CN115047500A - Offshore region sea tide load displacement model refinement method based on GPS data - Google Patents
Offshore region sea tide load displacement model refinement method based on GPS data Download PDFInfo
- Publication number
- CN115047500A CN115047500A CN202210641874.XA CN202210641874A CN115047500A CN 115047500 A CN115047500 A CN 115047500A CN 202210641874 A CN202210641874 A CN 202210641874A CN 115047500 A CN115047500 A CN 115047500A
- Authority
- CN
- China
- Prior art keywords
- tide
- gps
- model
- sea
- parameters
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000006073 displacement reaction Methods 0.000 title claims abstract description 104
- 238000000034 method Methods 0.000 title claims abstract description 57
- 238000011160 research Methods 0.000 claims abstract description 21
- 238000007670 refining Methods 0.000 claims abstract description 18
- 238000004458 analytical method Methods 0.000 claims abstract description 14
- 239000013598 vector Substances 0.000 claims description 16
- 238000012937 correction Methods 0.000 claims description 8
- 230000001419 dependent effect Effects 0.000 claims description 5
- 238000005070 sampling Methods 0.000 claims description 5
- 239000013535 sea water Substances 0.000 claims description 5
- 238000012545 processing Methods 0.000 claims description 4
- 238000004613 tight binding model Methods 0.000 claims description 4
- 238000005259 measurement Methods 0.000 abstract description 8
- 238000004364 calculation method Methods 0.000 abstract description 4
- 238000003089 Pariser Parr Pople method Methods 0.000 description 21
- 230000000694 effects Effects 0.000 description 14
- 238000005516 engineering process Methods 0.000 description 12
- 230000008859 change Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 5
- 238000012544 monitoring process Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 3
- 239000007787 solid Substances 0.000 description 3
- 230000003068 static effect Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000015572 biosynthetic process Effects 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000013507 mapping Methods 0.000 description 2
- 238000013178 mathematical model Methods 0.000 description 2
- 238000003786 synthesis reaction Methods 0.000 description 2
- PEDCQBHIVMGVHV-UHFFFAOYSA-N Glycerine Chemical compound OCC(O)CO PEDCQBHIVMGVHV-UHFFFAOYSA-N 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000005305 interferometry Methods 0.000 description 1
- 239000005433 ionosphere Substances 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 239000011435 rock Substances 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000012876 topography Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/13—Receivers
- G01S19/35—Constructional details or hardware or software details of the signal processing chain
- G01S19/37—Hardware or software details of the signal processing chain
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B21/00—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant
- G01B21/02—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant for measuring length, width, or thickness
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
- G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
Landscapes
- Engineering & Computer Science (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- General Physics & Mathematics (AREA)
- Signal Processing (AREA)
- Computer Networks & Wireless Communication (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Physics (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Computational Mathematics (AREA)
- Pure & Applied Mathematics (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Algebra (AREA)
- Electromagnetism (AREA)
- Position Fixing By Use Of Radio Waves (AREA)
Abstract
The embodiment of the disclosure provides a method for refining sea tide load displacement model in offshore region based on GPS data, which belongs to the technical field of calculation and specifically comprises the following steps: step 1, gridding a research area to obtain a plurality of grid points; step 2, estimating a tide distribution parameter of the tide model of each grid point; step 3, extracting the GPS tide distribution parameters of each reference station and estimating the GPS tide distribution parameters of each grid point according to the GPS tide distribution parameters; and 4, establishing a grid refinement model according to the tide distribution parameters of the tide model, the GPS tide distribution parameters and a harmonic analysis function, and calculating a tide load displacement refinement result according to the grid refinement model. Through the scheme disclosed by the invention, the measurement efficiency, the precision and the adaptability are improved.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of calculation, in particular to a method for refining a sea tide load displacement model in an offshore area based on GPS data.
Background
At present, the coastal region is a region where the interaction between water zones, rock zones, atmospheric zones, biospheres and human society is the most frequent and active, is one of the most complex and fragile regions of the natural ecological environment on the earth, is also the most developed region of global economy and has dense population distribution, and more than half of the population lives in a range of 200km from a coastline all over the world. Therefore, monitoring of wide-range deformation in coastal areas is an important field for global ecological environment change research. Through the development of recent decades, the theory of space-to-ground observation technologies such as GPS and synthetic aperture radar interferometry is gradually improved, and with the increase of the number of observation satellites and the more mature sensors and imaging technologies, the dynamic monitoring of large-scale ground surface deformation in coastal areas by using GPS and InSAR technologies becomes practical gradually.
