A sea surface wind speed inversion method based on submarine optical fiber distributed acoustic sensing

Abstract

The present invention discloses a method for inverting sea surface wind speed based on submarine optical fiber distributed acoustic sensing. The method comprises: recording wind-generated ocean environmental noise through submarine optical fiber based on distributed acoustic wave sensing; obtaining actual sea surface wind speed data through buoys; obtaining the time-frequency power spectrum of noise and its effective frequency band; obtaining an effective sensing unit group after quality control of wind-generated ocean environmental noise; establishing an inversion parameter model, and obtaining inversion parameters according to the time-frequency power spectrum, effective frequency band and actual sea surface wind speed data of the effective sensing unit group; establishing a sea surface wind speed inversion model, and obtaining the inverted sea surface wind speed according to the inversion parameters and the time-frequency power spectrum and effective frequency band of the noise to be inverted. The present invention is based on high-density acoustic sensing technology, makes full use of widely distributed submarine optical fibers, effectively improves the current situation of scarce ocean on-site wind speed observation data, greatly reduces the cost of wind speed observation, and provides a new way for sea surface wind speed observation under extreme sea conditions.

Description

Technical Field

[0001] The invention relates to a sea surface wind speed inversion method, and in particular to a sea surface wind speed inversion method based on submarine optical fiber distributed acoustic sensing. Background Art

[0002] Under extreme weather conditions such as typhoons and storm surges, strong winds can cause serious damage to ships, ports, offshore platforms, and coastal communities. By observing changes in sea surface wind speed in real time, relevant departments can more accurately identify and warn of extreme weather systems, take preventive measures in a timely manner, and provide safety guidance, thereby effectively reducing casualties and property losses. Therefore, real-time observation of sea surface wind speed is an important means of disaster prevention and mitigation, and is also a necessary measure for the development of the marine economy. In addition, sea surface wind speed is an important input parameter in atmospheric models, and real-time observation of sea surface wind speed helps improve the accuracy of weather forecasts.

[0003] At present, the acquisition of sea surface wind speed data mainly relies on two methods: traditional buoy observation and satellite remote sensing inversion. The number of ocean buoys is limited, making it difficult to cover a wide sea area, and they are easily damaged in extreme sea conditions, with high long-term maintenance costs. Although satellite remote sensing technology is widely used to invert sea surface wind speed, it is often severely disturbed by precipitation under extreme weather conditions, resulting in wind speed inversion errors of up to 10 m / s. Therefore, it is necessary to study a new sea surface wind speed observation method to supplement and verify the traditional observation method.

[0004] Inverting sea surface wind speed based on ocean environmental noise recorded by submarine optical fiber distributed acoustic sensing system DAS (Distributed acoustic sensing) is a feasible new method. With the development of offshore power supply, power generation and communication industries, the number of submarine optical cables is increasing and their distribution is becoming more and more widespread. If these dense submarine cables can be used to invert sea surface wind speed, it can meet the long-term, continuous and economical wind speed inversion needs. In recent years, scientists have confirmed that there is a strong dependence between ocean environmental noise and sea surface wind speed. However, the current research on the relationship between ocean environmental noise recorded by submarine optical fiber DAS and sea surface wind speed is still in the qualitative analysis stage, and there has been no public report on the quantitative inversion of sea surface wind speed based on ocean environmental noise recorded by submarine optical fiber DAS. Summary of the invention

[0005] In order to solve the problems mentioned in the background technology, the present invention provides a sea surface wind speed inversion method based on submarine optical fiber distributed acoustic sensing.

[0006] The technical solution adopted by the present invention is:

[0007] The sea surface wind speed inversion method based on submarine optical fiber distributed acoustic sensing of the present invention comprises:

[0008] Step 1) The wind-generated ocean environmental noise signal of the sea surface above itself is recorded through the submarine optical fiber based on distributed acoustic sensing DAS and divided into several sensor unit groups, and the actual wind speed data of the sea surface is obtained through the buoy above.

[0009] Step 2) Each sensor unit group is transmitted to a computer device equipped with a signal analysis module at the shore end, and the time-frequency power spectrum is obtained after processing; at the same time, the effective frequency band of the time-frequency power spectrum of each sensor unit group is determined.

[0010] Step 3) After quality control of each sensor unit group, several effective sensor unit groups are screened out.

[0011] Step 4) An inversion parameter model is established in the signal analysis module, and the time-frequency power spectrum, effective frequency band and actual wind speed data of each effective sensor unit group are input into the inversion parameter model. After processing, the inversion parameter model outputs the inversion parameters.

[0012] Step 5) In the signal analysis module, a sea surface wind speed inversion model is established according to the inversion parameters, the wind-generated ocean environmental noise signal to be inverted is obtained and the same processing of the wind-generated ocean environmental noise signal in steps 1)-2) is performed, and the inversion parameters and the time-frequency power spectrum of each sensor unit group of the processed wind-generated ocean environmental noise signal to be inverted and its effective frequency band are input into the sea surface wind speed inversion model. After processing, the sea surface wind speed inversion model outputs the inverted sea surface wind speed and displays it on the computer device.

[0013] In the step 1), according to the laying path of the submarine optical fiber, the submarine optical fiber is evenly divided into M acoustic sensing units of equal length along its length direction, and each acoustic sensing unit receives the vibration signal generated by the wind field on the sea surface above it in the ocean environment where it is located, and then transmits it to the demodulator at the shore end connected to the submarine optical fiber for processing to obtain the wind-generated ocean environment noise signal.

[0014] In step 2), the wind-generated ocean ambient noise signal of each acoustic sensor unit is evenly divided into a number of preset time lengths. t 0 Unit noise data, t 0 The time interval for the buoy to obtain the actual wind speed data of the sea surface is constructed into a noise signal data set by each unit noise data of each acoustic sensor unit; according to the layout path of the submarine optical fiber, each N adjacent acoustic sensor units are grouped into a sensor unit group, and finally the noise signal data sets of the M / N sensor unit groups are input into the signal analysis module of the computer equipment for processing.

[0015] In the step 2), the signal analysis module performs bandpass filtering on each unit noise data to avoid high-frequency noise interference caused by factors such as precipitation, shipping and bubbles, and then converts it into a time-frequency power spectrum through short-time Fourier transform, and takes the average of the time-frequency power spectra of each unit noise data in each noise signal data set as the time-frequency power spectrum of the noise signal data set, and then takes the average of the time-frequency power spectra of each noise signal data set of each sensing unit group as the time-frequency power spectrum of the sensing unit group.

[0016] In the step 2), for each sensor unit group, the time-frequency power spectra of each unit noise data of each sensor unit group are added and averaged to obtain the average power spectrum, and then the average power spectrum is calculated over the total observation time. t Add up and divide by the total observation time t , and then the normalized power spectrum is obtained after the maximum value normalization, and the frequency band in the normalized power spectrum of each sensor unit group that is greater than the preset normalized power threshold is taken as the effective frequency band.

[0017] In the step 3), quality control is performed on each sensor unit group. For each sensor unit group, the signal-to-noise ratio of the sensor unit group is calculated based on the average power spectrum of the sensor unit group and its effective frequency band, and the sensor unit group with a signal-to-noise ratio greater than a preset signal-to-noise ratio threshold is regarded as a valid sensor unit group.

[0018] In the step 4), the inversion parameter model is as follows:

[0019]

[0020] in, L is the residual function; t is the total observation time; U ´( t ) is the time t The actual wind speed data on the sea surface is obtained from the buoys below; α and β are the first and second inversion parameters, respectively; f N and f 1´ are the upper and lower limits of the effective frequency band of the effective sensor unit group; h ´ is the average water depth where the effective sensor unit group is located; k is the wave number; P ´( t , f ) is the time t The frequency recorded by the effective sensor unit group is f The time-frequency power spectrum of the wind-generated ocean ambient noise signal; through the residual function L Fitting the final inversion parameters α、βAnd used in sea surface wind speed inversion model.

[0021] In step 5), the sea surface wind speed inversion model is as follows:

[0022]

[0023] in, U ( t ) is the time t The inverted sea surface wind speed under ; α and β are the first and second inversion parameters, respectively; f N and f 1 are the upper and lower limits of the effective frequency band of the sensor unit group to be inverted; h is the average water depth of the sensor unit group to be inverted; k is the wave number; P ( t , f ) is the time t The frequency recorded by the sensor unit group to be inverted is f Time-frequency power spectrum of wind-generated ocean ambient noise signal.

[0024] The electronic device of the present invention comprises: a memory and a processor coupled to each other, wherein the memory stores program data, and the processor calls the program data to execute the method as described above.

[0025] The computer-readable storage medium of the present invention stores program data thereon, and when the program data is executed by a processor, the method described above is implemented.

[0026] The present invention records the wind-generated ocean environment noise through the submarine optical fiber DAS to invert the sea surface wind speed. After each acoustic sensing unit on the submarine optical fiber receives the vibration signal generated by the sea surface wind field in the ocean environment, the signal is transmitted to the demodulator at the shore end, and the corresponding noise signal is output. The noise signal data of each sensing unit is transmitted to a computer device equipped with a signal analysis module for processing, and the time-frequency power spectrum of the noise signal is obtained. The effective frequency band is obtained by analyzing the noise signal power spectrum. Then, the noise data of each sensing unit group is quality controlled, and the sensing unit group that can be used to invert the wind speed is screened. A sea surface wind speed inversion model based on the noise time-frequency power spectrum is established. The time-frequency power spectrum of the noise signal of each sensing unit group after quality control is input into the sea surface wind speed inversion model to achieve high temporal and spatial resolution sea surface wind speed inversion.

[0027] The beneficial effects of the present invention are:

[0028] The present invention records wind-generated ocean environmental noise through submarine optical fiber DAS to achieve high temporal and spatial resolution inversion of sea surface wind speed above submarine optical fiber, improves the problem of scarcity of wind speed data observed by buoys in the past, avoids damage to sea surface observation instruments by extreme sea conditions and interference of precipitation on satellite remote sensing, and reduces the cost required for deployment, recovery and maintenance of equipment; at the same time, establishes a quantitative correspondence between DAS-recorded ocean environmental noise and sea surface wind speed, fills the gap of using submarine optical fiber DAS to invert sea surface wind speed, verifies the feasibility of using submarine optical fiber DAS to invert sea surface wind speed, and provides a new idea for wind speed observation. The present invention uses the submarine cable optical fiber that is widely distributed today, which can effectively improve the current situation of scarcity of wind speed observation data in the ocean field, greatly reduce the cost of wind speed observation, and provide supplement and verification for traditional sea surface wind speed observation methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is a flow chart of the method of the present invention;

[0030] Figure 2 A schematic diagram of actual wind speed data of the sea surface obtained by the buoy of the present invention;

[0031] Figure 3 It is the time-frequency power spectrum and effective frequency band diagram of the noise signal of the present invention, wherein, Figure 3 (a) is the time-frequency power spectrum of the noise signal of the present invention, Figure 3 (b) is an effective frequency band diagram of the noise signal of the present invention;

[0032] Figure 4 It is a schematic diagram of the inversion parameter fitting results of the inversion parameter model of the present invention;

[0033] Figure 5 It is a schematic diagram of the sea surface wind speed inversion result of the present invention. DETAILED DESCRIPTION

[0034] The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0035] like Figure 1 As shown, the sea surface wind speed inversion method based on submarine optical fiber distributed acoustic sensing of the present invention is specifically as follows:

[0036] First, the submarine optical fiber based on distributed acoustic wave sensing DAS records the wind-generated ocean environmental noise signal on the sea surface above itself. According to the actual laying route of the submarine optical fiber, the submarine optical fiber is evenly divided into 8640 acoustic sensing units of equal length along its length direction. The length of each acoustic sensing unit is 2 m. Each acoustic sensing unit receives the tiny mechanical vibration signal generated by the wind field on the sea surface above itself in the marine environment where it is located, and then transmits it to the demodulator at the shore end connected to the submarine optical fiber for processing to obtain the wind-generated ocean environmental noise signal. After demodulation, the unit of the wind-generated ocean environmental noise signal is expressed in the standardized strain rate unit 1ɛ / s≡1 (m / m) / s. The submarine optical fiber can be an internal or external optical fiber of an existing submarine cable, submarine optical cable or submarine pipeline, or it can be a submarine optical fiber specially laid in advance. The route, burial depth and water depth of the submarine optical fiber are all known in advance. The optical fiber laid on the seabed and the shore-based demodulator connected to the optical fiber together constitute a submarine optical fiber DAS system. Then, the wind-generated ocean environmental noise signal of each acoustic sensor unit is evenly divided into several 1-hour unit noise data, that is, the time interval for the buoy to obtain the actual wind speed data of the sea surface. The unit noise data of each acoustic sensor unit is constructed into a noise signal data set; according to the layout path of the submarine optical fiber, every 20 adjacent acoustic sensor units are grouped into a sensor unit group, and finally the noise signal data sets of the 432 sensor unit groups are input into the signal analysis module of the computer equipment for processing.

[0037] At the same time, it is also necessary to obtain the actual wind speed data on the sea surface through the buoy located on the sea surface above the submarine optical fiber. In order to obtain the measured data of the sea surface wind speed, a wind speed observation buoy is installed near the submarine optical fiber. The buoy outputs the measured wind speed data once an hour. During the total observation time of 69 hours, the wind speed data sequence collected by the buoy is as follows: Figure 2 shown.

[0038] The signal analysis module then performs a bandpass filter on each unit noise data using a 0.05-1 Hz bandpass filter to avoid high-frequency noise interference caused by factors such as precipitation, shipping, and bubbles. The noise signal in the time domain is then converted to the time-frequency domain through a 100 s Hanning window and a short-time Fourier transform with a 50% overlap rate, thereby obtaining the time-frequency power spectrum of each unit noise data in units of (ɛ / s). 2 / Hz. The time-frequency power spectra of each unit noise data in each noise signal data set are averaged as the time-frequency power spectrum of the noise signal data set, and then the time-frequency power spectra of each noise signal data set of each sensing unit group are averaged as the time-frequency power spectrum of the sensing unit group.

[0039] At the same time, determine the effective frequency band of the time-frequency power spectrum of each sensor unit group. For each sensor unit group, add the time-frequency power spectra of each unit noise data of each sensor unit group and average them to obtain the average power spectrum. Then superimpose the average power spectrum on the total observation time of 69h and divide it by the total observation time of 69h. Then perform maximum value normalization to obtain the normalized power spectrum. The frequency band in the normalized power spectrum of each sensor unit group that is greater than the preset normalized power threshold is taken as the effective frequency band. Taking the sensor unit group composed of the 2100th to 2119th acoustic sensor units as an example, the average water depth of these 20 acoustic sensor units is 15.7 m. First, add and average the time-frequency power spectra of these 20 acoustic sensor units to obtain the average power spectrum of the sensor unit group, such as Figure 3 As shown in (a), the colors in the figure represent the average time-frequency power spectrum of the noise signal of the sensor unit group consisting of the 2100-2119 acoustic sensor units within the total observation time of 69 hours. The closer the color is to red, the stronger the noise power is. Next, the average time-frequency power spectrum is processed. First, it is superimposed within the total observation time of 69 hours and divided by the total observation time. Then, the result is normalized to the maximum value to obtain the normalized average power spectrum of the sensor unit group consisting of the 2100-2119 acoustic sensor units within 69 hours, as shown in Figure 3 As shown in (b), the frequency band with normalized average power greater than 0.8 is judged as the effective frequency band, ranging from 0.12-0.22 Hz.

[0040] Then, after quality control of each sensor unit group, several effective sensor unit groups are screened out. For the sensor unit group composed of the 2100th to 2119th acoustic sensor units, the signal-to-noise ratio (SNR) of the sensor unit group is calculated based on the average power spectrum and effective frequency band of the sensor unit group as follows:

[0041] SNR=10 log 10 {(∑ fN f=f1 P ´( f )) / (∑ 1.0 f=0.9 P ´( f ))}

[0042] in, P ´( f ) is the total observation time of the sensor unit group t The average power spectrum within .

[0043] The calculated signal-to-noise ratio (SNR) of the sensor unit group consisting of the 2100th to 2119th acoustic sensor units is 14.9 dB, which is greater than 10 dB. Therefore, it can be determined that the data of this sensor unit group can be used for wind speed inversion.

[0044] The inversion parameter model is established in the signal analysis module as follows:

[0045]

[0046] in, L is the residual function; t is the total observation time; U ´( t ) is the time t The actual wind speed data on the sea surface is obtained from the buoys below; α and β are the first and second inversion parameters, respectively; f N and f 1´ are the upper and lower limits of the effective frequency band of the effective sensor unit group; h ´ is the average water depth where the effective sensor unit group is located; k is the wave number; P ´( t , f ) is the time t The frequency recorded by the effective sensor unit group is f The time-frequency power spectrum of the wind-generated ocean ambient noise signal; through the residual function L Fitting the final inversion parameters α、β And used in sea surface wind speed inversion model.

[0047] The actual wind speed data of the sea surface U '( t ), the time-frequency power spectrum of the sensor unit group composed of the 2100-2119 acoustic sensor units P ´( t , f ) and the effective frequency band information obtained through frequency band identification [ f 1´ , f N ´] Input to the residual function L In the residual function L The final first inversion parameters are obtained by fitting α and the second inversion parameter β ,like Figure 4 As shown in the figure, in order to determine the inversion parameters, the least squares method is used to fit the inversion parameter model to obtain the optimal solution of the two inversion parameters. The first inversion parameter α =6954.85, the second inversion parameter β=-0.6654, goodness of fit R 2 =0.8477, which shows that the inversion model can fit the measured data well and has high accuracy.

[0048] In the signal analysis module, the sea surface wind speed inversion model is established according to the inversion parameters, as follows:

[0049]

[0050] in, U ( t ) is the time t The inverted sea surface wind speed under ; α and β are the first and second inversion parameters, respectively; f N and f 1 are the upper and lower limits of the effective frequency band of the sensor unit group to be inverted; h is the average water depth of the sensor unit group to be inverted; k is the wave number; P ( t , f ) is the time t The frequency recorded by the sensor unit group to be inverted is f Time-frequency power spectrum of wind-generated ocean ambient noise signal.

[0051] Wave Number in Sea Surface Wind Speed ​​Inversion Model k With frequency f The following relations are satisfied:

[0052] f 2 =( gk / 2 π ) tanh (2 ph )

[0053] in, g is the acceleration due to gravity.

[0054] After obtaining the inversion parameters of the inversion model, the inversion parameters and the time-frequency power spectrum and effective frequency band of each sensor unit group of the wind-generated ocean environmental noise signal to be inverted are input into the sea surface wind speed inversion model. After processing, the sea surface wind speed inversion model outputs the inverted sea surface wind speed and displays it on a computer device, such as Figure 5As shown. According to the prediction results of the model, the inverted wind speed was compared with the measured wind speed data provided by the buoy. The results showed that the average error of the inverted wind speed was 1.23 m / s and the root mean square error was 1.61 m / s. These error values ​​are acceptable in practical applications. Compared with the traditional wind speed measurement method, the inversion model has obvious advantages in terms of temporal and spatial resolution because dense and continuous sensor units are distributed on the submarine optical fiber and data can be monitored and transmitted in real time.

[0055] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0056] The above description of the embodiments is to facilitate the understanding and application of the present invention by those skilled in the art. It is obvious that those skilled in the art can easily make various modifications to the above embodiments and apply the general principles described herein to other embodiments without creative work. Therefore, the present invention is not limited to the above embodiments. Improvements and modifications made by those skilled in the art to the present invention based on the disclosure of the present invention should be within the protection scope of the present invention.

Claims

1. A method for inverting sea surface wind speed based on submarine optical fiber distributed acoustic sensing, characterized in that: include: Step 1) The wind-generated ocean environmental noise signal of the sea surface above itself is recorded through the submarine optical fiber based on distributed acoustic wave sensing DAS and divided into several sensor unit groups, and the actual wind speed data of the sea surface is obtained through the buoy above; Step 2) transmitting each sensor unit group to a computer device equipped with a signal analysis module at the shore end, and obtaining a time-frequency power spectrum after processing; and determining the effective frequency band of the time-frequency power spectrum of each sensor unit group; Step 3) After quality control of each sensor unit group, a number of effective sensor unit groups are screened out; Step 4) establishing an inversion parameter model in the signal analysis module, inputting the time-frequency power spectrum, effective frequency band and actual wind speed data of each effective sensor unit group into the inversion parameter model, and outputting the inversion parameters after processing by the inversion parameter model; Step 5) in the signal analysis module, a sea surface wind speed inversion model is established according to the inversion parameters, the wind-generated ocean environmental noise signal to be inverted is obtained and the same processing of the wind-generated ocean environmental noise signal in steps 1)-2) is performed, the inversion parameters and the time-frequency power spectrum of each sensor unit group of the processed wind-generated ocean environmental noise signal to be inverted and its effective frequency band are input into the sea surface wind speed inversion model, and the sea surface wind speed inversion model outputs the inverted sea surface wind speed after processing and displays it on the computer device; In the step 4), the inversion parameter model is as follows: in, L is the residual function; t is the total observation time; U ´( τ ) is the time τ The actual wind speed data on the sea surface is obtained from the buoys below; α and β are the first and second inversion parameters, respectively; f N and f 1´ are the upper and lower limits of the effective frequency band of the effective sensor unit group; h ´ is the average water depth where the effective sensor unit group is located; k is the wave number; P ´( τ , f ) is the time τ The frequency recorded by the effective sensor unit group is f Time-frequency power spectrum of wind-generated ocean ambient noise signal.

2. The method for inverting sea surface wind speed based on submarine optical fiber distributed acoustic sensing according to claim 1 is characterized in that: include: In the step 1), according to the laying path of the submarine optical fiber, the submarine optical fiber is evenly divided into M acoustic sensing units of equal length along its length direction, and each acoustic sensing unit receives the vibration signal generated by the wind field on the sea surface above it in the ocean environment where it is located, and then transmits it to the demodulator at the shore end connected to the submarine optical fiber for processing to obtain the wind-generated ocean environment noise signal.

3. The method for inverting sea surface wind speed based on submarine optical fiber distributed acoustic sensing according to claim 2 is characterized in that: include: In step 2), the wind-generated ocean ambient noise signal of each acoustic sensor unit is evenly divided into a number of preset time lengths. t 0 Unit noise data, t 0 The time interval for the buoy to obtain the actual wind speed data of the sea surface is constructed into a noise signal data set by each unit noise data of each acoustic sensor unit; according to the layout path of the submarine optical fiber, each N adjacent acoustic sensor units are grouped into a sensor unit group, and finally the noise signal data sets of the M / N sensor unit groups are input into the signal analysis module of the computer equipment for processing.

4. The method for inverting sea surface wind speed based on submarine optical fiber distributed acoustic sensing according to claim 3 is characterized in that: include: In the step 2), the signal analysis module performs bandpass filtering on each unit noise data, and then converts it into a time-frequency power spectrum through short-time Fourier transform, takes the average of the time-frequency power spectra of each unit noise data in each noise signal data set as the time-frequency power spectrum of the noise signal data set, and then takes the average of the time-frequency power spectra of each noise signal data set of each sensing unit group as the time-frequency power spectrum of the sensing unit group.

5. The method for inverting sea surface wind speed based on submarine optical fiber distributed acoustic sensing according to claim 3 is characterized in that: include: In the step 2), for each sensor unit group, the time-frequency power spectra of each unit noise data of each sensor unit group are added and averaged to obtain the average power spectrum, and then the average power spectrum is calculated over the total observation time. t Add up and divide by the total observation time t , and then the normalized power spectrum is obtained after the maximum value normalization, and the frequency band in the normalized power spectrum of each sensor unit group that is greater than the preset normalized power threshold is taken as the effective frequency band.

6. The method for inverting sea surface wind speed based on submarine optical fiber distributed acoustic sensing according to claim 5 is characterized in that: include: In the step 3), quality control is performed on each sensor unit group. For each sensor unit group, the signal-to-noise ratio of the sensor unit group is calculated based on the average power spectrum of the sensor unit group and its effective frequency band, and the sensor unit group with a signal-to-noise ratio greater than a preset signal-to-noise ratio threshold is regarded as a valid sensor unit group.

7. The method for inverting sea surface wind speed based on submarine optical fiber distributed acoustic sensing according to claim 1, characterized in that: include: In step 5), the sea surface wind speed inversion model is as follows: in, U ( τ ) is the time τ The inverted sea surface wind speed under ; α and β are the first and second inversion parameters, respectively; f N and f 1 are the upper and lower limits of the effective frequency band of the sensor unit group to be inverted; h is the average water depth of the sensor unit group to be inverted; k is the wave number; P ( τ , f ) is the time τ The frequency recorded by the sensor unit group to be inverted is f Time-frequency power spectrum of wind-generated ocean ambient noise signal.

8. An electronic device, characterized in that: include: A memory and a processor coupled to each other, wherein the memory stores program data, and the processor calls the program data to execute the method according to any one of claims 1 to 7.

9. A computer-readable storage medium having program data stored thereon, characterized in that: When the program data is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

Images