A system and method for constructing an ocean gravity field model based on adaptive fusion of multi-source altimetry data

Abstract

The present application provides a system and method for constructing an ocean gravity field model by adaptively fusing multi-source altimetry data. The system removes time-varying noise on the sea surface, removes the reference geoid height and the average dynamic topography of the ocean after preprocessing the multi-source altimetry data; calculates the multi-source fusion weighting factor of each type of satellite altimeter data, and dynamically adjusts the multi-source fusion weighting factor of each type of satellite altimeter data for iterative updating in combination with the evaluation accuracy of the ocean gravity field; calculates the multi-source fusion weights required for the fusion of multi-source altimetry data, and calculates the geoid height grid data or the vertical deviation grid data through gridding processing; inverts the ocean gravity field, and restores the reference gravity field model to generate an ocean gravity field model. The advantages of the present application are: it can fully tap the value of satellite altimetry data of different observation modes and different observation tasks, and fully, flexibly and efficiently utilize multi-source altimetry data to improve the spatial resolution of the ocean gravity field.

Description

Technical Field

[0001] The present application belongs to the field of ocean remote sensing mapping, and specifically relates to a system and method for constructing an ocean gravity field model by adaptively fusing multi-source altimetry data. Background Art

[0002] Radar altimeter is an important active microwave remote sensor, which is carried on ocean remote sensing satellites. It transmits radar pulse signals to the sea surface and receives echo signals to obtain ocean observation data such as sea surface height (SSH), effective wave height, and wind speed. Gravity-related parameters such as ocean geoid and vertical deviation can be extracted from satellite altimeter SSH data, and the ocean gravity field can be further inverted. Satellite altimeter has a rapid global coverage capability and can obtain global ocean gravity field information from space on a large scale, greatly improving the breadth and depth of human understanding of the ocean. Compared with traditional gravity detection methods (such as ship measurement and airborne), satellite altimeter technology can complete the workload of the past century in a few months; compared with gravity satellites, satellite altimeter can obtain high-resolution ocean gravity fields on a global scale. Therefore, satellite radar altimeter has become the most effective means to construct a high-resolution ocean gravity field model.

[0003] The spatial resolution of the ocean gravity field model mainly depends on the ground track density of the altimetry satellite. It is difficult to obtain high-resolution global ocean gravity field information by relying solely on a single satellite altimeter measurement. Domestic and foreign scholars have constructed a series of global ocean gravity field models by using multiple altimetry satellites to fuse and invert the ocean gravity field. The most authoritative of these are the SIO series models from the Scripps Institution of Oceanography in the United States and the DTU series models from the Technical University of Denmark. They improve spatial resolution by continuously combining new altimetry data, but the resolution of the latest version of the global ocean gravity field model obtained by combining all currently available satellite altimetry data has not yet reached a true 1'×1' resolution.

[0004] Different types of satellite altimetry data have their own advantages in the construction of ocean gravity field models. Geodetic mission (GM) data with high spatial coverage density dominates the construction of ocean gravity field models. Precise repetitive mission (ERM) data with higher accuracy can effectively supplement track gaps. Synthetic aperture altimetry data has higher accuracy, low resolution mode (LRM) data has better track diversity, GNSS-R altimetry data has rich signal sources, and wide swath interferometric altimetry and dual satellite follow-up altimetry can simultaneously obtain two-dimensional spatial information along and across the track, which can significantly improve the spatial resolution of the ocean gravity field. However, there is still a lack of a set of methods that can give full play to the value of satellite altimetry data of different observation tasks and different observation modes, and adaptively fuse multi-source altimetry data to improve the spatial resolution of the ocean gravity field. Summary of the invention

[0005] The purpose of this application is to overcome the defects of the prior art that it is unable to adaptively fuse multi-source altimetry data and cannot fully utilize the value of satellite altimetry data for different observation tasks and different observation modes.

[0006] In order to achieve the above purpose, the present application proposes a system for constructing an ocean gravity field model by adaptively fusing multi-source altimetry data, the system comprising:

[0007] Multi-source altimetry data classification preprocessing module: used to classify multi-source altimetry data according to satellite observation modes, perform preprocessing and data editing to remove abnormal values ​​of altimetry data, remove sea surface time-varying noise from sea surface height data according to satellite observation task classification, and remove the height values ​​of reference geoid height and ocean average dynamic terrain model to obtain preprocessed satellite line sea surface height data;

[0008] Multi-source fusion factor adaptive iteration module: used to calculate the multi-source fusion weighting factor of each type of satellite altimeter data involved in the fusion according to the high sea surface precision of the multi-source satellite altimeter; combined with the high sea surface precision of the multi-source altimeter data and the evaluation accuracy obtained by the ocean gravity field accuracy evaluation module, dynamically adjust the multi-source fusion weighting factor of each type of satellite altimeter data for iterative update, for adaptive fusion of multi-source altimeter data;

[0009] Multi-source altimetry data fusion module: used to perform cross-point adjustment or differential processing along the survey line sea surface height data of multi-source satellite altimeters, calculate the survey line geoid height data or sea surface height slope information after eliminating gross errors, and use the accuracy of the geoid height or sea surface height slope, the distance to the grid point to be calculated, and the multi-source fusion weighting factor of the satellite altimeter to calculate the multi-source fusion weights required for multi-source altimetry data fusion, and calculate the geoid height grid data or vertical deviation grid data through gridding processing;

[0010] The ocean gravity field model construction module is used to invert the ocean gravity field based on the vertical deviation method or the geoid height method from the vertical deviation grid data or the geoid height grid data, and restore the reference gravity field model corresponding to the removed reference geoid height model to generate the ocean gravity field model; and

[0011] Marine gravity field accuracy assessment module: used to use the refined measured gravity data as verification data, interpolate the marine gravity field model to the measured gravity point to obtain the model gravity value at that location, use the measured gravity value at the corresponding location to calculate the gravity residual value at the measured point, and calculate the assessment accuracy of the marine gravity field model based on the residual.

[0012] As an improvement of the above system, the processing process of the multi-source altimetry data classification preprocessing module includes:

[0013] Step A1: Classify the multi-source altimetry data according to the satellite observation mode, perform waveform re-tracking and error correction and data editing, and remove abnormal values ​​of the altimetry data;

[0014] Step A2: Classify the multi-source altimetry data according to the satellite observation mission, remove the sea surface time-varying noise from the sea surface height data, use collinear adjustment to weaken the ERM mission data, and use low-pass filtering to filter out the time-varying noise from the GM mission data;

[0015] Step A3: Use the latitude and longitude information to interpolate to the satellite survey line sea surface height position to calculate the corresponding reference geoid height and ocean average dynamic terrain, remove the reference geoid height and ocean average dynamic terrain model height values ​​from the sea surface high, and obtain the residual height ΔN:

[0016] ΔN=SSH-MDT-N ref

[0017] Where SSH is the sea surface height obtained by satellite altimetry; MDT is the height value of the mean dynamic terrain model; N ref is the reference model geoid height.

[0018] As an improvement of the above system, the processing process of the multi-source fusion factor adaptive iteration module includes:

[0019] Step B1: Calculate the initial value of the multi-source fusion weighting factor of each type of satellite altimeter data involved in the fusion according to the high sea surface precision of the multi-source satellite altimeter;

[0020] The initial value of the multi-source fusion weighting factor is:

[0021]

[0022] Among them, W s j is the multi-source fusion weighting factor of the j-th type of satellite altimeter data; m s j represents the high accuracy of the sea surface of the jth type of satellite altimeter data, which is usually determined by an external reliable prior value; l represents the number of types of satellite altimeter data involved in the fusion, and different missions of the same model of satellite altimeter are regarded as different types;

[0023] Step B2: combining the sea surface high precision of multi-source altimetry data and the evaluation accuracy of each type of satellite altimetry data obtained by the ocean gravity field accuracy evaluation module, dynamically adjusting the multi-source fusion weighting factor of each type of satellite altimeter data for iterative calculation, for adaptive fusion of multi-source altimetry data;

[0024] The multi-source fusion weighting factor is iteratively updated as:

[0025]

[0026] Among them, m g j Represents the evaluation accuracy of the ocean gravity field inverted from the j-th type of satellite altimeter data.

[0027] As an improvement of the above system, the processing process of the multi-source height measurement data fusion module includes:

[0028] Step C1: Perform cross-point adjustment or differential processing along the survey line for the sea surface height data of the multi-source satellite altimeter, and calculate the survey line geoid height data or sea surface height slope information after eliminating gross errors;

[0029] Step C2: Calculate the multi-source fusion weights required for multi-source altimetry data fusion by using the accuracy of the geoid height / sea surface height slope, the distance to the grid point to be determined, and the multi-source fusion weighting factor of the satellite altimeter;

[0030] If there are multiple altimetry data within a certain sea area near the grid point, the weight P of the i-th observation point of the multi-source satellite altimetry data is i for:

[0031]

[0032] or

[0033]

[0034] Where, d i is the distance from the ith observation point to the grid point to be determined; σ i is the slope accuracy of the geoid height / sea surface height at the ith observation point; W s j is the multi-source fusion weighting factor of the satellite altimeter corresponding to the i-th observation point;

[0035] Step C3: Gridding the geoid height data or sea surface height slope data of the multi-source satellite altimeter to generate geoid height grid data or vertical line deviation grid data.

[0036] As an improvement of the above system, the processing process of the ocean gravity field model construction module includes:

[0037] Step D1: Invert the ocean gravity field based on the vertical deviation method or the geoid height method by fusing the vertical deviation grid data or the geoid height grid data obtained by fusing the multi-source altimetry data;

[0038] Step D2: restoring the reference gravity field model corresponding to the removed reference geoid height model to the inverted ocean gravity anomaly to generate a high-resolution ocean gravity field model;

[0039] The global ocean gravity field model expression is:

[0040] g=Δg+g ref

[0041] Where Δg is the inverted gravity anomaly; g ref is the reference gravity field model.

[0042] As an improvement of the above system, the process of inverting the ocean gravity field using the vertical deviation method includes:

[0043] The discrete expression of the inverse Vening-Meinesz formula for inverting gravity anomalies from the vertical deviation grid is:

[0044]

[0045] in, is the gravity anomaly of the grid point p to be determined; γ0 is the average normal gravity value of the earth; and are the minimum and maximum latitudes of the calculation area; α qp ξ is the azimuth from the mobile grid point q to the grid point p in the calculation area; q and η q are the north-south and east-west components of the vertical deviation of the mobile grid point q in the calculation area, respectively. is the latitude of the mobile grid point q in the calculation area; and Δλ are the grid spacings in latitude and longitude; F1 and are the one-dimensional Fourier transform and inverse transform operators respectively; H′ is the derivative of the kernel function H, expressed as:

[0046]

[0047] As an improvement of the above system, the process of inverting the ocean gravity field using the geoid height method includes:

[0048] The discrete expression of the inverse Stokes formula for inverting gravity anomaly from the geoid high grid is:

[0049]

[0050] in, is the gravity anomaly of the grid point p to be determined; γ0 is the average normal gravity value of the earth; R is the average radius of the earth; is the geoid height of the grid point p to be determined; and λ q are the latitude and longitude of the mobile grid point q in the calculation area, respectively; and They are the minimum and maximum latitudes of the calculation area respectively; and Δλ are the grid spacings in latitude and longitude; F1 and are the one-dimensional Fourier transform and inverse transform operators, respectively, and the kernel function for:

[0051]

[0052] As an improvement of the above system, the processing process of the ocean gravity field accuracy assessment module includes:

[0053] Step E1: Interpolate the ocean gravity field model to the ship's measured gravity point to obtain the corresponding gravity value G k , and then use the refined ship-measured gravity value at the corresponding position Calculate the gravity residual value RES of the ocean gravity field model at the ship measurement point k ;

[0054]

[0055] Step E2: Count the gravity residual values ​​of the ocean gravity field model at the ship measurement point and calculate the accuracy of the residual values ​​of the ocean gravity field model.

[0056] As an improvement of the above system, the accuracy of the residual value of the ocean gravity field model is the root mean square RMS j :

[0057]

[0058] Among them, RMS j is the RMS value of the ocean gravity field inverted from the j-th type satellite altimeter data; N is the number of measured points for evaluating the j-th type satellite altimeter.

[0059] The present application also provides a method for constructing an ocean gravity field model by adaptively fusing multi-source altimetry data, which is implemented based on the above system and includes:

[0060] Step 1) The multi-source altimetry data classification preprocessing module classifies the multi-source altimetry data according to the satellite observation mode, performs preprocessing and data editing respectively to eliminate the abnormal values ​​of the altimetry data, removes the sea surface time-varying noise from the sea surface height data according to the satellite observation task classification, and removes the height values ​​of the reference geoid height and the ocean average dynamic terrain model to obtain the preprocessed satellite line sea surface height data;

[0061] Step 2) performing intersection adjustment or differential processing along the survey line on the satellite survey line sea level data, and calculating the survey line geoid height data or sea level height slope information after eliminating gross errors;

[0062] Step 3) the multi-source fusion factor adaptive iteration module calculates the initial value of the multi-source fusion weighting factor of each type of satellite altimeter data involved in the fusion according to the high sea surface precision of the multi-source satellite altimeter;

[0063] Step 4) according to the accuracy of the geoid height or the sea surface height slope, the distance to the grid point to be determined, and the multi-source fusion weighting factor obtained by the multi-source fusion factor adaptive iteration module, the multi-source fusion weight required for the fusion of multi-source altimeter data is calculated, and the geoid height or the sea surface height slope data of the survey line of the single-satellite altimeter are gridded to obtain the geoid height grid data or the vertical line deviation grid data of the single-satellite altimeter;

[0064] Step 5) the ocean gravity field model construction module uses the geoid height grid data or the vertical deviation grid data of the single-satellite altimeter to invert the ocean gravity field based on the geoid height method or the vertical deviation method, and restores the reference gravity field model corresponding to the removed reference geoid height model to generate the ocean gravity field grid data of the single-satellite altimeter;

[0065] Step 6) The ocean gravity field accuracy assessment module performs accuracy assessment on the ocean gravity field grid data of the single satellite altimeter respectively; combining the sea surface high precision of the multi-source altimetry data and the assessment accuracy obtained by the ocean gravity field accuracy assessment module, dynamically adjusts the fusion weighting factor of each type of satellite altimetry data for iterative update, and calculates the adaptive update value of the multi-source fusion weighting factor;

[0066] Step 7) using the accuracy of the geoid height or the sea surface height slope, the distance to the grid point to be determined, and the multi-source fusion weighting factor of the satellite altimeter, the multi-source fusion weights required for the fusion of multi-source altimetry data are calculated, and the geoid height data or the vertical deviation grid data are calculated through gridding processing;

[0067] Step 8) The ocean gravity field model construction module fuses the vertical deviation grid data or the geoid height grid data obtained by fusing the multi-source altimetry data, inverts the ocean gravity field based on the vertical deviation method or the geoid height method, and restores the reference gravity field model corresponding to the removed reference geoid height model to generate a high-resolution ocean gravity field model.

[0068] Compared with the prior art, the advantages of this application are:

[0069] 1. This application has for the first time established an ocean gravity field model construction system that adaptively fuses multi-source satellite altimetry data with different observation modes and different observation tasks, which can construct an ocean gravity field model with higher resolution.

[0070] 2. The method for constructing an ocean gravity field model by adaptively fusing multi-source satellite altimetry data proposed in this application can fully tap the value of satellite altimetry data of different observation modes and different observation tasks, and fully, flexibly and efficiently utilize multi-source altimetry data to improve the spatial resolution of the ocean gravity field. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 The figure shows the structure diagram of the ocean gravity field model constructed by adaptive fusion of multi-source altimetry data;

[0072] Figure 2 Shown is a flow chart of the method for constructing an ocean gravity field model by adaptively fusing multi-source altimetry data. DETAILED DESCRIPTION

[0073] The technical solution of the present application is described in detail below with reference to the accompanying drawings.

[0074] The present application proposes a system and method for constructing an ocean gravity field model by adaptively fusing multi-source altimetry data, which is not only applicable to the vertical deviation method, but also to the geoid height method. By adaptively fusing satellite altimetry data of different observation tasks (ERM, GM) and different observation modes (LRM, synthetic aperture mode, wide swath interferometry, dual-satellite follow-up altimetry, and GNSS-R altimetry, etc.), multi-source altimetry data can be fully, flexibly and efficiently utilized to improve the spatial resolution of the ocean gravity field.

[0075] Example 1

[0076] like Figure 1 As shown, the present application proposes an ocean gravity field model construction system for adaptive fusion of multi-source altimetry data, including a multi-source altimetry data classification preprocessing module, a multi-source fusion factor adaptive iteration module, a multi-source altimetry data fusion module, an ocean gravity field model construction module and an ocean gravity field accuracy assessment module.

[0077] The design of each module is as follows:

[0078] 1. Multi-source altimetry data classification preprocessing module: used to classify multi-source altimetry data according to satellite observation mode, perform preprocessing and data editing to remove altimetry data outliers, remove sea surface time-varying noise from sea surface height data according to satellite observation task classification, and remove reference geoid height model and ocean average dynamic terrain model to obtain high-precision satellite line sea surface height data (time, longitude, latitude, altitude);

[0079] The processing process of the multi-source altimetry data classification preprocessing module includes:

[0080] Step 1: Classify and process by observation mode: classify multi-source altimetry data by satellite observation mode, perform waveform re-tracking, error correction and data editing respectively to eliminate abnormal values ​​of altimetry data;

[0081] Step 2: Classify and process according to observation tasks; classify multi-source altimetry data according to satellite observation tasks, remove sea surface time-varying noise from sea surface height data, use collinear adjustment to weaken ERM mission data, and use low-pass filtering to filter out time-varying noise from GM mission data;

[0082] Step 3: Reference model removal: Use the longitude and latitude information to interpolate to the satellite survey line sea surface height position to calculate the corresponding reference geoid height and ocean average dynamic terrain, and remove the reference geoid height model and ocean average dynamic terrain model from the sea surface height.

[0083] ΔN=SSH-MDT-N ref

[0084] Where ΔN is the residual height used for subsequent inversion; SSH is the sea surface height obtained by satellite altimetry; MDT is the height value of the mean dynamic terrain model; N ref is the reference model geoid height.

[0085] 2. Multi-source fusion factor adaptive iteration module: used to calculate the multi-source fusion weighting factor of each type of satellite altimeter data involved in the fusion according to the high sea surface precision of the multi-source satellite altimeter; combined with the high sea surface precision of the multi-source satellite altimeter and the evaluation accuracy obtained by the ocean gravity field accuracy evaluation module, dynamically adjust the multi-source fusion weighting factor of each type of satellite altimeter data for iterative update, for adaptive fusion of multi-source altimeter data;

[0086] The processing process of the multi-source fusion factor adaptive iteration module includes:

[0087] Step 1: Calculate the initial value of the multi-source fusion weighting factor: According to the high sea surface precision of the multi-source satellite altimeter, calculate the initial value of the multi-source fusion weighting factor of each type of satellite altimeter data involved in the fusion;

[0088] The initial value of the multi-source fusion weighting factor is:

[0089]

[0090] Among them, W s j is the multi-source fusion weighting factor of the j-th type of satellite altimeter data; m s jrepresents the sea surface high accuracy of the jth type of satellite altimeter data, which is usually determined by an external reliable prior value; l represents the number of satellite altimeter data involved in the fusion (different missions of the same model of satellite altimeter are divided into different categories, such as ERS-1 / GM and ERS-1 / ERM are two types of altimeter data involved in the fusion).

[0091] Step 2: Adaptive update of multi-source fusion weighting factors; Combine the sea surface high precision of multi-source satellite altimeters and the evaluation accuracy of each type of satellite altimeter data obtained by the ocean gravity field accuracy evaluation module, dynamically adjust the multi-source fusion weighting factors of each type of satellite altimeter data for iterative calculation, and use them for adaptive fusion of multi-source altimeter data;

[0092] The multi-source fusion weighting factor is iteratively updated as:

[0093]

[0094] Among them, W s j is the multi-source fusion weighting factor of the j-th type of satellite altimeter data; m s j represents the sea surface high accuracy of the jth type satellite altimeter, which is usually determined by an external reliable prior value; m g j represents the evaluation accuracy of the ocean gravity field inverted from the j-th type of satellite altimeter data; l represents the number of satellite altimeter data involved in the fusion (different tasks of the same model of satellite altimeter are divided into different categories, such as ERS-1 / GM and ERS-1 / ERM are two types of altimeter data involved in the fusion).

[0095] 3. Multi-source altimetry data fusion module: used to perform cross-point adjustment or differential processing along the survey line for the sea surface height data (time, longitude, latitude, height) of the multi-source satellite altimeter, calculate the survey line geoid height data or sea surface height slope information after eliminating the gross error, and use the accuracy of the geoid height or sea surface height slope, the distance to the grid point to be calculated, and the multi-source fusion weighting factor of the satellite altimeter to calculate the multi-source fusion weights required for the fusion of multi-source altimetry data, and calculate the high-resolution geoid height data (longitude, latitude, geoid height) or vertical deviation grid data (longitude, latitude, meridian component, meridian component) through gridding processing;

[0096] The processing of the multi-source altimetry data fusion module includes:

[0097] Step 1: Calculation of geoid height or sea surface height slope: It is used to perform cross-point adjustment or differential processing along the survey line sea surface height data (time, longitude, latitude, height) of the multi-source satellite altimeter, and calculate the survey line geoid height data (time, longitude, latitude, height, accuracy) or sea surface height slope information (time, longitude, latitude, slope, azimuth, accuracy) after eliminating gross errors;

[0098] Step 2: Multi-source fusion weight calculation: It is used to calculate the multi-source fusion weight required for multi-source altimetry data fusion by using the accuracy of geoid height / sea surface height slope, the distance to the grid point to be determined, and the multi-source fusion weight factor of the satellite altimeter;

[0099] If there are multiple altimetry data within a certain sea area near the grid point, the weight of the i-th observation point of the multi-source satellite altimetry data is

[0100]

[0101] or

[0102]

[0103] Where, d i is the distance from the ith observation point to the grid point to be determined; σ i is the slope accuracy of the geoid height / sea surface height at the ith observation point; W s j is the multi-source fusion weighting factor of the satellite altimeter corresponding to the i-th observation point.

[0104] Step 3: Generate high-resolution grid data; grid the geoid height data or sea surface height slope data of the multi-source satellite altimeter to generate high-resolution geoid height grid data (longitude, latitude, geoid height) or vertical deviation grid data (longitude, latitude, meridian component, meridian component);

[0105] 4. Ocean gravity field model construction module: used to fuse the vertical deviation grid data or geoid height grid data obtained by multi-source altimetry data, invert the ocean gravity field based on the vertical deviation method or geoid height method, and restore the reference gravity field model corresponding to the removed reference geoid height model to generate a high-resolution ocean gravity field model;

[0106] The processing of the ocean gravity field model building module includes:

[0107] Step 1: Inversion of ocean gravity field: It is used to invert the ocean gravity field based on the vertical deviation method or the geoid height method by fusing the vertical deviation grid data or the geoid height grid data obtained by fusing the multi-source altimetry data;

[0108] The discrete expression of the inverse Vening-Meinesz formula for inverting gravity anomalies from the vertical deviation grid is:

[0109]

[0110] In the formula, is the gravity anomaly of the grid point p to be determined; γ0 is the average normal gravity value of the earth; and are the minimum and maximum latitudes of the calculation area; α qp ξ is the azimuth from the mobile grid point q to the grid point p in the calculation area; q and η q are the north-south and east-west components of the vertical deviation of the mobile grid point q in the calculation area, respectively. is the latitude of the mobile grid point q in the calculation area; and Δλ are the grid spacings in latitude and longitude; F1 and are the one-dimensional Fourier transform and inverse transform respectively. H′ is the derivative of the kernel function H, expressed as

[0111]

[0112] The discrete expression of the inverse Stokes formula for inverting gravity anomaly from the geoid high grid is:

[0113]

[0114] In the formula, is the gravity anomaly of the grid point p to be determined; γ0 is the average normal gravity value of the earth; R is the average radius of the earth; is the geoid height of the grid point p to be determined; and λ q are the latitude and longitude of the mobile grid point q in the calculation area, respectively; and They are the minimum and maximum latitudes of the calculation area respectively; and Δλ are the grid spacings in latitude and longitude; F1 and are the one-dimensional FFT forward transform and inverse transform operators respectively, and the kernel function is:

[0115]

[0116] Step 2: Restore the reference gravity field model; restore the reference gravity field model corresponding to the removed reference geoid height model to the inverted ocean gravity anomaly to generate a high-resolution ocean gravity field model.

[0117] The global ocean gravity field model expression is:

[0118] g=Δg+gref

[0119] Where Δg is the inverted gravity anomaly; g ref is the reference gravity field model.

[0120] 5. Marine gravity field accuracy assessment module: It is used to use the refined measured gravity data as verification data, interpolate the marine gravity field model to the measured gravity point to obtain the model gravity value at that location, use the measured gravity value at the corresponding location to calculate the gravity residual value at the measured point, and calculate the assessment accuracy of the marine gravity field model based on the residual.

[0121] The processing of the ocean gravity field accuracy assessment module includes:

[0122] Step 1: Calculation of gravity residual: Interpolate the ocean gravity field model to the ship-measured gravity point to obtain the corresponding gravity value G k , and then use the refined ship-measured gravity value at the corresponding position Calculate the gravity residual value of the ocean gravity field model at the ship measurement point;

[0123]

[0124] Step 2: Model accuracy assessment; statistically analyze the gravity residual values ​​of the ocean gravity field model at the ship measurement point and calculate the accuracy of the residual values ​​of the ocean gravity field model; there are many ways to calculate the accuracy, such as root mean square.

[0125]

[0126] Among them, RMS j is the RMS value of the ocean gravity field inverted from the j-th type satellite altimeter data; N is the number of measured points for evaluating the j-th type satellite altimeter.

[0127] Example 2

[0128] like Figure 2 As shown, the present application also proposes a method for constructing an ocean gravity field model by adaptively fusing multi-source altimetry data, which is implemented based on the above system and includes:

[0129] Step 1) The multi-source altimetry data classification preprocessing module classifies the multi-source altimetry data according to the satellite observation mode, performs preprocessing and data editing respectively to eliminate the altimetry data outliers, removes the sea surface time-varying noise from the sea surface height data according to the satellite observation task classification, and removes the reference geoid height model and the ocean average dynamic terrain model to obtain high-precision satellite line sea surface height data (time, longitude, latitude, altitude);

[0130] Step 2) The sea surface height data (time, longitude, latitude, height) of the survey line of the multi-source satellite altimeter are respectively subjected to intersection adjustment or differential processing along the survey line, and the geoid height data (time, longitude, latitude, height, accuracy) or sea surface height slope information (time, longitude, latitude, slope, azimuth, accuracy) of the survey line is calculated after the gross error is eliminated;

[0131] Step 3) the multi-source fusion factor adaptive iteration module calculates the initial value of the multi-source fusion weighting factor of each type of satellite altimeter data involved in the fusion according to the high sea surface precision of the multi-source satellite altimeter;

[0132] Step 4) According to the accuracy of the geoid height or the sea surface height slope, the distance to the grid point to be determined, and the multi-source fusion weighting factor obtained by the multi-source fusion factor adaptive iteration module, the multi-source fusion weight required for the fusion of multi-source altimetry data is calculated, and the geoid height or sea surface height slope data of the survey line of the single-satellite altimeter is gridded to obtain the geoid height grid data (longitude, latitude, geoid height) or the vertical line deviation grid data (longitude, latitude, meridian component, and meridian component) of the single-satellite altimeter;

[0133] Step 5) the ocean gravity field model construction module uses the geoid height grid data or the vertical deviation grid data of the single-satellite altimeter to invert the ocean gravity field based on the geoid height method or the vertical deviation method, and restores the reference gravity field model corresponding to the removed reference geoid height model to generate the ocean gravity field grid data of the single-satellite altimeter;

[0134] Step 6) The ocean gravity field accuracy assessment module performs accuracy assessment on the ocean gravity field grid data of the single satellite altimeter respectively; combining the sea surface high precision of the multi-source altimetry data and the assessment accuracy obtained by the ocean gravity field accuracy assessment module, dynamically adjusts the fusion weighting factor of each type of satellite altimetry data for iterative update, and calculates the adaptive update value of the multi-source fusion weighting factor;

[0135] Step 7) Calculate the multi-source fusion weights required for multi-source altimetry data fusion using the accuracy of the geoid height or sea surface height slope, the distance to the grid point to be determined, and the multi-source fusion weighting factor of the satellite altimeter, and calculate the high-resolution geoid height data (longitude, latitude, geoid height) or vertical deviation grid data (longitude, latitude, meridian component, meridian component) through gridding processing;

[0136] Step 8) The ocean gravity field model construction module fuses the vertical deviation grid data or the geoid height grid data obtained by fusing the multi-source altimetry data, inverts the ocean gravity field based on the vertical deviation method or the geoid height method, and restores the reference gravity field model corresponding to the removed reference geoid height model to generate a high-resolution ocean gravity field model.

[0137] The present application may also provide a computer device, comprising: at least one processor, a memory, at least one network interface and a user interface. The various components in the device are coupled together through a bus system. It is understood that the bus system is used to achieve connection and communication between these components. In addition to the data bus, the bus system also includes a power bus, a control bus and a status signal bus.

[0138] The user interface may include a display, a keyboard or a pointing device, such as a mouse, a trackball, a touch pad or a touch screen.

[0139] It is understood that the memory in the embodiments disclosed in the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus random access memory (DRRAM). The memories described herein are intended to include, but are not limited to, these and any other suitable types of memories.

[0140] In some embodiments, the memory stores the following elements, executable modules or data structures, or a subset thereof, or an extended set thereof: an operating system and applications.

[0141] The operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and process hardware-based tasks. The application includes various application programs, such as a media player (Media Player), a browser (Browser), etc., which are used to implement various application services. The program for implementing the method of the embodiment of the present disclosure can be included in the application.

[0142] In the above embodiment, the processor may also call a program or instruction stored in the memory, specifically, a program or instruction stored in an application program, and is used to:

[0143] Execute the steps of the above method.

[0144] The above method can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor may be a general processor, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The above-disclosed methods, steps and logic block diagrams can be implemented or executed. The general processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the above-disclosed method can be directly embodied as a hardware decoding processor to execute, or the hardware and software modules in the decoding processor are combined to execute. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0145] It is understood that the embodiments described in the present application can be implemented by hardware, software, firmware, middleware, microcode or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described in the present application or a combination thereof.

[0146] For software implementation, the technology of the present application can be implemented by executing the functional modules (such as procedures, functions, etc.) of the present application. The software code can be stored in a memory and executed by a processor. The memory can be implemented in the processor or outside the processor.

[0147] The present application may also provide a non-volatile storage medium for storing a computer program. When the computer program is executed by a processor, each step in the above method embodiment can be implemented.

[0148] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present application and are not intended to limit it. Although the present application is described in detail with reference to the embodiments, a person skilled in the art should understand that any modification or equivalent replacement of the technical solution of the present application does not depart from the spirit and scope of the technical solution of the present application and should be included in the scope of the claims of the present application.

Claims

1. A system for constructing an ocean gravity field model by adaptively fusing multi-source altimetry data, characterized in that: The system comprises: Multi-source altimetry data classification preprocessing module: used to classify multi-source altimetry data according to satellite observation modes, perform preprocessing and data editing to remove abnormal values ​​of altimetry data, remove sea surface time-varying noise from sea surface height data according to satellite observation task classification, and remove the height values ​​of reference geoid height and ocean average dynamic terrain model to obtain preprocessed satellite line sea surface height data; Multi-source fusion factor adaptive iteration module: used to calculate the multi-source fusion weighting factor of each type of satellite altimeter data involved in the fusion according to the high sea surface precision of the multi-source satellite altimeter; combined with the high sea surface precision of the multi-source altimeter data and the evaluation accuracy obtained by the ocean gravity field accuracy evaluation module, dynamically adjust the multi-source fusion weighting factor of each type of satellite altimeter data for iterative update, for adaptive fusion of multi-source altimeter data; Multi-source altimetry data fusion module: used to perform cross-point adjustment or differential processing along the survey line sea surface height data of multi-source satellite altimeters, calculate the survey line geoid height data or sea surface height slope information after eliminating gross errors, and use the accuracy of the geoid height or sea surface height slope, the distance to the grid point to be calculated, and the multi-source fusion weighting factor of the satellite altimeter to calculate the multi-source fusion weights required for multi-source altimetry data fusion, and calculate the geoid height grid data or vertical deviation grid data through gridding processing; The ocean gravity field model construction module is used to invert the ocean gravity field based on the vertical deviation method or the geoid height method from the vertical deviation grid data or the geoid height grid data, and restore the reference gravity field model corresponding to the removed reference geoid height model to generate the ocean gravity field model; and Marine gravity field accuracy assessment module: used to use the refined measured gravity data as verification data, interpolate the marine gravity field model to the measured gravity point to obtain the model gravity value at that location, use the measured gravity value at the corresponding location to calculate the gravity residual value at the measured point, and calculate the assessment accuracy of the marine gravity field model based on the residual.

2. The ocean gravity field model construction system of multi-source altimetry data adaptive fusion according to claim 1 is characterized in that: The processing process of the multi-source altimetry data classification preprocessing module includes: Step A1: Classify the multi-source altimetry data according to the satellite observation mode, perform waveform re-tracking and error correction and data editing, and remove abnormal values ​​of the altimetry data; Step A2: Classify the multi-source altimetry data according to the satellite observation mission, remove the sea surface time-varying noise from the sea surface height data, use collinear adjustment to weaken the ERM mission data, and use low-pass filtering to filter out the time-varying noise from the GM mission data; Step A3: Use the latitude and longitude information to interpolate to the satellite survey line sea surface height position to calculate the corresponding reference geoid height and ocean average dynamic terrain, remove the reference geoid height and ocean average dynamic terrain model height values ​​from the sea surface high, and obtain the residual height ΔN: ΔN=SSH-MDT-N ref Where SSH is the sea surface height obtained by satellite altimetry; MDT is the height value of the mean dynamic terrain model; N ref is the reference model geoid height.

3. The ocean gravity field model construction system of multi-source altimetry data adaptive fusion according to claim 1 is characterized in that: The processing process of the multi-source fusion factor adaptive iteration module includes: Step B1: Calculate the initial value of the multi-source fusion weighting factor of each type of satellite altimeter data involved in the fusion according to the high sea surface precision of the multi-source satellite altimeter; The initial value of the multi-source fusion weighting factor is: Among them, W s j is the multi-source fusion weighting factor of the j-th type of satellite altimeter data; m s j represents the high accuracy of the sea surface of the jth type of satellite altimeter data, which is usually determined by an external reliable prior value; l represents the number of types of satellite altimeter data involved in the fusion, and different missions of the same model of satellite altimeter are regarded as different types; Step B2: combining the sea surface high precision of multi-source altimetry data and the evaluation accuracy of each type of satellite altimetry data obtained by the ocean gravity field accuracy evaluation module, dynamically adjusting the multi-source fusion weighting factor of each type of satellite altimeter data for iterative calculation, for adaptive fusion of multi-source altimetry data; The multi-source fusion weighting factor is iteratively updated as: Among them, m g j Represents the evaluation accuracy of the ocean gravity field inverted from the j-th type of satellite altimeter data.

4. The ocean gravity field model construction system of multi-source altimetry data adaptive fusion according to claim 1 is characterized in that: The processing process of the multi-source altimetry data fusion module includes: Step C1: Perform cross-point adjustment or differential processing along the survey line for the sea surface height data of the multi-source satellite altimeter, and calculate the survey line geoid height data or sea surface height slope information after eliminating gross errors; Step C2: Calculate the multi-source fusion weights required for multi-source altimetry data fusion by using the accuracy of the geoid height / sea surface height slope, the distance to the grid point to be determined, and the multi-source fusion weighting factor of the satellite altimeter; If there are multiple altimetry data within a certain sea area near the grid point, the weight P of the i-th observation point of the multi-source satellite altimetry data is i for: or Where, d i is the distance from the ith observation point to the grid point to be determined; σ i is the slope accuracy of the geoid height / sea surface height at the ith observation point; W s j is the multi-source fusion weighting factor of the satellite altimeter corresponding to the i-th observation point; Step C3: Gridding the geoid height data or sea surface height slope data of the multi-source satellite altimeter to generate geoid height grid data or vertical line deviation grid data.

5. The ocean gravity field model construction system of multi-source altimetry data adaptive fusion according to claim 1 is characterized in that: The processing process of the ocean gravity field model construction module includes: Step D1: Invert the ocean gravity field based on the vertical deviation method or the geoid height method by fusing the vertical deviation grid data or the geoid height grid data obtained by fusing the multi-source altimetry data; Step D2: restoring the reference gravity field model corresponding to the removed reference geoid height model to the inverted ocean gravity anomaly to generate a high-resolution ocean gravity field model; The global ocean gravity field model expression is: g=Δg+g ref Where Δg is the inverted gravity anomaly; g ref is the reference gravity field model.

6. The ocean gravity field model construction system of multi-source altimetry data adaptive fusion according to claim 5 is characterized in that: The process of inverting the ocean gravity field using the vertical deviation method includes: The discrete expression of the inverse Vening-Meinesz formula for inverting gravity anomalies from the vertical deviation grid is: in, is the gravity anomaly of the grid point p to be determined; γ0 is the average normal gravity value of the earth; and are the minimum and maximum latitudes of the calculation area; α qp ξ is the azimuth from the mobile grid point q to the grid point p in the calculation area; q and η q are the north-south and east-west components of the vertical deviation of the mobile grid point q in the calculation area, respectively; is the latitude of the mobile grid point q in the calculation area; and Δλ are the grid spacings in latitude and longitude; F1 and are the one-dimensional Fourier transform and inverse transform operators respectively; H' is the derivative of the kernel function H, expressed as:

7. The ocean gravity field model construction system of multi-source altimetry data adaptive fusion according to claim 5 is characterized in that: The process of inverting the ocean gravity field using the geoid height method includes: The discrete expression of the inverse Stokes formula for inverting gravity anomaly from the geoid high grid is: in, is the gravity anomaly of the grid point p to be determined; γ0 is the average normal gravity value of the earth; R is the average radius of the earth; is the geoid height of the grid point p to be determined; and λ q are the latitude and longitude of the mobile grid point q in the calculation area, respectively; and They are the minimum and maximum latitudes of the calculation area respectively; and Δλ are the grid spacings in latitude and longitude; F1 and are the one-dimensional Fourier transform and inverse transform operators, respectively, and the kernel function for:

8. The ocean gravity field model construction system of multi-source altimetry data adaptive fusion according to claim 1 is characterized in that: The processing process of the ocean gravity field accuracy assessment module includes: Step E1: Interpolate the ocean gravity field model to the ship's measured gravity point to obtain the corresponding gravity value G k , and then use the refined ship-measured gravity value at the corresponding position Calculate the gravity residual value RES of the ocean gravity field model at the ship measurement point k ; Step E2: Count the gravity residual values ​​of the ocean gravity field model at the ship measurement point and calculate the accuracy of the residual values ​​of the ocean gravity field model.

9. The ocean gravity field model construction system of multi-source altimetry data adaptive fusion according to claim 8 is characterized in that: The accuracy of the residual value of the ocean gravity field model is the root mean square RMS j : Among them, RMS j is the RMS value of the ocean gravity field inverted from the j-th type satellite altimeter data; N is the number of measured points for evaluating the j-th type satellite altimeter.

10. A method for constructing an ocean gravity field model by adaptively fusing multi-source altimetry data, based on the system of any one of claims 1 to 9, the method comprising: Step 1) The multi-source altimetry data classification preprocessing module classifies the multi-source altimetry data according to the satellite observation mode, performs preprocessing and data editing respectively to eliminate the abnormal values ​​of the altimetry data, removes the sea surface time-varying noise from the sea surface height data according to the satellite observation task classification, and removes the height values ​​of the reference geoid height and the ocean average dynamic terrain model to obtain the preprocessed satellite line sea surface height data; Step 2) performing intersection adjustment or differential processing along the survey line on the satellite survey line sea level data, and calculating the survey line geoid height data or sea level height slope information after eliminating gross errors; Step 3) the multi-source fusion factor adaptive iteration module calculates the initial value of the multi-source fusion weighting factor of each type of satellite altimeter data involved in the fusion according to the high sea surface precision of the multi-source satellite altimeter; Step 4) according to the accuracy of the geoid height or the sea surface height slope, the distance to the grid point to be determined, and the multi-source fusion weighting factor obtained by the multi-source fusion factor adaptive iteration module, the multi-source fusion weight required for the fusion of multi-source altimeter data is calculated, and the geoid height or the sea surface height slope data of the survey line of the single-satellite altimeter are gridded to obtain the geoid height grid data or the vertical line deviation grid data of the single-satellite altimeter; Step 5) the ocean gravity field model construction module uses the geoid height grid data or the vertical deviation grid data of the single-satellite altimeter to invert the ocean gravity field based on the geoid height method or the vertical deviation method, and restores the reference gravity field model corresponding to the removed reference geoid height model to generate the ocean gravity field grid data of the single-satellite altimeter; Step 6) The ocean gravity field accuracy assessment module performs accuracy assessment on the ocean gravity field grid data of the single satellite altimeter respectively; combining the sea surface high precision of the multi-source altimetry data and the assessment accuracy obtained by the ocean gravity field accuracy assessment module, dynamically adjusts the fusion weighting factor of each type of satellite altimetry data for iterative update, and calculates the adaptive update value of the multi-source fusion weighting factor; Step 7) using the accuracy of the geoid height or the sea surface height slope, the distance to the grid point to be determined, and the multi-source fusion weighting factor of the satellite altimeter, the multi-source fusion weights required for the fusion of multi-source altimetry data are calculated, and the geoid height data or the vertical deviation grid data are calculated through gridding processing; Step 8) The ocean gravity field model construction module fuses the vertical deviation grid data or the geoid height grid data obtained by fusing the multi-source altimetry data, inverts the ocean gravity field based on the vertical deviation method or the geoid height method, and restores the reference gravity field model corresponding to the removed reference geoid height model to generate a high-resolution ocean gravity field model.

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