CN109557585A - A kind of CRS dip decomposition method based on differential evolution algorithm - Google Patents
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Abstract
The invention belongs to oil-gas exploration seism processing fields, more particularly to a kind of CRS dip decomposition method based on differential evolution algorithm, by to the discrete completion of angle of emergence α, if inclination angle is divided into stem portion, differential evolution algorithm is based respectively on to each part and obtains the parameter with highest similarity, is finally overlapped each section.The present invention proposes the global parameter optimization method based on differential evolution algorithm, to seek three independent variables α, RNIP, and RNMaximum coherence value, using measure coherent value size related coefficient three parameters are searched for as objective function.Lesser part is divided into the angle parameter range of entire search space, can identify the lineups information for interfering with each other and leading to cover.It being combined by dividing at inclination angle with global optimization scheme, effectively having restored complicated subsurface structure situation, having improved the effect of common reflection surface stack.
Description
Technical field
The invention belongs to oil-gas exploration seism processing fields, and in particular to a kind of CRS based on differential evolution algorithm
Dip decomposition method.
Background technique
The 1980's Mos, German professor Hubral propose CRS stack (Common Reflection
Surface CRS) technology, abbreviation CRS superposition.CRS stacking method does not both need that medium is descended to carry out any shape hypothesis over the ground,
It does not need to seek rate pattern, makes full use of the coherence of seismic wave, anisotropically medium is descended to be all suitable for uniform yet.Folded
Add in pore diameter range, adjacent trace gather self-energy is superimposed as much as possible, enhances seismic signal energy, so as to effectively improve
The processing quality of low signal-to-noise ratio seismic data.It can not only obtain the zero bias stability of high quality by CRS superposition, moreover it is possible to obtain
Multiple Expressive Features wave attributes kinematics parameters, these parameters have fully demonstrated the geological information of underground medium, thus
CRS superposition is an important developing direction in deep layer and complex area seismic data process.
Two-dimentional CRS formula includes three subsurface wavefield parameters, these three parameters are respectively that normal direction seismic wave is emitted on ground
Angle α, normal incidence point (NIP) wave wave-front curvature radius RNIPAnd normal direction (N) wave wave-front curvature radius RN, these three earthquakes are calculated
It is chosen, initial parameter is searched on conventional zero-offset stacked section, calculated projection Fresnel and bring determining superposition hole
Diameter carries out CRS superposition in superposition pore diameter range, tests all wave field attributes three parameter (α, RN, RNIP), therefrom choose tool
There are wave field attributes three parameter (α, the R of maximum coherence valueN, RNIP), obtained superposition operator can generate for the local structure of underground
Optimal illumination obtains the effect of optimal superposition.Three-dimensional CRS superposition determines required parameter using similar process.
CRS stacking method enhances the continuity and signal-to-noise ratio of reflection line-ups, conventional parameter choosing on stacked section
Taking is tested all parameters, the α of selection, RNIP, and RNWith maximum coherence value, however it is countless to three parameter testings
Coherent value is obviously unrealistic.In conventional parameter search, angle of emergence α is searched at first, is searched on the basis of α is determined later
Rope RNIPAnd RN, therefore this mode cause to select at the every bit in stacked section it is most strong from some angle
Reflected energy, and the reflective information of other angles and diffraction information will be disturbed elimination, lead to CRS stacked section kinematics
Characteristic Distortion.Especially in the area that the complex or extraordinary wave of construction is quite developed, due to diffraction energy after CRS superposition
Missing, seriously affects the effect of stacked section, subsequent seismic migration imaging quality can also reduce, therefore the problem affects
The practicability of CRS stacking method.Two-dimentional CRS superposition can obtain the imaging results of high quality under complicated geological conditions, so
And 3 subsurface wavefield parameters are needed since three-dimensional CRS is superimposed, the result of three-dimensional CRS superposition is influenced very by parameter
Greatly.In the current fine granularing scalability development phase, how accurately Selecting All Parameters, the influence for solving inclination angle discrimination, selection are more efficient
The problems such as have great importance.
In order to improve the accuracy of parameter search, Yang Kai (2002) proposes the method for dip scanning, replaces tradition CRS parameter
Influence is discriminated against at optimizing search, the inclination angle for attempting to eliminate CRS.Yang Kai (2005) is successfully solved using the method for dip decomposition
The Angle discrimination phenomenon occurred in two-dimentional CRS parameter search, so that the effect of two dimension CRS superposition is more accurate.Sun little Dong etc.
(2006) simulated annealing is introduced in complicated structure area, solves three parameters of complicated earth surface CRS superposition travel-time equation
Lotus root closes bring parameter search and optimization problem.Xue Fan (2012) not only solves inclination angle discrimination using dip decomposition method and does
It disturbs, angle of emergence filtering processing also is carried out to stacked section using dip decomposition method.Yang Kai (2013) etc. proposes to be superimposed traditional CRS
Imaging method improves, and CRS stack (CRS-OIS) method of Efferent tube mode is applied to GPU computing platform,
To solve inclination angle collision problem.
Summary of the invention
According to the above-mentioned deficiencies of the prior art, the present invention provides a kind of dip decomposition side CRS based on differential evolution algorithm
Method optimizes search to parameter using differential evolution method, can not only obtain more accurate parameter, can also eliminate and incline
The phenomenon that angle is discriminated against, and it is more efficient.
A kind of CRS dip decomposition method based on differential evolution algorithm of the present invention, it is characterised in that: by out
The discrete completion of firing angle α is based respectively on differential evolution algorithm to each part and obtains with highest if inclination angle is divided into stem portion
Each section, is finally overlapped by the parameter of similarity.
Wherein, preferred embodiment is as follows:
Before the partition process of inclination angle, in order to improve R in search spaceNIPLower limit, minimum NMO velocity is needed, by this
Minimum NMO velocity calculates lower limit
Lb is expressed as lower boundary, αminAnd αmaxFor the boundary of angular range, v0It is earth's surface speed, VminIt is NMO velocity,
Similarly, it can use maximum NMO velocity and calculate RNIPThe upper limit, wherein CRS NMO velocity is using each time sampling point
CRS parameter obtains.Theoretically RNFor infinitely large quantity, lesser value range can be determined by limiting search space.But it is right
For coherent value, RNVariation caused by value is very small, therefore does not have to consider RNLimitation range.
Shooting angle α is most important parameter during the parameter search that traditional CRS is superimposed, RNIPAnd RNIt is to be calculated using α
It obtains.Dip decomposition processing is by decomposing inclination angle domain, if inclination angle search range is divided into stem portion, preferably 3~5
Point.Division for search range, such asIt means that and is divided into 5 lineups search models
Enclose, oneIt is interior, it is next then to existIt is interior, it successively searches in this way.In each inclination angle range, based on poor
Evolution algorithm is divided to carry out CRS parameter search.It is divided into smaller part in this way, not only reduces search space, improves the receipts of optimization
Holding back property, and can identify the lineups information for interfering with each other and leading to offset, improve subsequent seism processing result
Quality.
It is described differential evolution algorithm is based respectively on to each part to obtain the realization process of the parameter with highest similarity
It is as follows:
1) selection target function: processing is optimized using similarity factor as objective function；
2) initialization of population；
3) mutation operation: reference vector is made a variation to obtain variation vector；
4) trial vector crossover operation: is obtained using crossing formula to reference vector and variation vector；
5) selection operation: the operation is based on greedy mechanism, has more Gao Shiying between Selection experiment vector sum reference vector
Individual of the vector of angle value as next-generation population；The target function value of offspring individual is made to be always above parent individuality in this way
Target function value is intended to optimal solution so as to cause result always.
6) terminate and be recycled into downstream: cyclic process 2) to process 5) process flow, termination condition is that judgement changes
Whether the value of objective function continues growing after generation calculates, and process 2 is continued cycling through if continuing growing) to process 5) processing stream
Journey obtains the parameter with highest similarity if not continuing to increase.
Wherein, the process 1) in similarity factor are as follows:
In formula: Xj(i) amplitude of i-th j-th sampled point is indicated；N is the number of sampling points in every trace record；M is note
Total road number in record, the magnitude range of similarity factor are [0,1].
The process 2) the specific implementation process is as follows:
Parameter vector x is tieed up using NP-Di,G, i=1 ..., NP carry out initialization of population and obtain reference vector, wherein parameter
Vector includes parameter angle of emergence α, normal incidence point wave wave-front curvature radius RNIPAnd normal direction wave wave-front curvature radius RN, then D
Value is that 3, NP is Population Size, and the reasonable value of NP individual amount is between 5D to 10D in population, population scale that the present invention selects
NP=20, G are the number of iterations.
The process 3) the specific implementation process is as follows:
It makes a variation to reference vector, variation vector is obtained by following formula
In formula, r1,r2,r3∈{1,.....,Np, F is zoom factor, and the value for the F that the present invention selects is 0.9362.To it
In the differences of two vectors zoom in and out, and be added to obtain a variation vector v with third reference vectori.G+1。
The process 4) the specific implementation process is as follows:
Reference vector and variation vector obtain trial vector as the following formula
In formula, CR is crossover probability, and for crossover probability CR between 0 and 1, CR value is bigger, the probability intersected
Crossover probability CR=0.7455, j ∈ [1, D] bigger, that the present invention chooses, jrandIt is 1 to the random integers between D.In order to keep away
Exempt from the invalid intersection of group, therefore, to assure that trial vector, reference vector and variation vector are different from.
The present invention has the advantages that the global parameter optimization method based on differential evolution algorithm is proposed, to seek three solely
Vertical variable α, RNIP, and RNMaximum coherence value, using measure coherent value size related coefficient three ginsengs are searched for as objective function
Number.Lesser part is divided into the angle parameter range of entire search space, can identify to interfere with each other leads to the same of coverage
Phase axis information.It being combined by dividing at inclination angle with global optimization scheme, effectively having restored complicated subsurface structure situation, improved
The effect of common reflection surface stack.
Detailed description of the invention
Fig. 1 is the CRS dip decomposition techniqueflow chart of the invention based on differential evolution algorithm；
Fig. 2 is the CRS dip decomposition of the differential evolution algorithm of model data；
Fig. 3 is the CRS dip decomposition of the differential evolution algorithm of real data.
Specific embodiment
The present invention is described in detail below with reference to the accompanying drawings and embodiments.It should be appreciated, however, that the offer of attached drawing is only
For a better understanding of the present invention, they should not be interpreted as limitation of the present invention.
Embodiment 1:
As shown in Figure 1, a kind of CRS dip decomposition method based on differential evolution algorithm, by discrete complete to angle of emergence α
At being based respectively on differential evolution algorithm to each part and obtain the ginseng with highest similarity if inclination angle is divided into stem portion
Each section, is finally overlapped by number.
Before the partition process of inclination angle, in order to improve R in search spaceNIPLower limit, minimum NMO velocity is needed, by this
Minimum NMO velocity calculates lower limit
Lb is expressed as lower boundary, αminAnd αmaxFor the boundary of angular range, v0It is earth's surface speed, VminIt is NMO velocity,
Similarly, it can use maximum NMO velocity and calculate RNIPThe upper limit, wherein CRS NMO velocity is using each time sampling point
CRS parameter obtains.Theoretically RNFor infinitely large quantity, lesser value range can be determined by limiting search space.But it is right
For coherent value, RNVariation caused by value is very small, therefore does not have to consider RNLimitation range.
Shooting angle α is most important parameter during the parameter search that traditional CRS is superimposed, RNIPAnd RNIt is to be calculated using α
It obtains.Dip decomposition processing is by decomposing inclination angle domain, if inclination angle search range is divided into stem portion, preferably 3~5
Point.Division for search range, such asIt means that and is divided into 5 lineups search models
Enclose, oneIt is interior, it is next then to existIt is interior, it successively searches in this way.In each inclination angle range, based on poor
Evolution algorithm is divided to carry out CRS parameter search.It is divided into smaller part in this way, not only reduces search space, improves the receipts of optimization
Holding back property, and can identify the lineups information for interfering with each other and leading to offset, improve subsequent seism processing result
Quality.
It is described differential evolution algorithm is based respectively on to each part to obtain the realization process of the parameter with highest similarity
It is as follows:
1) processing, the similarity factor selection target function: are optimized using similarity factor as objective function are as follows:
In formula: Xj(i) amplitude of i-th j-th sampled point is indicated；N is the number of sampling points in every trace record；M is note
Total road number in record, the magnitude range of similarity factor are [0,1].The optimization object function of the present embodiment is similarity factor, is passed through
Compare α, RNIP, and RNCoherent value, select maximum coherence value as CRS parameter.Similarity factor is the ginseng for measuring coherent value size
Number, similarity factor is higher, then coherence is better, and coherent value is higher.For scale parameter k, what value no matter is assigned, as amplitude Xj
(i) by kXj(i) when replacing, S function value is constant.
2) initialization of population: parameter vector x is tieed up using NP-Di,G, i=1 ..., NP carries out initialization of population and obtains benchmark
Vector, wherein parameter vector includes parameter angle of emergence α, normal incidence point wave wave-front curvature radius RNIPAnd normal direction wave wavefront is bent
Rate radius RN, then it is Population Size that the value of D, which is 3, NP, and the reasonable value of NP individual amount is between 5D to 10D in population, the present invention
The population scale NP=20, G of selection are the number of iterations；
3) mutation operation: reference vector is made a variation to obtain variation vector by following formula；
In formula, r1,r2,r3∈{1,.....,Np, F is zoom factor, and the value for the F that the present invention selects is 0.9362.To it
In the differences of two vectors zoom in and out, and be added to obtain a variation vector v with third reference vectori.G+1。
4) trial vector crossover operation: is obtained using crossing formula to reference vector and variation vector
In formula, CR is crossover probability, and for crossover probability CR between 0 and 1, CR value is bigger, the probability intersected
Crossover probability CR=0.7455, j ∈ [1, D] bigger, that the present invention chooses, jrandIt is 1 to the random integers between D.In order to keep away
Exempt from the invalid intersection of group, therefore, to assure that trial vector, reference vector and variation vector are different from.
5) selection operation: the operation is based on greedy mechanism, has more Gao Shiying between Selection experiment vector sum reference vector
Individual of the vector of angle value as next-generation population；The target function value of offspring individual is made to be always above parent individuality in this way
Target function value is intended to optimal solution so as to cause result always.
6) terminate and be recycled into downstream: cyclic process 2) to process 5) process flow, termination condition is that judgement changes
Whether the value of objective function continues growing after generation calculates, and process 2 is continued cycling through if continuing growing) to process 5) processing stream
Journey obtains the parameter with highest similarity if not continuing to increase.It is iteration time that NP-D, which ties up parameter G in parameter vector,
Number, usually as stop criterion, G is bigger, and solution is 200 iteration closer to global optimum, the maximum value of G.Meeting
After termination condition, the parameter with highest similarity factor will be used to be overlapped processing.
Relative to earthquake reverse-time migration method, the CRS dip decomposition method based on differential evolution algorithm has higher essence
Degree and migration imaging quality.In order to prove the application effect of this method, comparative test is overlapped first with analogue data.Figure
2 (a) are shown progress routine CRS stack result, and the parameter after carrying out differential evolution processing, which is shown, in Fig. 2 (b) carries out
The superimposed processing of CRS.It can be seen that signal-to-noise ratio after handling by CRS from the comparison of two figures to be remarkably reinforced, lineups are continuous
Property improve, resolution ratio also obviously increases, furthermore it can be seen that the presence of diffracted wave, and nascent more from ordinate 3 seconds
Subwave leads to inclination angle interference phenomenon.Stacked section after optimization processing then significantly reduces the influence of inclination angle interference, effect
It is superimposed significantly better than conventional CRS, lineups are more obvious continuous, can preferably can reflect underground real construction condition.
In order to further verify the practical application effect of this patent method, to certain exploratory area real data carry out based on difference into
Change the CRS dip decomposition processing of algorithm.Fig. 3 shows the stacked section obtained using different disposal method, respectively Fig. 3
(a) CMP stacked section, the CRS stacked section of Fig. 3 (b) routine, Fig. 3 (c) are cutd open using the CRS superposition after difference optimization algorithm
Face and Fig. 3 (d) utilize the CRS stacked section after difference optimization algorithm and dip decomposition.It can by comparison diagram 3 (a) and 3 (b)
To see, compared with traditional CMP superposition, conventional CRS superposition has shown that higher signal-to-noise ratio, lineups are increased
By force.Fig. 3 (b) and Fig. 3 (c) are compared, can be with by the determining CRS parameter of optimization difference algorithm search as superposition parameter
It obtains being superimposed more preferably stacked section than conventional CRS, signal-to-noise ratio improves, the enhancing of lineups continuity.Compared with Fig. 3 (c), pass through
Optimization difference searching algorithm and the Fig. 3 (d) that dip decomposition is handled that conflicts can obtain more reflected energies, especially more
The information of more diffraction lineups is more clear by the result that the image that dip decomposition is handled is handled than no dip decomposition,
Restore to interfere with each other the lineups information for leading to offset.Due to there is no to utilize excessive premise item in dip decomposition treatment process
Part, therefore can use and provide other conditions to improve shown here as a result, for example, making by using NMO velocity model
For constraint condition.
It can be seen from the figure that the CRS dip decomposition method of the differential evolution algorithm proposed using this patent can have
Effect ground improves the resolution ratio and precision of seismic imaging, to be preferably subsequent reservoir description and exploitation service.
The various embodiments described above are merely to illustrate the present invention, wherein the structure of each component, connection type and manufacture craft etc. are all
It can be varied, all equivalents and improvement carried out based on the technical solution of the present invention should not exclude
Except protection scope of the present invention.
Claims (10)
1. a kind of CRS dip decomposition method based on differential evolution algorithm, it is characterised in that: by the discrete completion of angle of emergence α,
If inclination angle is divided into stem portion, differential evolution algorithm is based respectively on to each part and obtains the parameter with highest similarity,
Finally each section is overlapped.
2. a kind of CRS dip decomposition method based on differential evolution algorithm according to claim 1, it is characterised in that: institute
It states and inclination angle is divided into 3~5 parts.
3. a kind of CRS dip decomposition method based on differential evolution algorithm according to claim 1, it is characterised in that described
To each part be based respectively on differential evolution algorithm obtain the parameter with highest similarity realization process it is as follows:
1) selection target function: processing is optimized using similarity factor as objective function；
2) initialization of population；
3) mutation operation: reference vector is made a variation to obtain variation vector；
4) trial vector crossover operation: is obtained using crossing formula to reference vector and variation vector；
5) selection operation: the operation is based on greedy mechanism, has higher fitness value between Selection experiment vector sum reference vector
Individual of the vector as next-generation population；
6) terminate and be recycled into downstream: cyclic process 2) to process 5) process flow, termination condition is to judge iteration meter
Whether the value of objective function continues growing after calculation, and process 2 is continued cycling through if continuing growing) arrive process 5) process flow,
If not continuing to increase, the parameter with highest similarity is obtained.
4. a kind of CRS dip decomposition method based on differential evolution algorithm according to claim 3, it is characterised in that described
Process 1) in similarity factor are as follows:
In formula: Xj(i) amplitude of i-th j-th sampled point is indicated；N is the number of sampling points in every trace record；M is total in record
Road number, the magnitude range of similarity factor is [0,1].
5. a kind of CRS dip decomposition method based on differential evolution algorithm according to claim 3, it is characterised in that described
Process 2) the specific implementation process is as follows:
Parameter vector x is tieed up using NP-Di,G, i=1 ..., NP carry out initialization of population and obtain reference vector, wherein parameter vector
Include parameter angle of emergence α, normal incidence point wave wave-front curvature radius RNIPAnd normal direction wave wave-front curvature radius RN, then the value of D be
3, NP be Population Size, and G is the number of iterations.
6. a kind of CRS dip decomposition method based on differential evolution algorithm according to claim 5, it is characterised in that: institute
The numerical value of NP is stated between 5D to 10D.
7. a kind of CRS dip decomposition method based on differential evolution algorithm according to claim 3, it is characterised in that described
Process 3) the specific implementation process is as follows:
It makes a variation to reference vector, variation vector is obtained by following formula
In formula, r1,r2,r3∈{1,.....,Np, F is zoom factor.
8. a kind of CRS dip decomposition method based on differential evolution algorithm according to claim 6, it is characterised in that: institute
The value for stating F is 0.9362.
9. a kind of CRS dip decomposition method based on differential evolution algorithm according to claim 3, it is characterised in that described
Process 4) the specific implementation process is as follows:
Reference vector and variation vector obtain trial vector as the following formula
In formula, CR is crossover probability, and crossover probability CR is between 0 and 1, j ∈ [1, D], jrandFor 1 to random whole between D
Number.
10. a kind of CRS dip decomposition method based on differential evolution algorithm according to claim 9, it is characterised in that: institute
State crossover probability CR=0.7455.
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| CN113504568A (en) * | 2021-07-09 | 2021-10-15 | 吉林大学 | Median filtering method based on niche differential evolution algorithm |
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