CN109521421A - A kind of Ground Penetrating Radar thin layer object recognition and detection method - Google Patents
A kind of Ground Penetrating Radar thin layer object recognition and detection method Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/885—Radar or analogous systems specially adapted for specific applications for ground probing
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Abstract
The invention discloses a kind of Ground Penetrating Radar thin layer object recognition and detection method, includes the following steps (1), successively extracts from target region of interest and each to be classified as an A-scan target；Every one of A-scan radar target data is synchronized respectively and squeezes Short Time Fourier Transform (Synchrosqueezing Short Fourier transform)；(2), the temporal frequency energy information figure of signal is obtained；(3), coating region is found out according to Energy distribution；(4), according to temporal frequency energy information figure, the thickness of thin layer is measured, realizes the positioning and identification of thin layer target.This method is not necessarily to carry out the training of data；Can more complete reservation thin layer target information, thus promoted target positioning precision, for layer with a thickness of
Description
Technical field
The present invention relates to ground penetrating radar detection field, specially a kind of spy based on synchronous extruding Short Time Fourier Transform
The positioning of radar thin layer and recognition methods.
Background technique
Ground Penetrating Radar is a kind of effective shallow underground target acquisition technology that recent decades develop rapidly, it is one
Kind of non-destructive detection means, have fast speed of detection, high resolution, it is flexible to operation, detect many advantages, such as at low cost,
It has been widely used in buried target, such as the detection and positioning in cavity, pipeline, land mine.
When electromagnetic wave is propagated in layered medium, if the wave impedance of interface both sides medium is discontinuous, electromagnetic wave is dividing
Reflection can be generated on interface.It is initially set up herein containing lamellate layered medium model, derives that thin bed reflection coefficient passes through again
Thin bed reflection coefficient is analyzed to analyze the reflection of electromagnetic wave characteristic of thin layer according to thin bed reflection coefficient analysis as a result, obtaining: thin layer
Reflection to electromagnetic wave, can be equivalent to thin layer to the filtering characteristic of filter action thin layer and the type of thin layer of electromagnetic wave and
If the thickness of the closely related thin layer of thickness is less than λ/8, electromagnetic wave can be superimposed in the reflection of interface upper and lower level, directly
Connect the thickness that layer can not be judged from time-domain.
Currently used thin layer recognition positioning method mainly has following five kinds: 1, based on the method for Short Time Fourier Transform,
The fixed defect of window, causes time frequency resolution not adjust suitably, i.e., window corresponds to high frequency resolution when big due in the presence of
With low temporal resolution；And hour window then corresponds to high time resolution and low frequency resolution ratio；2, Continuous Wavelet Transform, tool
Have multiresolution feature and better Voice segment, i.e., have preferable temporal resolution, low frequency in the high frequency region of signal
There is preferable frequency resolution in area.Since some time, in many fields, CWT always is very popular time frequency analysis
Method；3, generalized S-transform is widely used in geophysics；4, sliding window method, be primarily used to improve spectrogram can
The property read, improves the time-frequency focusing of signal component.Its basic ideas is: the energy value of any point in time-frequency spectrum is assigned to
In another point, but there are a defects for this method, i.e., it is irreversible, cannot reconstruct initial signal by inverse transformation；
These methods are distributed what still presence obscured in entire section in time-frequency figure, especially in low frequency range.For this purpose,
Daubechies etc. proposes joint wavelet transformation extruding wavelet transformation synchronous with the new method-of recombination
(Synchrosqueezing Wavelet Transform:SSWT), this method are that the time-frequency spectrum after wavelet transformation is carried out to weight
Group can be obtained higher time-frequency precision curve.
Summary of the invention
In order to solve the problems, such as traditional buried target localization method there is many drawbacks, the present invention provides a kind of base of Lee
In the synchronous Ground Penetrating Radar thin layer object recognition and detection method for squeezing Short Time Fourier Transform.
The object of the present invention is achieved like this:
A kind of Ground Penetrating Radar thin layer object recognition and detection method, which comprises the following steps:
(1), it is successively extracted from target region of interest and each is classified as an A-scan target；To every one of A-scan
Radar target data synchronizes respectively squeezes Short Time Fourier Transform (Synchrosqueezing Short Fourier
transform)；
(2), the temporal frequency energy information figure of signal is obtained；
(3), target region of interest is found out according to Energy distribution；
(4), according to temporal frequency energy information figure, the thickness for measuring thin layer realizes the positioning and identification of thin layer target.
The step (1) specifically:
1), Gpr Signal is B-scan image, and each column for extracting image are successively mentioned from target region of interest
It takes and each is classified as an A-scan target；
2) Short Time Fourier Transform (STFT) first, is carried out to signal, the coefficient after obtaining Short Time Fourier Transform:
Wherein b is time shift method, and g (t) is fixed window function；
Enable ψ (t)=g (t) ej2πft, formula (1) may be expressed as:
WhereinFor the complex conjugate of function ψ (t)；
According to the rule in Parseval theorem and Fourier Transformation Properties about change of scale and translation, formula (2)
It can be written as:
In formula, x (ξ) is the Fourier transformation of signal x (t),Indicate the complex conjugate of the Fourier transformation of ψ (t)；
First consider the case where signal is harmonic wave, takes x (t)=A cos2 π f0T., then:
Wushu (4) substitutes into formula (3) and obtains:
3) the instantaneous frequency w of signal s (t), is calculateds(a,b)
Due toTherefore the instantaneous frequency of signal x (t) are as follows:
Obviously, for shaped like x (t)=Acos2 π f0T. simple component signal, as available from the above equation:
4) it, is recombinated via compression, obtains synchronous extruding wavelet transformation magnitude Ts(ω,b)
For more generally multicomponent data processingAnd meetHere ψ 'n(t) ψ is indicatedn(t) derivative；Since Short Time Fourier Transform is linear transformation, therefore
The Short Time Fourier Transform result of multicomponent data processing x (t) can be expressed as N number of component xn(t) superposition of S-transformation, that is:
And
The instantaneous frequency of each simple component can be indicated by formula (6) are as follows:
So, the instantaneous frequency of multicomponent data processing x (t) may be expressed as:
Wherein, δ is impulse function, and being similar to synchronous extruding transformation [1] will be to frequency separation to realize convenient for algorithm
Integral is write as the form of summation；
The synchronous of signal x (t) squeezes Short Time Fourier Transform are as follows:
Wherein, flIt is the synchronous transformed frequency of extruding, LfIt is in Fourier spectrum in short-term with flCentered on frequency separation
Half length, fkFor the discretization frequency sampling point of frequency separation in Fourier spectrum in short-term, and Δ f=fk-fk-1The formula is indicated in short-term
Frequency separation [f in Fourier spectruml-Lf,fl+Lf] in time-frequency spectrum be overlapped and be placed on frequency flPlace, i.e., a frequency zones
Between Short Time Fourier Transform time-frequency spectrum " extruding " a to Frequency point on, to make that synchronous to squeeze Short Time Fourier Transform very big
Ground improves the frequency resolution of Short Time Fourier Transform.
The step 2) specifically: be directed to every one of A-Scan data zi, i=1 ..., m m is the road of B-scan data
Number, to each A-Scanzi, i=1 ..., m synchronize extruding Short Time Fourier Transform, obtain transformed instantaneous spectrum
zfi, i=1 ..., m, and by transformed signal zfiIt is rearranged, obtains the instantaneous spectrum zf of signal.
The step 3) specifically: the instantaneous spectrum zf of signal, the lateral variance curve of the energy diagram of measurement signal transient spectrum
Figure, according to the range given threshold of variance curve figure, so that it is determined that direct wave region；
The step 4) specifically: according to the temporal frequency energy information figure for removing direct wave, temporal frequency energy letter
Breath figure has very high time frequency resolution, therefore when extracting instantaneous spectral component, obtained frequency division section is relative to other several
Time-Frequency Analysis Method has better resolution ratio, determines thin layer according to the biggish place of energy in temporal frequency energy information figure
Region can identify the thickness of thin layer in conjunction with the thickness and temporal resolution of thin layer, realize the positioning and identification of thin layer target.
Positive beneficial effect: the present invention does not need to carry out the training of data, so being easy to carry out on-line checking；It can be completeer
Whole reservation target information, thus promoted target positioning precision, for layer with a thickness of thin layer data for, also can recognize that thin
The thickness of layer, so being not easy to miss thin layer target；It is able to ascend thin layer identification accuracy, effectively distinguishes stronger clutter and target
Echo；Based on algorithm it is relatively simple, algorithm operation time is short, ensure that quickly carry out the positioning of thin layer target accuracy.
Detailed description of the invention
Fig. 1 is the flow chart of Ground Penetrating Radar thin subsurface layer recognition methods of the invention；
Fig. 2 is signal, (c) thickness with a thickness of λ/2 of signal, (b) of the thin layer signal graph (a) of different thickness with a thickness of λ
It is the signals of λ/4, (d) with a thickness of the signal of λ/8；
Fig. 3 be various Time-Frequency Analysis Methods to a thickness of λ/8 tlc analysis result figure, (a) with a thickness of λ/8 signal,
(b) CWT analyzes result, (c) STFT analysis result, (d) SSTFT and analyzes result；
Fig. 4 is the synchronous time-frequency energy spectrogram squeezed after Short Time Fourier Transform of thin layer signal of different thickness；
Fig. 5 is practical thin layer signal graph；
Fig. 6 is the synchronous time-frequency energy spectrogram squeezed after Short Time Fourier Transform.
Specific embodiment
The present invention will be further described in detail with reference to the accompanying drawing.
It is synchronous to squeeze Short Time Fourier Transform (Synchrosqueezing Short Fourier Transform；
SSWT).Here transformation is mainly Short Time Fourier Transform, and recombinates the thinking that rule is reference time-frequency spectrum rearrangement method, if
It is compared with the spectrum rearrangement method based on wavelet transformation, then their main distinction is to carry out Fourier in short-term to signal
Different to the processing again of time-frequency spectrum after transformation, the former only resets frequency domain, and the latter is then the m- frequency of clock synchronization
It resets simultaneously in rate domain.This method ensure that therefore the mathematics invertibity of wavelet transformation synchronizes crowded while improving time-frequency precision
Pressure Short Time Fourier Transform not only has the high time frequency resolution of time-frequency spectrum rearrangement method, but also is reversible, and is used for thin
In the identification of alternating layers, have a good application prospect.
The present invention is a kind of based on synchronous extruding Short Time Fourier Transform (Synchrosqueezing Short Fourier
Transform；SSWT) method of signal analysis identifies the method positioned to carry out Ground Penetrating Radar buried target, such as Fig. 1-6 institute
Show, specific as follows:
1, it is successively extracted first from target region of interest and each is classified as an A-scan target；To every one of A-scan
Radar target data synchronizes respectively squeezes Short Time Fourier Transform (Synchrosqueezing Short Fourier
transform).Comprising the following steps:
1.1, Gpr Signal is B-scan image, and each column for extracting image are successively mentioned from target region of interest
It takes and each is classified as an A-scan target.
1.2, Short Time Fourier Transform (STFT) first is carried out to signal, the coefficient after obtaining Short Time Fourier Transform:
Wherein b is time shift method, and g (t) is fixed window function.
Enable ψ (t)=g (t) ej2πft, formula (1) may be expressed as:
WhereinFor function ψ (t) complex conjugate according in Parseval theorem and Fourier Transformation Properties about
The rule of change of scale and translation, formula (2) can be written as:
In formula, x (ξ) is the Fourier transformation of signal x (t),Indicate that the complex conjugate of the Fourier transformation of ψ (t) is first
Consider the case where signal is harmonic wave, takes x (t)=A cos2 π f0T., then:
Wushu (4) substitutes into formula (3) and obtains:
1.3, the instantaneous frequency w of signal s (t) is calculateds(a, b):
Due toTherefore the instantaneous frequency of signal x (t) are as follows:
Obviously, for shaped like x (t)=Acos 2_f0t simple component signal, as available from the above equation:
1.4, it is recombinated via compression, obtains synchronous extruding wavelet transformation magnitude Ts(ω,b)
For more generally multicomponent data processingAnd meetHere ψ 'n(t) ψ is indicatedn(t) derivative is since Short Time Fourier Transform is linear transformation, therefore
The Short Time Fourier Transform result of multicomponent data processing x (t) can be expressed as N number of component xn(t) superposition of S-transformation, i.e.,
And
Each simple component xn(t) instantaneous frequency can be indicated by formula (6) are as follows:
So, the instantaneous frequency of multicomponent data processing x (t) may be expressed as:
Wherein, δ is that impulse function will be to frequency separation to realize convenient for algorithm similar to synchronous extruding transformation [1]
Integral is write as the form of summation.
The synchronous of definition signal x (t) squeezes Short Time Fourier Transform are as follows:
Wherein, flIt is the synchronous transformed frequency of extruding, LfIt is in Fourier spectrum in short-term with flCentered on frequency separation
Half length, fkFor the discretization frequency sampling point of frequency separation in Fourier spectrum in short-term, and Δ f=fk-fk-1The formula is indicated in short-term
Frequency separation [f in Fourier spectruml-Lf,fl+Lf] in time-frequency spectrum be overlapped and be placed on frequency flPlace, that is, a frequency zones
Between Short Time Fourier Transform time-frequency spectrum " extruding " a to Frequency point on, to make that synchronous to squeeze Short Time Fourier Transform very big
Ground improves the frequency resolution of Short Time Fourier Transform.
2, the temporal frequency energy information figure of signal is obtained.Specifically includes the following steps:
For every one of A-Scan data zi, i=1 ..., m m is the road number of B-scan data, to each A-Scan
zi, i=1 ..., m synchronize extruding Short Time Fourier Transform, obtain transformed instantaneous spectrum zfi, i=1 ..., m, and will
Transformed signal zfiIt is rearranged, is arranged successively transformed signal zfi, i=1 ..., m, obtain signal synchronize it is crowded
Temporal frequency energy information figure zf after pressing Short Time Fourier Transform；
3, specifically include following two step:
Ground Penetrating Radar two dimension B-Scan echo is made of the one-dimensional A-Scan echo data of multiple tracks.In step 1
On the basis of the B-Scan image amplitude component extracted, for every one of A-Scan data, according to its spy in time t direction
Point is it is found that smaller in variance of the nontarget area temporal frequency energy information figure in the direction t；In target area, temporal frequency energy
The variance for measuring hum pattern is larger.Using two statistics of energy and variance, chooses energy and variance is all the region of peak value, and tie
Suitable threshold value is closed, determines thin layer mesh target area；Wherein, the threshold values in this section can be by manually presetting or iterating to calculate mode
It obtains, this is routine techniques.
4, according to the temporal frequency energy information figure for removing direct wave, when which has very high
Frequency division resolution, therefore when extracting instantaneous spectral component, obtained frequency division section has relative to other several Time-Frequency Analysis Methods
Better resolution ratio, according to the biggish position of energy in temporal frequency energy information figure, the vertical seat of descender line on the band of position
Mark difference just represents the corresponding depth of thin layer.Spread speed v by the depth extracted multiplied by electromagnetic wave in the medium, just obtains depth
The thickness H of degree, it may be assumed that
H=t0v (13)
Effect using localization method provided by the invention is as shown in Figure 6.
It is unspecified in specification to partly belong to that well known to a person skilled in the art the prior arts.
The present invention does not need to carry out the training of data, so being easy to carry out on-line checking；It being capable of more complete reservation target letter
Breath, thus promoted target positioning precision, for layer with a thickness of thin layer data for, also can recognize that the thickness of thin layer, institute
To be not easy to miss thin layer target；It is able to ascend thin layer identification accuracy, effectively distinguishes stronger clutter and target echo；Based on
Algorithm is relatively simple, and algorithm operation time is short, ensure that the accuracy for quickly carrying out the positioning of thin layer target.
Specific embodiment is presented above, but the present invention is not limited to described embodiment.Base of the invention
This thinking is above-mentioned basic scheme, and for those of ordinary skill in the art, various changes are designed in introduction according to the present invention
The model of shape, formula, parameter do not need to spend creative work.It is right without departing from the principles and spirit of the present invention
The change, modification, replacement and modification that embodiment carries out are still fallen in protection scope of the present invention.
Claims (5)
1. a kind of Ground Penetrating Radar thin layer object recognition and detection method, which comprises the following steps:
(1), it is successively extracted from target region of interest and each is classified as an A-scan target；To every one of A-scan radar mesh
Mark data synchronize respectively squeezes Short Time Fourier Transform (Synchrosqueezing Short Fourier
transform)；
(2), the temporal frequency energy information figure of signal is obtained；
(3), target region of interest is found out according to Energy distribution；
(4), according to temporal frequency energy information figure, the thickness for measuring thin layer realizes the positioning and identification of thin layer target.
2. a kind of Ground Penetrating Radar thin layer object recognition and detection method according to claim 1, which is characterized in that the step
(1) specifically:
1), Gpr Signal is B-scan image, and each column for extracting image successively extract often from target region of interest
One is classified as an A-scan target；
2) Short Time Fourier Transform (STFT) first, is carried out to signal, the coefficient after obtaining Short Time Fourier Transform:
Wherein b is time shift method, and g (t) is fixed window function；
Enable ψ (t)=g (t) ej2πft, formula (1) may be expressed as:
WhereinFor the complex conjugate of function ψ (t)；
According to the rule in Parseval theorem and Fourier Transformation Properties about change of scale and translation, formula (2) can be write
Are as follows:
In formula, x (ξ) is the Fourier transformation of signal x (t),Indicate the complex conjugate of the Fourier transformation of ψ (t)；First consider
The case where signal is harmonic wave, takes x (t)=Acos2 π f0T., then:
Wushu (4) substitutes into formula (3) and obtains:
3) the instantaneous frequency w of signal s (t), is calculateds(a,b)
Due toTherefore the instantaneous frequency of signal x (t) are as follows:
Obviously, for shaped like x (t)=Acos2 π f0T. simple component signal, as available from the above equation:
4) it, is recombinated via compression, obtains synchronous extruding wavelet transformation magnitude Ts(ω,b)
For more generally multicomponent data processingAnd meetHere ψ 'n(t) ψ is indicatedn(t) derivative；Since Short Time Fourier Transform is linear transformation, therefore
The Short Time Fourier Transform result of multicomponent data processing x (t) can be expressed as N number of component xn(t) superposition of S-transformation, that is:
And
The instantaneous frequency of each simple component can be indicated by formula (6) are as follows:
So, the instantaneous frequency of multicomponent data processing x (t) may be expressed as:
Wherein, δ is impulse function, is similar to synchronous squeeze and converts [1], to realize convenient for algorithm, by the integral to frequency separation
Write as the form of summation；
The synchronous of signal x (t) squeezes Short Time Fourier Transform are as follows:
Wherein, flIt is the synchronous transformed frequency of extruding, LfIt is in Fourier spectrum in short-term with flCentered on frequency separation half it is long
Degree, fkFor the discretization frequency sampling point of frequency separation in Fourier spectrum in short-term, and Δ f=fk-fk-1The formula is indicated in Fu in short-term
Frequency separation [f in leaf spectruml-Lf,fl+Lf] in time-frequency spectrum be overlapped and be placed on frequency flPlace, i.e., frequency separation
In Short Time Fourier Transform time-frequency spectrum " extruding " a to Frequency point, so that the synchronous Short Time Fourier Transform that squeezes be made greatly to mention
The high frequency resolution of Short Time Fourier Transform.
3. a kind of Ground Penetrating Radar thin layer object recognition and detection method according to claim 1, which is characterized in that the step
2) specifically:
For every one of A-Scan data zi, i=1 ..., m m is the road number of B-scan data, to each A-Scanzi, i=
1 ..., m synchronizes extruding Short Time Fourier Transform, obtains transformed instantaneous spectrum zfi, i=1 ..., m, and will be after transformation
Signal zfiIt is rearranged, obtains the instantaneous spectrum zf of signal.
4. a kind of Ground Penetrating Radar thin layer object recognition and detection method according to claim 1, which is characterized in that the step
3) specifically:
The instantaneous spectrum zf of signal, the lateral variance curve figure of the energy diagram of measurement signal transient spectrum, according to the model of variance curve figure
Given threshold is enclosed, so that it is determined that direct wave region.
5. a kind of Ground Penetrating Radar thin layer object recognition and detection method according to claim 1, which is characterized in that the step
4) specifically:
According to the temporal frequency energy information figure for removing direct wave, which differentiates with very high time-frequency
Rate, therefore when extracting instantaneous spectral component, obtained frequency division section has better relative to other several Time-Frequency Analysis Methods
Resolution ratio, according to energy in temporal frequency energy information figure it is biggish place determine coating region, in conjunction with thin layer thickness and when
Between resolution ratio can identify the thickness of thin layer, realize the positioning and identification of thin layer target.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110109186A (en) * | 2019-04-18 | 2019-08-09 | 河海大学 | A kind of Coherent Noise in GPR Record three-dimensional Time-Frequency Analysis Method |
| CN110135390A (en) * | 2019-05-24 | 2019-08-16 | 哈尔滨工业大学 | The specific emitter identification method inhibited based on main signal |
| CN110161123A (en) * | 2019-06-21 | 2019-08-23 | 南昌航空大学 | A kind of new defect inspection method based on magnetic striction wave guide |
| CN111007572A (en) * | 2019-11-22 | 2020-04-14 | 北京中科蓝图科技有限公司 | Automatic identification method, device and system for road underground cavity |
| CN111427091A (en) * | 2020-05-06 | 2020-07-17 | 芯元(浙江)科技有限公司 | Seismic exploration signal random noise suppression method by squeezing short-time Fourier transform |
| CN111679275A (en) * | 2020-08-06 | 2020-09-18 | 中南大学 | Underground pipeline identification method based on ground penetrating radar |
| CN113064166A (en) * | 2021-03-22 | 2021-07-02 | 石家庄铁道大学 | Method and device for detecting thickness of thin layer defect of multilayer concrete structure and terminal |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104199093A (en) * | 2014-09-01 | 2014-12-10 | 中国海洋石油总公司 | Seismic signal resolution enhancement method based on time-frequency domain energy adaptive weighting |
| CN104820786A (en) * | 2015-05-13 | 2015-08-05 | 西安交通大学 | Method for analyzing instantly weighted synchronous extrusion wavelet bispectrum |
| CN105005042A (en) * | 2015-07-27 | 2015-10-28 | 河南工业大学 | Ground penetrating radar underground target locating method |
| CN105403883A (en) * | 2015-10-29 | 2016-03-16 | 河南工业大学 | Ground penetrating radar underground target position detection method |
| CN106950600A (en) * | 2017-02-16 | 2017-07-14 | 中国石油大学(华东) | A kind of minimizing technology of near surface scattering surface ripple |
| CN107390267A (en) * | 2017-07-27 | 2017-11-24 | 西安交通大学 | A kind of seismic data attenuation compensation method of synchronous extruding transform domain |
-
2018
- 2018-01-27 CN CN201810080476.9A patent/CN109521421A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104199093A (en) * | 2014-09-01 | 2014-12-10 | 中国海洋石油总公司 | Seismic signal resolution enhancement method based on time-frequency domain energy adaptive weighting |
| CN104820786A (en) * | 2015-05-13 | 2015-08-05 | 西安交通大学 | Method for analyzing instantly weighted synchronous extrusion wavelet bispectrum |
| CN105005042A (en) * | 2015-07-27 | 2015-10-28 | 河南工业大学 | Ground penetrating radar underground target locating method |
| CN105403883A (en) * | 2015-10-29 | 2016-03-16 | 河南工业大学 | Ground penetrating radar underground target position detection method |
| CN106950600A (en) * | 2017-02-16 | 2017-07-14 | 中国石油大学(华东) | A kind of minimizing technology of near surface scattering surface ripple |
| CN107390267A (en) * | 2017-07-27 | 2017-11-24 | 西安交通大学 | A kind of seismic data attenuation compensation method of synchronous extruding transform domain |
Non-Patent Citations (1)
| Title |
|---|
| 黄忠来等: "同步挤压S变换", 《中国科学: 信息科学》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110109186A (en) * | 2019-04-18 | 2019-08-09 | 河海大学 | A kind of Coherent Noise in GPR Record three-dimensional Time-Frequency Analysis Method |
| CN110135390A (en) * | 2019-05-24 | 2019-08-16 | 哈尔滨工业大学 | The specific emitter identification method inhibited based on main signal |
| CN110161123A (en) * | 2019-06-21 | 2019-08-23 | 南昌航空大学 | A kind of new defect inspection method based on magnetic striction wave guide |
| CN111007572A (en) * | 2019-11-22 | 2020-04-14 | 北京中科蓝图科技有限公司 | Automatic identification method, device and system for road underground cavity |
| CN111427091A (en) * | 2020-05-06 | 2020-07-17 | 芯元(浙江)科技有限公司 | Seismic exploration signal random noise suppression method by squeezing short-time Fourier transform |
| CN111679275A (en) * | 2020-08-06 | 2020-09-18 | 中南大学 | Underground pipeline identification method based on ground penetrating radar |
| CN113064166A (en) * | 2021-03-22 | 2021-07-02 | 石家庄铁道大学 | Method and device for detecting thickness of thin layer defect of multilayer concrete structure and terminal |
| CN113064166B (en) * | 2021-03-22 | 2023-01-06 | 石家庄铁道大学 | Method and device for detecting thickness of thin layer defect of multilayer concrete structure and terminal |
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