CN113761808A - Earth surface tide displacement acquisition method based on GPS and empirical tide model, and application method and system thereof - Google Patents

Earth surface tide displacement acquisition method based on GPS and empirical tide model, and application method and system thereof Download PDF

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CN113761808A
CN113761808A CN202111311438.8A CN202111311438A CN113761808A CN 113761808 A CN113761808 A CN 113761808A CN 202111311438 A CN202111311438 A CN 202111311438A CN 113761808 A CN113761808 A CN 113761808A
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tide
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CN113761808B (en
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彭葳
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Changsha University of Science and Technology
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Abstract

The invention discloses a surface tide displacement acquisition method based on a GPS (global positioning system) and an empirical tide model, and an application method and a system thereof, wherein the method comprises the following steps: based on a tide harmony analysis method, calculating the harmony parameters of the tide partials by using an empirical tide model, calculating the harmony parameters of the tide partials by using observation data based on a GPS reference website and calculating the harmony parameters of the tide partials by using a PPP (Point-to-Point protocol) measurement technology; constructing a sample data set by using the calculated harmonic parameters, and introducing machine learning to perform network training to obtain a prediction model corresponding to the target tide; finally, the tidal displacement correction model is utilized to obtain the surface tidal displacement, and the tide dividing displacement of the target tide dividing in the surface tidal displacement correction model is calculated by utilizing a prediction model or utilizing a PPP (Point-to-Point protocol) measurement technology; other tidal separation displacements are calculated by using an empirical tidal model. In conclusion, a high-precision and high-efficiency earth surface tide displacement correction model is established, and the method is suitable for high-precision and large-range InSAR deformation measurement.

Description

Earth surface tide displacement acquisition method based on GPS and empirical tide model, and application method and system thereof
Technical Field
The invention belongs to the technical field of surface deformation monitoring, and particularly relates to a surface tide displacement acquisition method based on a GPS (global positioning system) and an empirical tide model, and an application method and a system thereof.
Background
The synthetic aperture differential interferometry technology is widely applied to ground deformation measurement, and with the increase of the spatial scale, the solid tide and ocean tide load displacement of InSAR differential interferometry is increased. The solid tide and ocean tide load effect is the elastic response of the solid earth to the change of the solar and monthly gravitation and the redistribution of the ocean mass, and is a nonlinear space large-scale signal. Previous studies have shown that in the EROcean tidal loads in the DInSAR interferograms of the S1/2, ENVISAT ASAR data, while the effects of solid tide effects are generally neglected at a width scale of the order of 100 km. The Sentinel-1A/B task provides data with larger spatial scale for determining ground deformation, the splicing length of a plurality of SAR images on SAR satellite orbits on the west coast of the United states can reach thousands of kilometers, and the maximum displacement value of solid tide and ocean tide load effect measured by a DInSAR interferogram exceeds 78 mm. However, in the spatially large-scale DInSAR interferogram, the amount of pixels after multi-view still reaches 107To 108The single-point calculation of the traditional solid tide and ocean tide load model cannot meet the fast calculation requirement of the pixel level under the space scale, so that the establishment of a correction model capable of efficiently and accurately determining the solid tide and ocean tide load displacement is necessary for the long-strip DInSAR interferometry.
The existing algorithm for load displacement of solid tide and ocean tide mainly comprises the following steps:
1. a tidal displacement estimation method according to an empirical tidal model corresponding to the solid tide and ocean tide loads in the IERS2010 protocol;
2. the displacement of ocean tidal loads is determined using PPP measurement techniques.
The solid tide model in the empirical tide model has higher precision, and the error value is usually less than 1% of the displacement of the solid tide, but the inaccuracy of the sea tide model in the offshore area may cause the error of the displacement of the sea tide load. The sea tide model is a grid model with proper resolution, grid points represent harmonic parameters of sea tide partial tide, and the sea tide load model can predict the earth surface deformation displacement caused by the sea tide load effect by substituting the tide harmonic parameters of the grid points into the sea tide load model. Many global tidal models with spatial resolution of 0.125 ° x 0.125 ° or 0.5 ° x 0.5 ° do not accurately reflect changes in tidal height near the coast.
Researchers determine ocean tide load displacement by using a static/dynamic PPP measuring technology, and the dynamic PPP measuring technology can accurately measure the harmonic parameters of main tide tides when a GPS observation time sequence is more than 1000 days, wherein the displacement precision of M2, S2, O1, N2 and Q1 tides is on the level of 0.2 mm. However, the number of GPS stations is limited, and therefore certain procedures limit the space in which they can be used, especially when large scale surface deformation monitoring in coastal spaces is envisaged, the number of GPS stations limits the monitoring range and the efficiency of calculation of tidal load displacement.
In conclusion, the PPP measurement technology based on the GPS and the tidal displacement estimation method based on the empirical tidal model have respective advantages and disadvantages, and how to fully utilize the respective advantages and evade shortages is the research point of the invention, so that a high-precision and high-efficiency surface tidal displacement correction model is established, and a foundation is laid for quickly and accurately correcting the surface tidal displacement of each pixel position in the long strip DInSAR interferogram.
Disclosure of Invention
The invention aims to provide a method for acquiring surface tide displacement based on a GPS (global positioning system) and an empirical tide model, an application method and a system, wherein the method constructs a tide displacement correction model, integrates tide displacement data based on the GPS and the empirical tide model, fully utilizes the high-precision characteristics of a PPP (point-to-point protocol) measurement technology on partial tide displacement measurement, effectively overcomes the problem of quantity constraint of GPS observation stations by constructing a prediction model, improves the acquisition precision of the surface tide displacement, improves the measurement efficiency by introducing machine learning to construct the prediction model, constructs a high-precision and high-efficiency tide displacement correction model, is suitable for high-precision and large-range InSAR deformation measurement application, and lays a foundation for quickly and accurately correcting the surface tide displacement of each pixel position in a long strip DInSAR interferogram.
In one aspect, the invention provides a surface tide displacement acquisition method based on a GPS and an empirical tide model, which comprises the following steps:
step S1: combining the global tidal tide model and the regional tidal tide model to form an empirical tide model, and calculating a harmonic parameter of the tide division by using the empirical tide model; calculating the reconciliation parameter of the tide based on the observation data of the GPS reference website and by utilizing the PPP measurement technology; wherein, the harmonic parameters are amplitude and phase;
step S2: constructing a sample data set by utilizing the harmonic parameters calculated by the empirical tide model and the harmonic parameters calculated by utilizing a PPP (Point-to-Point protocol) measurement technology, and introducing machine learning to carry out network training to obtain a prediction model corresponding to the target tide division;
taking the harmonic parameters calculated by the empirical tide models corresponding to the same position and the same partial tide and the harmonic parameters calculated by the PPP measurement technology as a group of sample data corresponding to a class of partial tides; and the harmonic parameters calculated by the empirical tide model are used for constructing network input parameters or are used as the network input parameters; constructing a network output parameter by using the harmonic parameter calculated by the PPP measurement technology or using the network output parameter as the network output parameter;
the target tide division is as follows: partial or total moisture of M2, S2, N2, O1 and Q1;
step S3: constructing a tidal displacement correction model, and obtaining the surface tidal displacement by using the tidal displacement correction model;
the tide division displacement of the target tide division in the surface tide displacement correction model is calculated by using the prediction model or using a PPP (Point-to-Point protocol) measurement technology; other tide division displacements are calculated by adopting the empirical tide model; and then, the tide dividing displacements at the same position are added to obtain the earth surface tide displacement at the corresponding position.
In the method, the problem that the displacement precision of a PPP measuring technology in measuring the tide of M2, S2, O1, N2 and Q1 reaches the level of 0.2 mm, but the number of GPS observation stations is limited is considered, and on the basis of the observation data of the existing GPS observation stations, machine learning fitting is introduced to search the relation between the measuring results of two measuring methods of an empirical tide model and the PPP measuring technology, so that a prediction model is obtained; the prediction model can obtain a prediction result close to the precision of a PPP measurement technology measurement result according to the measurement result of the empirical tide model, thereby overcoming the problem of quantity constraint of GPS observation stations and fully utilizing the high-precision measurement characteristics of the PPP measurement technology on partial tide.
In addition, the invention selects the global sea tide model and the regional sea tide model to form the empirical tide model, can effectively overcome the problem that the global sea tide model cannot accurately reflect the change of the tide height near the coast, and improves the measurement precision.
Optionally, the target tide includes: m2, N2, O1 and Q1 fen; in the surface tide displacement correction model, the empirical tide model is adopted to calculate K2, K1 and P1 tide displacement, and M2, N2, O1 and Q1 tide displacement are calculated by a prediction model or a PPP measurement technology.
Optionally, the process of calculating the tide splitting displacement of the target tide splitting at any position H by using the prediction model in step S3 is as follows:
acquiring a blending parameter of the target tide division under the position H based on the empirical tide model;
and obtaining a network output parameter based on the harmonic parameter and by using the prediction model so as to calculate the tide splitting displacement of the target tide splitting.
Optionally, the network input parameters in step S2 are: the tide split phasor is constructed by the harmonic parameters calculated by the empirical tide model; the network output parameters are: the expression of the partial tide phase quantity constructed by the harmonic parameters calculated by the PPP measurement technology is as follows:
Figure 668483DEST_PATH_IMAGE001
in the formula (I), the compound is shown in the specification,
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is composed of
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Moisture separating at position
Figure 282239DEST_PATH_IMAGE004
The amount of the moisture-separating phase of the water,
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is composed of
Figure 812895DEST_PATH_IMAGE003
Moisture separating at position
Figure 551043DEST_PATH_IMAGE004
The amplitude of the vibration of the vehicle,
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is composed of
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Moisture separating at positionkThe phase of (c).
Optionally, the prediction model in step S2 employs a least squares support vector machine.
In a second aspect, the present invention provides an application method based on the above-mentioned surface tidal displacement acquisition method, which is applied to correct a differential interferogram, and which includes: obtaining surface tide displacement by using the tide displacement correction model, wherein the surface tide displacement is sea tide load displacement correction data;
and correcting the sea tide load displacement of the corresponding pixel position in the corresponding differential interference pattern by utilizing the earth surface tide displacement.
In a third aspect, the present invention provides a system based on the above method for acquiring surface tidal displacement, which includes:
the empirical tide model calculation module is used for calculating the harmonic parameters of the tide division by utilizing the empirical tide model;
the PPP measurement technology calculation module is used for calculating the reconciliation parameters of the tide based on the observation data of the GPS reference website by utilizing the PPP measurement technology;
the prediction model building module is used for building a sample data set by utilizing the harmonic parameters calculated by the empirical tide model and the harmonic parameters calculated by the PPP measurement technology, and introducing machine learning to carry out network training to obtain a prediction model corresponding to the target tide division;
and the tidal displacement correction value calculation module is used for obtaining the earth surface tidal displacement by utilizing the tidal displacement correction model.
Optionally, the system further comprises: a differential interferogram correction module;
and the differential interference pattern correction module corrects the sea tide load displacement corresponding to the pixel position in the differential interference pattern by using the earth surface tide displacement.
In a third aspect, the present invention provides a system comprising:
one or more processors;
a memory storing one or more computer programs;
the processor invokes the computer program to implement: in the method for acquiring the surface tide displacement, the surface tide displacement is acquired by using a tide displacement correction model;
or the processor calls the computer program to implement: in the differential interference pattern correction method, the tidal displacement correction model is utilized to obtain the earth surface tidal displacement, and then the earth surface tidal displacement is utilized to correct the sea tide load displacement of the corresponding pixel position in the corresponding differential interference pattern.
In a fourth aspect, the present invention provides a readable storage medium storing a computer program;
the computer program is invoked by a processor to implement: in the method for acquiring the surface tide displacement, the surface tide displacement is acquired by using a tide displacement correction model;
or the computer program is invoked by a processor to implement: in the differential interference pattern correction method, the tidal displacement correction model is utilized to obtain the earth surface tidal displacement, and then the earth surface tidal displacement is utilized to correct the sea tide load displacement of the corresponding pixel position in the corresponding differential interference pattern.
Advantageous effects
1. According to the method for acquiring the earth surface tidal displacement based on the GPS and the empirical tidal model, the constructed tidal displacement correction model integrates the advantages of the empirical tidal model and the PPP measurement technology, and the measurement precision of the earth surface tidal displacement is improved. In consideration of the problems that the PPP measurement technology is high in displacement accuracy in measuring partial tides, but the number of the GPS observation stations is limited, the machine learning fitting is introduced to search the relation between the measurement results of two measurement methods of the empirical tide model and the PPP measurement technology on the basis of the observation data of the existing GPS observation station, so that a prediction model is obtained, the prediction model can obtain the prediction result close to the measurement result accuracy of the PPP measurement technology according to the measurement result of the empirical tide model, and therefore the problem of quantity constraint of the GPS observation stations is solved, and the high-accuracy measurement effect of the PPP measurement technology on partial tides is fully utilized. Moreover, the empirical tide model selected by the invention combines the global tide model and the regional tide model, so that the problem that the global tide model cannot accurately reflect the change of the tide height near the coast can be effectively solved, and the measurement precision is improved.
2. The method for acquiring the surface tide displacement can be applied to correcting sea tide load displacement of a corresponding pixel position in a differential interference diagram, and particularly, the tide displacement correction model constructed by the method is not limited by the position of a GPS observation station any more, so that the method can be suitable for long-strip DInSAR interferometry.
Drawings
FIG. 1 is a schematic diagram of a technical idea of a method for acquiring surface tidal displacement based on a GPS and an empirical tidal model according to an embodiment of the present invention;
a1 and b1 in fig. 2-1 are schematic diagrams of Q1 partial tide phasor estimation values of dynamic PPP method estimation and FES2014b + osu. usawest estimation, and c1 is a schematic diagram of vector differences of Q1 partial tide phasors of dynamic PPP method estimation and FES2014b + osu. usawest estimation, respectively;
FIG. 2-2 is a phasor comparison diagram corresponding to the Q1 tide for the dynamic PPP method estimation, FES2014b + osu. usawest estimation;
a2 and b2 in fig. 3-1 are schematic diagrams of estimated values of O1 partial tide phasors for dynamic PPP method estimation and FES2014b + osu. usawest estimation, and c2 is a schematic diagram of vector differences of O1 partial tide phasors for dynamic PPP method estimation and FES2014b + osu. usawest estimation, respectively;
FIG. 3-2 is a phasor comparison diagram corresponding to the O1 tide for the dynamic PPP method estimation, FES2014b + osu. usawest estimation;
a3 and b3 in fig. 4-1 are schematic diagrams of P1 partial tide phasor estimation values of dynamic PPP method estimation and FES2014b + osu. usawest estimation, and c3 is a schematic diagram of vector differences of P1 partial tide phasor of dynamic PPP method estimation and FES2014b + osu. usawest estimation, respectively;
FIG. 4-2 is a phasor comparison diagram corresponding to the P1 tide for the dynamic PPP method estimation, FES2014b + osu. usawest estimation;
a4 and b4 in fig. 5-1 are schematic diagrams of estimated values of the split tide phasor of K1 for dynamic PPP method estimation and FES2014b + osu. usawest estimation, and c4 is a schematic diagram of the vector difference of the split tide phasor of K1 for dynamic PPP method estimation and FES2014b + osu. usawest estimation, respectively;
FIG. 5-2 is a phasor comparison diagram corresponding to the K1 tide for the dynamic PPP method estimation, FES2014b + osu. usawest estimation;
a5 and b5 in fig. 6-1 are schematic diagrams of N2 partial tide phasor estimation values of dynamic PPP method estimation and FES2014b + osu. usawest estimation, and c5 is a schematic diagram of vector differences of N2 partial tide phasors of dynamic PPP method estimation and FES2014b + osu. usawest estimation, respectively;
FIG. 6-2 is a phasor comparison diagram corresponding to the N2 tide for the dynamic PPP method estimation, FES2014b + osu. usawest estimation;
a6 and b6 in fig. 7-1 are schematic diagrams of the estimated value of M2 partial tide phasor for dynamic PPP method estimation and FES2014b + osu. usawest estimation, and c6 is a schematic diagram of the vector difference of M2 partial tide phasor for dynamic PPP method estimation and FES2014b + osu. usawest estimation, respectively;
FIG. 7-2 is a phasor comparison diagram corresponding to M2 tide for dynamic PPP method estimation, FES2014b + osu. usawest estimation;
a7 and b7 in fig. 8-1 are schematic diagrams of the estimated value of the split tide phasor of S2 for dynamic PPP method estimation and FES2014b + osu. usawest estimation, and c7 is a schematic diagram of the vector difference of the split tide phasor of S2 for dynamic PPP method estimation and FES2014b + osu. usawest estimation, respectively;
FIG. 8-2 is a phasor comparison diagram corresponding to the S2 tide for the dynamic PPP method estimation, FES2014b + osu. usawest estimation;
a8 and b8 in fig. 9-1 are schematic diagrams of estimated values of the split tide phasor of K2 for estimation of the dynamic PPP method and FES2014b + osu. usawest, and c8 is a schematic diagram of vector differences of the split tide phasor of K2 for estimation of the dynamic PPP method and FES2014b + osu. usawest, respectively;
FIG. 9-2 is a phasor comparison diagram corresponding to the K2 tide for the dynamic PPP method estimation, FES2014b + osu. usawest estimation; as can be seen from fig. 2-1 to 9-2, the magnitudes of M2, N2, O1 and Q1 tides are large, and the spatial estimation accuracy is high;
FIGS. 10-1 to 10-3 are schematic diagrams of stdDev values of the difference between the model of the present invention and the FES2014b + osu. usawest model in three dimensions U, N, E, respectively, wherein coastal regions where the difference between the two methods is large can be determined based on FIGS. 10-1 to 10-3;
the a, b plots in FIG. 11 are plots of the LOS direction sea tide load displacement StdDev values along the rising rail (193) and falling rail (13), respectively, based on the model of the present invention.
Detailed Description
The present invention will be further described with reference to the following examples.
The invention provides a surface tide displacement acquisition method based on a GPS and an empirical tide model, which is used for measuring the ocean tide load displacement correction value (surface tide displacement). The invention also provides an application method thereof, which comprises the following steps: the measured ocean tidal load displacement correction is applied to correct the corresponding differential interferogram. The method for acquiring the surface tide displacement provided by the invention integrates the PPP technology based on the GPS and the empirical tide model, and the method is specifically set out below.
Example 1:
surface tidal Displacement in this example
Figure 486135DEST_PATH_IMAGE007
Expressed as:
Figure 16474DEST_PATH_IMAGE008
wherein the content of the first and second substances,
Figure 769666DEST_PATH_IMAGE009
representing Q1, O1, N2 and M2 tide-dividing displacements with higher estimation precision of the dynamic PPP method;
Figure 447772DEST_PATH_IMAGE010
displacement of K2, K1, P1 partial tide representing sea tide load model estimation.
In this embodiment, the sum of the tide division displacements of seven main tides, Q1, O1, N2, M2, K2, K1 and P1 is used as the ground surface tide displacement, and in other possible embodiments, other tide division displacements of the tide division may be added, which is not specifically limited in this invention. In the present invention, the Q1, O1, N2, and M2 tide-dividing displacements are obtained by a dynamic PPP method, that is, Q1, O1, N2, and M2 tide-dividing displacements are set as target tide-dividing displacements in the present embodiment, and in other feasible embodiments, the target tide-dividing displacements may be adjusted, which is not specifically limited by the present invention.
Dynamic PPP technique: the method can be used for determining the ocean tide load displacement, and the existing research proves that the dynamic PPP technology can accurately measure the harmonic parameters of the main ocean tide when the GPS observation time sequence is more than 1000 days, wherein the displacement precision of M2, S2, O1, N2 and Q1 tide is on the level of 0.2 mm.
The embodiment utilizes the observation data of a GPS observation station in a research area, and introduces a dynamic PPP technology to determine the amplitude and the phase (phase delay parameter) of the tide Q1, O1, N2 and M2.
Empirical tidal models: the load green function model based on the combination of the global tide model and the regional tide model can be understood as follows: from a global tide model grid
Figure 911989DEST_PATH_IMAGE011
Zhonghui regional sea tide model grid
Figure 359151DEST_PATH_IMAGE012
And then calculating the subtracted area by a load green function based on the regional tide model
Figure 283245DEST_PATH_IMAGE012
For calculation points
Figure 386330DEST_PATH_IMAGE013
The displacement of the tide and the tide is calculated by the load Green function based on the global tide model in other areas. Wherein the calculated point can be obtained by inputting the position of the calculated point to the empirical tide model
Figure 421282DEST_PATH_IMAGE013
And (4) regulating parameters of the sea tide and the tide.
It should be understood that the process of calculating amplitude, phase and tidal volume displacement, whether using PPP measurement techniques or empirical tidal models, is achievable in the prior art and therefore the specific calculation process is not set forth in detail.
Based on the above theoretical statement, the present embodiment provides a surface tide displacement acquisition method based on GPS and empirical tide models, which includes the following steps:
step S1: combining the global tidal tide model and the regional tidal tide model to form an empirical tide model, and calculating a harmonic parameter of the tide division by using the empirical tide model; calculating the reconciliation parameter of the tide based on the observation data of the GPS reference website and by utilizing the PPP measurement technology; wherein, the harmonic parameters are amplitude and phase.
In this case, for a certain research area and a set research time range, the harmonic parameters (amplitude and phase) of M2, N2, O1, and Q1 tides are calculated by using the observation data of the GPS reference site in the research area and the dynamic PPP measurement technique. It should be understood that the harmonic parameters have temporal and spatial characteristics, so as to obtain harmonic parameter data based on time series; due to the space characteristic, the phasor can be constructed according to the following form:
Figure 722951DEST_PATH_IMAGE014
Figure 552366DEST_PATH_IMAGE015
wherein the content of the first and second substances,
Figure 408327DEST_PATH_IMAGE016
is composed of
Figure 778128DEST_PATH_IMAGE017
Moisture separating at position
Figure 137566DEST_PATH_IMAGE018
The amount of the moisture-separating phase of the water,
Figure 200199DEST_PATH_IMAGE019
is composed of
Figure 277877DEST_PATH_IMAGE017
Moisture separating at position
Figure 153167DEST_PATH_IMAGE018
The amplitude of the vibration of the vehicle,
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is composed of
Figure 866225DEST_PATH_IMAGE017
Moisture separating at position
Figure 634461DEST_PATH_IMAGE018
The phase of (d); in the data of the GPS, among others,
Figure 346065DEST_PATH_IMAGE017
is a certain
Figure 680094DEST_PATH_IMAGE021
Latitude and longitude of the GPS site location.
Similarly, the amplitude and phase of each partial tide corresponding to the time and position are calculated by using the empirical tide model, and the phasor is also constructed according to the form.
Step S2: and constructing a sample data set by utilizing the harmonic parameters calculated by the empirical tide model and the harmonic parameters calculated by utilizing a PPP (Point-to-Point protocol) measurement technology, and introducing machine learning to carry out network training to obtain a prediction model corresponding to the target tide.
The prediction model is used for searching the relation between the harmonic parameters calculated by the empirical tide model and the harmonic parameters calculated by the PPP measurement technology, and further realizing the prediction of the harmonic parameters of M2, N2, O1 and Q1 tide at other non-GPS observation point positions. Thus, data from empirical tidal models are used as independent variables and data from PPP measurement techniques are used as dependent variables.
In this embodiment, the harmonic parameters of M2, N2, O1, and Q1 tides obtained by using the PPP measurement technique and the harmonic parameters of M2, N2, O1, and Q1 tides obtained by using the empirical tide model are used to construct a sample data set, and the harmonic parameters calculated by the empirical tide model and the harmonic parameters calculated by the PPP measurement technique at the same position and corresponding to the same tide are used as a set of sample data corresponding to a class of tide. And further taking phasors constructed based on the harmonic parameters as input and output parameters of the network, and finally obtaining a prediction model through model training.
As in this embodiment, a least squares support vector machine based on a polynomial kernel function is selected, and the model in the training phase is represented as:
Figure 84531DEST_PATH_IMAGE022
Figure 871221DEST_PATH_IMAGE023
in the formula (I), the compound is shown in the specification,
Figure 386516DEST_PATH_IMAGE024
tide constructed for reconciliation parameters calculated using PPP measurement techniques
Figure 575052DEST_PATH_IMAGE018
In that
Figure 150390DEST_PATH_IMAGE021
The phasor corresponding to the position of the GPS observation station,
Figure 424376DEST_PATH_IMAGE025
is a least squares support vector machine based on polynomial kernel functions (Suykens, 1999),
Figure 743362DEST_PATH_IMAGE026
is the deviation of the constant value of the time,
Figure 284940DEST_PATH_IMAGE027
tidal singling constructed for harmonic parameters calculated from empirical tidal models
Figure 31179DEST_PATH_IMAGE018
In that
Figure 526882DEST_PATH_IMAGE021
And the GPS observes phasor corresponding to the station position.
In the embodiment, in the spatial dimension, as the GPS reference station is independently observed, the phasor space modeling is carried out according to the spatial variation characteristics of the tide, the spatial random error can be eliminated, and the tide displacement value with high precision and high resolution is predicted; in other possible embodiments, phasors may not be selected as network input and output parameters; such as directly using amplitude and displacement as input and output parameters.
Step S3: and constructing a tidal displacement correction model, and obtaining the earth surface tidal displacement by using the tidal displacement correction model.
The prediction model can be used for predicting the harmonic parameters/phasors of M2, N2, O1 and Q1 tide at the position of the non-GPS observation station, and the prediction result is close to the measurement precision of the PPP measurement technology.
Therefore, in the embodiment, for the M2, N2, O1 and Q1 tide division displacements in the research area, after the harmonic parameters and the tide division phasors of M2, N2, O1 and Q1 tide division are calculated by using the empirical tide model, the harmonic parameters and the tide division phasors are input into the prediction model to obtain the output tide division phasors, so that the tide division displacements of M2, N2, O1 and Q1 are calculated; directly obtaining the tide displacement aiming at K2, K1 and P1 by using an empirical tide model; and finally, adding according to the formula (1) to obtain the earth surface tide displacement, like a tide harmonic formula. It should be understood that the M2, N2, O1, Q1 tide shifts of the GPS rover position within the research area may also be calculated using PPP measurement techniques, which are not specifically limited by the present invention.
Example 2:
the embodiment provides an application method based on the above earth surface tidal displacement acquisition method, which is applied to a corrected differential interferogram, and comprises the following steps: obtaining surface tide displacement by using the tide displacement correction model, wherein the surface tide displacement is sea tide load displacement correction data; and correcting the sea tide load displacement of the corresponding pixel position in the corresponding differential interference pattern by utilizing the earth surface tide displacement.
Example 3:
the embodiment provides a system based on the above method for acquiring surface tidal displacement, which includes:
the empirical tide model calculation module is used for calculating the harmonic parameters of the tide division by utilizing the empirical tide model;
the PPP measurement technology calculation module is used for calculating the reconciliation parameters of the tide based on the observation data of the GPS reference website by utilizing the PPP measurement technology;
the prediction model building module is used for building a sample data set by utilizing the harmonic parameters calculated by the empirical tide model and the harmonic parameters calculated by the PPP measurement technology, and introducing machine learning to carry out network training to obtain a prediction model corresponding to the target tide division;
and the tidal displacement correction value calculation module is used for obtaining the earth surface tidal displacement by utilizing the tidal displacement correction model.
In some implementations, it further includes a differential interferogram correction module; and the differential interference pattern correction module corrects the sea tide load displacement corresponding to the pixel position in the differential interference pattern by using the earth surface tide displacement.
It should be understood that, the specific implementation process of the above unit module refers to the method content, and the present invention is not described herein in detail, and the division of the above functional module unit is only a division of a logic function, and there may be another division manner in the actual implementation, for example, multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or may not be executed. Meanwhile, the integrated unit can be realized in a hardware form, and can also be realized in a software functional unit form.
Example 4:
the present embodiment provides a system, which includes: one or more processors; a memory storing one or more computer programs.
Wherein the processor invokes the computer program to implement: the method for acquiring the surface tide displacement obtains the surface tide displacement by using the tide displacement correction model.
In some implementations, the constructed tidal displacement correction model is imported into memory; in other implementations, the tidal displacement correction model is built in a system, and the processor invokes the computer program to implement:
combining the global tidal tide model and the regional tidal tide model to form an empirical tide model, and calculating a harmonic parameter of the tide division by using the empirical tide model; calculating the reconciliation parameter of the tide based on the observation data of the GPS reference website and by utilizing the PPP measurement technology; wherein, the harmonic parameters are amplitude and phase;
constructing a sample data set by utilizing the harmonic parameters calculated by the empirical tide model and the harmonic parameters calculated by utilizing a PPP (Point-to-Point protocol) measurement technology, and introducing machine learning to carry out network training to obtain a prediction model corresponding to the target tide division;
and constructing a tidal displacement correction model based on the prediction model and the empirical tidal model.
The processor invokes the computer program to implement:
in the differential interference pattern correction method, the tidal displacement correction model is utilized to obtain the earth surface tidal displacement, and then the earth surface tidal displacement is utilized to correct the sea tide load displacement of the corresponding pixel position in the corresponding differential interference pattern.
The specific implementation process of each step refers to the explanation of the foregoing method.
It should be understood that in the embodiments of the present invention, the Processor may be a Central Processing Unit (CPU), and the Processor may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The memory may include both read-only memory and random access memory, and provides instructions and data to the processor. The portion of memory may also include non-volatile random access memory. For example, the memory may also store device type information.
Example 5:
the present embodiment provides a readable storage medium storing a computer program. Wherein the computer program is invoked by a processor to implement: the method for acquiring the surface tide displacement obtains the surface tide displacement by using the tide displacement correction model.
In some implementations, the constructed tidal displacement correction model is imported into a readable storage medium; in other implementations, the tidal displacement correction model is built in a system, and the processor invokes the computer program to implement:
combining the global tidal tide model and the regional tidal tide model to form an empirical tide model, and calculating a harmonic parameter of the tide division by using the empirical tide model; calculating the reconciliation parameter of the tide based on the observation data of the GPS reference website and by utilizing the PPP measurement technology; wherein, the harmonic parameters are amplitude and phase;
constructing a sample data set by utilizing the harmonic parameters calculated by the empirical tide model and the harmonic parameters calculated by utilizing a PPP (Point-to-Point protocol) measurement technology, and introducing machine learning to carry out network training to obtain a prediction model corresponding to the target tide division;
and constructing a tidal displacement correction model based on the prediction model and the empirical tidal model.
The computer program is invoked by a processor to implement:
in the differential interference pattern correction method, the tidal displacement correction model is utilized to obtain the earth surface tidal displacement, and then the earth surface tidal displacement is utilized to correct the sea tide load displacement of the corresponding pixel position in the corresponding differential interference pattern.
The specific implementation process of each step refers to the explanation of the foregoing method.
The readable storage medium is a computer readable storage medium, which may be an internal storage unit of the controller according to any of the foregoing embodiments, for example, a hard disk or a memory of the controller. The readable storage medium may also be an external storage device of the controller, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the controller. Further, the readable storage medium may also include both an internal storage unit of the controller and an external storage device. The readable storage medium is used for storing the computer program and other programs and data required by the controller. The readable storage medium may also be used to temporarily store data that has been output or is to be output.
Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned readable storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Application example:
for further clear explanation of the InSAR solid tide and ocean tide load displacement model based on multi-source tide data fusion and the application thereof in the large-scale DInSAR space, a Sentinel-1 image of the whole American west coast (the coverage range is 31 degrees N-50 degrees N, 101 degrees W-130 degrees W) and data of a regional GPS reference station network are covered by a region. The model of the present invention is not limited to specific SAR image data, and is only illustrated here.
The experimental area included oceans, plains and alpine regions of the west coast of the united states, with less vegetation coverage in the southern california region and greater forest coverage in the northern region. The GPS data adopts 1038 reference stations of the PBO network, the time range is 1 month and 1 day in 2014 to 12 months and 31 days in 2018, and the total time is 1826 days of observation data. InSAR data adopts the ascending rail data of Sentinel-1B SLC, the track number is 137, and the imaging time is about 01:59:30 UTC.
The process is as follows:
Figure 649559DEST_PATH_IMAGE028
fast modeling of ocean tidal load displacement: and (3) calculating amplitude and phase delay parameters of 8 main partial tides according to a global tide model and a global and regional tide combined model by taking the positions of 1038 GPS (global positioning system) stations of the PBO (peripheral-based optical fiber) network as calculation points of the displacement time sequence of the ocean tide load. Meanwhile, amplitude and phase delay parameters of 8 main tides are estimated according to a GPS dynamic PPP method. And comparing and analyzing the sea tide models and the amplitude of the tide division estimated by the GPS dynamic PPP method, and further determining that the sea tide model which is most suitable for the research area is an FES2014b + osu. The relative change of M2, N2, O1 and S2 tide-dividing displacement spaces estimated based on GPS reference station network data is large, the estimation accuracy is high, and by combining K2, K1 and P1 tide-dividing displacements estimated by an FES2014b + osu. In an offshore area, the method for acquiring the earth surface tide displacement has a larger difference with an FES2014b + osu. usawest model, the maximum stdDev of the difference in the vertical direction of the earth surface tide displacement and the FES2014b + osu. usawest model is 1.93mm, the corresponding stdDev value of the vertical sea tide displacement is 17.9mm, and the precision of the new model can be improved by 10.7% to the maximum extent; the north directions are respectively 0.37mm, 3.61mm and 10.3 percent; the oriental directions are respectively 0.91mm, 5.85mm and 15.6 percent; along the LOS direction (up-track, incident angle 29 °, time interval 12 days) 1.89mm, 18.16mm, 10.4%, respectively, which causes several millimeters of error for the timing InSAR measurements.
Figure 812687DEST_PATH_IMAGE029
According to the spatial variation characteristics of the tidal load tide-dividing displacement of the ocean, the tidal phase-dividing quantity of an empirical tidal model is used as an independent variable, the tidal phase-dividing quantity measured by a PPP technology is used as a dependent variable, the ocean tidal load displacement of the position of a GPS website point is subjected to spatial modeling by utilizing a high-order polynomial function, and the spatial high-precision Q1, O1, and the like are predicted,N2, M2 tide shift.
Thirdly, gradually correcting the strip-shaped differential interference pattern by utilizing an ICAMS advanced atmospheric correction algorithm (Cao et al, 2021) based on an ECMWF ERA-5 meteorological model and the correction data of the load displacement of the solid tide and the ocean tide of the new model; and linear fitting is carried out on the strip belt differential interference image after atmospheric delay error, solid tide and ocean tide load correction, and the influence of track residual error is eliminated. The linear fitting process is prior art and its implementation is not specifically stated. According to the one-by-one analysis of various long wavelength signals in the strip-band differential interferograms, the atmospheric retardation error is the most important influence factor in the strip-band differential interferograms, but is not related to the spatial scale of the differential interferograms, and the solid tide and ocean tide load displacement can be increased along with the increase of the spatial coverage range of the differential interferograms. Thus, the solid tide and ocean tide loading effects become the main physical source signals in extensive differential interferometry at coastal areas. The signal related to the terrain in the long strip band differential interference image after the atmospheric delay error correction of the ICAMS advanced atmospheric correction algorithm is weakened, and the stdDev value of the differential interference image is reduced by 38.1% -50.3%. The spatial variation characteristic of the trend signal in the strip-band differential interferogram after atmospheric delay error correction is similar to the spatial characteristic of solid tide and ocean tide loads, and the strip-band differential interferogram is further corrected for solid tide and ocean tide load displacement, so that the stdDev value of the differential interferogram is further reduced by 3.9% -19.3%, and the size of the stdDev value depends on the spatial relative variation of earth surface tide displacement. The residual trend signal in the long-strip differential interferogram is inconsistent with the spatial characteristics of ocean tidal load and approaches to a linear surface, so that the residual trend signal can be considered as the influence of a residual track error, bilinear fitting is carried out on the residual track error, and the trend signal in the long-strip differential interferogram is basically eliminated after the residual track error is eliminated.
The model of the invention can effectively correct sea tide load displacement in the strip-belt differential interferometry, the residual displacement of the interference pattern fitted by the polynomial function approaches to zero and does not change along with the change of the sea tide load displacement trend, and the new model is proved to have higher spatial correction precision. If the bilinear plane is directly adopted to fit the original differential interference pattern after the atmospheric delay error correction, the correction of the original differential interference pattern is basically consistent with that of a new tidal model in the inland region, but a larger tidal load displacement residual error is generated in the offshore region with the tidal load displacement larger than 10 mm-18 mm.
To sum up, the tidal displacement correction model integrates a GPS reference station network consisting of 1038 continuous stations and an FES2014+ osu. usawest model, takes the ocean tidal load displacement value of the empirical model as a dependent variable, and adopts a high-order polynomial function to perform spatial modeling on the ocean tide load displacement of the position of the GPS website point, so that the precision of a complex coastline area can be improved. The long-strip differential interference pattern generated on the basis of the Sentinel-1 SLC image on the west coast of the United states is taken as an analysis object, and the experimental result shows that: (1) according to the space characteristics of the sea tide load tide-dividing phasor, the sea tide moisture-dividing parameter is estimated from the PPP coordinate time sequence of the GPS reference station network, so that the sea tide load displacement precision of a complex coastline area can be effectively improved, and the improvement amplitude can reach 11.7%. (2) The spatial large-scale signals in the long strip differential interference image mainly include atmospheric delay errors, orbit errors, solid tide and ocean tide load effects, wherein the superposition influence of the solid tide and the ocean tide load effects can reach 77.5mm, the residual tide displacement generated by fitting the long strip differential interference image by adopting a bilinear plane can reach 20.3mm, the error generated by fitting the long strip differential interference image by adopting a framing bilinear plane can reach 7.2mm, and the error of the splicing position of adjacent images in the offshore area in the long strip differential interference image is larger; (3) based on the sea tide load displacement correction model and the solid tide model, the trend change of the surface tide displacement can be effectively eliminated, the stdDev value of the differential interference graph is further reduced by 3.9% -19.3%, and compared with a traditional plane fitting method, the sea tide load displacement correction model and the solid tide model can effectively improve the area with large offshore surface sea tide load displacement magnitude and complex space change.
It should be emphasized that the examples described herein are illustrative and not restrictive, and thus the invention is not to be limited to the examples described herein, but rather to other embodiments that may be devised by those skilled in the art based on the teachings herein, and that various modifications, alterations, and substitutions are possible without departing from the spirit and scope of the present invention.

Claims (10)

1. A surface tide displacement acquisition method based on a GPS and an empirical tide model is characterized by comprising the following steps: the method comprises the following steps:
step S1: combining the global tidal tide model and the regional tidal tide model to form an empirical tide model, and calculating a harmonic parameter of the tide division by using the empirical tide model; calculating the reconciliation parameter of the tide based on the observation data of the GPS reference website and by utilizing the PPP measurement technology; wherein, the harmonic parameters are amplitude and phase;
step S2: constructing a sample data set by utilizing the harmonic parameters calculated by the empirical tide model and the harmonic parameters calculated by utilizing a PPP (Point-to-Point protocol) measurement technology, and introducing machine learning to carry out network training to obtain a prediction model corresponding to the target tide division;
taking the harmonic parameters calculated by the empirical tide models corresponding to the same position and the same partial tide and the harmonic parameters calculated by the PPP measurement technology as a group of sample data corresponding to a class of partial tides; and the harmonic parameters calculated by the empirical tide model are used for constructing network input parameters or are used as the network input parameters; constructing a network output parameter by using the harmonic parameter calculated by the PPP measurement technology or using the network output parameter as the network output parameter;
the target tide division is as follows: partial or total moisture of M2, S2, N2, O1 and Q1;
step S3: constructing a tidal displacement correction model, and obtaining the surface tidal displacement by using the tidal displacement correction model;
the tide division displacement of the target tide division in the surface tide displacement correction model is calculated by using the prediction model or using a PPP (Point-to-Point protocol) measurement technology; other tide division displacements are calculated by adopting the empirical tide model; and then, the tide dividing displacements at the same position are added to obtain the earth surface tide displacement at the corresponding position.
2. The method of claim 1, wherein: the target tide division comprises: m2, N2, O1 and Q1 fen; in the surface tide displacement correction model, the empirical tide model is adopted to calculate K2, K1 and P1 tide displacement, and M2, N2, O1 and Q1 tide displacement are calculated by a prediction model or a PPP measurement technology.
3. The method of claim 1, wherein: the process of calculating the tide separating displacement of the target tide at any position H by using the prediction model in step S3 is as follows:
acquiring a blending parameter of the target tide division under the position H based on the empirical tide model;
and obtaining a network output parameter based on the harmonic parameter and by using the prediction model so as to calculate the tide splitting displacement of the target tide splitting.
4. The method of claim 1, wherein: in step S2, the network input parameters are: the tide split phasor is constructed by the harmonic parameters calculated by the empirical tide model; the network output parameters are: the expression of the partial tide phase quantity constructed by the harmonic parameters calculated by the PPP measurement technology is as follows:
Figure 38009DEST_PATH_IMAGE001
in the formula (I), the compound is shown in the specification,
Figure 286588DEST_PATH_IMAGE002
is composed of
Figure 150639DEST_PATH_IMAGE003
Moisture separating at position
Figure 965011DEST_PATH_IMAGE004
The amount of the moisture-separating phase of the water,
Figure 754850DEST_PATH_IMAGE005
is composed of
Figure 287463DEST_PATH_IMAGE003
Moisture separating at position
Figure 689625DEST_PATH_IMAGE004
The amplitude of the vibration of the vehicle,
Figure 561766DEST_PATH_IMAGE006
is composed of
Figure 86289DEST_PATH_IMAGE003
Moisture separating at positionkThe phase of (c).
5. The method of claim 1, wherein: the prediction model in step S2 uses a least squares support vector machine.
6. A method of use of the surface tidal displacement capture method of any of claims 1 to 5, wherein: applied to a modified differential interferogram, comprising: obtaining surface tide displacement by using the tide displacement correction model, wherein the surface tide displacement is sea tide load displacement correction data;
and correcting the sea tide load displacement of the corresponding pixel position in the corresponding differential interference pattern by utilizing the earth surface tide displacement.
7. A system based on the method of any one of claims 1-5, characterized by: the method comprises the following steps:
the empirical tide model calculation module is used for calculating the harmonic parameters of the tide division by utilizing the empirical tide model;
the PPP measurement technology calculation module is used for calculating the reconciliation parameters of the tide based on the observation data of the GPS reference website by utilizing the PPP measurement technology;
the prediction model building module is used for building a sample data set by utilizing the harmonic parameters calculated by the empirical tide model and the harmonic parameters calculated by the PPP measurement technology, and introducing machine learning to carry out network training to obtain a prediction model corresponding to the target tide division;
and the tidal displacement correction value calculation module is used for obtaining the earth surface tidal displacement by utilizing the tidal displacement correction model.
8. The system of claim 7, wherein: further comprising: a differential interferogram correction module;
and the differential interference pattern correction module corrects the sea tide load displacement corresponding to the pixel position in the differential interference pattern by using the earth surface tide displacement.
9. A system, characterized by: the method comprises the following steps:
one or more processors;
a memory storing one or more computer programs;
the processor invokes the computer program to implement: the method of claim 1 wherein the surface tidal displacement is obtained using a tidal displacement correction model;
or the processor calls the computer program to implement: the method of claim 6 wherein the tidal displacement correction model is used to obtain surface tidal displacement, and said surface tidal displacement is used to correct the sea tide load displacement at the corresponding pixel location in the corresponding differential interferogram.
10. A readable storage medium, characterized by: a computer program is stored;
the computer program is invoked by a processor to implement: the method of claim 1 wherein the surface tidal displacement is obtained using a tidal displacement correction model;
or the computer program is invoked by a processor to implement: the method of claim 6 wherein the tidal displacement correction model is used to obtain surface tidal displacement, and said surface tidal displacement is used to correct the sea tide load displacement at the corresponding pixel location in the corresponding differential interferogram.
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