CN117826259A - Deep diffraction field speed modeling method and device, electronic equipment and storage medium - Google Patents
Deep diffraction field speed modeling method and device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN117826259A CN117826259A CN202410023446.XA CN202410023446A CN117826259A CN 117826259 A CN117826259 A CN 117826259A CN 202410023446 A CN202410023446 A CN 202410023446A CN 117826259 A CN117826259 A CN 117826259A
- Authority
- CN
- China
- Prior art keywords
- diffraction
- angle
- point
- aperture
- deep
- 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 54
- 238000012937 correction Methods 0.000 claims abstract description 29
- 238000003384 imaging method Methods 0.000 claims abstract description 27
- 238000001228 spectrum Methods 0.000 claims abstract description 21
- 238000000926 separation method Methods 0.000 claims abstract description 19
- 230000004044 response Effects 0.000 claims abstract description 18
- 230000003993 interaction Effects 0.000 claims abstract description 7
- 238000013508 migration Methods 0.000 claims description 11
- 230000005012 migration Effects 0.000 claims description 11
- 238000010276 construction Methods 0.000 claims description 10
- 230000006870 function Effects 0.000 claims description 10
- 238000012545 processing Methods 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 5
- 230000036962 time dependent Effects 0.000 claims description 4
- 238000005070 sampling Methods 0.000 claims 1
- 239000011435 rock Substances 0.000 abstract 1
- 238000004458 analytical method Methods 0.000 description 9
- 239000000243 solution Substances 0.000 description 8
- 238000010586 diagram Methods 0.000 description 6
- 238000004891 communication Methods 0.000 description 4
- 239000011159 matrix material Substances 0.000 description 4
- 230000001066 destructive effect Effects 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 230000002093 peripheral effect Effects 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 230000006378 damage Effects 0.000 description 1
- 230000005284 excitation Effects 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 238000002347 injection Methods 0.000 description 1
- 239000007924 injection Substances 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000010183 spectrum analysis Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000002123 temporal effect Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Abstract
The application provides a deep diffraction field speed modeling method, a deep diffraction field speed modeling device, electronic equipment and a storage medium, and relates to the field of deep resource exploration, wherein the method comprises the following steps: obtaining diffraction wave separation data, constructing a common diffraction point multi-aperture angle gather through angle deviation based on an edge diffraction model and a scatterer physical model, and capturing diffraction response in the multi-aperture; establishing a correction relation between the inclination angle of the diffraction point and the offset speed according to the angle gather; further, according to the correction relation, calculating a diffraction field velocity spectrum of each diffraction point based on the local similarity and the mixed weight of the difference boot-strap for each co-diffraction point multi-aperture angle gather; picking up a diffraction wave velocity value of a focusing signal through man-machine interaction; and finally, constructing a model through interpolation. The method can effectively capture deep diffraction weak energy, improve the focusing quality of a velocity spectrum, construct a refined velocity model and provide support for imaging of unconventional reservoirs such as deep dry-hot rock.
Description
Technical Field
The present application relates to the field of deep resource exploration, and in particular, to a deep diffraction field speed modeling method, device, electronic apparatus, and storage medium.
Background
In the processing and imaging of seismic data, a reasonable velocity model is critical to high-precision imaging. In diffraction wave velocity modeling, the prior art mostly works on post-stack or offset gathers. The low signal to noise ratio characteristic of deep reservoir diffraction response brings challenges to migration velocity modeling, and most of diffraction wave velocity modeling research is performed after stack or in migration gathers, such as a velocity continuation and local maximum peak diffraction velocity analysis method, a residual diffraction time difference velocity analysis method and the like. However, the existing method still has the technical problems of low correlation degree of the diffracted wave signals and poor accuracy of the diffracted wave offset speed.
Disclosure of Invention
The invention aims to provide a deep diffraction field speed modeling method, a deep diffraction field speed modeling device, electronic equipment and a storage medium, which enhance the correlation of diffraction wave signals and improve the accuracy of diffraction wave offset speed.
