CN112415590A - Local plane wave domain elastic wave imaging method and device - Google Patents
Local plane wave domain elastic wave imaging method and device Download PDFInfo
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Abstract
The invention provides a local plane wave domain elastic wave imaging method and device. The method comprises the following steps: obtaining the reflection rate of the elastic wave according to a speed disturbance formula; inputting a smooth elastic wave velocity model and an elastic wave reflectivity model by utilizing the reflectivity of the elastic wave according to a Born approximation theory, calculating the forward multi-wave local plane wave data of the elastic wave Gaussian beam Born, and taking the forward multi-wave local plane wave data as inversion observation data; using a Born forward simulation of elastic wave Gaussian beam superposition to obtain forward simulation local plane wave data; and constructing an objective function in the multi-wave type local plane wave domain, performing iterative solution on the objective function by using a conjugate gradient algorithm, and taking a solution corresponding to the minimum value of the objective function as an elastic wave imaging result. According to the invention, under the least square offset frame, the elastic wave imaging section is updated iteratively, so that the imaging amplitude approaches to the real reflectivity of the stratum, the purpose of amplitude-preserving imaging is achieved, the imaging resolution is improved, and crosstalk noise is suppressed.
Description
Technical Field
The invention relates to the technical field of seismic data imaging for oil geophysical exploration, in particular to a local plane wave domain elastic wave imaging method and device.
Background
The Gaussian beam migration is used as a flexible, accurate and efficient depth domain migration imaging method, has the advantages of ray migration imaging and wave equation migration imaging, and is suitable for imaging of complex geological structures, particularly three-dimensional steeply inclined structures. However, in the conventional gaussian beam offset imaging technology, because the offset operator is only the conjugate transpose of the positive operator and is not the inverse operator thereof, the imaging is blurred, and the formation high-fidelity imaging cannot be realized. With the rapid development of computer technology and the requirement for precise imaging of complex-structured oil and gas reservoirs, least square migration becomes a new idea of amplitude-preserving migration imaging.
At present, the least square Gaussian beam offset imaging technology is mainly limited to the aspect of sound waves, and the least square offset method for isotropic and anisotropic medium elastic wave Gaussian beams is rarely discussed. The least squares offset is usually solved iteratively by a gradient-guided method, each iteration involving a forward propagation from the seismic source to the subsurface scattering point and a backward propagation from the receiver position, and thus requiring a large amount of computation. And, for the application of three-dimensional least squares offset imaging, the amount of computation is multiplied more. In addition, the inversion method is not suitable for the reasons that the frequency range of the seismic source sub-wave is limited, and actual seismic observation data usually contain noise and non-primary reflection waves to some extent.
Disclosure of Invention
The embodiment of the invention mainly aims to provide a local plane wave domain elastic wave imaging method and device, which can realize flexible and efficient elastic wave Gaussian beam least square offset imaging and obtain an elastic wave imaging section with accurate amplitude and high resolution.
In order to achieve the above object, an embodiment of the present invention provides a local plane wave domain elastic wave imaging method, where the method includes:
obtaining the reflection rate of the elastic wave according to a speed disturbance formula;
inputting a smooth elastic wave velocity model and an elastic wave reflectivity model by utilizing the reflection rate of the elastic wave according to a Born approximation theory, calculating to obtain multi-mode local plane wave data on which the elastic wave Gaussian beam Born is forward, and taking the multi-mode local plane wave data on which the elastic wave Gaussian beam Born is forward as inversion observation data;
using a Born forward simulation of elastic wave Gaussian beam superposition to obtain forward simulation local plane wave data;
and constructing an objective function in a multi-wave type local plane wave domain according to the inversion observation data and the forward simulation local plane wave data, performing iterative solution on the objective function by using a conjugate gradient algorithm, and taking a solution corresponding to a minimum value of the objective function as an elastic wave imaging result.
Optionally, in an embodiment of the present invention, the obtaining forward simulated local plane wave data by using a Born forward simulation of superposition of elastic wave gaussian beams includes: determining a seismic wave field expression based on the Born forward modeling of the elastic wave Gaussian beam stacking by utilizing the Born forward modeling of the elastic wave Gaussian beam stacking; and obtaining forward simulation local plane wave data according to the seismic wave field expression.
Optionally, in an embodiment of the present invention, the constructing an objective function in a multi-wave local plane wave domain according to the inversion observation data and the forward simulated local plane wave data includes: and in a multi-wave type local plane wave domain, determining data residual errors of the inversion observation data and the forward simulation local plane wave data, and taking the two norms of the data residual errors as the target function.
Optionally, in an embodiment of the present invention, the iteratively solving the objective function by using a conjugate gradient algorithm, and taking a solution corresponding to the minimum value of the objective function as an elastic wave imaging result includes: and offsetting the data residual in the target function, and carrying out iterative solution by using a conjugate gradient algorithm according to the offset of the data residual to obtain a solution corresponding to the minimum value of the target function, wherein the solution is used as an elastic wave imaging result.
The embodiment of the invention also provides a local plane wave domain elastic wave imaging device, which comprises:
the reflectivity module is used for obtaining the reflectivity of the elastic wave according to a speed disturbance formula;
the observation data module is used for inputting a smooth elastic wave velocity model and an elastic wave reflectivity model according to a Born approximation theory by utilizing the reflection rate of the elastic wave, calculating to obtain multi-wave type local plane wave data on which the elastic wave Gaussian beam Born is forward, and taking the multi-wave type local plane wave data on which the elastic wave Gaussian beam Born is forward as inversion observation data;
the simulation data module is used for utilizing the Born forward simulation of the elastic wave Gaussian beam superposition to obtain forward simulation local plane wave data;
and the imaging result module is used for constructing an objective function in a multi-wave type local plane wave domain according to the inversion observation data and the forward simulation local plane wave data, performing iterative solution on the objective function by using a conjugate gradient algorithm, and taking a solution corresponding to a minimum value of the objective function as an elastic wave imaging result.
Optionally, in an embodiment of the present invention, the analog data module includes: the seismic wave field unit is used for determining a seismic wave field expression based on the Born forward modeling of the elastic wave Gaussian beam stacking by utilizing the Born forward modeling of the elastic wave Gaussian beam stacking; and the simulation data unit is used for obtaining forward simulation local plane wave data according to the seismic wave field expression.
Optionally, in an embodiment of the present invention, the imaging result module is specifically configured to determine, in a multi-wave local plane wave domain, a data residual of the inversion observation data and the forward modeling local plane wave data, and use a two-norm of the data residual as the target function.
Optionally, in an embodiment of the present invention, the imaging result module is further specifically configured to perform offset on a data residual in the objective function, perform iterative solution by using a conjugate gradient algorithm according to the offset of the data residual, obtain a solution corresponding to a minimum value of the objective function, and use the solution as an elastic wave imaging result.
The invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method when executing the program.
The present invention also provides a computer-readable storage medium storing a computer program for executing the above method.
According to the invention, under a least square offset frame, the elastic wave imaging section is updated in an iterative manner, so that the imaging amplitude approaches to the real reflectivity of the stratum, the purpose of amplitude-preserving imaging is achieved, the imaging resolution is improved along with the increase of the iteration times, and crosstalk noise is suppressed.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a flowchart of a local plane wave domain elastic wave imaging method according to an embodiment of the present invention;
FIG. 2 is a flowchart of the Born forward modeling of elastic wave Gaussian beam superposition according to an embodiment of the present invention;
FIG. 3 is a flow chart of a local plane wave domain elastic wave Gaussian beam least squares migration method according to an embodiment of the present invention;
FIGS. 4A-4F are schematic representations of a PP/PS velocity field, a PP/PS smooth velocity field, and a PP/PS reflectivity model incorporating a simple model of dimple formation in an embodiment of the present invention;
FIGS. 5A-5D are schematic diagrams of a conventional elastic wave Gaussian beam offset PP/PS imaging profile and a local plane wave domain elastic wave Gaussian beam least squares offset PP/PS imaging profile in an embodiment of the invention;
FIGS. 6A-6F are schematic diagrams of the residual data of the local plane wave domain of the PP/PS wave pattern after 1, 5 and 10 iterations according to an embodiment of the present invention;
FIGS. 7A-7B are schematic diagrams of PP/PS imaging amplitude versus true PP/PS reflectivity amplitude of the formation after 1 iteration and 10 iterations in an embodiment of the present invention;
FIG. 8 is a schematic structural diagram of a local plane wave domain elastic wave imaging device according to an embodiment of the present invention;
FIG. 9 is a block diagram of an exemplary simulation data module;
fig. 10 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a local plane wave domain elastic wave imaging method and device.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a flowchart of a local plane wave domain elastic wave imaging method according to an embodiment of the present invention, where the method includes:
and step S1, obtaining the reflection rate of the elastic wave according to a speed disturbance formula.
