Internal solitary wave amplitude inversion method based on SWOT observation data

Abstract

The invention relates to the technical field of ocean remote sensing image processing, and discloses an internal solitary wave amplitude inversion method based on SWOT observation data, which comprises the following steps of: acquiring sea surface height abnormal data of an SWOT satellite Ka-band radar interferometer L3, performing preprocessing, and performing adaptive noise filtering, maximum between-class variance method enhancement and morphological edge detection respectively; overlapping analysis is carried out, and pixel points which are all judged to be effective are reserved to serve as sea surface height abnormal data of internal solitary wave disturbance; inverting the first-order amplitude of the internal solitary wave; introducing a non-linear term in the extended KdV equation, and carrying out non-linear correction on the vertical modal function; and performing inversion to obtain the high-order amplitude of the internal solitary wave. According to the method disclosed by the invention, real and effective internal solitary wave disturbance information can be extracted as an input source of amplitude inversion, so that accurate inversion of the large-amplitude internal solitary wave is realized, and the technical potential of an SWOT satellite in quantitative research and practical application of the internal solitary wave is expanded.

Description

Technical Field

[0001] The present invention relates to the technical field of marine remote sensing image processing, and particularly to a method for inverting the amplitude of internal solitary waves based on SWOT observation data. Background Art

[0002] As a nonlinear wave phenomenon widely existing in the inner layer of the ocean, internal solitary waves play an important role in influencing ocean mixing, material transport, nutrient distribution, and the safety of ocean engineering. In recent years, the precise observation and physical parameter inversion of internal solitary waves, especially amplitude inversion, have become one of the key technical issues in marine scientific research and practical applications. Although existing optical remote sensing and synthetic aperture radar technologies have been widely used in the identification of the spatial distribution of internal solitary waves, there are still significant limitations in the quantitative observation of sea surface height anomalies and the detection of three-dimensional structures, making it difficult to meet the requirements for accurate inversion of the amplitude of large-scale internal solitary waves.

[0003] The rapid development of three-dimensional imaging radar altimetry technology provides a new means to solve the above problems. Especially the Ka-band radar interferometer (KaRIn) carried by the Surface Water and Ocean Topography (SWOT) satellite launched on December 16, 2022, has the ability to observe the sea surface height with high spatial resolution (250 m) and wide swath (100 km on each side), providing sea surface height change data with centimeter-level accuracy, laying an important data foundation for the extraction of internal solitary wave perturbation characteristics and amplitude inversion.

[0004] However, the SWOT satellite still faces the following key technical challenges in the application of internal solitary wave amplitude inversion: on the one hand, the marine background environment is complex, and the internal solitary wave perturbation signal in the SWOT sea surface height data is easily masked by background noise. There is an urgent need to develop an extraction method that can effectively highlight the internal solitary wave perturbation characteristics and provide high-quality input data for subsequent amplitude inversion; on the other hand, there is currently a lack of an amplitude inversion method based on SWOT sea surface height observation data and suitable for large-amplitude internal solitary waves. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides a method for inverting the amplitude of internal solitary waves based on SWOT observation data, to extract real and effective internal solitary wave perturbation information as the input source for amplitude inversion, and then achieve the accurate inversion of large-amplitude internal solitary waves, thereby expanding the technical potential of the SWOT satellite in the quantitative research and practical application of internal solitary wave dynamic processes.

[0006] To achieve the above object, the technical solution of the present invention is as follows: An internal solitary wave amplitude inversion method based on SWOT observation data comprises the following steps: Step 1: Obtain the L3 sea surface height anomaly data of the SWOT satellite Ka-band radar interferometer, and use the quality identification and threshold screening method to remove abnormal pixels and large-scale background fields; Step 2, the data obtained in step 1 are processed by adaptive noise filtering, and then the maximum inter-class variance method is used to enhance the stripe features in the sea surface height anomaly data, and the connectivity analysis is performed on the enhanced data to remove the small-scale noise area of non-internal solitary wave disturbance; Step 3, using the Sobel operator to perform morphological edge detection on the data obtained in step 1; Step 4, performing overlapping analysis on the pixel points respectively retained after processing in step 2 and step 3, retaining the pixel points determined to be valid in both steps as the sea surface height anomaly data of internal solitary wave disturbance; using this data as the height change caused by the internal solitary wave on the sea surface, inverting the first-order amplitude of the internal solitary wave; Step 5: Based on the first-order amplitude of the internal solitary wave obtained by inversion, the nonlinear term in the extended KdV equation is introduced to perform nonlinear correction on the vertical modal function to obtain the corrected nonlinear vertical modal function; the higher-order amplitude of the internal solitary wave is inverted using the corrected nonlinear vertical modal function.

[0007] In the above scheme, in step 1, the unedited sea surface height anomaly data contained in the unsmoothed variant of the SWOT satellite Ka-band radar interferometer L3 sea surface height anomaly data is selected as the basic data source for extracting internal solitary wave disturbance information.

[0008] In the above scheme, step 1 specifically includes the following processing steps: Step 1.1, based on the quality identifier provided in the L3 level unsmoothed variant of the SWOT satellite Ka-band radar interferometer, the obtained unedited sea surface height anomaly data is quality marked; the identification rules used include: selecting Flag 102 to remove default value error pixels; selecting Flag 101 to remove land area pixels; selecting Flag 100 to remove swath edge pseudo image pixels; selecting Flag 70 to remove spacecraft event-affected pixels; selecting Flag 50 to remove coastal and polar error pixels; selecting Flag 20 to remove sea ice-affected pixels; through the above identification processing, potential low-quality or abnormal data are removed; Step 1.2. Apply the threshold screening method to the sea surface height anomaly data after the quality marking process in Step 1.1 to remove abnormal pixels. The specific method is as follows: Calculate the average value of the global sea surface height anomaly data, and set the range of reasonable data values as the average value ± 50 cm. Pixels outside this range are determined as abnormal pixels and removed. Step 1.3. Download the global gridded sea surface height anomaly data provided by Copernicus Marine Service with a spatial resolution of 1 / 8° and a temporal resolution of 1 day as the background field data; Subtract the observed data after removing abnormal pixels obtained in Step 1.2 from this background field data pixel by pixel to obtain the sea surface height anomaly data with large-scale background variations removed.

[0009] In the above scheme, the specific method of Step 2 is as follows: Step 2.1. Adaptive noise filtering process: Calculate the global histogram distribution of the sea surface height anomaly data, and remove abnormal data below the 20th percentile and above the 99.9th percentile; On this basis, perform mean filtering on the global data using a 3×3 window to further smooth local noise. Step 2.2. Use the Otsu method to enhance the internal solitary wave fringe features for the data after noise filtering: First step, perform local adaptive Otsu segmentation using a 50×50 pixel sliding window; Second step, perform global Otsu segmentation on the global data to improve the recognition of internal solitary wave fringe features. Step 2.3. Perform connectivity analysis on the data after enhancing features in Step 2.2, calculate the number of pixel points in the connected region, and remove regions with less than 500 connected pixel points to remove small-scale noise regions of non-internal solitary wave disturbances and highlight the internal solitary wave fringe features.

[0010] In the above scheme, the specific method of Step 3 is as follows: Step 3.1. Calculate the Sobel operator in the x and y directions respectively using a 3×3 window. Step 3.2. Take the modulus of the calculation results of the Sobel operator in the x and y directions. Step 3.3. Calculate the global histogram distribution of the Sobel operator modulus values, and remove data points below the 60th percentile, which are regarded as non-edge pixels. Step 3.4. Perform mean filtering on the remaining data using a 3×3 window to further smooth the edge features.

[0011] In the above scheme, the first-order amplitude of the internal solitary wave in Step 4 is calculated as follows: ; In the formula, represents the first-order amplitude of the internal solitary wave; SSHAThe obtained sea surface height anomaly data of internal solitary waves; g is the acceleration due to gravity; z is the depth integration variable; H is the water depth, provided by the gridded water depth dataset of the global ocean bathymetry dataset; is the buoyancy frequency at depth z, calculated from the water density profile, which is provided by the WOA2023 monthly mean gridded stratification data; is the vertical mode function at depth z, solved according to the homogeneous eigenvalue problem and needs to be normalized so that its maximum value = 1; The following is the solution formula: ; ; In the formula, is the linear phase velocity, indicates that the vertical mode functions at the sea surface and the seabed are 0.

[0012] In the above scheme, in step 5, the calculation method of the nonlinear term is as follows: ; ; In the formula, is the nonlinear coefficient in the continuously stratified model, and the calculation formula is as follows: ; In the formula, indicates that the nonlinear values at the sea surface and the seabed are 0; Normalize the nonlinear term : ; Among them, is the nonlinear term; represents the nonlinear term after normalization; C is a constant to be determined, and it needs to be determined using the condition of , To make the corresponding depth; The corrected nonlinear vertical mode function is: .

[0013] In the above scheme, in step 5, the inversion formula of the high-order amplitude of the internal solitary wave is as follows: ; In the formula, SSHAis the obtained sea surface height anomaly data of internal solitary wave perturbations; g is the acceleration due to gravity; z is the depth integration variable; H is the water depth, provided by the gridded water depth dataset of the global ocean bathymetry dataset; represents at depth the buoyancy frequency; represents the corrected non - linear vertical mode function at depth z.

[0014] Through the above technical solutions, a method for inverting the amplitude of internal solitary waves based on SWOT observation data provided by the present invention has the following beneficial effects: 1. Optimization and pre - processing method based on SWOT observation data The present invention uses the unedited sea surface height anomaly data of the SWOT KaRIn L3 - level unsmoothed variant, and screens the original observation data according to specific Flag marks (Flag 20, 50, 70, 100, 101, 102), removing abnormal pixels including land areas, sea ice effects, swath edge artifacts, spacecraft event interferences, and polar errors. At the same time, a global threshold is introduced to remove abnormal extreme values, improving the quality of the original data for extracting internal solitary wave information; 2. High - efficiency extraction algorithm for internal solitary wave perturbation information The internal solitary wave information extraction algorithm proposed by the present invention uses the global sea surface height anomaly re - analysis product provided by the Copernicus Marine Service, removes the large - scale background change field, and effectively separates the internal solitary wave signal. Further, through adaptive noise filtering (including upper and lower truncation based on the global histogram and 3×3 window mean filtering), the Otsu's method double - scale segmentation method enhances the internal solitary wave fringe features, combines Sobel morphological edge detection and connected component analysis, removes non - fluctuating small pixel regions, and significantly improves the integrity and accuracy of internal solitary wave perturbation extraction; 3. High - precision non - linear inversion model for large - amplitude internal solitary waves The present invention constructs a non - linear amplitude inversion method applicable to large - amplitude internal solitary waves, calculates the first - order amplitude of internal solitary waves based on the sea surface height anomaly data extracted by SWOT, further introduces the non - linear term T(z) in the extended KdV equation to obtain the vertical mode function considering non - linear effects, and establishes a strongly non - linear internal solitary wave amplitude inversion model to achieve high - precision inversion of the amplitude of internal solitary waves. The inversion process combines the WOA2023 ocean stratification data and the water depth data of the global ocean bathymetry dataset, with good environmental adaptability and parameter generality, thus achieving high - precision inversion of large - amplitude internal solitary waves in different regions. Description of the Drawings

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art.

[0016] Figure 1 Schematic flow chart of an internal solitary wave amplitude inversion method based on SWOT observation data disclosed by the present invention; Figure 2 SWOT KaRIn L3-level unsmoothed and unedited original sea surface height anomaly data; Figure 3 Sea surface height anomaly data after preprocessing; Figure 4 Internal solitary wave fringe feature map extracted based on enhanced processing and edge detection; Figure 5 Specific display of the SWOT internal solitary wave feature extraction results in the western Indian Ocean region in the embodiment of the present invention. Among them, (a) is the sea surface backscattering coefficient (NRCS) image of the western Indian Ocean region obtained by the SWOT satellite; (b) is the sea surface height anomaly of the internal solitary wave perturbation in the western Indian Ocean extracted from the SWOT sea surface height anomaly data after being processed by the method of the present invention; Figure 6 Specific display of the SWOT internal solitary wave feature extraction results in the Andaman Sea region in the embodiment of the present invention. Among them, (a) is the sea surface backscattering coefficient (NRCS) image of the Andaman Sea region obtained by the SWOT satellite; (b) is the sea surface height anomaly of the internal solitary wave perturbation in the Andaman Sea extracted from the SWOT sea surface height anomaly data after being processed by the method of the present invention. Detailed implementation manners

[0017] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention.

[0018] The present invention provides an internal solitary wave amplitude inversion method based on SWOT observation data, as Figure 1 shown, including the following steps: Step 1: Obtain the SWOT satellite Ka-band radar interferometer L3 sea surface height anomaly data, and use the quality flag and threshold screening method to eliminate abnormal pixels and remove the large-scale background field.

[0019] Select the unedited sea surface height anomaly data included in the unsmoothed variant of the SWOT satellite Ka-band radar interferometer (KaRIn) L3 (Level-3) sea surface height anomaly data as the basic data source for extracting internal solitary wave perturbation information, as Figure 2 shown.

[0020] Perform the following processing on the obtained data: Step 1.1: Based on the quality identifiers (Flags) provided in the L3 unsmoothed variant of the SWOT satellite Ka-band radar interferometer, perform quality marking on the obtained unedited sea surface height anomaly data. The adopted identification rules include: select Flag 102 to exclude default value error pixels; select Flag 101 to exclude land area pixels; select Flag 100 to exclude swath edge pseudo-image pixels; select Flag 70 to exclude spacecraft event impact pixels; select Flag 50 to exclude coastal and polar region error pixels; select Flag 20 to exclude sea ice impact pixels. Through the above identification process, potential low-quality or abnormal data are removed. Step 1.2: Use the threshold screening method to remove abnormal pixels from the sea surface height anomaly data after the quality marking process in Step 1.1. The specific method is as follows: calculate the average value of the global sea surface height anomaly data, and set the average value ± 50 cm as the reasonable data value range. Pixels outside this range are determined as abnormal pixels and are removed. Step 1.3: Remove the large-scale background field: Download the global gridded sea surface height anomaly data provided by the Copernicus Marine Service (CMS) with a spatial resolution of 1 / 8° and a temporal resolution of 1 day as the background field data. Subtract the background field data pixel by pixel from the observation data after removing abnormal pixels obtained in Step 1.2 to obtain the sea surface height anomaly data with the large-scale background change removed, as Figure 3 shown, and the background noise is effectively suppressed.

[0021] Step 2: Perform adaptive noise filtering on the data obtained in Step 1, then use the Otsu method to enhance the fringe features in the sea surface height anomaly data, and perform connectivity analysis on the data after enhancing the features to remove small-scale noise regions that are not internal solitary wave perturbations.

[0022] The specific method is as follows: Step 2.1: Adaptive noise filtering process: Calculate the global histogram distribution of the sea surface height anomaly data, and remove abnormal data below the 20% quantile and above the 99.9% quantile. On this basis, perform mean filtering on the global data using a 3×3 window to further smooth local noise. Step 2.2: Use the Otsu method to enhance the internal solitary wave fringe features of the data after noise filtering: First step, perform local adaptive Otsu segmentation using a 50×50 pixel sliding window; Second step, perform overall Otsu segmentation on the global data to improve the recognition of internal solitary wave fringe features. Step 2.3, perform connectivity analysis on the data with enhanced features in Step 2.2, calculate the number of pixel points in the connected regions, and remove the regions with the number of connected pixel points less than 500 to remove small-scale noise regions of non-internal solitary wave perturbations and highlight the internal solitary wave stripe features, as Figure 4 shown, clearly showing the abnormal change in the sea surface height caused by internal solitary wave perturbations.

[0023] Step 3, perform morphological edge detection on the data obtained in Step 1 using the Sobel operator.

[0024] The specific method is as follows: Step 3.1, calculate the Sobel operator in the x-direction and y-direction respectively using a 3×3 window; Step 3.2, take the modulus of the calculation results of the Sobel operator in the x-direction and y-direction; Step 3.3, calculate the global histogram distribution of the Sobel operator modulus values, remove the data points below the 60% quantile, and regard them as non-edge pixels; Step 3.4, perform mean filtering on the remaining data using a 3×3 window to further smooth the edge features.

[0025] Step 4, perform an overlap analysis on the pixel points respectively retained after the processing in Step 2 and Step 3, retain the pixel points determined to be valid in both steps as the abnormal sea surface height data of internal solitary wave perturbations, use this data as the height change amount caused by internal solitary waves on the sea surface, and invert the first-order amplitude of the internal solitary wave.

[0026] The first-order amplitude of the internal solitary wave The calculation formula is as follows: ; In the formula, represents the first-order amplitude of the internal solitary wave; SSHA is the abnormal sea surface height data of the internal solitary wave perturbation extracted; g is the acceleration due to gravity; z is the depth integration variable; H is the water depth, provided by the gridded bathymetry dataset of the General Bathymetric Chart of the Oceans (GEBCO); is the buoyancy frequency at depth z, calculated from the water density profile, and the water density profile is provided by the monthly mean gridded data of WOA2023; is the vertical mode function at depth z, solved based on the homogeneous eigenvalue problem (Taylor-Goldstein equation), and needs to be normalized so that its maximum value =1; the following is the solution formula: ; ; In the formula, is the linear phase velocity, indicating that the vertical modal functions at the sea surface and the seabed are 0.

[0027] Step 5: On the basis of the retrieved first-order amplitude of the internal solitary wave, introduce the nonlinear term in the extended Korteweg-de Vries (KdV) equation to nonlinearly correct the vertical modal function, obtaining the corrected nonlinear vertical modal function. Using the corrected nonlinear vertical modal function, retrieve the high-order amplitude of the internal solitary wave.

[0028] Nonlinear term is calculated as follows: ; ; In the formula, is the nonlinear coefficient in the continuous stratification model, and its calculation formula is as follows: ; In the formula, indicates that the nonlinear values at the sea surface and the seabed are 0; Normalize the nonlinear term : ; Among them, is the nonlinear term; represents the normalized nonlinear term; C is a constant to be determined, which needs to be determined using the condition , To make the corresponding depth; The corrected nonlinear vertical modal function is: .

[0029] The inversion formula for the high-order amplitude of the internal solitary wave is as follows: ; In the formula, SSHA is the retrieved abnormal data of the disturbed sea surface height of the internal solitary wave; g is the acceleration due to gravity; z is the depth integration variable; H is the water depth, provided by the gridded water depth dataset of the global ocean bathymetry dataset; represents the buoyancy frequency at the depth ; represents the corrected nonlinear vertical modal function at the depth z.

[0030] Figure 5 In (a), it is an image of the sea surface backscattering coefficient (NRCS) in the western Indian Ocean region obtained by the SWOT satellite; Figure 5 In (b), it is the sea surface height anomaly of internal solitary wave perturbation in the western Indian Ocean extracted from the SWOT sea surface height anomaly data after being processed by the method of the present invention. Figure 6 In (a), it is an image of the sea surface backscattering coefficient (NRCS) in the Andaman Sea region obtained by the SWOT satellite; Figure 6 In (b), it is the sea surface height anomaly of internal solitary wave perturbation in the Andaman Sea extracted from the SWOT sea surface height anomaly data after being processed by the method of the present invention. It can be seen that the method of the present invention can accurately extract the sea surface height anomaly of internal solitary wave perturbation.

[0031] The method for extracting internal solitary wave information and inverting the amplitude based on SWOT satellite observation data proposed by the present invention makes full use of the high spatial resolution and wide swath sea surface height anomaly data provided by the SWOT satellite, and combines with the reanalysis ocean gridded data, which can effectively suppress the interference of ocean background noise on the extraction of internal solitary wave perturbation characteristics, and significantly improve the extraction accuracy and integrity of internal solitary wave perturbation characteristics. At the same time, the established large-amplitude internal solitary wave amplitude inversion model can fully consider the nonlinear characteristics and realize the high-precision inversion of the amplitude of internal solitary waves. This method provides a reliable technical means for ocean environmental dynamic monitoring, internal solitary wave disaster early warning and marine engineering safety risk assessment, and has good application prospects and popularization value.

[0032] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An internal solitary wave amplitude inversion method based on SWOT observation data, characterized in that, It includes the following steps: Step 1: Obtain the Ka-band radar interferometer L3 sea surface height anomaly data of the SWOT satellite, and use the quality identification and threshold screening method to eliminate abnormal pixels and remove the large-scale background field; Step 2: Perform adaptive noise filtering on the data obtained in Step 1, then use the Otsu method to enhance the fringe features in the sea surface height anomaly data, and perform connectivity analysis on the data after enhancing the features to remove small-scale noise regions disturbed by non-internal solitary waves; Step 3: Perform morphological edge detection on the data obtained in Step 1 using the Sobel operator; Step 4: Perform an overlapping analysis on the pixel points respectively retained after the processing in Step 2 and Step 3, and retain the pixel points determined to be valid in both steps as the sea surface height anomaly data disturbed by internal solitary waves; Use this data as the height change amount caused by internal solitary waves on the sea surface to invert the first-order amplitude of internal solitary waves; Step 5: On the basis of inverting the first-order amplitude of internal solitary waves, introduce the nonlinear term in the extended KdV equation to perform nonlinear correction on the vertical mode function to obtain the corrected nonlinear vertical mode function; Use the corrected nonlinear vertical mode function to invert the higher-order amplitude of internal solitary waves.

2. The internal solitary wave amplitude inversion method based on SWOT observation data according to claim 1, characterized in that In Step 1, select the unedited sea surface height anomaly data included in the unsmoothed variant of the Ka-band radar interferometer L3 sea surface height anomaly data of the SWOT satellite as the basic data source for extracting internal solitary wave perturbation information.

3. The internal solitary wave amplitude inversion method based on SWOT observation data according to claim 2, wherein, The specific processing process in Step 1 includes the following: Step 1.1: Based on the quality identifier provided in the L3-level unsmoothed variant of the SWOT satellite Ka-band radar interferometer, perform quality marking processing on the obtained unedited sea surface height anomaly data; The adopted identification rules include: Select the Flag102 identifier to eliminate default value error pixels; Select the Flag 101 identifier to eliminate land area pixels; Select the Flag 100 identifier to eliminate swath edge pseudo-image pixels; Select the Flag 70 identifier to eliminate spacecraft event impact pixels; Select the Flag 50 identifier to eliminate coastal and polar region error pixels; Select the Flag 20 identifier to eliminate sea ice impact pixels; Through the above identification processing, remove potential low-quality or abnormal data; Step 1.2: Use the threshold screening method to eliminate abnormal pixels from the sea surface height anomaly data after the quality marking processing in Step 1.

2. The specific method is: Calculate the average value of the global sea surface height anomaly data, and set the average value ± 50 cm as the reasonable data value range. Pixels outside this range are determined to be abnormal pixels and are eliminated; Step 1.3: Download the global gridded sea surface height anomaly data provided by the Copernicus Marine Service with a spatial resolution of 1 / 8° and a time resolution of 1 day as the background field data; Subtract the pixel-by-pixel corresponding observation data after eliminating abnormal pixels obtained in Step 1.2 from this background field data to obtain the sea surface height anomaly data with the large-scale background change removed.

4. The internal solitary wave amplitude inversion method based on SWOT observation data according to claim 1, wherein The specific method of Step 2 is as follows: Step 2.1, Adaptive noise filtering processing: Calculate the global histogram distribution of the sea surface height anomaly data, and remove the abnormal data below the 20% quantile and above the 99.9% quantile; On this basis, perform mean filtering on the global data using a 3×3 window to further smooth local noise; Step 2.2, Use the maximum inter-class variance method to enhance the internal solitary wave fringe features for the data after noise filtering: First step, perform local adaptive Otsu segmentation using a 50×50 pixel sliding window; Second step, perform overall Otsu segmentation on the global data to improve the recognition of internal solitary wave fringe features; Step 2.3, Perform connectivity analysis on the data after enhancing the features in Step 2.2, calculate the number of pixel points in the connected region, and remove the regions with the number of connected pixel points less than 500 to remove small-scale noise regions of non-internal solitary wave perturbations and highlight the internal solitary wave fringe features.

5. A method for inverting the amplitude of internal solitary waves based on SWOT observation data according to claim 1, characterized in that, The specific method of Step 3 is as follows: Step 3.1, Calculate the Sobel operator in the x direction and the y direction respectively using a 3×3 window; Step 3.2, Take the modulus of the calculation results of the Sobel operator in the x direction and the y direction; Step 3.3, Calculate the global histogram distribution of the Sobel operator modulus values, and remove the data points below the 60% quantile, which are regarded as non-edge pixels; Step 3.4, Perform mean filtering on the remaining data using a 3×3 window to further smooth the edge features.

6. The internal solitary wave amplitude inversion method based on SWOT observation data according to claim 1, wherein The first-order amplitude of the internal solitary wave in Step 4 The calculation formula is as follows: ； In the formula, represents the first-order amplitude of the internal solitary wave; SSHA is the obtained anomalous data of the disturbed sea surface height of the internal solitary wave; g is the acceleration due to gravity; z is the depth integration variable; H is the water depth, provided by the gridded water depth dataset of the global ocean bathymetry dataset; is the buoyancy frequency at a depth of z, calculated from the water density profile, and the water density profile is provided by the WOA2023 monthly average gridded stratification data; is the vertical mode function at a depth of z, solved based on the homogeneous eigenvalue problem and needs to be normalized so that its maximum value = 1; The following is the solution formula: ； ； In the formula, is the linear phase velocity, indicating that the vertical mode functions at the sea surface and the sea bottom are 0.

7. A method for inverting the amplitude of internal solitary waves based on SWOT observation data according to claim 6, characterized in that, In step 5, the non-linear term is calculated as follows: ； ； In the formula, is the non-linear coefficient in the continuous layering model, and its calculation formula is as follows: ； In the formula, indicates that the nonlinear values at the sea surface and the seabed are 0; Normalize the non-linear term as follows: ； Among them, is a non-linear term; represents the non-linear term after normalization processing; C is a constant to be determined, and it is necessary to use this condition to determine To make the corresponding depth; Corrected Nonlinear Vertical Mode Function is as follows: 。 8. A method for inverting the amplitude of internal solitary waves based on SWOT observation data according to claim 1, characterized in that In step 5, the inversion formula for the high-order amplitude of the internal solitary wave is as follows: ； In the formula, SSHA is the obtained sea surface height anomaly data of internal solitary wave disturbances; g is the acceleration due to gravity; z is the depth integration variable; H is the water depth, provided by the gridded water depth dataset of the global ocean bathymetry dataset; represents at depth the buoyancy frequency; represents the corrected non - linear vertical mode function at depth z.

Images