CN109917453A - Multiple dimensioned primary wave separation method based on Shearlet transformation - Google Patents
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Abstract
The present invention relates to a kind of multiple dimensioned primary wave separation methods based on Shearlet transformation, comprising: input original seismic data and prediction multiple wave；Construct Shearlet system；Mapping under Shearlet system is carried out to input data；The domain Shearlet multi-resolution decomposition is carried out, multiple dimensioned earthquake record and prediction multiple wave are obtained；Based on Multiscale Matching is carried out, using the true multiple wave information for including in original seismic data as criterion, the correction in amplitude and phase is carried out to prediction multiple wave；The prediction multiple wave Shearlet coefficient after correcting under each scale is chosen as threshold value, a point scale Shearlet soft-threshold is carried out and subtracts, the primary wave under each scale in original seismic data is separated；Multiple dimensioned primary wave separating resulting is merged, final primary wave separating resulting is obtained.The present invention is based on the multiple dimensioned primary wave separation methods of Shearlet transformation, and efficiently separating for primary wave may be implemented.
Description
Technical field
The invention belongs to technical field of geophysical exploration, and in particular to a kind of to carry out to collected seismic exploration data
The method of primary wave separation, in particular to a kind of multiple dimensioned primary wave separation method based on Shearlet transformation.
Background technique
Sufficient oil-gas geology resource is the important leverage of national economic development, in national economy rapid growth but oil gas
Resource today increasingly in short supply, how to carry out effective petroleum resources detection becomes particularly significant.Currently, China is land main old
Oily area underproduces to meet actual demand, and the exploration and exploitation of Marine oil and gas resource are especially urgent.Seismic prospecting is as most
The main means of the common exploitation method for verifying underground oil and gas resource distribution and Marine Geology and petroleum resources investigation.Closely
Nian Lai, the difficulty of gas and oil in sea are also increasing, and the oil-gas exploration of complex area proposes seismic data imaging precision
Higher requirement.
Collected land and marine seismic data is all simultaneously comprising primary reflection and multiple reflections in actual production
Wave, but in seism processing, we are mainly overlapped migration imaging using primary reflection and handle, and thus obtain energy
Enough sectional views for being more truly reflected underground structure, to carry out the delineation in target petroleum resources region.The presence of multiple wave
Meeting will appear illusion, directly cut so that velocity analysis and imaging results in seismic data post-processing become negatively affected
Subtract the accuracy of final geology data interpretation.Therefore, primary wave separation problem is always the research hotspot of seism processing.
Currently, the popular wave separation technique of industry is by propositions such as Berkhout and Verschuur
SRME (surface-related multiple elimination) method.Wherein, multiple wave refers to surface layer related multiple,
Primary wave separating resulting, which refers to, only carries out the back wave and interbed multiple of primary event in underground.SRME method using multiple wave with
Physical relation between primary wave regard the primary wave of initial estimation record as common detector gather, will be comprising primary wave and more
The original seismic data of subwave carries out earth's surface-consistent convolution to the two data and adds up as common-shot-gather, after
And realize traditional surface layer related multiple prediction.Due in actual production without primary wave as prediction process
Initial value, this process need to realize by iterative cycles.Estimate to carry out multiple wave prediction using real-time primary wave, then will
It is subtracted from input data, obtains a more accurate primary wave, the input as next iteration circulation.This data are driven
Dynamic prediction technique has the advantages that significant, it does not need to know subsurface structure situation in advance, all surface phases that may be present
Closing multiple wave can be predicted to, and have important practical significance and economic significance.It is enterprising in the theoretical basis of wave equation
Then prediction multiple wave is compared with original seismic data, then is subtracted by the prediction of row multiple wave, to realize primary wave
Separation.In actual treatment, predicts the multiple wave come and is all recorded with original big gun there are biggish difference on amplitude and phase,
Therefore, multiple wave, in the case where multiple wave is remaining as small as possible that primary reflection signal is as complete as possible is precisely effectively matched
Separate the final effect for directly determining seismic imaging in ground.
In seismic data process, traditional primary wave separation method is based on obvious between multiple wave and primary wave mostly
Physical property difference, such as periodicity or speed difference.Primary wave separation method based on the principle of least square is also to make
With a kind of more conventional method, this method can for amplitude existing for prediction multiple wave and practical multiple wave or phase difference into
The basic matching of row and correction, but Time Displacement Error existing for prediction multiple wave and practical multiple wave can not be directly corrected, and shaking
Also easily cause wave distortion and boundary effect in width matching process.
For the not good enough situation of conventional least square primary wave separation method treatment effect, Herrmann et al. will
Curvelet transformation introduces primary wave separation field, obviously mentions although the effect of actual treatment has than least squares method really
Height, but due to after being converted into the domain Curvelet data matrix it is excessively huge, computational efficiency is very low, time-consuming very long, not
Adapt to the actual demand that industry carries out efficient primary wave separation to mass data.Moreover, although Curvelet transformation can be with
Ground-to-ground shake data carry out rarefaction representation very well, but also have apparent limitation, its rotation operator can not fully digitalization,
This causes it to cannot achieve unified processing in continuous transformation and discrete transform.In order to overcome the defect of Curvelet transformation,
2005, composite wavelet theory and multi-scale geometric analysis were effectively related to one by Demetrio Labate and Guo et al.
It rises, is constructed by the Affine Systems with synthesis expansion of special shape a kind of close to optimal multidimensional function rarefaction representation side
Method --- Shearlet transformation, it has the directionality and better rarefaction representation property more more sensitive than Qu Bo and profile wave.
Multiple dimensioned property is one of characteristic possessed by seismic signal, and the seismic data of different scale carries different geology
Information.The data that seismic signal is divided into different scale are handled again, more efficiently can more accurately analyze inherent geology
Situation, therefore, carrying out multiple dimensioned primary wave separation in conjunction with sparse transformation has great research significance.
Summary of the invention
It is an object of the invention to provide a kind of based on Shearlet transformation for the deficiency in above-mentioned actual production technology
Multiple dimensioned primary wave separation method, it is too low to solve the domain Curvelet primary wave separation method computational efficiency, conventional minimum two
Multiply that primary wave separation method precision is low, the remaining serious problem of multiple wave in separating resulting.
The purpose of the present invention is what is be achieved through the following technical solutions:
A kind of multiple dimensioned primary wave separation method based on Shearlet transformation, comprising the following steps:
A, original seismic data and prediction multiple wave are inputted；
B, according to the actual conditions of input data, Shearlet system is constructed, defines Shearlet are as follows:
In formula, OsFor directional operator, AaFor parabolic linear content operator, TmFor translation operator, Shearlet is converted by letter
Number, which is stretched, translated and rotated, generates basic function, has nonlinearity erron degree of approximation, and be successively to segment in frequency space
's；The parameter that must be provided in building process comprises determining that the scale number n of required Shearlet system, it is necessary to be greater than or wait
In 1；Determine the respective number of row and column；Determine shearing wave series, the array of specially one 1 × n size, wherein each is first
Element all has to be larger than or is equal to 0；Logical value is determined, which dictates that whether shearlet system constructs on GPU；Two-dimensional directional filter
Wave device；And define the one-dimensional orthogonal mirror filter of the Wavelet Component of shearing wave；
C, the mapping under Shearlet system is carried out to original seismic data and prediction multiple wave, data is transformed into
The domain Shearlet obtains Shearlet coefficient；
D, the domain Shearlet multi-resolution decomposition is carried out to original seismic data and prediction multiple wave, it is pre- under acquisition is multiple dimensioned
Survey multiple wave and original seismic data, i.e., by retaining i-th layer of selected layer of Shearlet coefficient, by other layers of Shearlet
Coefficient all zeroizes method, by original seismic data P and prediction multiple wave MpIt is divided into the identical multi-Scale Data of counter structure,
It is denoted as P1,P2,...,PiAnd Mp1,Mp2,...,Mpi, wherein i is the sum of multi-Scale Data and the scale of Shearlet system
Number；
E, Based on Multiscale Matching is carried out to original seismic data and prediction multiple wave, it is true with include in original seismic data
Multiple wave information is criterion, carries out the correction in amplitude and phase to prediction multiple wave, keeps difference between the two as small as possible,
To obtain the prediction multiple wave after the correction under each scale；
F, corresponding to the multiple wavelength-division scale of prediction after multiple dimensioned original seismic data and correction to carry out the domain Shearlet threshold
Value subtracts, i.e., in the domain Shearlet, under each scale, using the Shearlet coefficient of the prediction multiple wave after matching and correlation as
Threshold value takes soft threshold method to the Shearlet coefficient of itself and original earthquake data, and criterion is will to be less than the coefficient guarantor of threshold value
The coefficient for staying, and being greater than threshold value will be subtracted the amount of a threshold size, obtain multiple dimensioned primary wave separating resulting,
SpFor the Shearlet coefficient of original seismic data, and TmIt is the Shearlet of the multiple wave after amplitude matches
The mould of coefficient；
G, the multiple dimensioned domain Shearlet soft-threshold primary wave separating resulting is merged and is reconstructed, obtain final primary wave separation
As a result.
Step A, input data are offshore seismic exploration data or land seismic exploration data.
Compared with prior art, the beneficial effects of the present invention are: the present invention is based on shearlet transformation multiple dimensioned one
Subwave separation method, solves that traditional least squares method primary wave separating resulting precision is low to separate meter with the domain Curvelet primary wave
The problem of calculation takes long time can obtain accurate matched effect while quick calculate.The present invention utilizes seismic signal institute
The multiple dimensioned characteristic having chooses the Shearlet transformation with multidirectional, best sparsity, multiple dimensioned earth's surface
Show original seismic data and prediction multiple wave, and is targetedly done according to signal in the difference of different scale different directions
Best match corrects out, then carries out the multiple dimensioned domain Shearlet soft-threshold and subtracts, and realizes quickly and effectively primary wave separation.
In addition, the present invention has had the program bag under two kinds of environment of Matlab and Linux, Geophysical Data Processing and explanation personnel are
Primary wave separation can be carried out by program bag, there is preferable value for applications.
Detailed description of the invention
The multiple dimensioned primary wave separation method flow chart that Fig. 1 is converted based on Shearlet；
The frequency domain subdivision graph of Fig. 2 Shearlet transformation；
Fig. 3 divides the Shearlet basic function of scale and angle to approach curvilinear singular point；
Work area original seismic data described in Fig. 4 the embodiment of the present application；
Predict multiple wave in work area described in Fig. 5 the embodiment of the present application；
The first scale of the domain Shearlet original seismic data in Fig. 6 a the embodiment of the present application；
The second scale of the domain Shearlet original seismic data in Fig. 6 b the embodiment of the present application；
Third scale original seismic data in the domain Shearlet in Fig. 6 c the embodiment of the present application；
The 4th scale original seismic data of the domain Shearlet in Fig. 6 d the embodiment of the present application；
The first scale prediction of the domain Shearlet multiple wave in Fig. 7 a the embodiment of the present application；
The second scale prediction of the domain Shearlet multiple wave in Fig. 7 b the embodiment of the present application；
Third scale prediction multiple wave in the domain Shearlet in Fig. 7 c the embodiment of the present application；
The 4th scale prediction multiple wave of the domain Shearlet in Fig. 7 d the embodiment of the present application；
Prediction multiple wave in Fig. 8 a the embodiment of the present application after the first scale of the domain Shearlet matching and correlation；
Prediction multiple wave in Fig. 8 b the embodiment of the present application after the second scale of the domain Shearlet matching and correlation；
Prediction multiple wave in Fig. 8 c the embodiment of the present application after the domain Shearlet third scale matching and correlation；
Prediction multiple wave in Fig. 8 d the embodiment of the present application after the 4th scale matching and correlation of the domain Shearlet；
Threshold value subraction the first scale primary wave separating resulting in the domain Shearlet in Fig. 9 a the embodiment of the present application；
Threshold value subraction the second scale primary wave separating resulting in the domain Shearlet in Fig. 9 b the embodiment of the present application；
The domain Shearlet threshold value subraction third scale primary wave separating resulting in Fig. 9 c the embodiment of the present application；
Threshold value subraction the 4th scale primary wave separating resulting in the domain Shearlet in Fig. 9 d the embodiment of the present application；
Primary wave separates final result in Figure 10 the embodiment of the present application.
Specific embodiment
With reference to the accompanying drawing with example to further detailed description of the invention.
In order to make those skilled in the art better understand the technical solutions in the application, below in conjunction with the application reality
The attached drawing in example is applied, the technical scheme in the embodiment of the application is clearly and completely described, it is clear that described implementation
Example is only some embodiments of the present application, instead of all the embodiments.Based on the embodiment in the application, the common skill in this field
The application protection all should belong in art personnel every other embodiment obtained without creative labor
Range.
The embodiment of the present application carries out primary wave separation according to flow chart shown in Fig. 1.Fig. 2 is the frequency domain of Shearlet transformation
Subdivision graph, Fig. 3 expression divide the Shearlet basic function of scale and angle to approach curvilinear singular point.Fig. 4 is marine somewhere
The real seismic record in seismic exploration work area, Fig. 5 are the prediction multiple wave data in the work area.
As shown in Figure 1, a kind of multiple dimensioned primary wave separation method based on Shearlet transformation, comprising the following steps:
A, the original seismic data and prediction multiple wave in the purpose work area are inputted；
B, according to the actual conditions of input data, Shearlet system is constructed, Shearlet is converted by carrying out to function
Flexible, translation and rotation generate basic function, have nonlinearity erron degree of approximation, and successively segment in frequency space；Structure
The parameter that must be provided with during building comprises determining that the scale number n of required Shearlet system, is 4 in the embodiment of the present application；
It determines the respective number of row and column, is the sampling number and road number of earthquake record in the embodiment of the present application；Determine shearing wave series,
It is specially the array [1,1,2,2] of 1 × 4 size in the embodiment of the present application；It determines logical value, takes and patrol in the embodiment of the present application
Collecting value is 0；Two-dimensional directional filter takes modulate2 (dfilters (' cd', ' d'), ' c') in the embodiment of the present application；And
And define the one-dimensional orthogonal mirror filter of the Wavelet Component of shearing wave；
C, after building Shearlet system, original seismic data and prediction multiple wave to the purpose work area are carried out
Data are transformed into the domain Shearlet, obtain Shearlet coefficient by the mapping under Shearlet system；
D, the original seismic data to the purpose work area and prediction multiple wave carry out the domain Shearlet multi-resolution decomposition, obtain
The prediction multiple wave and original seismic data under four scales are obtained, respectively as shown in Fig. 6 a- Fig. 6 d and Fig. 7 a- Fig. 7 d；
E, the multiple dimensioned original seismic data to the purpose work area and prediction multiple wave carry out a point scale Corresponding matching, with
The true multiple wave information for including in original seismic data is criterion, carries out the correction in amplitude and phase to prediction multiple wave,
Keep difference between the two as small as possible, so that the prediction multiple wave after the correction under each scale is obtained, such as Fig. 8 a- Fig. 8 d institute
Show；
F, the multiple wavelength-division scale of prediction after the multiple dimensioned original seismic data to the purpose work area and correction it is corresponding into
The domain row Shearlet threshold value subtracts, i.e., in the domain Shearlet, under four scales, by the prediction multiple wave after matching and correlation
Shearlet coefficient takes soft-threshold as threshold value, to the Shearlet coefficient of itself and original earthquake data according to the following formula
Method, criterion are the amounts that the coefficient for remaining the coefficient for being less than threshold value, and being greater than threshold value will be subtracted a threshold size,
Wherein, SpFor the Shearlet coefficient of original seismic data, and TmIt is the Shearlet coefficient of the multiple wave after amplitude matches
Mould, the primary wave separating resulting of four scales is obtained, as shown in Fig. 9 a- Fig. 9 d；
G, four domain scale Shearlet soft-threshold primary wave separating resultings are merged into reconstruct, obtains a final wavelength-division
From as a result, as shown in Figure 10.The method of the present invention has used advanced Shearlet sparse transformation, it is multiple dimensioned with seismic data
Characteristic effectively combine, method flow calculating speed is fast, high-efficient, efficiently solves the domain Curvelet primary wave separation method meter
The too low problem of efficiency is calculated, the method for the present invention is higher than conventional least square primary wave separation method precision, overcomes its separation knot
The remaining serious problem of multiple wave, has preferably separated primary wave in fruit.
Claims (2)
1. a kind of multiple dimensioned primary wave separation method based on Shearlet transformation, which comprises the following steps:
A, original seismic data and prediction multiple wave are inputted；
B, according to the actual conditions of input data, Shearlet system is constructed, defines Shearlet are as follows:
In formula, OsFor directional operator, AaFor parabolic linear content operator, TmFor translation operator, Shearlet transformation by function into
Row is flexible, translate and rotation generates basic function, has nonlinearity erron degree of approximation, and successively segment in frequency space；
The parameter that must be provided in building process comprises determining that the scale number n of required Shearlet system, it is necessary to be greater than or equal to 1；
Determine the respective number of row and column；Determine shearing wave series, the array of specially one 1 × n size, wherein each element
It has to be larger than or equal to 0；Logical value is determined, which dictates that whether shearlet system constructs on GPU；Two-dimensional directional filtering
Device；And define the one-dimensional orthogonal mirror filter of the Wavelet Component of shearing wave；
C, the mapping under Shearlet system is carried out to original seismic data and prediction multiple wave, data is transformed into Shearlet
Domain obtains Shearlet coefficient；
D, the domain Shearlet multi-resolution decomposition is carried out to original seismic data and prediction multiple wave, the prediction under acquisition is multiple dimensioned is more
Subwave and original seismic data, i.e., by retaining i-th layer of selected layer of Shearlet coefficient, by other layers of Shearlet coefficient
All zeroize method, by original seismic data P and prediction multiple wave MpIt is divided into the identical multi-Scale Data of counter structure, is denoted as
P1,P2,...,PiAnd Mp1,Mp2,...,Mpi, wherein i is the sum of multi-Scale Data and the scale parameter of Shearlet system；
E, Based on Multiscale Matching is carried out to original seismic data and prediction multiple wave, it is true multiple with include in original seismic data
Wave information is criterion, carries out the correction in amplitude and phase to prediction multiple wave, keeps difference between the two as small as possible, thus
Prediction multiple wave after obtaining the correction under each scale；
F, to the prediction multiple wave after multiple dimensioned original seismic data and correction, the corresponding domain the Shearlet threshold value that carries out of scale is divided to subtract
It goes, i.e., in the domain Shearlet, under each scale, using the Shearlet coefficient of the prediction multiple wave after matching and correlation as threshold
Value takes soft threshold method to the Shearlet coefficient of itself and original earthquake data, and criterion is will to be less than the coefficient reservation of threshold value
The coefficient for getting off, and being greater than threshold value will be subtracted the amount of a threshold size, obtain multiple dimensioned primary wave separating resulting,
SpFor the Shearlet coefficient of original seismic data, and TmIt is the Shearlet coefficient of the multiple wave after amplitude matches
Mould；
G, the multiple dimensioned domain Shearlet soft-threshold primary wave separating resulting is merged and is reconstructed, obtain final primary wave separation knot
Fruit.
2. a kind of multiple dimensioned primary wave separation method based on Shearlet transformation according to claim 1, feature exist
In: step A, input data are offshore seismic exploration data or land seismic exploration data.
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