The PP and PS reflectivity of the elastic medium is obtained by adopting the conventional speed disturbance formula.
And step S2, inputting a smooth elastic wave velocity model and an elastic wave reflectivity model according to a Born approximation theory by using the reflection rate of the elastic wave, calculating to obtain multi-mode local plane wave data on which the elastic wave Gaussian beam Born is forward, and taking the multi-mode local plane wave data on which the elastic wave Gaussian beam Born is forward as inversion observation data.
The method comprises the steps of inputting smooth PP and PS speed models and PP and PS reflectivity models by utilizing the reflectivity of elastic waves and based on a first-order Born approximation theory, and calculating the forward multi-mode local plane wave data of the elastic wave Gaussian beam Born as the inverted observation data.
And step S3, performing Born forward simulation by using elastic wave Gaussian beam superposition to obtain forward simulation local plane wave data.
The Born forward modeling process of elastic wave Gaussian beam superposition specifically comprises the following steps: the seismic wavefield of Born forward modeling based on elastic wave gaussian beam stacking is represented as:
uv(r,s,ω)＝ω2∫ΩdxMwv(x)S(ω)Gw(x,s,ω)Gv(x,r,ω)
wherein u isv(r, S, ω) is a v-wave vector field, S (ω) is a frequency domain seismic source function, Mwv(x) Representing the reflectivity of different modes at point x, Gw(x, s, ω) represents the response of a source of w-wave units at the location of the s-point at the x-point in the subsurface, Gv(x, r, ω) represents the response of the v-wave unit source excited at the x point in the subsurface to the r location at the surface.
Under the assumption of a high frequency approximation of the seismic wavefield, the green's function can be expressed in the form of a gaussian beam stack:
wherein,
representing the slowness vector associated with the wave pattern,
it represents the v-gaussian beam emerging from point x' to point x.
And (3) superposing local plane waves of different plane wave slowness components at the central position of the earth surface sparse Gaussian beam to obtain simulated multi-wave type local plane wave data:
wherein, phi represents a coefficient,
representing a synthetic with
The slowness vector Gaussian beam corresponds to a v-wave local plane wave. And completing the Born forward modeling process of elastic wave Gaussian beam superposition by the calculation to obtain forward modeling local plane wave data.
And step S4, constructing an objective function in a multi-wave type local plane wave domain according to the inversion observation data and the forward simulation local plane wave data, performing iterative solution on the objective function by using a conjugate gradient algorithm, and taking a solution corresponding to a minimum value of the objective function as an elastic wave imaging result.
The method comprises the steps of constructing an objective function in a multi-wave type local plane wave domain, and adopting a least square offset method to carry out iterative solution. The least square objective function constructed by the multi-mode local plane wave domain is expressed as:
φ(m)＝||Dobs-Lm||2
wherein D isobsRepresenting observation data multi-wave type local plane waves, i.e. inversion observation data, L representing elastic wave Gaussian beam Born positive operator, m representing reflection coefficient, Lm representing elastic wave Gaussian beam Born forward modeling seismic wave field process, i.e. forward modeling local plane wavesSee step S3 for plane wave data.
And solving the minimum value of the objective function phi (m), and correcting the imaging according to the data residual error of the forward simulated data and the actual observed data of the Gaussian beam Born. And (3) iterative solving of the least square deviation PP and PS imaging sections by adopting a conjugate gradient algorithm, wherein the iterative algorithm is expressed as follows:
mk+1＝mk+αsk
where α represents the step size, skDenotes the conjugate gradient of the kth iteration, mkAnd mk+1Representing imaging for the kth and (k + 1) th iterations, respectively.
sk＝gk+βsk-1
Wherein, gk＝LT(Dobs-Lmk)，
LTRepresenting the elastic wave gaussian beam offset operator.
The elastic wave gaussian beam offset formula is expressed as a conjugate of the forward equation, and therefore, the expression for elastic wave gaussian beam offset is:
wherein the superscript symbol denotes complex conjugation.
As an embodiment of the present invention, as shown in fig. 2, the obtaining forward simulated local plane wave data by using a Born forward simulation of elastic wave gaussian beam superposition includes:
step S21, determining a seismic wave field expression based on the Born forward modeling of the elastic wave Gaussian beam stacking by utilizing the Born forward modeling of the elastic wave Gaussian beam stacking;
and step S22, obtaining forward modeling local plane wave data according to the seismic wave field expression.
As an embodiment of the present invention, constructing an objective function in a multi-wave local plane wave domain according to the inversion observation data and the forward simulated local plane wave data includes: and in a multi-wave type local plane wave domain, determining data residual errors of the inversion observation data and the forward simulation local plane wave data, and taking the two norms of the data residual errors as the target function.
In this embodiment, iteratively solving the objective function by using a conjugate gradient algorithm, and taking a solution corresponding to the minimum value of the objective function as an elastic wave imaging result includes: and offsetting the data residual in the target function, and carrying out iterative solution by using a conjugate gradient algorithm according to the offset of the data residual to obtain a solution corresponding to the minimum value of the target function, wherein the solution is used as an elastic wave imaging result.
In an embodiment of the invention, as shown in fig. 3, a flow chart of a local plane wave domain elastic wave gaussian beam least squares migration method is shown. Constructing an objective function in a multi-wave type local plane wave domain, namely forward modeling two norms of residual errors of local plane wave data and actual local plane wave data:
φ(m)＝||Dobs-Lm||2 (1)
wherein D isobsThe observation data is expressed by the multi-mode local plane wave, L is expressed by the Born positive operator of the elastic wave Gaussian beam, and m is expressed by the reflection coefficient.
(1) In the formula, Lm represents the process of elastic wave gaussian beam Born forward modeling of the seismic wave field, and the seismic wave field modeled by Born forward modeling based on the superposition of elastic wave gaussian beams is represented as:
uv(r,s,ω)＝ω2∫ΩdxMwv(x)S(ω)Gw(x,s,ω)Gv(x,r,ω) (2)
wherein u isv(r, S, ω) is a v-wave vector field, S (ω) is a frequency domain seismic source function, Mwv(x) Representing the reflectivity of different modes at point x, Gw(x, s, ω) represents the response of a source of w-wave units at the location of the s-point at the x-point in the subsurface, Gv(x, r, ω) represents the response of the v-wave unit source excited at the x point in the subsurface to the r location at the surface.
Under the assumption of a high frequency approximation of the seismic wavefield, the green's function can be expressed in the form of a gaussian beam stack:
wherein,
representing the slowness vector associated with the wave pattern,
it represents the v-gaussian beam emerging from point x' to point x.
And (3) superposing local plane waves of different plane wave slowness components at the central position of the earth surface sparse Gaussian beam to obtain simulated multi-wave type local plane wave data:
wherein, phi represents a coefficient,
representing a synthetic with
The slowness vector Gaussian beam corresponds to a v-wave local plane wave.
Solving the minimum two-norm of the data residual error in the formula (1) by adopting a conjugate gradient algorithm, wherein the iterative format of the conjugate gradient is as follows:
mk+1＝mk+αsk (5)
where α represents the step size, skDenotes the conjugate gradient of the kth iteration, mkAnd mk+1Representing imaging for the kth and (k + 1) th iterations, respectively.
sk＝gk+βsk-1 (6)
Wherein, gk＝LT(Dobs-Lmk)，
LTRepresenting the elastic wave gaussian beam offset operator. gkThe calculation formula of (2) is expressed as a gradient solving process, namely, the data residual error of the multi-mode local plane wave domain is subjected to deviation. The elastic wave gaussian beam offset operator is expressed as a conjugate of the forward operator, so the expression for elastic wave gaussian beam offset is:
wherein the superscript symbol denotes complex conjugation.
Since both the gaussian beam Born forward and the migration process require data to be superimposed on the local plane wave domain of the multi-mode, the local plane wave data is more sparse. Therefore, the elastic wave Gaussian beam least square offset imaging method for constructing the target functional in the multi-wave local plane wave domain can enable the inversion result to be more stable and accurate. In addition, in the iterative process, mutual conversion between multi-wave type local plane wave domain data and multi-component data is saved, and the working efficiency can be effectively improved for the three-dimensional elastic wave Gaussian beam least square offset imaging method.
