CN107783185B

CN107783185B - A kind of processing method and processing device of tomographic statics

Info

Publication number: CN107783185B
Application number: CN201710826133.8A
Authority: CN
Inventors: 曾庆才; 曾同生; 欧阳永林; 王兴; 宋雅莹; 胡新海; 康敬程; 王露; 代春萌; 姜仁
Original assignee: China Petroleum and Natural Gas Co Ltd
Current assignee: China Petroleum and Natural Gas Co Ltd
Priority date: 2017-09-14
Filing date: 2017-09-14
Publication date: 2019-05-07
Anticipated expiration: 2037-09-14
Also published as: CN107783185A

Abstract

The embodiment of the present application discloses a kind of processing method and processing device of tomographic statics, the method includes carrying out Neural Network Inversion to initial velocity model, obtains the first near-surface velocity model；Ray tracing is carried out to first near-surface velocity model based on small grid unit, calculates the ray degree of covering of the small grid unit；According to the ray degree of covering of the small grid unit, sliding-model control based on Moving grids unit is carried out to first near-surface velocity model, the difference of the ray degree of covering of any two grid cell in the first near-surface velocity model after Moving grids cell discretization is less than first threshold；Neural Network Inversion is carried out to the first near-surface velocity model after the Moving grids cell discretization, obtains the second near-surface velocity model；Static correction value is determined according to second near-surface velocity model.Using each embodiment of the application, the accuracy and stability of tomographic statics processing can be improved.

Description

A kind of processing method and processing device of tomographic statics

Technical field

The present invention relates to seismic data processing technical fields, particularly, be related to a kind of tomographic statics processing method and Device.

Background technique

In The Foreland Basins In Central-western China area petroleum resources are abundant, are the key areas of oil-gas exploration and development.Exploration practices table Bright earthquake accurate imaging is the key means that these areas reduce exploration risk, and still, these regional earth's surface elevations rise and fall greatly, low Change acutely on reduction of speed tape speed and thickness space, refracting interface is unstable, and complicated near surface structure leads to these areas Static correction problem is extremely prominent, and static correction problem has become the big bottleneck for restricting these regional subsurface structure accurate imagings.

A variety of static correcting methods were applied for the protrusion static correction problem of Complex Mountain at present, including elevation calculation, Field model ing static correction, refraction static correction and tomographic statics.Wherein, for the protrusion static correction problem of Complex Mountain, layer Analysis inverting static correction is advanced relative to other several method theories, and precision is higher, has to complex near-surface structure suitable well Ying Xing, static correction effect are better than other several methods.

But since the near surface structure of Complex Mountain is extremely complex, low velocity layer speed and thickness are vertical and horizontal acutely Variation causes the near-surface velocity inverting of tomographic statics to there are problems that pathosis and multi-solution, so that Conventional chromatography static correction It is difficult to meet the needs of actual seismic exploration in precision.

Summary of the invention

A kind of processing method and processing device for being designed to provide tomographic statics of the embodiment of the present application, can overcome routine The pathosis and multiresolution issue of near-surface velocity inverting in tomographic statics improve the accuracy of Static Correction of Tomographic Inversion and steady It is qualitative.

A kind of processing method and processing device of tomographic statics provided by the present application is by including that following manner is realized:

A kind of processing method of tomographic statics, which comprises

Neural Network Inversion is carried out to initial velocity model, obtains the first near-surface velocity model；

Ray tracing is carried out to first near-surface velocity model based on small grid unit, calculates the small grid unit Ray degree of covering；

According to the ray degree of covering of the small grid unit, first near-surface velocity model is carried out based on change net The sliding-model control of lattice unit, any two grid cell in the first near-surface velocity model after Moving grids cell discretization Ray degree of covering difference be less than first threshold；

Neural Network Inversion is carried out to the first near-surface velocity model after the Moving grids cell discretization, obtains second Near-surface velocity model；

Static correction value is determined according to second near-surface velocity model.

The processing method of the tomographic statics of the embodiment of the present application, it is described anti-to initial velocity model progress neural network It drills, obtains the first near-surface velocity model, comprising:

Initial velocity model is constructed according to surface survey data；

Sliding-model control is carried out to the initial velocity model based on big grid cell, to initial after big grid discretization Rate pattern carries out Neural Network Inversion, obtains the first near-surface velocity model.

The processing method of the tomographic statics of the embodiment of the present application, it is described according to the covering time of the ray of the small grid unit Number, carries out the sliding-model control based on Moving grids unit to first near-surface velocity model, comprising:

It is less than or equal to the small grid unit area of second threshold to ray degree of covering, by the first near-surface velocity mould Type carries out the sliding-model control based on the first grid cell；

Second threshold is greater than to ray degree of covering and is less than the small grid unit area of third threshold value, it is close by described first Earth's surface rate pattern carries out the sliding-model control based on the second grid cell；

It is more than or equal to the small grid unit area of third threshold value to ray degree of covering, by the first near-surface velocity mould Type carries out the sliding-model control based on third grid cell；

The third threshold value is greater than second threshold, and the size of first grid cell is greater than second grid cell Size, the size of second grid cell are greater than the size of the third grid cell, the size of the third grid cell More than or equal to the size of the small grid unit.

It is greater than the small net of preset degree threshold value to near-surface velocity variation degree in first near-surface velocity model First near-surface velocity model is carried out the sliding-model control based on third grid cell by lattice unit area.

The size of the grid cell includes Δ x1 × Δ y1 × Δ z1, Δ x1 be reception channel away from n times, Δ y1 is shot point Away from n times, Δ z1 is n times of depth sampling interval；

The value of the n of the small grid unit includes: n=1；

The value range of the n of first grid cell includes: 5≤n≤8；

The value range of the n of second grid cell includes: 3≤n < 5；

The value range of the n of the third grid cell includes: 1≤n < 3.

The processing method of the tomographic statics of the embodiment of the present application, the initial velocity to after big grid cell discretization Model carries out Neural Network Inversion, obtains the first near-surface velocity model, comprising:

Pick up earthquake primary travel time；

Forward modeling is carried out to the initial velocity model after the big grid cell discretization, when obtaining the first forward modeling travelling；

To the initial velocity after big grid cell discretization when being travelled based on the earthquake primary travel time and the first forward modeling Model carries out Neural Network Inversion, obtains the first near-surface velocity model.

The processing method of the tomographic statics of the embodiment of the present application, first to after the Moving grids cell discretization Near-surface velocity model carries out Neural Network Inversion, obtains the second near-surface velocity model, comprising:

Forward modeling is carried out to the first near-surface velocity model of the Moving grids cell discretization, obtains the second forward modeling travelling When；

To first after the Moving grids cell discretization when being travelled based on the earthquake primary travel time and the second forward modeling Near-surface velocity model carries out Neural Network Inversion, obtains the second near-surface velocity model.

The processing method of the tomographic statics of the embodiment of the present application, the Neural Network Inversion include using backpropagation mind Inverting is carried out through network.

The processing method of the tomographic statics of the embodiment of the present application, it is described to be determined according to second near-surface velocity model Static correction value, comprising:

Low velocity layer thickness, low velocity layer speed, refractor velocity, root are obtained according to second near-surface velocity model The static correction value is determined according to the low velocity layer thickness, low velocity layer speed, refractor velocity.

The processing method of the tomographic statics of the embodiment of the present application, further includes:

Judge whether the static correction value meets preset correction accuracy condition；

If the result judged be it is no, execute iterative processing:

Last time is obtained into the second near-surface velocity model as first near-surface velocity model and is based on Moving grids list Member carries out sliding-model control and carries out Neural Network Inversion accordingly, obtains the second new near-surface velocity model；

New static correction value is obtained according to the second new near-surface velocity model；

Until meeting the correction accuracy condition, static correction is carried out according to the static correction value for meeting correction accuracy condition Processing.

The processing method of the tomographic statics of the embodiment of the present application, described to judge whether the static correction value meets preset Correction accuracy condition, comprising:

Judge the practical sinking speed that the low velocity layer speed opposite cover investigation measures at near-surface investigation control point position Whether the absolute value of the error amount of tape speed is less than preset velocity error threshold value and judges at near-surface investigation control point position It is pre- whether the absolute value of the error amount for the practical low velocity layer thickness that the low velocity layer thickness opposite cover investigation measures is less than If thickness error threshold value, if it is judged that be it is yes, then meet preset correction accuracy condition；

Or

Judge the practical static correction value that the static correction value opposite cover investigation measures at near-surface investigation control point position Whether the absolute value of error amount is less than preset static correction value error threshold, if it is judged that be it is yes, then meet preset school Positive precision conditions.

The processing method of the tomographic statics of the embodiment of the present application, judges whether the static correction value meets preset correction Precision conditions, comprising:

Low-and high-frequency decomposition is carried out to the static correction value, high frequency static correction value is applied to earthquake data before superposition；

The earthquake data before superposition of application high frequency static correction value is handled to obtain stacked section, judges the stacked section In reference lamina reflection line-ups whether can continuously track, based on including whether reference lamina reflection line-ups can continuously be tracked and sentence Disconnected result determines whether the static correction value meets preset correction accuracy condition.

On the other hand, the embodiment of the present application also provides a kind of tomographic statics device, comprising:

First inverting module obtains the first near-surface velocity mould for carrying out Neural Network Inversion to initial velocity model Type；

Moving grids descretization module is chased after for carrying out ray to first near-surface velocity model based on small grid unit Track calculates the ray degree of covering of the small grid unit, according to the ray degree of covering of the small grid unit, to described One near-surface velocity model carries out the sliding-model control based on Moving grids unit, the first near surface after Moving grids cell discretization The difference of the ray degree of covering of any two grid cell in rate pattern is less than first threshold；

Second inverting module, for carrying out nerve to the first near-surface velocity model after the Moving grids cell discretization Network inverting obtains the second near-surface velocity model；

Static correction value determining module, for determining static correction value according to second near-surface velocity model.

A kind of tomographic statics device of the embodiment of the present application, depositing including processor and storage processor executable instruction Reservoir, when described instruction is executed by the processor realize the following steps are included:

Sliding-model control based on small grid unit is carried out to first near-surface velocity model, to the small grid from The first near-surface velocity model after dispersion carries out ray tracing, calculates the ray degree of covering of small grid unit；

To in the first near-surface velocity model after the small grid discretization, the ray degree of covering is less than the first threshold The small grid unit area of value carries out the first grid cell sliding-model control, obtains the first near-surface velocity of Moving grids discretization The ray degree of covering of model, first grid cell is more than or equal to first threshold；

Neural Network Inversion is carried out to the first near-surface velocity model of the Moving grids cell discretization, it is close to obtain second Earth's surface rate pattern；

A kind of processing method for tomographic statics that this specification one or more embodiment provides, can be by first to first Beginning rate pattern carries out Neural Network Inversion, obtains the first near-surface velocity model；It is close to first to be then based on small grid unit Earth's surface rate pattern carries out ray tracing, calculates the ray degree of covering of small grid unit, is covered according to the ray of small grid unit Lid number carries out the sliding-model control based on Moving grids unit to first near-surface velocity model, and Moving grids unit from The difference of the ray degree of covering of any two grid cell in the first near-surface velocity model after dispersion is less than the first threshold Value.So that the grid cell of the first near-surface velocity model after Moving grids discretization has, ray is covered and ray covers Number distribution is relatively uniform, improves the accuracy and stability of inverting.Using each embodiment of the application, conventional layer can be overcome The pathosis and multiresolution issue of near-surface velocity inverting in static correction are analysed, the accuracy and stabilization of tomographic statics processing are improved Property.

Detailed description of the invention

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

Fig. 1 is a kind of flow diagram of the processing method embodiment for tomographic statics that this specification provides；

The schematic diagram of static correction value is obtained in one embodiment that Fig. 2 provides for this specification；

Fig. 3 is the specific example mesorelief near-surface velocity theoretical model schematic diagram that this specification provides；

The initial velocity model schematic diagram used in the specific example that Fig. 4 provides for this specification；

It is obtained in the specific example that Fig. 5 provides for this specification using the inversion method that this specification embodiment provides Near-surface velocity model schematic diagram；

The earth's surface elevation map in a certain area to be measured in another specific example that Fig. 6 provides for this specification；

It is obtained in another specific example that Fig. 7 provides for this specification using the inversion method that this specification embodiment provides The low velocity layer speed (a) and thickness (b) plan view arrived；

It is obtained in another specific example that Fig. 8 provides for this specification using the inversion method that this specification embodiment provides Low velocity layer velocity error (a) and thickness error (b) schematic diagram；

It is obtained in another specific example that Fig. 9 provides for this specification using the method that this specification embodiment provides Excitation point static correction value (a) and receiver static correction amount (b) schematic diagram；

The static correction value error (a) that Conventional chromatography inverting obtains in another specific example that Figure 10 provides for this specification And static correction value error (b) schematic diagram obtained using the method that this specification embodiment provides；

Main profile application Conventional chromatography inverting in the middle part of work area is crossed in another specific example that Figure 11 provides for this specification The superposition of the stacked profile map (a) of obtained static correction and the static correction obtained using the method that this specification embodiment provides Sectional view (b)；

Figure 12 be cross-track application Conventional chromatography in the middle part of work area in another specific example that this specification provides The stacked profile map (a) for the static correction that inverting obtains and the obtained static correction of method provided using this specification embodiment Stacked profile map (b)；

Figure 13 is a kind of modular structure schematic diagram for tomographic statics Installation practice that this specification provides.

Specific embodiment

In order to make those skilled in the art more fully understand the technical solution in this specification, below in conjunction with this explanation Attached drawing in book one or more embodiment carries out the technical solution in this specification one or more embodiment clear, complete Site preparation description, it is clear that described embodiment is only specification a part of the embodiment, instead of all the embodiments.Based on saying Bright book one or more embodiment, it is obtained by those of ordinary skill in the art without making creative efforts all The range of this specification example scheme protection all should belong in other embodiments.

Fig. 1 is a kind of embodiment of the method process signal of the processing method of tomographic statics that this specification provides Figure.Although present description provides as the following examples or method operating procedure shown in the drawings or apparatus structure, based on normal Rule may include more or partially less operation after merging in the method or device without creative labor Step or modular unit.In the step of there is no necessary causalities in logicality or structure, these steps execute sequence Or the modular structure of device is not limited to this specification embodiment or execution shown in the drawings sequence or modular structure.The method Or device in practice, server or the end product of modular structure are in application, can be according to shown in embodiment or attached drawing Method or modular structure carry out sequence execution or it is parallel execute (such as parallel processor or multiple threads environment, It even include the implementation environment of distributed treatment, server cluster).

Specific one embodiment as shown in Figure 1, a kind of processing method for tomographic statics that this specification provides one In a embodiment, the method may include:

S2, Neural Network Inversion is carried out to initial velocity model, obtains the first near-surface velocity model.

It, can be to be measured by obtaining in order to improve the convergent speed of near-surface velocity model refutation process in the present embodiment The surface survey data in region constructs initial velocity model according to the surface survey data.The surface survey data can be with Data, little refraction data including micrometering well measurements etc. can be measured by micro logging and little refraction and be obtained at control point position Accurate low velocity layer information and refracting layer information, the low velocity layer information may include low velocity layer speed and sinking speed Tape thickness, the refracting layer information may include refractor velocity.It, can be by right in one embodiment that this specification provides Low velocity layer information and refracting layer information at difference control point position in area's to be measured carry out interpolation smoothing processing, obtain area to be measured Initial velocity model.Initial velocity model is constructed according to the surface survey data, is conducive to the essence for improving initial velocity model Exactness.

In one embodiment that this specification provides, when carrying out inverting to the initial velocity model, big net can be based on Lattice unit carries out grid cell discretization to the initial velocity model.In one or more embodiment that this specification provides In, the size of the grid cell may include Δ x1 × Δ y1 × Δ z1, Δ x1 be reception channel away from n times, Δ y1 is shot point Away from n times, Δ z1 is temporally calculate n times of depth sampling interval of sampling interval, and the specific value of n should be fast in conjunction near surface The variation tendency and calculation amount of degree comprehensively consider.Preferably, the value range of the corresponding n of the big grid cell may include: 6 ≤n≤10.In the present embodiment, since the grid cell used to initial velocity model discretization is larger, discrete model space Size substantially reduces, therefore, can be with to Neural Network Inversion is carried out based on the initial velocity model after big grid cell discretization It is quickly obtained the first near-surface velocity model.

In another embodiment that this specification provides, mind is carried out to the initial velocity model after big grid cell discretization Through network inverting, the first near-surface velocity model is obtained, may include:

S202, earthquake primary travel time is picked up.

First break pickup is carried out according to original earthquake data, earthquake primary travel time is obtained, the first arrival to pickup can be passed through Carry out quality monitoring, editor, modification, it is ensured that first arrival time is accurate.

In one or more embodiment of this specification, the general interfering noise weight of seismic data of Complex Mountain acquisition, Cause first arrival identification error big.In order to obtain high-precision first arrival time, the accuracy of inverting is improved, it can be by first with small The effective first arrival signal of Relation acquisition of wave conversion and Lipchitz (Lipschitz) index, then use related algorithm automatic Picking First arrival.Original seismic data is indicated with f (t), can be indicated in the wavelet transformation of scale s and position t are as follows:

Wf (s, t)=f (t) * Ψ_s(t)=∫ f (τ) Ψ_s(t-τ)dτ (1)

Wherein,Ψ (t) is wavelet, and τ is shift factor.If the number of plies of wavelet decomposition is J layers, then the mathematical relationship of wavelet transformation and Lipschitz index can indicate are as follows:

Wherein, a₁,a₂,...,a_J-1,a_JIt is the wavelet module value of each layer.

The value of T can be calculated according to formula (2), define Lipschitz index is indicated with β, if setting T < 0, β > 0, it indicates that the signaling point is useful signal point, is otherwise noise signal point.It is can use in the present embodiment with certain decomposition ruler It spends and wavelet transformation is carried out to the first arrival signal containing noise, the modulus maximum point on every grade of scale is found, then, according to formula (2) Lipschitz index β is calculated, judges that the point is effective first arrival signaling point or interfering noise signaling point, removes noise signal point Modulus maximum, then reconstruct effective first arrival signal using wavelet inverse transformation, finally use the related algorithm first break picking time, from And the accuracy for the earthquake primary travel time for ensuring to obtain.

S204, forward modeling is carried out to the initial velocity model after big grid cell discretization, when obtaining the first forward modeling travelling.

In the present embodiment, can by the method for ray tracing to the initial velocity model after big grid cell discretization into Row forward modeling.Since Complex Mountain near surface structure is complicated, the speed of low velocity layer and thickness vertically and horizontally change acutely, conventional is penetrated Line tracing algorithm stability is poor, and Langan ray-tracing procedure has good adaptability, Er Qiexiao to complicated rate pattern Rate and precision are high.In one embodiment that this specification provides, forward modeling can be carried out using Langan ray-tracing procedure, The analytical expression described when ray position, directions of rays and ray are travelled during Langan ray tracing respectively can be with table It is shown as:

Wherein, r₀It is initial coordinate position of the ray in incidence point position,It is ray initially entering in incidence point position Penetrate direction, c₀It is initial velocity of the ray in incidence point position, s indicates ray arc length,For velocity gradient vector, t ' is forward modeling When travelling,For the higher order term more than second order and second order of velocity gradient, carried out by above-mentioned (3), (4), (5) three formula When solving available first forward modeling travelling.

S206, when being travelled based on the earthquake primary travel time and the first forward modeling to initial after big grid cell discretization Rate pattern carries out Neural Network Inversion, obtains the first near-surface velocity model.

In the present embodiment, travel-time equation, root can be established according to the initial velocity model after the big grid discretization When travelling according to first forward modeling and earthquake primary travel time carries out mind to the initial velocity model after big grid cell discretization Through network inverting, the first near-surface velocity model is obtained.

The travel-time equation can indicate are as follows:

T=F (p (y), v₀(y),e(y),v(y),d) (6)

Wherein, t is earthquake primary travel time, and F is a nonlinear function, and p (y) is low velocity layer thickness, v₀It (y) is sinking Speed belt speed, e (y) are earth's surface elevations, and v (y) is refractor velocity, and d is geophone offset, and y is line direction coordinate, wherein t, d, e It (y) is the known quantity obtained from original earthquake data, p (y), v₀(y), v (y) is amount to be asked.

It is linear anti-since the surface elevation change of Complex Mountain is violent, the speed of low velocity layer and thickness cross directional variations are fast Algorithm is easily trapped into local extremum, and non-linear inversion algorithm has the advantages that global optimizing, therefore non-linear inversion algorithm is more It is suitable for solving complicated near-surface velocity model.The near-surface velocity model of Complex Mountain and the function of earthquake primary travel time close It is that F is difficult with a determining functional relation expression.In one embodiment of this specification, Neural Network Inversion can be passed through Obtain the first near-surface velocity model.Reverse transmittance nerve network (BP neural network) analog human brain in neural network method Intelligence is realized from the arbitrary nonlinear mapping for being input to output, has and carry out nonlinear on the basis of master sample And the ability of pattern-recognition.Therefore, had using BP neural network progress inverting and sought independent of initial velocity model, the overall situation The advantages of excellent, nonlinear and pattern-recognition.In one embodiment of this specification, can by BP neural network into Row inverting obtains near-surface velocity model.It certainly, can also be using other neural network sides in other some embodiments Method carries out inverting, such as convolutional neural networks.

In one embodiment of this specification, the objective function of BP neural network inversion algorithm can be indicated are as follows:

Wherein, t_ijFor earthquake primary travel time, t_ij' for forward modeling travelling when, i be excitation point call number, j be geophone station rope Quotation marks.

BP neural network is made of one or more hidden layers of an input layer, an output layer and centre, nerve Member is basic element therein, and single neuron is the information process unit of a multiple input single output, input/output relation Are as follows:

O_j=f (∑ W_ijI_i-θ_j) (8)

F=(1+e^-x)^-1 (9)

Wherein, x=∑ W_ijI_i-θ_j, the serial number of j expression neuron, O_jIndicate its output information, I_iIndicate its i-th it is defeated Enter, θ_jIndicate threshold value, W_ijIt is its weighted value.The calculating process of BP neural network may include two parts content, study and prediction, Learning algorithm may include that sample data input BP network is obtained output data, defeated by network output data and given ideal Threshold value and weighted value in the error correction network model of data out, the threshold value can be first to shake relatively when the first forward modeling travelling To error threshold when travelling, by the weighted value and threshold value of the continuous corrective networks model of repetitive exercise, until reaching expected mesh Mark, finally obtains ideal BP neural network model.Prediction algorithm may include: the earthquake first arrival travelling that input is picked up When, the first near-surface velocity model is calculated according to the final BP neural network model that learning algorithm obtains.

The first near-surface velocity model that the above method that one or more embodiment of this specification provides obtains, can be with For instructing the partition with variable-step mesh in subsequent processing, the accuracy that subsequent meshes divide is improved.

S4, the sliding-model control based on Moving grids unit is carried out to first near-surface velocity model, to Moving grids list The first near-surface velocity model after first discretization carries out Neural Network Inversion, obtains the second near-surface velocity model.

In the present embodiment, ray tracing first can be carried out to first near-surface velocity model based on small grid unit, And according to the ray coverage density of small grid unit, the ray degree of covering of small grid unit is calculated.By utilizing grid cell Lesser the first near-surface velocity model of small grid dividing elements of size, and ray tracing is carried out, fine determination can be compared The distribution of ray divides the first near-surface velocity model for Moving grids and provides more accurate foundation.Then, according to the small grid The ray degree of covering of unit carries out the sliding-model control based on Moving grids unit to first near-surface velocity model, and The difference of the ray degree of covering of any two grid cell in the first near-surface velocity model after Moving grids cell discretization Value is less than first threshold；Neural Network Inversion is carried out to the first near-surface velocity model after Moving grids cell discretization, is obtained Second near-surface velocity model.The method provided through this embodiment can carry out inverting to the first near-surface velocity model When, each grid cell has ray covering and degree of covering distribution is relatively uniform, to improve the accuracy of inverting.

In one embodiment of this specification, the ray degree of covering according to the small grid unit, to described First near-surface velocity model carries out the sliding-model control based on Moving grids unit, may include: to be less than to ray degree of covering Equal to the small grid unit area of second threshold, the first near-surface velocity model is subjected to the discretization based on the first grid cell Processing；Second threshold is greater than to ray degree of covering and is less than the small grid unit area of third threshold value, by the first near surface speed Degree model carries out the sliding-model control based on the second grid cell；It is more than or equal to the small grid of third threshold value to ray degree of covering First near-surface velocity model is carried out the sliding-model control based on third grid cell by unit area；The third threshold value is big In second threshold, the size of first grid cell is greater than the size of the second grid cell, the ruler of second grid cell The very little size greater than third grid cell, the size of the third grid cell are more than or equal to the size of small grid unit.

For the above method provided by the embodiments of the present application by covering few small grid unit area to ray, utilization is larger First grid cell of size divides the first near-surface velocity model；Less small grid unit area is covered to ray, benefit The first near-surface velocity model is divided with the second grid cell of medium size；More small grid cellular zone is covered to ray Domain divides the first near-surface velocity model using the lesser third grid cell of size；To guarantee Moving grids mesh discretization The difference of the ray degree of covering of any two grid cell in the first near-surface velocity model after change is less than first threshold, So that Moving grids divide after the first near-surface velocity model in each grid cell there is ray to cover and degree of covering point Cloth is relatively uniform.

When it is implemented, the size of first grid cell, the second grid cell, third grid cell and The value of the size of the first threshold should comprehensively consider in conjunction with the Variation Features and calculation amount of near-surface velocity, to guarantee to become net Each grid cell has ray covering in rate pattern after lattice discretization and degree of covering distribution is relatively uniform.In this explanation In one embodiment that book provides, the value of the corresponding n of the small grid unit may include: n=1, the first grid list The value range of the corresponding n of member may include: 5≤n≤8, and the value range of the corresponding n of second grid cell can wrap Include: the value range of 3≤n < 5, the corresponding n of the third grid cell may include: 1≤n < 3.

Above-described embodiment that this specification provides, by calculating the ray degree of covering of each small grid unit, root According to the ray degree of covering of each small grid unit, it is discrete that the progress of Moving grids unit is based on to first near-surface velocity model Change is handled, the ray covering of any two grid cell in the first near-surface velocity model after the Moving grids cell discretization The difference of number is less than first threshold, so that the ray covering time of each grid cell in the first near-surface velocity model Number distribution is relatively uniform.The method for overcoming grid division unit in Conventional chromatography static correction near-surface velocity inverting exists and does not have The problem of being not covered with by ray or covered multi-solution caused by few region and pathosis, to improve inverting Accuracy and stability.

In another embodiment of this specification, near surface speed in the first near-surface velocity model of analysis can also be passed through Variation tendency is spent, or obtains the variation tendency of near-surface velocity by analysis surface survey data, the near-surface velocity can To include low velocity layer speed.Such as the change that low velocity layer speed data determines near-surface velocity can be measured by analyzing micro logging Change trend.According to the variation tendency of the near-surface velocity, first near-surface velocity model is carried out based on Moving grids list The sliding-model control of member.When it is implemented, can be big to near-surface velocity variation degree in first near-surface velocity model In the small grid unit area of preset degree threshold value, the first near-surface velocity model is carried out based on third grid cell discrete Change processing, the preset degree threshold value can be set according to actual conditions.Using the present embodiment above scheme, near-earth is chosen The relatively violent small grid unit area of table velocity variations carries out the first near-surface velocity model based on lesser grid cell Sliding-model control, to further increase the accuracy of inverting.

In one embodiment that this specification provides, to the first near-surface velocity model after Moving grids cell discretization Neural Network Inversion is carried out, the second near-surface velocity model is obtained, may include:

According to the carry out ray tracing forward modeling of the first earth's surface rate pattern of the Moving grids discretization, obtains seismic wave and penetrate When thread path and the second forward modeling are travelled.The travelling when side according to the first earth's surface velocity model building of the Moving grids discretization Journey, when being travelled according to second forward modeling and the earthquake primary travel time is to the first near-earth of the Moving grids cell discretization Table rate pattern carries out Neural Network Inversion, obtains the second near-surface velocity model.The ray tracing forward modeling and neural network The concrete mode of inverting is described again here referring to step S2.

S6, static correction value is determined according to second near-surface velocity model.

In the present embodiment, horizontal superposition theory requires excitation point, geophone station to be located on same level datum level, at this time instead Ejected wave time curve meets hyperbolic rule, and the speed and thickness change of actual hypsography, low velocity layer, excitation well depth etc. Factor causes time distance curve of reflection wave not to be able to satisfy hyperbolic rule, and therefore, it is necessary to excitation point, geophone station are passed through corresponding quiet school Positive quantity is corrected on the datum level, so that excitation point, geophone station are located on same level datum level.The bright book of this institute provides One embodiment in, can according to the second near-surface velocity model obtain low velocity layer thickness, low velocity layer speed and folding Interval velocity is penetrated, excitation point, geophone station are calculated according to the low velocity layer thickness, low velocity layer speed and refractor velocity Static correction value.

In one embodiment that this specification provides, with reference to Fig. 2, S indicates excitation point in Fig. 2, is located in high-speed layer, depth For h+h₀+h₁；R indicates geophone station, is located at earth's surface, and low velocity layer thickness p is h_s+h_L, low velocity layer speed is V₀, refractor velocity For V, horizontal dotted line indicates that datum level, bending dotted line indicate high-speed layer top interface, and the curved continuous lines being bent above dotted line indicate ground Table.Static corrections processing passes through the corresponding well depth correction of excitation pointWeathering correctionLandform CorrectionExcitation point is corrected on datum level by static correction value, passes through the corresponding weathering correction of receiving pointTopographical correctionReceiving point is corrected on datum level by static correction value, so that reflected wave in phase Axis meets dynamic correction, horizontal principle of stacking.

In another embodiment of this specification the method, the method can also include:

S802, judge whether the static correction value meets preset correction accuracy condition；

If S804, the result judged be it is no, execute iterative processing:

S806, obtained to the second near-surface velocity model as first near-surface velocity model last time based on change net Lattice unit carries out sliding-model control and carries out Neural Network Inversion accordingly, obtains the second new near-surface velocity model；

S808, new static correction value is obtained according to the second new near-surface velocity model；

S810, until meet the correction accuracy condition, carried out according to the static correction value for meeting correction accuracy condition Static corrections processing.

In the present embodiment, whether the static correction value obtained in judgment step S6 meets preset precision conditions, may include:

The low velocity layer speed and thickness data at near-surface investigation control point position in the second near-surface velocity model are obtained, And near-surface investigation measures at the position practical low velocity layer speed and thickness data；Calculate separately the second near surface speed The error amount of low velocity layer speed, the practical low velocity layer speed that thickness opposite cover investigation measures, thickness in degree model, sentences Whether the absolute value of the error amount of disconnected low velocity layer speed is less than preset velocity error threshold value, and judges low velocity layer thickness The absolute value of error amount whether be less than preset thickness error threshold value.If it is determined that result be yes, i.e., above-mentioned low velocity layer Speed is respectively less than corresponding default error threshold with the exhausted angle value of the error amount of thickness, then meets preset correction accuracy condition； If it is determined that result be it is no, that is, be unsatisfactory for preset correction accuracy condition.

Alternatively, the static correction value obtained at near-surface investigation control point position by the second near-surface velocity model is obtained, with And the practical static correction value that near-surface investigation measures at the position, calculate the quiet school obtained by the second near-surface velocity model The error amount for the practical static correction value that positive quantity opposite cover investigation measures, judges whether the absolute value of the error amount of static correction value is small In preset static correction value error threshold；If it is judged that be it is yes, i.e., the absolute value of the error amount of the described static correction value is less than Preset static correction value error threshold then meets preset correction accuracy condition；If it is determined that result be it is no, that is, be unsatisfactory for pre- If correction accuracy condition.

In above-described embodiment, the value size of preset error threshold be may be set according to actual conditions, can also basis Subsequent static corrections processing result carries out adjustment appropriate, to meet the requirement of static corrections processing precision.

If the result judged be it is no, execute iterative processing:

The second near-surface velocity model that last time is obtained is based on Moving grids as first near-surface velocity model Unit carries out sliding-model control and carries out Neural Network Inversion accordingly, obtains the second new near-surface velocity model, according to The second new near-surface velocity model obtains new static correction value.

Judge whether the new static correction value meets above-mentioned preset correction accuracy condition again, if it is determined that result Be it is no, then execute above-mentioned iterative processing steps again, i.e., by the second near-surface velocity model obtained in last iterative processing As next iteration processing in the first near-surface velocity model, and so on, until obtain static correction value meet described in Correction accuracy condition carries out static corrections processing according to the static correction value for meeting correction accuracy condition, to be effectively ensured quiet The accuracy of correction process.

Since the static correction value cross directional variations of Complex Mountain are violent, imaging meeting directly is overlapped using static correction value The problem for causing time distance curve of reflection wave distortion, stack velocity error big.Therefore, it is provided in this embodiment according to static correction value into It, can be by establishing suitable floating datum according to the static correction value of excitation point, geophone station, to excitation in row static corrections processing The static correction value progress low-and high-frequency decomposition of point, geophone station, is then applied to earthquake data before superposition, maximum journey for high frequency static correction value Degree guarantees the authenticity of seismic wave field and seismic velocity, improves seismic imaging effect.When it is implemented, by the acquisition The static correction value progress low-and high-frequency decomposition of excitation point, geophone station, is applied to earthquake data before superposition for high frequency static correction value, to application The earthquake data before superposition of high frequency static correction value carries out pretreatment and stack velocity analysis obtains stacked section.

It, can be quiet to judge by analyzing the feature in the stacked section in one embodiment that this specification provides The quality of correction.It is generally necessary to carry out reflecting the feature of earth formation and the feature of actual earth formation in analysis stacked section Between the goodness of fit determine the quality of static correction, when the goodness of fit is greater than goodness of fit threshold value, i.e. structure feature in stacked section It coincide substantially with the feature of actual earth formation, it is determined that the quality of static correction meets preset static correction condition.In addition, In one or more embodiment that this specification provides, it can also further pass through the reference lamina reflection in analysis stacked section The quality whether lineups can continuously be tracked to judge static correction, if the reference lamina reflection line-ups in stacked section can connect Continuous tracking, it is determined that the quality of static correction meets preset static correction condition, exports the quiet school of determining excitation point and geophone station Positive quantity；If the reference lamina reflection line-ups in stacked section cannot be tracked continuously, static correction condition is reset, is executed above-mentioned Iterative step further obtains more accurate static correction value, to be further ensured that the accuracy of static corrections processing.

In order to enable the scheme in the embodiment that this specification provides is clearer, this specification is additionally provided using above-mentioned The specific example in the reality of scheme region to be measured.

In a specific example of this specification, fluctuating near-surface velocity theoretical model is devised, as shown in figure 3, mould It is split shooting that theoretical type, which corresponds to analog observation system, 28000 meters of spread length, big gun spacing 50m, geophone station spacing 25m, 501 Big gun, Mei Bao 120, smallest offset is away from 25m, maximum offset 1500m.Fig. 4 be this specification embodiment provide method in adopt Initial velocity model schematic diagram, Fig. 5 are the second near-surface velocities obtained using the method that this specification embodiment provides The schematic diagram of model, as shown in Figure 5 this specification embodiment provide method obtain the second near-surface velocity model very close to The fluctuating near-surface velocity theoretical model, the above method inversion accuracy for illustrating that this specification embodiment provides are higher.

In another specific example of this specification, Fig. 6 is the earth's surface elevation map in Sichuan Changning area, area's earth's surface category In hill features, hypsography is larger, and the steep bank of the cliff of displacement is more, ravines and guillies criss-cross, height above sea level 471m-1507m, and relative relief is up to 1036 Rice.Area's surface geology complicated condition, lithology exposure are mainly Triassic system limestone, quartzy sandstone and Jurassic system sand shale, difference The speed of lithology, thickness space variation are fast.Complicated near surface structure leads to the static correction outstanding problem in the area, conventional static correction Application effect Shortcomings of the method in the area.

Fig. 7 is the low velocity layer velocity plane figure (figure in above method inverting Sichuan Changning that this specification embodiment provides A) and thickness plane figure (figure b), Fig. 8 are the above-mentioned sides of this specification embodiment offer at near-surface investigation control point position Method inversion result opposite cover investigates the error schematic diagram of measurement result, and that Fig. 8 (a) is indicated is low velocity layer velocity error, Fig. 8 (b) what is indicated is low velocity layer thickness error, the sinking band for the above method inverting that this specification embodiment provides as shown in Figure 8 Velocity error is no more than ± 10m/s, and thickness error is no more than ± 10m, and the above-mentioned chromatography for illustrating that this specification embodiment provides is anti- It is higher to drill method precision.

Fig. 9 is the static correction value of the excitation point and geophone station that are obtained by the method that this specification embodiment provides, Fig. 9 (a) it is corresponding be excitation point static correction value, it is the static correction value of geophone station that Fig. 9 (b) is corresponding.Figure 10 (a) is near-surface investigation The static correction value opposite cover that Conventional chromatography inverting obtains at control point position investigates the error of measurement result, and Figure 10 (b) is table The static correction value opposite cover investigation that the above method inverting that this specification embodiment provides at layer investigation control point position obtains The error of measurement result；The error of Conventional chromatography static correction is larger as shown in Figure 10, and the offer of this specification embodiment is above-mentioned The static correction value error that method obtains is no more than ± 10ms, shows the quiet school obtained by the above method that this specification embodiment provides Positive accuracy of measurement is higher.

Figure 11 was the stacked section (a) for the static correction that main profile application Conventional chromatography inverting obtains in the middle part of work area, this theory The stacked section (b) for the static correction that the above method that bright book embodiment provides obtains, comparison can see, and Conventional chromatography inverting is quiet The stacked section of correction cannot continuously be tracked (shown in arrow) at aberration, reference lamina reflection line-ups, and this specification embodiment The stacked section imaging effect of the above method of offer is good, reference lamina reflection line-ups can be tracked continuously, shows through this theory The precision that the above method that bright book embodiment provides carries out static corrections processing is higher.

Figure 12 was the stacked section (a) for the static correction that cross-track application Conventional chromatography inverting obtains in the middle part of work area, sheet The stacked section (b) for the static correction that the above method that specification embodiment provides obtains, comparison can see, Conventional chromatography inverting The stacked section of static correction cannot continuously be tracked (shown in arrow) at aberration, reference lamina reflection line-ups, and this specification is implemented The stacked section imaging effect for the above method that example provides is good, reference lamina reflection line-ups can be tracked continuously, shows to pass through this The precision that the above method that specification embodiment provides carries out static corrections processing is higher.

All the embodiments in this specification are described in a progressive manner, same and similar portion between each embodiment Dividing may refer to each other, and each embodiment focuses on the differences from other embodiments.Specifically it is referred to The description of aforementioned relevant treatment related embodiment, does not do repeat one by one herein.

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

Based on the processing method of tomographic statics described above, this specification one or more embodiment also provides one kind The device of tomographic statics.The device may include that the system of this specification embodiment the method, software has been used (to answer With), module, component, server etc. and combine the necessary device for implementing hardware.Based on same innovation thinking, this specification is real The device in one or more embodiments of example offer is applied as described in the following examples.The realization side solved the problems, such as due to device Case is similar to method, therefore the implementation of the specific device of this specification embodiment may refer to the implementation of preceding method, repetition Place repeats no more.Used below, the software and/or hardware of predetermined function may be implemented in term " unit " or " module " Combination.Although device described in following embodiment is preferably realized with software, the group of hardware or software and hardware The realization of conjunction is also that may and be contemplated.Specifically, Figure 13 is that a kind of tomographic statics device that this specification provides is implemented The modular structure schematic diagram of example, as shown in figure 13, the apparatus may include:

First inverting module 102 can be used for carrying out Neural Network Inversion to initial velocity model, obtain the first near surface Rate pattern；

Moving grids descretization module 104, can be used for based on small grid unit to first near-surface velocity model into Row ray tracing calculates the ray degree of covering of the small grid unit, according to the ray degree of covering of the small grid unit, Sliding-model control based on Moving grids unit is carried out to first near-surface velocity model, after Moving grids cell discretization The difference of the ray degree of covering of any two grid cell in one near-surface velocity model is less than first threshold；

Second inverting module 106 can be used for the first near-surface velocity model after the Moving grids cell discretization Neural Network Inversion is carried out, the second near-surface velocity model is obtained；

Static correction value determining module 108 can be used for determining static correction value according to second near-surface velocity model.

Described device provided by the above embodiment, by carrying out the first near-surface model based on the discrete of Moving grids unit Change is handled, so that each grid cell in the first near-surface velocity model after Moving grids cell discretization has ray covering And degree of covering distribution is relatively uniform, the pathosis and multi-solution for overcoming near-surface velocity inverting in Conventional chromatography static correction are asked Topic, to improve the accuracy and stability of tomographic statics processing.

Certainly, it is described referring to preceding method embodiment, in the other embodiments of described device, the first inverting module 102 may include:

Initial velocity model construction unit can be used for constructing initial velocity model according to surface survey data；

Big grid discretization unit can be used for carrying out at discretization the initial velocity model based on big grid cell Reason；

First inverting unit can be used for carrying out neural network to the initial velocity model after the big grid discretization anti- It drills, obtains the first near-surface velocity model.

In another embodiment for the described device that this specification provides, the Moving grids descretization module 104 be can wrap It includes:

In another embodiment for the described device that this specification provides, the Moving grids descretization module 104 can be with Include:

Described device provided by the above embodiment, by changing relatively violent small grid cellular zone to near-surface velocity Domain carries out sliding-model control to the first near-surface velocity model based on third grid cell, to further increase the essence of inverting Exactness.

In another embodiment for the described device that this specification provides, the first inverting unit may include:

First forward modeling subelement can be used for carrying out just the initial velocity model after the big grid cell discretization It drills, when obtaining the first forward modeling travelling；

First inverting subelement, to described big when can be used for being travelled according to the earthquake primary travel time and the first forward modeling Initial velocity model after grid cell discretization carries out Neural Network Inversion, obtains the first near-surface velocity model.

In another embodiment for the described device that this specification provides, the second inverting module 106 may include:

Second forward modeling unit carries out forward modeling for the first near-surface velocity model to the Moving grids cell discretization, When obtaining the second forward modeling travelling；

Second inverting unit, to the Moving grids list when for being travelled according to the earthquake primary travel time and the second forward modeling First near-surface velocity model of first discretization carries out Neural Network Inversion, obtains the second near-surface velocity model.

In another embodiment for the described device that this specification provides, the determining static correction value module 108 be can wrap It includes:

Low velocity layer thickness, low velocity layer speed, refractor velocity, root are obtained according to second near-surface velocity model The static correction value is determined according to the low velocity layer thickness, low velocity layer speed and refractor velocity.

In another embodiment for the described device that this specification provides, the tomographic statics device can also include:

Quality Control unit, can be used for judging whether the static correction value meets preset correction accuracy condition, if described sentence Disconnected result be it is no, then execute iterative processing, the last time obtained into the second near-surface velocity model as first near surface Rate pattern is based on Moving grids unit and carries out sliding-model control and carry out Neural Network Inversion accordingly, and it is close to obtain new second Earth's surface rate pattern obtains new static correction value according to the second new near-surface velocity model, until meeting the correction Precision conditions；

Static corrections processing unit can be used for carrying out at static correction according to the static correction value for meeting correction accuracy condition Reason.

In another embodiment for the described device that this specification provides, the Quality Control unit may include:

First correction accuracy judgment sub-unit can be used for judging the low velocity layer speed at near-surface investigation control point position Whether the absolute value of the error amount for the practical low velocity layer speed that degree opposite cover's investigation measures is less than preset velocity error threshold Value and judge that the low velocity layer thickness opposite cover investigates the practical low velocity layer measured at near-surface investigation control point position Whether the absolute value of the error amount of thickness is less than preset thickness error threshold value, if it is judged that be it is yes, then meet preset Correction accuracy condition；

In another embodiment for the described device that this specification provides, the Quality Control unit can also include:

Second correction accuracy judgment sub-unit can be used for judging the static correction value phase at near-surface investigation control point position Whether the absolute value of the error amount of the practical static correction value measured to near-surface investigation is less than preset static correction value error threshold, such as Fruit judging result be it is yes, then meet preset correction accuracy condition.

Third correction accuracy judgment sub-unit carries out low-and high-frequency decomposition to the static correction value, high frequency static correction value is answered Earthquake data before superposition is used, the earthquake data before superposition of application high frequency static correction value is handled to obtain stacked section, judges institute State whether the reference lamina reflection line-ups in stacked section can continuously be tracked, based on include reference lamina reflection line-ups whether can connect The judging result of continuous tracking determines whether the static correction value meets preset correction accuracy condition.

The described device that said one or multiple embodiments provide carries out Quality Control by the accuracy to static correction value, For precise requirements are not reached, it is further processed, so that the accuracy of static corrections processing is further ensured,

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

A kind of tomographic statics device that this specification one or more embodiment provides, can be by first to initial velocity Model carries out Neural Network Inversion, obtains the first near-surface velocity model；Small grid unit is then based on to the first near surface speed It spends model and carries out ray tracing, calculate the ray degree of covering of small grid unit, according to the ray degree of covering of small grid unit, Sliding-model control based on Moving grids unit is carried out to first near-surface velocity model, and after Moving grids cell discretization The difference of the ray degree of covering of any two grid cell in first near-surface velocity model is less than first threshold.To make The grid cell of the first near-surface velocity model after obtaining Moving grids discretization has ray covering and ray degree of covering is distributed It is relatively uniform, improve the accuracy and stability of inverting.Using each embodiment of the application, Conventional chromatography static correction can be overcome The pathosis and multiresolution issue of middle near-surface velocity inverting improve the accuracy and stability of tomographic statics processing.

Method or apparatus described in above-described embodiment that this specification provides can realize that business is patrolled by computer program It collects and records on a storage medium, the storage medium can be read and be executed with computer, realize this specification embodiment institute The effect of description scheme.Therefore, this specification also provides a kind of tomographic statics device, including processor and storage processor can The memory executed instruction, when described instruction is executed by the processor realize the following steps are included:

A kind of tomographic statics device described in above-described embodiment, can be by first carrying out nerve net to initial velocity model Network inverting obtains the first near-surface velocity model；It is then based on small grid unit and ray is carried out to the first near-surface velocity model Tracking calculates the ray degree of covering of small grid unit, according to the ray degree of covering of small grid unit, to first near-earth Table rate pattern carries out the sliding-model control based on Moving grids unit, and the first near-surface velocity after Moving grids cell discretization The difference of the ray degree of covering of any two grid cell in model is less than first threshold.So that Moving grids discretization The grid cell of the first near-surface velocity model afterwards has ray covering and the distribution of ray degree of covering is relatively uniform, improves anti- The accuracy and stability drilled.Using each embodiment of the application, near-surface velocity in Conventional chromatography static correction can be overcome anti- The pathosis and multiresolution issue drilled improve the accuracy and stability of tomographic statics processing.

The storage medium may include the physical unit for storing information, usually by after information digitalization again with benefit The media of the modes such as electricity consumption, magnetic or optics are stored.It may include: that letter is stored in the way of electric energy that the storage medium, which has, The device of breath such as, various memory, such as RAM, ROM；The device of information is stored in the way of magnetic energy such as, hard disk, floppy disk, magnetic Band, core memory, magnetic bubble memory, USB flash disk；Using optical mode storage information device such as, CD or DVD.Certainly, there are also it Readable storage medium storing program for executing of his mode, such as quantum memory, graphene memory etc..

It should be noted that this specification device described above can also include according to the description of related method embodiment Other embodiments, concrete implementation mode are referred to the description of embodiment of the method, do not repeat one by one herein.This explanation Various embodiments are described in a progressive manner in book, and same and similar part refers to each other i.e. between each embodiment Can, each embodiment focuses on the differences from other embodiments.It is situated between especially for hardware+program class, storage For matter+program embodiment, since it is substantially similar to the method embodiment, so be described relatively simple, related place referring to The part of embodiment of the method illustrates.

Device, module or the unit that above-described embodiment illustrates can specifically be realized, Huo Zheyou by computer chip or entity Product with certain function is realized.It is a kind of typically to realize that equipment is computer.Specifically, computer for example can be a People's computer, laptop computer, vehicle-mounted human-computer interaction device, cellular phone, camera phone, smart phone, individual digital help Reason, media player, navigation equipment, electronic mail equipment, game console, tablet computer, wearable device or these The combination of any equipment in equipment.

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

It is also known in the art that other than realizing controller in a manner of pure computer readable program code, it is complete Entirely can by by method and step carry out programming in logic come so that controller with logic gate, switch, specific integrated circuit, programmable Logic controller realizes identical function with the form for being embedded in microcontroller etc..Therefore this controller is considered one kind Hardware component, and the structure that the device for realizing various functions that its inside includes can also be considered as in hardware component.Or Person even, can will be considered as realizing the device of various functions either the software module of implementation method can be hardware again Structure in component.

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

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

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

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

It should also be noted that, the terms "include", "comprise" or its any other variant are intended to nonexcludability It include so that the process, method, commodity or the equipment that include a series of elements not only include those elements, but also to wrap Include other elements that are not explicitly listed, or further include for this process, method, commodity or equipment intrinsic want Element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that including described want There is also other identical elements in the process, method or equipment of element.

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

This specification one or more embodiment can computer executable instructions it is general on It hereinafter describes, such as program module.Generally, program module includes executing particular task or realization particular abstract data type Routine, programs, objects, component, data structure etc..This this specification one can also be practiced in a distributed computing environment Or multiple embodiments, in these distributed computing environments, by being held by the connected remote processing devices of communication network Row task.In a distributed computing environment, program module can be located at the local and remote computer including storage equipment In storage medium.

