Internal wave inversion method and system based on high-frequency tidal data and satellite remote sensing fusion

Abstract

This invention relates to the field of marine internal wave monitoring technology, and discloses an internal wave inversion method and system based on the fusion of high-frequency tidal data and satellite remote sensing. The method includes: S1, acquiring SAR satellite remote sensing data, 5-minute sampling interval tidal data, and CTD density profile data, and performing preprocessing; S2, performing spatiotemporal alignment of the SAR satellite remote sensing data, 5-minute sampling interval tidal data, and CTD density profile data; S3, using variational mode decomposition to decompose the preprocessed 5-minute sampling interval tidal data and screen for internal wave characteristic modes, etc. This method and system improves the spatial resolution of high-precision inversion of multiple parameters such as amplitude, period, propagation speed, direction, and energy dissipation to the ten-meter level, optimizes the temporal resolution to within one day, and improves the amplitude measurement accuracy to 0.1 meters, significantly outperforming the internal wave amplitude measurement accuracy and temporal resolution of existing SAR satellites.

Description

Technical Field

[0001] This invention relates to the field of marine internal wave monitoring technology, specifically to an internal wave inversion method and system based on the fusion of high-frequency tidal data and satellite remote sensing. Background Technology

[0002] Internal ocean waves, a ubiquitous dynamic phenomenon within the ocean, cause breakage, reflection, and polarity reversal during their propagation. These processes pose a serious threat to marine engineering facilities (such as wind turbine platforms, substation platforms, and oil drilling platforms) and military activities such as submarine concealment. Large-amplitude internal waves (a / H > 0.09) are particularly hazardous. These waves can reach amplitudes of tens of meters, increasing dramatically during the uphill phase, leading to waveform breakage and energy dissipation exceeding 50%. This not only disrupts the stability of ocean structures but also interferes with acoustic signal propagation, severely impacting marine resource development and national defense security.

[0003] Existing methods for monitoring internal waves are mainly divided into field monitoring and remote sensing monitoring, but both have significant limitations. Remote sensing monitoring often utilizes satellite synthetic aperture radar (SAR) for internal wave detection. While it can provide clear SAR images, displaying the spatial morphology, wavelength, and propagation direction of internal waves, it lacks synchronous and accurate measured sea surface height data as a verification benchmark, making it difficult to capture fine structures. Regarding temporal resolution, in modern satellite constellation modes, the revisit period for a specific area can be as short as a few days or even a day (near polar regions), but this still cannot meet the continuous observation requirements for high-frequency internal waves (periods ranging from several hours to several days). Field monitoring often uses observation equipment such as tide level buoys and pressure-type tide gauges. Although these offer high temporal resolution and accurate amplitude measurement, their spatial coverage is sparse, making it difficult to characterize the spatial propagation characteristics of internal waves. This results in insufficient accuracy in retrieving internal wave parameters (amplitude, propagation direction, energy dissipation). Deploying array-type tide gauges can expand the monitoring range, but it still cannot cover the entire area of ​​platforms such as wind farms, and it cannot accurately detect the propagation direction and velocity of internal waves. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the present invention aims to provide an internal wave inversion method based on the fusion of high-frequency tidal data and satellite remote sensing. This method can achieve high-precision inversion of multiple parameters such as internal wave amplitude, period, propagation speed, direction, and energy dissipation.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] The internal wave inversion method based on the fusion of high-frequency tidal data and satellite remote sensing includes: S1, acquiring SAR satellite remote sensing data, 5-minute sampling interval tidal data, and CTD density profile data, and preprocessing them; S2, performing spatiotemporal alignment on the SAR satellite remote sensing data, 5-minute sampling interval tidal data, and CTD density profile data; S3, using variational mode decomposition to decompose the preprocessed 5-minute sampling interval tidal data, screening internal wave characteristic modes, and extracting the amplitude, period, and energy proportion parameters of the internal waves; S4, utilizing SAR satellite remote sensing data, combined with the mutual... S5. Calculate the propagation direction using the Coriolis force correction term; S6. Establish a linear relationship between the SAR sea surface height anomaly gradient and the amplitude, and invert the propagation velocity of the internal wave based on the linear relationship; S7. Calculate the buoyancy frequency based on CTD density profile data, and calculate the horizontal velocity of the internal wave according to the amplitude, period, horizontal velocity of the internal wave, and buoyancy frequency; S8. Calculate the load time history curve of the internal wave using the Morison equation and the horizontal velocity of the internal wave; S9. Output the amplitude, propagation direction, propagation velocity, and load time history curve of the internal wave to the structural analysis software interface.

[0007] In this invention, preferably, the preprocessing in S1 includes: detrending and Butterworth low-pass filtering of tidal data at 5-minute sampling intervals; radiometric calibration, geometric correction and feature extraction of SAR satellite remote sensing data; and removal of density outliers from CTD density profile data using the 3σ criterion.

[0008] In this invention, preferably, the spatiotemporal alignment method described in S2 is as follows: the time window of the SAR satellite remote sensing data is aligned with the tide time period of the 5-minute sampling interval tide data, and the spatial aspect of the SAR satellite remote sensing data is generated by Kriging interpolation to generate the sea surface height field of the internal wave propagation path.

[0009] In this invention, preferably, the parameters of the variational mode decomposition in S3 are set as follows: the number of modes K is 3 to 8, the penalty factor α is 1000 to 5000, and the center frequency range is f_c ∈ [0.5, 1.2] mHz.

[0010] In this invention, preferably, the screening of internal wave characteristic modes in S3 is carried out by calculating the instantaneous frequency of each mode through Hilbert transform, and the mode with a screening period of 10 minutes to 2 hours is selected as the internal wave characteristic mode.

[0011] In this invention, preferably, the formula for calculating the propagation direction in S4 is:

[0012] + ,

[0013] in, The direction angle of propagation. For the horizontal displacement of the internal wave in directional components, For the horizontal displacement of the internal wave in directional components, Coriolis parameters, To delay the spread.

[0014] In this invention, preferably, the inversion formula for the propagation speed in S5 is:

[0015] ,

[0016] in, For the propagation speed of internal waves, This represents the sea surface height anomaly gradient. denoted as , where is the amplitude of the internal wave, and k is the calibration coefficient, which is calibrated using measured tidal level data at 5-minute sampling intervals.

[0017] In this invention, preferably, the formula for calculating the horizontal velocity of the internal wave in S6 is:

[0018] ,

[0019] in, Let N be the amplitude of the internal wave, N be the buoyancy frequency, and h be the water depth. Let t be the acceleration due to gravity, and t be the time. Angular frequency, The period of the internal wave.

[0020] The internal wave inversion system based on the fusion of high-frequency tidal data and satellite remote sensing includes: an acquisition module for acquiring SAR satellite remote sensing data, 5-minute sampling interval tidal data, and CTD density profile data, and preprocessing the 5-minute sampling interval tidal data; a spatiotemporal alignment module for spatiotemporally aligning the SAR satellite remote sensing data, 5-minute sampling interval tidal data, and CTD density profile data; an internal wave feature extraction module for decomposing the preprocessed 5-minute sampling interval tidal data using variational mode decomposition, screening internal wave characteristic modes, and extracting the amplitude, period, and energy proportion parameters of the internal waves; and a propagation direction calculation module for using SAR satellite remote sensing data... The system employs a combination of cross-correlation and Coriolis force correction terms to calculate the propagation direction; a propagation velocity inversion module to establish a linear relationship between the SAR sea surface height anomaly gradient and the amplitude, and to invert the propagation velocity of the internal wave based on this linear relationship; a horizontal velocity calculation module to calculate the buoyancy frequency based on CTD density profile data, and to calculate the horizontal velocity of the internal wave based on the amplitude, period, horizontal velocity of the internal wave, and buoyancy frequency; a load calculation module to calculate the load time history curve of the internal wave using the horizontal velocity of the internal wave and the Morison equation; and an output module to output the amplitude, propagation direction, propagation velocity, and load time history curve of the internal wave to the structural simulation software interface.

[0021] In this invention, preferably, the preprocessing includes: detrending and Butterworth low-pass filtering of 5-minute sampling interval tide data; radiometric calibration, geometric correction, and feature extraction of SAR satellite remote sensing data; and removal of density outliers from CTD density profile data using the 3σ criterion. The spatiotemporal alignment method is as follows: aligning the time window of the SAR satellite remote sensing data with the tide time period of the 5-minute sampling interval tide data, and using Kriging interpolation to generate the sea surface height field of the internal wave propagation path in the spatial aspect of the SAR satellite remote sensing data. The parameters of the variational mode decomposition are set as follows: the number of modes K is 3~8, the penalty factor α is 1000~5000, and the center frequency range is f_c∈[0.5, 1.2] mHz. The selection of internal wave characteristic modes is achieved by calculating the instantaneous frequency of each mode using Hilbert transform, and selecting modes with a selection period of 10 minutes to 2 hours as internal wave characteristic modes.

[0022] The formula for calculating the propagation direction is:

[0023] + ,

[0024] in, The direction angle of propagation. For the horizontal displacement of the internal wave in directional components, For the horizontal displacement of the internal wave in directional components, Coriolis parameters, To delay the transmission;

[0025] The inversion formula for the propagation speed is:

[0026] ,

[0027] in, For the propagation speed of internal waves, This represents the sea surface height anomaly gradient. The amplitude of the internal wave is given by , and k is the calibration coefficient, which is calibrated using the measured tidal level data at 5-minute sampling intervals.

[0028] The formula for calculating the horizontal velocity of the internal wave is:

[0029] ,

[0030] in, Let N be the amplitude of the internal wave, N be the buoyancy frequency, and h be the water depth. Let t be the acceleration due to gravity, and t be the time. Angular frequency, The period of the internal wave.

[0031] Compared with the prior art, the beneficial effects of the present invention are:

[0032] This method and system utilize satellite remote sensing to provide the "spatial truth" of wave propagation direction and velocity over a large area to calibrate the inversion results of the tide gauge array. Simultaneously, it uses tide data to provide the "sea surface truth" of minute-level instantaneous amplitude and period of internal waves to correct the inversion accuracy of SAR. Based on this, an mKdV theoretical model suitable for large-amplitude internal waves (a / H>0.09) is introduced as a physical constraint, ultimately achieving high-precision inversion of multiple parameters such as internal wave amplitude, period, propagation velocity, direction, and energy dissipation. This improves the spatial resolution of high-precision inversion of multiple parameters such as amplitude, period, propagation velocity, direction, and energy dissipation to the ten-meter level, optimizes the temporal resolution to within one day, and improves the amplitude measurement accuracy to 0.1 meters, significantly outperforming the internal wave amplitude measurement accuracy and temporal resolution of existing SAR satellites. This provides high-precision data support for safety assessment of marine engineering facilities, submarine stealth analysis, and national defense security. Attached Figure Description

[0033] Figure 1 This is a flowchart of an embodiment of the present invention for an internal wave inversion method based on the fusion of high-frequency tidal data and satellite remote sensing.

[0034] Figure 2 This is a schematic diagram of the internal wave inversion system based on the fusion of high-frequency tidal data and satellite remote sensing, according to another embodiment of the present invention. Detailed Implementation

[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] It should be noted that when a component is described as "fixed to" another component, it can be directly on the other component or may have a component in between. When a component is considered "connected to" another component, it can be directly connected to the other component or may have a component in between. When a component is considered "set on" another component, it can be directly set on the other component or may have a component in between. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0037] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0038] Please see Figure 1 A preferred embodiment of the present invention provides an internal wave inversion method based on the fusion of high-frequency tidal data and satellite remote sensing, comprising:

[0039] S1 collects SAR satellite remote sensing data, 5-minute sampling interval tide data, and CTD density profile data, and preprocesses the 5-minute sampling interval tide data.

[0040] SAR satellite remote sensing data is obtained by using SAR imagery data from relevant satellites (such as Sentinel-1 satellite) over target sea areas such as wind farms and booster stations. The spatial resolution can be selected as 10 meters and the revisit period is 1 day.

[0041] To obtain tidal data at 5-minute sampling intervals, a pressure-type tidal gauge can be used, deployed in the target sea area, to collect tidal data at 5-minute intervals, which can be continuously collected for 30 days.

[0042] CTD density profile data can be obtained by using a CTD device (Conductivity, Temperature, Depth, which can measure multiple parameters such as temperature, conductivity, pressure, dissolved oxygen, pH, chlorophyll, and turbidity) to simultaneously collect seawater density profile data in the area where the tide gauge is deployed, with a depth range of 0 to 200 meters and a sampling interval of 1 meter.

[0043] Specifically, preprocessing includes:

[0044] (1) Detrending and Butterworth low-pass filtering are applied to the tidal data at 5-minute sampling intervals to eliminate the tidal principal components.

[0045] Detrending: Using linear fitting methods in MATLAB to remove long-term drift trends in tidal data;

[0046] Butterworth low-pass filter: Design a 4th-order low-pass filter using the `butter` function, with a selectable cutoff frequency: `f_cut = 1 / (2 × 3600) Hz = 1.39 × 10⁻`. 4 Hz (corresponding to a period of 2 hours) is used to filter the detrended data and retain the internal wave signal.

[0047] (2) Perform radiometric calibration, geometric correction and feature extraction on SAR satellite remote sensing data.

[0048] Radiometric calibration: SAR images are radiometrically calibrated using SNAP software, converting pixel values ​​into backscattering coefficients;

[0049] Geometric correction: Using the WGS-84 coordinate system, the image is geometrically corrected using GCPs (Ground Control Points), with the error controlled within 1 pixel;

[0050] Feature extraction: Threshold segmentation was used to extract the bright and dark stripes on the sea surface corresponding to the internal waves, and SSHA (Sea Surface Height Anomaly) data was extracted using the sarpy library.

[0051] (3) Density outliers were removed from the CTD density profile data using the 3σ criterion.

[0052] S2 performs spatiotemporal alignment of SAR satellite remote sensing data, 5-minute sampling interval tide data, and CTD density profile data.

[0053] Specifically, the spatiotemporal alignment method is as follows: the time window of the SAR satellite remote sensing data is aligned with the tide time period of the 5-minute sampling interval tide data, and the spatial aspect of the SAR satellite remote sensing data is generated by Kriging interpolation to generate the sea surface height field of the internal wave propagation path.

[0054] S3 employs variational mode decomposition to decompose the preprocessed 5-minute sampling interval tidal data, screen internal wave characteristic modes, and extract the amplitude, period, and energy ratio parameters of the internal waves.

[0055] Specifically, the parameters for submodal decomposition are set as follows: the number of modes K ranges from 3 to 8, the penalty factor α ranges from 1000 to 5000, and the center frequency range is f_c ∈ [0.5, 1.2] mHz. The selection of internal wave characteristic modes involves calculating the instantaneous frequency of each mode using the Hilbert transform, and selecting modes with a selection period of 10 minutes to 2 hours as internal wave characteristic modes.

[0056] For each decomposed mode u_i, perform a Hilbert transform and calculate the instantaneous frequency f_i = (1 / (2π))・dφ / dt (φ is the instantaneous phase). Select modes f_i∈ [0.14, 1.67] mHz (corresponding to periods of 10 minutes to 2 hours) as internal wave characteristic modes. Calculate the peak value of the internal wave characteristic mode as the amplitude A, the interval between adjacent peak values ​​as the period T, and the ratio of mode energy to total energy as the energy proportion E. P .

[0057] S4 uses SAR satellite remote sensing data, combined with cross-correlation method and Coriolis force correction term to calculate propagation direction.

[0058] Specifically, the formula for calculating the direction of propagation is:

[0059] + ,

[0060] in, The direction angle of propagation. For the horizontal displacement of the internal wave in directional components, For the horizontal displacement of the internal wave in directional components, Coriolis parameters, To delay the spread, This is a Coriolis force correction term.

[0061] Select two adjacent internal wave crests in the sea surface height field, with coordinates (x1, y1) and (x2, y2) respectively. Calculate Δx = x2 - x1 and Δy = y2 - y1. The calculation is the initial propagation direction calculated using the cross-correlation method. .

[0062] The Coriolis parameter is calculated using the latitude φ of the target sea area. The Coriolis parameter is f = 2Ωsinφ (Ω is the Earth's rotational angular velocity, which can be taken as 7.292 × 10⁻). 5(rad / s); the Coriolis parameter multiplied by the propagation delay of the internal wave yields the Coriolis force correction term, which, when used to correct the initial propagation direction, gives the internal propagation direction.

[0063] S5. Establish the linear relationship between the SAR sea surface height anomaly gradient and amplitude, and invert the propagation velocity of the internal wave based on the linear relationship.

[0064] Specifically, the inverse formula for propagation speed is:

[0065] ,

[0066] in, For the propagation speed of internal waves, This represents the sea surface height anomaly gradient. denoted as , where is the amplitude of the internal wave, and k is the calibration coefficient, which is calibrated using measured tidal level data at 5-minute sampling intervals.

[0067] S6 calculates the buoyancy frequency based on CTD density profile data, and calculates the horizontal velocity of the internal wave based on amplitude, period, horizontal velocity of the internal wave and buoyancy frequency.

[0068] Based on the preprocessed CTD density profile data, using the formula Calculate the buoyancy frequency N, where g is the gravitational acceleration, ρ is the seawater density, and z is the depth.

[0069] Specifically, the formula for calculating the horizontal velocity of internal waves is:

[0070] ,

[0071] Where u is the horizontal velocity of the internal wave. Let N be the amplitude of the internal wave, N be the buoyancy frequency, and h be the water depth. Let t be the acceleration due to gravity, and t be the time. Angular frequency, The period of the internal wave.

[0072] S7. Using the horizontal velocity of the internal wave, the load time history curve of the internal wave is calculated using the Morison equation.

[0073] The internal wave dynamic load is calculated using the drag force formula. The calculation uses the Morison equation, where F is the load, C_D is the drag coefficient (1.2 for cylinders), D_u is the diameter of the marine structure pile foundation, ρ is the seawater density, and u is the horizontal velocity of the internal wave. The load F can be obtained from the calculation, thus yielding the internal wave load time history curve.

[0074] When the ratio of the amplitude A of the internal wave to the water depth H is greater than 0.09, the internal wave becomes strongly nonlinear and its propagation speed needs to be calculated using the mKdV equation; otherwise, the error will be large. This method is for the case of large amplitude internal waves (A / H>0.09).

[0075] S8 outputs the amplitude, propagation direction, propagation speed, and load time history curve of the internal wave to the structural analysis software interface.

[0076] The internal wave load time history curve can be output to structural analysis software through the dedicated ANSYS / SACS interface, providing input data for stress distribution calculation and fatigue life assessment of marine engineering structures such as wind farms, thus completing the closed loop from parameter inversion to engineering application.

[0077] Please see Figure 2 This invention also provides an internal wave inversion system based on the fusion of high-frequency tidal data and satellite remote sensing, comprising:

[0078] Acquisition module 1 is used to acquire SAR satellite remote sensing data, 5-minute sampling interval tide data, and CTD density profile data, and to preprocess the 5-minute sampling interval tide data;

[0079] Spatiotemporal alignment module 2 is used to perform spatiotemporal alignment of SAR satellite remote sensing data, 5-minute sampling interval tide data, and CTD density profile data;

[0080] The internal wave feature extraction module 3 is used to decompose the preprocessed 5-minute sampling interval tide data using variational mode decomposition, screen the internal wave feature modes, and extract the amplitude, period and energy ratio parameters of the internal waves.

[0081] The propagation direction calculation module 4 is used to calculate the propagation direction using SAR satellite remote sensing data, combined with the cross-correlation method and the Coriolis force correction term.

[0082] The propagation velocity inversion module 5 is used to establish a linear relationship between the SAR sea surface height anomaly gradient and the amplitude, and to invert the propagation velocity of the internal wave based on the linear relationship.

[0083] The horizontal velocity calculation module 6 is used to calculate the buoyancy frequency based on CTD density profile data. It calculates the horizontal velocity of the internal wave based on the amplitude, period, horizontal velocity of the internal wave and buoyancy frequency.

[0084] Load calculation module 7 is used to calculate the load time history curve of the internal wave using the horizontal velocity of the internal wave and the Morison equation.

[0085] Output module 8 is used to output the amplitude, propagation direction, propagation speed, and load time history curve of the internal wave to the structural simulation software interface.

[0086] In a preferred embodiment of the present invention, in the internal wave inversion system based on the fusion of high-frequency tidal data and satellite remote sensing:

[0087] Preprocessing includes:

[0088] Detrending and Butterworth low-pass filtering were applied to the tidal data at 5-minute sampling intervals.

[0089] Radiometric calibration, geometric correction, and feature extraction are performed on SAR satellite remote sensing data.

[0090] Density outliers were removed from CTD density profile data using the 3σ criterion.

[0091] The spatiotemporal alignment method is as follows: the time window of SAR satellite remote sensing data is aligned with the tide time period of 5-minute sampling interval tide data, and the spatial aspect of SAR satellite remote sensing data is generated by Kriging interpolation to generate the sea surface height field of internal wave propagation path.

[0092] The parameters for variational mode decomposition are set as follows: the number of modes K ranges from 3 to 8, the penalty factor α ranges from 1000 to 5000, and the center frequency range is f_c ∈ [0.5, 1.2] mHz;

[0093] The selection of internal wave characteristic modes involves calculating the instantaneous frequency of each mode using Hilbert transform, and selecting modes with a selection period of 10 minutes to 2 hours as internal wave characteristic modes.

[0094] The formula for calculating the direction of propagation is:

[0095] + ,

[0096] in, The direction angle of propagation. For the horizontal displacement of the internal wave in directional components, For the horizontal displacement of the internal wave in directional components, Coriolis parameters, To delay the transmission;

[0097] The inverse formula for propagation speed is:

[0098] ,

[0099] in, For the propagation speed of internal waves, This represents the sea surface height anomaly gradient. The amplitude of the internal wave is given by , and k is the calibration coefficient, which is calibrated using the measured tidal level data at 5-minute sampling intervals.

[0100] The formula for calculating the horizontal velocity of internal waves is:

[0101] ,

[0102] in, Let N be the amplitude of the internal wave, N be the buoyancy frequency, and h be the water depth. Let t be the acceleration due to gravity, and t be the time. Angular frequency, The period of the internal wave.

[0103] The above description is a detailed description of the preferred embodiments of the present invention. However, the embodiments are not intended to limit the scope of the patent application of the present invention. All equivalent changes or modifications made under the technical spirit of the present invention should fall within the patent scope covered by the present invention.

Claims

1. An internal wave inversion method based on the fusion of high-frequency tidal data and satellite remote sensing, characterized in that, include: S1: Collect SAR satellite remote sensing data, tidal level data at 5-minute sampling intervals, and CTD density profile data, and perform preprocessing. S2 performs spatiotemporal alignment of SAR satellite remote sensing data, 5-minute sampling interval tide data, and CTD density profile data. S3 uses variational mode decomposition to decompose the preprocessed 5-minute sampling interval tide data, screen the internal wave characteristic modes, and extract the amplitude, period and energy ratio parameters of the internal waves. S4, using SAR satellite remote sensing data, combined with the cross-correlation method and the Coriolis force correction term to calculate the propagation direction; S5, establish a linear relationship between the SAR sea surface height anomaly gradient and the amplitude, and invert the propagation velocity of the internal wave based on the linear relationship; S6. Calculate the buoyancy frequency based on CTD density profile data, and calculate the horizontal velocity of the internal wave according to the amplitude, period, water depth and buoyancy frequency. S7. Using the horizontal velocity of the internal wave, the load time history curve of the internal wave is calculated through the Morison equation. S8 outputs the amplitude, propagation direction, propagation speed, and load time history curve of the internal wave to the structural analysis software interface.

2. The internal wave inversion method based on the fusion of high-frequency tidal data and satellite remote sensing as described in claim 1, characterized in that, The preprocessing described in S1 includes: Detrending and Butterworth low-pass filtering were applied to the tidal data at 5-minute sampling intervals. Radiometric calibration, geometric correction, and feature extraction are performed on SAR satellite remote sensing data. Density outliers were removed from CTD density profile data using the 3σ criterion.

3. The internal wave inversion method based on the fusion of high-frequency tidal data and satellite remote sensing as described in claim 1, characterized in that, The spatiotemporal alignment method described in S2 is as follows: the time window of the SAR satellite remote sensing data is aligned with the tide time period of the 5-minute sampling interval tide data, and the spatial aspect of the SAR satellite remote sensing data is generated by Kriging interpolation to generate the sea surface height field of the internal wave propagation path.

4. The internal wave inversion method based on the fusion of high-frequency tidal data and satellite remote sensing as described in claim 1, characterized in that, The parameters of the variational mode decomposition described in S3 are set as follows: the number of modes K is 3~8, the penalty factor α is 1000~5000, and the center frequency range is f_c∈[0.5, 1.2] mHz.

5. The internal wave inversion method based on the fusion of high-frequency tidal data and satellite remote sensing as described in claim 1, characterized in that, The screening of internal wave characteristic modes described in S3 involves calculating the instantaneous frequency of each mode using the Hilbert transform, and selecting modes with a screening period of 10 minutes to 2 hours as internal wave characteristic modes.

6. The internal wave inversion method based on the fusion of high-frequency tidal data and satellite remote sensing as described in claim 1, characterized in that, The formula for calculating the propagation direction mentioned in S4 is: + ， in, The direction angle of propagation. For the horizontal displacement of the internal wave in directional components, For the horizontal displacement of the internal wave in directional components, Coriolis parameters, To delay the spread.

7. The internal wave inversion method based on the fusion of high-frequency tidal data and satellite remote sensing as described in claim 1, characterized in that, The inversion formula for the propagation speed described in S5 is: ， in, For the propagation speed of internal waves, This represents the sea surface height anomaly gradient. denoted as , where is the amplitude of the internal wave, and k is the calibration coefficient, which is calibrated using measured tidal level data at 5-minute sampling intervals.

8. The internal wave inversion method based on the fusion of high-frequency tidal data and satellite remote sensing as described in claim 1, characterized in that, The formula for calculating the horizontal velocity of internal waves, as described in S6, is: ， in, Let N be the amplitude of the internal wave, N be the buoyancy frequency, and h be the water depth. Let t be the acceleration due to gravity, and t be the time. Angular frequency, The period of the internal wave.

9. An internal wave inversion system based on the fusion of high-frequency tidal data and satellite remote sensing, characterized in that, include: The acquisition module is used to acquire SAR satellite remote sensing data, 5-minute sampling interval tide data, and CTD density profile data, and to preprocess the 5-minute sampling interval tide data. The spatiotemporal alignment module is used to perform spatiotemporal alignment on SAR satellite remote sensing data, 5-minute sampling interval tide data, and CTD density profile data. The internal wave feature extraction module is used to decompose the preprocessed 5-minute sampling interval tide data using variational mode decomposition, screen the internal wave feature modes, and extract the amplitude, period and energy ratio parameters of the internal waves. The propagation direction calculation module is used to calculate the propagation direction using SAR satellite remote sensing data, combined with the cross-correlation method and the Coriolis force correction term. The propagation velocity inversion module is used to establish a linear relationship between the SAR sea surface height anomaly gradient and the amplitude, and to invert the propagation velocity of the internal wave based on the linear relationship. The horizontal velocity calculation module is used to calculate the buoyancy frequency based on CTD density profile data, and to calculate the horizontal velocity of the internal wave according to the amplitude, period, water depth and buoyancy frequency. The load calculation module is used to calculate the load time history curve of the internal wave using the horizontal velocity of the internal wave and the Morison equation. The output module is used to output the amplitude, propagation direction, propagation speed, and load time history curve of the internal wave to the structural simulation software interface.

10. The internal wave inversion system based on the fusion of high-frequency tidal data and satellite remote sensing as described in claim 9, characterized in that, The preprocessing includes: Detrending and Butterworth low-pass filtering were applied to the tidal data at 5-minute sampling intervals. Radiometric calibration, geometric correction, and feature extraction are performed on SAR satellite remote sensing data. Density outliers were removed from CTD density profile data using the 3σ criterion. The spatiotemporal alignment method is as follows: the time window of the SAR satellite remote sensing data is aligned with the tide time period of the 5-minute sampling interval tide data, and the spatial aspect of the SAR satellite remote sensing data is generated by Kriging interpolation to generate the sea surface height field of the internal wave propagation path. The parameters of the variational mode decomposition are set as follows: the number of modes K is 3~8, the penalty factor α is 1000~5000, and the center frequency range is f_c∈[0.5, 1.2] mHz; The selection of internal wave characteristic modes involves calculating the instantaneous frequency of each mode using Hilbert transform, and selecting modes with a selection period of 10 minutes to 2 hours as internal wave characteristic modes. The formula for calculating the propagation direction is: + ， in, The direction angle of propagation. For the horizontal displacement of the internal wave in directional components, For the horizontal displacement of the internal wave in directional components, Coriolis parameters, To delay the transmission; The inversion formula for the propagation speed is: ， in, For the propagation speed of internal waves, This represents the sea surface height anomaly gradient. The amplitude of the internal wave is given by , and k is the calibration coefficient, which is calibrated using the measured tidal level data at 5-minute sampling intervals. The formula for calculating the horizontal velocity of the internal wave is: , in, Let N be the amplitude of the internal wave, N be the buoyancy frequency, and h be the water depth. Let t be the acceleration due to gravity, and t be the time. Angular frequency, The period of the internal wave.

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