Ocean tides are the phenomenon of periodic fluctuations in seawater caused by the influence of the induced tidal force of celestial bodies such as the sun and the moon. Ocean tides can influence the form and physical parameters of the solid earth in the form of loads, such as displacement loads, gravity loads, inclination loads, strain loads and the like, wherein the earth surface displacement caused by sea tide loads can reach 10cm in the vertical direction, and the vertical deformation gradient reaches 3cm/100 KM. The sea tide load displacement has long wavelength characteristic (wavelength is about 102-103 km), the spatial variation of the sea tide load displacement increases along with the increase of the range and is easily coupled with some geophysical deformation, and the monitoring result of the surface deformation is further influenced. Therefore, for geodetic techniques to achieve dynamic monitoring of coastal areas with millimeter or even sub-millimeter accuracy, the effect of sea tide load displacement must be estimated and corrected with the same or even higher accuracy. However, the sea tide load displacement estimation method is influenced by factors such as sea tide model precision, and it is difficult to obtain a high-precision sea tide load displacement estimation result in a coastal region. The existing sea tide load displacement estimation method mainly comprises the following steps:
the sea tide load displacement estimation method based on the sea tide model is the most common method for sea tide load displacement estimation, according to a load deformation theory provided by Farrell, the sea tide model is used for providing sea tide height, and the sea tide load displacement of any load point on the earth surface can be obtained through discrete convolution by using a displacement load Green's function based on the earth model, but the method is influenced by the accuracy of the sea tide model. At present, more than 20 kinds of global sea tide models internationally released can provide high-precision deep sea area tide height data, the tide height data precision of the global sea tide models in shallow sea areas is poor due to the influence of complex seabed terrain friction, sea water density, coastlines and the like in the shallow sea areas, and in addition, the global sea tide models have data loss in some sea areas, and the high-precision sea tide load displacement in offshore areas is difficult to estimate only based on the global sea tide models. Aiming at the defects of the global tide model, 22 regional tide models are released internationally, and the regional tide model is used for replacing the data of the global tide model in the shallow sea area, so that the tide load displacement estimation can be refined to a certain extent. However, the precision of the regional sea tide model is also greatly affected by the shallow sea topography, and some shallow sea regions do not have the regional sea tide model or the model data is not updated for a long time, so that the method for refining sea tide load displacement estimation by using the regional sea tide model cannot be realized in some sea regions or the refining effect is not ideal.
The sea tide load displacement estimation method based on the GPS technology can estimate the sea tide load displacement from a long-time observation sequence of a survey station by utilizing the GPS precise single-point positioning technology, and can be divided into static PPP estimation and dynamic PPP estimation. The static PPP method is to solve the harmonic parameters of the main tide and the coordinates of the survey station as unknown parameters in the GPS precise point location data processing, so that the amplitude and phase delay parameters of each tide of the survey station can be obtained, but the method has too many unknown parameters and is easy to reduce the estimation intensity of the tide load displacement. The dynamic PPP method is based on a tide harmonic function, and extracts tide division amplitude and phase delay parameters of tide waves from a high-sampling dynamic PPP coordinate time sequence. Many researches have shown that based on the GPS static or dynamic PPP technology, M2, N2, O1 and Q1 tide load displacements can be accurately estimated from the time domain, but due to the influence of the GPS revisit period, orbit errors, multipath effects, etc., K2 and K1 tide convergence and estimation accuracy are poor, and the estimation accuracy of S2 and P1 tide is also low due to the influence of the GPS unmodeled error. In addition, because the distribution of the GPS sites is limited, the sea tide load displacement estimation based on the GPS technology is difficult to obtain the whole sea tide load spatial variation characteristics of the coastal region.
According to the analysis of the existing sea tide load displacement estimation methods, the inherent defects of the methods can be found, and the high-precision sea tide load displacement estimation result cannot be obtained, so that the sea tide load displacement in the GPS and InSAR interferometric measurement technology cannot be well removed, and the measurement result cannot reach the due theoretical precision.
Therefore, it is necessary to design a method for refining the sea tide load displacement model in the offshore area based on the GPS data, which can obtain high precision and reflect the overall spatial variation characteristics of the offshore area.
Disclosure of Invention
In view of this, the embodiments of the present disclosure provide a method for refining a sea tide load displacement model in an offshore area based on GPS data, so as to at least partially solve the problems of poor measurement efficiency, poor accuracy and poor adaptability in the prior art.
The embodiment of the disclosure provides a method for refining a sea tide load displacement model in an offshore area based on GPS data, which comprises the following steps:
and 4, establishing a grid refinement model according to the tide distribution parameters of the tide model, the GPS tide distribution parameters and a harmonic analysis function, and calculating a tide load displacement refinement result according to the grid refinement model.
According to a specific implementation manner of the embodiment of the present disclosure, the step 1 specifically includes:
and gridding the research area through the researched boundary vector data to obtain a high-resolution grid point capable of reflecting the research overall tidal current load displacement information.
According to a specific implementation manner of the embodiment of the present disclosure, the step 2 specifically includes:
according to the ocean tide theory, calculating the tide amplitude and phase at each measuring station in the frequency domain by utilizing a displacement load Green function based on discrete convolution as the tide parameters of the ocean tide model:
wherein d is Model (L, B, t) is the ocean tidal load displacement value of the grid point with longitude and latitude coordinates (L, B) at the time t, and H (L ', B', t) is the global sea area S G The instantaneous tide height of a certain load point (psi, A) respectively represents the spherical angular distance and the azimuth angle between the survey station and the load point, and rho W Is the seawater density, R represents the earth radius, and G (psi, A) is the displacement load Green's function.
According to a specific implementation manner of the embodiment of the present disclosure, the step 3 specifically includes:
step 3.1, dynamic precise single-point positioning processing with 10min sampling interval is carried out on GPS reference station network data by utilizing resolving software to obtain a coordinate time sequence of each survey station without sea tide load displacement correction, and sea tide load displacement tide distribution parameters are inverted from the coordinate time sequence based on a least square harmonic analysis method;
and 3.2, after the GPS tide distribution parameters of each reference station are extracted, quickly obtaining the tide distribution parameters of the sea tide model of the reference station by utilizing SPOTL software, taking the constructed tide distribution phasor as an independent variable and the tide distribution phasor constructed by the GPS tide distribution parameters of each reference station as a dependent variable, and establishing the GPS tide distribution phasor model by adopting methods such as global least square polynomial fitting and the like so as to obtain the GPS tide distribution parameters of each lattice point.
According to a specific implementation manner of the embodiment of the present disclosure, the step 4 specifically includes:
step 4.1, combining the tide separating parameters of the sea tide model and the GPS tide separating parameters of each grid point to obtain a grid refinement model of a plurality of main tide separating parameters;
and 4.2, substituting all main tide wave tide parameters into a harmonic analysis function to obtain a sea tide load displacement refinement result.
The refinement scheme of the sea tide load displacement model in the offshore area based on the GPS data comprises the following steps: step 1, gridding a research area to obtain a plurality of grid points; step 2, estimating a tide distribution parameter of the tide model of each grid point; step 3, extracting the GPS tide distribution parameters of each reference station and estimating the GPS tide distribution parameters of each grid point according to the GPS tide distribution parameters; and 4, establishing a grid refinement model according to the tide distribution parameters of the tide model, the GPS tide distribution parameters and a harmonic analysis function, and calculating a tide load displacement refinement result according to the grid refinement model.
The beneficial effects of the embodiment of the disclosure are: according to the scheme, high-resolution gridding is carried out on the research area, the sea tide load displacement model refinement based on grid points is beneficial to rapidly obtaining sea tide load refined displacement at any position of the research area and rapidly constructing a sea tide load displacement diagram required by large-scale InSAR technology correction, and geodetic measurement precision is improved.
(2) When grid point GPS tide distribution parameters are estimated, random errors of GPS dynamic PPP inversion tide distribution parameters are fully considered, prior information of space change of the tide distribution parameters of the tide model is based, the tide distribution phasor of the tide model is used as an independent variable, the GPS tide distribution phasor is used as a dependent variable, and the random errors of the GPS tide distribution phasor and the high-precision GPS tide distribution parameters at a prediction grid point are eliminated through a global least square polynomial and other mathematical models in a fitting mode.
(3) The high-precision tide-dividing parameters M2, N2, O1 and Q1 estimated by the GPS in the research area and the high-precision tide-dividing parameters S2, K2, K1 and P1 of the sea tide model are combined, so that the shallow sea effect problem of the sea tide model can be effectively improved, and the sea tide load displacement estimation precision in the offshore area is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings required to be used in the embodiments will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for refining a sea tide load displacement model in an offshore area based on GPS data according to an embodiment of the present disclosure;
fig. 2 is a schematic diagram of a high-fraction gridding of GPS reference survey station distribution and its 1 'x 1' in the special administrative district of hong kong in china according to an embodiment of the present disclosure;
fig. 3 is a vertical displacement phasor vector comparison diagram before and after 8 tidal waves at 12 reference stations in the special administrative district of hong kong in china are refined by the high-precision offshore model in china, which is estimated by different global tidal models provided by the embodiment of the present disclosure;
FIG. 4 is a comparison graph of RMS values of errors in the combined forecast of each partial tide phasor between 7 global tide models provided by the embodiments of the present disclosure;
FIG. 5 is a schematic diagram illustrating phasor vector differences between a HAMTIDE 11A-based global tide model value and a GPS dynamic PPP estimation value according to an embodiment of the present disclosure;
fig. 6 is a schematic diagram of an error RMS in the comprehensive prediction of each tide relative to an OTL model reference value before and after the OTL random error fitting estimated by the GPS dynamic PPP provided by the embodiment of the present disclosure and an error RSS in the total prediction of the sea tide;
fig. 7 is a root mean square of errors in vertical coordinates of 12 reference stations in a special administrative area of hong kong in china according to an embodiment of the present disclosure;
fig. 8 shows the refinement effect of Grid _ Precision _ Model and the conventional osu.
Detailed Description
The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.
The embodiments of the present disclosure are described below with specific examples, and other advantages and effects of the present disclosure will be readily apparent to those skilled in the art from the disclosure of the present disclosure. It is to be understood that the described embodiments are merely illustrative of some, and not restrictive, of the embodiments of the disclosure. The disclosure may be embodied or carried out in various other specific embodiments, and various modifications and changes may be made in the details within the description without departing from the spirit of the disclosure. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
It is noted that various aspects of the embodiments are described below within the scope of the appended claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the disclosure, one skilled in the art should appreciate that one aspect described herein may be implemented independently of any other aspects and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. Additionally, such an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present disclosure, and the drawings only show the components related to the present disclosure rather than the number, shape and size of the components in actual implementation, and the type, amount and ratio of the components in actual implementation may be changed arbitrarily, and the layout of the components may be more complicated.
In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects may be practiced without these specific details.
The embodiment of the disclosure provides a GPS data-based method for refining a sea tide load displacement model in an offshore region, which can be applied to a high-precision sea tide load displacement estimation process in the offshore region.
Referring to fig. 1, a schematic flow chart of a method for refining a sea tide load displacement model in an offshore area based on GPS data according to an embodiment of the present disclosure is provided. As shown in fig. 1, the method mainly comprises the following steps:
the step 1 specifically comprises:
and gridding the research area through data such as the researched boundary vector and the like to obtain a high-resolution grid point capable of reflecting the research overall tidal load displacement information.
In specific implementation, the grid points are divided into a series of high-spatial resolution grid points according to the latitude and longitude range of the research area and the GPS station distribution, whether the grid points are located on the land of the research area instead of the sea is judged by using the researched sea-land boundary vector data, and grid points of the interested area are selected by using vector data such as an administrative boundary and the like.
further, the step 2 specifically includes:
according to the ocean tide theory, calculating the tide amplitude and phase at each measuring station in the frequency domain by utilizing a displacement load Green function based on discrete convolution as the tide dividing parameters of the ocean tide model.
In specific implementation, according to the ocean tide theory, the earth surface deformation caused by global ocean tide changes can be calculated based on the formula (1) discrete convolution integral by utilizing a displacement load Green function:
wherein d is Model (L, B, t) is the ocean tidal load displacement value of the grid point with longitude and latitude coordinates (L, B) at the time t, and H (L ', B', t) is the global sea area S G The instantaneous tide height of a certain load point (psi, A) respectively represents the spherical angular distance, the azimuth angle, rho between the survey station and the load point W Is the seawater density, R represents the earth radius, and G (psi, A) is the displacement load Green's function. By the formula, the tide amplitude and phase at each station can be obtained in the frequency domain.
on the basis of the above embodiment, the step 3 specifically includes:
step 3.1, performing dynamic precise single-point positioning processing with sampling intervals of 10min on GPS reference station network data by using resolving software to obtain a coordinate time sequence of each survey station without sea tide load displacement correction, and inverting sea tide load displacement tide-dividing parameters from the coordinate time sequence based on a least square harmonic analysis method;
and 3.2, after the GPS tide distribution parameters of each reference station are extracted, quickly obtaining the tide distribution parameters of the sea tide model of the reference station by utilizing SPOTL software, taking the constructed tide distribution phasor as an independent variable and the tide distribution phasor constructed by the GPS tide distribution parameters of each reference station as a dependent variable, and establishing the GPS tide distribution phasor model by adopting methods such as global least square polynomial fitting and the like so as to obtain the GPS tide distribution parameters of each lattice point.
In specific implementation, based on long-term observation data of a GPS reference station network, a time sequence under a WGS84 coordinate system of a measurement station is solved by using a dynamic precise single-point positioning technology, and the time sequence of the coordinates can be converted into a N, E, U three-dimensional direction according to a conversion formula. Based on the tidal harmony analysis function, each partial tide parameter can be estimated from the three-dimensional dynamic PPP coordinate time series, and the functional expression is generally represented by the sum of the displacement vectors of the half-day partial tides M2, S2, N2, K2 and the full-day partial tides K1, O1, P1 and Q1:
wherein k represents the kth GPS reference station; a. the j,k And G j,k Is the amplitude and phase delay of the partial tide j; chi shape j Is the initial phase angle; omega j Is the angular frequency; f. of j And mu j Node factors and astronomical angles, respectively.
In order to obtain the GPS tide distribution parameters of the grid points, a GPS tide distribution parameter space model based on the tide distribution parameters of the sea tide model is established first, and then the GPS tide distribution parameters of each grid point are predicted, wherein the modeling process is as follows:
at survey station k, the complex form of sea tide model and GPS tide j is:
where i is the imaginary part of the complex number, and x is A Model,k,j cosΦ Model,k,j ；y＝A Model,k,j sinΦ Model,k,j ； u＝A GPS,k,j cosΦ GPS,k,j ；v＝A GPS,k,j sinΦ GPS,k,j 。
The phasor difference Z existing in the moisture separation of the two methods is obtained by the formula (3) k,j Comprises the following steps:
Z k,j ＝Z GPS,k,j -Z Model,k,j ＝Z system,j -Z residual,k,j (4)
in the formula, the phasor difference Z k，j Can be decomposed into random errors Z associated with a particular survey station k residual，k，j Multipath effects, e.g. of GPS, and systematic error Z common to all stations system，j For example, the error of unmodeled solid Tide in the research area, the error of OTL (ocean Tide loading) model value, etc.
