CN112880634B - Elevation reference frame dynamic monitoring method based on CORS station network - Google Patents
Elevation reference frame dynamic monitoring method based on CORS station network Download PDFInfo
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Abstract
The invention discloses a CORS station network-based elevation reference frame dynamic monitoring method, which comprises the steps of utilizing continuous observation data of an SDCORS station network, and eliminating elevation abnormal changes caused by surface environment loads by reconstructing a ground height time sequence and combining atmospheric pressure, land water and sea level data of the whole world and the region so as to obtain a time sequence of normal height changes of stations; the checking with the leveling data on 16 CORS sites shows that the maximum difference value between the normal height change based on the CORS data and the normal height change of the two-stage leveling is 29.5mm, and the average difference value is 3.5 mm; and under the condition that the CORS station has long-term continuous observation data and the data quality is good, dynamically correcting the historical observation normal height of the CORS station by using the normal height change determined by the CORS station data, and realizing dynamic maintenance of the area elevation reference frame based on the CORS station data.
Description
Technical Field
The invention relates to dynamic monitoring and maintenance of an area elevation reference frame, in particular to a dynamic monitoring method of an elevation reference frame based on a CORS station network.
Background
The geodetic coordinate frame not only can provide a geometric and physical reference for mapping and engineering, but also can provide important reference information for researching and detecting changes of the earth, the climate and the like. The method plays an important role in the construction of geographic information systems, navigation and positioning, satellite orbit determination, earth surface movement and other practical applications. With the development of the national mapping geographic information cause, relatively perfect continuously-operating reference station networks are built in many provinces and regions of China. The Shandong province satellite positioning continuous operation comprehensive application service System (SDCORS) project is implemented by the Shandong province national resource hall construction and the Shandong province national surveying and mapping institute organization, is formally started in 2007 and passes the acceptance in 2011. The system is incorporated into a CORS system established in the city and the industry, can provide real-time positioning and regional reference frame service for Shandong province, and plays an important role in establishing and maintaining national and regional geodetic reference frames.
The geodetic frame is a physical implementation of the geocentric coordinate reference system, and the physical points that make up the coordinate reference frame are usually fixed to the earth's surface, making maintaining the accuracy of the coordinate reference frame a dynamic problem because the dynamic nature of the earth, frame points, can change as the earth's surface moves. With respect to the elevation frame of reference, the geophysical factors that cause a station to vary in elevation are largely divided into two main categories, one being tidal deformation, which mainly includes solid earth tides, ocean load tides and extreme tides, and the other being surface mass load variations caused by mass migration of the atmosphere at the earth's surface and water in various states. There are mainly atmospheric mass loads, hydrological loads, marine non-tidal weight loads, etc., which have a pronounced seasonal variation characteristic (non-linear periodic variation). For the first type of tidal deformation, the original data at the CORS station has been compensated and corrected by a corresponding mathematical model, and for the second type of deformation, the load Green function processing is mainly adopted at present. And correcting the influence of non-structural deformation on the coordinate time sequence of the CORS station by using a satellite-to-earth observation data set related geophysical model.
In order to maintain the high precision and the current situation of the national geodetic coordinate system, the existing elevation reference frame needs to be continuously and dynamically monitored and maintained. Because the traditional measuring mode of separating the plane control network from the elevation control network is limited by technical conditions and operation modes, the efficiency of normal high change of the station is low through leveling measurement and monitoring, and long-term continuous monitoring is not facilitated. In recent years, with the increasing maturity of an interconnection sharing mechanism and a real-time high-precision positioning technology of a CORS station network, all-weather high-precision characteristics observed by CORS stations and accumulated historical data continuously observed for a long time are fully utilized through a plurality of CORS station networks continuously operating in a region, so that transient and long-term changes of regional and even national crustal movement, weather and the like can be monitored, and various high-precision space positioning services and multi-source information services can be provided.
The conventional method for maintaining elevation references is realized by using high-level leveling. This approach has two significant drawbacks: firstly, the measurement method consumes a great amount of manpower and material resources. Secondly, the period of one-time retest is very long, and after the retest is usually finished, the measured elevation value is changed and cannot be used due to ground settlement, so that an alternative method is urgently needed to be found, the change of the elevation value of the point position can be dynamically monitored, and the change monitoring and maintenance of the elevation reference frame are realized.
Disclosure of Invention
The invention mainly solves the technical problem of how to provide an elevation reference frame dynamic monitoring method based on a CORS station network, which can obtain a point location real-time change elevation reference value through continuous and multi-year precision satellite positioning data processing combined with water vapor load correction, realize area frame elevation data dynamic change monitoring and further replace high-level leveling to maintain the elevation reference.
In order to solve the technical problems, the invention adopts the technical scheme that:
a dynamic monitoring method for an elevation reference frame based on a CORS station network comprises the following specific steps:
the method comprises the following steps: generating a three-dimensional coordinate time sequence of the CORS site,
selecting a plurality of stable international or national reference stations as constraints in an area or at the periphery, resolving three-year observation data of a CORS station network site, producing a single-day solution, simultaneously obtaining continuous comprehensive solutions for a plurality of years, and resolving to obtain a three-dimensional coordinate change time sequence of the CORS site relative to the comprehensive solutions;
step two: carrying out the analysis and reconstruction of the ground high time series of the CORS station,
aiming at a single daily solution time sequence of the large-ground height of each CORS station, taking a Chebyshev function as a basis function, carrying out low-frequency parameter estimation on the time sequence, and separating linear terms; reconstructing a geodetic height nonlinear change time sequence according to the low-frequency parameters, using the geodetic height nonlinear change time sequence as a reference of gross error detection, detecting and eliminating gross errors according to 3 times of residual error standard deviation, and obtaining a clean geodetic height nonlinear change time sequence; carrying out period estimation on scattered points of the time sequence, estimating to obtain period parameters, and then reconstructing a geodetic high nonlinear change time sequence;
step three: the elevation abnormal change caused by the atmospheric pressure load is calculated,
(1) and at each meteorological station, taking the average atmospheric pressure in the observation period as a reference value, and subtracting the actual atmospheric pressure of each month from the reference value to obtain a change value of the atmospheric pressure of each month relative to the reference value, and converting to obtain the change of the equivalent water height, wherein the 1hPa (hectopa) atmospheric pressure change corresponds to the vertical load deformation of 1 cm.
(2) And utilizing global atmospheric pressure model data to expand to obtain a load spherical harmonic coefficient model, and utilizing atmospheric pressure data to calculate and obtain the elevation abnormal change of the CORS site caused by the atmospheric pressure change by adopting a removal recovery method based on the load spherical harmonic coefficient model and a load Green function integral formula.
Step four: calculating the abnormal elevation change caused by land water load
(1) By utilizing the GLDAS model hydrological data, taking the average land water reserve (equivalent water height) in an observation period as a reference value, and subtracting the land water reserve of each month from the reference value to obtain a change value of the land water reserve of each month relative to the reference value;
(2) expanding a global land-water reserve change grid to obtain a spherical harmonic coefficient model, and calculating to obtain the elevation abnormal change of the CORS station caused by land-water reserve change by using the spherical harmonic coefficient model based on the earth load deformation theory;
step five: calculating the abnormal change of elevation caused by the change of sea level in the area
(2) Using AVISO global sea level height data, taking average sea level height in an observation period as a reference value, making a difference between the sea level height of each month and the reference value, obtaining a change value of the sea level height of each month relative to the reference value, and expanding to obtain a spherical harmonic coefficient model;
(2) and calculating to obtain the abnormal elevation change of the CORS site caused by the sea level change by utilizing the sea level change data of the high-resolution area and adopting a removal recovery method based on a load spherical harmonic coefficient model and a load Green function integral formula.
Step six: calculating the elevation change value of the CORS station point location, and realizing dynamic monitoring and maintenance of the elevation datum.
And determining a point position geodetic height change value and the elevation abnormal correction number determined in the second step to the fifth step according to the geodetic height time sequence value reconstructed in the first step, thereby determining the variation of the unit normal height and realizing the dynamic monitoring and maintenance of the elevation datum.
The method can effectively solve the problems in the prior art, mainly utilizes the continuous observation data of the CORS station network selected in the surface deformation area, adopts a data processing strategy facing surface deformation monitoring, processes to obtain a three-dimensional coordinate time sequence and carries out time sequence analysis. The normal height change of the CORS station is obtained by integrating the time series analysis result of the CORS station and observation data such as atmospheric, land hydrological and sea level changes, dynamic monitoring of an elevation reference frame in an area is realized, the technical scheme for dynamically monitoring the elevation reference frame is summarized, and the usability of the elevation reference frame under the condition of objective ground settlement is improved; is a technical innovation of one-time expansibility in the prior art, and has good popularization and use values.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without inventive efforts, wherein:
FIG. 1 is a schematic diagram of a dynamic monitoring structure of an elevation reference frame based on a CORS station network according to the present invention;
FIG. 2 is a schematic diagram illustrating an elevation anomaly change calculation process based on a removal recovery method according to the present invention;
FIG. 3 is a CORS site for time series analysis in an embodiment of the present invention;
FIG. 4 is the result of the earth high time series analysis and reconstruction of the CORS station of the present invention;
FIG. 5 is a chart of time series of abnormal changes in elevation of total load for 16 CORS stations in accordance with the present invention;
fig. 6 is a water-level route diagram of Shandong province in 2015 and 2019 in the example of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to the attached drawings 1-6, in order to meet application requirements of infrastructure construction, city planning, climate earthquake and the like in Shandong province, the dynamic monitoring method for the elevation reference frame based on the CORS station network disclosed by the invention combines an SDCORS system with a quasi-geoid refinement model; a large amount of observation data accumulated so far are built by fully utilizing the SDCORS station network, and a ground height time sequence of the CORS station is reconstructed; calculating the abnormal change of the elevation caused by the environmental load based on a removal recovery method by combining the atmospheric, land hydrology and sea level data in the area; finally, obtaining a normal high-variation time sequence of the station, and realizing dynamic monitoring and maintenance of the Shandong province elevation reference frame;
the method comprises the following specific steps:
the method comprises the following steps: generating a three-dimensional coordinate time sequence of the CORS site,
selecting a plurality of stable international or national base stations in or around a region as constraints, processing CORS station data from 2015 to 2019, calculating three-year observation data of a CORS station network site, producing a single-day solution, further obtaining a weekly solution from 2015 to 2019 of the CORS station, simultaneously obtaining a continuous multi-year comprehensive solution, and calculating to obtain a three-dimensional coordinate change time sequence of the CORS station relative to the comprehensive solution;
step two: carrying out the analysis and reconstruction of the ground high time series of the CORS station,
aiming at a high single-day solution time sequence in the earth U direction of each CORS station, performing low-frequency parameter estimation on the time sequence by taking a Chebyshev function as a basis function, and separating linear terms; reconstructing a geodetic height nonlinear change time sequence according to the low-frequency parameters, using the geodetic height nonlinear change time sequence as a reference of gross error detection, detecting and eliminating gross errors according to 3 times of residual error standard deviation, and obtaining a clean geodetic height nonlinear change time sequence; carrying out period estimation on scattered points of the time sequence, estimating to obtain period parameters, and then reconstructing a geodetic high nonlinear change time sequence;
according to the linear terms and the nonlinear low-frequency period parameters of the earth height time sequence of the CORS station, the earth height change at two given moments can be interpolated or extrapolated
Δh＝v·(t2-t1)+Δh′(t2)-Δh′(t1) (1)
In the formula t1、t2For a given two moments, v is the high linear rate of earth, Δ h' (t)1)、Δh′(t2) Respectively obtaining the high-ground nonlinear change values obtained by the low-frequency periodic parameter reconstruction calculation at the two moments;
step three: the elevation abnormal change caused by the atmospheric pressure load is calculated,
calculating global load model quantity by utilizing a global atmospheric pressure, land, water and sea level grid time sequence model and integrating earth load deformation and an earth gravitational field theory, and calculating regional load refinement quantity based on a load spherical harmonic coefficient model and a removal recovery method of a regional load Green function to finally obtain the elevation abnormal change of a CORS site;
the method for calculating the abnormal change of the elevation caused by the earth surface environmental load based on the removal recovery method mainly comprises the following steps:
(2) according to the load elastic deformation theory, converting the global atmospheric pressure, land water, sea level and other data into equivalent water height, subtracting the equivalent water height from the average value to obtain the equivalent water height variation of each month,
specifically, at each meteorological station, taking the average atmospheric pressure in an observation period as a reference value, and subtracting the actual atmospheric pressure of each month from the reference value to obtain a change value of the atmospheric pressure of each month relative to the reference value, and converting to obtain a change of equivalent water height, wherein the 1hPa (hectopa) atmospheric pressure change corresponds to 1cm vertical load deformation;
(2) and utilizing global atmospheric pressure model data to expand to obtain a load spherical harmonic coefficient model, and utilizing atmospheric pressure data to calculate and obtain the elevation abnormal change of the CORS site caused by the atmospheric pressure change by adopting a removal recovery method based on the load spherical harmonic coefficient model and a load Green function integral formula.
Equivalent water height change Δ h at ground point (R, θ, λ)wCan be expressed as a normalized load spherical harmonic series:
in the formula: r is the mean radius of the earth;
normalizing the load spherical harmonic coefficient for n orders and m times;
the association Legendre function is fully normalized.
According to the load deformation theory, the load influence of the ground and earth external elevation anomaly (geohorizon) is as follows:
in the formula: g is a universal gravitation constant; rhowIs the density of water; rhoeIs the earth average density; m is the total mass of the earth; a is the radius of the earth's major semiaxis; r is the geocentric distance; gamma is normal gravity; k'nIs the n-order bit loading lux number.
Calculating the area load influence of the quasi-geoid based on the Green function integral:
the gravitational potential of a unit mass at a certain point on the surface, namely the direct influence, is as follows:
wherein g is a value of gravity,
for a fully normalized association Legendre function, ψ is the spherical angular distance between the calculated point (r, θ, λ) and the ground flow point (r ', θ ', λ '):
cosψ＝cosθcosθ′+sinθsinθsinθ′cos(λ′-λ) (5)
the unit mass generates load on the solid earth, the earth deforms due to the load, the gravitational potential change is caused, and the indirect influence is as follows:
the total change of the quasi-geoid caused by the unit mass is the sum of the direct influence and the indirect influence, that is, the green function corresponding to the quasi-geoid is:
knowing the equivalent water height change Δ h on the groundwThen the geoid change is the spatial convolution of the equivalent water height change with the green function:
Δζ＝ρw∫SΔhwU(ψ)dS (8)
where dS is the ground flow integral bin.
