Depth reference epoch reduction method based on GNSS and satellite altimetry
By combining GNSS and satellite altimetry data to calculate crustal deformation and sea level change rates, and constructing a depth datum epoch reduction model, the problems of reduced depth datum accuracy and time-variability in traditional methods are solved, achieving more accurate and stable depth datum management.
Patent Information
- Application Number
- CN202510345495.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-24
- Publication Date
- 2025-09-30
- Estimated Expiration
- 2045-03-24
AI Technical Summary
Traditional depth datum determination methods lose accuracy over time and are unable to effectively cope with time-varying effects, resulting in unstable depth datums.
By combining GNSS and satellite altimetry data, and calculating the vertical deformation of the crust and the rate of change of absolute sea level height, a depth benchmark epoch reduction model is constructed to perform epoch reduction and verification.
The accuracy and long-term stability of depth benchmark epoch reduction are improved, the benchmark is adjusted dynamically to avoid drift problems caused by changes in sea level height and crustal movement, and to ensure the scientific nature and consistency of the benchmark.
Smart Images
Figure CN119879868B_ABST
Abstract
Description
Technical Field
[0001] The invention discloses a depth reference epoch reduction method based on GNSS and satellite altimetry, and belongs to the technical field of ocean surveying and mapping. Background Art
[0002] Due to the complexity of tidal waves and observational asynchrony, a depth datum cannot be defined at a single point like an elevation datum and propagated by equipotential surfaces or their approximations in a conservative force field. Instead, it is implemented by calculating tidal harmonic constants. If the harmonic constants exhibit a certain trend, the depth datum determined from them will inevitably exhibit a certain regularity. Traditional depth datums are represented by depth datum values determined at discrete tide gauges. In the calculation of depth datum values, the datum values determined at the same location and using the same calculation method exhibit a certain degree of time variability. That is, the depth datum values vary significantly depending on the timing of tide observations and the length of the series. Therefore, depth datums are often defined by a specific epoch, the depth datum epoch. This epoch is usually defined as a specific period of time. Based on the 18.61-year period of the ascending node of the moon, this period is usually 19 years.
[0003] With the accumulation of tidal data and global sea level changes, traditional methods for determining depth datums have shown significant deficiencies and flaws in practical applications. The depth datum values determined by various tide gauges at different times and locations use inconsistent tidal observation data at different time scales, resulting in the marine depth datum being in different time bases. Furthermore, the depth datum values currently used by long-term tide gauges were determined relatively early, and the algorithms used in different periods are inconsistent and have not been revised, leaving room for improvement in their timeliness. Furthermore, the depth datum lacks a clearly defined depth datum epoch, resulting in a significant degree of time variability when determined using tidal data from different periods and time periods.
[0004] To achieve the construction of a unified depth datum for the ocean and avoid the time-variability associated with depth datums determined from observational data from different periods and time periods, epoch reduction must be performed when determining the depth datum for tide gauge data that lack a specific 19-year tidal time scale. Currently, methods for implementing epoch reduction for depth datums primarily rely on transferring depth datum values from synchronized observations from nearby long-term tide gauges using methods such as distance-weighted interpolation, the ratio of the subtreme low tide level, the tidal range ratio, least squares fitting, and differential correction. However, the accuracy of depth datum values determined by these methods depends primarily on the quality of synchronized observation data. Factors such as tidal properties, transfer distance, synchronization duration, and topographical environment can also affect transfer accuracy. These methods are unable to address the impact of time-variability and have certain limitations.
[0005] Tide data observed at tide gauges reflect changes in relative sea level height, whose rate of change is the difference between the absolute sea level change rate and the vertical crustal deformation rate. For tide gauges where the absolute sea level rise rate is large but the crust rise rate is small, or where subsidence is occurring, the observed relative sea level height appears to be rising, and vice versa. For stations where long-term tide gauge data does not cover a 19-year timescale, the relative sea level change determined by the absolute sea level change rate and the vertical crustal deformation rate provides an innovative methodological basis for implementing depth reference epoch reduction. With the continuous accumulation of satellite altimetry data and the continuous improvement of GNSS technology, a practical data foundation and theoretical basis have been provided for depth reference epoch reduction. Summary of the Invention
[0006] The purpose of the present invention is to provide a depth reference epoch reduction method based on GNSS and satellite altimetry to solve the problem in the prior art that the accuracy of the depth reference value decreases over time.
[0007] The depth reference epoch reduction method based on GNSS and satellite altimetry includes:
[0008] S1. Collect and process GNSS data, tide gauge data, and satellite altimetry data to obtain time series of crustal vertical deformation and absolute sea level height;
[0009] S2. Processing the time series of vertical crust deformation to calculate the linear rate of vertical crust deformation;
[0010] S3. Processing the absolute sea level height time series to obtain the absolute sea level height change rate;
[0011] S4. Calculate the depth reference value based on tide gauge station data;
[0012] S5. Perform epoch reduction calculation on the depth reference value and verify the epoch reduction calculation result.
[0013] S1 includes site selection, which requires synchronous observation data with a time scale of more than one year, a data efficiency of more than 80%, and the presence of a GNSS station and a long-term tide gauge station within 50 km.
[0014] The collection and processing includes using a harmonic analysis iterative method to process missing values and zero drift of tide station data, wherein the tide station data is tide level data;
[0015] The mean of the effective along-track points within a 1° range around the tide gauge station is selected as the absolute sea level height time series.
[0016] The cubic spline interpolation method and the triple mean square error are used as the threshold to process the missing values and outliers of GNSS data and satellite altimetry data, respectively. The satellite altimetry data is a tidal height series inverted from satellite altimetry.
[0017] The short-term data drift caused by GNSS antenna movement and earthquake factors is unified to obtain the time series of vertical crustal deformation.
[0018] S2 involves calculating the linear rate of crustal vertical deformation from a time series of crustal vertical deformation using a linear regression model.
[0019] S3 includes using singular spectrum analysis to perform principal component analysis on the absolute sea level time series, eliminating seasonal periodic signals, and using linear regression model to obtain the absolute sea level change rate. .
[0020] S5 includes the linear rate of vertical crustal deformation between the observation epoch and the target epoch based on tide observation data and absolute sea level change rate , construct the mathematical model of depth reference epoch reduction:
[0021] ;
[0022] Where, 、 denote the observation epoch and target epoch respectively, The epoch return number for the depth reference value.
[0023] The epoch-corrected depth reference value is obtained by adding the epoch-corrected depth reference value of S4 to the epoch-corrected depth reference value.
[0024] Compared to existing technologies, the present invention has the following advantages: By combining multiple data sources, including GNSS, tide gauges, and satellite altimetry, the present invention can more comprehensively capture the dynamic characteristics of vertical crustal deformation and absolute sea level changes. Traditional methods primarily rely on tide gauge data, which is limited in accuracy due to factors such as the quality of synchronized observation data and transmission distance. The fusion of multi-source data not only overcomes the shortcomings of a single data source but also improves the accuracy of depth reference epoch reduction through cross-validation, particularly on long-term timescales, and can more accurately reflect trends in sea level changes. Traditional depth reference determination methods struggle to effectively address the effects of time variability. The present invention, by introducing the calculation of the absolute sea level change rate and the vertical crustal deformation rate, provides a new theoretical basis for depth reference epoch reduction. This method can dynamically adjust the depth reference value, avoiding the problem of reference surface drift caused by sea level changes and crustal movement, thereby ensuring the long-term stability and scientific nature of the depth reference. BRIEF DESCRIPTION OF THE DRAWINGS
[0025] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION
[0026] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention are described clearly and completely below. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.
[0027] The depth reference epoch reduction method based on GNSS and satellite altimetry includes:
[0028] S1. Collect and process GNSS data, tide gauge data, and satellite altimetry data to obtain time series of crustal vertical deformation and absolute sea level height;
[0029] S2. Processing the time series of vertical crust deformation to calculate the linear rate of vertical crust deformation;
[0030] S3. Processing the absolute sea level height time series to obtain the absolute sea level height change rate;
[0031] S4. Calculate the depth reference value based on tide gauge station data;
[0032] S5. Perform epoch reduction calculation on the depth reference value and verify the epoch reduction calculation result.
[0033] S1 includes site selection, which requires synchronous observation data with a time scale of more than one year, a data efficiency of more than 80%, and the presence of a GNSS station and a long-term tide gauge station within 50 km.
[0034] The collection and processing includes using a harmonic analysis iterative method to process missing values and zero drift of tide station data, wherein the tide station data is tide level data;
[0035] The mean of the effective along-track points within a 1° range around the tide gauge station is selected as the absolute sea level height time series.
[0036] The cubic spline interpolation method and the triple mean square error are used as the threshold to process the missing values and outliers of GNSS data and satellite altimetry data, respectively. The satellite altimetry data is a tidal height series inverted from satellite altimetry.
[0037] The short-term data drift caused by GNSS antenna movement and earthquake factors is unified to obtain the time series of vertical crustal deformation.
[0038] S2 involves calculating the linear rate of crustal vertical deformation from a time series of crustal vertical deformation using a linear regression model.
[0039] S3 includes using singular spectrum analysis to perform principal component analysis on the absolute sea level time series, eliminating seasonal periodic signals, and using linear regression model to obtain the absolute sea level change rate. .
[0040] S5 includes the linear rate of vertical crustal deformation between the observation epoch and the target epoch based on tide observation data and absolute sea level change rate , construct the mathematical model of depth reference epoch reduction:
[0041] ;
[0042] Where, 、 denote the observation epoch and target epoch respectively, The epoch return number for the depth reference value.
[0043] The epoch-corrected depth reference value is obtained by adding the epoch-corrected depth reference value of S4 to the epoch-corrected depth reference value.
[0044] The present invention innovatively proposes a depth benchmark epoch reduction model based on the absolute sea level change rate and the crustal vertical deformation rate. Traditional methods mainly rely on statistical analysis of tide gauge data, while this model, by combining satellite altimetry and GNSS data, can more accurately quantify the rate of change of relative sea level height, thereby providing a more scientific mathematical basis for the epoch reduction of the depth benchmark. This innovation not only solves the problem that traditional methods cannot cope with time variability, but also provides a new technical means for the long-term maintenance of the depth benchmark. The present invention proposes an innovative mechanism for collaborative processing and verification of multi-source data. Through the joint processing of GNSS, satellite altimetry and tide gauge data, not only can more accurate crustal deformation and absolute sea level change information be obtained, but also the reliability and consistency of the data can be ensured through cross-validation. This collaborative processing mechanism not only improves the accuracy of depth benchmark epoch reduction, but also provides an operational technical framework for subsequent datum updates and maintenance, and has important practical value.
[0045] The technical process of the present invention is as follows Figure 1As shown, the method includes processing GNSS observation data, extracting crustal deformation sequence, obtaining crustal vertical deformation rate and uncertainty, processing satellite altimetry data, extracting sea surface height time series, obtaining absolute sea surface height rate, obtaining long-term tide gauge station data, processing tide level data in combination with crustal deformation sequence, correcting relative sea surface height by combining crustal vertical deformation rate, uncertainty and tide level data with absolute sea surface height rate, obtaining depth reference epoch reduction result and verifying it. The present invention extracts absolute sea surface height change rate of 16 long-term tide gauge stations in the experimental sea area, and performs statistics on sea surface height change rate of 16 long-term tide gauge stations as shown in Table 1.
[0046] Table 1. Sea level change rate at 16 long-term tide gauge stations (unit: mm)
[0047] ;
[0048] according to conduct Calculate and obtain the depth reference plane as shown in Table 2.
[0049] Table 2. Epoch reduction verification results (unit: cm)
[0050] ;
[0051] Table 2 shows the results of the long-term tide gauge data after depth benchmark epoch conversion. Using the tide data from 2000 to 2011 as the benchmark, the data from 2011 to 2018 were further corrected for deformation (2011-2018 deformation = (The values are rounded to two decimal places in cm in the table). The final epoch-corrected baseline values were obtained. A comparative analysis using the baseline values from 2000 to 2018 as the "true values" showed that the average baseline value decreased from 7.03 cm to 3.95 cm after epoch-correction. Although the correction effect was less than ideal at some stations due to shoreline changes and environmental factors, overall, epoch-correction significantly improved the accuracy of the baseline values.
[0052] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents, and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.
Claims
1. A depth reference epoch reduction method based on GNSS and satellite altimetry, characterized in that: include: S1. Collect and process GNSS data, tide gauge data, and satellite altimetry data to obtain time series of crustal vertical deformation and absolute sea level height; S2. Processing the time series of vertical crust deformation to calculate the linear rate of vertical crust deformation; S3. Processing the absolute sea level height time series to obtain the absolute sea level height change rate; S4. Calculate the depth reference value based on tide gauge station data; S5. Perform epoch reduction calculation on the depth reference value and verify the epoch reduction calculation result; S5 includes the linear rate of vertical crustal deformation between the observation epoch and the target epoch based on tide observation data with a 19-year epoch period. and absolute sea level change rate , construct the mathematical model of depth reference epoch reduction: ； Where, 、 denote the observation epoch and target epoch respectively, is the epoch return number of the depth reference value; The epoch-corrected depth reference value is obtained by adding the epoch-corrected depth reference value of S4 to the epoch-corrected depth reference value.
2. The depth reference epoch reduction method based on GNSS and satellite altimetry according to claim 1, characterized in that: S1 includes site selection, which requires synchronous observation data with a time scale of more than one year, a data efficiency of more than 80%, and the presence of a GNSS station and a long-term tide gauge station within 50 km.
3. The depth reference epoch reduction method based on GNSS and satellite altimetry according to claim 1, characterized in that: The collection and processing includes using a harmonic analysis iterative method to process missing values and zero drift of tide station data, wherein the tide station data is tide level data; The mean of the effective along-track points within a 1° range around the tide gauge station is selected as the absolute sea level height time series.
4. The method for calculating depth reference epochs based on GNSS and satellite altimetry according to claim 3, wherein: The cubic spline interpolation method and the triple mean square error are used as the threshold to process the missing values and outliers of GNSS data and satellite altimetry data, respectively. The satellite altimetry data is a tidal height series inverted from satellite altimetry. The short-term data drift caused by GNSS antenna movement and earthquake factors is unified to obtain the time series of vertical crustal deformation.
5. The method for depth reference epoch reduction based on GNSS and satellite altimetry according to claim 4, characterized in that: S2 involves calculating the linear rate of crustal vertical deformation from a time series of crustal vertical deformation using a linear regression model.
6. The method for depth reference epoch reduction based on GNSS and satellite altimetry according to claim 5, characterized in that: S3 includes using the singular spectrum analysis method to perform principal component analysis on the absolute sea level time series, eliminating seasonal periodic signals, and using a linear regression model to obtain the absolute sea level change rate. .