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

The above description is only an example of the present application, is not intended to limit this application.For those skilled in the art For, various changes and changes are possible in this application.All any modifications made within the spirit and principles of the present application are equal Replacement, improvement etc., should be included within the scope of the claims of this application.

Claims

1. a kind of processing method of tomographic statics characterized by comprising

Ray tracing is carried out to first near-surface velocity model based on small grid unit, calculates penetrating for the small grid unit Line degree of covering；

According to the ray degree of covering of the small grid unit, first near-surface velocity model is carried out based on Moving grids list The sliding-model control of member, any two grid cell in the first near-surface velocity model after Moving grids cell discretization are penetrated The difference of line degree of covering is less than first threshold, wherein the ray degree of covering according to the small grid unit, to described First near-surface velocity model carries out the sliding-model control based on Moving grids unit, comprising:

To ray degree of covering be less than or equal to second threshold small grid unit area, by first near-surface velocity model into Sliding-model control of the row based on the first grid cell；

Second threshold is greater than to ray degree of covering and is less than the small grid unit area of third threshold value, by first near surface Rate pattern carries out the sliding-model control based on the second grid cell；

To ray degree of covering be more than or equal to third threshold value small grid unit area, by first near-surface velocity model into Sliding-model control of the row based on third grid cell；

The third threshold value is greater than second threshold, and the size of first grid cell is greater than the ruler of second grid cell Very little, the size of second grid cell is greater than the size of the third grid cell, and the size of the third grid cell is big In the size for being equal to the small grid unit；

Neural Network Inversion is carried out to the first near-surface velocity model after the Moving grids cell discretization, obtains the second near-earth Table rate pattern；

2. the processing method of tomographic statics according to claim 1, which is characterized in that it is described to initial velocity model into Row Neural Network Inversion obtains the first near-surface velocity model, comprising:

Initial velocity model is constructed according to surface survey data；

Sliding-model control is carried out to the initial velocity model based on big grid cell, to the initial velocity after big grid discretization Model carries out Neural Network Inversion, obtains the first near-surface velocity model.

3. the processing method of tomographic statics according to claim 1, which is characterized in that described according to the small grid list The ray degree of covering of member, carries out the sliding-model control based on Moving grids unit to first near-surface velocity model, comprising:

It is greater than the small grid list of preset degree threshold value to near-surface velocity variation degree in first near-surface velocity model First near-surface velocity model is carried out the sliding-model control based on third grid cell by first region.

4. the processing method of tomographic statics according to claim 1, which is characterized in that described according to the small grid list The ray degree of covering of member, carries out the sliding-model control based on Moving grids unit to first near-surface velocity model, comprising:

The size of the grid cell includes Δ x1 × Δ y1 × Δ z1, Δ x1 be reception channel away from n times, Δ y1 is shooting distance N times, Δ z1 is n times of depth sampling interval；

The value of the n of the small grid unit includes: n=1；

The value range of the n of first grid cell includes: 5≤n≤8；

The value range of the n of second grid cell includes: 3≤n < 5；

The value range of the n of the third grid cell includes: 1≤n < 3.

5. the processing method of tomographic statics according to claim 2, which is characterized in that described to big grid mesh discretization Initial velocity model after change carries out Neural Network Inversion, obtains the first near-surface velocity model, comprising:

Pick up earthquake primary travel time；

To the initial velocity after the big grid cell discretization when being travelled based on the earthquake primary travel time and the first forward modeling Model carries out Neural Network Inversion, obtains the first near-surface velocity model.

6. the processing method of tomographic statics according to claim 5, which is characterized in that described to the Moving grids unit The first near-surface velocity model after discretization carries out Neural Network Inversion, obtains the second near-surface velocity model, comprising:

Forward modeling is carried out to the first near-surface velocity model of the Moving grids cell discretization, when obtaining the second forward modeling travelling；

To the first near-earth after the Moving grids cell discretization when being travelled based on the earthquake primary travel time and the second forward modeling Table rate pattern carries out Neural Network Inversion, obtains the second near-surface velocity model.

7. the processing method of tomographic statics according to claim 1, which is characterized in that the Neural Network Inversion includes Inverting is carried out using reverse transmittance nerve network.

8. the processing method of tomographic statics according to claim 1-7, which is characterized in that described according to Second near-surface velocity model determines static correction value, comprising:

Low velocity layer thickness, low velocity layer speed, refractor velocity are obtained according to second near-surface velocity model, according to institute It states low velocity layer thickness, low velocity layer speed, refractor velocity and determines the static correction value.

9. the processing method of tomographic statics according to claim 8, which is characterized in that further include:

If the result judged be it is no, execute iterative processing:

Last time is obtained the second near-surface velocity model as first near-surface velocity model to carry out based on Moving grids list Member sliding-model control and carry out Neural Network Inversion accordingly, obtain the second new near-surface velocity model；

Until meeting the correction accuracy condition, carried out at static correction according to the static correction value for meeting correction accuracy condition Reason.

10. the processing method of tomographic statics according to claim 9, which is characterized in that the judgement static correction Whether amount meets preset correction accuracy condition, comprising:

Judge the practical low velocity layer speed that the low velocity layer speed opposite cover investigation measures at near-surface investigation control point position Whether the absolute value of the error amount of degree is less than preset velocity error threshold value and judges described at near-surface investigation control point position It is preset whether the exhausted angle value of the error amount for the practical low velocity layer thickness that low velocity layer thickness opposite cover investigation measures is less than Thickness error threshold value, if it is judged that be it is yes, then meet preset correction accuracy condition；

Alternatively,

Judge the error for the practical static correction value that the static correction value opposite cover investigation measures at near-surface investigation control point position Whether the absolute value of value is less than preset static correction value error threshold, if it is judged that be it is yes, then meet preset correction essence Degree condition.

11. according to the processing method of tomographic statics described in claim 9 or 10, which is characterized in that judge the quiet school Whether positive quantity meets preset correction accuracy condition, comprising:

The earthquake data before superposition of application high frequency static correction value is handled to obtain stacked section, is judged in the stacked section Whether reference lamina reflection line-ups can continuously be tracked, based on the judgement knot that whether can continuously track including reference lamina reflection line-ups Fruit determines whether the static correction value meets preset correction accuracy condition.

12. a kind of tomographic statics device characterized by comprising

First inverting module obtains the first near-surface velocity model for carrying out Neural Network Inversion to initial velocity model；

Moving grids descretization module, for carrying out ray tracing to first near-surface velocity model based on small grid unit, The ray degree of covering for calculating the small grid unit, according to the ray degree of covering of the small grid unit, to described first Near-surface velocity model carries out the sliding-model control based on Moving grids unit, the first near surface speed after Moving grids cell discretization The difference for spending the ray degree of covering of any two grid cell in model is less than first threshold, wherein described according to The ray degree of covering of small grid unit carries out at the discretization based on Moving grids unit first near-surface velocity model Reason, comprising:

Second inverting module, for carrying out neural network to the first near-surface velocity model after the Moving grids cell discretization Inverting obtains the second near-surface velocity model；

13. a kind of tomographic statics device, which is characterized in that including processor and depositing for storage processor executable instruction Reservoir, when described instruction is executed by the processor realize the following steps are included: