CN115235391B - Method for measuring ice thickness based on A0 modal dispersion curve - Google Patents
Method for measuring ice thickness based on A0 modal dispersion curve Download PDFInfo
- Publication number
- CN115235391B CN115235391B CN202210770471.5A CN202210770471A CN115235391B CN 115235391 B CN115235391 B CN 115235391B CN 202210770471 A CN202210770471 A CN 202210770471A CN 115235391 B CN115235391 B CN 115235391B
- Authority
- CN
- China
- Prior art keywords
- dispersion curve
- ice
- expected
- ice layer
- thickness
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B17/00—Measuring arrangements characterised by the use of infrasonic, sonic or ultrasonic vibrations
- G01B17/02—Measuring arrangements characterised by the use of infrasonic, sonic or ultrasonic vibrations for measuring thickness
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
- G06F17/141—Discrete Fourier transforms
- G06F17/142—Fast Fourier transforms, e.g. using a Cooley-Tukey type algorithm
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/15—Correlation function computation including computation of convolution operations
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Computational Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Algebra (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Discrete Mathematics (AREA)
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
Abstract
The invention discloses a method for measuring ice thickness based on an A0 modal dispersion curve, which comprises the steps of collecting an A0 modal impact signal excited by knocking an ice surface through an accelerometer; extracting an A0 modal dispersion curve by adopting deconvolution time-frequency analysis; setting an expected sequence d = [ d ] of ice layer thickness according to thickness measurement resolution requirement 1 ,d 2 ,…,d M ],d i Represents the ith desired ice layer thickness, i =1,2.., M; solving an expected frequency dispersion curve of the A0 mode corresponding to the thickness of each expected ice layer; calculating a correlation coefficient rho (d) of the expected frequency dispersion curve of the A0 mode corresponding to each expected ice layer thickness respectively for the frequency dispersion curve of the A0 mode i ) (ii) a Obtaining an ice layer thickness measurement D:the invention has the advantages of small working intensity, simple operation and convenient implementation, can ensure the safety of polar science researchers, does not need expensive underwater vehicles and carried acoustic emission sonars, does not need integrated acoustic emission equipment, only needs an accelerometer to be used for collecting natural acoustic signals such as knocking on ice, and has more economical efficiency.
Description
Technical Field
The invention belongs to the field of acoustic measurement, relates to a method for measuring the thickness of an ice layer, and particularly relates to a method for measuring the thickness of the ice layer based on an accelerometer for acquiring an A0 modal dispersion curve of the ice layer.
Background
The scientific investigation of the north pole is one of the main contents of the north pole strategy in China. As development and utilization of the north pole become more and more realistic, exploring the north pole has important strategic significance. The polar region sea ice structure is an important parameter which has profound influence on global climate, and one leading technical difficulty in the field of global change research is the problem of measuring the thickness of the sea ice.
The in-situ measurement method is a conventional method for researching and obtaining the thickness of the sea ice, is realized by drilling the ice core, has high precision and reliable data, is time-consuming and labor-consuming, and is only suitable for ice section measurement of a thin ice layer. The latest patent of the same kind of technology, namely a parametric array ice layer section detection underwater robot and an ice layer section detection method, provides that an underwater vehicle and a carried elevation sonar are used for measuring ice thickness under ice by utilizing echo, and problems of vehicle deployment and recovery and under-ice positioning navigation exist at present; systems for polar sea ice thickness detection propose systems based on electromagnetic methods to measure ice thickness on ice, with expensive equipment and susceptibility to arctic weather and light.
Researches find that a strong A0 mode similar to plate wave propagation exists in an ice layer of an ice-covered water area, and the mode has obvious dispersion characteristics in a low frequency band and is sensitive to ice layer thickness parameters.
Disclosure of Invention
Aiming at the prior art, the invention aims to provide a method for measuring the thickness of an ice layer based on an accelerometer to acquire an A0 modal dispersion curve in the ice layer, and the method has the advantages of convenience in arrangement, safety in operation and economy compared with the existing method.
In order to solve the technical problem, the method for measuring the thickness of the ice layer comprises the following steps:
step 1: acquiring an A0 modal impact signal excited by knocking the ice surface at a known distance through an accelerometer;
step 2: extracting an A0 modal dispersion curve by adopting deconvolution time-frequency analysis;
and step 3: setting an expected sequence d = [ d ] of ice layer thickness according to thickness measurement resolution requirement 1 ,d 2 ,…,d M ]Wherein d is i Represents the ith desired ice layer thickness, i =1,2.., M;
and 4, step 4: solving an expected frequency dispersion curve of the A0 mode corresponding to the thickness of each expected ice layer;
and 5: calculating a correlation coefficient rho (d) of the expected frequency dispersion curve of the A0 mode corresponding to each expected ice layer thickness respectively by the frequency dispersion curve of the A0 mode i );
further, the extracting of the A0 modal dispersion curve by deconvolution time-frequency analysis includes:
performing short-time Fourier transform on the A0 modal impact signal to obtain a conventional time-frequency spectrogram B (n, k), wherein n and k are respectively an nth sampling point in a time domain and a kth sampling point in a frequency domain of the spectrogram;
and (3) performing deconvolution operation on B (n, k), solving by a Bayesian iterative method, and expressing as:
in the formula, P m (n, k) is a spectrogram after the mth iteration optimization based on a deconvolution method, m represents the iteration number,a two-dimensional correlation operation is represented,representing a two-dimensional convolution operation, P when m =1 m (n, k) = B (n, k), S (n, k) is a point scattering function, and satisfies:
in the formula, T is the number of rectangular window function points of short-time Fourier transform, and N is the number of discrete Fourier transform points;
to P m (n, k) performing a conventional thresholding operation: if P m If (n, k) is greater than the set threshold, the point X = (n, k) is a discrete point of the modal dispersion curve of A0, i.e., the corresponding angular frequency ω is the same as the set threshold k =2πkf s Arrival time t of A0 mode of/N 1 (ω k )=n/f s ,f s The accelerometer sampling frequency.
Further, solving the expected frequency dispersion curve of the A0 mode corresponding to each expected ice layer thickness includes:
desired thickness d of ice layer i The corresponding A0 modal dispersion curve is:
in the formula, c g (ω k ,d i ) In the A0 mode at an angular frequency of omega k Expected ice layer thickness of d i The group velocity of the time is calculated by the following formula:
in the formula, c p (ω k ,d i ) In the A0 mode at an angular frequency of omega k Desired ice layer thickness d i The phase velocity of the time of flight is,k r the horizontal wave number of the A0 mode.
Further, the correlation coefficient ρ (d) of the expected dispersion curve of the A0 mode corresponding to each expected ice layer thickness is respectively associated with the dispersion curve of the A0 mode i ) The method specifically comprises the following steps:
in the formula, K is the discrete point number of the A0 modal dispersion curve obtained in the step 2,andrespectively the mean value and the standard deviation of the modal A0 dispersion curve obtained in the step 2,andrespectively desired ice thickness d i Mean and standard deviation of the corresponding expected dispersion curves.
The invention has the beneficial effects that: the method comprises the steps of firstly analyzing a wave equation of an A0 mode based on an elastic wave theory, and solving the equation based on a numerical method to obtain acoustic parameters of the mode, such as horizontal wave number, phase velocity, group velocity and the like. Performing high-resolution deconvolution time-frequency analysis on the A0 mode received by the ice layer accelerometer to extract a time-frequency curve of the A0 mode of the ice layer; and finally, constructing a cost function to realize the measurement of the ice thickness. Compared with the prior art, the invention has the following advantages:
1. compared with an in-situ measurement method, the method has the advantages of small working strength, simple operation and convenient implementation, and effectively ensures the safety of the scientific expedition personnel in the polar region;
2. compared with the method for measuring the ice thickness underwater, the method has the advantages that an expensive underwater vehicle and a carried acoustic emission sonar are not needed, and the method is more economical;
3. compared with the device for measuring the ice thickness on the ice, the device does not need to integrate acoustic emission equipment, and only needs an accelerometer to be used for collecting natural acoustic signals such as knocking on the ice;
4. the outfield experiment verifies the actual effect of the method for measuring the thickness of the ice layer.
Drawings
FIG. 1 is a flow chart of the accelerometer thickness measurement of the present invention;
FIG. 2 is a graph showing the waveform of the impact signal collected at 140m in the present invention;
FIG. 3 is a deconvolution time-frequency curve according to the present invention;
FIG. 4 is a plot of the desired dispersion in the present invention;
FIG. 5 is a correlation coefficient of a dispersion curve according to the present invention;
fig. 6 is a group velocity dispersion curve of the A0 mode.
Detailed Description
The invention is further described with reference to the drawings and examples.
Research shows that three acoustic modes of A0, S0 and SH mainly exist in the ice layer acoustic wave due to the fact that the ice layer macroscopically has a plate-shaped configuration. According to the propagation characteristics of the ice layer sound wave, the energy of the A0 mode is larger than that of the S0 mode and the SH mode. The inherent dispersion characteristic of the A0 mode is more sensitive to ice layer thickness and frequency than the S0 mode and the SH mode. The dispersion characteristic here means that the same mode has different group velocities at different frequencies, that is, the mode is collected by the accelerometer after propagating for a certain distance, and the time when different frequency components of the mode arrive at the receiving point is in sequence. As shown in fig. 6, when the thickness of the ice layer is constant, the group velocity of the A0 mode gradually increases from 0 to a certain maximum value and then gradually decreases to a stable value as the frequency increases. The thickness of the ice layer has a one-to-one correspondence relationship with the dispersion curve of the A0 mode.
Therefore, the A0 mode signal is used as a reference signal for measuring the ice thickness, and the problem of noise interference on the S0 mode and the SH mode is effectively avoided.
An accelerometer is arranged on the ice surface to collect acoustic signals. Tapping the ice surface at a distance r from the accelerometer can excite the A0 mode in the ice layer. Intercepting the A0 modal waveform, wherein different frequencies of the waveform reach time, and an instant frequency curve is as follows:
in the formula, is t 0 As signal intercept time point, d is ice layer thickness, c g (omega, d) is the group velocity of the A0 mode when the angular frequency is omega and the thickness of the ice layer is d, and the group velocity calculation formula
In which k and c p The horizontal wave number and the phase velocity of the A0 mode are respectively solved through an ice water coupling acoustic propagation model.
Existing extraction dispersion methods include hilbert-yellow transform, short-time fourier transform, wavelet transform, and wigner distribution. However, the actually acquired signal noise is still low, and the method is verified to be incapable of effectively extracting the frequency dispersion curve of the ice layer A0 mode, so that the practicability of the method is reduced.
The application provides that a deconvolution method aiming at A0 modal dispersion is adopted to obtain a high-resolution time-frequency spectrogram, and a dispersion curve is extracted through thresholding. The deconvolution algorithm can be solved by a Bayesian iterative method, represented as
In the formula, m represents the number of iterations,a two-dimensional correlation operation is represented,representing two-dimensional convolution operation, B is a spectrogram of an accelerometer for acquiring an A0 modal signal, P is a spectrogram optimized based on a deconvolution method, n and k are respectively a time domain sampling point and a frequency domain sampling point of the spectrogram, and particularly S is a specific point scattering function in the method
In the formula, T is the number of rectangular window function points, and N is the number of discrete Fourier transform points.
The verification proves that the method can effectively extract the A0 mode in the ice layer, overcome the interference of environmental noise and improve the practicability of the ice sound positioning method.
And performing cross correlation on the actual A0 modal dispersion curve and a theoretical dispersion curve solved based on the expected ice layer thickness to obtain a correlation coefficient, wherein the expected ice thickness with the maximum correlation coefficient is the estimated actual ice layer thickness.
The first embodiment is as follows:
the invention discloses a method for measuring the thickness of an ice layer, which comprises the following steps:
step 1: acquiring an A0 modal impact signal excited by knocking the ice surface at a known distance through an accelerometer;
step 2: extracting an A0 modal dispersion curve by adopting deconvolution time-frequency analysis;
and 3, step 3: setting an expected sequence of ice layer thicknesses d = [ d ] according to thickness measurement resolution requirements 1 ,d 2 ,…,d M ]In which d is i Represents the ith desired ice layer thickness, i =1,2.., M;
and 4, step 4: solving an A0 modal expected frequency dispersion curve corresponding to each expected ice layer thickness;
and 5: calculating a correlation coefficient rho (d) of the expected frequency dispersion curve of the A0 mode corresponding to each expected ice layer thickness respectively by the frequency dispersion curve of the A0 mode i );
example two:
on the basis of the embodiment, the extracting of the A0 modal dispersion curve by deconvolution time-frequency analysis comprises the following steps:
performing short-time Fourier transform on the A0 modal impact signal to obtain a conventional time-frequency spectrogram B (n, k), wherein n and k are respectively an nth sampling point in a time domain and a kth sampling point in a frequency domain of the spectrogram;
and (3) performing deconvolution operation on B (n, k), solving by a Bayesian iterative method, and expressing that:
in the formula, P m (n, k) is a spectrogram after the mth iteration optimization based on a deconvolution method, m represents the iteration times,a two-dimensional correlation operation is represented,representing a two-dimensional convolution operation, when m =1, P m (n, k) = B (n, k), S (n, k) is a point scattering function, and satisfies:
in the formula, T is the number of rectangular window function points of short-time Fourier transform, and N is the number of discrete Fourier transform points;
to P m (n, k) performing a conventional thresholding operation: if P m If (n, k) is greater than the set threshold, the point X = (n, k) is a discrete point of the modal dispersion curve of A0, i.e. the corresponding angular frequency ω is equal to k =2πkf s Arrival time t of A0 mode of/N 1 (ω k )=n/f s ,f s The accelerometer sampling frequency.
Example three:
on the basis of the above embodiment, solving the expected frequency dispersion curve of the A0 mode corresponding to each expected ice layer thickness includes:
desired thickness d of ice layer i The corresponding modal A0 dispersion curve is:
in the formula, c g (ω k ,d i ) In the A0 mode at an angular frequency of omega k Expected ice layer thickness of d i The group velocity of the time is calculated by the following formula:
in the formula, c p (ω k ,d i ) In the A0 mode at an angular frequency of omega k Desired ice layer thickness d i The phase velocity of the time of flight is,k r the horizontal wave number of the A0 mode.
Example four:
based on the above embodiment, the A0 modal dispersion curveCorrelation coefficient rho (d) of A0 mode expected dispersion curve with line corresponding to each expected ice layer thickness i ) The method specifically comprises the following steps:
wherein K is the discrete point number of the modal A0 frequency dispersion curve obtained in the step 2,andrespectively the mean value and the standard deviation of the modal A0 dispersion curve obtained in the step 2,andrespectively desired ice thickness d i Mean and standard deviation of the corresponding expected dispersion curves.
Examples are given below with specific parameters:
as shown in fig. 1, the present invention comprises the steps of:
step 1: an accelerometer is arranged on the ice surface to collect acoustic signals. Tapping the ice surface at a distance r from the accelerometer excites the A0 mode in the ice layer. As shown in FIG. 2, the A0 mode waveform fragment excited by the impact sound source at 140m is collected for the accelerometer, the duration of the A0 mode is 20-40ms, and the sampling frequency f of the accelerometer is s =40kHz;
Step 2: and extracting a modal dispersion curve based on deconvolution time-frequency analysis. The application provides that a deconvolution method aiming at A0 modal dispersion is adopted to obtain a high-resolution time-frequency spectrogram, and a dispersion curve is extracted through thresholding.
Firstly, performing short-time Fourier transform on the A0 mode signal in the step 1 to obtain a conventional time-frequency spectrogram B (n, k), wherein n and k are time domain sampling points and frequency domain sampling points of the spectrogram respectively.
Next, a deconvolution operation is performed on B (n, k). The deconvolution algorithm can be solved by a Bayesian iterative method, denoted as
In the formula P m (n, k) is a spectrogram after the mth iteration optimization based on a deconvolution method, m represents the iteration number,a two-dimensional correlation operation is represented,representing a two-dimensional convolution operation, P when m =1 m (n, k) = B (n, k). In particular, S is a point scattering function specific to the method
In the formula, T is the number of rectangular window function points, and N is the number of discrete Fourier transform points.
Finally, to P m (n, k) performing a conventional thresholding operation. If P m (n, k) is greater than the threshold, the point X = (n, k) is a discrete point of the modal A0 dispersion, i.e. the corresponding angular frequency ω is ω k =2πkf s Arrival time t of A0 mode of/N 1 (ω k )=n/f s 。
As shown in fig. 3, in a time-frequency spectrogram obtained by deconvolving a waveform of the A0 mode, a window function length T is 512 points, a number N of fourier points is 1024 points, main energy of the A0 mode is concentrated in 200 to 1600Hz, and as frequency increases, a relative arrival time of the A0 mode gradually decreases, which means that a group velocity increases, and when the frequency is higher than 1000Hz, the relative arrival time thereof gradually increases, a group velocity thereof decreases, energy of the frequency higher than 1600Hz is smaller, and the arrival time of the A0 mode of 200 to 1600Hz is mainly used as a parameter for estimating the ice layer thickness.
And step 3: root of herbaceous plantThe desired sequence of ice layer thicknesses is set autonomously according to the thickness measurement resolution. Here, it is desirable that the ice thickness is set to 0.1m-2.0m with a spacing of 0.1m, i.e., d c =[0.1 0.2 0.3...2.0]。
And 4, step 4: and solving an expected frequency dispersion curve. For each desired ice layer thickness, the arrival time of the A0 mode, i.e., the dispersion curve, is
In the formula, c g (ω k ,d c ) In the A0 mode at an angular frequency of omega k The desired thickness of the ice layer is d c Group velocity of time. Group velocity is calculated by
In the formula, c p (ω k ,d c ) In the A0 mode at an angular frequency of omega k The desired thickness of the ice layer is d c The phase velocity of the time of flight is,k r for the horizontal wave number of the A0 mode, solve for k r The classical expression of the method is:
|H(k r )|=0
wherein |. Represents a determinant, H is an A0 mode matrix, and the fluid sound pressure wave equationElastic wave equation and boundary continuity condition control, k 0 =ω/c 0 ,c 0 Is the speed of sound in water.
And 5: calculating the correlation between the signal frequency dispersion curve and the expected curve, and defining the frequency dispersion curve t extracted based on the deconvolution method in the step 2 1 (ω k ) And the ice thickness calculated in step 4 is d c Time theory dispersion curve t 2 (ω k ,d c ) Has a correlation coefficient of
Wherein K is the discrete point number of the frequency dispersion curve,andare each t 1 (ω k ) The mean and the standard deviation of (a) are,andrespectively, desired dispersion curve t 2 (ω k ) Mean and standard deviation of.
As shown in table 1, correlation coefficients for different desired ice thicknesses.
TABLE 1 correlation coefficient of expected ice thickness
Step 6: from the results of step 5, the estimated ice thickness is
As shown in fig. 5, when the desired ice layer thickness is 0.5m, the correlation coefficient is the largest, i.e., the estimated ice layer thickness is D =0.5m, with an error of about 0.03m compared to the actually measured value of 0.47 m.
The measured data result proves that the method can effectively measure the thickness of the ice layer through the accelerometer on the ice.
It is to be understood that the invention is not limited to the examples described above, but that modifications and variations may be effected thereto by those of ordinary skill in the art in light of the foregoing description, and that all such modifications and variations are intended to be within the scope of the invention as defined by the appended claims.
Claims (4)
1. A method for measuring ice thickness based on an A0 modal dispersion curve is characterized by comprising the following steps:
step 1: acquiring an A0 modal impact signal excited by knocking the ice surface at a known distance through an accelerometer;
and 2, step: extracting an A0 modal dispersion curve by adopting deconvolution time-frequency analysis;
and 3, step 3: setting an expected sequence of ice layer thicknesses d = [ d ] according to thickness measurement resolution requirements 1 ,d 2 ,…,d M ]In which d is i Represents the ith desired ice layer thickness, i =1, 2.., M;
and 4, step 4: solving an expected frequency dispersion curve of the A0 mode corresponding to the thickness of each expected ice layer;
and 5: calculating a correlation coefficient rho (d) of the expected frequency dispersion curve of the A0 mode corresponding to each expected ice layer thickness respectively for the frequency dispersion curve of the A0 mode i );
2. the method for measuring the ice thickness based on the A0 modal dispersion curve according to claim 1, characterized in that: the method for extracting the A0 modal dispersion curve by adopting deconvolution time-frequency analysis comprises the following steps:
performing short-time Fourier transform on the A0 modal impact signal to obtain a conventional time-frequency spectrogram B (n, k), wherein n and k are respectively an nth sampling point in a time domain and a kth sampling point in a frequency domain of the spectrogram;
and (3) performing deconvolution operation on B (n, k), solving by a Bayesian iterative method, and expressing that:
in the formula, P m (n, k) is a spectrogram after the mth iteration optimization based on a deconvolution method, m represents the iteration number,a two-dimensional correlation operation is represented,representing a two-dimensional convolution operation, P when m =1 m (n, k) = B (n, k), S (n, k) is a point scattering function, satisfying:
in the formula, T is the number of rectangular window function points of short-time Fourier transform, and N is the number of discrete Fourier transform points;
to P m (n, k) performing a conventional thresholding operation: if P m If (n, k) is greater than the set threshold, the point X = (n, k) is a discrete point of the modal dispersion curve of A0, i.e., the corresponding angular frequency ω is the same as the set threshold k =2πkf s Arrival time t of A0 mode of/N 1 (ω k )=n/f s ,f s The accelerometer sampling frequency.
3. The method for measuring the ice thickness based on the A0 modal dispersion curve according to claim 2, characterized in that: the solving of the expected frequency dispersion curve of the A0 mode corresponding to each expected ice layer thickness comprises:
desired thickness d of ice layer i The corresponding A0 modal dispersion curve is:
in the formula, c g (ω k ,d i ) In the A0 mode at an angular frequency of omega k Desired ice layer thickness d i The group velocity of time is calculated by the formula:
4. The method for measuring the ice thickness based on the A0 modal dispersion curve according to claim 3, characterized in that: the correlation coefficient rho (d) of the A0 modal expected frequency dispersion curve corresponding to each expected ice layer thickness respectively i ) The method specifically comprises the following steps:
in the formula, K is the discrete point number of the A0 modal dispersion curve obtained in the step 2,andrespectively is the mean value and the standard deviation of the modal dispersion curve A0 obtained in the step 2,andrespectively desired ice thickness d i Mean and standard deviation of the corresponding expected dispersion curves.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202210770471.5A CN115235391B (en) | 2022-06-30 | 2022-06-30 | Method for measuring ice thickness based on A0 modal dispersion curve |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202210770471.5A CN115235391B (en) | 2022-06-30 | 2022-06-30 | Method for measuring ice thickness based on A0 modal dispersion curve |
Publications (2)
Publication Number | Publication Date |
---|---|
CN115235391A CN115235391A (en) | 2022-10-25 |
CN115235391B true CN115235391B (en) | 2022-12-30 |
Family
ID=83672372
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202210770471.5A Active CN115235391B (en) | 2022-06-30 | 2022-06-30 | Method for measuring ice thickness based on A0 modal dispersion curve |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN115235391B (en) |
Citations (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2005091970A2 (en) * | 2004-03-06 | 2005-10-06 | Michael Trainer | Methods and apparatus for determining the size and shape of particles |
WO2008005311A2 (en) * | 2006-06-30 | 2008-01-10 | Carnegie Mellon University | Methods, apparatuses, and systems for damage detection |
CN104833323A (en) * | 2015-05-12 | 2015-08-12 | 中国科学院金属研究所 | Method for measuring the width of laser lapping welding seam by using reflected echo of S0 mode lamb wave |
CN104965025A (en) * | 2015-05-14 | 2015-10-07 | 南京航空航天大学 | Multi-zone damage detection method based on Lamb wave signal correlation coefficient |
CN105910559A (en) * | 2016-06-13 | 2016-08-31 | 南京航空航天大学 | Method for utilizing Lamb waves to detect thicknesses of frozen ice layers of rotor wing |
CN111044613A (en) * | 2019-12-26 | 2020-04-21 | 武汉工程大学 | Metal plate micro-defect detection method based on nonlinear Lamb wave |
CN111766625A (en) * | 2020-07-06 | 2020-10-13 | 中国科学技术大学 | Seismic background noise dispersion curve extraction method based on deep learning |
CN113358743A (en) * | 2021-05-12 | 2021-09-07 | 北京工业大学 | Lamb wave mode separation method based on time-frequency distribution similarity analysis |
CN113686964A (en) * | 2021-09-07 | 2021-11-23 | 哈尔滨工程大学 | Sea ice thickness observation method based on leakage mode acoustic waveguide characteristics |
CN114459648A (en) * | 2022-01-19 | 2022-05-10 | 哈尔滨工业大学 | Multi-mode Lamb wave data fusion-based baseline-free stress online monitoring system and monitoring method |
Family Cites Families (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US9442204B2 (en) * | 2012-08-06 | 2016-09-13 | Exxonmobil Upstream Research Company | Seismic inversion for formation properties and attenuation effects |
-
2022
- 2022-06-30 CN CN202210770471.5A patent/CN115235391B/en active Active
Patent Citations (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2005091970A2 (en) * | 2004-03-06 | 2005-10-06 | Michael Trainer | Methods and apparatus for determining the size and shape of particles |
WO2008005311A2 (en) * | 2006-06-30 | 2008-01-10 | Carnegie Mellon University | Methods, apparatuses, and systems for damage detection |
CN104833323A (en) * | 2015-05-12 | 2015-08-12 | 中国科学院金属研究所 | Method for measuring the width of laser lapping welding seam by using reflected echo of S0 mode lamb wave |
CN104965025A (en) * | 2015-05-14 | 2015-10-07 | 南京航空航天大学 | Multi-zone damage detection method based on Lamb wave signal correlation coefficient |
CN105910559A (en) * | 2016-06-13 | 2016-08-31 | 南京航空航天大学 | Method for utilizing Lamb waves to detect thicknesses of frozen ice layers of rotor wing |
CN111044613A (en) * | 2019-12-26 | 2020-04-21 | 武汉工程大学 | Metal plate micro-defect detection method based on nonlinear Lamb wave |
CN111766625A (en) * | 2020-07-06 | 2020-10-13 | 中国科学技术大学 | Seismic background noise dispersion curve extraction method based on deep learning |
CN113358743A (en) * | 2021-05-12 | 2021-09-07 | 北京工业大学 | Lamb wave mode separation method based on time-frequency distribution similarity analysis |
CN113686964A (en) * | 2021-09-07 | 2021-11-23 | 哈尔滨工程大学 | Sea ice thickness observation method based on leakage mode acoustic waveguide characteristics |
CN114459648A (en) * | 2022-01-19 | 2022-05-10 | 哈尔滨工业大学 | Multi-mode Lamb wave data fusion-based baseline-free stress online monitoring system and monitoring method |
Non-Patent Citations (2)
Title |
---|
基于Lamb波的积冰探测研究;李君婷;《中国优秀硕士学位论文全文数据库 工程科技II辑》;20220315;全文 * |
基于冰层波导的海冰厚度测量方法研究;马丁一;《2019中国西部声学学术交流会论文集》;20190831;全文 * |
Also Published As
Publication number | Publication date |
---|---|
CN115235391A (en) | 2022-10-25 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN105589066B (en) | A kind of method that underwater uniform motion ROV parameter is estimated using vertical vector battle array | |
CN107179535A (en) | A kind of fidelity based on distortion towed array strengthens the method for Wave beam forming | |
CN105911551B (en) | A kind of Sound speed profile inversion method based on weighted aggregation Kalman filtering algorithm | |
CN103076594B (en) | Method for positioning underwater sound pulse signal by double array elements on basis of cross-correlation | |
CN103176163B (en) | Phase model based ship line spectrum noise source position identification method | |
CN104678384B (en) | Method for estimating underwater target speed by using sound pressure difference cross-correlation spectrum analysis of beam fields | |
CN111487589B (en) | Target drop point positioning method based on multi-source sensor network | |
CN103197278B (en) | Warship line spectrum noise source positioning method based on change rate of Doppler frequency shift | |
CN113687308B (en) | Method for positioning seismic source on ice based on bending waves | |
CN104297740A (en) | Method for estimating Doppler spectrum of radar target on basis of phase analysis | |
CN103344961B (en) | Passive acoustic Doppler phase position method of joint measurement of ship speed and distance | |
CN104698431A (en) | Method for estimating fussy component space angle and ambiguity-resolving multi-channel SAR (segmentation and resassembly sublayer) orientation | |
CN104360251A (en) | Ultrasonic signal time delay estimation method for partial discharging of potential transformer | |
CN104765038A (en) | Method for tracing moving point sound source track based on inner product correlation principle | |
CN105717198A (en) | Single frequency and re-estimation MUSIC (multiple signal classification) method for structure-oriented impact locating | |
CN102323618B (en) | Coherent noise suppression method based on fractional order Fourier transformation | |
CN115235391B (en) | Method for measuring ice thickness based on A0 modal dispersion curve | |
CN113238208B (en) | Method for calculating forward acoustic scattering Doppler frequency shift of moving target in irregular track water | |
CN109116359B (en) | Method for estimating low-altitude wind shear wind field echo wind speed of airborne radar | |
CN115166817B (en) | Ice sound positioning method based on ice layer modal group slowness difference characteristics | |
CN101825722B (en) | Robust method for estimating instantaneous frequency of seismic signal | |
CN109085595B (en) | Method for estimating speed of air motion sound source by using signals received by hydrophone | |
CN108646248B (en) | Passive acoustic speed and distance measuring method for low-speed moving sound source | |
CN115236592A (en) | Ice sound positioning method based on single-array-element time-frequency curve matching | |
CN110470742A (en) | A kind of accurate detecting method of channel bend defect |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant |