CN105629306B - A kind of signal to noise ratio method for establishing model - Google Patents

A kind of signal to noise ratio method for establishing model Download PDF

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CN105629306B
CN105629306B CN201410583378.9A CN201410583378A CN105629306B CN 105629306 B CN105629306 B CN 105629306B CN 201410583378 A CN201410583378 A CN 201410583378A CN 105629306 B CN105629306 B CN 105629306B
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noise ratio
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刘志成
许璐
谢金娥
贾春梅
宋林
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China Petroleum and Chemical Corp
Sinopec Geophysical Research Institute
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Sinopec Geophysical Research Institute
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Abstract

The invention provides a kind of signal to noise ratio method for establishing model, belongs to the digital processing fields such as seismic prospecting data processing.This method includes：Step 1：Input noisy common midpoint gather x (t)；Step 2：Hilbert transform is carried out to common midpoint gather x (t), common midpoint gather x (t) instantaneous envelope trace gather a (t) and cosine phase function trace gather cos θ (t) is obtained, conventional dynamic school is carried out to cosine phase function trace gather cos θ (t) and is superimposed to obtain the stack power trace gather S of each CMP trace gathern(t)；Step 3：Using the fit correlation expression formula between stack power value S and signal to noise ratio snr, by stack power trace gather Sn(t) the signal to noise ratio model trace gather SNR (t) of the common midpoint gather is converted to；Step 4：All CMP trace gathers are carried out with step 1 to the processing of step 3, finally gives signal to noise ratio model section；Step 5：Weak signal Dynamic Extraction is carried out according to the SNR value each put in the signal to noise ratio model section, the basically identical purpose of overall data time-space domain signal to noise ratio is finally reached, so as to optimize migration imaging result.

Description

A kind of signal to noise ratio method for establishing model
Technical field
The invention belongs to the digital processing fields such as seismic prospecting data processing, and in particular to a kind of signal to noise ratio model is built Cube method, for Low SNR signal processing.
Background technology
There are earth's surface topography and geomorphology, near-surface rock character condition and complicated subsurface geology in southern china and western exploration acreage Construction simultaneously and is deposited, topographical elevation difference change acutely, low velocity layer thickness change greatly, high rock stratum crop out suddenly, subinverse cover and push away Cover construction it is more, these complicated seismic geological coditions propose bigger requirement to existing earthquake-capturing and treatment technology.Such as This complicated geological condition, the seismic signal signal to noise ratio collected is very low, and conventional denoising method and technology are not fully fitted With having significant limitation and inadaptability.
In the processing of complicated mountain front data, the unbalanced situation of time-space domain signal to noise ratio, such as sandstone area often occurs Signal to noise ratio is high, and Limestone pavement signal to noise ratio is low.This time-space domain signal to noise ratio it is inconsistent, it is most likely that be the imaging of complicated mountain front data One of the main reason for ineffective.
The content of the invention
It is an object of the invention to solve problem present in above-mentioned prior art, there is provided a kind of signal to noise ratio model foundation side Method, fitting is superimposed using the cosine phase of signal, forms signal to noise ratio model section, relied on the model to carry out weak signal dynamic and carry Take, finally make overall data time-space domain signal to noise ratio basically identical, so as to optimize migration imaging result.
The present invention is achieved by the following technical solutions：
A kind of signal to noise ratio method for establishing model, methods described include：
Step 1：Input noisy common midpoint gather x (t)；
Step 2：Hilbert transform is carried out to common midpoint gather x (t), obtains the instantaneous of common midpoint gather x (t) Envelope trace gather a (t) and cosine phase function trace gather cos θ (t), conventional dynamic school is carried out to cosine phase function trace gather cos θ (t) and is folded Add the stack power trace gather S for obtaining each CMP trace gathern(t)；
Step 3：Using the fit correlation expression formula between stack power value S and signal to noise ratio snr, by stack power trace gather Sn(t) the signal to noise ratio model trace gather SNR (t) of the common midpoint gather is converted to；
Step 4：All CMP trace gathers are carried out with step 1 to the processing of step 3, finally gives signal to noise ratio model section；
Step 5：Weak signal Dynamic Extraction is carried out according to the SNR value each put in the signal to noise ratio model section, is finally reached The purpose basically identical to overall data time-space domain signal to noise ratio, so as to optimize migration imaging result.
Fit correlation expression formula between the stack power value S and signal to noise ratio snr is as follows：
Wherein, a, b are general expression coefficient.
The step 2 is realized using following formula：
Hilbert transform expression formula is：
Instantaneous envelope expression formula is：
Instantaneous phase expression formula is：
Cosine phase function is：
Compared with prior art, the beneficial effects of the invention are as follows：Theoretical model and actual seismic data experiments result show： Rely on signal to noise ratio model to carry out weak signal Dynamic Extraction, finally make overall data time-space domain signal to noise ratio basically identical, so as to optimize Migration imaging result.
Brief description of the drawings
Fig. 1 zone CMP stack sections in front of the mountains
Two dimensional models of the Fig. 2 containing 45 ° of tomographies
The CMP trace gathers of Fig. 3 two dimensional models
Fig. 4 a) two dimensional model horizontal superposition
Fig. 4 b) two dimensional model PoSTM
Fig. 5 a) on disk SNR be 10, lower wall SNR be 5 when CMP stack section and PoSTM sections horizontal stacking chart (SR =2)
Fig. 5 b) on disk SNR be 10, lower wall SNR be 5 when CMP stack section and PoSTM sections PoSTM (SR=2)
Fig. 6 a) disk SNR is 10 on PoSTM (SR=5), PoSTM sections when lower wall SNR is 2,
Fig. 6 b) disk SNR is 10 on PoSTM (SR=10), PoSTM sections when lower wall SNR is 1
Fig. 7 a) disk SNR is 20 on PoSTM (SR=10), PoSTM sections when lower wall SNR is 2,
Fig. 7 b) disk SNR is 10 on PoSTM (SR=10), PoSTM sections when lower wall SNR is 1
Fig. 8 a) stack power value S and the actual calculated value graph of relation of signal to noise ratio (SNR)
Fig. 8 b) theoretical energy superposition match value and actual calculated value graph of relation
Fig. 9 weak signal Dynamic Extraction flow charts
Figure 10 a) ten layer models in model Analysis signal-to-noise ratio (SNR)
Figure 10 b) in model Analysis signal-to-noise ratio (SNR) plus model of making an uproar
Figure 10 c) Analysis signal-to-noise ratio (SNR) figure in model Analysis signal-to-noise ratio (SNR)
Figure 11 a) trace gather adds being superimposed after making an uproar with the prestack in signal to noise ratio illustraton of model
Figure 11 b) trace gather be superimposed with the cosine phase in signal to noise ratio illustraton of model fitting signal to noise ratio model
Figure 12 a) sandstone area signal to noise ratio model
Figure 12 b) Limestone pavement signal to noise ratio model
Figure 13 a) mountain front area based on signal to noise ratio model weak signal extract before section
Figure 13 b) mountain front area based on signal to noise ratio model weak signal extract after section
Figure 14 a) before processing mountain front area real data signal to noise ratio model
Figure 14 b) processing after mountain front area real data signal to noise ratio model
Figure 15 a) loess tableland area based on signal to noise ratio model weak signal extract before section
Figure 15 b) loess tableland area based on signal to noise ratio model weak signal extract after section
Figure 16 a) loess tableland area based on signal to noise ratio model weak signal extract before signal to noise ratio model section
Figure 16 b) loess tableland area based on signal to noise ratio model weak signal extract after signal to noise ratio model section
The step block diagram of Figure 17 the inventive method.
Embodiment
The present invention is described in further detail below in conjunction with the accompanying drawings：
The invention provides a kind of signal to noise ratio method for establishing model, and signal to noise ratio (SNR) model is relied on, weak signal is extracted Dynamics is changed to variable EP (x, y, t) by constant EP, to reach the basically identical purpose of overall data time-space domain signal to noise ratio, and incite somebody to action this Method is applied in sinopec complexity mountain front data.
In the processing of complicated mountain front data, the unbalanced situation of time-space domain signal to noise ratio, such as sandstone area often occurs Signal to noise ratio is high, and Limestone pavement signal to noise ratio is low.This phenomenon may influence migration imaging result, in order to verify the phenomenon, do following Validation test.
As shown in figure 1, sandstone area signal to noise ratio is higher, Limestone pavement signal to noise ratio is then than relatively low, in their adjacent regions, often Preferable migration imaging effect can be cannot get because signal to noise ratio drop is larger.
In order to prove this idea, this project has done theoretical model experiment：
As shown in Figure 2 be the two dimensional model containing 45 ° of tomographies, and Fig. 3 is one of CMP trace gathers, it is overlapped with Poststack, which adds, makes an uproar, and it is 10 to make its overall SNR, CMP stack section such as Fig. 4 a) shown in, it can be seen that there is two-layer structure, there is bar centre Tomography, there is stronger diffracted wave at breakpoint；Fig. 4 b) it is PoSTM (time migration after stack) section, diffracted wave convergence is preferable, section Clearly.
Two breakpoints are drawn a straight line, are divided into tomography upper lower burrs both sides, such as Fig. 5 a) it is that upper disk SNR is 10, lower wall CMP stack section when SNR is 5, now SR=2, wherein SR are that the drop size of adjacent area signal to noise ratio is expressed as signal to noise ratio Rate, dimension are multiples,Wherein HS --- the signal to noise ratio in high s/n ratio region, LS --- the noise in low signal-to-noise ratio region Than SR >=1；Fig. 5 b) it is PoSTM sections.It is visible in figure, it is smaller to the influential effect of migration imaging as SR=2.
Continue reduce lower wall signal to noise ratio, make SNR be 2, now SR be 5, it can be seen that Fig. 6 a) shown in PoSTM sections In upper disk breakpoint have slight phenomenon of making an arc.Lower wall signal to noise ratio is further reduced, it is 1 to make SNR, and now SR is 10, Fig. 6 b) institute Upper disk breakpoint in the PoSTM sections shown phenomenon of making an arc is even more serious, and section is smudgy.
Fig. 7 a) and Fig. 7 b) be SR all be 10 test comparison, it can be seen that Fig. 7 a) and Fig. 7 b) all generate it is very serious Make an arc.Wherein, Fig. 7 a) show, if upper lower burrs synchronously improve signal to noise ratio, without considering if reducing S/N rate SR, then Migration result can only improve the background of imaging section, can not solve decision point and make an arc phenomenon.Therefore, signal to noise ratio is unbalance to inclined It is very big to move Imaging.
In summary, following theoretical model conclusion (of pressure testing) can be obtained：
1st, as S/N rate SR >=10 (critical point), migration imaging will produce the unbalance effect of signal to noise ratio.
2nd, the unbalance effect of signal to noise ratio：
High s/n ratio region releases energy to periphery low signal-to-noise ratio region, its own migration imaging effect is deteriorated and (degenerates Say)；
Low signal-to-noise ratio region absorbs energy from periphery high s/n ratio region, its own migration imaging effect is improved and (improves Say).
3rd, the overall signal to noise ratio for improving data, can not solve the problems, such as migration imaging very well；Integrally improving signal to noise ratio Meanwhile S/N rate is reduced, it can just be more beneficial for migration imaging.
The present invention proposes a kind of signal to noise ratio method for establishing model based on cosine phase superposition fitting process.
(1) classical Hilbert transform
Hilbert transform (HT) is the important tool in signal analysis.It is assumed that a continuous time signal is x (t), Its Hilbert transform is h (t), then Hilbert transform expression formula is：
Instantaneous envelope expression formula is：
Instantaneous phase expression formula is：
Cosine phase function is：
Therefore：X (t)=cos θ (t) a (t) (5)
It can be seen that x (t) can be analyzed to cosine phase function cos θ (t) and instantaneous envelope a (t).
(2) technical thought and technology are realized
The CMP trace gather data containing different frequency wavelet and different signal to noise ratio are initially set up, obtain stack power value S and letter Make an uproar than the fit correlation expression formula between (SNR).Then Hilbert transform is carried out to the actual CMP seismic channel sets of input, obtained To the instantaneous envelope trace gather and cosine phase function trace gather in seismic signal road, cosine phase function trace gather is done into conventional dynamic school and folded Add, obtain the stack power trace gather of the CMP trace gathers, then by the conversion relational expression between stack power value and signal to noise ratio, obtain To the signal to noise ratio model trace gather of each CMP trace gather, identical computing is done to all CMP trace gathers, finally gives signal to noise ratio model Section, and the Dynamic Extraction for carrying out weak signal according to this eliminates the unbalance effect of signal to noise ratio.
As shown in figure 17, specific implementation step is：
Step 1：There is following transformational relation expression formula between stack power value S and signal to noise ratio (SNR), such as formula (6) institute Show：
Wherein, Sn(t) it is stack power value, curve such as Fig. 8 a) shown in, SNR (t) is snr value, and a, b are General Expression Formula coefficient.Different signal to noise ratio CMP trace gathers are built by different frequency wavelet, theoretical energy superposition value and actual calculated value is calculated Relation curve such as Fig. 8 b) shown in, numerical value test shows to work as a=2.2, during b=0.01, error of fitting 0.02, shows to be superimposed Transformed representation (6) between energy value S and signal to noise ratio (SNR) has generality in the range of allowable error；
Step 2：Noisy common midpoint gather x (t) is inputted, the signal road is common seismic signal road；
Step 3：Hilbert transform (formula 1) is carried out to x (t), obtains x (t) instantaneous envelope road a (t) (formula 2) With cosine phase trace gather cos θ (t) (formula 4), conventional dynamic school is carried out to cosine phase function trace gather and is superimposed to obtain each CMP The stack power trace gather S of trace gathern(t)；
Step 4：Using the conversion relational expression obtained in step 1, by stack power trace gather Sn(t) it is changed into the CMP SNR (t) trace gathers of trace gather, shown in transformation relation formula such as formula (7)
Step 5：All CMP trace gathers are carried out with step 1 to four processing, finally gives signal to noise ratio model section；
Step 6：Weak signal Dynamic Extraction is carried out according to SNR (t) model section, is finally reached overall data time-space domain letter Make an uproar than basically identical purpose, so as to optimize migration imaging result.
Fig. 9 gives the result signal for realizing steps flow chart and key step.
By theoretical model and actual seismic data experiments, this method carries that yupin effect is obvious, has stronger specific aim.
Figure 10 a) shown in ten layer models, signal to noise ratio successively from 1.0 to 0.1, adds the trace gather after making an uproar as schemed from top to bottom Shown in 10b), selection identification limit SNR=0.2, be signal more than 0.2, be noise less than 0.2, such as Figure 10 c) it is shown, it is perpendicular The right of line is signal, and the left side is noise, and identification limit SNR is 0.2.
Figure 11 a) and Figure 11 b) it is after analyzing signal to noise ratio, start signal to noise ratio modeling.Figure 11 a) it is that a prestack adds Stacked section after making an uproar, Figure 11 b) it is that tensionless winkler foundation moves the signal to noise ratio model that school superposition fitting process is formed, from the figure, it can be seen that disconnected Signal to noise ratio at point is most strong.Figure 12 a) and Figure 12 b) it is that sandstone area and Limestone pavement cosine phase are superimposed the letter that fitting process is formed Make an uproar and contrasted than model, it can be seen that for sandstone area signal to noise ratio apparently higher than Limestone pavement, this is also identical in actual geological condition, is said Bright this method is true and reliable.
The inventive method has been successfully applied in complicated mountain front and loess tableland low SNR data processing.
Figure 13 a) and Figure 13 b) be that mountain front area is based on section before and after the extraction of signal to noise ratio model weak signal, Figure 13 a) it is place Stacked section before reason, 13b) be (SNR=3) after processing stacked section, the section signal to noise ratio after processing significantly improves, especially The signal to noise ratio of Limestone pavement has more obvious raising, and the entire profile wave field enriches, very naturally.Such as Figure 14 a) and Figure 14 b) shown in, Wherein 14a) be before processing signal to noise ratio model, it is evident that sandstone area signal to noise ratio is higher, and Limestone pavement signal to noise ratio is relatively low, 14b) be place Signal to noise ratio model after reason, it can be seen that whole signal to noise ratio section is more balanced, and signal to noise ratio is basically identical.That is, this item Weak signal extractive technique based on wavelet polarity reconstruct used by mesh, employing the dynamic of the weak signal based on signal to noise ratio model After extraction, the signal to noise ratio of section is basically identical, and this is highly profitable to migration imaging.
Figure 15 a) and Figure 15 b) be weak signal extraction anterior-posterior horizontal superposition comparison diagram, 15a) be weak signal extraction before superposition Section, 15b) be weak signal extraction after stacked section, it is seen that the overall stacked section signal to noise ratio after extraction is more balanced, reaches Signal to noise ratio basically identical effect.Figure 16 a) and Figure 16 b) be stacked section signal to noise ratio model, it can be seen that weak signal Signal to noise ratio is integrally improved after extraction, and basically identical, is laid a solid foundation for later stage migration imaging.
Invention describes a kind of signal to noise ratio method for establishing model based on cosine phase superposition fitting process.Initially set up and contain There are different frequency wavelet and the CMP trace gather data of different signal to noise ratio, obtain the plan between stack power value S and signal to noise ratio (SNR) Close relational expression.Then Hilbert transform is carried out to the actual CMP seismic channel sets of input, obtains the instantaneous of seismic signal road Envelope trace gather and cosine phase function trace gather, cosine phase function trace gather is done into conventional dynamic school superposition, obtains the folded of the CMP trace gathers Add energy trace gather, then by the conversion relational expression between stack power value and signal to noise ratio, obtain the noise of each CMP trace gather Than model trace gather, similar process is done to all CMP trace gathers, finally gives signal to noise ratio model section, and carry out weak signal according to this Dynamic Extraction eliminates the unbalance effect of signal to noise ratio.Finally make overall data time-space domain signal to noise ratio basically identical, so as to optimize partially Move imaging results.
Above-mentioned technical proposal is one embodiment of the present invention, for those skilled in the art, at this On the basis of disclosure of the invention application process and principle, it is easy to make various types of improvement or deformation, be not limited solely to this Invent the method described by above-mentioned embodiment, therefore previously described mode is simply preferable, and and without limitation The meaning of property.

Claims (2)

  1. A kind of 1. signal to noise ratio method for establishing model, it is characterised in that：Methods described includes：
    Step 1：Input noisy common midpoint gather x (t)；
    Step 2：Hilbert transform is carried out to common midpoint gather x (t), obtains common midpoint gather x (t) instantaneous envelope Trace gather a (t) and cosine phase function trace gather cos θ (t), conventional dynamic school is carried out to cosine phase function trace gather cos θ (t) and is superimposed To the stack power trace gather S of each CMP trace gathern(t)；
    Step 3：Using the fit correlation expression formula between stack power value S and signal to noise ratio snr, by stack power trace gather Sn(t) Be converted to the signal to noise ratio model trace gather SNR (t) of common midpoint gather；
    Step 4：All CMP trace gathers are carried out with step 1 to the processing of step 3, finally gives signal to noise ratio model section；
    Step 5：Weak signal Dynamic Extraction is carried out according to the SNR value each put in the signal to noise ratio model section, is finally reached whole The basically identical purpose of body data time-space domain signal to noise ratio, so as to optimize migration imaging result；The stack power value S and signal to noise ratio Fit correlation expression formula between SNR is as follows：
    <mrow> <mi>S</mi> <mi>N</mi> <mi>R</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <msqrt> <mfrac> <mrow> <mi>a</mi> <mo>&amp;lsqb;</mo> <msub> <mi>S</mi> <mi>n</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>-</mo> <mi>b</mi> <mo>&amp;rsqb;</mo> </mrow> <mrow> <mn>1</mn> <mo>-</mo> <msub> <mi>S</mi> <mi>n</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <mi>b</mi> </mrow> </mfrac> </msqrt> </mrow>
    Wherein, a, b are general expression coefficient.
  2. 2. signal to noise ratio method for establishing model according to claim 1, it is characterised in that：The step 2 is using following What formula was realized：
    Hilbert transform expression formula is：
    <mrow> <mi>h</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mn>1</mn> <mi>&amp;pi;</mi> </mfrac> <msubsup> <mo>&amp;Integral;</mo> <mrow> <mo>-</mo> <mi>&amp;infin;</mi> </mrow> <mrow> <mo>+</mo> <mi>&amp;infin;</mi> </mrow> </msubsup> <mfrac> <mrow> <mi>x</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> <mrow> <mi>t</mi> <mo>-</mo> <mi>&amp;tau;</mi> </mrow> </mfrac> <mi>d</mi> <mi>&amp;tau;</mi> </mrow>
    Wherein, t represents original series function x (t) independent variable, and τ represents independent variable t time shift amount,
    Instantaneous envelope expression formula is：
    <mrow> <mi>a</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <msqrt> <mrow> <msup> <mi>x</mi> <mn>2</mn> </msup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <msup> <mi>h</mi> <mn>2</mn> </msup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </msqrt> </mrow>
    Instantaneous phase expression formula is：
    <mrow> <mi>&amp;theta;</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <mi>arccos</mi> <mrow> <mo>(</mo> <mfrac> <mrow> <mi>x</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> <mrow> <mi>a</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>)</mo> </mrow> </mrow>
    Cosine phase function is：
    <mrow> <mi>c</mi> <mi>o</mi> <mi>s</mi> <mi>&amp;theta;</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <mi>x</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> <mrow> <mi>a</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>.</mo> </mrow>
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