CN106199715A - Strong reflection separation method based on model and device - Google Patents

Abstract

The invention discloses a kind of strong reflection separation method based on model and device, described method includes: set up strong reflection model according to real well data；For strong reflection lineups carry out three wink Parameter analysis, obtain initial wavelet control parameter；Described control parameter is optimized, obtains most preferably mating wavelet with strong reflection lineups；Deduct most preferably mating wavelet from original earthquake data, obtain the data after strong reflection separates, draw model test conclusion；Conclusion (of pressure testing) is used for real data analysis, carries out strong reflection separation；Carry out well lie analysis, adjust parameter；Determine and finally mate Wavelet parameter, obtain whole district's strong reflection separating resulting.Strongly reflecting layer can be separated by the method, prominent reservoir weak signal, improve the precision of reservoir prediction.

Description

Strong reflection separation method based on model and device

Technical field

The present invention relates to the explanatory process field of seismic signal, be specifically related to a kind of based on model Strong reflection separation method and device.

Background technology

When seismic signal is processed, there is strong reflection lineups shielding reservoir weak signal Problem, in seismic prospecting, along with Modern seismic exploration targets by structural oil pool to little sand body, The transfer of the elusive reservoirs such as narrow river course, thin interbed, microcrack, the seismic response energy of reservoir targets Measuring more weak, be often submerged among background information even background noise, waveform is the most often distorted. Therefore, the weak variable signal of weak signal or space the most effectively detects becomes a difficult problem, existing Technology expands and includes High-resolution Processing, attributive analysis, prestack post-stack inversion, and The research of the nearly strongly reflecting layer separation method etc. risen.

Matching pursuit algorithm (Matching Pursuit) is proposed by Mallat etc., and this algorithm can be former Beginning signal decomposition becomes the linear combination of multiple atom, and these atoms are by one group of control parameter determination: Center time delay, mid frequency, phase place, scale factor and amplitude.By selecting difference ginseng The combination of number atom, is reconstructed primary signal, to reach the purpose such as denoising, frequency dividing.Pin Problem to strong reflection lineups shielding reservoir weak signal, matching pursuit algorithm is that one is newer Grain husk method, but in actual mechanical process, control parameter choose be one the most crucial Problem, the choosing of parameter seriously constrains effective application of the method.

Summary of the invention

For defect of the prior art, the invention provides a kind of strong reflection based on model and divide From method and apparatus, the method can utilize match tracing technology in conjunction with real data feature, Strongly reflecting layer is separated, prominent reservoir weak signal, improve the precision of reservoir prediction.

According to one aspect of the invention, it is provided that a kind of strong reflection separation method based on model, It is characterized in that, said method comprising the steps of:

(1) strong reflection model is set up according to real well data；

(2) for strong reflection lineups carry out three wink Parameter analysis, initially controlled parameter； Wherein, described control parameter is that γ={ u, f, φ, k}, u, f, φ and k represent wavelet center respectively Time delay, mid frequency, phase place and scale factor；

(3) described control parameter is optimized, obtains most preferably mating with strong reflection lineups Wavelet；

(4) deduct most preferably mating wavelet from original earthquake data, obtain strong reflection and separate After data, draw model test conclusion；

(5) experiment conclusion is used for real data analysis, carries out strong reflection separation；

(6) carrying out well lie analysis, if meeting the requirements entrance step (7), otherwise entering step Suddenly (5) carry out parameter adjustment；

(7) determine and finally mate Wavelet parameter, obtain whole district's strong reflection separating resulting.

