CN108107474B

CN108107474B - A kind of aliased data separation method and device based on sparse inversion

Info

Publication number: CN108107474B
Application number: CN201810104593.4A
Authority: CN
Inventors: 宋家文; 王文闯; 李培明; 李合群; 王宝彬; 马凯
Original assignee: China National Petroleum Corp; BGP Inc
Current assignee: China National Petroleum Corp; BGP Inc
Priority date: 2018-02-02
Filing date: 2018-02-02
Publication date: 2019-11-08
Anticipated expiration: 2038-02-02
Also published as: CN108107474A

Abstract

The application provides a kind of aliased data separation method and device based on sparse inversion.The described method includes: splitting to aliased seismic data, three-dimensional common detector gather data are obtained；Threshold contracting function is constructed according to the three-dimensional common detector gather data, the threshold contracting function includes the relationship between the number of iterations and seismic data amplitude range；Sparse constraint objective function is constructed based on the aliased seismic data, the sparse constraint objective function includes the functional relation between the aliased seismic data and useful signal data；According to the threshold contracting function, inverting is iterated to the sparse constraint objective function using the three-dimensional sliding space-time window of building and is solved, the SNR estimation and compensation data of the aliased seismic data are obtained.Using embodiment each in the application, the separating effect of seismic data is improved, improves the efficiency of data processing.

Description

A kind of aliased data separation method and device based on sparse inversion

Technical field

The application belongs to seismic data processing technology field more particularly to a kind of aliased data separation based on sparse inversion Method and device.

Background technique

Due to traditional seismic acquisition low efficiency, at high cost, it is not able to satisfy the industry of shake data acquisition to high-density Demand can improve earthquake-capturing day effect using aliasing acquisition technique, reduce high-density seismic acquisition cost in the prior art. But since aliasing acquisition technique is by the way of continuous agitation, aliasing, which acquires in data, has a large amount of adjacent big gun interference noise, Seriously reduce seismic data signal-to-noise ratio and image quality.Therefore, the SNR estimation and compensation of aliased data is aliasing acquisition data processing Necessary links.

In the prior art, aliasing acquisition data separation method can be divided into three classes: direct Denoising Algorithm, iterated denoising method and anti- Drill partition method.Direct Denoising Algorithm and iterated denoising method are mainly made an uproar using random character of the adjacent big gun interference on non-big gun collection to suppress Sound, they the shortcomings that be when seismic data aliasing degree is higher, these methods normally result in that serious noise is remaining and letter Number damage, aliased data separating effect is bad, it is difficult to meet actual production demand.Inverting partition method relevant spy signal-based Sign applies sparse constraint to seismic data to extract useful signal in transform domain, and utilizes can be predicted between signal and noise Property realize SNR estimation and compensation, such methods depend critically upon signal in the degree of rarefication and used threshold contracting function of transform domain, Higher cost is calculated, aliased data separating effect is bad.The many invertion separation methods all Shortcomings proposed at present, are such as based on The separation method that FK (Frequency-Wavenumber) transformation, linear Radon (Radon transform) are converted is in transform domain to bending The characterization of lineups is not sparse, affects aliasing acquisition data separating effect.Therefore, aliasing number can be improved by needing one kind in the industry According to the embodiment of separating effect.

Summary of the invention

The application is designed to provide a kind of aliased data separation method and device based on sparse inversion, sliding using three-dimensional The local linear feature of seismic data can be improved in dynamic space-time window, improves the separating effect of seismic data.Meanwhile it using dilute The constraint method of inversion and threshold contracting function are dredged, the convergence rate of iterative inversion is accelerated, improves the efficiency of data processing.

On the one hand this application provides a kind of aliased data separation method based on sparse inversion, comprising:

Aliased seismic data is split, three-dimensional common detector gather data are obtained；

Threshold contracting function is constructed according to the three-dimensional common detector gather data, the threshold contracting function includes iteration Relationship between number and seismic data amplitude range；

Sparse constraint objective function is constructed based on the aliased seismic data, the sparse constraint objective function includes described Functional relation between aliased seismic data and useful signal data；

According to the threshold contracting function, using building three-dimensional sliding space-time window to the sparse constraint objective function into Row iteration inverting solves, and obtains the SNR estimation and compensation data of the aliased seismic data.

Further, described according to the threshold contracting function in another embodiment of the method, utilize building Three-dimensional sliding space-time window is iterated inverting to the sparse constraint objective function and solves, and obtains the letter of the aliased seismic data It makes an uproar mask data, comprising:

Execute the first processing step:

It is selected from the currently active signal data in the sparse constraint objective function using the three-dimensional sliding space-time window Take local earthquake's data；

Threshold processing is carried out to local earthquake's data according to the threshold contracting function, obtains pretreatment useful signal Data；

Adjacent big gun interference noise is predicted according to the pretreatment useful signal data；

The adjacent big gun interference noise is subtracted from the three-dimensional common detector gather data, obtains iteration useful signal number According to；

Judge whether current iteration number is less than and preset total the number of iterations, if being less than, by the iteration useful signal number According to as the currently active signal data, first processing step is repeated, until the current iteration number is more than or equal to It is described when presetting total the number of iterations, using the iteration useful signal data as the SNR estimation and compensation data.

Further, in another embodiment of the method, it is described according to the threshold contracting function to the part Seismic data carries out threshold processing, obtains pretreatment useful signal data, comprising:

Three dimensional fast Fourier direct transform is carried out to local earthquake's data, obtains direct transform Fourier data；

The corresponding threshold value of current iteration number is obtained according to the threshold contracting function, by the direct transform Fourier number Seismic data in less than the threshold value is set to zero, obtains threshold and handles data；

Threshold processing data are subjected to three dimensional fast Fourier inverse transformation, obtain the pretreatment useful signal number According to.

Further, described that threshold processing data are subjected to three-dimensional quickly in another embodiment of the method Fourier inversion obtains the pretreatment useful signal data, comprising:

Threshold processing data are subjected to three dimensional fast Fourier inverse transformation, it is corresponding to obtain current three-dimensional sliding space-time window Part pretreatment useful signal data；

Judge whether the seismic data in the currently active signal data is all selected, if it is not, then sliding described three Dimension sliding space-time window obtains the corresponding local earthquake's data of next three-dimensional sliding space-time window, threshold trip of going forward side by side processing, under acquisition One three-dimensional corresponding part of space-time window of sliding pre-processes useful signal data；

If so, each three-dimensional corresponding part of space-time window of sliding is pre-processed useful signal data, is integrated, will be integrated Useful signal data afterwards, as the corresponding pretreatment useful signal data of the currently active signal data.

Further, in another embodiment of the method, the sparse constraint objective function includes:

In above formula,Indicating error term, d indicates that the aliased seismic data, m indicate useful signal data, Γ indicates aliasing operator,Indicate bound term, the sliding space-time of nx representation space X-direction The window number of window, the window number of the sliding space-time window of ny representation space Y-direction, nt indicate the sliding space-time in the direction time t The window number of window, F indicate three dimensional fast Fourier direct transform operator, W_ix,iy,itIt indicates in space X, space Y, time T direction On three-dimensional sliding space-time window operator, | | FW_ix,iy,itm||₀Expression takes L0 norm, and λ indicates regularization parameter.

Further, described according to the three-dimensional common detector gather data structure in another embodiment of the method Build threshold contracting function, comprising:

Fast Fourier direct transform is carried out to the three-dimensional common detector gather data, obtains the three-dimensional common receiver road Collect the peak swing value and minimum amplitude value of data；

According to the peak swing value and the minimum amplitude value, the threshold contracting function is constructed.

Further, in another embodiment of the method, the threshold contracting function includes:

In above formula, T (k) indicates that the threshold value of kth time iteration, N indicate total the number of iterations.

On the other hand, this application provides a kind of aliased data separator based on sparse inversion, comprising:

Aliased data splits module, for splitting to aliased seismic data, obtains three-dimensional common detector gather data；

Threshold function constructs module, for constructing threshold contracting function according to the three-dimensional common detector gather data；

Sparse objective function constructs module, for constructing sparse constraint objective function, institute based on the aliased seismic data Stating sparse constraint objective function includes the functional relation between the aliased seismic data and useful signal data；

Alternate analysis module, for sliding space-time window to described using the three-dimensional of building according to the threshold contracting function Sparse constraint objective function is iterated inverting solution, obtains the SNR estimation and compensation data of the aliased seismic data.

Further, in another embodiment of described device, the alternate analysis module is specifically used for:

Execute the first processing step:

In another aspect, present invention also provides a kind of aliased data separator based on sparse inversion, comprising: processor And the memory for storage processor executable instruction, the processor are realized above-mentioned based on sparse when executing described instruction The aliased data separation method of inverting.

Aliased data separation method and device provided by the present application based on sparse inversion, first fractionation aliasing acquire earthquake Data construct threshold contracting function according to the three-dimensional common detector gather data after fractionation, provide data for successive iterations inverting Basis.Sparse-constrained inversion technology is recycled, constructs the sparse constraint objective function of aliased seismic data, and using three-dimensional sliding Space-time window and threshold contracting function are iterated inverting to sparse constraint objective function and solve, and obtain three-dimensional common detector gather The useful signal data of data, complete the SNR estimation and compensation of aliased seismic data.Local earthquake is chosen using three-dimensional sliding space-time window The local linear feature of useful signal data is utilized in data, improves signal in the sparse degree of Fourier transform domain, improves The effect of aliased seismic data SNR estimation and compensation.Meanwhile letter is shunk using sparse-constrained inversion, Fast Fourier Transform (FFT), threshold Number etc., accelerates convergence rate, improves computational efficiency.

Detailed description of the invention

In order to illustrate the technical solutions in the embodiments of the present application or in the prior art more clearly, to embodiment or will show below There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this The some embodiments recorded in application, for those of ordinary skill in the art, in the premise of not making the creative labor property Under, it is also possible to obtain other drawings based on these drawings.

Fig. 1 is a kind of method stream of aliased data separation method one embodiment based on sparse inversion provided by the present application Journey schematic diagram；

Fig. 2 is the three-dimensional common detector gather schematic diagram split out in aliased seismic data in the embodiment of the present application；

Fig. 3 is the flow diagram of the aliased data separation method in another embodiment of the application based on sparse inversion；

Fig. 4 is the flow diagram of the aliased data separation method in the another embodiment of the application based on sparse inversion；

Fig. 5 is the useful signal of aliased seismic data one three-dimensional common detector gather data separating in the embodiment of the present application Schematic diagram data；

Fig. 6 is that the adjacent big gun interference of three-dimensional common detector gather data separating in aliased seismic data in the embodiment of the present application is shown It is intended to；

Fig. 7 is the stacked section schematic diagram in the embodiment of the present application before aliased seismic data separation；

Fig. 8 is the useful signal stacked section schematic diagram in the embodiment of the present application after aliased seismic data separation；

Fig. 9 is that the modular structure of aliased data separator one embodiment provided by the present application based on sparse inversion is shown It is intended to；

Figure 10 is the module knot of another aliased data separator embodiment based on sparse inversion provided by the present application Structure schematic diagram.

Specific embodiment

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 merely a part but not all of the embodiments of the present application.Based on the embodiment in the application, this field is common The application protection all should belong in technical staff's every other embodiment obtained without creative efforts Range.

The acquisition of seismic data is the basis of seismic prospecting, in order to improve the collecting efficiency of seismic data, can usually be adopted With aliasing acquisition technique, it can excited simultaneously using different focus, corresponding different wave detector receives different shakes simultaneously The seismic signal that source generates.The collecting efficiency of seismic data can be improved in aliasing acquisition technique, still, while exciting Shi Youlin big gun Bring interference noise needs are rejected from aliased seismic data, to obtain effective seismic data.

The embodiment of the present application can use three-dimensional sliding space-time window, be carried out using sparse-constrained inversion to aliased seismic data Separating treatment.Sparse-constrained inversion can actually indicate to calculate from noisy seismic channel using deconvolution principle The method of the amplitude and time of reflection coefficient with sparse distribution feature.Sparse-constrained inversion is nonlinear optimal problem, is led to Nonlinear optimal problem can be often converted into linear optimization problem, then be solved with linear optimization algorithm.Utilize the application reality The method for applying example improves the separating effect of seismic data, meanwhile, it uses sparse-constrained inversion method and threshold shrinks letter Number, accelerates the convergence rate of iterative inversion, improves the efficiency of data processing.

Specifically, Fig. 1 is a kind of aliased data separation method one embodiment based on sparse inversion provided by the present application Method flow schematic diagram, the aliased data separation method provided by the present application based on sparse inversion includes:

S1, aliased seismic data is split, obtains three-dimensional common detector gather data.

It may include the collected seismic data of multiple wave detectors in aliased seismic data, the embodiment of the present application is to aliasing It shakes data and carries out deconsolidation process, the seismic data of common receiver is splitted out, obtain three-dimensional common detector gather data.Example Such as: three-dimensional common detector gather data can be splitted out by aliased seismic data multiplied by an aliasing operator, it specifically can be with The fractionation of aliased seismic data is carried out with the following method:

m₀=Γ^Hd (1)

In above formula, d can indicate aliased seismic data, m₀It can indicate the three-dimensional common detector gather after splitting, Γ can To indicate that aliasing operator, aliasing operator may include the firing time and location information of focus, Γ^HIt can indicate aliasing operator Conjugation.

Fig. 2 is the three-dimensional common detector gather schematic diagram split out in aliased seismic data in the embodiment of the present application, such as Fig. 2 Shown, ordinate can indicate the time in figure, and the first row can indicate big gun wire size (can be understood as X-direction) in abscissa, the Two rows can indicate useful signal data and adjacent big gun interference point in shot point pile No. (can be understood as Y-direction) aliased seismic data Relevant and random feature is not shown.Useful signal data can indicate effective seismic data in aliased seismic data, i.e., Except the seismic data of denoising.

S2, threshold contracting function is constructed according to the three-dimensional common detector gather data, the threshold contracting function includes Relationship between the number of iterations and seismic data amplitude range.

After obtaining three-dimensional common detector gather data, three-dimensional common detector gather data can be handled, according to three The amplitude for tieing up common detector gather data, determines threshold contracting function.Threshold contracting function can indicate sparse-constrained inversion In iterative process, each time when iteration in seismic data amplitude range, i.e. threshold contracting function can indicate the number of iterations With the corresponding relationship between seismic data amplitude range.It can be by simulated experiment or according to the iterative data of history, building Threshold contracting function provides accurate data basis for subsequent sparse-constrained inversion, accelerates the convergence rate of iterative inversion.

In the application one embodiment, threshold contracting function can be constructed with the following method:

Three-dimensional Fourier's direct transform can be carried out to each three-dimensional common detector gather data, count each three-dimensional inspection altogether Wave point trace gather data corresponding peak swing value Tmax and minimum amplitude value Tmin.Fast Fourier transform (FFT), is discrete Fourier The fast algorithm of transformation, can be according to the characteristics such as odd, even, empty, real of discrete fourier transform, to the algorithm of Discrete Fourier Transform Improve acquisition.According to the corresponding peak swing value Tmax of each three-dimensional common detector gather data and minimum amplitude value Tmin determines the amplitude range of useful signal data during iterative inversion, further determines that out threshold contracting function.Threshold Contracting function, that is, the number of iterations can be according to reality with the physical relationship of the amplitude of seismic data corresponding when iteration each time It needs to be configured, can be determined by modes such as numerical simulation, mathematical statistics.

Different three-dimensional common detector gather data may correspond to different peak swing value Tmax and minimum amplitude value Tmin, correspondingly, different three-dimensional common detector gather data may correspond to different threshold contracting functions.To aliased seismic When data carry out the SNR estimation and compensation of sparse-constrained inversion, corresponding door can be used to different three-dimensional common detector gather data Sill contracting function.

In the application one embodiment, threshold contracting function can use following formula:

In above formula, T (k) can indicate that the threshold of kth time iteration (or indicates the vibration of seismic data when kth time iteration The maximum value or minimum value of amplitude), N can total the number of iterations, can need to be arranged according to practical iteration precision, the application one N=50 can be taken in a embodiment.

S3, sparse constraint objective function is constructed based on the aliased seismic data, the sparse constraint objective function includes Functional relation between the aliased seismic data and useful signal data.

It may include two parts in sparse constraint objective function, a part is the constraint to noise, and a part is to anti- The constraint of coefficient is penetrated, it can be using the constraint portions of norm building reflection coefficient.Useful signal data can indicate aliased seismic Effective seismic data is the seismic data for removing denoising in data.It may include aliased seismic number in sparse constraint objective function According to the functional relation between useful signal data.Iterative inversion may include: according to known geology, geophysical information, really A fixed initial model, the then field-effect of forward modelling model are modified using the difference (remaining value) of calculated value and observation Initial model, then forward modelling field value again, remakes model modification according to comparison result.It iterates in this way, until calculated value Reach preset precision with the difference (or mean square error) of observation or the number of iterations reaches the number of iterations of threshold value, it is final to obtain To inversion result.

Can using useful signal data as the variable of sparse constraint objective function, by sparse constraint objective function into Row iteration inverting solves sparse constraint target letter, and by continuous iteration, it is optimal can to obtain the acquisition of sparse constraint objective function Corresponding useful signal data when solution.Specific construction method the embodiment of the present application of sparse constraint objective function does not limit specifically It is fixed, it can be constructed with reference to sparse-constrained inversion technology in the prior art.

In the application one embodiment, the sparse constraint objective function may include:

In above formula,It can indicate error term, d can indicate that aliased seismic data, m can indicate effective Signal data, Γ can indicate aliasing operator,It can indicate bound term, nx can be with table Show space X direction sliding space-time window window number, ny can with representation space Y-direction sliding space-time window window number, Nt can indicate the window number of the sliding space-time window in time t direction, and F can indicate three dimensional fast Fourier direct transform operator, W^ix _,iy,itIt can indicate the three-dimensional sliding space-time window operator on space X, space Y, time T direction, | | FW_ix,iy,itm||₀It can be with Expression takes L₀Norm, λ can indicate regularization parameter.

S4, space-time window is slided to the sparse constraint objective function using the three-dimensional of building according to the threshold contracting function It is iterated inverting solution, obtains the SNR estimation and compensation data of the aliased seismic data.

Corresponding useful signal data in sparse constraint objective function are chosen based on three-dimensional sliding space-time window, are received using threshold The amplitude range of seismic data when contracting function determines iteration, further screens the seismic data of iteration.To sparse constraint Objective function is iterated inverting, solves sparse constraint objective function, obtains that sparse constraint objective function optimal solution is corresponding to be had Signal data is imitated, realizes the separation to aliased seismic data.It, can be by the three-dimensional common detector gather after fractionation when primary iteration Data m₀As initial useful signal data, successively iteration updates until the total the number of iterations of arrival, obtains final effective letter Number completes the inverting of aliased seismic data.

It include noise and useful signal data in the three-dimensional common detector gather data split out in aliased seismic data.This Apply for that embodiment can choose the part number in the corresponding useful signal data of current iteration number using three-dimensional sliding space-time window According to, using the local linear feature of useful signal data, inverting is iterated based on sparse constraint objective function, solve it is sparse about Beam objective function.It specifically can be in space X, space Y, the three-dimensional sliding space-time window of time T direction building, from current iteration number pair The corresponding part seismic data of successively selection three-dimensional sliding space-time window in the useful signal data answered.It can use threshold contraction Function controls the precision and convergence rate of iterative inversion, such as: can use threshold contracting function to the useful signal data of selection It is further processed, as the basic data of iterative inversion, reduces calculation amount.Obtain the optimal of sparse constraint objective function Corresponding useful signal data when solution, until the useful signal data that the total the number of iterations of arrival or iterative inversion obtain, which have reached, to be needed The required precision wanted.Corresponding useful signal data when can be up to total the number of iterations or reach required precision, as final SNR estimation and compensation data, realize the SNR estimation and compensation of aliased seismic data.

Specific iterative inversion process can be carried out with reference to sparse-constrained inversion technology in the prior art, the embodiment of the present application It is not especially limited.

In the embodiment of the present application when carrying out the inverting of sparse constraint objective function based on three-dimensional sliding space-time window, Ke Yiyi It is secondary that sparse constraint iterative inversion is carried out respectively to the multiple three-dimensional common detector gather data split out in aliased seismic data, it will Aliased data in each three-dimensional common detector gather data carries out SNR estimation and compensation, realizes the noise point of all aliased seismic datas From.

Aliased data separation method provided by the embodiments of the present application based on sparse inversion slides space-time window pair using three-dimensional The seismic data of sparse-constrained inversion process is screened, and the local linear feature of seismic data can be improved, improve earthquake The separating effect of data.Meanwhile sparse-constrained inversion method and threshold contracting function are used, accelerate the convergence of iterative inversion Speed improves the efficiency of data processing.

Fig. 3 is the flow diagram of the aliased data separation method in another embodiment of the application based on sparse inversion, such as Shown in Fig. 3, the aliased data separation method in the embodiment of the present application based on sparse inversion includes:

Carry out aliased seismic data fractionation can after constructing threshold contracting function and sparse constraint objective function To carry out the iterative inversion of sparse constraint objective function using following steps:

S20, space-time window is slided from the currently active signal data in the sparse constraint objective function using the three-dimensional Middle selection local earthquake data.

The currently active signal data can refer to the corresponding useful signal data of current iteration number, can be in space X, space Y, the three-dimensional sliding space-time window of the direction time T building, chooses part from the corresponding the currently active signal of current iteration inverting number Seismic data.

S21, threshold processing is carried out to local earthquake's data according to the threshold contracting function, it is effective obtains pretreatment Signal data.

It is handled using local earthquake data of the threshold contracting function to selection, filters out and meet iteration threshold requirement Seismic data improves iteration precision and convergence rate as pretreatment useful signal data.

It is described that threshold is carried out to local earthquake's data according to the threshold contracting function in the application one embodiment Processing obtains pretreatment useful signal data, may include:

Specifically, according to current iteration number and threshold contracting function, the corresponding door of current iteration number can be obtained Sill threshold value T.After carrying out three dimensional fast Fourier direct transform to local earthquake's data of selection, by the direct transform Fourier number of acquisition Seismic data in less than the corresponding threshold threshold value of current iteration number is set to zero, obtains threshold and handles data.It specifically can be with Threshold is obtained using following formula and handles data:

In above formula, f (m) can indicate that direct transform Fourier data, T are the corresponding threshold values of current iteration number.

It can use above-mentioned formula (2), obtain the corresponding threshold value of current iteration number, such as: if current iteration number It is 1, then k=1 can be substituted into above-mentioned formula (2), in conjunction with the maximum amplitude and minimum amplitude of three-dimensional common detector gather data, It can determine the corresponding threshold value T of current iteration number.

After obtaining the corresponding threshold processing data of current iteration number, threshold processing data are subjected to three dimensional fast Fourier Inverse transformation, it can obtain the corresponding pretreatment useful signal data of current iteration number.

On the basis of the above embodiments, described that threshold processing data are carried out three in the application one embodiment Fast Fourier Transform Inverse is tieed up, the pretreatment useful signal data is obtained, may include:

Local earthquake's data in the currently active signal data are successively chosen using three-dimensional sliding space-time window, and to selection Local earthquake's data carry out Fast Fourier Transform (FFT) processing and threshold processing, obtain the corresponding part of current three-dimensional sliding space-time window Pre-process useful signal data.Three-dimensional sliding space-time window is slided again, from selection another part in the currently active signal data Local earthquake's data carry out identical data processing, until the seismic data in the currently active signal data is all chosen simultaneously Fast Fourier Transform (FFT) and threshold processing are carried out, it is pre- to obtain the corresponding part of the corresponding three-dimensional sliding space-time window of different space-time positions Handle useful signal data.The corresponding part pretreatment of the three-dimensional sliding space-time window effectively letter that each space-time position of acquisition is arrived Number is integrated, it can will treated that local pre-processing seismic data is put into is right in three-dimensional common detector gather data At the position answered, the corresponding pretreatment useful signal data of the currently active signal data are obtained.

S22, adjacent big gun interference noise is predicted according to the pretreatment useful signal data.

It, can be based on pretreatment useful signal number after obtaining the corresponding pretreatment useful signal data of current iteration number According to the adjacent big gun interference noise of prediction.The prediction of adjacent big gun noise can be specifically carried out with reference noise Predicting Technique, such as will pretreatment Abnormal data in useful signal data is as adjacent big gun interference noise.It, can be pre- with reference formula (5) in the application one embodiment Survey adjacent big gun interference noise:

N=(Γ^HΓ-I)m (5)

In above formula, n can indicate that adjacent big gun interference noise, Γ can indicate aliasing operator, Γ^HIt can indicate aliasing operator Conjugation, I can be with unit matrix.

S23, the adjacent big gun interference noise is subtracted from the three-dimensional common detector gather data, obtain iteration useful signal Data.

After obtaining adjacent big gun interference noise, adjacent big gun interference noise can be subtracted from current three-dimensional common detector gather data It goes, obtains iteration useful signal data, realize that the iteration of useful signal data updates.

S24, judge whether current iteration number is less than total the number of iterations, if being less than, then follow the steps S25, otherwise, execute Step S26.Total the number of iterations, that is, previously described presets total the number of iterations, is hereinafter properly termed as total the number of iterations, total iteration The size of number can be preset, and the embodiment of the present application is not especially limited.

S25, using the iteration useful signal data as the currently active signal data, repeat S20-S24,

S26, using the iteration useful signal data as the SNR estimation and compensation data.

Specifically, judge whether current iteration useful signal data are less than total the number of iterations, if it is less, can will obtain The currently active signal data of the iteration useful signal data obtained as next iteration inverting, repeats step S20-S24.I.e. pair Updated iteration useful signal data choose local earthquake's data using three-dimensional sliding space-time window, and to selecting locally Shake data are handled, and new iteration useful signal data are obtained, until the number of iterations reaches total the number of iterations.Work as the number of iterations When reaching total the number of iterations, then using the iteration useful signal data of newest acquisition as current three-dimensional common detector gather data SNR estimation and compensation data complete the SNR estimation and compensation processing of current three-dimensional common detector gather data.To other three-dimensional common receiver roads Collect data and SNR estimation and compensation is carried out using identical method, until completing the SNR estimation and compensation of all three-dimensional common detector gather data Processing.

Fig. 4 is the flow diagram of the aliased data separation method in the another embodiment of the application based on sparse inversion, such as Shown in Fig. 4, the technical solution of the application is introduced below with reference to specific example:

B1, aliased seismic data is split, the three-dimensional common detector gather data after being split.Specific fractionation side Method can refer to above-described embodiment, and details are not described herein again.

B2, threshold contracting function is constructed according to current three-dimensional common detector gather data.Specific construction method can refer to Above-described embodiment, details are not described herein again.

B3, the corresponding sparse constraint target letter of current three-dimensional common detector gather data is constructed based on aliased seismic data Number.Specific construction method can refer to above-described embodiment, and details are not described herein again.It can be by the three-dimensional common detector gather after fractionation Data m₀Initial useful signal data as sparse constraint objective function iterative inversion.Different three-dimensional common detector gathers The form of the corresponding sparse constraint objective function of data can be identical, but initial effective letter in sparse constraint objective function Number may be different.

B4, it is corresponded to according to the corresponding threshold contracting function calculating current iteration number of current three-dimensional common detector gather data Threshold value T.

B5, (when primary iteration, initially effective can be used from the currently active signal data using three-dimensional sliding space-time window Signal data is as the currently active signal data) in successively choose local earthquake's data, space can be used in the embodiment of the present application X, the three-dimensional sliding space-time window that space Y, time T direction sampling point number are 20 × 20 × 100.

B6, local earthquake's data of selection are done with three dimensional fast Fourier direct transform, obtains direct transform Fourier data.

B7, threshold processing is carried out to direct transform Fourier data, the seismic data zero setting of threshold value will be less than, obtains threshold Handle data.The introduction of above-described embodiment can be specifically referred to, details are not described herein again.

B8, three dimensional fast Fourier inverse transformation is done to threshold treated threshold processing data, obtains current three-dimensional sliding The corresponding part pretreatment useful signal data of space-time window；

B9, judge whether the seismic data in the currently active signal data is all selected, if it is not, then repeating step B5 extremely Step B9, the next three-dimensional corresponding part of space-time window of sliding for obtaining the currently active signal model pre-process useful signal number According to until to the processing for completing all seismic datas in the currently active signal data；If so, thening follow the steps B10.

B10, the part pretreatment useful signal data that the corresponding three-dimensional sliding space-time window of different space-times obtains are merged into It is whole, obtain the corresponding pretreatment useful signal data of the currently active signal data.

B11, the corresponding pretreatment useful signal data of the currently active signal data using acquisition predict that adjacent big gun interference is made an uproar Sound n.

B12, the adjacent big gun interference noise that prediction is subtracted from current three-dimensional common detector gather data, update useful signal number According to acquisition iteration useful signal data.

B13, judge whether the number of iterations reaches total the number of iterations, if so, B14 is executed, if it is not, then repeating step B4 extremely Step B13, using iteration useful signal data as the currently active signal data of next iteration inverting.It carries out current three-dimensional total The next iteration inversion procedure of geophone station trace gather data completes current three-dimensional common receiver road until reaching total the number of iterations Collect the SNR estimation and compensation of data, obtains current three-dimensional common detector gather data SNR estimation and compensation data.

Fig. 5 is the useful signal of aliased seismic data one three-dimensional common detector gather data separating in the embodiment of the present application Schematic diagram data, as shown in figure 5, ordinate can indicate the time in figure, the first row can indicate that big gun wire size (can be in abscissa It is interpreted as X-direction), the second row can indicate shot point pile No. (can be understood as Y-direction), and the adjacent big gun of random like has interfered The elimination of effect.Fig. 6 is the adjacent big gun interference of three-dimensional common detector gather data separating in aliased seismic data in the embodiment of the present application Schematic diagram, as shown in fig. 6, ordinate can indicate the time in figure, in abscissa the first row can indicate big gun wire size (it is understood that For X-direction), the second row can indicate shot point pile No. (can be understood as Y-direction), without discovery useful signal in the noise of elimination Damage.

B14, judge whether three-dimensional common detector gather data are disposed, if so, B15 is executed, if otherwise repeating to walk Rapid B2 to step B14 carries out at the SNR estimation and compensation based on sparse inversion other three-dimensional common detector gather data after splitting Reason.

B15, the corresponding SNR estimation and compensation data of each three-dimensional common detector gather data are integrated, obtains aliased seismic The SNR estimation and compensation data of data complete the SNR estimation and compensation of entire aliasing acquisition seismic data.Fig. 7 is aliasing in the embodiment of the present application Stacked section schematic diagram before seismic data separation, abscissa can indicate that Taoist monastic name, ordinate can indicate the time in figure, such as scheme Shown in 7, a large amount of neighbour's big gun serious interference reduces earthquake signal-to-noise ratio and image quality.Fig. 8 is aliasing in the embodiment of the present application Useful signal stacked section schematic diagram after shaking data separating, abscissa can indicate Taoist monastic name in figure, when ordinate can indicate Between, as shown in figure 8, the method in the embodiment of the present application eliminates adjacent big gun after carrying out SNR estimation and compensation processing to aliased seismic data Interference, useful signal are highlighted, and lineups continuity enhancing, signal-to-noise ratio significantly improves.

Aliased data separation method provided by the present application based on sparse inversion, first fractionation aliasing acquire seismic data, Three dimensional fast Fourier direct transform is done to common detector gather, determines threshold contracting function.Then useful signal data are carried out Three-dimensional sliding window Fourier direct transform, the processing of transform domain data threshold, three-dimensional sliding space-time window Fast Fourier Transform Inverse and Useful signal data are pre-processed to merge.Adjacent big gun interference noise is predicted according to pretreatment useful signal data are obtained, and altogether from three-dimensional It is subtracted in geophone station trace gather, updates useful signal data.Finally to all three-dimensional common detector gather iteration refutation processes, It completes aliasing and acquires data SNR estimation and compensation.Local earthquake's data are chosen using three-dimensional sliding space-time window, useful signal number is utilized According to local linear feature, improve signal in the sparse degree of Fourier transform domain, improve aliased seismic data noise point From effect.Meanwhile using sparse-constrained inversion, Fast Fourier Transform (FFT), threshold contracting function etc., convergence rate is accelerated, Improve computational efficiency.

Based on the aliased data separation method described above based on sparse inversion, this specification one or more embodiment A kind of aliased data separator based on sparse inversion is also provided.The device may include having used this specification implementation The example system (including distributed system) of the method, software (application), module, component, server, client etc. and in conjunction with must The device for the implementation hardware wanted.Based on same innovation thinking, in one or more embodiments that this specification embodiment provides Device is as described in the following examples.Since the implementation that device solves the problems, such as is similar to method, this specification is implemented The implementation of the specific device of example may refer to the implementation of preceding method, and overlaps will not be repeated.It is used below, term The combination of the software and/or hardware of predetermined function may be implemented in " unit " or " module ".Although described in following embodiment Device is preferably realized with software, but the realization of the combination of hardware or software and hardware is also that may and be contemplated.

Specifically, Fig. 9 is the mould of aliased data separator one embodiment provided by the present application based on sparse inversion Block structure schematic diagram, as shown in figure 9, the aliased data separator provided herein based on sparse inversion includes: aliasing Data split module 91, and threshold function constructs module 92, and sparse objective function constructs module 93, alternate analysis module 94.

Aliased data splits module 91, can be used for splitting aliased seismic data, obtains three-dimensional common receiver road Collect data；

Threshold function constructs module 92, can be used for shrinking letter according to the three-dimensional common detector gather data building threshold Number；

Sparse objective function constructs module 93, can be used for constructing sparse constraint target letter based on the aliased seismic data Number, the sparse constraint objective function includes the functional relation between the aliased seismic data and useful signal data；

Alternate analysis module 94 can be used for utilizing the three-dimensional sliding space-time window of building according to the threshold contracting function Inverting is iterated to the sparse constraint objective function to solve, and obtains the SNR estimation and compensation data of the aliased seismic data.

Aliased data separator provided by the present application based on sparse inversion, it is provided by the embodiments of the present application based on sparse The aliased data separation method of inverting uses sparse-constrained inversion method and threshold contracting function, accelerates iterative inversion Convergence rate improves the efficiency of data processing.Meanwhile the local line of seismic data can be improved using three-dimensional sliding space-time window Property feature, improves the separating effect of seismic data.

On the basis of the above embodiments, the alternate analysis module is specifically used for:

Execute the first processing step:

According to the three-dimensional common detector gather data and the adjacent big gun interference noise, iteration useful signal data are obtained；

Aliased data separator provided by the present application based on sparse inversion is chosen effective using three-dimensional sliding space-time window Local earthquake's data in signal accelerate the speed of iteration convergence, mention in conjunction with Fast Fourier Transform (FFT) and threshold contracting function High computational efficiency.The local linear feature of useful signal is utilized, the processing of space-time window is slided by three-dimensional, signal is improved and exists The sparse degree of Fourier transform domain, significantly improves the separating effect of aliased seismic data, improves signal-to-noise ratio, improve Image quality.

It should be noted that device described above can also include other embodiment party according to the description of embodiment of the method Formula, concrete implementation mode are referred to the description of related method embodiment, do not repeat one by one herein.

It is above-mentioned that this specification specific embodiment is described.Other embodiments are in the scope of the appended claims It is interior.In some cases, the movement recorded in detail in the claims or step can be come according to the sequence being different from embodiment It executes and desired result still may be implemented.In addition, process depicted in the drawing not necessarily require show it is specific suitable Sequence or consecutive order are just able to achieve desired result.In some embodiments, multitasking and parallel processing be also can With or may be advantageous.

The above-mentioned aliased data separation method or device by sparse inversion that this specification embodiment provides can be based on Corresponding program instruction is executed to realize, such as using the c++ language of windows operating system in the end PC reality by processor in calculation machine Existing, Linux system is realized or other are for example realized using android, iOS system programming language in intelligent terminal, and Processing logic realization based on quantum computer etc..A kind of aliased data based on sparse inversion that this specification provides separates dress In another embodiment set, Figure 10 is that another aliased data separator based on sparse inversion provided by the present application is implemented The modular structure schematic diagram of example, as shown in Figure 10, the aliased data based on sparse inversion point that another embodiment of the application provides It may include processor 101 and for the memory 102 of storage processor executable instruction from device,

Processor 101 and memory 102 pass through bus 103 and complete mutual communication；

The processor 101 is used to call the program instruction in the memory 102, above-mentioned respectively based on sparse anti-to execute Method provided by the aliased data separation method embodiment drilled, for example, aliased seismic data is split, obtains three Tie up common detector gather data；Threshold contracting function is constructed according to the three-dimensional common detector gather data, the threshold is shunk Function includes the relationship between the number of iterations and seismic data amplitude range；It is sparse about based on aliased seismic data building Beam objective function, the sparse constraint objective function include that the function between the aliased seismic data and useful signal data closes System；According to the threshold contracting function, changed using the three-dimensional sliding space-time window of building to the sparse constraint objective function For inverting, the SNR estimation and compensation data of the aliased seismic data are obtained.

It should be noted that specification device described above can also include it according to the description of related method embodiment His embodiment, concrete implementation mode are referred to the description of embodiment of the method, do not repeat one by one herein.In the application Various embodiments are described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, often What a embodiment stressed is the difference from other embodiments.For hardware+program class embodiment, Since it is substantially similar to the method embodiment, so being described relatively simple, related place is said referring to the part of embodiment of the method It is bright.

This specification embodiment is not limited to meet industry communication standard, standard computer data processing sum number According to situation described in storage rule or this specification one or more embodiment.The right way of conduct is made in certain professional standards or use by oneself In formula or the practice processes of embodiment description embodiment modified slightly also may be implemented above-described embodiment it is identical, it is equivalent or The implementation result being anticipated that after close or deformation.Using these modifications or deformed data acquisition, storage, judgement, processing side The embodiment of the acquisitions such as formula still may belong within the scope of the optional embodiment of this specification embodiment.

In the 1990s, the improvement of a technology can be distinguished clearly be on hardware improvement (for example, Improvement to circuit structures such as diode, transistor, switches) or software on improvement (improvement for method flow).So And with the development of technology, the improvement of current many method flows can be considered as directly improving for hardware circuit. Designer nearly all obtains corresponding hardware circuit by the way that improved method flow to be programmed into hardware circuit.Cause This, it cannot be said that the improvement of a method flow cannot be realized with hardware entities module.For example, programmable logic device (Programmable Logic Device, PLD) (such as field programmable gate array (Field Programmable Gate Array, FPGA)) it is exactly such a integrated circuit, logic function determines device programming by user.By designer Voluntarily programming comes a digital display circuit " integrated " on a piece of PLD, designs and makes without asking chip maker Dedicated IC chip.Moreover, nowadays, substitution manually makes IC chip, this programming is also used instead mostly " is patrolled Volume compiler (logic compiler) " software realizes that software compiler used is similar when it writes with program development, And the source code before compiling also write by handy specific programming language, this is referred to as hardware description language (Hardware Description Language, HDL), and HDL is also not only a kind of, but there are many kind, such as ABEL (Advanced Boolean Expression Language)、AHDL(Altera Hardware Description Language)、Confluence、CUPL(Cornell University Programming Language)、HDCal、JHDL (Java Hardware Description Language)、Lava、Lola、MyHDL、PALASM、RHDL(Ruby Hardware Description Language) etc., VHDL (Very-High-Speed is most generally used at present Integrated Circuit Hardware Description Language) and Verilog.Those skilled in the art also answer This understands, it is only necessary to method flow slightly programming in logic and is programmed into integrated circuit with above-mentioned several hardware description languages, The hardware circuit for realizing the logical method process can be readily available.

Controller can be implemented in any suitable manner, for example, controller can take such as microprocessor or processing The computer for the computer readable program code (such as software or firmware) that device and storage can be executed by (micro-) processor can Read medium, logic gate, switch, specific integrated circuit (Application Specific Integrated Circuit, ASIC), the form of programmable logic controller (PLC) and insertion microcontroller, the example of controller includes but is not limited to following microcontroller Device: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20 and Silicone Labs C8051F320 are deposited Memory controller is also implemented as a part of the control logic of memory.It is also known in the art that in addition to Pure computer readable program code mode is realized other than controller, can be made completely by the way that method and step is carried out programming in logic Controller is obtained to come in fact in the form of logic gate, switch, specific integrated circuit, programmable logic controller (PLC) and insertion microcontroller etc. Existing identical function.Therefore this controller is considered a kind of hardware component, and to including for realizing various in it The device of function can also be considered as the structure in hardware component.Or even, it can will be regarded for realizing the device of various functions For either the software module of implementation method can be the structure in hardware component again.

System, device, module or the unit that above-described embodiment illustrates can specifically realize by computer chip or entity, Or it is realized by the product with certain function.It is a kind of typically to realize that equipment is computer.Specifically, computer for example may be used Think personal computer, laptop computer, vehicle-mounted human-computer interaction device, cellular phone, camera phone, smart phone, individual Digital assistants, media player, navigation equipment, electronic mail equipment, game console, tablet computer, wearable device or The combination of any equipment in these equipment of person.

Although this specification one or more embodiment provides the method operating procedure as described in embodiment or flow chart, It but may include more or less operating procedure based on conventional or without creativeness means.The step of being enumerated in embodiment Sequence is only one of numerous step execution sequence mode, does not represent and unique executes sequence.Device in practice or When end product executes, can be executed according to embodiment or the execution of method shown in the drawings sequence or parallel (such as it is parallel The environment of processor or multiple threads, even distributed data processing environment).The terms "include", "comprise" or its Any other variant is intended to non-exclusive inclusion so that include the process, methods of a series of elements, product or Equipment not only includes those elements, but also including other elements that are not explicitly listed, or further include for this process, Method, product or the intrinsic element of equipment.In the absence of more restrictions, being not precluded is including the element There is also other identical or equivalent elements in process, method, product or equipment.The first, the second equal words are used to indicate name Claim, and does not indicate any particular order.

For convenience of description, it is divided into various modules when description apparatus above with function to describe respectively.Certainly, implementing this The function of each module can be realized in the same or multiple software and or hardware when specification one or more, it can also be with The module for realizing same function is realized by the combination of multiple submodule or subelement etc..Installation practice described above is only It is only illustrative, for example, in addition the division of the unit, only a kind of logical function partition can have in actual implementation Division mode, such as multiple units or components can be combined or can be integrated into another system or some features can be with Ignore, or does not execute.Another point, shown or discussed mutual coupling, direct-coupling or communication connection can be logical Some interfaces are crossed, the indirect coupling or communication connection of device or unit can be electrical property, mechanical or other forms.

The present invention be referring to according to the method for the embodiment of the present invention, the process of device (system) and computer program product Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.

These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates, Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or The function of being specified in multiple boxes.

These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one The step of function of being specified in a box or multiple boxes.

In a typical configuration, calculating equipment includes one or more processors (CPU), input/output interface, net Network interface and memory.

Memory may include the non-volatile memory in computer-readable medium, random access memory (RAM) and/or The forms such as Nonvolatile memory, such as read-only memory (ROM) or flash memory (flash RAM).Memory is computer-readable medium Example.

Computer-readable medium includes permanent and non-permanent, removable and non-removable media can be by any method Or technology come realize information store.Information can be computer readable instructions, data structure, the module of program or other data. The example of the storage medium of computer includes, but are not limited to phase change memory (PRAM), static random access memory (SRAM), moves State random access memory (DRAM), other kinds of random access memory (RAM), read-only memory (ROM), electric erasable Programmable read only memory (EEPROM), flash memory or other memory techniques, read-only disc read only memory (CD-ROM) (CD-ROM), Digital versatile disc (DVD) or other optical storage, magnetic cassettes, tape magnetic disk storage, graphene stores or other Magnetic storage device or any other non-transmission medium, can be used for storage can be accessed by a computing device information.According to herein In define, computer-readable medium does not include temporary computer readable media (transitory media), such as the data of modulation Signal and carrier wave.

It will be understood by those skilled in the art that this specification one or more embodiment can provide as method, system or calculating Machine program product.Therefore, this specification one or more embodiment can be used complete hardware embodiment, complete software embodiment or The form of embodiment combining software and hardware aspects.Moreover, this specification one or more embodiment can be used at one or It is multiple wherein include computer usable program code computer-usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) on the form of computer program product implemented.

All the embodiments in this specification are described in a progressive manner, same and similar portion between each embodiment Dividing may refer to each other, and each embodiment focuses on the differences from other embodiments.Especially for system reality For applying example, since it is substantially similar to the method embodiment, so being described relatively simple, related place is referring to embodiment of the method Part explanation.In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", The description of " specific example " or " some examples " etc. means specific features described in conjunction with this embodiment or example, structure, material Or feature is contained at least one embodiment or example of this specification.In the present specification, to the signal of above-mentioned term Property statement be necessarily directed to identical embodiment or example.Moreover, particular features, structures, materials, or characteristics described It may be combined in any suitable manner in any one or more of the embodiments or examples.In addition, without conflicting with each other, this The technical staff in field can be by the spy of different embodiments or examples described in this specification and different embodiments or examples Sign is combined.

The foregoing is merely the embodiments of this specification one or more embodiment, are not limited to book explanation Book one or more embodiment.To those skilled in the art, this specification one or more embodiment can have various Change and variation.All any modification, equivalent replacement, improvement and so within the spirit and principles of the present application should all include Within scope of the claims.

Claims

1. a kind of aliased data separation method based on sparse inversion characterized by comprising

Threshold contracting function is constructed according to the three-dimensional common detector gather data, the threshold contracting function includes the number of iterations With the relationship between seismic data amplitude range；

Sparse constraint objective function is constructed based on the aliased seismic data, the sparse constraint objective function includes the aliasing Functional relation between seismic data and useful signal data；

According to the threshold contracting function, changed using the three-dimensional sliding space-time window of building to the sparse constraint objective function It is solved for inverting, obtains the SNR estimation and compensation data of the aliased seismic data；

The sparse constraint objective function includes:

In above formula,Indicate error term, d indicates that the aliased seismic data, m indicate useful signal data, Γ table Show aliasing operator,Indicate bound term, the sliding space-time window of nx representation space X-direction Window number, the window number of the sliding space-time window of ny representation space Y-direction, nt indicate the sliding space-time window in the direction time t Window number, F indicate three dimensional fast Fourier direct transform operator, W_ix,iy,itIt indicates on space X, space Y, time T direction Three-dimensional sliding space-time window operator, | | FW_ix,iy,itm||₀Expression takes L₀Norm, λ indicate regularization parameter.

2. a kind of aliased data separation method based on sparse inversion as described in claim 1, which is characterized in that the basis The threshold contracting function is iterated inverting to the sparse constraint objective function using the three-dimensional sliding space-time window of building and asks Solution, obtains the SNR estimation and compensation data of the aliased seismic data, comprising:

Execute the first processing step:

Utilize three-dimensional sliding space-time window selection office from the currently active signal data in the sparse constraint objective function Portion's seismic data；

Threshold processing is carried out to local earthquake's data according to the threshold contracting function, obtains pretreatment useful signal number According to；

The adjacent big gun interference noise is subtracted from the three-dimensional common detector gather data, obtains iteration useful signal data；

Judge whether current iteration number is less than and preset total the number of iterations, if being less than, the iteration useful signal data are made For the currently active signal data, first processing step is repeated, until the current iteration number is more than or equal to described When presetting total the number of iterations, using the iteration useful signal data as the SNR estimation and compensation data.

3. a kind of aliased data separation method based on sparse inversion as claimed in claim 2, which is characterized in that the basis The threshold contracting function carries out threshold processing to local earthquake's data, obtains pretreatment useful signal data, comprising:

The corresponding threshold value of current iteration number is obtained according to the threshold contracting function, it will be in the direct transform Fourier data Seismic data less than the threshold value is set to zero, obtains threshold and handles data；

Threshold processing data are subjected to three dimensional fast Fourier inverse transformation, obtain the pretreatment useful signal data.

4. a kind of aliased data separation method based on sparse inversion as claimed in claim 3, which is characterized in that described by institute It states threshold processing data and carries out three dimensional fast Fourier inverse transformation, obtain the pretreatment useful signal data, comprising:

Threshold processing data are subjected to three dimensional fast Fourier inverse transformation, obtain the corresponding office of current three-dimensional sliding space-time window Portion pre-processes useful signal data；

Judge whether the seismic data in the currently active signal data is all selected, if it is not, then sliding described three-dimensional sliding Dynamic space-time window, obtains the corresponding local earthquake's data of next three-dimensional sliding space-time window, and threshold trip of going forward side by side processing obtains next The corresponding part pretreatment useful signal data of three-dimensional sliding space-time window；

If so, each three-dimensional corresponding part of space-time window of sliding is pre-processed useful signal data, integrated, after integration Useful signal data, as the corresponding pretreatment useful signal data of the currently active signal data.

5. a kind of aliased data separation method based on sparse inversion as described in claim 1, which is characterized in that the basis The three-dimensional common detector gather data construct threshold contracting function, comprising:

Fast Fourier direct transform is carried out to the three-dimensional common detector gather data, obtains the three-dimensional common detector gather number According to peak swing value and minimum amplitude value；

6. a kind of aliased data separation method based on sparse inversion as claimed in claim 5, which is characterized in that the threshold Contracting function includes:

7. a kind of aliased data separator based on sparse inversion characterized by comprising

Sparse objective function constructs module, described dilute for constructing sparse constraint objective function based on the aliased seismic data Dredging constrained objective function includes the functional relation between the aliased seismic data and useful signal data；

Alternate analysis module, for sliding space-time window to described sparse using the three-dimensional of building according to the threshold contracting function Constrained objective function is iterated inverting solution, obtains the SNR estimation and compensation data of the aliased seismic data；

The sparse constraint objective function includes:

8. a kind of aliased data separator based on sparse inversion as claimed in claim 7, which is characterized in that the iteration Separation module is specifically used for:

Execute the first processing step:

9. a kind of aliased data separator based on sparse inversion, which is characterized in that including processor and at storage The memory of device executable instruction is managed, the processor is realized when executing described instruction such as any one of claim 1 to 6 institute The step of stating method.