CN109212605A - pseudo-differential operator storage method and device - Google Patents
pseudo-differential operator storage method and device Download PDFInfo
- Publication number
- CN109212605A CN109212605A CN201811138476.6A CN201811138476A CN109212605A CN 109212605 A CN109212605 A CN 109212605A CN 201811138476 A CN201811138476 A CN 201811138476A CN 109212605 A CN109212605 A CN 109212605A
- Authority
- CN
- China
- Prior art keywords
- pseudo
- differential operator
- point
- sampling
- father
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 37
- 238000005070 sampling Methods 0.000 claims abstract description 86
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 15
- 238000010606 normalization Methods 0.000 claims description 25
- 238000004364 calculation method Methods 0.000 claims description 11
- 238000004590 computer program Methods 0.000 claims description 5
- 230000008859 change Effects 0.000 claims description 2
- 239000011159 matrix material Substances 0.000 description 18
- 230000010287 polarization Effects 0.000 description 11
- 238000000926 separation method Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 5
- 238000003384 imaging method Methods 0.000 description 5
- 238000000354 decomposition reaction Methods 0.000 description 3
- 239000006185 dispersion Substances 0.000 description 3
- 230000005012 migration Effects 0.000 description 3
- 238000013508 migration Methods 0.000 description 3
- 238000001615 p wave Methods 0.000 description 3
- 241000208340 Araliaceae Species 0.000 description 2
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 2
- 235000003140 Panax quinquefolius Nutrition 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000009795 derivation Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 239000004744 fabric Substances 0.000 description 2
- 235000008434 ginseng Nutrition 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 230000018199 S phase Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000005336 cracking Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000007500 overflow downdraw method Methods 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/32—Transforming one recording into another or one representation into another
Landscapes
- Engineering & Computer Science (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Acoustics & Sound (AREA)
- Environmental & Geological Engineering (AREA)
- Geology (AREA)
- General Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- Geophysics (AREA)
- Radar Systems Or Details Thereof (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the invention provides a kind of pseudo-differential operator storage method and devices, wherein this method comprises: being directed to the corresponding pseudo-differential operator of each space lattice, carries out nonuniform sampling；Store the corresponding sampled data of each pseudo-differential operator.The program can reduce the size of pseudo-differential operator while not reducing differential operator precision, and then store the corresponding sampled data of each pseudo-differential operator, realizing reduces amount of storage while not reducing differential operator precision, the storage capacity under two-dimensional case is advantageously reduced, is conducive to be used for the algorithm of direct solution Kelvin-Christoffel eigen[value to handle three-dimensional problem.
Description
Technical field
The present invention relates to seismic data processing technology field, in particular to a kind of pseudo-differential operator storage method and device.
Background technique
Different from integration method or Gaussian beam migration imaging, vector elastic wave field reverse-time migration utilizes all-wave equation, will not lose
The detailed information for losing seismic wave field has preferably imaging output for steep configuration.It is right since multiband fusion method is utilized
In high speed confining bed, water layer etc., multiband fusion provides the illumination of more perspective, for sonic prospecting, there is to obtain day
Solely thick advantage.In one's early years, since techno-absence and cost limit, usually the image-forming condition of traditional acoustic reverse-time migration is directly answered
For multi-wave seismic data, the horizontal component of vector elastic wave field and vertical component are directly imaged.But vertical component
And it is not equal to compressional component, also non-equivalence is in shear component for horizontal component, so traditional cross-correlation imaging method is physically
Meaning is insufficient, and can bring the illusion in imaging, so needing for vector wave field to be divided into pure before carrying out vector Seismic imaging
Net longitudinal wave and shear wave wave field.
Currently, elastic wave vector wavefield decomposition has following several method:
First method: direct solution Kelvin-Christoffel eigen[value, Kelvin-Christoffel are intrinsic
Equation can be obtained by wave equation direct derivation, which depict the propagation characteristic of the seismic wave fields such as phase velocity and group velocity,
There is very strong practical significance in seismic theory research.For mathematics level, Kelvin-Christoffel eigen[value is
One typical eigenvalue problem, defining polarization vector has untrivialo solution, can by solving Kelvin-Christoffel eigen[value
To obtain the polarization vector of wave field in length and breadth, the dispersion relation of pseudo- P wave He puppet S wave can be obtained, according to frequency-Beam Domain and when
The relationship of m- spatial domain carries out two-dimensional Fourier transform, available pure wave wave equation.Then, it is thus proposed that utilize identical
Method, carried out trivector seismic wave field projection separation calculate.The theory of this kind of calculation method is clear, realizes difficulty
Less.But has a problem in that needing to solve the Kelvin- of each space networks lattice point for heterogeneous anisotropic media
Christoffel, calculation amount is huge, is difficult to carry out to three-dimensional problem.
Second method: it based on pseudo- P wave, the puppet S wave wave equation under high-frequency approximation theory deduction VTI medium, and carries out
Numerical simulation.Under the premise of weak anisotropy, the approximate solution of the dispersion relation of puppet P wave and puppet S wave is obtained, numerical simulation is weak each
Seismic wave field under anisotropy TTI medium.This method is referred to as acoustics and assumes method, as its name suggests, under acoustics supposed premise,
It is assumed that shear wave velocity is unrelated with velocity of longitudinal wave in wave equation, sets shear wave velocity directly as zero, be deduced anisotropic medium
Under pseudo- P wave dispersion equation, obtain Frequency-Space Domain wave equation after Fourier transformation.But this method is only false
If the shear wave phase velocity in wave equation is zero, but for many positions in model, the group velocity of shear wave is simultaneously not zero, this
The generation that many pseudo- shear waves can be brought, needs to carry out additional calculating, eliminates pseudo- shear wave.
The third method: there is different polarization characteristics to carry out Wave Decomposition in length and breadth according to longitudinal and shear wave, be a kind of side of classics
To projecting method.
Fourth method: the divergence and curl of vector elastic wave field correspond to different wave field structures, utilize Helmholtz
Definition can obtain projection of the vector elastic wave field in horizontal wave number and vertical wavenumber, carry out Wave Decomposition in length and breadth.But it is this
Efficiently calculation method is applicable only to isotropic medium, for anisotropic medium, the polarization direction of longitudinal and shear wave with
The direction of propagation is different, and anisotropy is stronger, and above-mentioned difference is bigger.
For isotropic medium, algorithm above is applicable in, but is directed to the wave field separation of heterogeneous anisotropic media,
Direct solution Kelvin-Christoffel eigen[value is most accurate, and the most apparent algorithm of physical significance, is cutd open for grid
Point, the wave field separation of homogeneous anisotropy's medium needs to solve the intrinsic side of Kelvin-Christoffel for each mesh point
Journey, this just brings great calculation amount and storage capacity；Need to solve Kelvin- corresponding to each space lattice
Christoffel eigen[value is stored in memory headroom after obtaining pseudo-differential operator corresponding to each space lattice,
This just brings huge storage capacity, and due to current GPU limited memory, storage capacity is marginally acceptable under two-dimensional case, still
When handling three-dimensional problem, the problem of storage capacity, restricts always the development of the algorithm.
Summary of the invention
The embodiment of the invention provides a kind of pseudo-differential operator storage methods, are stored up with solving pseudo-differential operator in the prior art
The big technical problem of storage.This method comprises:
For the corresponding pseudo-differential operator of each space lattice, nonuniform sampling is carried out；
Store the corresponding sampled data of each pseudo-differential operator.
The embodiment of the invention also provides a kind of pseudo-differential operator storage devices, to solve pseudo-differential operator in the prior art
The big technical problem of storage capacity.The device includes:
Sampling module carries out nonuniform sampling for being directed to the corresponding pseudo-differential operator of each space lattice；
Memory module, for storing the corresponding sampled data of each pseudo-differential operator.
The embodiment of the invention also provides a kind of computer equipments, including memory, processor and storage are on a memory
And the computer program that can be run on a processor, the processor are realized above-mentioned arbitrary quasi- when executing the computer program
Differential operator storage method, to solve the big technical problem of pseudo-differential operator storage capacity in the prior art.
The embodiment of the invention also provides a kind of computer readable storage medium, the computer-readable recording medium storage
There is the computer program for executing above-mentioned arbitrary pseudo-differential operator storage method, is stored with solving pseudo-differential operator in the prior art
Measure big technical problem.
In embodiments of the present invention, by carrying out nonuniform sampling, making to the corresponding pseudo-differential operator of each space lattice
The size that can reduce pseudo-differential operator while not reducing differential operator precision is obtained, and then stores each pseudo-differential operator pair
The sampled data answered, realizing reduces amount of storage while not reducing differential operator precision, advantageously reduces under two-dimensional case
Storage capacity, be conducive to be used for the algorithm of direct solution Kelvin-Christoffel eigen[value to handle three-dimensional problem.
Detailed description of the invention
The drawings described herein are used to provide a further understanding of the present invention, constitutes part of this application, not
Constitute limitation of the invention.In the accompanying drawings:
Fig. 1 (a) to Fig. 1 (c) is elastic wave vector wave field separation under a kind of uniform VTI medium provided in an embodiment of the present invention
Schematic diagram；
Fig. 2 is a kind of flow chart of pseudo-differential operator storage method provided in an embodiment of the present invention；
Fig. 3 is a kind of schematic diagram of original pseudo-differential operator provided in an embodiment of the present invention；
Fig. 4 is a kind of schematic diagram in sampling interval provided in an embodiment of the present invention；
Fig. 5 is a kind of structural block diagram of pseudo-differential operator storage device provided in an embodiment of the present invention.
Specific embodiment
To make the objectives, technical solutions, and advantages of the present invention clearer, right below with reference to embodiment and attached drawing
The present invention is described in further details.Here, exemplary embodiment and its explanation of the invention is used to explain the present invention, but simultaneously
It is not as a limitation of the invention.
So-called pseudo-differential operator is exactly by solving Kelvin-Christoffel eigen[value, by the phase of VTI or TTI
Speed formula brings equation coefficient matrix into, so that eigenvalue problem is set up.Solve non trivial solution just and be the phase velocity of the direction of propagation
Corresponding polarization direction.In order to comprehensively illustrate the analytic method of this problem, we introduce anisotropy basic theories first
And from VTI (perpendicular magnetic anisotropy), HTI (transverse anisotropy) vector wave equation, discussion is gradually spread out.
It is generally known that ball medium model is a model heterogeneous, there is non-fully elasticity, anisotropy, more
The features such as phase, the elastic wave vector equation comprising anisotropic character are the basis reasons for studying Seismic anisotropy feature
By, and the reason of causing anisotropy, has very much, the origin cause of formation is also complex.Intrinsic anisotropy, Causes of Cracking anisotropy and
Long wavelength's anisotropy is anisotropic three main reasons of ball medium.Thomsen has counted global anisotropy data,
According to anisotropic power, anisotropic medium is divided into: weak anisotropy, strong anisotropy and extreme anisotropy.And
Most commonly seen anisotropic medium is weak anisotropy medium.
In study of seismology, seismic aeolotropy refers to any comprising internal structure on the scale of seismic wave field
The uniformity material in (cyclicity thin interbed or the crack aligned), elastic characteristic change with direction.It is either each
To the same sex or Anisotropic elastic wave vector equation, the description of elastic characteristic all relies on elastic coefficient matrix, or claims
Be elasticity tensor.The elastic coefficient matrix of different anisotropic mediums is described below.
The coefficient of elasticity tensor of VTI medium can indicate are as follows:
It can easily be seen that in VTI medium coefficient matrix there are five independent variables.
The coefficient of elasticity tensor of HTI medium can indicate are as follows:
It can easily be seen that in HTI medium coefficient matrix there are five independent variables.
The coefficient of elasticity tensor of HTI medium can indicate are as follows:
It can easily be seen that independent variable has nine in HTI medium coefficient matrix.
TTI medium is one step of the progress variation of VTI medium, is to claim axis according to the cloth of field layout in VTI medium
If made of deflection, so needing exist for introducing inclination angle theta and azimuthTwo parameters, wherein inclination angle theta represents symmetry axis and sight
The angle of examining system system vertical direction axis, azimuthRepresent the angle of symmetry axis and observation system trunnion axis.The bullet of TTI medium
Property tensor can by Bond convert, be formed by VTI dielectric resilient variation in coefficient matrix:
There are 21 independent parameters in TTI medium, but according to the actual situation, usually only consider inclination angle theta, orientation
AngleIt is defined as zero, that is, the anisotropic character of seismic wave is only discussed in X-O-Z plane, by such simplification, TTI
The elastic coefficient matrix independence coefficient of medium can be reduced to 13 by 21:
Wherein:
In above-mentioned coefficient matrix,For five isolated components of VTI coefficient of elasticity tensor.
And the coefficient of elasticity tensor of isotropic medium can be indicated with Lame coefficient lambda and μ:
From the perspective of the derivation of equation, coefficient of elasticity tensor has specific physical significance, constructs stress tensor
Bridge between strain tensor, but from the perspective of practical application, we can not be direct according to coefficient of elasticity tensor
It goes to measure anisotropic strong and weak and variation, this is because indicating that the phase velocity feature of seimic wave propagation has been hidden in elasticity
Among amount, in addition, for seismic prospecting, this point most important perpendicular to the velocity of longitudinal wave of observation system symmetry axis
It can not intuitively be obtained from elastic coefficient matrix.In order to which theoretical and reality to be combined, discussed convenient for practical, the prior art
21 independent variables in TTI elasticity tensor are simplified and have been summarized, purpose be exactly by anisotropic parameters from
It is independent in tensor matrix, and ε is defined, tri- parameters of δ, γ go to indicate anisotropic feature.If using vp0And vs0
Indicate the P wave and SV phase velocity of wave along medium symmetry axis, it can be as follows by thompson parameter definition:
It is apparent that, the pseudo- p wave interval velocity of seismic wave field can be by v from thompson parameterp0,vs0, ε, δ tetra- ginsengs
Number indicates that pseudo- shear wave velocity then can be by vs0, γ expression.And ε, δ, γ are the ginseng of three Scaling Rules anisotropy sizes
Number, also there is obvious physical significance.For isotropic medium, it is only necessary to which thompson parameter is all defined as zero
, and for anisotropic medium, the anisotropy that ε characterizes pseudo- P wave is strong and weak, and the anisotropy that γ measures puppet S wave is strong
It is weak, and δ is then illustrated when seismic wave vertical incidence, the space second dervative of pseudo- P phase velocity of wave, as shown in formula (9).
Different medium has different expression-forms, for VTI medium, wave equation is as follows according to coefficient matrix difference:
For HTI medium, wave equation is as follows:
For OA medium, wave equation is as follows:
It is not difficult to find out that, different elastic parameters characterize Jie of the anisotropy under different actual conditions from above three formula
Matter wave equation, so also have difference slightly for Kelvin-Christoffel equation, below we with
Illustrate that the wave separation algorithm based on pseudo-differential operator, Kelvin-Christoffel equation are expressed as follows for VTI medium:
Wherein, for VTI medium, element is as follows:
Wherein, Γ11,Γ12,Γ22It is the matrix element of Kelvin-Christoffel equation, p=(px,pz) it is the phase velocity
The polarization vector in direction is spent, ρ represents Media density, and v represents phase velocity.Problem described in Kelvin-Christoffel equation
It is a very typical eigenvalue problem, in wave field propagation, polarization vector will not be zero, so in order to enable Kelvin-
Christoffel equation is set up, and the determinant of exclusive requirement coefficient matrix is zero, and formulae express is as follows:
For two-dimensional problems, for giving any direction of propagation of plane, Kelvin-Christoffel equation can be obtained
Two different phase velocity solutions, have corresponded to the phase velocity of P wave and SV wave, the two solutions be at any time it is orthogonal,
This is because the coefficient matrix of Kelvin-Christoffel equation is symmetrical matrix.But it is different from isotropism situation
It is the longitudinal wave polarization vector that solves in addition to specific direction, has certain angle with the wave field direction of propagation.This is also to be called pseudo- P
The reason of wave.
TTI medium is one step of the progress variation of VTI medium, is to claim axis according to the cloth of field layout in VTI medium
If made of deflection, so needing exist for introducing inclination angle theta and azimuthEven parameter, wherein inclination angle theta represents symmetry axis and sight
The angle of examining system system vertical direction axis, azimuthRepresent the angle of symmetry axis and observation system trunnion axis.The bullet of TTI medium
Property tensor can by Bond convert, be formed by VTI dielectric resilient variation in coefficient matrix:
After Bond is converted, the elastic coefficient matrix of TTI medium becomes six from four isolated components in the case of VTI
Isolated component brings this six isolated components into Kelvin-Christoffel equation, the pseudo- p wave in the case of available TTI
With the analytical form of pseudo- S phase velocity of wave, and then the polarization vector expression formula of pseudo- p wave He puppet S wave is obtained, respectively with pP,pSVIt indicates,
Its formula is as follows:
We defineFor the direction of propagation of wave, it is not difficult to find that pP·pSV=0, this is illustrated
The polarization direction of puppet P wave and puppet S wave is orthogonal under TTI medium, but pP×k≠0,pSVK ≠ 0, it means that no matter puppet P
There are angles with the direction of propagation of wave for the polarization direction of wave or puppet S wave, this is also the original of pseudo- P wave He puppet S wave title origin
Cause.
Equally, we can not also convert wave field, only convert to dividing operator.It has been proposed that by wave-number domain
Dividing operator ipPx,ipPz,pSVx,ipSVzSpatial domain is transformed to, filter in spatial domain is carried out, essence is by point of Dellinger
It has been generalized to anisotropic medium from isotropic medium from algorithm, as follows with formulae express:
In formula, UxAnd UzVertical component and horizontal component of the representative vector wave field in spatial domain, qP and qSV generation respectively
The wave field of table space domain puppet P wave and puppet S wave, LPx,LPz,LSVx,LSVzIt is to ipPx,ipPz,pSVx,ipSVzAfter carrying out Fourier transformation
Spatial domain expression-form, be referred to as pseudo-differential operator, the feature of pseudo-differential operator is related with elastic parameter, and [] represents
The convolutional calculation of spatial domain, that is, filter in spatial domain.It is filtered in spatial domain, the wave field after just being separated.Fig. 1
It (a) is that the separating resulting that filter in spatial domain obtains is carried out using pseudo-differential operator to Fig. 1 (c), Fig. 1 (a) is impulse response wave field
Snapshot (left figure is X-component, and right figure is Z component)；Fig. 1 (b) be separation after wave field snapshot (left figure be quasi-P component, the right side
Figure is qS component)；Fig. 1 (c) is pseudo-differential operator (left figure is x-component, and right figure is z-component), the quasi-differential that this separation is chosen
Operator size is 51*51.From the results of view, although separating resulting still has part residual, overall effect can also reach expected.
From the results of view, the size-dependence of pseudo-differential operator the accuracy of separating resulting, too big pseudo-differential operator can greatly increase
Add the storage capacity of computer, then reducing the storage capacity of pseudo-differential operator how under the premise of not losing precision, just becoming
Where the value of key and the application.The application proposition is described below utilizes nonuniform sampling, reduces pseudo-differential operator storage
The method of storage.
In embodiments of the present invention, a kind of pseudo-differential operator storage method is provided, as shown in Fig. 2, this method comprises:
Step 201: being directed to the corresponding pseudo-differential operator of each space lattice, carry out nonuniform sampling；
Step 202: storing the corresponding sampled data of each pseudo-differential operator.
Process as shown in Figure 2 is it is found that in embodiments of the present invention, by calculating the corresponding quasi-differential of each space lattice
Son carries out nonuniform sampling, allows to reduce the size of pseudo-differential operator while not reducing differential operator precision, in turn
The corresponding sampled data of each pseudo-differential operator is stored, realizing reduces amount of storage while not reducing differential operator precision,
The storage capacity under two-dimensional case is advantageously reduced, is conducive to the algorithm of direct solution Kelvin-Christoffel eigen[value
For handling three-dimensional problem.
When it is implemented, amount of storage is reduced while not reducing pseudo-differential operator precision to realize, in the present embodiment
In, for the corresponding pseudo-differential operator of each space lattice, carry out nonuniform sampling, comprising:
0 to 1 normalization is carried out to each pseudo-differential operator；
The spatial sampling interval that the pseudo-differential operator after each normalization is calculated using non-linear projection algorithm, according to described
Spatial sampling interval is sampled, wherein the spatial sampling interval makes the important area of pseudo-differential operator after normalization
Domain carries out intensive sampling, and the insignificant region of pseudo-differential operator after normalization carries out sparse sampling, and the important area is
The biggish region of data distribution variable density, the insignificant region are the lesser region of data distribution variable density.
Specifically, the spatial sampling interval for the pseudo-differential operator that can be calculated using the following equation after each normalization:
Wherein, DpFor the pseudo-differential operator after normalization, αcFor maximum sampling interval, αfFor minimum sampling interval, β is indicated
The rate of decay of the maximum sampling interval to minimum sampling interval, DsIt adopts in space for the corresponding pseudo-differential operator of each space lattice
Sample interval, DsDetermine the distance between two neighboring sampled point.
When it is implemented, after the spatial sampling interval of pseudo-differential operator is calculated, it can according to spatial sampling interval
It is sampled, for example, being sampled according to the spatial sampling interval, comprising:
Father's point is set by the center of pseudo-differential operator, the size of father's point is the space nearest apart from father's point
The spatial sampling interval of mesh point is sampled by circulation following steps to traverse the model space of entire pseudo-differential operator:
Every 60 degree of settings, one sub- point around father's point, the distance between sub- point father's point corresponding with itself is spatial sampling
Interval；
Antithetical phrase point is sampled；
When sub- point is greater than the preset ratio of the corresponding father's point size of the sub- point at a distance from all father's points, which is regarded
For father's point.
Specifically, above-mentioned preset ratio can be 80%.
By the above-mentioned method of sampling, the storage capacity of original pseudo-differential operator at least can be reduced by 80 percent.
The core for the above-mentioned pseudo-differential operator storage method that the application proposes is that a kind of nonuniform sampling theorem is utilized,
The gradually mapping that father's point is put to son is carried out to original pseudo-differential operator mesh generation space, it, can be with after traversing the entire model space
Down-sampled to the progress of original storage capacity biggish pseudo-differential operator, the algorithm of storage capacity of terminating an agreement down-sampled in this way there is no in the field
Precedent.
We provide the direction x and the direction z pseudo-differential operator of an anisotropic model below, if Fig. 3 is (on the left of in Fig. 3
For the direction x pseudo-differential operator, right side is the direction z pseudo-differential operator) shown in, we drop it using nonuniform sampling principle
Sampling, calculates separately its spatial sampling interval, down-sampled percentage and computational efficiency.The operator can act on wave field and carry out
Convolution calculates, and to achieve the purpose that separating P wave from S wave, but original pseudo-differential operator storage capacity is excessive, we carry out it
It is non-homogeneous down-sampled described in text, it is calculated by formula, its available spatial sampling interval figure, as shown in Figure 4.
We are very easy to find from Fig. 4, which has original pseudo-differential operator important area (middle section)
Very high sample rate, and to the insignificant area (peripheral portion) of pseudo-differential operator, the sampling interval is larger, and such way can be with
Guarantee the precision of original pseudo-differential operator, and can largely reduce its storage capacity.
For this model example, the grid before sampling is always counted as 2601 mesh points, by non-homogeneous down-sampled, is adopted
Grid after sample, which is always counted, is reduced to 739, and sampling is than being 28.4121%, that is to say, that we reduce data volume for original
The 28.4121% of beginning data, and calculating the time is 0.853130 second, it was demonstrated that the algorithm has very high computational efficiency.
Based on the same inventive concept, a kind of pseudo-differential operator storage device is additionally provided in the embodiment of the present invention, it is such as following
Embodiment described in.Since the principle that pseudo-differential operator storage device solves the problems, such as is similar to pseudo-differential operator storage method, because
The implementation of this pseudo-differential operator storage device may refer to the implementation of pseudo-differential operator storage method, and overlaps will not be repeated.
Used below, the combination of the software and/or hardware of predetermined function may be implemented in term " unit " or " module ".Although with
Device described in lower embodiment is preferably realized with software, but the combined realization of hardware or software and hardware
It may and be contemplated.
Fig. 5 is a kind of structural block diagram of the pseudo-differential operator storage device of the embodiment of the present invention, as shown in figure 5, the device
Include:
Sampling module 501 carries out nonuniform sampling for being directed to the corresponding pseudo-differential operator of each space lattice；
Memory module 502, for storing the corresponding sampled data of each pseudo-differential operator.
In one embodiment, the sampling module, comprising:
Normalization unit, for carrying out 0 to 1 normalization to each pseudo-differential operator；
Interval calculation unit, for calculating the space of the pseudo-differential operator after each normalization using non-linear projection algorithm
Sampling interval, wherein the spatial sampling interval adopts the important area of pseudo-differential operator after normalization intensively
Sample, the insignificant region of pseudo-differential operator after normalization carry out sparse sampling, and the important area is data distribution density
Greater than the region of preset value, the insignificant region is the region that data distribution density is less than preset value；
Sampling unit, for being sampled according to the spatial sampling interval.
In one embodiment, the interval calculation unit is calculated by the following formula the calculation of the quasi-differential after each normalization
The spatial sampling interval of son:
Wherein, DpFor the pseudo-differential operator after normalization, αcFor maximum sampling interval, αfFor minimum sampling interval, β is indicated
The rate of decay of the maximum sampling interval to minimum sampling interval, DsIt adopts in space for the corresponding pseudo-differential operator of each space lattice
Sample interval, DsDetermine the distance between two neighboring sampled point.
In one embodiment, the sampling unit, specifically for setting one for the center of pseudo-differential operator
Father's point, the size of father's point are the spatial sampling interval of the space networks lattice point nearest apart from father's point, by circulation following steps come
The model space for traversing entire pseudo-differential operator is sampled:
Every 60 degree of settings, one sub- point around father's point, the distance between sub- point father's point corresponding with itself is spatial sampling
Interval；
Antithetical phrase point is sampled；
When sub- point is greater than the preset ratio of the corresponding father's point size of the sub- point at a distance from all father's points, which is regarded
For father's point.
In one embodiment, the preset ratio is 80%.
In another embodiment, a kind of software is additionally provided, the software is for executing above-described embodiment and preferred reality
Apply technical solution described in mode.
In another embodiment, a kind of storage medium is additionally provided, above-mentioned software is stored in the storage medium, it should
Storage medium includes but is not limited to: CD, floppy disk, hard disk, scratch pad memory etc..
The embodiment of the present invention realizes following technical effect: by the corresponding pseudo-differential operator of each space lattice, into
Row nonuniform sampling, allows to reduce the size of pseudo-differential operator while not reducing differential operator precision, and then stores
The corresponding sampled data of each pseudo-differential operator, realize reduces amount of storage while not reducing differential operator precision, favorably
In reducing the storage capacity under two-dimensional case, be conducive to for the algorithm of direct solution Kelvin-Christoffel eigen[value being used for
Handle three-dimensional problem.
Obviously, those skilled in the art should be understood that each module of the above-mentioned embodiment of the present invention or each step can be with
It is realized with general computing device, they can be concentrated on a single computing device, or be distributed in multiple computing devices
On composed network, optionally, they can be realized with the program code that computing device can perform, it is thus possible to by it
Store and be performed by computing device in the storage device, and in some cases, can be held with the sequence for being different from herein
The shown or described step of row, perhaps they are fabricated to each integrated circuit modules or will be multiple in them
Module or step are fabricated to single integrated circuit module to realize.In this way, the embodiment of the present invention be not limited to it is any specific hard
Part and software combine.
The foregoing is only a preferred embodiment of the present invention, is not intended to restrict the invention, for the skill of this field
For art personnel, the embodiment of the present invention can have various modifications and variations.All within the spirits and principles of the present invention, made
Any modification, equivalent substitution, improvement and etc. should all be included in the protection scope of the present invention.
Claims (12)
1. a kind of pseudo-differential operator storage method characterized by comprising
For the corresponding pseudo-differential operator of each space lattice, nonuniform sampling is carried out；
Store the corresponding sampled data of each pseudo-differential operator.
2. pseudo-differential operator storage method as described in claim 1, which is characterized in that corresponding quasi- for each space lattice
Differential operator carries out nonuniform sampling, comprising:
0 to 1 normalization is carried out to each pseudo-differential operator；
The spatial sampling interval that the pseudo-differential operator after each normalization is calculated using non-linear projection algorithm, according to the space
Sampling interval is sampled, wherein the spatial sampling interval make the important area of pseudo-differential operator after normalization into
Row intensive sampling, the insignificant region of pseudo-differential operator after normalization carry out sparse sampling, and the important area is data
Distribution density is greater than the region of preset value, and the insignificant region is the region that data distribution density is less than preset value.
3. pseudo-differential operator storage method as claimed in claim 2, which is characterized in that be calculated by the following formula each normalizing
The spatial sampling interval of pseudo-differential operator after change:
Wherein, DpFor the pseudo-differential operator after normalization, αcFor maximum sampling interval, αfFor minimum sampling interval, β indicates maximum
The rate of decay of the sampling interval to minimum sampling interval, DsBetween spatial sampling for the corresponding pseudo-differential operator of each space lattice
Every DsDetermine the distance between two neighboring sampled point.
4. pseudo-differential operator storage method as claimed in claim 2, which is characterized in that carried out according to the spatial sampling interval
Sampling, comprising:
Father's point is set by the center of pseudo-differential operator, the size of father's point is the space lattice nearest apart from father's point
The spatial sampling interval of point is sampled by circulation following steps to traverse the model space of entire pseudo-differential operator:
Every 60 degree of settings, one sub- point around father's point, the distance between sub- point father's point corresponding with itself is between spatial sampling
Every；
Antithetical phrase point is sampled；
When sub- point is greater than the preset ratio of the corresponding father's point size of the sub- point at a distance from all father's points, which is considered as father
Point.
5. pseudo-differential operator storage method as claimed in claim 4, which is characterized in that the preset ratio is 80%.
6. a kind of pseudo-differential operator storage device characterized by comprising
Sampling module carries out nonuniform sampling for being directed to the corresponding pseudo-differential operator of each space lattice；
Memory module, for storing the corresponding sampled data of each pseudo-differential operator.
7. pseudo-differential operator storage device as claimed in claim 6, which is characterized in that the sampling module, comprising:
Normalization unit, for carrying out 0 to 1 normalization to each pseudo-differential operator；
Interval calculation unit, for calculating the spatial sampling of the pseudo-differential operator after each normalization using non-linear projection algorithm
Interval, wherein the spatial sampling interval makes the important area of pseudo-differential operator after normalization carry out intensive sampling,
The insignificant region of pseudo-differential operator after normalization carries out sparse sampling, and the important area is that data distribution density is greater than in advance
If the region of value, the insignificant region is the region that data distribution density is less than preset value；
Sampling unit, for being sampled according to the spatial sampling interval.
8. pseudo-differential operator storage device as claimed in claim 7, which is characterized in that the interval calculation unit passes through following
Formula calculates the spatial sampling interval of the pseudo-differential operator after each normalization:
Wherein, DpFor the pseudo-differential operator after normalization, αcFor maximum sampling interval, αfFor minimum sampling interval, β indicates maximum
The rate of decay of the sampling interval to minimum sampling interval, DsBetween spatial sampling for the corresponding pseudo-differential operator of each space lattice
Every DsDetermine the distance between two neighboring sampled point.
9. pseudo-differential operator storage device as claimed in claim 7, which is characterized in that
The sampling unit, specifically for setting father's point for the center of pseudo-differential operator, the size of father's point be away from
The spatial sampling interval of the space networks lattice point nearest from father's point traverses entire pseudo-differential operator by circulation following steps
The model space is sampled:
Every 60 degree of settings, one sub- point around father's point, the distance between sub- point father's point corresponding with itself is between spatial sampling
Every；
Antithetical phrase point is sampled；
When sub- point is greater than the preset ratio of the corresponding father's point size of the sub- point at a distance from all father's points, which is considered as father
Point.
10. pseudo-differential operator storage device as claimed in claim 9, which is characterized in that the preset ratio is 80%.
11. a kind of computer equipment including memory, processor and stores the meter that can be run on a memory and on a processor
Calculation machine program, which is characterized in that the processor is realized described in any one of claim 1 to 5 when executing the computer program
Pseudo-differential operator storage method.
12. a kind of computer readable storage medium, which is characterized in that the computer-readable recording medium storage has perform claim
It is required that the computer program of 1 to 5 described in any item pseudo-differential operator storage methods.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811138476.6A CN109212605A (en) | 2018-09-28 | 2018-09-28 | pseudo-differential operator storage method and device |
| PCT/CN2019/099892 WO2020063131A1 (en) | 2018-09-28 | 2019-08-09 | Method and device for storing pseudo-differential operator |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811138476.6A CN109212605A (en) | 2018-09-28 | 2018-09-28 | pseudo-differential operator storage method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109212605A true CN109212605A (en) | 2019-01-15 |
Family
ID=64982116
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811138476.6A Pending CN109212605A (en) | 2018-09-28 | 2018-09-28 | pseudo-differential operator storage method and device |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN109212605A (en) |
| WO (1) | WO2020063131A1 (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020063131A1 (en) * | 2018-09-28 | 2020-04-02 | 中国科学院地质与地球物理研究所 | Method and device for storing pseudo-differential operator |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113435074B (en) * | 2021-03-24 | 2024-02-09 | 中国地震局工程力学研究所 | M-UFSPML model, construction method, intelligent terminal and server |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101505423A (en) * | 2009-02-25 | 2009-08-12 | 西安交通大学 | Non-uniform sampling method for video image |
| US20100088035A1 (en) * | 2008-10-06 | 2010-04-08 | Etgen John T | Pseudo-analytical method for the solution of wave equations |
| CN102008305A (en) * | 2010-12-16 | 2011-04-13 | 中国科学院深圳先进技术研究院 | Dynamic magnetic resonance imaging method |
| CN102105814A (en) * | 2008-06-19 | 2011-06-22 | 雪佛龙美国公司 | System and method for seismic trace analysis |
| CN103886760A (en) * | 2014-04-02 | 2014-06-25 | 李涛 | Real-time vehicle type detection system based on traffic video |
| CN104133241A (en) * | 2014-07-31 | 2014-11-05 | 中国科学院地质与地球物理研究所 | Wave field separating method and device |
| CN105137486A (en) * | 2015-09-01 | 2015-12-09 | 中国科学院地质与地球物理研究所 | Elastic wave reverse-time migration imaging method and apparatus in anisotropic media |
| CN106772585A (en) * | 2017-01-26 | 2017-05-31 | 中国科学院地质与地球物理研究所 | Analysis method and device is intended in a kind of optimization based on elastic wave decoupling equation |
| CN106970419A (en) * | 2017-04-10 | 2017-07-21 | 东华理工大学 | A kind of non-homogeneous bent ripple 3D seismic data method for reconstructing based on linear Bregman algorithms |
| CN107153216A (en) * | 2017-07-05 | 2017-09-12 | 中国科学院地质与地球物理研究所 | Determine method, device and the computer-readable storage medium of the Poynting vector of seismic wave field |
| CN107272058A (en) * | 2017-07-05 | 2017-10-20 | 中国科学院地质与地球物理研究所 | Imaging method, imaging device and computer-readable storage medium |
| CN107340540A (en) * | 2017-07-05 | 2017-11-10 | 中国科学院地质与地球物理研究所 | Direction wave decomposition method, device and the computer-readable storage medium of elastic wave field |
| CN108196303A (en) * | 2017-12-29 | 2018-06-22 | 中国石油天然气集团公司 | Elastic wave field separation method, device, storage medium and equipment |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109212605A (en) * | 2018-09-28 | 2019-01-15 | 中国科学院地质与地球物理研究所 | pseudo-differential operator storage method and device |
-
2018
- 2018-09-28 CN CN201811138476.6A patent/CN109212605A/en active Pending
-
2019
- 2019-08-09 WO PCT/CN2019/099892 patent/WO2020063131A1/en active Application Filing
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102105814A (en) * | 2008-06-19 | 2011-06-22 | 雪佛龙美国公司 | System and method for seismic trace analysis |
| US20100088035A1 (en) * | 2008-10-06 | 2010-04-08 | Etgen John T | Pseudo-analytical method for the solution of wave equations |
| CN101505423A (en) * | 2009-02-25 | 2009-08-12 | 西安交通大学 | Non-uniform sampling method for video image |
| CN102008305A (en) * | 2010-12-16 | 2011-04-13 | 中国科学院深圳先进技术研究院 | Dynamic magnetic resonance imaging method |
| CN103886760A (en) * | 2014-04-02 | 2014-06-25 | 李涛 | Real-time vehicle type detection system based on traffic video |
| CN104133241A (en) * | 2014-07-31 | 2014-11-05 | 中国科学院地质与地球物理研究所 | Wave field separating method and device |
| CN105137486A (en) * | 2015-09-01 | 2015-12-09 | 中国科学院地质与地球物理研究所 | Elastic wave reverse-time migration imaging method and apparatus in anisotropic media |
| CN106772585A (en) * | 2017-01-26 | 2017-05-31 | 中国科学院地质与地球物理研究所 | Analysis method and device is intended in a kind of optimization based on elastic wave decoupling equation |
| CN106970419A (en) * | 2017-04-10 | 2017-07-21 | 东华理工大学 | A kind of non-homogeneous bent ripple 3D seismic data method for reconstructing based on linear Bregman algorithms |
| CN107153216A (en) * | 2017-07-05 | 2017-09-12 | 中国科学院地质与地球物理研究所 | Determine method, device and the computer-readable storage medium of the Poynting vector of seismic wave field |
| CN107272058A (en) * | 2017-07-05 | 2017-10-20 | 中国科学院地质与地球物理研究所 | Imaging method, imaging device and computer-readable storage medium |
| CN107340540A (en) * | 2017-07-05 | 2017-11-10 | 中国科学院地质与地球物理研究所 | Direction wave decomposition method, device and the computer-readable storage medium of elastic wave field |
| CN108196303A (en) * | 2017-12-29 | 2018-06-22 | 中国石油天然气集团公司 | Elastic wave field separation method, device, storage medium and equipment |
Non-Patent Citations (2)
| Title |
|---|
| ALIP MOHAMMED 等: "Sampling and Pseudo-Differential Operators", 《ADVANCES AND APPLICATIONS》 * |
| 王俊: "基于快速多极子算法的大规模重力数据正反演方法研究", 《中国博士学位论文全文数据库 基础科学辑》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020063131A1 (en) * | 2018-09-28 | 2020-04-02 | 中国科学院地质与地球物理研究所 | Method and device for storing pseudo-differential operator |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2020063131A1 (en) | 2020-04-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104133241B (en) | Wave field separation method and apparatus | |
| CN105137486B (en) | Anisotropic medium Elastic Wave reverse-time migration imaging method and its device | |
| Song et al. | Modeling of pseudoacoustic P-waves in orthorhombic media with a low-rank approximation | |
| CN107479092B (en) | A kind of frequency domain high order ACOUSTIC WAVE EQUATION the Forward Modeling based on directional derivative | |
| Du et al. | Recursive integral time-extrapolation methods for waves: A comparative review | |
| CN105652320B (en) | Reverse-time migration imaging method and device | |
| Wang et al. | Up/down and P/S decompositions of elastic wavefields using complex seismic traces with applications to calculating Poynting vectors and angle-domain common-image gathers from reverse time migrations | |
| CN103149585A (en) | Elastic migration seismic wave field construction method and elastic migration seismic wave field construction device | |
| CN109212605A (en) | pseudo-differential operator storage method and device | |
| CN107340540B (en) | Direction wave decomposition method, device and the computer storage medium of elastic wave field | |
| Langrenne et al. | Measurement of confined acoustic sources using near-field acoustic holography | |
| CN108196303A (en) | Elastic wave field separation method, device, storage medium and equipment | |
| Farra et al. | Weak-anisotropy approximations of P-wave phase and ray velocities for anisotropy of arbitrary symmetry | |
| Gao et al. | An Efficient Multiscale Finite‐Element Method for Frequency‐Domain Seismic Wave Propagation | |
| CN106597535A (en) | Method of improving elastic wave reverse time migration offset computation rate and space resolution | |
| CN106707353A (en) | Nuclear magnetic resonance T2 spectrum inversion method considering baseline shifting under low signal-to-noise ratio | |
| Zhang et al. | Optimized Chebyshev Fourier migration: A wide-angle dual-domain method for media with strong velocity contrasts | |
| CN105974471B (en) | A kind of quick forward modelling method of the more GPU of seismic data based on asynchronous flow | |
| Cheng et al. | Propagating pure wave modes in general anisotropic media, Part I: P-wave propagator | |
| Müller et al. | Quantitative passive imaging by iterative holography: the example of helioseismic holography | |
| CN104750954A (en) | Method and device for simulating earthquake waves in complex anisotropic media | |
| CN110618459B (en) | Seismic data processing method and device | |
| Eshraghi et al. | Scattering of elastic waves by nonaxisymmetric three‐dimensional dipping layer | |
| Verweij et al. | Transmission and reflection of transient elastodynamic waves at a linear slip interface | |
| Geevers | Finite Element Methods for Seismic Modelling |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190115 |