CN107659314A - The rarefaction expression of distributing optical fiber sensing space-time two-dimension signal and compression method - Google Patents
The rarefaction expression of distributing optical fiber sensing space-time two-dimension signal and compression method Download PDFInfo
- Publication number
- CN107659314A CN107659314A CN201710852133.5A CN201710852133A CN107659314A CN 107659314 A CN107659314 A CN 107659314A CN 201710852133 A CN201710852133 A CN 201710852133A CN 107659314 A CN107659314 A CN 107659314A
- Authority
- CN
- China
- Prior art keywords
- mrow
- signal
- optical fiber
- time
- space
- 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.)
- Granted
Links
Classifications
-
- H—ELECTRICITY
- H03—ELECTRONIC CIRCUITRY
- H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
- H03M7/00—Conversion of a code where information is represented by a given sequence or number of digits to a code where the same, similar or subset of information is represented by a different sequence or number of digits
- H03M7/30—Compression; Expansion; Suppression of unnecessary data, e.g. redundancy reduction
- H03M7/3059—Digital compression and data reduction techniques where the original information is represented by a subset or similar information, e.g. lossy compression
- H03M7/3062—Compressive sampling or sensing
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Complex Calculations (AREA)
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
Abstract
The invention discloses the rarefaction expression of distributing optical fiber sensing space-time two-dimension signal and compression method, the design feature of temporal and spatial correlations and directionality for distribution type fiber-optic sound/vibration sensing space-time two-dimension signal, using small echo and shearing wave multiscale analysis ability, realize that distribution type fiber-optic sound/vibration sensing space-time two-dimension signal is represented and compressed in the rarefaction of shearing wavelet domain.Alleviate the contradictions such as real-time storage, transmission and processing that existing distribution type fiber-optic sound/vibration sensor-based system is brought with the performance indications lifting generation super large data volume such as detection frequency band, monitoring distance and spatial resolution.
Description
Technical field
The rarefaction expression of distributing optical fiber sensing space-time two-dimension signal and compression method, for the dilute of space-time two-dimension signal
Thinization represents and compression, belongs to transducing signal rarefaction expression and compression technique area
Background technology
In recent years, the Distributed Optical Fiber Sensing Techniques based on optical time domain reflection technology obtain research both at home and abroad extensively and profoundly
And application, such as phase sensitivity optical time domain reflectometer, polarization sensitive optical time domain reflection instrument and Brillouin optical time-domain reflectometer.Particularly
Representative of the phase sensitivity optical time domain reflection technology (Φ-OTDR) as Distributed Optical Fiber Sensing Techniques, utilize buried laying or overhead
The spatial distribution and time-varying information of optical cable sensing physical quantity such as sound wave, vibration along thread environment, are to realize a wide range of environment
A kind of important technical of dynamic monitoring, played a significant role in infrastructure security monitoring field.Nearest 2 years, it is based on
The distribution type fiber-optic sound/vibration sensor-based system of linear demodulation, photo-signal can be obtained and modulated with extraneous vibration or sound wave
Linear relationship between the phase place change of introducing, I~Δ φ (t), further increase system detectio sensitivity, frequency band range and
Intelligent Understanding ability to perceiving target in environment.
The number that distribution type fiber-optic sound/vibration sensor-based system generally produces in a wide range of round-the-clock uninterrupted monitoring process
It is bigger according to measuring.Such as the distribution type fiber-optic sound/vibration sensor-based system that monitoring distance is tens kilometers, according to space point
Tens meters of calculating of resolution, the monitoring range is equivalent to the thousands of individual sensors of placement；When system light pulse repetition rate up to
KHz, i.e., the data sampling rate of each sensing node reach kHz, can be calculated, distribution type fiber-optic sound, oscillating pickup system
Kind per second of uniting produces 1-10M Monitoring Data, and each data are calculated by 2 Byte, then data volume each second is 2M-20M
Byte, data volume is 7.2G-72G per hour.In round-the-clock uninterrupted monitoring process, 24 hours every day can produce
172.8G-1.7T data.With the continuous improvement of distribution type fiber-optic sound/vibration sensor-based system performance indications, for example, single-point
Detect the sensing section that frequency band is more and more wider, and monitoring distance is more and more remote, spatial resolution more and more higher, is obtained in its unit interval
Points and sensing data amount can also at double, Ji Shibei, hundreds of times increase.To data storage later, real-time Transmission and place
Reason is exerted heavy pressures on.If this contradiction can not be solved very well, high performance system index in actual applications can not be real
Play a role.The existing sparse representation method based on wavelet basis can solve the compression problem of one-dimensional time signal, be passed in point type
Certain effect is achieved on sensor, and the characteristics of 2-d wavelet base shortage anisotropy, it is impossible to flexibly reflection function is each
Changing rule on individual direction, therefore the sparse representation method based on wavelet basis is not suitable for the two of distributed optical fiber sensing system
Dimensional signal space-time structure, it is difficult to obtain good compression effectiveness.
The content of the invention
It is an object of the invention to：Solve the existing sparse representation method based on wavelet basis and can only solve point sensor to exist
Compression in one-dimensional time signal；The characteristics of lacking anisotropy because of 2-d wavelet base, it is impossible to which flexibly reflection function is in each side
Upward changing rule, can not solve the compression on two-dimensional time signal caused by distribution type fiber-optic sound/vibration sensor-based system
The problem of；Provide rarefaction expression and the compression method of a kind of distributing optical fiber sensing space-time two-dimension signal.
The technical solution adopted by the present invention is as follows：
The rarefaction expression of distributing optical fiber sensing space-time two-dimension signal and compression method, it is characterised in that following steps：
Optical fiber sound/vibration transducing signal along step 1, collection laying optical cable, when structure optical fiber sound/vibration senses
Null response signal, carry out denoising；
Step 2, space-time response signal is sensed to the optical fiber sound/vibration after denoising based on Shearlet conversion carried out
Rarefaction represents and compression；
After step 3, rarefaction are represented and compressed, signal is reconstructed null response when recovering optical fiber sound/vibration sensing
Signal.
Further, the step 2 comprises the following steps that：
Step 2.1, useful signal concentration in optical fiber sound/vibration sensing space-time response signal is chosen based on wavelet package transforms
Frequency range carry out first time SNR estimation and compensation；
After step 2.2, first time SNR estimation and compensation, non-linear space is realized to wavelet packet coefficient by OTSU adaptive thresholds
Second of SNR estimation and compensation；
Step 2.3, the frequency-domain sparse based on optical fiber sound/vibration sensing space-time response signal after SNR estimation and compensation twice,
The rarefaction expression and compression of optical fiber sound/vibration sensing space-time response signal after denoising are realized using Shearlet conversion.
Further, the step 2.1 is specific as follows：
Optical fiber sound/vibration sensing space-time response signal matrix X is taken successivelyM,NEach column vector, obtaining one-dimensional time rings
Induction signal X:,n(n=1,2 ... N)；
Based on " db6 " wavelet packet to one-dimensional time response signal X:,n(n=1,2 ... N) carries out p layer WAVELET PACKET DECOMPOSITIONs and obtained
Obtain energy-distributing feature of the signal in different frequency bands；
Space-time response signal is sensed by the wavelet packet coefficient portfolio restructuring signal and former optical fiber sound/vibration of different frequency range
Comparative analysis, extract the optical fiber sound/vibration sensing space-time response signal Major Concentrated Frequency Band be distributed in corresponding small echo
In bag coefficient, the useful signal component in combination wavelet bag coefficient reconstruct collection Mid Frequency, that is, obtained after obtaining first time SNR estimation and compensation
The optical fiber sound/vibration sensing space-time response signal X arrived1。
Further, the step 2.2 is specific as follows：
By optical fiber sound/vibration sensing space-time response signal XM,NWith the optical fiber sound/shake obtained after first time SNR estimation and compensation
Dynamic sensing space-time response signal X1Difference be designated as background signal B；
Spatial noise position is first determined by background signal B very noisy points position, the light that will be obtained after first time SNR estimation and compensation
Fine sound/vibration sensing space-time response signal X1In corresponding points zero setting, realize SNR estimation and compensation spatially, specific steps are such as
Under：Based on OTSU algorithms to the adaptive selected thresholds of background signal B, the point that threshold value is exceeded in background signal B is labeled as making an uproar by force
Sound point, record its position and by the position zero setting, realize second of noise point of optical fiber sound/vibration sensing space-time response signal
From.
5. the rarefaction of distributing optical fiber sensing space-time two-dimension signal according to claim 4 represents and compression side
Method, it is characterised in that the step 2.3 comprises the following steps that：
Step 2.31, space-time response signal X is sensed to the optical fiber sound/vibration after second of SNR estimation and compensation2Carry out
Shearlet is converted；
Frequency band selection is carried out in Shearlet transform domains and coefficient is chosen after step 2.32, Shearlet conversion.
Further, the step 2.31 comprises the following steps that：
Step 2.311, space-time response signal X first is sensed to the optical fiber sound/vibration after second of SNR estimation and compensation2Carry out more
Yardstick subdivision；Specially：
Space-time response signal X is sensed to the optical fiber sound/vibration after denoising with wavelet packet2Decomposed, obtain low frequency system
Number f'JWith high frequency coefficient fj, wherein j expression current decomposition yardsticks；
Step 2.312, the space-time response signal travel direction after multiple dimensioned subdivision is localized；Specially：
To the high frequency coefficient f under each yardstickjSubdivision is carried out using the window function with direction and dimensional variation, definition is horizontal
Bore D0With vertical cone D1, wherein D0Region isD1Region is
ξ1、ξ2For Shearlet transform domain reference axis,Represent that component is the n-dimensional vector space of real number on frequency domain；Respectively to D0And D1Area
High frequency coefficient f on each yardstick in domainjSelection 2j+1, j=0~J-1 window function W, the window function frequency domain is：
Wherein d is region parameter, and j is current decomposition yardstick, and l is that Shearlet ripples framework constructs coefficient, to each yardstick
It is satisfied by constraints：
The segmentation of Shearlet frequency domains is completed using window function W, obtains the component of signal of different scale different directions frequency band
fj,p, j expression current decomposition yardsticks (j=0~J-1), p expressions current decomposition direction (p=0~2j+3-1)。
Further, the step 2.32 comprises the following steps that：
Using energy shared by each frequency band as index for selection when frequency band is chosen, the frequency band that signal energy is concentrated is chosen, by selection
Low-frequency band f'JIt is designated as l0, high-frequency sub-band fj,pIt is designated as l1~ln, n is chooses high-frequency sub-band number, by the frequency band coefficient summation of selection
It is designated as high-energy frequency band H；
After frequency band is chosen, Shearlet coefficient selections are carried out：According to coefficient retaining ratio T2Choose high-energy frequency band
Significantly value coefficient, wherein coefficient retaining ratio T2For the greatest coefficient retaining ratio of high-energy frequency band, note signal to noise ratio snr is signal
The ratio of average and signal standards deviation, T2Selection is determined that choosing SNR is by the signal to noise ratio snr of reconstruct space-time response signal
Maximum SNRmaxWhen corresponding T2Value, significantly the amplitude of value coefficient and its position, composition Shearlet are dilute for record high-energy frequency band
Sparse coefficient, the Shearlet rarefactions for completing signal are represented and compressed.
Further, the step 3 comprises the following steps that：
Structure is respectively equal to original Shearlet conversion coefficients square with the frequency band number identical full 0 matrix chosen, size
Battle array；
Shearlet sparse coefficients, other positions will be filled out on the significantly value coefficient correspondence position of the high-energy frequency band of reservation
It is zero, builds Shearlet sparse coefficient matrixes；
Then to the inverse transformation of Shearlet sparse coefficient matrixes travel direction localization, i.e., to low-frequency band and high frequency
Band coefficient carries out two-dimensional inverse Fourier transform respectively, obtains low-frequency band the reconstruction of time and space signal l0' and each high-frequency sub-band the reconstruction of time and space
Signal l1'~ln', by l0'~ln' it is added the optical fiber distributed type sound/vibration space-time response signal recovered after being decompressed.
In summary, by adopting the above-described technical solution, the beneficial effects of the invention are as follows：
1st, the present invention makees ratio using Shearlet sparse representation methods and Traditional Wavelet (Wavelet) sparse representation method
Compared with it is 12.97dB that Shearlet sparse representation methods, which obtain maximum signal to noise ratio SNR' when compression factor reaches 0.2%,；Pressing
In the case of contracting ratio identical, Shearlet sparse representation methods signal to noise ratio snr ' compared with Wavelet it is higher by 3-4dB, reduced overall
Reconstruction signal signal to noise ratio is significantly better than Wavelet sparse representation methods, and thousand can be obtained under conditions of signal to noise ratio is higher
Other compression factor is classified, the rarefaction representation and compression result of the inventive method are significantly better than directly on 2D signal using small
Ripple base carries out rarefaction representation and compression method；
2nd, the present invention alleviates existing distribution type fiber-optic sound/vibration sensor-based system with detection frequency band, monitoring distance and space
The lifting of the performance indications such as resolution ratio produces the contradictions such as real-time storage, transmission and processing that super large data volume is brought；
3rd, the present invention has directive design feature for distribution type fiber-optic sound/vibration sensing space-time two-dimension signal,
The sparse representation method based on Shearlet bases is proposed, the directive space-time two-dimension letter of tool can not be applicable by solving existing method
The problem of number structure, realize data be effectively compressed represented with rarefaction while, the further of data is realized by reconstruct
Denoising, achieve significantly superior compression effectiveness and signal to noise ratio；
4th, the present invention represents space-time two-dimension signal denoising twice by rarefaction, substantially improves rarefaction representation and compression
Effect；
5th, the present invention is improved Shearlet rarefactions method for expressing, it is proposed that frequency band is chosen chooses what is be combined with coefficient
Adaptive shortening thresholding algorithm, effectively improve the effect of rarefaction representation.
Brief description of the drawings
Fig. 1 is the distribution type fiber-optic sound/vibration sensing system architecture figure of the present invention；
Fig. 2 is the original space-time response signal figure that the present invention gathers；
Fig. 3 is rarefaction expression and the compression method flow chart of optical fiber distributed type sound/vibration transducing signal of the present invention；
Fig. 4 carries out the front and rear contrast of WAVELET PACKET DECOMPOSITION denoising for certain spatial point time series signal in the present invention：(a) wavelet packet
Decompose the time response signal before denoising；(b) the time response signal after WAVELET PACKET DECOMPOSITION denoising；
Fig. 5 is the space-time response signal figure after denoising of WAVELET PACKET DECOMPOSITION in the present invention；
Fig. 6 is the space-time response signal figure after OTSU adaptive threshold second denoisings in the present invention；
Fig. 7 is Shearlet Dividing in frequency domain in the present invention with choosing schematic diagram；
Fig. 8 is the horizontal cone of Shearlet frequency domains in the present invention and vertical cone schematic diagram；
Fig. 9 is the optical fiber distributed type sound/vibration signal graph after the present invention recovers；
Figure 10 based on Shearlet and is based on existing Wavelet compression results comparison diagram for the present invention.
Embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, it is right below in conjunction with drawings and Examples
The present invention is further elaborated.It should be appreciated that specific embodiment described herein is only to explain the present invention, not
For limiting the present invention.
Present invention process object is optical fiber distributed type sound/vibration transducing signal, typically by based on phase sensitivity optical time domain reflection
The distribution type fiber-optic sound/vibration sensor-based system of technology produces.
Embodiment one
Distribution type fiber-optic sound/vibration sensing system hardware is made up of three parts, and detecting optical cable, optical signal demodulation are set
Standby, signal transacting main frame, system architecture are as shown in Figure 1.Detecting optical cable generally use general single mode telecommunication optical fiber or especial sound,
Sensitizing type sensing optic cable is vibrated, also can be directly using typically along underground piping, power transmission cable, the buried laying of town road
Piece vacant fibre core of the communications optical cable laid.Optical signal demodulation equipment is the core of the system, and its internal composition device is main
Including optics and the class of electricity device two.The optical signals photodetector demodulated is converted into electric signal, then by adopting at a high speed
Truck carries out signal synchronous collection, and last digital electric signal gives signal transacting main frame by the interface such as network real-time Transmission.Signal
Processing main frame is common computer main frame (PC) or FPGA/DSP embedded main boards, analysis, processing for Fibre Optical Sensor signal, is led to
Cross signal specific Processing Algorithm and obtain the event information for causing sound wave, vibration etc., and its position is determined by optical time domain reflection principle,
And Intelligent Recognition and classification are carried out to sensed event.
Embodiment two
On the basis of embodiment one, optical fiber distributed type sound, the vibrating sensing signal of system acquisition, as shown in Fig. 2 being
Null response is believed during the optical fiber distributed type sound/vibration that certain station nearby collects along the optical cable of track laying when train passes through
Number, there is certain span in time, space, and there is certain orientation on space-time two-dimension figure.The sensing of the space-time two-dimension is believed
Number it is designated as XM,N=x (m, n), m=1,2 ... M, n=1,2 ... N }, wherein m is time dimension, and n is space dimensionality, M, N tables
Show time, spatial sequence length, there is M=50000, N=60 in the present embodiment.Distribution type fiber-optic sound, vibration sensing system
Hardware acquisition parameter is as follows：Data space acquisition length is 1km, space neighbouring sample point spacing 16.3m, data time sample rate
3.3KHz, temporally adjacent sampled point spacing 0.3ms.
Rarefaction expression is carried out to the distributing optical fiber sensing signal of collection and compression method is as shown in Figure 3：It is primarily based on
Wavelet package transforms choose the frequency range that useful signal is concentrated, and carry out first time SNR estimation and compensation；Then by OTSU adaptive thresholds to
Wavelet packet coefficient after SNR estimation and compensation realizes second of SNR estimation and compensation of non-linear space；Based on light after SNR estimation and compensation twice
The frequency-domain sparse of fine sound/vibration sensing space-time response signal, when being realized using Shearlet conversion after denoising null response believe
Number rarefaction represent and compression.
Embodiment three
On the basis of embodiment two, space-time response signal is sensed to optical fiber sound/vibration and carries out multi-scale wavelet bag point
Solution, chooses useful signal collection Mid Frequency, and step is as follows：
The time-frequency distributions of signal with different type are different from structure, therefore the energy in the different scale component of WAVELET PACKET DECOMPOSITION
It is distributed variant.Optical fiber sound/vibration sensing space-time response signal matrix X is taken successivelyM,NEach column vector, when obtaining one-dimensional
Between response signal X:,n(n=1,2 ... N), based on " db6 " wavelet packet to one-dimensional time response signal X:,n(n=1,2 ... N)
P layers WAVELET PACKET DECOMPOSITION (having p=6 in the present embodiment) is carried out, signal can be divided into 2 by low frequency to high frequency in frequency domainpIt is individual not
Same frequency range, d is designated as by the wavelet packet coefficient of each frequency rangek(k=1,2 ... 2p), energy of the signal in different frequency bands is obtained with this
Distribution characteristics.Because the optical fiber distributed type sound/vibration signal that embodiment one obtains is distributed mainly on low-frequency range, ambient noise leads to
Often it is distributed in whole frequency band.By the wavelet packet coefficient portfolio restructuring signal of different frequency range, (optical fiber sound/vibration passes with original signal
Feel space-time response signal) comparative analysis, extract optical fiber sound/vibration sensing space-time response signal focus primarily upon the 2nd,
3 frequency ranges, are distributed mainly on d2With d3In coefficient.Therefore, d is combined2With d3Wavelet packet coefficient can reconstruct useful in 2,3 frequency ranges
Component of signal.It is as shown in Figure 4 through the one-dimensional time response signal contrast before and after wavelet packet denoising by taking the point of space as an example.Will be through
One-dimensional time response signal X' after wavelet packet denoising:,n(n=1,2 ... N) merges the optical fiber sound after obtaining denoising/shake
Dynamic sensing space-time response signal, i.e., mainly include vibration signal caused by train operation, be designated as X1, as shown in Figure 5.Meanwhile will
Original fiber sound/vibration sensing space-time response signal XM,NWith the optical fiber sound/vibration sensing space-time response signal X after denoising1
Difference be designated as background signal B.
Example IV
On the basis of embodiment three, OTSU adaptive thresholds space is carried out again to the space-time matrix after wavelet packet denoising
Denoising, realize further SNR estimation and compensation.Specific steps：
Spatial noise position is first determined by background signal B very noisy points position, by train operation vibration signal X1In pair
Zero setting should be put, realizes SNR estimation and compensation spatially.Comprise the following steps that：Background is believed based on OTSU (maximum between-cluster variance) algorithms
Number adaptive selected thresholds of B, the point that threshold value is exceeded in background signal B is labeled as very noisy point, records its position, and zero setting.
OTSU algorithm threshold value methods：Using the maximum variance of target area and background area average gray for judge according to
According to if matrix size is M × N, gray level number is L, and binarization segmentation threshold value is thr, then matrix can be divided into target a-quadrant
With background B area, its tonal range is respectively 0~thr and thr~L-1, if there is N in AAIndividual pixel, there is N in BBIndividual pixel, then
There is NA+NBThe ratio that the number of pixel accounts for entire image in=M × N, A, B area is respectively：
If A, the average gray of B area is respectively μAAnd μB, then the average gray of view picture figure may be calculated：
μ=μAωA+μBωB (3)
The inter-class variance g of view picture figure is calculated as：
G=ωA(μ-μA)2+ωB(μ-μB)2=ωAωB(μA-μB)2 (4)
From 0 to L-1, value, g take maximum g to threshold value thr successivelymaxWhen corresponding threshold value thr be required segmentation threshold
otsu_thr。
Based on threshold value obtained by OTSU algorithms, the point that threshold value is exceeded in background signal B is labeled as very noisy point, records its position
Put, the optical fiber sound/vibration after denoising is sensed into space-time response signal X1In corresponding very noisy point position zero setting, realize denoising
Second of SNR estimation and compensation of optical fiber sound/vibration sensing space-time response signal afterwards.Optical fiber sound after second of SNR estimation and compensation
Sound/vibrating sensing space-time response signal is designated as X2, as shown in Figure 6.
Embodiment five
On the basis of example IV, based on optical fiber sound/vibration sensing space-time response signal after SNR estimation and compensation twice
Frequency-domain sparse, the rarefaction table of optical fiber sound/vibration sensing space-time response signal after denoising is realized using Shearlet conversion
Show as follows with compression step：
(1) space-time response signal X is sensed to the optical fiber sound/vibration after second of SNR estimation and compensation2Carry out Shearlet changes
Change.Shearlet conversion is made up of multiple dimensioned subdivision and direction two steps of localization.
The first step, multiple dimensioned subdivision is first carried out, if Decomposition order is J, there is J=3 in the present embodiment, i.e. Decomposition order is
3.With harr wavelet packets to the space-time response signal X after denoising2Three layers of decomposition are carried out, can obtain low frequency coefficient f'JWith three yardsticks
Under high frequency coefficient fj(j=0~J-1), wherein j represent current decomposition yardstick.Its Dividing in frequency domain figure as shown in fig. 7,
Shearlet transform domain reference axis are respectively ξ1、ξ2, central square region is low-frequency band, corresponds to correspond to chi respectively from inside to outside
Spend the high-frequency sub-band for 0~J-1.
Second step, travel direction localizes again after multiple dimensioned subdivision, in order to obtain the high fdrequency component on different directions, to each
High frequency coefficient f under individual yardstickjSubdivision is carried out using the window function with direction and dimensional variation.The horizontal cone D of definition0With vertical cone
D1, wherein D0Region isD1Region isSuch as Fig. 8 institutes
Show, D1For darker regions, D0For light areas.
Respectively to D0And D1High frequency coefficient f on each yardstick in regionjSelection 2j+1(j=0~J-1) individual window function W, the window
Function frequency domain is：
Wherein d is region parameter, and j is current decomposition yardstick, and l is that Shearlet ripples framework constructs coefficient.To each yardstick
(j=0,1,2) it is satisfied by constraints：
The segmentation of Shearlet frequency domains is completed using window function W, obtains the component of signal of different scale different directions frequency band
fj,p, j expression current decomposition yardsticks, p expression current decompositions direction.As shown in fig. 7, when black region is decomposition scale j=2
One group of window function.
(2) frequency band is carried out in Shearlet transform domains after Shearlet conversion to choose and coefficient selection.
Using energy shared by each frequency band as index for selection when frequency band is chosen, the frequency band that signal energy is concentrated is chosen, can more be had
Effect ground reflection component of signal feature, reaches more preferable compression factor.The present embodiment chooses threshold using frequency band gross energy 1% as frequency band
Value T1, have chosen higher than the low-frequency band of frequency band selected threshold and four high-frequency sub-bands, be designated as l0And l1,l2,l3,l4, (for convenience
Narration, by the low-frequency band f' of selectionJIt is designated as l0, high-frequency sub-band fj,pIt is designated as l1~ln), each frequency band Shearlet transform coefficient matrixs
Size is equal to original space-time response signal matrix.Five frequency band coefficient summations are designated as high-energy frequency band H.
After frequency band is chosen, Shearlet coefficient selections are carried out.According to coefficient retaining ratio T2Choose high-energy frequency band
Significantly value coefficient, wherein coefficient retaining ratio T2For the greatest coefficient retaining ratio of high-energy frequency band, work as T2When=0.1%, that is, protect
The greatest coefficient retaining ratio for staying high-energy frequency band is 0.1%.Remember the ratio that signal to noise ratio snr is signal average and signal standards deviation
Value, T2Selection is determined that it is maximum SNR to choose SNR by the signal to noise ratio snr of reconstruct space-time response signalmaxWhen corresponding T2Value.
High-energy the frequency band significantly amplitude of value coefficient and its position are recorded, Shearlet sparse coefficients is formed, completes signal
Shearlet rarefactions represent and compression.
Compression factor Compress_ratio can be by coefficient retaining ratio T2Calculate, its corresponding relation is：
Compress_ratio=T2*2 (7)；
Embodiment six
On the basis of embodiment five, based on Shearlet rarefactions expression and the Shearlet sparse coefficient matrixes compressed
The method for carrying out the sensing space-time response signal reconstruct of optical fiber sound/vibration：
Five full 0 matrixes are built, size is respectively equal to original Shearlet transform coefficient matrixs.By the high-energy frequency of reservation
Shearlet sparse coefficients, other positions zero, the sparse systems of structure Shearlet are filled out on the significantly value coefficient correspondence position of band
Matrix number.Then to the inverse transformation of Shearlet sparse coefficient matrixes travel direction localization, i.e., to low-frequency band and four height
Frequency sub-band coefficients carry out two-dimensional inverse Fourier transform respectively, obtain the reconstruction of time and space signal l of low-frequency band0' and during each high-frequency sub-band
Empty reconstruction signal l1',l2',l3',l4'.By l0'~l4' it is added the optical fiber sound/vibration sensing space-time recovered after being decompressed
Response signal, as shown in Figure 9.
For the compression performance and effect of the present invention, using compression factor (Compression_ratio) and signal to noise ratio
(SNR') two indices are as rarefaction representation and the Performance Evaluating Indexes of compression.Wherein compression factor such as formula (7) calculates, and believes herein
Make an uproar and then defined than SNR' by the ratio of signal area mean power and background area mean power, signal area is by the track recovered
Information is obtained by morphology opening and closing operations.By Shearlet sparse representation methods and Traditional Wavelet (Wavelet) rarefaction representation
Method is made comparisons, as a result as shown in Figure 10.In Figure 10, Shearlet sparse representation methods obtain when compression factor reaches 0.2%
It is 12.97dB to maximum signal to noise ratio SNR'；In the case of compression factor identical, Shearlet sparse representation method signal to noise ratio
SNR' is higher by 3-4dB compared with Wavelet, and reduced overall reconstruction signal signal to noise ratio is significantly better than Wavelet sparse representation methods, and
The other compression factor of thousand classifications can be obtained under conditions of signal to noise ratio is higher.Find out from result, the sparse table of the inventive method
Show to be significantly better than with compression result and carry out rarefaction representation and compression method using wavelet basis directly on 2D signal, illustrate this hair
The validity of bright method.
What is enumerated in the embodiment of the present invention is the rarefaction expression and compression of optical fiber distributed type sound/vibration transducing signal
Specific implementation method, wavelet packet functions and the multi-resolution decomposition number of plies in the inventive method, the Shearlet multi-resolution decomposition number of plies
And high-frequency sub-band selection etc. is selected and adjusted according to practical situations.This method may apply to other be based on MZI,
During Micelson and other full distributed or dot matrix optical fiber sound, the signal rarefaction of vibration sensing system are represented and compressed.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention
All any modification, equivalent and improvement made within refreshing and principle etc., should be included in the scope of the protection.
Claims (8)
1. rarefaction expression and the compression method of distributing optical fiber sensing space-time two-dimension signal, it is characterised in that following steps：
Optical fiber sound/vibration transducing signal along step 1, collection laying optical cable, structure optical fiber sound/vibration sensing space-time ring
Induction signal, carry out denoising；
Step 2, based on Shearlet conversion the optical fiber sound/vibration after denoising is sensed space-time response signal carry out it is sparse
Change and represent and compress；
After step 3, rarefaction are represented and compressed, signal is reconstructed and recovers optical fiber sound/vibration sensing space-time response signal.
2. rarefaction expression and the compression method of distributing optical fiber sensing space-time two-dimension signal according to claim 1, its
It is characterised by, the step 2 comprises the following steps that：
Step 2.1, the frequency that useful signal is concentrated in optical fiber sound/vibration sensing space-time response signal is chosen based on wavelet package transforms
Duan Jinhang first time SNR estimation and compensations；
After step 2.2, first time SNR estimation and compensation, the second of non-linear space is realized to wavelet packet coefficient by OTSU adaptive thresholds
Secondary SNR estimation and compensation；
Step 2.3, the frequency-domain sparse based on optical fiber sound/vibration sensing space-time response signal after SNR estimation and compensation twice, are utilized
The rarefaction expression and compression of optical fiber sound/vibration sensing space-time response signal after denoising are realized in Shearlet conversion.
3. rarefaction expression and the compression method of distributing optical fiber sensing space-time two-dimension signal according to claim 2, its
Be characterised by, the step 2.1 it is specific as follows：
Optical fiber sound/vibration sensing space-time response signal matrix X is taken successivelyM,NEach column vector, obtain one-dimensional time response letter
Number X:,n(n=1,2 ... N)；
Based on " db6 " wavelet packet to one-dimensional time response signal X:,n(n=1,2 ... N) carries out p layer WAVELET PACKET DECOMPOSITIONs and believed
Number different frequency bands energy-distributing feature；
Pair of space-time response signal is sensed with former optical fiber sound/vibration by the wavelet packet coefficient portfolio restructuring signal of different frequency range
Than analysis, extract optical fiber sound/vibration sensing space-time response signal Major Concentrated Frequency Band and be distributed in corresponding wavelet packet system
In number, the useful signal component in combination wavelet bag coefficient reconstruct collection Mid Frequency, that is, obtain what is obtained after first time SNR estimation and compensation
Optical fiber sound/vibration sensing space-time response signal X1。
4. rarefaction expression and the compression method of the distributing optical fiber sensing space-time two-dimension signal according to claim 2,3,
Characterized in that, the step 2.2 is specific as follows：
By optical fiber sound/vibration sensing space-time response signal XM,NPassed with the optical fiber sound/vibration obtained after first time SNR estimation and compensation
Feel space-time response signal X1Difference be designated as background signal B；
Spatial noise position is first determined by background signal B very noisy points position, the optical fiber sound that will be obtained after first time SNR estimation and compensation
Sound/vibrating sensing space-time response signal X1In corresponding points zero setting, realize SNR estimation and compensation spatially, comprise the following steps that：Base
In OTSU algorithms to the adaptive selected thresholds of background signal B, the point that threshold value is exceeded in background signal B is labeled as very noisy point,
Record its position and by the position zero setting, realize second of SNR estimation and compensation of optical fiber sound/vibration sensing space-time response signal.
5. rarefaction expression and the compression method of distributing optical fiber sensing space-time two-dimension signal according to claim 4, its
It is characterised by, the step 2.3 comprises the following steps that：
Step 2.31, space-time response signal X is sensed to the optical fiber sound/vibration after second of SNR estimation and compensation2Carry out Shearlet changes
Change；
Frequency band selection is carried out in Shearlet transform domains and coefficient is chosen after step 2.32, Shearlet conversion.
6. a kind of rarefaction of distributing optical fiber sensing space-time two-dimension signal according to claim 5 represents and compression side
Method, it is characterised in that the step 2.31 comprises the following steps that：
Step 2.311, space-time response signal X first is sensed to the optical fiber sound/vibration after second of SNR estimation and compensation2Carry out multiple dimensioned
Subdivision；Specially：
Space-time response signal X is sensed to the optical fiber sound/vibration after denoising with wavelet packet2Decomposed, obtain low frequency coefficient f'J
With high frequency coefficient fj, wherein j expression current decomposition yardsticks；
Step 2.312, the space-time response signal travel direction after multiple dimensioned subdivision is localized；Specially：
To the high frequency coefficient f under each yardstickjSubdivision is carried out using the window function with direction and dimensional variation, defines horizontal cone D0
With vertical cone D1, wherein D0Region isD1Region is
ξ1、ξ2For Shearlet transform domain reference axis,Represent that component is the n-dimensional vector space of real number on frequency domain；Respectively to D0And D1Area
High frequency coefficient f on each yardstick in domainjSelection 2j+1, j=0~J-1 window function W, the window function frequency domain is：
<mrow>
<mtable>
<mtr>
<mtd>
<mrow>
<msubsup>
<mi>W</mi>
<mrow>
<mi>j</mi>
<mo>,</mo>
<mi>l</mi>
</mrow>
<mrow>
<mo>(</mo>
<mi>d</mi>
<mo>)</mo>
</mrow>
</msubsup>
<mo>:</mo>
<mo>{</mo>
<mrow>
<mo>(</mo>
<msub>
<mi>&xi;</mi>
<mn>1</mn>
</msub>
<mo>,</mo>
<msub>
<mi>&xi;</mi>
<mn>2</mn>
</msub>
<mo>)</mo>
</mrow>
<mo>:</mo>
<msub>
<mi>&xi;</mi>
<mn>1</mn>
</msub>
<mo>&Element;</mo>
<mo>&lsqb;</mo>
<mo>-</mo>
<msup>
<mn>2</mn>
<mrow>
<mn>2</mn>
<mi>j</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msup>
<mo>,</mo>
<mo>-</mo>
<msup>
<mn>2</mn>
<mrow>
<mn>2</mn>
<mi>j</mi>
<mo>-</mo>
<mn>4</mn>
</mrow>
</msup>
<mo>&rsqb;</mo>
<mo>&cup;</mo>
<mo>&lsqb;</mo>
<msup>
<mn>2</mn>
<mrow>
<mn>2</mn>
<mi>j</mi>
<mo>-</mo>
<mn>4</mn>
</mrow>
</msup>
<mo>,</mo>
<msup>
<mn>2</mn>
<mrow>
<mn>2</mn>
<mi>j</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msup>
<mo>&rsqb;</mo>
<mo>,</mo>
<mo>|</mo>
<mrow>
<msub>
<mi>&xi;</mi>
<mn>2</mn>
</msub>
<mo>/</mo>
<msub>
<mi>&xi;</mi>
<mn>1</mn>
</msub>
<mo>+</mo>
<mi>l</mi>
<msup>
<mn>2</mn>
<mrow>
<mo>-</mo>
<mi>j</mi>
</mrow>
</msup>
</mrow>
<mo>|</mo>
<mo>&le;</mo>
<msup>
<mn>2</mn>
<mrow>
<mo>-</mo>
<mi>j</mi>
</mrow>
</msup>
<mo>}</mo>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mo>(</mo>
<mi>d</mi>
<mo>,</mo>
<mi>j</mi>
<mo>,</mo>
<mi>l</mi>
<mo>&Element;</mo>
<mi>Z</mi>
<mo>,</mo>
<mn>0</mn>
<mo>&le;</mo>
<mi>d</mi>
<mo>&le;</mo>
<mn>1</mn>
<mo>,</mo>
<mn>0</mn>
<mo>&le;</mo>
<mi>j</mi>
<mo>&le;</mo>
<mn>2</mn>
<mo>,</mo>
<mo>-</mo>
<msup>
<mn>2</mn>
<mi>j</mi>
</msup>
<mo>&le;</mo>
<mi>l</mi>
<mo>&le;</mo>
<msup>
<mn>2</mn>
<mi>j</mi>
</msup>
<mo>-</mo>
<mn>1</mn>
<mo>&cap;</mo>
<mi>l</mi>
<mo>&NotEqual;</mo>
<mn>0</mn>
<mo>)</mo>
</mrow>
</mtd>
</mtr>
</mtable>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>5</mn>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
Wherein d is region parameter, and j is current decomposition yardstick, and l is that Shearlet ripples framework constructs coefficient, full to each yardstick
Sufficient constraints：
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>d</mi>
<mo>=</mo>
<mn>0</mn>
</mrow>
<mn>1</mn>
</munderover>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>l</mi>
<mo>=</mo>
<mo>-</mo>
<msup>
<mn>2</mn>
<mi>j</mi>
</msup>
</mrow>
<msup>
<mn>2</mn>
<mi>j</mi>
</msup>
</munderover>
<msup>
<mrow>
<mo>|</mo>
<msubsup>
<mi>W</mi>
<mrow>
<mi>j</mi>
<mo>,</mo>
<mi>l</mi>
</mrow>
<mrow>
<mo>(</mo>
<mi>d</mi>
<mo>)</mo>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<msub>
<mi>&xi;</mi>
<mn>1</mn>
</msub>
<mo>,</mo>
<msub>
<mi>&xi;</mi>
<mn>2</mn>
</msub>
<mo>)</mo>
</mrow>
<mo>|</mo>
</mrow>
<mn>2</mn>
</msup>
<mo>=</mo>
<mn>1</mn>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>6</mn>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
The segmentation of Shearlet frequency domains is completed using window function W, obtains the component of signal f of different scale different directions frequency bandj,p,j
Current decomposition yardstick (j=0~J-1) is represented, p represents current decomposition direction (p=0~2j+3-1)。
7. rarefaction expression and the compression method of distributing optical fiber sensing space-time two-dimension signal according to claim 6, its
It is characterised by, the step 2.32 comprises the following steps that：
Using energy shared by each frequency band as index for selection when frequency band is chosen, the frequency band that signal energy is concentrated is chosen, by the low frequency of selection
Band f'JIt is designated as l0, high-frequency sub-band fj,pIt is designated as l1~ln, n is selection high-frequency sub-band number, and the frequency band coefficient summation of selection is designated as
High-energy frequency band H；
After frequency band is chosen, Shearlet coefficient selections are carried out：According to coefficient retaining ratio T2Choose the amplitude of high-energy frequency band
Coefficient, wherein coefficient retaining ratio T2For the greatest coefficient retaining ratio of high-energy frequency band, note signal to noise ratio snr be signal average with
The ratio of signal standards deviation, T2Selection is determined that it is maximum to choose SNR by the signal to noise ratio snr of reconstruct space-time response signal
SNRmaxWhen corresponding T2Value, high-energy the frequency band significantly amplitude of value coefficient and its position are recorded, forms the sparse systems of Shearlet
Number, the Shearlet rarefactions for completing signal are represented and compressed.
8. rarefaction expression and the compression method of distributing optical fiber sensing space-time two-dimension signal according to claim 7, its
It is characterised by, the step 3 comprises the following steps that：
Structure is respectively equal to original Shearlet transform coefficient matrixs with the frequency band number identical full 0 matrix chosen, size；
Shearlet sparse coefficients will be filled out on the significantly value coefficient correspondence position of the high-energy frequency band of reservation, other positions are
Zero, build Shearlet sparse coefficient matrixes；
Then to the inverse transformation of Shearlet sparse coefficient matrixes travel direction localization, i.e., to low-frequency band and high-frequency sub-band system
Number carries out two-dimensional inverse Fourier transform respectively, obtains low-frequency band the reconstruction of time and space signal l0' and each high-frequency sub-band the reconstruction of time and space signal
l1'~ln', by l0'~ln' it is added the optical fiber distributed type sound/vibration space-time response signal recovered after being decompressed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710852133.5A CN107659314B (en) | 2017-09-19 | 2017-09-19 | Sparse representation and compression method of distributed optical fiber sensing space-time two-dimensional signal |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710852133.5A CN107659314B (en) | 2017-09-19 | 2017-09-19 | Sparse representation and compression method of distributed optical fiber sensing space-time two-dimensional signal |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107659314A true CN107659314A (en) | 2018-02-02 |
| CN107659314B CN107659314B (en) | 2021-02-19 |
Family
ID=61129679
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710852133.5A Active CN107659314B (en) | 2017-09-19 | 2017-09-19 | Sparse representation and compression method of distributed optical fiber sensing space-time two-dimensional signal |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107659314B (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108898765A (en) * | 2018-05-11 | 2018-11-27 | 国网湖北省电力有限公司检修公司 | A kind of overhead transmission line external force damage alarm method based on analysis of vibration signal |
| CN109951223A (en) * | 2019-03-15 | 2019-06-28 | 山东大学 | A kind of method that random single pulse sampling restores signal and frequency in phase sensitive OTDR sensing |
| CN110031081A (en) * | 2019-03-26 | 2019-07-19 | 长江大学 | A kind of phi-OTDR distributed optical fiber vibration signal antinoise method and system based on modified wavelet packet |
| CN110260897A (en) * | 2019-06-18 | 2019-09-20 | 华中科技大学 | A kind of Brillouin optical time domain analysis instrument denoising method dictionary-based learning and system |
| CN110415198A (en) * | 2019-07-16 | 2019-11-05 | 南京信息工程大学 | A kind of Method of Medical Image Fusion based on laplacian pyramid Yu parameter adaptive Pulse Coupled Neural Network |
| CN112615626A (en) * | 2020-12-16 | 2021-04-06 | 中油奥博(成都)科技有限公司 | DAS mass data frequency wave number domain data compression storage method |
| CN114387748A (en) * | 2022-01-29 | 2022-04-22 | 湖北闪复管道科技有限公司 | Anti-interference perimeter intrusion alarm system based on positioning type optical fiber multi-dimensional protection |
| CN114857504A (en) * | 2022-06-06 | 2022-08-05 | 华南理工大学 | Pipeline safety monitoring method based on distributed optical fiber sensor and deep learning |
| CN116385642A (en) * | 2023-03-31 | 2023-07-04 | 浙江大学 | Three-dimensional scalar information compression reconstruction method based on spherical Shearlet |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102663679A (en) * | 2012-03-02 | 2012-09-12 | 西北工业大学 | Image denoising method based on Shearlet contraction and improved TV model |
| CN102890820A (en) * | 2012-09-18 | 2013-01-23 | 西安电子科技大学 | Image denoising method based on shearlet transformation and Wiener filtering |
| CN104615877A (en) * | 2015-01-28 | 2015-05-13 | 辽宁工程技术大学 | Method for conducting signal denoising based on wavelet packet |
| CN106097272A (en) * | 2016-06-13 | 2016-11-09 | 河北工程大学 | Image processing method based on interpolation shearing wave and device |
| CN106355564A (en) * | 2016-09-12 | 2017-01-25 | 天津大学 | Speckle noise filtering method for optical coherence sectional image |
-
2017
- 2017-09-19 CN CN201710852133.5A patent/CN107659314B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102663679A (en) * | 2012-03-02 | 2012-09-12 | 西北工业大学 | Image denoising method based on Shearlet contraction and improved TV model |
| CN102890820A (en) * | 2012-09-18 | 2013-01-23 | 西安电子科技大学 | Image denoising method based on shearlet transformation and Wiener filtering |
| CN104615877A (en) * | 2015-01-28 | 2015-05-13 | 辽宁工程技术大学 | Method for conducting signal denoising based on wavelet packet |
| CN106097272A (en) * | 2016-06-13 | 2016-11-09 | 河北工程大学 | Image processing method based on interpolation shearing wave and device |
| CN106355564A (en) * | 2016-09-12 | 2017-01-25 | 天津大学 | Speckle noise filtering method for optical coherence sectional image |
Non-Patent Citations (2)
| Title |
|---|
| 刘帅奇: "基于稀疏表示的Shearlet域SAR图像去噪", 《电子与信息学报》 * |
| 吴一全等: "基于Shearlet域各向异性扩散和稀疏表示的图像去噪", 《应用科学学报》 * |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108898765A (en) * | 2018-05-11 | 2018-11-27 | 国网湖北省电力有限公司检修公司 | A kind of overhead transmission line external force damage alarm method based on analysis of vibration signal |
| CN109951223A (en) * | 2019-03-15 | 2019-06-28 | 山东大学 | A kind of method that random single pulse sampling restores signal and frequency in phase sensitive OTDR sensing |
| CN109951223B (en) * | 2019-03-15 | 2021-09-21 | 山东大学 | Method for recovering signal and frequency in phase-sensitive OTDR (optical time Domain reflectometer) sensing by random single-pulse sampling |
| CN110031081A (en) * | 2019-03-26 | 2019-07-19 | 长江大学 | A kind of phi-OTDR distributed optical fiber vibration signal antinoise method and system based on modified wavelet packet |
| CN110260897A (en) * | 2019-06-18 | 2019-09-20 | 华中科技大学 | A kind of Brillouin optical time domain analysis instrument denoising method dictionary-based learning and system |
| CN110415198A (en) * | 2019-07-16 | 2019-11-05 | 南京信息工程大学 | A kind of Method of Medical Image Fusion based on laplacian pyramid Yu parameter adaptive Pulse Coupled Neural Network |
| CN112615626A (en) * | 2020-12-16 | 2021-04-06 | 中油奥博(成都)科技有限公司 | DAS mass data frequency wave number domain data compression storage method |
| CN112615626B (en) * | 2020-12-16 | 2022-07-01 | 中油奥博(成都)科技有限公司 | DAS mass data frequency wave number domain data compression storage method |
| CN114387748A (en) * | 2022-01-29 | 2022-04-22 | 湖北闪复管道科技有限公司 | Anti-interference perimeter intrusion alarm system based on positioning type optical fiber multi-dimensional protection |
| CN114857504A (en) * | 2022-06-06 | 2022-08-05 | 华南理工大学 | Pipeline safety monitoring method based on distributed optical fiber sensor and deep learning |
| CN116385642A (en) * | 2023-03-31 | 2023-07-04 | 浙江大学 | Three-dimensional scalar information compression reconstruction method based on spherical Shearlet |
| CN116385642B (en) * | 2023-03-31 | 2023-09-12 | 浙江大学 | Three-dimensional scalar information compression reconstruction method based on spherical Shearlet |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107659314B (en) | 2021-02-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107659314A (en) | The rarefaction expression of distributing optical fiber sensing space-time two-dimension signal and compression method | |
| Liu et al. | Vehicle detection and classification using distributed fiber optic acoustic sensing | |
| CN102879822B (en) | A kind of seismic multi-attribute fusion method based on contourlet transformation | |
| CN101303764B (en) | Method for self-adaption amalgamation of multi-sensor image based on non-lower sampling profile wave | |
| CN102800074B (en) | Synthetic aperture radar (SAR) image change detection difference chart generation method based on contourlet transform | |
| CN105974468B (en) | A kind of method that can be carried out at the same time five dimension Reconstruction of seismic data and noise compacting | |
| CN103424777B (en) | A kind of method that improves seismic imaging resolution ratio | |
| CN110361778A (en) | A kind of Reconstruction of seismic data method based on generation confrontation network | |
| CN103226028B (en) | Method for identifying and detecting disturbance signals of phase-sensitive optical time domain reflectometer | |
| CN106842321A (en) | Reconstruction of seismic data method and apparatus | |
| CN106707334A (en) | Method for improving seismic data resolution | |
| CN108983287A (en) | A kind of anti-alias Reconstruction of seismic data method of warp wavelet based on convex set projection algorithm | |
| CN104216010A (en) | Method for increasing quality of seismic data by using harmonic waves of controllable seismic focus | |
| CN104570116A (en) | Geological marker bed-based time difference analyzing and correcting method | |
| CN107179550A (en) | A kind of seismic signal zero phase deconvolution method of data-driven | |
| van den Ende et al. | Deep deconvolution for traffic analysis with distributed acoustic sensing data | |
| CN110208856A (en) | A kind of desert Complex Noise drawing method based on manifold subregion 2D-VMD | |
| CN104730576A (en) | Curvelet transform-based denoising method of seismic signals | |
| CN102338886B (en) | Polarization filtering method for effectively attenuating surface waves in three-component seismic record | |
| You et al. | Multiscale ambient noise tomography of short-period Rayleigh waves across northern Taiwan | |
| CN104635264A (en) | Processing method and apparatus of pre-stack seismic data | |
| CN103645504A (en) | Weak earthquake signal processing method based on generalized instantaneous phase and P norm negative norm | |
| CN104392421A (en) | NSCT (nonsubsampled contourlet transform) based catenary image enhancement method | |
| Wu et al. | Multi-scale progressive fusion attention network based on small sample training for DAS noise suppression | |
| CN102298768B (en) | High-resolution image reconstruction method based on sparse samples |
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 |