CN109212608A - Borehole microseismic signal antinoise method based on 3D shearlet transformation - Google Patents
Borehole microseismic signal antinoise method based on 3D shearlet transformation Download PDFInfo
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- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
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Abstract
The present invention discloses the borehole microseismic signal antinoise method based on 3 Dshearlet transformation, comprising the following steps: Step 1: three component datas of borehole microseismic are switched to 3-D data set；Step 2: carrying out shear transformation to the data set to obtain shearing factor；Step 3: carrying out threshold process to the shearing factor obtains coefficient after threshold value: Step 4: obtaining signal after time domain denoising using inverse shear transformation to coefficient after the threshold value；Step 5: first three surface of signal obtains useful signal after exporting the time domain denoising.Present invention utilizes the correlations that coefficient in microseism data and shearing domain is distributed, and handle three component signal using 3Dshearle, and denoise using multi-scale thresholds function, achieve excellent performance in terms of useful signal retains with noise attentuation.
Description
Technical field
The present invention relates to seismic data processing technical fields, and more particularly, the present invention relates to be based on 3D shearlet
The borehole microseismic signal antinoise method of transformation.
Background technique
Micro-seismic monitoring plays an important role in reservoir performance monitor and resource characteristic research.But microseism data are past
Toward the strong pollution by unnecessary noise, this causes the signal-to-noise ratio (SNR) of on-the-spot record lower.Therefore, it is micro- for inhibiting noise
The important step of monitoring quality is improved in seismic processing.
In terms of obtaining microseism data, monitoring is becoming increasingly popular in well.Borehole microseismic data are by three components
(3C) composition: x, y and z-component have very strong correlation between them.However, the frequency of borehole microseismic data is above the ground level
Microseism signal.Therefore for the noise-reduction method of common seismic signal, borehole microseismic denoising is not suitable for it.In recent years, domestic
Some effort have been made for seismic data noise attenuation method in research well in outer scholar.In the prior art, it is thus proposed that
A kind of adaptive signal detection and data Denoising Algorithm based on apex offset parabolic Radon transform.This method utilizes parabola
Approach raising algorithm speed.And its groundwork concentrates on signal detection rather than noise suppressed.There is researcher to develop one
Novel filtering method of the kind based on mathematical morphology.The shortcomings that the method improve the extensions of top cap filter difficulty.But it is only limitted to
It is handled using low-frequency noise.For non-stationary property, propose a kind of based on set empirical mode decomposition (EEMD) and displacement entropy
Non-Stationary random noise suppressing method.There is researcher to pass through the adaptive direction vector median filter that converts based on Radon
Propose a kind of new denoising scheme.It successfully detects and has decayed noise.But both methods is only in input signal
It is effective when noise is relatively high.
Shearlet transformation (ST) provides multiple dimensioned, an expression system for multi-direction and position function composition.Two dimension
The algorithm that shear transformation (2DST) is combined with threshold value has been widely used for image denoising and microseism processing.But it is answered
When for borehole microseismic data, the correlation between 3C data is almost had ignored.
Summary of the invention
The present invention is to solve current technology shortcoming, provides the borehole microseismic based on 3D shearlet transformation
Signal antinoise method more thoroughly removes background noise under the premise of retaining useful signal.
Technical solution provided by the invention are as follows: the borehole microseismic signal antinoise method based on 3D shearlet transformation, packet
Include following steps:
Step 1: three component datas of borehole microseismic are switched to 3-D data set；
Step 2: carrying out shear transformation to the data set to obtain shearing factor；
Step 3: carrying out threshold process to the shearing factor obtains coefficient after threshold value:
Step 4: obtaining signal after time domain denoises using inverse shear transformation to coefficient after the threshold value；
Step 5: first three surface of signal obtains useful signal after exporting the time domain denoising；
Wherein, coefficient S H' after the threshold valueψ(Sj) obtained by following calculating process:
Wherein, S is the data set, SHψ(Sj) it is shearing factor, j is scale, λjFor the threshold value changed with scale j；μ is
First coefficient, and meet 0≤μ≤1.
Preferably, the shear transformation meets in the step 2:
SHψ(Sj)=< S, ψj,l,k>
Wherein, SHψ(Sj) indicate to carry out shearlet transformation, < S, ψ to Sj,l,k> indicate that continuous shearlet transformation will letter
Number S projects to the generating function ψ that scale is j, direction is l and the time is kj,l,kOn.
Preferably, the generating function ψj,l,kMeet:
{ψj,l,k=| det A |j/2ψ(BlAjx-k):j∈Z,l∈Z2,k∈Z2}
Wherein,And l=(l1,l2)∈Z2, BlTo shear matrix, and meetZ and
Z2It is one group of imaginary number and the empty vector of two dimension respectively.
Preferably, the generating function ψj,l,kFrequency domain beFor arbitrary ζ=(ζ1,ζ2,ζ3)∈R3, the frequency
DomainMeet:
Wherein,Compact schemes are Compact schemes areω is frequency, also, is worked asWhen,When | ω | have when≤1
Preferably, in the step 2,
S=X+N
Wherein, X is the useful signal, and N is Complex-valued additive random noise.
Preferably,
SHψ(Sj)=SHψ(Xj)+SHψ(Nj)
Wherein, SHψ(Xj) indicate useful signal shearing factor, SHψ(Nj) indicate noise shearing factor.
Preferably, signal S ' satisfaction after time domain denoises in the step 4:
Preferably, the step 5 chooses first three two-dimensional coefficient matrix of signal S ' after time domain denoises as output.
Preferably, first coefficient meets:
Wherein, α is the second coefficient, and meets 0≤α≤1.
The present invention at least has following the utility model has the advantages that the present invention provides in a kind of well based on 3D shearlet transformation
Microseism signal antinoise method is utilized microseism data and shears the correlation of coefficient distribution in domain, at 3Dshearle
Three component signal is managed, and is denoised using multi-scale thresholds function, is achieved in terms of useful signal retains with noise attentuation excellent
Performance.
Detailed description of the invention
Fig. 1 is that (black is the first scale, and colourless is the second scale, grey for the energy profile of each scale coefficient of the present invention
For third scale).
Fig. 2 be each scale coefficient of the present invention Higher Order Cumulants distribution (black is the first scale, and colourless is the second scale,
Grey is third scale).
Fig. 3 is the purified signal of analog signal of the invention.
Fig. 4 is the signals with noise figure of analog signal of the invention.
Fig. 5 is WT denoising result figure of the invention.
Fig. 6 is 2DST denoising result figure of the invention.
Fig. 7 is 3DST denoising result figure of the invention.
Fig. 8 is three kinds of method single track denoising result figures of the invention.
Fig. 9 is three kinds of method denoising result spectrograms of the invention.
Figure 10 is the real data of actual signal of the invention.
Figure 11 is the WT denoising result of actual signal of the invention.
Figure 12 is the 2DST denoising result of actual signal of the invention.
Figure 13 is the 3DST denoising result of actual signal of the invention.
Specific embodiment
Present invention will be described in further detail below with reference to the accompanying drawings, to enable those skilled in the art referring to specification text
Word can be implemented accordingly.
The present invention can there are many different forms to implement, and should not be construed as limited to embodiment set forth herein, phase
Instead, these embodiments are provided, so that the disclosure will be thorough and complete.In the accompanying drawings, for clarity, structure can be exaggerated
With the size and relative size in region.
The present invention provides a kind of borehole microseismic signal antinoise methods based on 3D shearlet transformation, we apply
Random noise in three-dimensional shear transformation (3DST) removal borehole microseismic data.Using the correlation between three-component, will count
According to 3D matrix is reassembled into, then handled by shear transformation.
The following steps are included:
Step 1: three component datas of borehole microseismic are switched to 3-D data set；
Step 2: carrying out shear transformation to the data set to obtain shearing factor；
Step 3: carrying out threshold process to the shearing factor obtains coefficient after threshold value:
Step 4: obtaining signal after time domain denoises using inverse shear transformation to coefficient after the threshold value；
Step 5: first three surface of signal obtains useful signal after exporting the time domain denoising；
Wherein, coefficient S H' after the threshold valueψ(Sj) obtained by following calculating process:
Wherein, S is the data set, SHψ(Sj) it is shearing factor, j is scale, λjFor the threshold value changed with scale j；μ is
First coefficient, and meet 0≤μ≤1.
In specific example,
Correlation between three-component, it is similar to three frame video datas.Based on this, we are by three components of borehole microseismic
Signal is converted to three-dimensional data (also referred to as 3D data).By x, y and z-component stack composition 3-D data set S.To three dimension
Shear transformation is carried out according to collection S to obtain the shearing factor SH of different scale jψ(Sj)。
S=X+N
Wherein, X is useful signal, and N is Complex-valued additive random noise.
S can be represented as in the domain shearlet:
SHψ(Sj)=SHψ(Xj)+SHψ(Nj)
Wherein, SHψ(Xj) indicate useful signal shearing factor, SHψ(Nj) indicate noise shearing factor.
According to the sparsity that shearlet is converted, SHψ(X) value is concentrated and is greater than SHψ(N).So according in shearlet
Useful signal is different from the amplitude of noise in domain, can separate useful signal and noise.
As dimension d=3, the shearlet transform definition of signal are as follows:
SHψ(Sj)=< S, ψj,l,k>
Wherein, SHψ() represents shearlet transformation, and<>represents inner product operation, and continuous shearlet is converted signal S
Projecting to scale is j, the generating function ψ that direction is l and the time is kj,l,kOn.
Generating function ψj,l,kMeet:
{ψj,l,k=| det A |j/2ψ(BlAjx-k):j∈Z,l∈Z2,k∈Z2}
Wherein, Z and Z2It is one group of imaginary number and the empty vector of two dimension respectively.
In addition,And l=(l1,l2)∈Z2, shear matrix BlAnd it is defined as
Generating function ψj,l,kFrequency domain beMeet:
For arbitrary ζ=(ζ1,ζ2,ζ3)∈R3, wherein ψ1And ψ2Meet:
(1)Compact schemes areAnd work asShi You
(2)Compact schemes areAnd work as | ω | have when≤1ω is frequency.
We handle shearing factor SH using improved wavelet thresholdψ(Sj):
Wherein, SH 'ψ(Sj) coefficient that is that treated, λ is threshold parameter.And
α is the second coefficient, meets 0≤α≤1.
But this threshold function table is not directly applicable shearing factor.Since shearlet is a multi-scale transform,
The distribution of useful signal is also different on various scales.We clearly demonstrate by the energy and higher order cumulants of shearing wave coefficient
This feature.Higher order cumulants are expressed as follows:
Wherein, cnIt is the n-th order accumulation of stochastic variable x, Φ (ω) is the characteristic function of x, and Ψ (ω)=ln Φ (ω) is
The second feature function of x.
Under normal circumstances, the energy of noise is lower than signal energy, and its Higher Order Cumulants goes to zero.As shown in Figs. 1-2,
Distribution of the two indices on each scale is closely similar.In addition, coefficient energy and Higher Order Cumulants with scale increase
And increase.Therefore, we may safely draw the conclusion, and each scale includes that the degree of useful signal is different.On all scales
It the use of identical threshold value is unreasonable.
Based on the above analysis, the invention proposes a new threshold function tables (as coefficient after threshold value), and expression formula is such as
Under:
Wherein, j indicates scale, λjIndicate the threshold value with dimensional variation.For scale dimension applications relevant to useful signal compared with
Small threshold value, for the biggish threshold value of scale similar in noise.
In order to verify the feasibility and applicability of denoising method proposed by the present invention, as shown in Figure 3-4, we illustrate one
A several borehole microseismic events based on Ricker small echo.The expression formula of Ricker small echo is as follows:
X (t)=(1-2 π2ω2t2)exp(-π2ω2t2)
Wherein, ω is frequency, and t is the time, and exp () is exponential function.
Per pass signal has 128 sampled points, basic frequency 200Hz in Fig. 3-4.In order to handle more true generated data,
It is embedded in the white Gaussian noise of the SNR with -5dB.SNR is defined as:
Wherein, X (t, d) is purified signal,It is denoising result, t is the time, and d is number, and M and N are letter respectively
Number length and width.
Fig. 5-7 is that WT (wavelet transformation), 2DST (two dimension shearing transformation) and 3DST (three-dimensional shear transformation) algorithm are applied to
The denoising result of noisy record.In order to show the advantage of 3DST, we compare the superior effect of region A and region B.From WT's
As a result in, it may be seen that useful signal substantially restores, but still there is too many noise in region A.Fig. 6 is shown
2DST's as a result, this method inhibits the ability of noise to be better than WT in region A, and useful signal decaying is very serious, especially exists
In the B of region.Back wave is almost filtered out simultaneously by a small margin.In contrast, 3DST can restore small distortion under very noisy
Small echo.The removal of background noise is more thorough, and the recovery effects of event are obvious.
In order to preferably observe denoising result, we illustrate the denoising waveforms of three kinds of method one-channel records in fig. 8.It is logical
It crosses and compares, it may be seen that still having some residual noises after WT and 2DST.In contrast, the method in the present invention
It can inhibit noise, and can preferably retain the amplitude of microseism signal.Fig. 9 shows the frequency spectrum comparison result of three kinds of methods.
For frequency spectrum, the result of 3DST algorithm is in high and low frequency region closer to purified signal.
SNR and mean square error (MSE) are to measure two important indicators of filter result performance.Higher SNR and lesser
MSE means better result.As shown in table 1, three kinds of methods are compared using two indices, we are it can be concluded that knot
By 3DST performance in three kinds of methods is best.
The SNR and MSE of 1 three kinds of methods of table
In order to further test it is proposed that method real data processing in validity, we are applied to
In the 15 borehole microseismics record that some region of China is collected.As shown in Figure 10, the presence of random noise seriously obscures
Micro-seismic event.Shown in the denoising result of WT, 2DST and 3DST such as Figure 11, Figure 12 and Figure 13.From the result of WT, Wo Menke
To see that it still has many noises, or even produce some pseudo- axis.As for 2DST, it can inhibit most of noise, but
It will lead to the decaying of some small-signals simultaneously.On the contrary, the method in the present invention substantially increases signal-to-noise ratio and has restored event
Continuity.
In the present invention, we have proposed a kind of new algorithms based on 3DST, and apply it to borehole microseismic data
Noise suppressed in.It is utilized microseism data and shears the correlation of coefficient distribution in domain.The experimental results showed that with WT and
2DST denoising method is compared, which can obtain better performance in terms of useful signal retains with noise attentuation.
Although the embodiments of the present invention have been disclosed as above, but its is not only in the description and the implementation listed
With it can be fully applied to various fields suitable for the present invention, for those skilled in the art, can be easily
Realize other modification, therefore without departing from the general concept defined in the claims and the equivalent scope, the present invention is simultaneously unlimited
In specific details and legend shown and described herein.
Claims (9)
1. the borehole microseismic signal antinoise method based on 3D shearlet transformation, which comprises the following steps:
Step 1: three component datas of borehole microseismic are switched to 3-D data set；
Step 2: carrying out shear transformation to the data set to obtain shearing factor；
Step 3: carrying out threshold process to the shearing factor obtains coefficient after threshold value:
Step 4: obtaining signal after time domain denoises using inverse shear transformation to coefficient after the threshold value；
Step 5: first three surface of signal obtains useful signal after exporting the time domain denoising；
Wherein, coefficient S H' after the threshold valueψ(Sj) obtained by following calculating process:
Wherein, S is the data set, SHψ(Sj) it is shearing factor, j is scale, λjFor the threshold value changed with scale j；μ is first
Coefficient, and meet 0≤μ≤1.
2. the borehole microseismic signal antinoise method of 3D shearlet transformation as described in claim 1, which is characterized in that
Shear transformation described in the step 2 meets:
SHψ(Sj)=< S, ψj,l,k>
Wherein, SHψ(Sj) indicate to carry out shearlet transformation, < S, ψ to Sj,l,k> indicate that signal S is thrown in continuous shearlet transformation
Shadow is to the generating function ψ that scale is j, direction is l and the time is kj,l,kOn.
3. the borehole microseismic signal antinoise method as claimed in claim 2 based on 3D shearlet transformation, feature exist
In the generating function ψj,l,kMeet:
{ψj,l,k=| det A |j/2ψ(BlAjx-k):j∈Z,l∈Z2,k∈Z2}
Wherein,And l=(l1,l2)∈Z2, BlTo shear matrix, and meetZ and Z2Respectively
It is one group of imaginary number and the empty vector of two dimension.
4. the borehole microseismic signal antinoise method as claimed in claim 3 based on 3D shearlet transformation, feature exist
In the generating function ψj,l,kFrequency domain beFor arbitrary ζ=(ζ1,ζ2,ζ3)∈R3, the frequency domainMeet:
Wherein,Compact schemes areCompact schemes areω is frequency, also, is worked asWhen,When | ω | have when≤1
5. the borehole microseismic signal antinoise method as described in claim 1 based on 3D shearlet transformation, feature exist
In, in the step 2,
S=X+N
Wherein, X is the useful signal, and N is Complex-valued additive random noise.
6. the borehole microseismic signal antinoise method as claimed in claim 5 based on 3D shearlet transformation, feature exist
In,
SHψ(Sj)=SHψ(Xj)+SHψ(Nj)
Wherein, SHψ(Xj) indicate useful signal shearing factor, SHψ(Nj) indicate noise shearing factor.
7. the borehole microseismic signal antinoise method as described in claim 1 based on 3D shearlet transformation, feature exist
In signal S ' satisfaction after time domain denoising in the step 4:
8. the borehole microseismic signal antinoise method as claimed in claim 5 based on 3D shearlet transformation, feature exist
In,
The step 5 chooses first three two-dimensional coefficient matrix of signal S ' after time domain denoises as output.
9. the borehole microseismic signal antinoise method as claimed in claim 5 based on 3D shearlet transformation, feature exist
In the first coefficient μ meets:
Wherein, α is the second coefficient, and meets 0≤α≤1.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109917453A (en) * | 2019-01-25 | 2019-06-21 | 吉林大学 | Multiple dimensioned primary wave separation method based on Shearlet transformation |
| CN110646851A (en) * | 2019-10-17 | 2020-01-03 | 东北大学 | Adaptive threshold seismic random noise suppression method based on Shearlet transformation |
| CN112363218A (en) * | 2020-12-07 | 2021-02-12 | 鞍钢集团矿业有限公司 | 3D-Shearlet transformation and scale self-adaption based seismic random noise suppression method |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103399348A (en) * | 2013-08-15 | 2013-11-20 | 电子科技大学 | Denoising method for seismic signal based on Shearlet transform |
| CN106597541A (en) * | 2017-02-22 | 2017-04-26 | 中国石油大学(华东) | Shearlet transformation-based seismic data reconstruction method |
| CN107845079A (en) * | 2017-11-15 | 2018-03-27 | 浙江工业大学之江学院 | 3D shearlet medicine CT video denoising methods based on compact schemes |
| CN107991707A (en) * | 2017-12-05 | 2018-05-04 | 吉林大学 | A kind of borehole microseismic first break picking method based on kurtosis characteristic in shear let domains |
| WO2018093747A1 (en) * | 2016-11-17 | 2018-05-24 | Saudi Arabian Oil Company | Use of wavelet cross-correlation for virtual source denoising |
-
2018
- 2018-06-11 CN CN201810591853.5A patent/CN109212608B/en not_active Expired - Fee Related
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103399348A (en) * | 2013-08-15 | 2013-11-20 | 电子科技大学 | Denoising method for seismic signal based on Shearlet transform |
| WO2018093747A1 (en) * | 2016-11-17 | 2018-05-24 | Saudi Arabian Oil Company | Use of wavelet cross-correlation for virtual source denoising |
| CN106597541A (en) * | 2017-02-22 | 2017-04-26 | 中国石油大学(华东) | Shearlet transformation-based seismic data reconstruction method |
| CN107845079A (en) * | 2017-11-15 | 2018-03-27 | 浙江工业大学之江学院 | 3D shearlet medicine CT video denoising methods based on compact schemes |
| CN107991707A (en) * | 2017-12-05 | 2018-05-04 | 吉林大学 | A kind of borehole microseismic first break picking method based on kurtosis characteristic in shear let domains |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109917453A (en) * | 2019-01-25 | 2019-06-21 | 吉林大学 | Multiple dimensioned primary wave separation method based on Shearlet transformation |
| CN110646851A (en) * | 2019-10-17 | 2020-01-03 | 东北大学 | Adaptive threshold seismic random noise suppression method based on Shearlet transformation |
| CN110646851B (en) * | 2019-10-17 | 2021-01-22 | 东北大学 | Adaptive threshold seismic random noise suppression method based on Shearlet transformation |
| CN112363218A (en) * | 2020-12-07 | 2021-02-12 | 鞍钢集团矿业有限公司 | 3D-Shearlet transformation and scale self-adaption based seismic random noise suppression method |
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