After the abnormal elevation changes caused by the atmospheric pressure load, the land water load and the sea level change are respectively obtained, the abnormal elevation changes caused by the total load can be obtained by summing the abnormal elevation changes:
Δζ＝ΔζAir+ΔζLws+ΔζOcn (9)
in which Δ ζ total load is abnormally changed in elevation, ΔζAirFor abnormal changes in elevation, Δ, caused by atmospheric pressure loadingζLwsFor abnormal changes in elevation, Δ, caused by land-water loadsζOcnThe elevation abnormal change caused by the change of the sea level of the area.
Step four: calculating the abnormal elevation change caused by land water load
(1) By utilizing the GLDAS model hydrological data, taking the average land water reserve (equivalent water height) in an observation period as a reference value, and subtracting the land water reserve of each month from the reference value to obtain a change value of the land water reserve of each month relative to the reference value;
(2) expanding a global land-water reserve change grid to obtain a spherical harmonic coefficient model, and calculating to obtain the elevation abnormal change of the CORS station caused by land-water reserve change by using the spherical harmonic coefficient model based on the earth load deformation theory;
step five: calculating the abnormal change of elevation caused by the change of sea level in the area
(3) Using AVISO global sea level height data, taking average sea level height in an observation period as a reference value, making a difference between the sea level height of each month and the reference value, obtaining a change value of the sea level height of each month relative to the reference value, and expanding to obtain a spherical harmonic coefficient model;
(2) and calculating to obtain the abnormal elevation change of the CORS site caused by the sea level change by utilizing the sea level change data of the high-resolution area and adopting a removal recovery method based on a load spherical harmonic coefficient model and a load Green function integral formula.
Step six: calculating the elevation change value of the CORS station point location, and realizing dynamic monitoring and maintenance of the elevation datum.
In a specific implementation process, a point location ground height change value is determined according to the ground height time sequence value of the CORS station reconstructed in the first step, and the elevation abnormal correction number determined in the second step, the fifth step is added, so that the variation of the normal height of a ground unit is determined, and dynamic monitoring and maintenance of the elevation datum are realized.
In the specific implementation process, the elevation abnormal change caused by the atmospheric pressure load is calculated in the third step, and the atmospheric, land water reserves and sea level changes of the earth surface layer are non-tidal, and the surface non-tidal load changes can be uniformly expressed by the equivalent water height change of the ground.
In the specific implementation process, the elevation abnormal change caused by land water load is calculated in the fourth step and the elevation abnormal change caused by regional sea level change is calculated in the fifth step, global atmospheric pressure, land water and sea level data are prepared and sorted firstly, the average value of the data in three months from 1 month to 3 months in 2015 is used as comparison reference data, and then the data of each month is subtracted from the average value to obtain the variation of each month relative to the reference value. And then calculating the elevation abnormal change caused by the surface environmental load by adopting a removal-recovery method. Wherein, the sources of global atmospheric pressure, land water and sea level are as follows:
atmospheric pressure data: two data with a spatial resolution of 0.125 ° × 0.125 ° are obtained by using a European middle Weather forecast center (ECMWF) global atmospheric pressure model, one per month, for a period of time ranging from 2015 to 2019, 8 months. And the China ground climate data month value data set downloaded by the China weather data network comprises 61 weather station data of Shandong province and surrounding areas, and the time period of the data set is also 2015, 1 month to 2019, 8 months and one month. The method comprises the steps of extracting required atmospheric pressure data from ECMWF global atmospheric pressure model data (NC files), converting the data into vertical load deformation of 1cm corresponding to equivalent water height and 1hPa (hectopascal) atmospheric pressure change, and obtaining the global atmospheric pressure data.
Land water data. Global Land Data Assimilation System (GLDAS) Data of the mada aviation center and the national environmental forecast center were used. The mode outputs various hydrological parameters (such as soil humidity, soil temperature, evaporation capacity, rainfall, runoff, snow capacity and the like) of the land surface through land surface modeling and data assimilation technology. The model uses rainfall observations and solar radiation, etc. as input parameters with spatial resolution of 0.25 ° x 0.25 °, one value per month. The time period is 2015 for 1 month to 2019 for 11 months. And extracting required land-water data from GLDAS model hydrological data (NC files), and converting the land-water data into corresponding equivalent water heights so as to obtain global land-water data.
And thirdly, sea level change data. The Sea Level anomaly Data is gridding fusion Data MSLA (maps of Sea Level anomaly) provided by a satellite Oceanographic Data center of French national space research Center (CNES), generated by average Sea Level anomaly of multiple global Satellites at altimetry months, and provided with resolution of 0.25 degrees multiplied by 0.25 degrees, and the time period is 1 month in 2015 to 11 months in 2019. And extracting required sea level data from AVISO model sea level height data (NC files), and converting the sea level data into corresponding equivalent water height, thereby obtaining global sea level data.
In a specific implementation process, the elevation abnormal change time series of 16 CORS stations are finally calculated in the sixth step, the time period is 2015 for 1 month to 2019 for 8 months, the time resolution is 1 month, the total load elevation abnormal change information of 16 CORS stations is counted in table 2, and fig. 5 is the total load elevation abnormal change time series of 16 CORS stations.
Experiments and analyses
CORS station data processing
Selecting a plurality of stable international or national reference stations as constraints in or around the Shandong province region, processing CORS station data from 2015 to 2019, resolving to obtain a single-day solution of CORS stations, and further obtaining a weekly solution from 2015 to 2019 of CORS stations. And finally, obtaining a three-dimensional coordinate cycle solution time series (N, E, U directions) of 20 CORS stations by calculation, wherein the time resolution is 7 days, and the time period is 2015, 6 months and 1 days to 2019, 7 months and 30 days. The CORS sites for which the time period was sufficient for time series analysis were then selected, and the 16 CORS sites used for the final experiment are listed in Table 1, with the spatial distribution shown in FIG. 3, for time series analysis.
TABLE 1 CORS site for time series analysis
| Serial number | Name of station | Serial number | Name of station | Serial number | Name of station | Serial number | Name of station |
| 1 | Estuary HEKO | 5 | Wuch in Wucheng | 9 | Dongming DOMI | 13 | Plough ZQRS |
| 2 | Jining JINI | 6 | Camping XIAY | 10 | Huta | 14 | ZiziLZWT |
| 3 | Pingyin PYRS | 7 | Yucheng YuCH | 11 | Juch city JUCH | 15 | Zhou village ZHCU |
| 4 | Birthday light SDSG | 8 | Linqing LINQ | 12 | Birthday light SHGU | 16 | DEZH, texas |
And respectively analyzing and reconstructing the periodic solution time series of the geodetic height of each CORS station to finally obtain the geodetic height nonlinear change time series of 16 CORS stations, as shown in FIG. 4.
Calculating elevation anomaly changes
Firstly, preparing and arranging global atmospheric pressure, land water and sea level data, taking the average value of the data from 1 month to 3 months in 2015 as comparison reference data, and subtracting the average value from the data of each month to obtain the variation of each month relative to the reference value. And then calculating the elevation abnormal change caused by the surface environmental load by adopting a removal-recovery method. Wherein, the sources of global atmospheric pressure, land water and sea level are as follows:
atmospheric pressure data: two data with a spatial resolution of 0.125 ° × 0.125 ° are obtained by using a European middle Weather forecast center (ECMWF) global atmospheric pressure model, one per month, for a period of time ranging from 2015 to 2019, 8 months. And the China ground climate data month value data set downloaded by the China weather data network comprises 61 weather station data of Shandong province and surrounding areas, and the time period of the data set is also 2015, 1 month to 2019, 8 months and one month. The method comprises the steps of extracting required atmospheric pressure data from ECMWF global atmospheric pressure model data (NC files), converting the data into vertical load deformation of 1cm corresponding to equivalent water height and 1hPa (hectopascal) atmospheric pressure change, and obtaining the global atmospheric pressure data.
Land water data. Global Land Data Assimilation System (GLDAS) Data of the mada aviation center and the national environmental forecast center were used. The mode outputs various hydrological parameters (such as soil humidity, soil temperature, evaporation capacity, rainfall, runoff, snow capacity and the like) of the land surface through land surface modeling and data assimilation technology. The model uses rainfall observations and solar radiation, etc. as input parameters with spatial resolution of 0.25 ° x 0.25 °, one value per month. The time period is 2015 for 1 month to 2019 for 11 months. And extracting required land-water data from GLDAS model hydrological data (NC files), and converting the land-water data into corresponding equivalent water heights so as to obtain global land-water data.
And thirdly, sea level change data. The Sea Level anomaly Data is gridding fusion Data MSLA (maps of Sea Level anomaly) provided by a satellite Oceanographic Data center of French national space research Center (CNES), generated by average Sea Level anomaly of multiple global Satellites at altimetry months, and provided with resolution of 0.25 degrees multiplied by 0.25 degrees, and the time period is 1 month in 2015 to 11 months in 2019. And extracting required sea level data from AVISO model sea level height data (NC files), and converting the sea level data into corresponding equivalent water height, thereby obtaining global sea level data.
Finally, calculating to obtain elevation abnormal change time sequences of 16 CORS sites, wherein the time period is from 1 month in 2015 to 8 months in 2019, the time resolution is 1 month, the total load elevation abnormal change information of 16 CORS sites is counted in table 2, and fig. 5 is the total load elevation abnormal change time sequence of 16 CORS sites.
TABLE 2 statistics of total load elevation anomaly change information (unit: mm) for all CORS stations
CORS station normal height change determination and result verification
And calculating the normal height change of the CORS station according to the earth height change and the elevation abnormal change of the CORS station at two given moments. In order to verify whether the elevation reference frame maintenance method based on the CORS station meets the requirement of national level leveling, the normal height of the CORS station obtained by adopting two-stage leveling can be used for solving the normal height change of the level. The normal height change of the data of the CORS station is compared with the normal height change of leveling measurement, the normal height change and the leveling measurement result are obtained from different observation data and are mutually independent, and the accuracy of the normal height change of the CORS station can be checked.
The result of the provincial level of Shandong in 2015 is 4441.20KM, wherein the first-level is 1039.2KM, and the second-level is 3402 KM. The second-class leveling network of Shandong province is measured on the basis of the first-class leveling network of the country, the leveling network covers the whole Shandong province, 168 second-class leveling lines are shared in total, and only 51 leveling lines in the 2019 measuring area are selected for networking in the calculation, as shown in fig. 6 (a). The second-class leveling net comprises 17 closed rings, the height difference per kilometer calculated by independently utilizing the ring closing difference of the closed rings is +/-1.8945 mm, the height difference of the weakest point is +/-24.18 mm, and the height difference of the weakest measuring section is +/-5.52 mm.
The Shandong province's water-level results in 2019 are 3432.4KM totally, and are all at the second-class level. The second-class leveling net covers the east, west and north of Shandong province, and has 34 total second-class leveling routes, as shown in FIG. 6 (b). The second-class leveling net comprises 11 closed rings, the height difference per kilometer calculated by utilizing the ring closing difference of the closed rings is +/-1.3472 mm, the error in the weakest point elevation is +/-19.08 mm, and the error in the height difference of the weakest measuring section is +/-3.80 mm.
The leveling data processing software adopts precision leveling data preprocessing software developed by China surveying and mapping scientific research institute and software of a Coboty leveling and settlement observation data processing system to complete the data resolving of a second-class leveling line with a total of 2854 kilometers in an earth surface deformation area. In the experiment, the normal height of the level in the two stages of 16 CORS sites is differentiated by using the adjustment results of the level networks in the two stages of 2015 and 2019 in the measurement area. Normal high changes in level were obtained, see table below.
Level Normal high variation for Table 316 CORS sites
| Serial number | Normal high level (m) in 2015 year | Normal high level (m) in 2019 | Normal high variation of level (mm) |
| 1. | 4.795 | 4.778 | -17 |
| 2. | 39.532 | 39.505 | -27 |
| 3. | 76.642 | 76.63 | -12 |
| 4. | 5.679 | 5.554 | -125 |
| 5. | 24.221 | 24.154 | -67 |
| 6. | 4.015 | 3.809 | -206 |
| 7. | 21.278 | 21.214 | -64 |
| 8. | 34.946 | 34.822 | -124 |
| 9. | 57.895 | 57.791 | -104 |
| 10. | 14.18 | 14.162 | -18 |
| 11. | 48.667 | 48.523 | -144 |
| 12. | 17.003 | 16.768 | -235 |
| 13. | 123.915 | 123.923 | 8 |
| 14. | 45.607 | 45.565 | -42 |
| 15. | 85.067 | 85.071 | 4 |
| 16. | 23.08 | 22.94 | -140 |
And according to the leveling observation time of the CORS station in the two-stage leveling measurement, calculating to obtain the normal high change of the CORS station corresponding to the two-stage observation time by utilizing a linear term and a nonlinear low-frequency period parameter obtained by analyzing a geoid high time sequence of the CORS station and combining a geoid level change time sequence calculated by utilizing data such as atmospheric, hydrological and sea level changes.
ΔH＝Δh-Δζ (10)
In the formula, Δ H is a normal height change, Δ H is an earth height change, and Δ ζ elevation changes abnormally.
TABLE 416 CORS site Normal high Change comparison results in mm
The normal height change of the CORS sites is obtained through two-stage leveling measurement, the normal height change of the CORS sites is obtained through the combination of the ground height time series analysis of the CORS sites and the elevation abnormal change, therefore, the normal height change of the CORS sites and the normal height change of the leveling are compared at 16 sites, a table 4 shows the normal height change comparison result of the 16 CORS sites, the maximum value of the normal height change difference obtained through two mutually independent observation means is 29.5mm, and the average value is 3.5 mm. Analysis table 4 shows that the normal height variation difference values of 13 of the 16 CORS stations are smaller than the error in the elevation of the leveling points in 2015 or 2019, the normal height variation difference values of only 3 CORS stations of YUCH, tomming DOMI and zhongcun ZHCU slightly exceed the error in the elevation of the leveling points, the normal height variation difference values of only the tomming DOMI station exceed 20mm, and other stations are smaller than 19 mm. The results show that the high accuracy of determining the normal high variation by the CORS station data is high.
According to the national first-class and second-class leveling regulations, the total mean error limit of the second-class leveling is 2mm/km, and the bound route closure difference and the ring closure difference are limited
The leveling route lengths corresponding to the maximum difference and the average difference of the normal height variation of the CORS station and leveling are calculated according to the above and are shown in the following table.
TABLE 5 leveling Path Length corresponding to Normal high variance Difference
As can be seen from Table 4, in 16 CORS stations, the effective data time period of 11 CORS stations is not enough to cover the two-stage level observation time, the geodetic height time sequence of the CORS stations needs extrapolation calculation to obtain the normal height change of the two-stage level observation time, the extrapolation time is generally 1-2 months, the extrapolation time of the HEKO station reaches 6 months, and the extrapolation time of the HUTA station reaches 1 year. The normal high variation results (tables 4 and 5) determined by the data of the CORS station have higher precision in consideration of factors such as leveling errors, incomplete matching of data time periods and leveling observation time and the like. Under the condition that the CORS station has long-term continuous observation data and good data quality, the historical observation normal height of the CORS station can be dynamically corrected by using the normal height change determined by the data of the CORS station, and the dynamic maintenance of the area elevation reference frame based on the data of the CORS station is realized.
According to the method, continuous observation data of the SDCORS station network are utilized, the time sequence of the geodetic height is reconstructed, and the atmospheric pressure, land water and sea level data of the whole world and the region are combined to eliminate the abnormal change of the elevation caused by the surface environment load, so that the time sequence of the normal height change of the station is obtained. The examination of the correlated level data at 16 CORS sites showed that the maximum difference between the normal height variation based on CORS data and the normal height variation of the two-stage leveling was 29.5mm, with an average difference of 3.5 mm. Therefore, under the condition that the CORS site has long-term continuous observation data and the data quality is good, the historical observation normal height of the CORS site can be dynamically corrected by using the normal height change determined by the CORS site data, and the dynamic maintenance of the area elevation reference frame based on the CORS site data is realized.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by the present specification, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (4)
1. A dynamic monitoring method of an elevation reference frame based on a CORS station network is characterized by comprising the following steps: the method comprises the following specific steps:
the method comprises the following steps: generating a three-dimensional coordinate time sequence of a CORS site, selecting a plurality of stable international or national reference stations as constraints in an area or the periphery, resolving three-year observation data of the CORS site network site, producing a single-day solution, simultaneously obtaining a continuous multi-year comprehensive solution, and resolving to obtain a three-dimensional coordinate change time sequence of the CORS site relative to the comprehensive solution;
step two: carrying out the analysis and reconstruction of the ground high time series of the CORS station,
aiming at a single daily solution time sequence of the large-ground height of each CORS station, taking a Chebyshev function as a basis function, carrying out low-frequency parameter estimation on the time sequence, and separating linear terms; reconstructing a geodetic height nonlinear change time sequence according to the low-frequency parameters, using the geodetic height nonlinear change time sequence as a reference of gross error detection, detecting and eliminating gross errors according to 3 times of residual error standard deviation, and obtaining a clean geodetic height nonlinear change time sequence; carrying out period estimation on scattered points of the time sequence, estimating to obtain period parameters, and then reconstructing a geodetic high nonlinear change time sequence;
step three: calculating the elevation abnormal change value caused by the atmospheric pressure load,
(1) at each meteorological station, the difference between the actual measurement atmospheric pressure of each month and the average atmospheric pressure in the observation period is obtained, the change value of the atmospheric pressure of each month relative to the average atmospheric pressure in the observation period is obtained, the change of the equivalent water height is obtained through conversion, and the 1hPa atmospheric pressure change corresponds to the 1cm vertical load deformation; (2) the method comprises the steps that a load spherical harmonic coefficient model is obtained by utilizing global atmospheric pressure model data in an expansion mode, and an elevation abnormal change value of a CORS site caused by atmospheric pressure change is obtained by utilizing atmospheric pressure data in a calculation mode through a removal recovery method based on the load spherical harmonic coefficient model and a load Green function integral formula;
step four: calculating the elevation abnormal change value caused by land water load
(1) By utilizing the hydrological data of the GLDAS model, the difference between the land water reserve of each month and the equivalent water height of the average land water reserve in the observation period is obtained to obtain a change value of the land water reserve of each month relative to the equivalent water height of the average land water reserve in the observation period; (2) expanding a global land-water reserve change grid to obtain a spherical harmonic coefficient model, and calculating to obtain an elevation abnormal change value of the CORS station due to land-water reserve change by using the spherical harmonic coefficient model based on the earth load deformation theory;
step five: calculating the abnormal elevation change value caused by the sea level change of the area
(1) Obtaining a change value of the sea level height of each month relative to the average sea level height in the observation period by using AVISO global sea level height data and making a difference between the sea level height of each month and the average sea level height in the observation period, and expanding to obtain a spherical harmonic coefficient model; (2) calculating to obtain an elevation abnormal change value of the CORS site caused by sea level change by using sea level change data of a high-resolution area and adopting a removal recovery method based on a load spherical harmonic coefficient model and a load Green function integral formula;
step six: and calculating the abnormal change value of the point position elevation of the CORS station, and realizing dynamic monitoring and maintenance of the elevation datum.
2. The elevation reference frame dynamic monitoring method based on the CORS station network as claimed in claim 1, wherein: and determining the point location geodetic height variation value and the elevation abnormal variation value determined in the third step to the sixth step by the reconstructed CORS station geodetic height time sequence value in the second step, thereby determining the variation of the unit normal height and realizing the dynamic monitoring and maintenance of the elevation datum.
3. The elevation reference frame dynamic monitoring method based on the CORS station network as claimed in claim 1, wherein: two steps of interpolation or extrapolation of the variation of the geodetic height at given two moments according to the linear term and the nonlinear low-frequency period parameter of the geodetic height time sequence of the CORS station
Δh＝v·(t2-t1)+Δh′(t2)-Δh′(t1) (1)
In the formula t1、t2For a given two moments, v is the high linear rate of earth, Δ h' (t)1)、Δh′(t2) Respectively, the earth high nonlinear variation values obtained by the low-frequency period parameter reconstruction calculation at the two moments.
4. The elevation reference frame dynamic monitoring method based on the CORS station network as claimed in claim 1, wherein: the method for calculating the elevation abnormal change value caused by the earth surface environmental load based on the removal recovery method mainly comprises the following steps:
(1) converting global atmospheric pressure, land water and sea level data into equivalent water heights according to a load elastic deformation theory, and subtracting the equivalent water heights from an average value to obtain equivalent water height variation of each month, wherein the average value is the average of the equivalent water heights of the first three months;
specifically, average atmospheric pressure in an observation period is taken as a reference value at each meteorological station, difference is made between actual measurement atmospheric pressure of each month and the reference value, a change value of atmospheric pressure of each month relative to the reference value is obtained, and a change of equivalent water height is obtained through conversion, wherein 1hPa atmospheric pressure change corresponds to 1cm vertical load deformation;
(2) the method comprises the steps that a load spherical harmonic coefficient model is obtained by utilizing global atmospheric pressure model data in an expansion mode, and an elevation abnormal change value of a CORS site caused by atmospheric pressure change is obtained by utilizing atmospheric pressure data in a calculation mode through a removal recovery method based on the load spherical harmonic coefficient model and a load Green function integral formula;
equivalent water height variation at calculation point (R, theta, lambda)ΔhwCan be expressed as a normalized load spherical harmonic series:
in the formula: r is the mean radius of the earth;
normalizing the load spherical harmonic coefficient for n orders and m times;
association Legendre function for full normalization;
according to the load deformation theory, the influence of the abnormal load of the ground and the earth external elevation is as follows:
in the formula: g is a universal gravitation constant; rhowIs the density of water; rhoeIs the earth average density; m is the total mass of the earth; a is the radius of the earth's major semiaxis; r is the geocentric distance; gamma is normal gravity; k'nThe load is the Lefu number of n-order bit;
calculating the area load influence of the quasi-geoid based on the Green function integral:
the gravitational potential of a unit mass at a certain point on the surface, namely the direct influence, is as follows:
wherein g is a value of gravity,
for a fully normalized association Legendre function, ψ is the spherical angular distance between the calculated point (r, θ, λ) and the ground flow point (r ', θ ', λ '):
cosψ＝cosθcosθ′+sinθsinθ′cos(λ′-λ) (5)
the unit mass generates load on the solid earth, the earth deforms due to the load, the gravitational potential change is caused, and the indirect influence is as follows:
the total change of the quasi-geoid caused by the unit mass is the sum of the direct influence and the indirect influence, that is, the green function corresponding to the quasi-geoid is:
knowing the equivalent water height change Δ h on the groundwThen the geoid change is the spatial convolution of the equivalent water height change with the green function:
Δζ＝ρw∫SΔhwU(ψ)dS (8)
wherein dS is a ground flow integral bin;
after the abnormal elevation changes caused by the atmospheric pressure load, the land water load and the sea level change are respectively obtained, the abnormal elevation changes caused by the total load can be obtained by summing the abnormal elevation changes:
Δζ＝ΔζAir+ΔζLws+ΔζOcn (9)
in the formula, Δ ζ is abnormal in total load elevation, Δ ζAirFor abnormal changes in elevation due to atmospheric load, Δ ζLwsΔ ζ, an abnormal change in elevation due to land-water loadOcnThe elevation abnormal change caused by the change of the sea level of the area.
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