Alternatively, described for strong reflection lineups carry out three wink Parameter analysis, obtain initial Join parameter to include:

Described three wink Parameter analysis include: using the time at instantaneous amplitude largest enveloping as wavelet Center u time delay_n, using the instantaneous frequency at instantaneous amplitude largest enveloping as mid frequency f_n With using the instantaneous phase at instantaneous amplitude largest enveloping as sub-wave phase φ_n, initially controlled Parameter { u_n,f_n,φ_n}；And

Determine that scale factor k, described scale factor k are calculated by below equation:

$g_{{\mathrm{\&gamma;}}_n}\left(t\right)=\arg \underset{g_{{\mathrm{\&gamma;}}_n}\&Element;D}{\max }\frac{|<R^{\left(n\right)}f,g_{{\mathrm{\&gamma;}}_n}>|}{\left|\right|g_{{\mathrm{\&gamma;}}_n}\left|\right|};$

Wherein, hereIt is small echo redundant dictionary, is over-complete dictionary of atoms； <f, g>represents the inner product of f and g,It is by waveletCarry out Normalization.

Alternatively, described Wavelet parameter is optimized, obtains optimal with strong reflection lineups Gamete ripple includes:

According to formulaOptimization wavelet in search dictionary Center u time delay_n, mid frequency f_n, phase_n, then disturbance near initial value, To hunting zone [u_n-Δu,u_n+ Δ u], [f_n-Δf,f_n+ Δ f], [φ_n-Δφ,φ_n+ Δ φ], [k_n-Δk,k_n+ Δ k], Δ u is time interval, and Δ f is frequency interval, and Δ φ is phase intervals, Δ k It it is scale parameter interval.

Alternatively, described Wavelet parameter is optimized, obtains optimal with strong reflection lineups Gamete ripple includes:

According to model test result and real data feature, optimization obtains with described strong reflection The wavelet of good coupling.

Alternatively, described control parameter also includes wavelet amplitude a_n, and obtained by below equation Wavelet amplitude:

$a_n=\frac{|<R^{\left(n\right)}f,g_{{\mathrm{\&gamma;}}_n}>|}{\left|\right|g_{{\mathrm{\&gamma;}}_n}|{|}^2}.$

According to another aspect of the present invention, it is provided that a kind of strong reflection segregation apparatus based on model, It is characterized in that, described device includes following:

Model building module, for setting up strong reflection model according to real well data；

Parameter initialization module, for for strong reflection lineups carry out three wink Parameter analysis, To initially controlling parameter；Wherein, described control parameter is γ={ u, f, φ, k}, u, f, φ and k Represent wavelet center time delay, mid frequency, phase place and scale factor respectively；

Parameter optimization module, for being optimized described control parameter, obtains same with strong reflection Phase axle most preferably mates wavelet；

Data computation module, for deducting most preferably mating wavelet from original earthquake data, Data after separating to strong reflection, draw model test conclusion；

Strong reflection separation module, is used for real data analysis by experiment conclusion, carries out strong reflection and divides From；

Analyze module, be used for carrying out real data well lie analysis；

Determine module, be used for determining and finally mate Wavelet parameter, obtain whole district's strong reflection and separate knot Really.

Alternatively, in described parameter initialization module, described three wink Parameter analysis include: by wink Time amplitude largest enveloping at time as wavelet center u time delay_n, instantaneous amplitude is maximum Instantaneous frequency at envelope is as mid frequency f_nInstantaneous with by instantaneous amplitude largest enveloping Phase place is as sub-wave phase φ_n, initially controlled parameter { u_n,f_n,φ_n}；And

Determine that scale factor k, described scale factor k are calculated by below equation:

$g_{{\mathrm{\&gamma;}}_n}\left(t\right)=\arg \underset{g_{{\mathrm{\&gamma;}}_n}\&Element;D}{\max }\frac{|<R^{\left(n\right)}f,g_{{\mathrm{\&gamma;}}_n}>|}{\left|\right|g_{{\mathrm{\&gamma;}}_n}\left|\right|};$

Wherein, hereIt is small echo redundant dictionary, is over-complete dictionary of atoms； <f, g>represents the inner product of f and g,It is by waveletCarry out normalizing Change.

Alternatively, in described parameter optimization module, according to formulaOptimization wavelet center u time delay in search dictionary_n、 Mid frequency f_n, phase_n, then disturbance near initial value, obtain hunting zone [u_n-Δu,u_n+ Δ u], [f_n-Δf,f_n+ Δ f], [φ_n-Δφ,φ_n+ Δ φ], [k_n-Δk,k_n+ Δ k], Δ u Being time interval, Δ f is frequency interval, and Δ φ is phase intervals, and Δ k is scale parameter interval.

Alternatively, described parameter optimization module, according to model test result and real data feature, Optimization obtains the wavelet most preferably mated with described strong reflection.

Alternatively, device according to claim 6, described control parameter also includes wavelet Amplitude a_n, and by below equation acquisition wavelet amplitude:

$a_n=\frac{|<R^{\left(n\right)}f,g_{{\mathrm{\&gamma;}}_n}>|}{\left|\right|g_{{\mathrm{\&gamma;}}_n}|{|}^2}.$

As shown from the above technical solution, the strong reflection separation method based on model that the present invention provides And device, the control parameter mating wavelet is optimized and asks for, with preferably to strongly reflecting layer Separate, the weak reflectance signature of the prominent reservoir that underlies.The application of real data shows, the most instead Penetrate the seismic signature after separation and have the higher goodness of fit with well data.

Accompanying drawing explanation

Fig. 1 is the stream of the strong reflection separation method based on model provided according to embodiments of the present invention Cheng Tu；

Fig. 2 is two dimensional model schematic diagram according to an embodiment of the invention；

Fig. 3 is forward modeling profile according to an embodiment of the invention；

Fig. 4 is strong reflection separation profile according to an embodiment of the invention；

Fig. 5 is RMS amplitude curve comparison figure before and after separation according to an embodiment of the invention；

Fig. 6 is energy rate of increase curve comparison figure before and after separation according to an embodiment of the invention；

Fig. 7 is that wavelet deducts comparative bid parameter according to an embodiment of the invention；

Fig. 8 is frequency disturbance comparison diagram according to an embodiment of the invention；

Fig. 9 is mistake well profile comparison diagram before and after separation according to an embodiment of the invention.

Detailed description of the invention

Below in conjunction with the accompanying drawings, the detailed description of the invention of invention is further described.Hereinafter implement Example is only used for clearly illustrating technical scheme, and can not limit this with this Bright protection domain.

Matching pursuit algorithm (Matching Pursuit) is proposed by Mallat etc., and this algorithm is A kind of effective signal Its Sparse Decomposition method, can by the linear combination that signal decomposition is multiple atom, By the combination of selected different parameters atom, primary signal is reconstructed, to reach special Purpose.Algorithm fundamental formular expression formula is:

$f=<f,g_{{\mathrm{\&gamma;}}_0}>g_{{\mathrm{\&gamma;}}_0}+R^{1}f---\left(1\right)$

Arbitrary signal in wherein f is Hilbert space,Be the 1st iteration it After the inner product of the residual error basic function selected with during the 1st iteration, R¹F is iteration institute for the first time The residual error produced, R simultaneously¹F is also that f existsResidual error after upper approximation, and R¹F andJust it is Hand over, meet:

$\left|\right|f|{|}^2=|<f,g_{{\mathrm{\&gamma;}}_0}>{|}^2+\left|\right|R^{1}f|{|}^2---\left(2\right)$

After m iteration, obtain:

$f=\underset{n=0}{\overset{m-1}{\&Sigma;}}<R^{n}f,g_{{\mathrm{\&gamma;}}_n}>g_{{\mathrm{\&gamma;}}_n}+R^{m}f---\left(3\right)$

R^mF is last residual error item.

In order to makeRepresent most preferably approaching of signal f, it is necessary to make residual error Xiang Jinke Can little, then in product termShould be the biggest.It is accomplished by finding one to connect most with f Near atom, this process elaborates the implication of match tracing clearly.M iteration is signal Resolve into m atom combination and the m time iteration after residue remnants.In each iterative process In, it is all first to find out atom maximally related with primary signal, then carries out next step decomposition.Weight Carry out again, until post fit residuals value meets setting threshold value or iterations reaches the very big of setting Value.WaveletCan be by γ_n={ u_n,f_n,φ_n,k_nPortray, u_n、f_n、φ_nAnd k_nIt is referred to as wavelet control Parameter processed, represents required wavelet respectivelyCenter time delay, mid frequency, phase place and Scale factor.

The present invention utilizes three-step approach to ask for mating the control parameter of wavelet.First with Morlet small echo As ground atom, by wavelet center time delay, mid frequency, phase place and scale factor Characterizing coupling wavelet, four control parameter group of Morlet small echo become parameter array γ, γ=u, f, φ, k}, and represent respectively wavelet center time delay, mid frequency, phase place and yardstick because of Son.

Morlet Wavelet temporal territory expression formula is

$wt=\exp (-\ln 2\frac{f_m^2{(t-u)}^2}{k})\exp \left(i\right(2{\mathrm{\&pi;f}}_m(t-u)+\mathrm{\&phi;}\left)\right)---\left(4\right)$

f_mBeing wavelet mid frequency, time delay centered by u, k is scale factor, and φ is phase place. Wavelet is presented herein below and controls the concrete acquiring method of parameter.

The first step, utilize complex seismic trace analytical technology calculate three wink attribute, instantaneous amplitude Time at largest enveloping is as center u time delay_n, the instantaneous frequency at this time point and Instantaneous phase is equally as initial value f_nAnd φ_n, obtain three parameter { u of preresearch estimates_n,f_n,φ_n}。 For Morlet wavelet, it is also understood that scale factor k, the most just can determine that one uniquely Morlet wavelet, scale factor is calculated by formula (5),

$g_{{\mathrm{\&gamma;}}_n}\left(t\right)={\mathrm{argmax}}_{g_{{\mathrm{\&gamma;}}_n}\&Element;D}\frac{|<R^{\left(n\right)}f,g_{{\mathrm{\&gamma;}}_n}>|}{\left|\right|g_{{\mathrm{\&gamma;}}_n}\left|\right|}---\left(5\right)$

HereIt is small echo redundant dictionary, is over-complete dictionary of atoms.<f, g>generation The inner product of table f and g,It is by waveletCarry out normalization.Utilize Three originally determined parameters u_n, f_nAnd φ_n, with formula (5), parameter k is carried out traversal search, Obtain optimal value.

Second step, asks for the most optimized parameter to determine the wavelet of couplingSearching with formula (5) In rope dictionary, these four parameters of optimization, then disturbance near initial value, obtain hunting zone [u_n-Δu,u_n+ Δ u], [f_n-Δf,f_n+ Δ f], [φ_n-Δφ,φ_n+ Δ φ], [k_n-Δk,k_n+ Δ k], Δ u Being time interval, Δ f is frequency interval, and Δ φ is phase intervals, and Δ k is scale parameter interval.

3rd step, estimates amplitude parameter a_n, utilize formula (6) to obtain wavelet amplitude；

$a_n=\frac{|<R^{\left(n\right)}f,g_{{\mathrm{\&gamma;}}_n}>|}{\left|\right|g_{{\mathrm{\&gamma;}}_n}|{|}^2}---\left(6\right)$

For strong reflection lineups separate, this process only needs iteration once, and the In two steps, should obtain the most anti-with this according to model test result and real data feature, optimization Penetrate the wavelet of optimal coupling, then coupling wavelet is deducted from original seismic data, gone Except the record after strong reflection, and then next step analysis can be carried out.

Choosing for match parameter, the present invention passes through model test, correctly choosing parameter Provide certain reference function, be effectively improved the method effect in actual applications.Model Specifically comprising the following steps that of test

1) strong reflection model is set up.

2) strong reflection lineups are carried out three wink Parameter analysis, obtain initial match parameter.

3) optimize Wavelet parameter, obtain the wavelet most preferably mated with strong reflection lineups.

4) coupling wavelet is deducted from original seismic data, obtain the note after strong reflection separates Record, draws model test conclusion.

5) experiment conclusion is used for real data analysis, carries out strong reflection separation；

6) carry out well lie analysis, according to analytical effect, step (7) can be entered or enter Enter the 5th) adjustment of stepping line parameter.

7) determine final coupling Wavelet parameter, obtain rational strong reflection separating resulting, enter Row reservoir prediction analysis etc..

Strong reflection separation method flow chart based on model is as shown in Figure 1.

Wavelet control parameter is chosen and is discussed by the present invention first, mainly frequency and son Ripple deducts the discussion of ratio.Analysis of experiments shows, in order to obtain best separating effect, Ying Jiang Coupling wavelet all deducts, and original frequency should be near strong reflection lineups dominant frequency, and disturbance model Enclose smaller.

According to one embodiment of present invention, it is provided that a kind of strong reflection separation side based on model Method, it is characterised in that said method comprising the steps of:

Strong reflection model is set up according to real well data；Make following strong anti-in the present embodiment Penetrate model (model schematic as in figure 2 it is shown, this model just drill profile as it is shown on figure 3, white Line is the strong reflection axle of pickup), upper strata represents slow formation, and speed is 1000m/s, and density is 1.65g/cm³；Underliing for mudstone stratum, setting speed is 2500m/s, and density is 2.2g/cm³； Centre is sandstone, and setting speed is 3500m/s, and density is 2.275g/cm³；Sandstone formation is adjacent Impedance interface above.Position, three mouthfuls of well Well1, Well2 and Well3 well points cumulative sand is thick Degree is respectively 18m, 5.4m and 13.7m.As Fig. 4 shows strong reflection separation profile, extract RMS amplitude attribute in window when lineups up 10ms, down 40ms, sends out by analyzing Existing, this model shows more consistent feature on RMS amplitude curve.In order to further Analyze the Changing Pattern of curve, the rate of change of RMS amplitude before and after processing has been done statistical Analysis, will without sand body seismic channel RMS amplitude as background value RmsAmp, other seismic channels RMS amplitude value is designated as variable R msAmp (x), x=1, and 2,3 ..., if energy rate of increase is Ratio(x)。

Ratio (x)=(RmsAmp (x)-RmsAmp)/RmsAmp (7)

The reflection of this parameter is the root-mean-square energy situation of change relative to background value, can be intuitively The attribute character of the geological data before and after displaying strong reflection separation, as shown in Figure 5.Before and after process Rate of increase there occurs significantly change, after process, rate of increase becomes big, and this explanation is relative to background For strong reflection, Sandstone Section response on amplitude attribute becomes apparent from, and this is to real data Application serves positive directive function.

It is discussed additionally, emphasis of the present invention deducts ratio to frequency disturbance and wavelet.This Bright under conditions of frequency is fixing, be individually subtracted coupling the 0.6 of wavelet, 0.8 times, 1 times (all Deduct) contrast, comparing result such as Fig. 6.It follows that the feelings all deducted in wavelet Under condition, energy rate of increase is maximum.

Under conditions of coupling wavelet all deducts, after setpoint frequency range of disturbance, algorithm is automatic Optimum Matching wavelet is found, such as Fig. 7 in disturbance interval.It follows that for real data Spectral change feature, in order to accurately reflect preliminary wavelet feature and lineups energy variation rule, Strengthen the relative change of this energy to greatest extent, in relatively stable strong reflection district, it is proposed that give Fixed relatively microvariations scope, about 1-2Hz.

For real data feature, interval of interest seismic data is analyzed, is determined data Dominant frequency is at about 25Hz.Combining with theoretical analysis, passed through well profile parameter testing, finally determined It is 24Hz-25Hz that wavelet controls parameters frequency, and zero phase, scale parameter selects k=0.17, initially Wavelet center is the T6c layer bit time (strong reflection crest) explained time delay, and disturbance is interval 2ms.The shale strong reflection of Zhang Jia beach, work area is carried out whole district's separation, after obtaining strong reflection separation Data volume, in order to subsequent analysis.

The present invention have chosen two mouthfuls of typical well W1 and W2 and carried out crossing the contrast of well profile and divided Analysis, such as Fig. 8.Before strongly reflecting layer separates, two mouthfuls of wells have strong reflection clearly at T6c, Feature differentiation is the most difficult.Separated by strong reflection, it is clear that W1 well is by force Obvious amplitude-frequency response (shown in oval) occurs under reflecting layer, and W2 well amplitude-frequency response is faint. From the log data of two mouthfuls of wells, 8 sections of sand body gross thickness of W1 well length reach 31.6m, and W2 8 sections of sand body gross thickness of well length are only 6.6m, have concordance well with the section after processing.

The core of matching pursuit algorithm is accurately asking for of coupling wavelet, so asking for controlling parameter The problem becoming most critical.Tradition application lacks the process such as modelling verification and detail parameters analysis, Lack reliability, apply DeGrain.The present invention is based on model test, to coupling wavelet Control parameter and ask for having carried out more detail discussion, ask for mating wavelet more accurately, and Strong reflection lineups are carried out optimal separation, the weak reflectance signature of the prominent reservoir that underlies.Actual money The application of material shows, the seismic signature after strong reflection separates has the higher goodness of fit with well data, Application effect is obvious.

In the description of the present invention, illustrate a large amount of detail.It is to be appreciated, however, that this Inventive embodiment can be put into practice in the case of not having these details.In some instances, It is not shown specifically known method, structure and technology, in order to the not fuzzy reason to this specification Solve.

Similarly, it will be appreciated that disclose to simplify the present invention and help to understand each invented party One or more in face, above in the description of the exemplary embodiment of the present invention, this Each bright feature is grouped together in single embodiment, figure or descriptions thereof sometimes. But, should the method for the disclosure not explained the most required for protection in reflecting an intention that Application claims feature more more than the feature being expressly recited in each claim.More true Say with cutting, as the following claims reflect as, inventive aspect is less than above All features of disclosed single embodiment.Therefore, it then follows the claim of detailed description of the invention Book is thus expressly incorporated in this detailed description of the invention, and the most each claim itself is as this The independent embodiment of invention.

It will be understood by those skilled in the art that and the module in the equipment in embodiment can be carried out Adaptively change and they are provided in different one or more of this embodiment and set In Bei.Can the module in embodiment or unit or assembly be combined into a module or unit or Assembly, and multiple submodule or subelement or sub-component can be put them in addition.Except At least some in such feature and/or process or unit is mutually exclusive part, permissible Use any combination to public in this specification (including adjoint claim, summary and accompanying drawing) All features of opening and the disclosedest any method or all processes of equipment or unit It is combined.Unless expressly stated otherwise, this specification (includes adjoint claim, plucks Want and accompanying drawing) disclosed in each feature can be by providing identical, equivalent or the replacing of similar purpose Replace for feature.

Although additionally, it will be appreciated by those of skill in the art that embodiments more described herein Including some feature included in other embodiments rather than further feature, but different enforcement The combination of the feature of example means to be within the scope of the present invention and formed different enforcement Example.Such as, in the following claims, embodiment required for protection one of arbitrarily Can mode use in any combination.

The all parts embodiment of the present invention can realize with hardware, or with at one or many The software module run on individual processor realizes, or realizes with combinations thereof.This area It will be appreciated by the skilled person that microprocessor or digital signal processor can be used in practice (DSP) some in the equipment of a kind of browser terminal according to embodiments of the present invention are realized Or all some or all functions of parts.The present invention is also implemented as performing this Part or all equipment of the method described by or device program (such as, calculate Machine program and computer program).The program of such present invention of realization can be stored in meter On calculation machine computer-readable recording medium, or can be to have the form of one or more signal.Such letter Number can download from internet website and to obtain, or provide on carrier signal, or to appoint What his form provides.

The present invention will be described rather than enters the present invention to it should be noted above-described embodiment Row limits, and those skilled in the art are without departing from the scope of the appended claims Alternative embodiment can be designed.In the claims, any ginseng between bracket should not will be located in Examine symbol construction and become limitations on claims.Word " comprises " not exclude the presence of and is not listed in right Element in requirement or step.Word "a" or "an" before being positioned at element does not excludes the presence of Multiple such elements.The present invention can by means of include some different elements hardware and Realize by means of properly programmed computer.If listing the unit claim of equipment for drying In, several in these devices can be specifically to be embodied by same hardware branch.Word First, second and third use do not indicate that any order.Can be by these word explanations For title.

It is last it is noted that various embodiments above is only in order to illustrate technical scheme, It is not intended to limit；Although the present invention being described in detail with reference to foregoing embodiments, It will be understood by those within the art that: it still can be to described in foregoing embodiments Technical scheme modify, or the most some or all of technical characteristic carried out equivalent replace Change；And these amendments or replacement, do not make the essence of appropriate technical solution depart from the present invention each The scope of embodiment technical scheme, it all should be contained in the claim of the present invention and description In the middle of scope.

Claims (10)

1. a strong reflection separation method based on model, it is characterised in that described method bag Include following steps:

(1) strong reflection model is set up according to real well data；

(2) for strong reflection lineups carry out three wink Parameter analysis, initially controlled parameter； Wherein, described control parameter is that γ={ u, f, φ, k}, u, f, φ and k represent wavelet center respectively Time delay, mid frequency, phase place and scale factor；

(3) described control parameter is optimized, obtains most preferably mating with strong reflection lineups Wavelet；

(4) deduct most preferably mating wavelet from original earthquake data, obtain strong reflection and separate After data, draw model test conclusion；

(5) experiment conclusion is used for real data analysis, carries out strong reflection separation；

(6) carrying out well lie analysis, if meeting the requirements entrance step (7), otherwise entering step Suddenly (5) carry out parameter adjustment；

(7) determine and finally mate Wavelet parameter, obtain whole district's strong reflection separating resulting.

Method the most according to claim 1, it is characterised in that described same for strong reflection Phase axle carry out three wink Parameter analysis, obtain initial matching parameter and include:

Described three wink Parameter analysis include: using the time at instantaneous amplitude largest enveloping as wavelet Center u time delay_n, using the instantaneous frequency at instantaneous amplitude largest enveloping as mid frequency f_n With using the instantaneous phase at instantaneous amplitude largest enveloping as sub-wave phase φ_n, initially controlled Parameter { u_n,f_n,φ_n}；And

Determine that scale factor k, described scale factor k are calculated by below equation:

$g_{{\mathrm{\&gamma;}}_n}\left(t\right)=\arg \underset{g_{{\mathrm{\&gamma;}}_n}\&Element;D}{max}\frac{|<R^{\left(n\right)}f,g_{{\mathrm{\&gamma;}}_n}>|}{\left|\right|g_{{\mathrm{\&gamma;}}_n}\left|\right|};$

Wherein, hereIt is small echo redundant dictionary, is over-complete dictionary of atoms； <f, g>represents the inner product of f and g,It is by waveletCarry out Normalization.

Method the most according to claim 1, it is characterised in that described Wavelet parameter is entered Row optimizes, and obtains most preferably mating wavelet with strong reflection lineups and includes:

According to formulaOptimization wavelet in search dictionary Center u time delay_n, mid frequency f_n, phase_n, then disturbance near initial value, To hunting zone [u_n-Δu,u_n+ Δ u], [f_n-Δf,f_n+ Δ f], [φ_n-Δφ,φ_n+ Δ φ], [k_n-Δk,k_n+ Δ k], Δ u is time interval, and Δ f is frequency interval, and Δ φ is phase intervals, Δ k It it is scale parameter interval.

4. according to the method described in claim 1 or 3, it is characterised in that described to wavelet ginseng Number is optimized, and obtains most preferably mating wavelet with strong reflection lineups and includes:

According to model test result and real data feature, optimization obtains with described strong reflection The wavelet of good coupling.

Method the most according to claim 2, it is characterised in that described control parameter is also wrapped Enclosed tool wave-amplitude a_n, and by below equation acquisition wavelet amplitude:

$a_n=\frac{|<R^{\left(n\right)}f,g_{{\mathrm{\&gamma;}}_n}>|}{{\left|\right|g_{{\mathrm{\&gamma;}}_n}\left|\right|}^2}.$

6. a strong reflection segregation apparatus based on model, it is characterised in that described device bag Include following:

Model building module, for setting up strong reflection model according to real well data；

Parameter initialization module, for for strong reflection lineups carry out three wink Parameter analysis, To initially controlling parameter；Wherein, described control parameter is γ={ u, f, φ, k}, u, f, φ and k Represent wavelet center time delay, mid frequency, phase place and scale factor respectively；

Parameter optimization module, for being optimized described control parameter, obtains same with strong reflection Phase axle most preferably mates wavelet；

Data computation module, for deducting most preferably mating wavelet from original earthquake data, Data after separating to strong reflection, draw model test conclusion；

Strong reflection separation module, is used for real data analysis by experiment conclusion, carries out strong reflection and divides From；

Analyze module, be used for carrying out real data well lie analysis；

Determine module, be used for determining and finally mate Wavelet parameter, obtain whole district's strong reflection and separate knot Really.

Device the most according to claim 6, it is characterised in that described parameter initialization mould In block, described three wink Parameter analysis include: using the time at instantaneous amplitude largest enveloping as son Ripple center u time delay_n, using the instantaneous frequency at instantaneous amplitude largest enveloping as mid frequency f_nWith using the instantaneous phase at instantaneous amplitude largest enveloping as sub-wave phase φ_n, initially controlled Parameter { u processed_n,f_n,φ_n}；And

Determine that scale factor k, described scale factor k are calculated by below equation:

$g_{{\mathrm{\&gamma;}}_n}\left(t\right)=\arg \underset{g_{{\mathrm{\&gamma;}}_n}\&Element;D}{max}\frac{|<R^{\left(n\right)}f,g_{{\mathrm{\&gamma;}}_n}>|}{\left|\right|g_{{\mathrm{\&gamma;}}_n}\left|\right|};$

Wherein, hereIt is small echo redundant dictionary, is over-complete dictionary of atoms； <f, g>represents the inner product of f and g,It is by wavelet g_γnCarry out normalizing Change.

Device the most according to claim 6, it is characterised in that described parameter optimization module In, according to formulaOptimization wavelet center in search dictionary Time delay u_n, mid frequency f_n, phase_n, then disturbance near initial value, searched Rope scope [u_n-Δu,u_n+ Δ u], [f_n-Δf,f_n+ Δ f], [φ_n-Δφ,φ_n+ Δ φ], [k_n-Δk,k_n+ Δ k], Δ u is time interval, and Δ f is frequency interval, and Δ φ is phase intervals, and Δ k is scale parameter interval.

9. according to the device described in claim 6 or 8, it is characterised in that described parameter optimization Module, according to model test result and real data feature, optimization obtains and described strong reflection The wavelet of optimal coupling.

Device the most according to claim 6, described control parameter also includes wavelet amplitude a_n, And by below equation acquisition wavelet amplitude:

$a_n=\frac{|<R^{\left(n\right)}f,g_{{\mathrm{\&gamma;}}_n}>|}{{\left|\right|g_{{\mathrm{\&gamma;}}_n}\left|\right|}^2}.$