In a first aspect, the present invention provides a deep diffraction field speed modeling method, the method comprising:
obtaining diffraction wave separation data, constructing a common diffraction point multi-aperture angle gather through angle deviation based on an edge diffraction model and a scatterer physical model, and capturing diffraction response in the multi-aperture;
establishing a correction relation between the inclination angle and the offset speed of the diffraction point according to the multi-aperture angle gather of the co-diffraction point;
calculating a diffraction field velocity spectrum of each diffraction point according to the correction relation of the inclination angle and the offset velocity of the diffraction point and aiming at each common diffraction point multi-aperture angle gather based on the local similarity and the mixed weight of the difference boot-strap;
in the diffraction field velocity spectrum of each diffraction point, a diffraction wave velocity value of a focusing signal is picked up in a man-machine interaction mode;
and (3) constructing a target diffraction field speed model by carrying out interpolation processing on the diffraction wave speed value.
In an alternative embodiment, obtaining diffracted wave separation data includes:
and obtaining the seismic data directly used for migration, removing reflection components from the seismic data, and obtaining diffraction wave separation data.
In an alternative embodiment, constructing the co-diffraction point multi-aperture angular gathers by angular offset includes:
generating angle gather data by using the angle shift for any imaging point in the subsurface space, and calculating a diffraction incident angle and a diffraction exit angle of a local diffraction point position by ray tracing:
wherein alpha represents an included angle between an incident ray and a vertical axis, namely a diffraction incident angle; beta represents the included angle between the ray and the vertical axis, namely the diffraction emergence angle;
determining the inclination angle of the diffraction point according to the included angle between the outgoing line and the perpendicular line of the local reflection interface:
wherein θ is the inclination of the diffraction point.
In an alternative embodiment, capturing the multi-aperture intra-diffraction response includes:
based on the three-dimensional angle domain offset frame, diffraction energy in a first aperture of a scatterer model is selected through imaging rays, and a section inclination angle field is used for controlling the ray beam to acquire a diffraction main energy band in a second aperture.
In an alternative embodiment, the correction of the inclination angle of the diffraction point and the offset speed includes:
wherein, τ (θ) is used to represent the travel correction formula of the diffraction response in the offset tilt domain, θ is the tilt of the diffraction point, γ=v m /υ，v m For the offset velocity, v is the original velocity, ζ= (x) m -x d )/z d ，x m To shift imaging point, x d ,z d For the actual diffraction point position τ 0 For a double travel at normal incidence.
In an alternative embodiment, the local similarity and the mixing weight of the differential boot-strap are determined by:
wherein w is hyb (j, k) is a mixed weight, w sw A weight function parameterized for a time-dependent parameter b (k); w (w) νs To be based on local similarity weight function, κ [ q (j, k), q r (k)]Is a local similarity.
In a second aspect, the present invention provides a deep diffraction field speed modeling apparatus, the apparatus comprising:
the common diffraction point multi-aperture angle gather construction module is used for acquiring diffraction wave separation data, constructing a common diffraction point multi-aperture angle gather through angle deviation based on an edge diffraction model and a scatterer physical model, and capturing diffraction response in a multi-aperture;
the correction relation establishing module is used for establishing a correction relation between the inclination angle and the offset speed of the diffraction point according to the multi-aperture angle gather of the common diffraction point;
the calculation module is used for calculating a diffraction field velocity spectrum of each diffraction point according to the correction relation of the inclination angle and the offset velocity of the diffraction point and aiming at each common diffraction point multi-aperture angle gather based on the local similarity and the mixed weight of the difference boot-strap;
the pickup module is used for picking up diffraction wave speed values of the focusing signals in a diffraction field speed spectrum of each diffraction point in a man-machine interaction mode;
the modeling module is used for constructing a target diffraction field speed model by carrying out interpolation processing on the diffraction wave speed value.
In an alternative embodiment, the co-diffraction point multi-aperture angular gather construction module is further configured to:
and obtaining the seismic data directly used for migration, removing reflection components from the seismic data, and obtaining diffraction wave separation data.
In a third aspect, the invention provides an electronic device comprising a processor and a memory, the memory storing computer-executable instructions executable by the processor to implement the deep diffraction field speed modeling method of any of the preceding embodiments.
In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when invoked and executed by a processor, cause the processor to implement the deep diffraction field speed modeling method of any of the preceding embodiments.
Aiming at a deep unconventional reservoir, the method, the device, the electronic equipment and the storage medium for modeling the deep diffraction field speed fully utilize the wide illumination propagation rule of diffracted waves, and the diffraction point multi-aperture angle gather based on the Huygens principle is constructed, so that the gather has ultrahigh superposition times, can capture the diffracted energy in a plurality of apertures, enhance the correlation of diffracted wave signals and improve the accuracy of the diffracted wave offset speed.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for modeling deep diffraction field velocity according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a co-injection point multi-aperture angular gather construction according to an embodiment of the present disclosure;
FIG. 3 is a schematic illustration of interpolation according to an embodiment of the present disclosure;
FIG. 4 is a flowchart of a specific deep diffraction field velocity modeling method according to an embodiment of the present application;
FIG. 5 is a block diagram of a deep diffraction field velocity modeling apparatus according to an embodiment of the present application;
fig. 6 is a block diagram of an electronic device according to an embodiment of the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, as provided in the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
A common velocity processing method is superimposed velocity spectrum analysis based on CMP gathers. For the problem of velocity spectrum in AVO, rattliffe and Adler (2000), sarkar et al (2001), yan and Tsvankin (2008) and Fomel (2009), velocity analysis and modeling methods in the case of complex waveforms and polarity reversal were studied. In addition to the overlay speed, the offset speed analysis controls the quality of the final imaging, with the most common being the angular domain based offset speed analysis. The method of velocity analysis was studied on the angle domain co-imaging point gathers based on the wave field prolongation method by Biondi and Symes (2004). In addition to gather-based velocity analysis, fomel (2003) implements a velocity continuation-based offset velocity analysis method. Sava et al (2005) improved the resolution and continuity of the conventional method by analyzing the diffracted wave that is not fully focused and the reflected wave that is not fully offset. Fomel et al (2007) propose a local maximum variance based method that uses velocity prolongation and local peak techniques to perform velocity analysis based on the degree of diffraction rather than reflected wave focusing after separation. By ray tracing the diffracted wave that is not completely deflected, coimbra et al (2013) update the deflection speed with the residual diffraction time difference. Merzlikin and Fomel (2017) propose a method of path summation imaging along the velocity dimension that avoids using multiple velocities for shifting and picking up the velocities. Lin Peng (2020) is aimed at the separated diffracted wave, and by combining an inclination angle domain diffracted wave travel time correction formula and a focusing method, the correlation of diffracted wave signals is enhanced, and the accuracy of the diffracted wave offset speed is improved. Decker and Fomel (2021) calculate the probability of diffraction wave distribution and the energy gradient distribution weight by using the diffraction wave focusing spectrum based on the idea of path integration, and analyze the diffraction wave offset velocity from the probability point of view.
However, since the deep reservoir diffraction response has the characteristic of low signal to noise ratio, challenges are brought to the migration velocity modeling, and based on this, the embodiment of the application provides a deep diffraction field velocity modeling method, device, electronic equipment and storage medium, which enhances the correlation of diffraction wave signals and improves the accuracy of the migration velocity of diffraction waves.
The embodiment of the application provides a deep diffraction field speed modeling method, which is shown in fig. 1 and mainly comprises the following steps:
step S110, diffraction wave separation data are obtained, a common diffraction point multi-aperture angle gather is constructed through angle offset based on an edge diffraction model and a scatterer physical model, and diffraction responses in multiple apertures are captured.
In one embodiment, seismic data for migration directly may be acquired, and a plane wave destructive filter is used on the seismic data to remove reflected components to obtain diffracted wave separation data.
When the angle deviation is used for constructing the multi-aperture angle gather of the co-diffraction point, an angle domain deviation method can be used for obtaining a diffraction main energy band in a second aperture by using the position of the imaging rays which vertically exit the earth surface and selecting diffraction energy in a first Fresnel aperture based on a scatterer model and determining the position of the rays which exit the earth surface by using ray tracing based on an inclination angle field of an underground reflection interface. FIG. 2 shows a schematic diagram of a co-incident spot multi-aperture angular gather construction, wherein (a) is a three-dimensional wavefront surface generated by single shot excitation, (b) is a multi-aperture diffraction energy selection mode based on an edge diffraction and scatterer model, (c) is a peak energy and main energy band of a diffraction wave response, and (d) is a co-diffraction spot multi-aperture angular gather wave field characteristic.
The co-diffraction point multi-aperture angular gathers described above are a matrix-like storage format, with rows representing depth locations of the subsurface space, and columns representing seismic amplitude values corresponding to each angle.
And for the same imaging point, projecting all signals which are based on angle offset and meet the kinematic imaging condition of the diffracted wave into an inclination angle-scattering angle-imaging point depth imaging space, and superposing signals corresponding to different scattering angles according to the scattering angle direction to obtain an inclination angle, so as to obtain the multi-aperture angle gather of the co-diffraction point.
For each imaging point, the multi-aperture angular gather is the energy at the imaging point at a different offset tilt angle for the second dimension when the first dimension is depth.
Step S120, a correction relationship between the inclination angle and the offset speed of the diffraction point is established according to the multi-aperture angle gather of the common diffraction point.
Step S130, according to the correction relation between the inclination angle and the offset speed of the diffraction points, for each co-diffraction point multi-aperture angle gather, a diffraction field velocity spectrum of each diffraction point is calculated based on the local similarity and the mixed weight of the difference boot-strap.
In one embodiment, in a velocity field used for constructing the co-diffraction point multi-aperture gather, a series of different gamma values are used for calculating the constructed co-diffraction point multi-aperture gather to obtain new time correction through the correction relation between the inclination angle of the diffraction point and the offset velocity, and the diffraction field velocity spectrum is obtained by applying local similarity and mixed weight calculation of differential boot-strap to the energy corresponding to the new correction.
The correction relation between the inclination angle and the offset speed of the diffraction point can be represented by the following formula:
wherein, τ (θ) is used to represent the travel correction formula of the diffraction response in the offset tilt domain, θ is the tilt of the diffraction point, γ=v m /υ，v m For the offset velocity, v is the original velocity, ζ= (x) m -x d )/z d ，x m To shift imaging point, x d ,z d For the actual diffraction point position τ 0 For a double travel at normal incidence.
In step S140, in the diffraction field velocity spectrum of each diffraction point, the diffraction wave velocity value of the focusing signal is picked up by a man-machine interaction method.
Step S150, a target diffraction field speed model is constructed by interpolation processing of the diffraction wave speed value.
In one embodiment, the interpolation may include inverse distance weighting or kriging, etc. For example, this can be achieved by the following interpolation function:
wherein x and y are coordinates to be interpolated, x 1 ,y 1 ,x 2 ,y 2 The four vertices that together form the coordinate area, Q is the velocity value corresponding to the four vertices, and the interpolation diagram is shown in fig. 3.
The finally constructed target diffraction field speed model is a data matrix, wherein the rows of the data matrix represent depth, the columns represent transverse coordinates, the numerical values are speed values, and the data matrix can be displayed in a graph through software.
For easy understanding, the method for modeling the velocity of the god diffraction field provided in the embodiment of the present application is described in detail below.
In an alternative embodiment, the diffracted wave separation data is obtained, and the seismic data directly used for migration may be obtained, and the reflected component is removed from the seismic data to obtain the diffracted wave separation data.
Specifically, the reflected component may be removed by performing a reflected wave extension non-superimposed transformation on the seismic data, where the reflected wave in the seismic data may be described using a plane wave destructive filter:
D＝S(I-C(p))
wherein D is a diffracted wave after separation, S= [ S ] 1 ,S 2 ,…S N ] T For seismic data, I is an identity matrix, p= [ p ] 1 ,p 2 ,…,p N-1 ]For the same phase axis slope, N is the trace number of the seismic data, C (p) is the plane wave destruction operator:
C i,i+1 (p i ) A plane wave destructive filter representing the prediction of the jth trace of seismic data from the ith trace gather of seismic data. The scalar form is:
wherein Z is t ,Z x The Z-transforms in the temporal direction and in the spatial direction are indicated, respectively.
In an alternative embodiment, constructing the co-diffraction point multi-aperture angular gathers by angular offset includes:
generating angle gather data by using the angle shift for any imaging point in the subsurface space, and calculating a diffraction incident angle and a diffraction exit angle of a local diffraction point position by ray tracing:
wherein alpha represents an included angle between an incident ray and a vertical axis, namely a diffraction incident angle; beta represents the included angle between the ray and the vertical axis, namely the diffraction emergence angle;
determining the inclination angle of the diffraction point according to the included angle between the outgoing line and the perpendicular line of the local reflection interface:
wherein θ is the inclination of the diffraction point.
In an alternative embodiment, capturing the multi-aperture intra-diffraction response includes:
based on the three-dimensional angle domain offset frame, diffraction energy in a first aperture of a scatterer model is selected through imaging rays, and a section inclination angle field is used for controlling the ray beam to acquire a diffraction main energy band in a second aperture. In practical application, an angle domain offset method can be utilized, the position of the imaging ray vertically emergent from the earth surface is utilized, the diffraction energy in the first Fresnel aperture is selected based on a scatterer model, and then the position of the ray emergent from the earth surface is determined through ray tracing based on an inclination angle field of an underground reflection interface, so that a diffraction main energy band in the second aperture is obtained.
In an alternative embodiment, the local similarity and the mixing weight of the differential boot-strap are determined by:
wherein w is hyb (j, k) is a mixed weight, w sw A weight function parameterized for a time-dependent parameter b (k); w (w) νs To be based on local similarity weight function, κ [ q (j, k), q r (k)]Is a local similarity.
FIG. 3 illustrates a specific deep diffraction field velocity modeling method. The method constructs a high-superposition-number co-diffraction point multi-aperture angle gather through deflection, generates a diffraction field velocity spectrum by using a deflection velocity field and the multi-aperture angle gather, and establishes a diffraction field velocity model by interactively picking up velocity values of a focusing velocity spectrum.
In conclusion, the method and the device construct a common diffraction point multi-aperture angle gather based on two physical models of edge diffraction and scatterers according to a Huygens principle, can capture diffraction response energy of super-high superposition times, and solve the problem of diffraction field velocity field modeling under the condition of low signal-to-noise ratio; by defining the diffraction wave velocity spectrum through the mixed weight, the non-relevant signal interference effect can be eliminated, the focusing performance of the non-stationary signal diffraction velocity spectrum can be improved, and the stability and accuracy of calculation can be enhanced.
The invention provides a common diffraction point multi-aperture angle gather based on a Huygens principle, which has ultrahigh superposition times, can capture diffraction energy in a plurality of apertures, and provides basic data for deep complex medium imaging by constructing diffraction field speeds matched with the common diffraction point multi-aperture angle gather.
Based on the above method embodiment, the embodiment of the present application further provides a deep diffraction field speed modeling apparatus, as shown in fig. 5, which mainly includes the following parts:
the common-diffraction-point multi-aperture angle gather construction module 510 is configured to acquire diffraction wave separation data, construct a common-diffraction-point multi-aperture angle gather by angular offset based on an edge diffraction model and a scatterer physical model, and capture a multi-aperture intra-diffraction response;
the correction relation establishing module 520 is configured to establish a correction relation between the inclination angle and the offset speed of the diffraction point according to the multi-aperture angle gather of the common diffraction point;
a calculation module 530, configured to calculate, for each co-diffraction point multi-aperture angular gather, a diffraction field velocity spectrum of each diffraction point based on the local similarity and the hybrid weight of the differential boot-strap according to the correction relationship between the inclination angle and the offset velocity of the diffraction point;
a pickup module 540, configured to pick up a diffraction wave velocity value of the focusing signal in a human-computer interaction manner in a diffraction field velocity spectrum of each diffraction point;
the modeling module 550 is configured to construct a target diffraction field velocity model by performing interpolation processing on the diffracted wave velocity value.
In one possible implementation, the co-diffraction point multi-aperture angular gather construction module 510 is further configured to:
and obtaining the seismic data directly used for migration, removing reflection components from the seismic data, and obtaining diffraction wave separation data.
In one possible implementation, the co-diffraction point multi-aperture angular gather construction module 510 is further configured to:
generating angle gather data by using the angle shift for any imaging point in the subsurface space, and calculating a diffraction incident angle and a diffraction exit angle of a local diffraction point position by ray tracing:
wherein alpha represents an included angle between an incident ray and a vertical axis, namely a diffraction incident angle; beta represents the included angle between the ray and the vertical axis, namely the diffraction emergence angle;
determining the inclination angle of the diffraction point according to the included angle between the outgoing line and the perpendicular line of the local reflection interface:
wherein θ is the inclination of the diffraction point.
In one possible implementation, the co-diffraction point multi-aperture angular gather construction module 510 is further configured to:
based on the three-dimensional angle domain offset frame, diffraction energy in a first aperture of a scatterer model is selected through imaging rays, and a section inclination angle field is used for controlling the ray beam to acquire a diffraction main energy band in a second aperture.
In one possible embodiment, the correction relationship between the inclination angle and the offset speed of the diffraction point includes:
wherein, τ (θ) is used to represent the travel correction formula of the diffraction response in the offset tilt domain, θ is the tilt of the diffraction point, γ=v m /υ，v m For the offset velocity, v is the original velocity, ζ= (x) m -x d )/z d ，x m To shift imaging point, x d ,z d For the actual diffraction point position τ 0 For a double travel at normal incidence.
In one possible implementation, the local similarity and the mixture weight of the differential boot-strap are determined by:
wherein w is hyb (j, k) is a mixed weight, w sw A weight function parameterized for a time-dependent parameter b (k); w (w) νs To be based on local similarity weight function, κ [ q (j, k), q r (k)]Is a local similarity.
The implementation principle and the generated technical effects of the deep diffraction field speed modeling device provided in the embodiment of the present application are the same as those of the foregoing method embodiment, and for a brief description, reference may be made to corresponding matters in the foregoing deep diffraction field speed modeling method embodiment where the embodiment of the deep diffraction field speed modeling device is not mentioned.
The embodiment of the present application further provides an electronic device, as shown in fig. 6, which is a schematic structural diagram of the electronic device, where the electronic device 100 includes a processor 61 and a memory 60, and the memory 60 stores computer executable instructions that can be executed by the processor 61, and the processor 61 executes the computer executable instructions to implement any one of the deep diffraction field speed modeling methods described above.
In the embodiment shown in fig. 6, the electronic device further comprises a bus 62 and a communication interface 63, wherein the processor 61, the communication interface 63 and the memory 60 are connected by means of the bus 62.
The memory 60 may include a high-speed random access memory (RAM, random Access Memory), and may further include a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory. The communication connection between the system network element and at least one other network element is achieved via at least one communication interface 63 (which may be wired or wireless), and may use the internet, a wide area network, a local network, a metropolitan area network, etc. Bus 62 may be an ISA (Industry Standard Architecture ) bus, a PCI (Peripheral Component Interconnect, peripheral component interconnect standard) bus, or EISA (Extended Industry Standard Architecture ) bus, among others. The bus 62 may be classified as an address bus, a data bus, a control bus, or the like. For ease of illustration, only one bi-directional arrow is shown in FIG. 6, but not only one bus or type of bus.
The processor 61 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in the processor 61 or by instructions in the form of software. The processor 61 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but also digital signal processors (Digital Signal Processor, DSP for short), application specific integrated circuits (Application Specific Integrated Circuit, ASIC for short), field-programmable gate arrays (Field-Programmable Gate Array, FPGA for short) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the embodiments of the present application may be embodied directly in hardware, in a decoded processor, or in a combination of hardware and software modules in a decoded processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor 61 reads the information in the memory, and in combination with its hardware, performs the steps of the deep diffraction field speed modeling method of the foregoing embodiment.
The embodiment of the application further provides a computer readable storage medium, where the computer readable storage medium stores computer executable instructions that, when invoked and executed by a processor, cause the processor to implement the above deep diffraction field velocity modeling method, and the detailed implementation of the method can be found in the foregoing embodiments and will not be repeated herein.
The computer program product of the deep diffraction field speed modeling method, the deep diffraction field speed modeling device, the electronic equipment and the storage medium provided by the embodiment of the application comprise a computer readable storage medium storing program codes, the instructions included in the program codes can be used for executing the method described in the method embodiment, and specific implementation can be seen in the method embodiment and will not be repeated here.
The relative steps, numerical expressions and numerical values of the components and steps set forth in these embodiments do not limit the scope of the present application unless specifically stated otherwise.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer readable storage medium executable by a processor. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. A method of deep diffraction field velocity modeling, the method comprising:
obtaining diffraction wave separation data, constructing a common diffraction point multi-aperture angle gather through angle deviation based on an edge diffraction model and a scatterer physical model, and capturing diffraction response in the multi-aperture;
establishing a correction relation between the inclination angle and the offset speed of the diffraction point according to the multi-aperture angle gather of the common diffraction point;
calculating a diffraction field velocity spectrum of each diffraction point according to the correction relation of the inclination angle and the offset velocity of the diffraction point and aiming at each common diffraction point multi-aperture angle gather based on the local similarity and the mixed weight of the difference boot-strap;
in the diffraction field velocity spectrum of each diffraction point, a diffraction wave velocity value of a focusing signal is picked up in a man-machine interaction mode;
and constructing a target diffraction field speed model by carrying out interpolation processing on the diffraction wave speed value.
2. The deep diffraction field speed modeling method as claimed in claim 1, wherein obtaining diffraction wave separation data includes:
and obtaining seismic data directly used for migration, and removing reflection components from the seismic data to obtain diffraction wave separation data.
3. The deep diffraction field speed modeling method as claimed in claim 2, wherein constructing the co-diffraction point multi-aperture angular gathers by angular offset includes:
generating angle gather data by using the angle shift for any imaging point in the subsurface space, and calculating a diffraction incident angle and a diffraction exit angle of a local diffraction point position by ray tracing:
wherein alpha represents an included angle between an incident ray and a vertical axis, namely a diffraction incident angle; beta represents the included angle between the ray and the vertical axis, namely the diffraction emergence angle;
determining the inclination angle of the diffraction point according to the included angle between the outgoing line and the perpendicular line of the local reflection interface:
wherein θ is the inclination of the diffraction point.
4. The deep diffraction field speed modeling method as claimed in claim 1, wherein capturing the multi-aperture intra-diffraction response includes:
based on the three-dimensional angle domain offset frame, diffraction energy in a first aperture of a scatterer model is selected through imaging rays, and a section inclination angle field is used for controlling the ray beam to acquire a diffraction main energy band in a second aperture.
5. The deep diffraction field speed modeling method as claimed in claim 1, wherein the correction relation of the inclination angle and the offset speed of the diffraction point includes:
wherein, τ (θ) is used to represent the travel correction formula of the diffraction response in the offset tilt domain, θ is the tilt of the diffraction point, γ=v m /υ，v m For the offset velocity, v is the original velocity, ζ= (x) m -x d )/z d ，x m To shift imaging point, x d ,z d For the actual diffraction point position τ 0 For a double travel at normal incidence.
6. The deep diffraction field speed modeling method of claim 1, wherein the local similarity and the mixed weight of the differential boot-strap are determined by:
wherein w is hyb (j, k) is a mixed weight, w sw A weight function parameterized for a time-dependent parameter b (k); w (w) νs To be based on local similarity weight function, κ [ q (j, k), q r (k)]Is a local similarity; q is the amplitude of the wave,c(k)＝t k N/∑ j x (j), x is the offset, j, k represents the track number and the time sampling point number.
7. A deep diffraction field speed modeling apparatus, the apparatus comprising:
the common diffraction point multi-aperture angle gather construction module is used for acquiring diffraction wave separation data, constructing a common diffraction point multi-aperture angle gather through angle deviation based on an edge diffraction model and a scatterer physical model, and capturing diffraction response in a multi-aperture;
the correction relation establishing module is used for establishing a correction relation between the inclination angle and the offset speed of the diffraction point according to the multi-aperture angle gathers of the co-diffraction points;
the calculation module is used for calculating a diffraction field velocity spectrum of each diffraction point based on the local similarity and the mixed weight of the difference boot-strap for each common diffraction point multi-aperture angle gather according to the correction relation of the inclination angle and the offset velocity of the diffraction point;
the pickup module is used for picking up diffraction wave speed values of the focusing signals in a diffraction field speed spectrum of each diffraction point in a man-machine interaction mode;
and the modeling module is used for constructing a target diffraction field speed model by carrying out interpolation processing on the diffraction wave speed value.
8. The deep diffraction field speed modeling apparatus as claimed in claim 7, wherein the co-diffraction point multi-aperture angular gather construction module is further configured to:
and obtaining seismic data directly used for migration, and removing reflection components from the seismic data to obtain diffraction wave separation data.
9. An electronic device comprising a processor and a memory, the memory storing computer-executable instructions executable by the processor to implement the deep diffraction field speed modeling method of any of claims 1-7.
10. A computer readable storage medium storing computer executable instructions which, when invoked and executed by a processor, cause the processor to implement the deep diffraction field speed modeling method of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410023446.XA CN117826259A (en) | 2024-01-05 | 2024-01-05 | Deep diffraction field speed modeling method and device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410023446.XA CN117826259A (en) | 2024-01-05 | 2024-01-05 | Deep diffraction field speed modeling method and device, electronic equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117826259A true CN117826259A (en) | 2024-04-05 |
Family
ID=90511419
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410023446.XA Pending CN117826259A (en) | 2024-01-05 | 2024-01-05 | Deep diffraction field speed modeling method and device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117826259A (en) |
-
2024
- 2024-01-05 CN CN202410023446.XA patent/CN117826259A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US4964103A (en) | Three dimensional before stack depth migration of two dimensional or three dimensional seismic data | |
| US4887244A (en) | Method for seismic trace interpolation using a forward and backward application of wave equation datuming | |
| RU2460095C2 (en) | Wavefront randon transform | |
| Van den Ende et al. | A self-supervised deep learning approach for blind denoising and waveform coherence enhancement in distributed acoustic sensing data | |
| CN108369289A (en) | The method for analyzing design geophysical reconnaissance using full wave field inversion point spread function | |
| US7460437B2 (en) | Seismic data processing method and system for migration of seismic signals incorporating azimuthal variations in the velocity | |
| CN104268412A (en) | Angle gather ray chromatography migration velocity analyzing method and device | |
| EA032186B1 (en) | Seismic adaptive focusing | |
| CN110023790B (en) | Seismic acquisition geometric full-waveform inversion | |
| US20180017692A1 (en) | Device and method for estimating pre-stack wavelet model from seismic gathers | |
| Wang et al. | Seismic velocity inversion transformer | |
| Johnson et al. | Seafloor map generation for autonomous underwater vehicle navigation | |
| Hu et al. | Wavefield reconstruction of teleseismic receiver function with the stretching‐and‐squeezing interpolation method | |
| CN109307889A (en) | Optimization stack method and system based on the weighting of space-time variable coefficient | |
| CN105093300A (en) | Geologic body boundary identification method and apparatus | |
| CN115469362B (en) | Energy flow density vector calculation method in seismic exploration | |
| CN117826259A (en) | Deep diffraction field speed modeling method and device, electronic equipment and storage medium | |
| CN109188522B (en) | Velocity field construction method and device | |
| CN108828655B (en) | Method and device for processing trace gather recording and stacking profiles and computer storage medium | |
| Kuehnert et al. | Locating Rockfalls Using Inter‐Station Ratios of Seismic Energy at Dolomieu Crater, Piton de la Fournaise Volcano | |
| CN1308703C (en) | A Seismic method and apparatus for generating a semblance panel and computing the reflector dip | |
| Liu et al. | Wave-equation diffraction imaging using pseudo dip-angle gather | |
| CN108693560B (en) | Scattered wave imaging method and system based on cross-correlation channel | |
| RU2126984C1 (en) | Method determining depth and speed parameters of medium and construction of its image by seismic data- prime system | |
| CN111914609B (en) | Well-seismic combined prestack geostatistical elastic parameter inversion method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination |