CN105530012B - The compressed sensing based sparse one-dimensional well data of wavelet field compresses and reconstructing method - Google Patents
The compressed sensing based sparse one-dimensional well data of wavelet field compresses and reconstructing method Download PDFInfo
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- CN105530012B CN105530012B CN201510796871.3A CN201510796871A CN105530012B CN 105530012 B CN105530012 B CN 105530012B CN 201510796871 A CN201510796871 A CN 201510796871A CN 105530012 B CN105530012 B CN 105530012B
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- 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
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- 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
- H03M7/50—Conversion to or from non-linear codes, e.g. companding
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Abstract
The present invention is a kind of compressed sensing based data compression and reconstruction method, belongs to signal compression process field.This method can with the rate lower than nyquist sampling theorem on specific sparse domain compression sampling, can preferably utilize the compressibility of signal, the redundancy section of as few as possible acquisition signal, compression transmission, Accurate Reconstruction during signal acquisition.Wherein compression sampling is completed in wavelet transformed domain.Data reconstruction therein uses orthogonal matching pursuit algorithm.In the application of oil field actual data transfer, the occupancy of the communication resource is reduced, network transmission delay is alleviated, has saved production run cost.
Description
Technical field
The present invention relates to compressed sensing based one-dimensional well data compression and reconstructing methods, belong to signal processing technology neck
Domain.
Background technique
In practical oil field manufacturing parameter real time monitoring, data sampling rate need to be reduced to save communication flows.Oil well
Load, displacement, temperature, pressure and electrical parameter these information mainly pass through wireless network transmissions.One type is internal special
Wireless network, such as ZigBee-network, this kind of general bandwidth of network is smaller, is transmitted between oil well by multi-hop mode；It is another kind of to be
Third party's Operation Network, such as GPRS or 3G network, this kind of network bandwidth is relatively large, is transmitted by single-hop base station, according to number
According to flow rate calculation expense.Load, displacement and electrical parameter information data amount are larger, and directly there are many drawbacks for transmission.For private network
Network will increase the data load and transmission delay of network, reduce the service quality of network, increases and sends terminal and transmission in network
The power consumption of routing node results even in network paralysis under extreme case.For network of charging, increases data traffic and not only mention
High production run cost, and occupy commercial communication resource.
Summary of the invention
The purpose of the invention is to reduce the sample rate of signal, the consumption of well data transfer resource is reduced, proposes base
In the one-dimensional well data compression of compressed sensing and reconstructing method, this method can be with the rate lower than nyquist sampling theorem
The compression sampling on specific sparse domain can preferably utilize the compressibility of signal, during signal acquisition as far as possible
The redundancy section of few acquisition signal, compression transmission, Accurate Reconstruction.
The purpose of the present invention is what is be achieved through the following technical solutions.
Compressed sensing based one-dimensional well data compression of the invention and reconstructing method, step are as follows:
1) in substrate Ψ ∈ R after being denoised to well data xN×NIt opens into and spatially carries out sparse decomposition: x=Ψ α；
The denoising refers to that Windowed filtering removes noise after data x is carried out Fourier transformation.
The sparse decomposition refers to data x carrying out base decomposition in wavelet transformed domain.
2) compression sampling is carried out to the N-dimensional sparse vector α that step 1) obtains, obtains signal y, y has M sampled point: y=Φ
X=Φ Ψ α；
The compression refers to that choosing M dimension gaussian random matrix is that observing matrix Φ is multiplied with data x, and M is natural number,
Due to M < < N, i.e. realization data compression.
3) the transmission data of well data monitoring system are used for the observation y that step 2) obtains.
4) receiving end receiving step 3) transmission data y, carry out orthogonal matching pursuit (OMP, Orthogonal Matching
Pursuit optimal solution) is reconstructed.
5) defining the initial residual error of OMP algorithm is e0=y, the initial set of matches of definition are combined into, define columns initial value
I=1.
6) column vector of correlation maximum: q=argmax is calculated | ei-1,(φψ)j|, wherein line number j=1,2 ..., d,
Columns i, sparse coefficient show nonzero value in i to record at this time, and size is
7) the basis vector q that step 6) searches out is added to set Ai=Ai-1After ∪ q, to AiIt is orthogonal to carry out Schmidt
Change, and seeks new residual error
8) multiple loop iteration step 7) obtains final matched sparse coefficient α and set until residual error is less than threshold value
A, i.e. (φ ψ) '.
9) using sparse coefficient obtained in step 8), original signal x=Ψ α is restored.
One-dimensional well data compression and reconstruct are completed since then.
Beneficial effect
The present invention reduces data sampling rate by adopting compressed sensing algorithm, using well data in the dilute of wavelet transformed domain
Data dimension needed for thin characteristic reduces reconstruct, realizes high-resolution reconstruction with orthogonal matching pursuit algorithm.Reduce oil well
The complexity and data volume of field data compression, save transfer resource.
Detailed description of the invention
Fig. 1, which is that the present invention is based on the sparse one-dimensional well data compressions of the wavelet field of compressed sensing, realizes frame with reconfiguration system
Figure；
Fig. 2 is that wavelet field carries out sparse decomposition analogous diagram to well data in step 1)；
Residual error and the number of iterations relational graph when Fig. 3 is step 5) to step 8) reconstruction signal；
Fig. 4 is the sparse coefficient reconstructed in step 8)；
Fig. 5 is that the present invention is based on the compressions of the one-dimensional well data of compressed sensing and reconstructing method simulated effect figure.
Specific embodiment
The present invention will be further described with reference to the accompanying drawings and examples.
Embodiment
As shown in Figure 1, handling with the method for the present invention oil well one-dimensional data, by taking load data as an example.
1) as shown in Fig. 2 (a), to load value x in well datapayloadIn substrate Ψ ∈ R after denoisingN×NOpen into space
Upper carry out sparse decomposition: xpayload=Ψ α；
The denoising refers to load value xpayloadWindowed filtering removes noise, choosing after carrying out Fourier transformation
20 rectangular windows are taken, as shown in Fig. 2 (b).
The sparse decomposition refers to the load value after denoising carrying out base decomposition in wavelet transformed domain.It is small to choose " db1 "
Wave, the wavelet decomposition number of plies are 6, are obtained shown in sparse coefficient α such as Fig. 2 (c).
2) compression sampling is carried out to the N-dimensional sparse vector α that step 1) obtains, obtains signal y, y has M sampled point: y=Φ
X=Φ Ψ α；
The compression refers to that choosing M dimension gaussian random matrix is that observing matrix Φ is multiplied with data x, and M is natural number,
Due to M < < N, i.e. realization data compression.Observing matrix chooses gaussian random observing matrix.
3) the transmission data of well data monitoring system are used for the observation y that step 2) obtains.
4) receiving end receiving step 3) transmission data y, carry out orthogonal matching pursuit (OMP, Orthogonal Matching
Pursuit optimal solution) is reconstructed.
5) defining the initial residual error of OMP algorithm is e0=y, the initial set of matches of definition are combined into, define columns initial value
I=1.
6) column vector of correlation maximum: q=argmax is calculated | ei-1,(φψ)j|, wherein line number j=1,2 ..., d,
Columns i, sparse coefficient show nonzero value in i to record at this time, and size is
7) the basis vector q that step 6) searches out is added to set Ai=Ai-1After ∪ q, to AiIt is orthogonal to carry out Schmidt
Change, and seeks new residual errorAs shown in Figure 3.
8) multiple loop iteration step 7) obtains final matched sparse coefficient α and set until residual error is less than threshold value
A, i.e. (φ ψ) '.
9) using sparse coefficient obtained in step 8), as shown in Fig. 4 (b), original signal x=Ψ α is restored, such as Fig. 5 institute
Show.
One-dimensional well data compression and reconstruct are completed since then.
Claims (4)
1. the compressed sensing based sparse one-dimensional well data of wavelet field compresses and reconstructing method, which is characterized in that comprising as follows
Step:
Step 1: denoising is carried out to well data x, then in substrate Ψ ∈ RN×NIt opens into spatially to the oil after denoising
Well data carry out sparse decomposition: x=Ψ α；
The denoising refers to that Windowed filtering removes noise after data x is carried out Fourier transformation；
The sparse decomposition refers to data x carrying out base decomposition in wavelet transformed domain；
Step 2: carrying out compression sampling to the N-dimensional sparse vector α that step 1 obtains, obtain observation y,yThere is M sampled point: y=
Φ x=Φ Ψ α；
The compression refers to that choosing M dimension gaussian random matrix is that observing matrix Φ is multiplied with data x, and M is natural number, due to M
< < N, i.e. realization data compression；
Step 3: the observation y that step 2 is obtained is as the transmission data of well data monitoring system；
Step 4: receiving end receiving step three transmits data, carries out orthogonal matching pursuit and reconstructs optimal solution；
Step 5: the definition initial residual error of orthogonal matching pursuit algorithm is e0=y, the initial set of matches of definition are combined intoDefinition
Columns initial value i=1；
Step 6: the column vector of correlation maximum: q=argmax is calculated | ei-1,(φψ)j|, wherein line number j=1,2 ..., d,
Columns i, sparse coefficient show nonzero value in i to record at this time, and size is
Step 7: the basis vector q that step 6 searches out is added to set Ai=Ai-1After ∪ q, to AiIt is orthogonal to carry out Schmidt
Change, and seeks new residual error
Step 8: multiple loop iteration step 7 obtains final matched sparse coefficient α sum aggregate until residual error is less than threshold value
Conjunction A, i.e. (φ ψ) '；
Step 9: using sparse coefficient obtained in step 8, original signal x=Ψ α is restored；One-dimensional well data is completed since then
Compression and reconstruct.
2. the sparse one-dimensional well data compression of compressed sensing based wavelet field and reconstructing method according to claim 1,
It is characterized in that, Fourier transformation adding window denoising preferred value is 20 rectangular windows in step 1.
3. the sparse one-dimensional well data compression of compressed sensing based wavelet field and reconstructing method according to claim 1,
It is characterized in that, sparse decomposition transform domain preferred value is " db1 " small echo in step 1, and the wavelet decomposition number of plies is 6.
4. the sparse one-dimensional well data compression of compressed sensing based wavelet field and reconstructing method according to claim 1,
It is characterized in that, the preferred observing matrix of Sampling Compression is gaussian random observing matrix in step 2.
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| CN102291152A (en) * | 2011-04-07 | 2011-12-21 | 湖南大学 | Singular value decomposition-based compressed sensing noisy signal reconfiguration system |
| CN104242947A (en) * | 2014-08-25 | 2014-12-24 | 南京邮电大学 | SAMP reconstructing method based on Haar wavelet tree |
| CN105050105A (en) * | 2015-08-21 | 2015-11-11 | 湘潭大学 | High-energy-efficiency low-information-density data collecting method based on compressed sensing |
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| CN102291152A (en) * | 2011-04-07 | 2011-12-21 | 湖南大学 | Singular value decomposition-based compressed sensing noisy signal reconfiguration system |
| CN104242947A (en) * | 2014-08-25 | 2014-12-24 | 南京邮电大学 | SAMP reconstructing method based on Haar wavelet tree |
| CN105050105A (en) * | 2015-08-21 | 2015-11-11 | 湘潭大学 | High-energy-efficiency low-information-density data collecting method based on compressed sensing |
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