO satellite altimetry data, UK Met Office temperature and salinity datasets, and Geruo13 glacier isostatic adjustment model correction data.

[0013] The terrestrial hydrological load data includes: GLDAS hydrological model data and CPC soil water data.

[0014] The surface solids load data includes global sedimentary thickness data from the CRUST 1.0 model.

[0015] Optionally, the spatiotemporal registration process includes: All multi-source surface quality load data are unified to the same temporal resolution, the same spatial resolution, and the same time span.

[0016] Missing data were filled using cubic spline interpolation.

[0017] All data were standardized to the equivalent water level unit.

[0018] Optionally, the calculation of the load Green's function specifically includes: Based on the aforementioned elastic Earth model, the Earth is discretized into a multi-layered structure.

[0019] Starting from the Earth's center, the vertical displacement component in the Love number of each load order is calculated by integrating layer by layer.

[0020] Based on the vertical displacement components, the load Green's function is calculated by summing the Legendre series.

[0021] Optionally, the expression for the load Green's function is: ; in, for Green's function of the load at the location; To calculate the spherical angular distance between the calculation point and the load point; The radius of the Earth; Earth mass; This is the load Love number; For Legendre polynomials; n max It is the maximum order.

[0022] Optionally, the expression for the spherical Green's function integral is: ; in, Indicates coarse latitude; Indicates longitude; For calculation points; for Vertical displacement at the location; for Green's function of the load at the location; To calculate the spherical angular distance between the calculation point and the load point; The load point; for Load quality changes at the location; integration region It is the entire sphere.

[0023] Optionally, the formula for calculating the GNSS vertical velocity field after deducting the load effect is: ; in, The vertical velocity of GNSS after deducting load effects; For the global GNSS vertical velocity field, This is due to the isostatic adjustment effect of glaciers; This is the load correction amount.

[0024] Optionally, the precise correction method for the GNSS vertical velocity field based on multi-source load modeling further includes: performing multi-dimensional cross-validation on the GNSS vertical velocity field after deducting load effects by combining uncorrected data and GRACE inversion results.

[0025] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a precise correction method for GNSS vertical velocity field based on multi-source load modeling. The method includes: acquiring multi-source surface mass load data; the multi-source surface mass load data includes: atmospheric load data, ocean non-tidal load data, land hydrological load data, and surface solid load data; performing spatiotemporal registration, outlier removal, and unit unification processing on the multi-source surface mass load data to construct a multi-source load dataset; effectively eliminating differences and noise interference in spatiotemporal dimensions, data accuracy, and measurement units among data from different sources, constructing a standardized, accurate, and reliable multi-source load dataset, thus laying a solid data foundation for subsequent high-precision load deformation calculations. Based on an elastic Earth model, the method employs Farrell's load Green's function theory to calculate the vertical displacement response of the Earth's surface under point loads. The load Green's function is obtained by solving for the load Love number and summing the Legendre series; the elastic Earth model is either the PREM Earth model or a custom Earth layering model; it can accurately reflect the elastic layering structure characteristics of the real Earth's interior, precisely quantifying the physical response relationship between surface mass load and surface vertical displacement, and avoiding deformation calculation errors caused by simplified Earth models. Based on the multi-source load dataset and the load Green's function, the spherical Green's function integral method is used to calculate the vertical displacement of each grid point on the global grid caused by all mass loads, constructing a global load deformation field. This enables refined and global quantitative characterization of load deformation on a global scale, overcoming the limitation that local load deformation calculations cannot adapt to global GNSS station corrections. Time series analysis is performed on the global load deformation field, and the least squares method is used to fit the linear trend in the load deformation sequence. Simultaneously, annual, semi-annual, and ten-year periodic signals are separated to extract the load correction at each global GNSS station location. This accurately extracts the specific load correction at each corresponding point of each global GNSS station, effectively distinguishing between the long-term trend and periodic fluctuation characteristics of load deformation, and avoiding correction extraction errors caused by time series signal aliasing. This paper subtracts the load correction at each global GNSS station location from the global GNSS vertical velocity field, and simultaneously deducts the glacial isostatic adjustment effect, to obtain the GNSS vertical velocity field after deducting the load effect, thus completing the precise correction. The global GNSS vertical velocity field is obtained by preprocessing and linear trend estimation based on coordinate time series data from continuous global GNSS observation stations. This approach can maximally restore the true vertical movement signal of crustal structure, achieving high-precision correction of the GNSS vertical velocity field. This application systematically corrects the GNSS vertical velocity field by integrating multi-source surface mass change information and using the Green's function integral method to calculate load deformation, thereby obtaining more realistic and reliable vertical velocity information. This effectively reduces the impact of surface load changes on GNSS observation results, improves the accuracy of the global GNSS vertical velocity field, and provides reliable data support for crustal deformation monitoring, sea-level change research, and Earth system mass change analysis. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is an application environment diagram of a GNSS vertical velocity field precision correction method based on multi-source load modeling in one embodiment of this application; Figure 2 A flowchart illustrating a precise correction method for the GNSS vertical velocity field based on multi-source load modeling, provided as an embodiment of this application; Figure 3 A schematic diagram of the EWH trend of mass change from 2005 to 2025 provided for an embodiment of this application: (a) global atmospheric model and (b) global ocean model; Figure 4 A schematic diagram of the EWH trend of global soil water quality changes from 2005 to 2025 provided for an embodiment of this application; Figure 5 A schematic diagram of the EWH trend of global canopy water quality changes from 2005 to 2025, provided for an embodiment of this application; Figure 6 A schematic diagram illustrating the EWH trend of global snowmelt quality changes from 2005 to 2025, provided for an embodiment of this application; Figure 7 A schematic diagram of a model showing the global solid mass change from 2005 to 2025, provided for an embodiment of this application; Figure 8 A schematic diagram of the EWH trend of a global mass change model (including hydrological and solid mass changes) from 2005 to 2025 provided for an embodiment of this application; Figure 9 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0028] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0029] The proposed method for precise correction of the GNSS vertical velocity field based on multi-source load modeling has good scientific validity and practicality. This method comprehensively considers the influence of various surface mass variations on GNSS vertical deformation, uses the Green's function integral method to calculate load deformation, and systematically corrects the GNSS velocity field, thereby improving the accuracy of the GNSS vertical velocity field. Through comprehensive analysis of multi-source data and result verification, more reliable vertical velocity field information can be obtained. This method can be widely applied to crustal deformation monitoring and related Earth science research fields.

[0030] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0031] The precise correction method for the GNSS vertical velocity field based on multi-source load modeling provided in this application can be applied to, for example... Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on other servers. Terminal 102 can send acquired multi-source surface mass load data to server 104. The multi-source surface mass load data includes: atmospheric load data, ocean non-tidal load data, land hydrological load data, and surface solid load data. After receiving the multi-source surface mass load data, server 104 performs spatiotemporal registration, outlier removal, and unit unification processing on the multi-source surface mass load data to construct a multi-source load dataset. Based on the elastic earth model, using Farrell's load Green's function theory, the vertical displacement response of the Earth's surface under point load is calculated. The load Green's function is obtained by solving for the load Love number and summing the Legendre series. The elastic earth model is the PREM earth model or a custom-defined Earth layering model. Based on the multi-source... Using the load dataset and the aforementioned load Green's function, the spherical Green's function integral method is employed to calculate the vertical displacement of each grid point on the global grid caused by all mass loads, constructing a global load deformation field. Time series analysis is performed on the global load deformation field, and the least squares method is used to fit the linear trend in the load deformation sequence. Simultaneously, annual, semi-annual, and ten-year period signals are separated to extract the load correction at each global GNSS station location. The global GNSS vertical velocity field is then subtracted from the load correction at each global GNSS station location, while also subtracting the glacier isostatic adjustment effect, resulting in a GNSS vertical velocity field after deducting the load effect, thus completing the precise correction. The global GNSS vertical velocity field is obtained by preprocessing and linear trend estimation based on the coordinate time series data of continuous global GNSS observation stations. Server 104 can feed back the obtained GNSS vertical velocity field after deducting the load effect to terminal 102. Furthermore, in some embodiments, the GNSS vertical velocity field precision correction method based on multi-source load modeling can also be implemented separately by the server 104 or the terminal 102. For example, the terminal 102 can directly perform GNSS vertical velocity field precision correction based on multi-source load modeling on multi-source surface mass load data, or the server 104 can obtain multi-source surface mass load data from the data storage system and perform GNSS vertical velocity field precision correction based on multi-source load modeling on the multi-source surface mass load data.

[0032] The terminal 102 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. The server 104 can be implemented using a standalone server or a server cluster composed of multiple servers, or it can be a cloud server.

[0033] In one exemplary embodiment, such as Figure 2 As shown, a precise correction method for the GNSS vertical velocity field based on multi-source load modeling is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps S1 to S6. Wherein: S1: Acquire multi-source surface mass load data; the multi-source surface mass load data includes: atmospheric load data, marine non-tidal load data, terrestrial hydrological load data, and surface solid load data.

[0034] S2: Perform spatiotemporal registration, outlier removal, and unit unification processing on the multi-source surface quality load data to construct a multi-source load dataset.

[0035] S3: Based on the elastic Earth model, the vertical displacement response of the Earth's surface under point load is calculated using the Farrell load Green's function theory. The load Green's function is obtained by solving for the load Love number and summing the Legendre series. The elastic Earth model is either the PREM Earth model or a custom Earth layering model.

[0036] S4: Based on the multi-source load dataset and the load Green's function, the vertical displacement of each grid point on the global grid caused by all mass loads is calculated using the spherical Green's function integration method, and the global load deformation field is constructed.

[0037] S5: Perform time series analysis on the global load deformation field, use the least squares method to fit the linear trend in the load deformation sequence, and separate the annual, semi-annual and ten-year period signals to extract the load correction at the location of each GNSS station worldwide.

[0038] S6: Subtract the load correction at the location of each GNSS station from the global GNSS vertical velocity field, and simultaneously deduct the glacier isostatic adjustment effect to obtain the GNSS vertical velocity field after deducting the load effect, thus completing the precise correction; the global GNSS vertical velocity field is a velocity field obtained by preprocessing and linear trend estimation based on the coordinate time series data of continuous global GNSS observation stations.

[0039] Steps S3 and S4 are the calculation steps for load deformation; steps S5 and S6 are the steps for estimating the load deformation rate. By implementing steps S1 to S6, this method comprehensively utilizes multi-source surface mass load data from the atmosphere, ocean, and terrestrial hydrology, and calculates the vertical deformation caused by the surface load based on Green's function integral theory, systematically correcting the GNSS vertical velocity field to obtain more realistic and reliable GNSS vertical velocity information.

[0040] As an optional implementation, in step S1, the atmospheric load data includes surface pressure data provided by the European Centre for Medium-Range Weather Forecasts (ERA5) reanalysis data.

[0041] The ocean non-tidal load data includes: AVISO satellite altimetry data, UK Met Office temperature and salinity datasets, and Geruo13 glacier isostatic adjustment model correction data.

[0042] The terrestrial hydrological load data includes: GLDAS hydrological model data and CPC soil water data.

[0043] The surface solids load data includes global sedimentary thickness data from the CRUST 1.0 model.

[0044] As an optional implementation, in step S2, the spatiotemporal registration process includes: All multi-source surface quality load data are unified to the same temporal resolution, the same spatial resolution, and the same time span.

[0045] Missing data were filled using cubic spline interpolation.

[0046] All data were standardized to the equivalent water level unit.

[0047] like Figures 3-8 As shown, after spatiotemporal registration, the equivalent water height represents the trend of quality change in the global multi-source load model from 2005 to 2025.

[0048] As an optional implementation, in step S3, the calculation of the load Green's function specifically includes the following sub-steps: S31: Based on the aforementioned elastic Earth model, the Earth is discretized into a multi-layered structure.

[0049] S32: Starting from the Earth's center, calculate the vertical displacement component in the Love number of each order of load by integrating layer by layer, up to the order. .

[0050] S33: Based on the vertical displacement components, the load Green's function is calculated by summing the Legendre series.

[0051] The expression for the load Green's function is: ; in, for Green's function of the load at the location; To calculate the spherical angular distance between the calculation point and the load point; The radius of the Earth; Earth mass; This is the load Love number; For Legendre polynomials; n max It is the maximum order.

[0052] As an optional implementation, in step S4, the expression for the spherical Green's function integral is: ; in, Indicates coarse latitude; Indicates longitude; For calculation points; for Vertical displacement at the location; for Green's function of the load at the location; To calculate the spherical angular distance between the calculation point and the load point; The load point; for Load quality changes at the location; integration region It is the entire sphere.

[0053] As an optional implementation, in step S5, a time series analysis is performed on the global load deformation field obtained in step S4. The least squares method is used to fit the linear trend in the load deformation sequence, and the long-term trend term of load deformation is extracted. At the same time, the annual cycle, semi-annual cycle and ten-year cycle signals are separated, and the long-term trend value of load deformation at the location of each GNSS station in the world is extracted, i.e., the load correction.

[0054] As an optional implementation, in step S6, coordinate time series data of continuous global GNSS observation stations are first acquired, the coordinate time series data are preprocessed and linear trend estimated, the vertical velocity of each GNSS observation station is calculated, and a global GNSS vertical velocity field is constructed.

[0055] The formula for calculating the GNSS vertical velocity field after deducting the load effect is as follows: ; in, The vertical velocity of GNSS after deducting load effects; For the global GNSS vertical velocity field, This is due to the isostatic adjustment effect of glaciers; This is the load correction amount.

[0056] As an optional implementation, the GNSS vertical velocity field precision correction method based on multi-source load modeling further includes: performing multi-dimensional cross-validation of the GNSS vertical velocity field after deducting load effects by combining uncorrected data and GRACE inversion results.

[0057] Specifically, the results of the Green's function method are truncated to the 60th order and compared point by point with the filtered GRACE product. The convergence of typical points under different truncation orders is analyzed. Combined with the comparison of uncorrected data and the calculation of load contribution, multi-dimensional cross-validation is achieved.

[0058] In summary, compared with the prior art, this application has the following beneficial effects: 1. This application overcomes the uncertainty of a single data source by integrating four major categories of load data, and can more comprehensively reflect the true spatial distribution of surface mass load.

[0059] 2. This application overcomes the inherent defects of the traditional spherical harmonic coefficient method (GRACE products only provide gravity fields within the 60th order). The Green's function integral method is based on Farrell's classical load theory and considers the elastic layered structure of the earth. It can fully reflect the contribution of load sources of any order and avoid the errors caused by simplified approximations.

[0060] This application also provides an application scenario in which the above-mentioned GNSS vertical velocity field precision correction method based on multi-source load modeling is applied. Specifically, the GNSS vertical velocity field precision correction method based on multi-source load modeling provided in this embodiment can be applied to the scenario of global crustal vertical motion monitoring and geodetic precision data processing. This scenario includes a multi-source surface load data acquisition stage, a load deformation modeling and calculation link, a GNSS observation data preprocessing stage, and a crustal vertical motion velocity field refinement stage. Multi-source mass load data from global atmosphere, ocean, terrestrial hydrology, and solid surface are entered into the load deformation modeling and calculation link. After spatiotemporal registration, outlier removal, and unit unification processing, the global load deformation field is calculated and periodic signals are separated based on the elastic earth model and Farrell load Green's function theory, forming a high-precision load correction. The GNSS vertical velocity field precision correction method based on multi-source load modeling provided in this embodiment belongs to the load effect correction stage in the GNSS vertical velocity field refinement processing link. Specifically, in the process of solving the coordinate time series of global continuous GNSS observation stations, the influence of multi-source surface mass load deformation and glacier isostatic adjustment effect can be successively deducted from the original GNSS velocity field to finally obtain a high-precision crustal vertical velocity field after eliminating non-tectonic movement interference. This provides a reliable observation basis for scientific research and engineering applications such as plate movement monitoring, seismic deformation inversion, sea level change analysis, and land subsidence assessment.

[0061] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 9 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs in the non-volatile storage media to run. The database stores multi-source surface mass load data. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a precise correction method for the GNSS vertical velocity field based on multi-source load modeling.

[0062] Those skilled in the art will understand that Figure 9The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0063] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0064] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0065] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Moreover, the collection, use and processing of the relevant data are carried out in compliance with the relevant data protection laws and policies of the country where the location is located, and with the authorization granted by the owner of the corresponding device.

[0066] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0067] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0068] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0069] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A precise correction method for the GNSS vertical velocity field based on multi-source load modeling, characterized in that, The precise correction method for the GNSS vertical velocity field based on multi-source load modeling includes: Acquire multi-source surface mass load data; the multi-source surface mass load data includes: atmospheric load data, marine non-tidal load data, terrestrial hydrological load data, and surface solid load data; The multi-source surface quality load data are subjected to spatiotemporal registration, outlier removal, and unit unification processing to construct a multi-source load dataset. Based on the elastic Earth model, the vertical displacement response of the Earth's surface under point load is calculated using the Farrell load Green's function theory. The load Green's function is obtained by solving for the load Love number and summing the Legendre series. The elastic Earth model is either the PREM Earth model or a custom Earth layering model. Based on the multi-source load dataset and the load Green's function, the spherical Green's function integration method is used to calculate the vertical displacement of each grid point on the global grid caused by all mass loads, and to construct the global load deformation field. Time series analysis was performed on the global load deformation field. The least squares method was used to fit the linear trend in the load deformation sequence. At the same time, the annual, semi-annual and ten-year period signals were separated, and the load correction at the location of each GNSS station worldwide was extracted. The global GNSS vertical velocity field is subtracted from the load correction at the location of each GNSS station worldwide, and the glacier isostatic adjustment effect is also deducted to obtain the GNSS vertical velocity field after deducting the load effect, thus completing the precise correction. The global GNSS vertical velocity field is a velocity field obtained by preprocessing and linear trend estimation based on the coordinate time series data of continuous global GNSS observation stations.

2. The precise correction method for GNSS vertical velocity field based on multi-source load modeling according to claim 1, characterized in that, The atmospheric load data includes surface pressure data provided by the European Centre for Medium-Range Weather Forecasts (ERA5) reanalysis data. The ocean non-tidal load data includes: AVISO satellite altimetry data, UK Met Office temperature and salinity datasets, and Geruo13 glacier isostatic adjustment model correction data; The terrestrial hydrological load data includes: GLDAS hydrological model data and CPC soil water data; The surface solids load data includes global sedimentary thickness data from the CRUST 1.0 model.

3. The precise correction method for GNSS vertical velocity field based on multi-source load modeling according to claim 1, characterized in that, The spatiotemporal registration process includes: All multi-source surface quality load data are unified to the same temporal resolution, the same spatial resolution, and the same time span; Missing data were filled using cubic spline interpolation; All data were standardized to the equivalent water level unit.

4. The precise correction method for GNSS vertical velocity field based on multi-source load modeling according to claim 1, characterized in that, The calculation of the load Green's function specifically includes: Based on the aforementioned elastic Earth model, the Earth is discretized into a multi-layered structure; Starting from the Earth's center, the vertical displacement components in the Love number of each order of load are calculated by integrating layer by layer; Based on the vertical displacement components, the load Green's function is calculated by summing the Legendre series.

5. The precise correction method for GNSS vertical velocity field based on multi-source load modeling according to claim 1, characterized in that, The expression for the load Green's function is: ； in, for Green's function of the load at the location; To calculate the spherical angular distance between the calculation point and the load point; The radius of the Earth; Earth mass; This is the load Love number; For Legendre polynomials; n max It is the maximum order.

6. The precise correction method for GNSS vertical velocity field based on multi-source load modeling according to claim 1, characterized in that, The expression for the integral of the spherical Green's function is: ； in, Indicates coarse latitude; Indicates longitude; For calculation points; for Vertical displacement at the location; for Green's function of the load at the location; To calculate the spherical angular distance between the calculation point and the load point; The load point; for Load quality changes at the location; integration region It is the entire sphere.

7. The precise correction method for GNSS vertical velocity field based on multi-source load modeling according to claim 1, characterized in that, The formula for calculating the GNSS vertical velocity field after deducting the load effect is as follows: ； in, The vertical velocity of GNSS after deducting load effects; For the global GNSS vertical velocity field, This is due to the isostatic adjustment effect of glaciers; This is the load correction amount.

8. The precise correction method for GNSS vertical velocity field based on multi-source load modeling according to claim 1, characterized in that, The precise correction method for the GNSS vertical velocity field based on multi-source load modeling also includes: performing multi-dimensional cross-validation of the GNSS vertical velocity field after deducting load effects by combining uncorrected data and GRACE inversion results.

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