In order to obtain the survey station high-precision GPS tide, the phasor space modeling of the GPS is carried out based on the prior information of the space change characteristic of the tide of the sea tide model, and the random error Z of the GPS estimation tide is reduced residual,k,j And predicting the high-precision GPS tide of each grid point, so the method comprises the following steps:
Z GPS,k,j ＝f(Z Model,k,j )＝u+v*i＝u(x,y)+v(x,y)*i (5)
in conclusion, the sea tide load displacement cos component and sin component estimated by the GPS can be sequentially subjected to spatial modeling based on the binary real variable function of the sea tide model tide split phasor, and further the GPS estimated tide split parameter of each lattice point is obtained.
And 4, establishing a grid refinement model according to the tide distribution parameters of the tide model, the GPS tide distribution parameters and a harmonic analysis function, and calculating a tide load displacement refinement result according to the grid refinement model.
Further, the step 4 specifically includes:
step 4.1, combining the tide separating parameters of the sea tide model and the GPS tide separating parameters of each grid point to obtain a grid refinement model of a plurality of main tide separating parameters;
and 4.2, substituting all the main tide wave tide parameters into a harmonic analysis function to obtain a sea tide load displacement refinement result.
In specific implementation, according to the IERS2010 protocol, the ocean tidal load displacement can be represented by a tidal harmonic function obtained by adding tidal harmonic displacement vectors, the harmonic function is used for combining the high-precision tidal load displacement estimated by any grid point GPS and the ocean tide model, and the expression can be represented as follows:
wherein hs represents Q1, O1, N2 and M2 which have higher estimation precision by a dynamic PPP method; hm represents the estimated tide points of S2, K2, K1 and P1 of the sea tide load model.
Evaluating the gridding refined tide-separating estimation precision by the error RMS in the formula tide-separating forecast synthesis and the error RSS in the total comprehensive forecast:
wherein N is the number of effective grid points in the test area, and A and phi are the combination of grid pointsThe tide amplitude and phase of the GPS and tide model data,
and
the amplitude and phase of the tide are reference values, m is the number of tide divisions, and m is usually 8.
The larger the sea tide load displacement time sequence fluctuation of any grid point is, the larger the influence of the sea tide load displacement on the point is. The space-time refinement effect of the sea tide load displacement model can be effectively reflected by using the standard deviation change rate of the sea tide load displacement time sequence before and after refinement of all grid points in a research area, so that the sea tide load displacement refinement percentage (Percent _ Refine) at any grid point is defined as shown in the following formula:
wherein Δ OTL ═ OTL refine -OTL model The sea tide load displacement time series correction values before and after refinement are shown.
According to the method for refining the sea tide load displacement model in the offshore area based on the GPS data, the sea tide load displacement refinement model in the offshore area is established by combining the high-precision tide displacement parameters of the high-resolution grid points estimated by the sea tide model and the GPS dynamic PPP, and the accuracy of sea tide load displacement estimation in the complex coastline area can be effectively improved. In the method, the research area is subjected to high-resolution gridding, the sea tide load displacement model is refined based on grid points, the sea tide load displacement model reflecting the whole situation and local details of the offshore area can be obtained, the sea tide load refined displacement at any position can be quickly obtained, a sea tide load displacement graph required by large-scale InSAR technology correction can be quickly constructed, and the geodetic measurement precision is improved; the system error and the random error of the sea tide load displacement parameter estimated by the GPS dynamic PPP method are fully considered, each tide phase quantity constructed by the sea tide load displacement parameter estimated by the sea tide model is used as prior information, the random error of the sea tide load displacement estimated by the GPS dynamic PPP is eliminated by adopting a function fitting mode, the influence of errors such as multipath effect on OTL estimation can be effectively reduced, the sea tide load displacement estimation precision of the GPS dynamic PPP method is improved, and the refinement of the sea tide load displacement in an offshore area is facilitated; the method combines M2, N2, O1 and Q1 high-precision tide-dividing parameters estimated by the GPS dynamic PPP of the grid points in the offshore area and S2, K2, K1 and P1 high-precision tide-dividing parameters estimated by a sea tide model, improves the shallow sea effect problem of the sea tide model, and can realize effective refinement of sea tide load displacement in the offshore area. The sea tide load displacement model refinement method is intuitive in principle, easy to realize programming and expand application, is a steady and reliable sea tide load displacement estimation refinement method, and can improve the precision of GNSS, InSAR and other precise ground measurement technologies.
The method will be described with reference to an embodiment, and the specific administrative district of hong kong in china will be taken as a specific embodiment, and the method for refining the sea tide load displacement model in the offshore region based on GPS data proposed by the present invention will be further described with reference to the accompanying drawings.
The special administrative district of hong Kong is located at the north end of the south China sea and mainly comprises a Jiulong peninsula and a plurality of small islands. The coastline of the special administrative district of hong kong in china is complicated and the influence of OTL is obvious. The specific implementation of the invention adopts GPS observation data which is freely provided by the administrative district and the administrative district of hong Kong in China from 2008 to 2017. The distribution of the GPS reference stations in hong kong special administrative district of china is shown in fig. 2, in which HKFN stations were replaced by T430 stations in 2014.
The method specifically comprises the following steps:
gridding study area: the method comprises the steps of carrying out 1 'x 1' high-resolution gridding on a special administrative region of hong Kong China, selecting effective gridding points capable of effectively reflecting the whole and local sea tide load displacement information of the special administrative region of hong Kong China according to a vector boundary file of the special administrative region of hong Kong China, wherein a triangular symbol in the drawing represents a GPS continuous operation reference station, a dotted line is 1 'x 1' gridding on the special administrative region of hong Kong China, and hollow circular dots are effective gridding points as shown in figure 2.
Estimating the tide distribution parameters of the grid point tide model: eight main tide-separating parameters of the tide models, such as HAMTIDE11A, Fes2004, DTU10, GOT04, NAO99b, EOT11A, TPXO7.2, TPXOATLAS and the like, before and after the refinement of a Chinese high-precision offshore model osu. As shown in fig. 3, a vertical displacement phasor vector comparison graph before and after 8 tidal waves at 12 reference stations in the special administrative area of hong kong in china are estimated for different global tide models and refined through a high-precision offshore model in china is shown, and it can be seen that the consistency of the estimation of different global tide models in the special administrative area of hong kong in china can be improved by using data of the high-precision offshore model in china, and the refining effect on the full-day tide (K1\ O1\ P1\ K1) is better than that on the half-day tide (M2\ S2\ N2\ K2), which indicates that the half-day tide precision of the special administrative area of hong kong in china of the high-precision offshore model in china is to be further improved. According to the error RMS in the flood-share forecasting synthesis defined by the formula (7), based on all effective lattice points of the special administrative district of hong Kong in China, RMS values in the vertical direction of each flood-share phasor between 7 global tidal models of DTU10, EOT11A, FES2004, GOT04P7, TPXXO 72, TPXOATLAS and NAO99b corrected by offshore data in China and osu, chiansea.2010+ HAMTIDE11A models are calculated, and as shown in FIG. 4, it can be seen that the difference of each model corrected by the offshore model in China is very small in the special administrative district of hong Kong in China. Therefore, in this example, the average value of the tide distribution parameters of the eight global tide models modified by the offshore model of china in the special administrative district of hong kong is selected as the OTL model reference value of the special administrative district of hong kong china.
Estimating grid point GPS tide-dividing parameters: the method comprises the steps of adopting a single-day RINEX format observation file provided by the special administrative district and the administrative district general administration of hong Kong China, carrying out single-day dynamic PPP calculation with a sampling rate of 600s through Bernese GNSS data processing software, and setting a height cut-off angle to be 3 degrees in the calculation process. The satellite orbit product, 30s satellite clock error, earth rotation parameter, ionosphere parameter and Code Deviation (DCB) file adopts CODE product. The model of tropospheric delay adopts GMF (Global Mapping function) Mapping model. Solid tides and extreme tides were corrected according to IERS protocol 2010. And (3) obtaining a coordinate time sequence without OTL correction at 12 reference stations in the special administrative district of hong Kong in China by calculation, and converting the coordinate time sequence to N, E, U three-dimensional directions according to a conversion formula. Based on the tide harmony analysis method of the formula (2), the GPS tide parameters at the reference station can be estimated from the three-dimensional dynamic PPP coordinate time sequence. FIG. 5 is a phasor vector difference at a GPS reference station between the HAMTIDE11A global tide model value and the GPS dynamic PPP estimate, and a U-to-eighths tide phasor vector difference between the HAMTIDE11A global tide model value and the GPS dynamic PPP estimate. (a) Is the total vector difference; (b) the system phasor vector difference of the model value and the GPS estimated value; (c) is the residual phasor vector difference of (a) minus (b). It can be seen that random errors and system errors exist in the tide distribution parameters estimated by the GPS dynamic PPP. In this example, based on equation (5), the GPS estimated tide split parameter is fitted to the GPS estimated tide split by using the least square 2-order polynomial surface mathematical model with the reference station tide split parameter phasor estimated by HAMTIDE11A as the independent variable and the GPS estimated tide split parameter as the dependent variable. The GPS estimation and the global least square 2-order polynomial fitting of each tide RMS and the total RSS of the GPS estimation are respectively calculated by utilizing a formula (7), as shown in FIG. 6, the fitted GPS estimation tide RMS and RSS are smaller than the direct GPS estimation, the verification proves that the global least square 2-order polynomial fitting adopted in the invention can effectively reduce the random error of the GPS estimation OTL, improve the accuracy of the GPS estimation tide parameters, and further estimate the high-accuracy GPS tide parameters of all effective lattice points of the special administrative district of hong Kong.
Establishing a grid refinement model: all effective lattice points facing the special administrative district of hong Kong China combine the S2, K2, K1 and P1 tide-separating parameters estimated by the global sea tide Model HAMTIDE11A and the M2, N2, O1 and Q1 tide-separating parameters estimated by GPS, and establish the Grid _ Precision _ Model (Grid _ Precision _ Model) of the sea tide load displacement Grid in the special administrative district of hong Kong China. In order to verify that the Grid _ Precision _ Model method based on the combined GPS and the sea tide Model can improve the estimation of the sea tide load displacement, in this example, the OTL displacement is corrected based on the vertical coordinate time series data of 12 reference stations 2017 in the special administrative area of hong kong, 1 month 1 day to 2017 in 1 month 31 day, and the root mean square of the coordinate errors before and after correction is counted, as shown in fig. 7, it can be seen that all three methods can effectively reduce the root mean square of the GPS vertical coordinate, and the traditional osu. Specifically, according to the change rate Percent by Percent by Percent according to Percent by Percent (formula (8 and Percent (formula (9 Percent) by Percent). The result shows that, for the sea tide load displacement refinement in the special administrative district of hong Kong in China, the Grid _ Precision _ Model and the regional sea tide Model refinement method are consistent in the spatial trend in the spatial domain; in the time domain, the Grid _ Precision _ Model has a better refining effect than the regional sea tide Model, the refining effect of the Grid _ Precision _ Model on the displacement time sequence of the sea tide load in the special administrative district of hong Kong in China is 8% -31%, and the refining effect of the regional sea tide Model method is only 5% -17%, so that the effectiveness of the method for refining the sea tide load displacement Model in the offshore area based on the GPS data is verified.
The above description is only for the specific embodiments of the present disclosure, but the scope of the present disclosure is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present disclosure should be covered within the scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
Claims (5)
1. A method for refining sea tide load displacement model in offshore region based on GPS data is characterized by comprising the following steps:
step 1, gridding a research area to obtain a plurality of grid points;
step 2, estimating a tide distribution parameter of the tide model of each grid point;
step 3, extracting the GPS tide distribution parameters of each reference station and estimating the GPS tide distribution parameters of each grid point according to the GPS tide distribution parameters;
and 4, establishing a grid refinement model according to the tide distribution parameters of the tide model, the GPS tide distribution parameters and a harmonic analysis function, and calculating a tide load displacement refinement result according to the grid refinement model.
2. The method according to claim 1, wherein step 1 specifically comprises:
and gridding the research area through the researched boundary vector data to obtain a high-resolution grid point capable of reflecting the research overall tidal current load displacement information.
3. The method according to claim 1, wherein the step 2 specifically comprises:
according to the ocean tide theory, calculating the tide amplitude and phase at each measuring station in the frequency domain by utilizing a displacement load Green function based on discrete convolution as the tide parameters of the ocean tide model:
wherein d is Model (L, B, t) is the ocean tidal load displacement value of the grid point with longitude and latitude coordinates (L, B) at the time t, and H (L ', B', t) is the global sea area S G The instantaneous tide height of a certain load point (psi, A) respectively represents the spherical angular distance and the azimuth angle between the survey station and the load point, and rho W Is the seawater density, R represents the earth radius, and G (psi, A) is the displacement load Green's function.
4. The method according to claim 1, wherein step 3 specifically comprises:
step 3.1, performing dynamic precise single-point positioning processing with sampling intervals of 10min on GPS reference station network data by using resolving software to obtain a coordinate time sequence of each survey station without sea tide load displacement correction, and inverting sea tide load displacement tide-dividing parameters from the coordinate time sequence based on a least square harmonic analysis method;
and 3.2, after the GPS tide distribution parameters of each reference station are extracted, quickly obtaining the tide distribution parameters of the sea tide model of the reference station by utilizing SPOTL software, taking the constructed tide distribution phasor as an independent variable and the tide distribution phasor constructed by the GPS tide distribution parameters of each reference station as a dependent variable, and establishing the GPS tide distribution phasor model by adopting a global least square polynomial fitting method so as to obtain the GPS tide distribution parameters of each grid point.
5. The method according to claim 1, wherein the step 4 specifically comprises:
step 4.1, combining the tide model tide dividing parameters of each grid point with the GPS tide dividing parameters to obtain a grid refinement model of a plurality of main tide dividing parameters;
and 4.2, substituting all main tide wave tide parameters into a harmonic analysis function to obtain a sea tide load displacement refinement result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210641874.XA CN115047500A (en) | 2022-06-08 | 2022-06-08 | Offshore region sea tide load displacement model refinement method based on GPS data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210641874.XA CN115047500A (en) | 2022-06-08 | 2022-06-08 | Offshore region sea tide load displacement model refinement method based on GPS data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115047500A true CN115047500A (en) | 2022-09-13 |
Family
ID=83161856
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210641874.XA Pending CN115047500A (en) | 2022-06-08 | 2022-06-08 | Offshore region sea tide load displacement model refinement method based on GPS data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115047500A (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20210033735A1 (en) * | 2019-08-01 | 2021-02-04 | Swift Navigation, Inc. | System and method for gaussian process enhanced gnss corrections generation |
| CN113761808A (en) * | 2021-11-08 | 2021-12-07 | 长沙理工大学 | Earth surface tide displacement acquisition method based on GPS and empirical tide model, and application method and system thereof |
| CN114002720A (en) * | 2021-09-14 | 2022-02-01 | 中国科学院空天信息创新研究院 | Ship positioning and meteorological data inversion method based on ocean tide load |
-
2022
- 2022-06-08 CN CN202210641874.XA patent/CN115047500A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20210033735A1 (en) * | 2019-08-01 | 2021-02-04 | Swift Navigation, Inc. | System and method for gaussian process enhanced gnss corrections generation |
| CN114002720A (en) * | 2021-09-14 | 2022-02-01 | 中国科学院空天信息创新研究院 | Ship positioning and meteorological data inversion method based on ocean tide load |
| CN113761808A (en) * | 2021-11-08 | 2021-12-07 | 长沙理工大学 | Earth surface tide displacement acquisition method based on GPS and empirical tide model, and application method and system thereof |
Non-Patent Citations (2)
| Title |
|---|
| 刘经南;张化疑;刘焱雄;陈武;周兴华;: "GNSS研究海潮负荷效应进展", 武汉大学学报(信息科学版), no. 01, 5 January 2016 (2016-01-05) * |
| 赵红;涂锐;刘智;蒋光伟;: "利用GPS动态PPP技术求解海潮负荷位移", 测绘学报, no. 08, 15 August 2017 (2017-08-15) * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zhang et al. | GPS PPP‐derived precipitable water vapor retrieval based on Tm/Ps from multiple sources of meteorological data sets in China | |
| Stammer et al. | Accuracy assessment of global barotropic ocean tide models | |
| Catalão et al. | Merging GPS and atmospherically corrected InSAR data to map 3-D terrain displacement velocity | |
| Pérez-Peña et al. | Velocity field and tectonic strain in Southern Spain and surrounding areas derived from GPS episodic measurements | |
| CN110376623B (en) | Satellite-borne GNSS-R mirror reflection point ocean tide correction positioning method and system | |
| Guan et al. | A method of establishing an instantaneous water level model for tide correction | |
| Jin et al. | The global mean sea surface model WHU2013 | |
| Yuan et al. | Determination of ocean tide loading displacements in Hong Kong using GPS technique | |
| Khan et al. | The M 2 ocean tide loading wave in Alaska: vertical and horizontal displacements, modelled and observed | |
| Lei et al. | Accuracy assessment of recent global ocean tide models around Antarctica | |
| CN109917382B (en) | Sea tide load displacement influence evaluation and correction method in coastal zone InSAR interferogram | |
| Yeh et al. | Vertical displacement due to ocean tidal loading around Taiwan based on GPS observations | |
| Scherneck et al. | Ocean tide and atmospheric loading | |
| Guo et al. | Sea level changes of China seas and neighboring ocean based on satellite altimetry missions from 1993 to 2012 | |
| Fei et al. | Dem Development and Precision Analysis for Antarctic Ice Sheet Using Cryosat‐2 Altimetry Data | |
| Oja et al. | Gravity anomaly field over Estonia | |
| CN114170524A (en) | Single-day tidal flat digital terrain construction method based on high-time-space satellite data fusion | |
| CN114002720A (en) | Ship positioning and meteorological data inversion method based on ocean tide load | |
| Yue et al. | Effects and correction of Atmospheric pressure loading deformation on GNSS reference stations in Mainland China | |
| Zhao et al. | Determination of ocean tide loading displacement by GPS PPP with priori information constraint of NAO99b global ocean tide model | |
| Ito et al. | High resolution mapping of Earth tide response based on GPS data in Japan | |
| CN115047500A (en) | Offshore region sea tide load displacement model refinement method based on GPS data | |
| Teke | The principle diurnal and semidiurnal tides of the ocean loading displacements and their long-term variability as observed by VLBI | |
| Véronneau et al. | The Canadian gravimetric geoid model of 2000 (CGG2000) | |
| Zhao et al. | Combining the tide gauge stations and GPS/GLONASS observations to validate global and regional ocean tide models around China coast |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |