A soliton internal wave velocity inversion method based on vector sound field energy fluctuation frequency

Abstract

The application discloses a kind of soliton internal wave velocity inversion methods based on vector sound field energy fluctuation frequency characteristics, comprising: a sound source and a vector hydrophone vertical array are placed on the sound propagation path expected by soliton internal wave in the set sea area;Sound source emits single-frequency sound signal, and vector hydrophone vertical array receives sound pressure and horizontal velocity signal;Calculate sound pressure-horizontal velocity cross spectrum, obtain the fluctuation spectrum of vector sound field at each depth by Fourier transform;Average processing obtains average energy fluctuation spectrum;Extracting spectral peak, construct the fluctuation frequency corresponding to each spectral peak into measured sound field energy fluctuation frequency vector;According to the ocean environmental parameter when there is no soliton internal wave, the horizontal wave number difference between different order normal modes of sound field is calculated;Combined with the preset soliton internal wave velocity range, the theoretical fluctuation frequency vector corresponding to each velocity value is constructed;The similarity of measured sound field energy fluctuation frequency vector and theoretical fluctuation frequency vector is calculated, the velocity value corresponding to the maximum value is taken, and inversion is realized.

Description

Technical Field

[0001] This invention belongs to the fields of underwater acoustic engineering, sonar technology, and marine engineering, and relates to a method for inverting the internal wave velocity of a soliton based on the frequency characteristics of vector sound field energy fluctuations. Background Technology

[0002] Soliton internal waves are a typical physical oceanographic phenomenon in shallow continental shelf waters. Densely stable, stratified seawater, disturbed by tidal currents and undulating topography, generates internal tidal waves. These waves, influenced by strong nonlinear factors during propagation, evolve into large-amplitude soliton internal waves. Soliton internal waves are a crucial component of the ocean dynamic system, playing a key role in mixing, transport, and energy transfer, and are essential for maintaining marine ecological balance and temperature-salinity stability. Soliton internal waves can induce strong shear currents, threatening the safety of offshore platforms and underwater vehicles; they also alter the spatiotemporal distribution of seawater sound velocity, impacting marine acoustic applications such as ocean acoustic detection and underwater communication.

[0003] The methods for monitoring internal waves of ocean solitons are divided into in-situ measurement, remote sensing, numerical simulation and acoustic inversion. The in-situ measurement method involves arranging an array of observation equipment such as temperature, salinity, depth and current meters at fixed points, and monitoring internal waves by measuring parameters such as seawater temperature, salinity, density and current velocity, as in reference [1] ("Design and application of internal solitary wave monitoring system based on Tiantong communication", published in the Journal of Tropical Oceanography, Vol. 5, 2024, starting page 180). The advantages of the in-situ measurement method are small measurement error and a complete data analysis system; the disadvantage is that it can only monitor local sea areas and it is difficult to capture the large-scale spatial distribution and propagation path of internal waves. Remote sensing observation mainly uses synthetic aperture radar (SAR) to capture the modulation signal of soliton internal waves on sea surface features, identifies internal waves through remote sensing images and inverts their parameters, as in reference [2] ("Study on soliton waves in the South Orkney Sea based on Sentinel-1 SAR imaging", published in the Journal of Ocean University of China, Vol. 2, February 2025, starting page 10). The advantage of remote sensing observation is that it can achieve large-area observation and capture the generation source, propagation path and spatial distribution law of internal waves; the disadvantage is that it can only capture the sea surface characterization of internal waves, cannot reflect the fine structure of underwater vertical profiles, and cannot communicate with underwater platforms in real time. Numerical simulation takes the field observation data as the initial condition, performs numerical simulation based on the internal wave dynamics model, and realizes predictive monitoring and parameter inversion optimization of internal waves through iterative correction of simulation results and observation data. The advantage of numerical simulation is that it can predict and warn of internal waves and simulate the entire process of internal wave generation, propagation and evolution. The disadvantage is that it is highly dependent on the initial data of field observation, and the accuracy of the simulation results is greatly affected by topography and hydrological boundary conditions. It can only be used as a supplementary monitoring method, such as the literature [3] ("Study on the spatiotemporal characteristics and generation mechanism of internal solitary waves in eastern Hainan Island", published in the 5th issue of Marine Surveying and Mapping in September 2025, starting page 52). The acoustic inversion method uses the sudden change in the seawater sound velocity profile caused by soliton internal waves to identify internal waves and invert their parameters through the change in the propagation characteristics of underwater acoustic signals. The advantage of acoustic inversion method is that it can capture the mesoscale propagation characteristics of internal waves underwater and can communicate with the underwater platform in real time, providing the underwater platform with soliton internal wave parameter information in a timely manner. For example, the acoustic inversion method proposed in reference [4] ("Algorithm improvement and experimental verification of monitoring internal waves by using acoustic field spectrum frequency drift", published in Acta Acustica, No. 5, September 2017, starting page 524) requires the use of full-ocean-depth vertical array for mode separation. This method requires accurate understanding of the underwater attitude of the vertical array, which is difficult to implement in engineering and has low flexibility. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and propose a soliton internal wave velocity inversion method based on the frequency characteristics of vector sound field energy fluctuations.

[0005] In view of this, the present invention proposes a method for inverting the internal wave velocity of a soliton based on the frequency characteristics of vector acoustic field energy fluctuations, comprising: Step S1: Place a sound source and a vector hydrophone vertical array on the expected sound propagation path of the soliton in the designated sea area. The vector hydrophone vertical array contains multiple array elements. Step S2: A single-frequency sound signal is emitted by the sound source, and the sound pressure signal and horizontal vibration velocity signal are received by the vertical array of the vector hydrophone; Step S3: Calculate the cross spectrum of sound pressure and horizontal vibration velocity at each array element, and obtain the vector sound field fluctuation spectrum at each depth through Fourier transform; Step S4: Average the vector sound field fluctuation spectrum at each depth to obtain the average energy fluctuation spectrum; Step S5: Extract the spectral peaks from the average energy fluctuation spectrum, and construct the measured sound field energy fluctuation frequency vector from the fluctuation frequencies corresponding to each spectral peak. Step S6: Based on the marine environmental parameters without soliton internal waves, calculate the horizontal wavenumber difference between different normal modes of the sound field; and combine the preset soliton internal wave velocity range to construct the theoretical fluctuation frequency vector corresponding to each preset velocity value. Step S7: Calculate the similarity between the measured sound field energy fluctuation frequency vector and the theoretical fluctuation frequency vector, take the preset velocity value corresponding to the maximum similarity value, and determine it as the velocity estimate of the soliton internal wave in the direction of sound propagation path, thereby realizing the inversion.

[0006] Preferably, the vector hydrophone vertical array in step S1 is a multi-channel receiving array used to synchronously acquire sound pressure signals and horizontal vibration velocity signals at different depths.

[0007] Preferably, the sound pressure-horizontal velocity cross spectrum in step S3 is... for:

[0008] in, The sound pressure spectrum. These represent distance, depth, and soliton internal wave propagation time, respectively. The value represents the horizontal vibration velocity spectrum, and "*" represents the conjugate operator.

[0009] Preferably, the horizontal axis of the vector sound field fluctuation spectrum obtained in step S3 is in period / hour.

[0010] Preferably, the measured sound field energy fluctuation frequency vector constructed in step S5 is: ,in N The number of fluctuation frequency values. Indicates the extracted first n A fluctuation frequency value.

[0011] Preferably, the marine environmental parameters in step S6 when there are no internal waves in the soliton include at least: seawater depth, seawater sound velocity profile, seabed sound velocity, density, and absorption coefficient.

[0012] Preferably, in step S6, the method for constructing the theoretical fluctuation frequency vector is as follows: For a certain velocity value within the preset soliton internal wave velocity range The frequency values ​​of sound field fluctuations caused by coupling of normal modes of different signs are calculated according to the following formula. :

[0013] in, , These represent the horizontal wavenumbers of the m-th and n-th normal modes, respectively. This indicates the operation of taking the real part; By sorting the fluctuation frequency values ​​caused by coupling of different normal modes from smallest to largest, a theoretical fluctuation frequency vector is constructed. ,in Q The number of fluctuation frequency values. Indicates the sorted order of the first... q Each frequency value.

[0014] Preferably, the similarity calculation method in step S7 is as follows: For the measured frequency vector of sound field energy fluctuation elements in In the theoretical fluctuation frequency vector Find the theoretical frequency value with the smallest absolute difference from it. , recorded as ; will be with vector All elements Corresponding target frequency value Construct the target frequency vector :

[0015] Calculate the frequency vector representing the sound field fluctuations With the target frequency vector Euclidean distance of overall difference The Euclidean distance between the two vectors is then mapped to a normalized similarity. :

[0016] Preferably, in step S7, the velocity estimate of the soliton internal wave in the direction of the sound propagation path is... for: , in, This represents the minimum and maximum values ​​of the preset range of wave velocities within a soliton.

[0017] Preferably, the method is used to suppress underwater isotropic noise interference.

[0018] Compared with the prior art, the advantages of the present invention are: 1. Unlike existing acoustic monitoring technologies that use scalar sound field sound pressure or sound intensity signals for internal wave monitoring, this invention is based on vector sound field information for internal wave monitoring. That is, on the basis of the original scalar sound field information processing, it also extends to the use of vector vibration velocity information and adopts a joint processing technology of sound pressure and vibration velocity signals, which can effectively suppress underwater isotropic noise interference and reduce the signal-to-noise ratio threshold for which the method is applicable.

[0019] 2. Unlike existing acoustic internal wave monitoring technologies that only utilize a single dominant frequency of sound intensity fluctuations, this invention extracts multiple fluctuation characteristic frequencies of the complex sound intensity of the vector sound field and uses a method of matching characteristic frequency vectors to optimize the internal wave velocity estimation. Its advantage lies in that it can make full use of the sound field fluctuation information caused by internal waves, effectively reduce the monitoring error caused by environmental interference when using single frequency information, improve the accuracy and reliability of internal wave parameter inversion, and enhance the adaptability of the monitoring system to complex marine acoustic field environments.

[0020] 3. Unlike existing acoustic internal wave monitoring techniques that only use a single hydrophone to receive energy fluctuations for velocity inversion, this invention uses a vertical array of vector hydrophones to collect vector sound field information. By accumulating and averaging the energy spectra of the received sound signals at different depths, the fluctuation characteristics of the vector sound field energy at different depths are fully integrated, thereby further improving the method's noise resistance and inversion accuracy. Attached Figure Description

[0021] Figure 1 This is a flowchart of the soliton internal wave velocity inversion method based on the frequency of sound field energy fluctuations in this invention; Figure 2 This is a schematic diagram of the deployment and waveguide parameters of the soliton internal wave monitoring system in an embodiment of the present invention; Figure 3 This is the change of the cross spectrum of vector sound field sound pressure and horizontal vibration velocity at each depth in the embodiments of the present invention over time; Figure 4 This is the cross-spectral fluctuation spectrum of vector sound field sound pressure-horizontal vibration velocity at each depth in the embodiments of the present invention; Figure 5 This is the sound pressure-horizontal vibration velocity cross-spectral fluctuation spectrum after depth averaging in this embodiment of the invention; Figure 6 This is the normalized similarity curve between the sound field fluctuation frequency vector and the theoretical fluctuation frequency vector in the embodiment of the present invention. Figure 7 These are 200 Monte Carlo statistics of internal wave velocity inversion under different signal-to-noise ratios in this embodiment of the invention, where (a) is the sound pressure autospectrum and (b) is the sound pressure-horizontal vibration velocity cross spectrum. Detailed Implementation

[0022] This invention proposes a method for inverting the internal wave velocity of a soliton based on the frequency of energy fluctuations in a vector acoustic field. This method relies on an internal wave monitoring system consisting of a sound source and a vertical array of vector hydrophones. The method includes: placing a sound source and a vertical array of vector hydrophones at a certain distance apart; emitting a single-frequency sound signal from the sound source; using the vertical array of vector hydrophones to record the sound pressure and horizontal velocity signals of the soliton internal wave propagation over a period of time; calculating the cross spectrum of sound pressure and horizontal velocity for each vector hydrophone; performing a Fourier transform on the cross spectrum to obtain the sound field fluctuation spectrum of each vector hydrophone; averaging the average; searching for high-energy line spectrum frequency values ​​in the averaged vector sound field fluctuation spectrum to construct a vector sound field energy fluctuation frequency vector; calculating the horizontal wavenumber difference between different normal modes of the sound field based on marine environmental parameters when there are no internal waves; calculating the theoretical fluctuation frequency vector corresponding to the preset soliton internal wave velocity based on the functional relationship between the horizontal wavenumber difference of the normal modes and the soliton internal wave velocity; calculating the similarity between the vector sound field energy fluctuation frequency vector and the theoretical fluctuation frequency vector; the velocity value corresponding to the maximum value of the similarity curve is the estimated value of the soliton internal wave velocity. The marine environmental parameters include at least: seawater depth, seawater sound velocity profile, seabed sound velocity, density, and absorption coefficient.

[0023] The processing flow of the method described in this invention is as follows: Step 1: First, deploy a soliton internal wave monitoring system consisting of a low-frequency sound source and a vertical array of vector hydrophones containing multiple array elements along the path of the soliton internal wave. In one embodiment, 16 elements are used.

[0024] Step 2: Calculate the horizontal wavenumbers of all normal modes in the sound field when there are no internal waves using the KRAKEN sound field calculation model. .

[0025] Step 3: Assume the range of propagation velocity of the soliton internal wave is... For a certain velocity value within the range , Calculate the frequency values ​​of sound field fluctuations caused by coupling of normal modes of different signs:

[0026] in, , These represent the horizontal wavenumbers of the m-th and n-th normal modes, respectively. This indicates the operation of taking the real part; Sort the sound field fluctuation frequency values ​​from smallest to largest to construct a theoretical frequency vector. ,in Q The number of fluctuation frequency values. Indicates the sorted order of the first... q Each frequency value.

[0027] Step 4: Calculate the wave propagation time of each vector hydrophone within the soliton. Internal sound pressure-velocity cross spectrum:

[0028] in, The sound pressure spectrum. This represents the horizontal vibration velocity spectrum; "*" represents the conjugate operator. These represent distance, depth, and propagation time of the soliton internal wave, respectively.

[0029] Step 5: Calculate the Fourier transform of the cross spectrum of sound pressure and horizontal vibration velocity of each vector hydrophone to obtain the vector sound field fluctuation spectrum at different depths.

[0030] Step 6: In order to make full use of the energy fluctuation information at each depth, the fluctuation spectrum of the vector hydrophone at each depth is averaged to obtain a smooth sound pressure-velocity cross spectrum fluctuation spectrum at each depth.

[0031] Step 7: Extract the spectral peaks from the average energy fluctuation spectrum, and construct a sound field energy fluctuation frequency vector from the fluctuation frequencies corresponding to each spectral peak. ,in N The number of fluctuation frequency values. Indicates the search result of the first... n Each fluctuation frequency value.

[0032] Step 8: For vectors elements in In the theoretical fluctuation frequency vector Find the theoretical frequency value with the smallest absolute difference from it. , recorded as . will be with vector All elements Corresponding target frequency value Construct the target frequency vector :

[0033] Step 9: Calculate the frequency vector representing the energy fluctuations of the sound field. With the target frequency vector Euclidean distance of overall difference The Euclidean distance between the two vectors is then mapped to a normalized similarity. The smaller the overall difference, the higher the normalized similarity value.

[0034] Step 10: Traverse the pre-defined speed range All values ​​within the range, when normalized similarity When the maximum value is reached, it corresponds to the estimated value of the component of the wave propagation velocity inside the soliton along the sound propagation path. :

[0035] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and embodiments.

[0036] Example This invention provides a method for inverting the propagation velocity of internal waves in a soliton using the frequency of vector acoustic field energy fluctuations. This method addresses practical application problems such as weak noise immunity and inability to communicate with underwater platforms in real time in existing internal wave monitoring methods. The implementation process is as follows: Figure 1 As shown, a soliton internal wave monitoring system, consisting of a low-frequency sound source and a vertical array of vector hydrophones capable of measuring sound pressure and particle velocity, is first deployed along the path of the soliton internal wave. The sound source emits a single-frequency sound signal, and the vertical array of vector hydrophones records the sound pressure and horizontal velocity signals over a period of time as the soliton internal wave travels along the sound propagation path. The sound pressure-horizontal velocity cross spectrum of each vector hydrophone is calculated, and a Fourier transform is performed on the cross spectrum to obtain the fluctuation spectrum of the cross spectrum of sound pressure and horizontal velocity for each vector hydrophone, which is then averaged. The line spectrum frequencies in the average fluctuation spectrum of the vector sound field energy are used to form a vector sound field energy fluctuation frequency vector. The difference in horizontal wavenumber between different normal modes of the sound field is calculated based on the marine environmental parameters when there are no internal waves. According to the functional relationship between the difference in horizontal wavenumber of normal modes and the velocity of the soliton internal wave, the theoretical fluctuation frequency vector corresponding to the preset soliton internal wave velocity can be calculated. The similarity between the vector sound field energy fluctuation frequency vector and the theoretical fluctuation frequency vector is calculated. The velocity value corresponding to the maximum value of the similarity curve is the velocity estimate of the soliton internal wave on the sound field propagation path.

[0037] The specific implementation process consists of the following steps: Step 1: First, deploy the soliton internal wave monitoring system, consisting of one low-frequency sound source and one 16-element vector hydrophone vertical array, along the path of the soliton internal wave. Figure 2 As shown. Figure 2The marine environmental parameters used in the numerical simulation of the method are also given, including: seawater depth H = 80 m, seabed sound speed 1600 m / s, density 1.5 g / cm3, absorption coefficient 0.5 dB / λ; sound source depth 49 m, transmitting a 200 Hz single-frequency signal; sound source and receiving array distance 30 km; and background sound velocity profile without soliton internal waves. for: in, c 1 = 1481 m / s c 2 = 1522 m / s, representing the speed of sound in the upper and lower layers of seawater, respectively. z 1 = 30 m z 2 = 15 m, representing the depths of the upper water layer and the thermocline, respectively. g = ( c 1- c 2) / ( z 1- z 2) represents the sound velocity gradient in the thermocline.

[0038] The soliton internal wave adopts a typical hyperbolic orthogonal secant model of the KdV equation. The internal wave causes a change in the depth of the thermocline through a function. describe: Where sech is the hyperbolic secant function. = 15 m is the amplitude of the wave inside the soliton. = 100 m is the width of the wave inside the soliton, and the velocity of the wave inside the soliton is = 1 m / s, The time of motion of the soliton's internal wave. The sound velocity profile after being disturbed by the soliton's internal wave. for: Step 2: Calculate the horizontal wavenumbers of all normal modes in the sound field when there are no internal waves using the KRAKEN sound field calculation model. The horizontal wave values ​​of the first 5 normal modes under the current numerical simulation conditions are as follows: Table 1

[0039] Step 3: Assume the range of propagation velocity of the soliton internal wave is... For a certain velocity value within the range The frequency values ​​of sound field fluctuations caused by coupling of normal modes of different signs are calculated according to the following formula:

[0040] The fluctuation frequency values ​​caused by coupling of different normal modes are sorted from smallest to largest to construct a theoretical frequency vector. ,in Q The number of fluctuation frequency values. Indicates the sorted order of the first... q Each frequency value.

[0041] Step 4: Calculate the wave propagation time of each vector hydrophone within the soliton. Internal sound pressure-velocity cross spectrum:

[0042] in, The sound pressure spectrum. The horizontal vibration velocity spectrum is shown; "*" represents the conjugate operator. The calculation results of the sound pressure-velocity cross spectrum of each depth vector hydrophone over time are as follows: Figure 3 As shown.

[0043] Step 5: Calculate the Fourier transform of the cross spectrum of sound pressure and horizontal velocity for each vector hydrophone to obtain their respective fluctuation spectrum (the horizontal axis unit is cycles per hour, cph: cycles per hour), such as... Figure 4 As shown.

[0044] Step 6: Average the fluctuation spectrum at each depth. The averaged fluctuation spectrum is shown below. Figure 5 As shown in the figure, the red dashed line represents the sound pressure-velocity cross-spectral fluctuation curve, and the blue solid line represents the sound pressure self-spectral fluctuation curve. Comparing the two fluctuation spectrum curves, it can be seen that the line spectrum in the sound pressure-horizontal velocity cross-spectral fluctuation spectrum is enhanced. This reflects the ability of the sound pressure-horizontal velocity cross-spectrum to suppress isotropic noise, and also demonstrates the technical advantages of this invention, which uses the vertical vibration of the vector hydrophone and the joint processing of sound pressure and horizontal velocity signals.

[0045] Step 7: Search for spectral peaks in the average energy fluctuation spectrum and construct an energy fluctuation frequency vector from the fluctuation frequencies corresponding to each peak. ,in N The number of fluctuation frequency values. Indicates the search result of the first... n The frequency values ​​are as follows. In the current embodiment, the frequencies of the first four high-energy spectral peaks are shown below: Table 2

[0046] Step 8: For vectors elements in In the theoretical fluctuation frequency vector Find the theoretical frequency value with the smallest absolute difference from it. , recorded as . will be with vector All elements Corresponding target frequency value Construct the target frequency vector :

[0047] Step 9: Calculate the frequency vector representing the sound field fluctuations With the target frequency vector Euclidean distance of overall difference The Euclidean distance between the two vectors is then mapped to a normalized similarity. The smaller the overall difference, the higher the normalized similarity value.

[0048] Step 10: Traverse the pre-defined speed range All values ​​within the range, when normalized similarity When the maximum value is reached, it corresponds to the estimated value of the component of the wave propagation velocity inside the soliton along the sound propagation path. ,like Figure 6 As shown, the velocity inversion results are consistent with the internal wave velocity values ​​given in the simulation.

[0049]

[0050] Step 11: The Monte Carlo method is used to simulate and verify the soliton internal wave velocity inversion method based on the sound field energy fluctuation frequency under different signal-to-noise ratios. To demonstrate the advantages of the vector sound field sound pressure and vibration velocity signal joint processing technology adopted in this invention compared with existing sound pressure signal processing technologies, a comparative analysis is performed on the fluctuation frequency inversion results based on the sound pressure self-spectrum and the sound pressure-horizontal vibration velocity cross-spectrum. The simulation is set with a velocity search range of 0 m / s ~ 2 m / s, a search step size of 0.1 m / s, a signal-to-noise ratio range of -15 dB ~ 0 dB, and 200 simulations. Figure 7 The statistical distribution of the internal wave propagation velocity estimation results obtained by matching the fluctuation frequencies of the sound pressure self-spectrum and the sound pressure-horizontal velocity cross-spectrum under different signal-to-noise ratio (SNR) conditions shows that the soliton internal wave velocity can be correctly estimated. The white dashed line in the figure represents the detection line, corresponding to the SNR threshold when the detection probability is 90%. (a) shows the sound pressure self-spectrum, with a detection SNR threshold of -7 dB, and (b) shows the sound pressure-horizontal velocity cross-spectrum, with a detection SNR threshold of -9.5 dB. The reduction in the latter's detection SNR threshold is also due to the noise suppression effect of the cross-spectrum.

[0051] Simulation results show that the method of the present invention can effectively realize the inversion of the internal wave velocity of solitons and has a certain ability to resist environmental noise, thereby improving the accuracy and reliability of the internal wave parameter inversion.

[0052] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the embodiments, those skilled in the art should understand that modifications or equivalent substitutions to the technical solutions of the present invention do not depart from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for inverting the internal wave velocity of a soliton based on the frequency characteristics of vector acoustic field energy fluctuations, comprising: Step S1: Place a sound source and a vector hydrophone vertical array on the expected sound propagation path of the soliton in the designated sea area. The vector hydrophone vertical array contains multiple array elements. Step S2: A single-frequency sound signal is emitted by the sound source, and the sound pressure signal and horizontal vibration velocity signal are received by the vertical array of the vector hydrophone; Step S3: Calculate the cross spectrum of sound pressure and horizontal vibration velocity at each array element, and obtain the vector sound field fluctuation spectrum at each depth through Fourier transform; Step S4: Average the vector sound field fluctuation spectrum at each depth to obtain the average energy fluctuation spectrum; Step S5: Extract the spectral peaks from the average energy fluctuation spectrum, and construct the measured sound field energy fluctuation frequency vector from the fluctuation frequencies corresponding to each spectral peak. Step S6: Based on the marine environmental parameters without soliton internal waves, calculate the horizontal wavenumber difference between different normal modes of the sound field; and combine the preset soliton internal wave velocity range to construct the theoretical fluctuation frequency vector corresponding to each preset velocity value. Step S7: Calculate the similarity between the measured sound field energy fluctuation frequency vector and the theoretical fluctuation frequency vector, take the preset velocity value corresponding to the maximum similarity value, and determine it as the velocity estimate of the soliton internal wave in the direction of sound propagation path, thereby realizing the inversion.

2. The soliton internal wave velocity inversion method based on the frequency characteristics of vector acoustic field energy fluctuations according to claim 1, characterized in that, The vector hydrophone vertical array in step S1 is a multi-channel receiving array used to synchronously acquire sound pressure signals and horizontal vibration velocity signals at different depths.

3. The soliton internal wave velocity inversion method based on the frequency characteristics of vector acoustic field energy fluctuations according to claim 1, characterized in that, The sound pressure-horizontal velocity cross spectrum in step S3 for: in, The sound pressure spectrum. These represent distance, depth, and soliton internal wave propagation time, respectively. The spectrum represents the horizontal vibration velocity, and "*" represents the conjugate operator.

4. The soliton internal wave velocity inversion method based on the frequency characteristics of vector acoustic field energy fluctuations according to claim 1, characterized in that, The horizontal axis of the vector sound field fluctuation spectrum obtained in step S3 is in period / hour.

5. The method for inverting the internal wave velocity of a soliton based on the frequency characteristics of vector acoustic field energy fluctuations according to claim 1, characterized in that, The measured sound field energy fluctuation frequency vector constructed in step S5 is: ,in N The number of fluctuation frequency values. Indicates the extracted first n A fluctuation frequency value.

6. The method for inverting the internal wave velocity of a soliton based on the frequency characteristics of vector acoustic field energy fluctuations according to claim 1, characterized in that, The marine environmental parameters for soliton internal waves in step S6 include at least: seawater depth, seawater sound velocity profile, seabed sound velocity, density, and absorption coefficient.

7. The soliton internal wave velocity inversion method based on the frequency characteristics of vector acoustic field energy fluctuations according to claim 5, characterized in that, In step S6, the method for constructing the theoretical fluctuation frequency vector is as follows: For a certain velocity value within the preset soliton internal wave velocity range The frequency values ​​of sound field fluctuations caused by coupling of normal modes of different signs are calculated according to the following formula. : in, , These represent the horizontal wavenumbers of the m-th and n-th normal modes, respectively. This indicates the operation of taking the real part; By sorting the fluctuation frequency values ​​caused by coupling of different normal modes from smallest to largest, a theoretical fluctuation frequency vector is constructed. ,in Q The number of fluctuation frequency values. Indicates the sorted order of the first... q Each frequency value.

8. The method for inverting the internal wave velocity of a soliton based on the frequency characteristics of vector acoustic field energy fluctuations according to claim 7, characterized in that, The similarity calculation method in step S7 is as follows: For the measured frequency vector of sound field energy fluctuation elements in In the theoretical fluctuation frequency vector Find the theoretical frequency value with the smallest absolute difference from it. , recorded as ; will be with vector All elements Corresponding target frequency value Construct the target frequency vector : Calculate the frequency vector representing the energy fluctuation of the measured sound field. With the target frequency vector Euclidean distance of overall difference The Euclidean distance between the two vectors is then mapped to a normalized similarity. : 。 9. The soliton internal wave velocity inversion method based on the frequency characteristics of vector acoustic field energy fluctuations according to claim 8, characterized in that, In step S7, the velocity estimate of the soliton internal wave in the direction of the sound propagation path is... for: ， in, These represent the minimum and maximum values ​​of the preset soliton internal wave velocity range, respectively.

10. The method for inverting the internal wave velocity of a soliton based on the frequency characteristics of vector acoustic field energy fluctuations according to claim 1, characterized in that, The method is used to suppress underwater isotropic noise interference.

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