The invention uses a simple synthetic model containing depression structure as an example to verify the effectiveness and feasibility of the method, and longitudinal wave and transverse wave velocity models are shown in fig. 4A and 4B, wherein the transverse wave velocity model is represented by a formula
And (4) calculating. The number of vertical and horizontal grid points of the velocity model is 361 and 401 respectively, and the distance between the horizontal grid and the vertical grid is 10 m. The results of smoothing the compressional wave velocity model and the shear wave velocity model are shown in fig. 4C and 4D, and the PP and PS reflectivities of the formation medium are calculated according to the velocity disturbance formula, as shown in fig. 4E and 4F. The actual observation data of forward modeling of the model is obtained by forward modeling of an elastic wave Gaussian beam, shot points are located at the position of 0-3950m of a velocity profile, each 50m is a shot, the total number of the shots is 80, a seismic source is a Rake wavelet with a dominant frequency of 15Hz, and time sampling is carried outAnd (4) at an interval of 0.2ms, obtaining the multi-mode local plane wave at the center position of the sparse Gaussian beam on the ground surface.
Conventional elastic wave gaussian beam deviation, i.e. PP imaging and PS imaging at 1 iteration, is shown in fig. 5A and 5C, and it can be seen that the imaging resolution is low, and although formation information can be given, the imaging amplitude retention is poor. Fig. 5B and 5D are local plane wave domain elastic wave gaussian beam least square offset PP imaging and PS imaging, respectively, and a comparison with fig. 5A and 5C shows that the resolution of the imaging result after iteration is improved, the underground illumination is enhanced, the imaging amplitude approaches to the true reflectivity of the formation, the imaging noise is suppressed, and the imaging quality is good.
Fig. 6A to 6F show local plane wave domain data residuals after local plane wave domain elastic wave gaussian beam least square offset 1 iteration, 5 iterations, and 10 iterations, fig. 6A to 6C are PP mode data residuals, and fig. 6D to 6F are PS mode data residuals. By comparison, it can be clearly found that the data residual after 10 iterations is almost 0 compared with the data residual after 1 iteration and 5 iterations, which indicates that the inversion method is stable and convergent.
And respectively extracting one of PP and PS imaging sections of 1 iteration and 10 iterations of least square offset, and comparing the imaging amplitude with the formation reflectivity amplitude, wherein the extraction position is a transverse 2000m position. As can be seen from fig. 7A and 7B, the imaging amplitude after 10 iterations is almost completely consistent with the true formation reflectivity, and therefore it is proved that the true formation reflectivity can be inverted by elastic wave gaussian beam least square offset.
In the invention, under the least square offset frame, the imaging sections of the PP and PS of the elastic wave are updated in an iterative manner, so that the imaging amplitude approaches to the real reflectivity of the stratum, and the purpose of amplitude-preserving imaging is achieved. And with the increase of the iteration times, the imaging resolution is improved, and the crosstalk noise is suppressed.
Fig. 8 is a schematic structural diagram of a local plane wave domain elastic wave imaging device according to an embodiment of the present invention, where the device includes:
the reflectivity module 10 is used for obtaining the reflectivity of the elastic wave according to a speed disturbance formula;
the observation data module 20 is configured to input a smooth elastic wave velocity model and an elastic wave reflectivity model according to a Born approximation theory by using the reflection rate of the elastic wave, calculate to obtain multi-mode local plane wave data on which the elastic wave gaussian beam Born is forward, and use the multi-mode local plane wave data on which the elastic wave gaussian beam Born is forward as inversion observation data;
the simulation data module 30 is configured to obtain forward simulation local plane wave data by using a Born forward simulation of elastic wave gaussian beam superposition;
and the imaging result module 40 is configured to construct an objective function in a multi-wave local plane wave domain according to the inversion observation data and the forward simulated local plane wave data, perform iterative solution on the objective function by using a conjugate gradient algorithm, and use a solution corresponding to a minimum value of the objective function as an elastic wave imaging result.
As an embodiment of the present invention, as shown in fig. 9, the analog data module 30 includes:
the seismic wave field unit is used for determining a seismic wave field expression based on the Born forward modeling of the elastic wave Gaussian beam stacking by utilizing the Born forward modeling of the elastic wave Gaussian beam stacking;
and the simulation data unit is used for obtaining forward simulation local plane wave data according to the seismic wave field expression.
As an embodiment of the present invention, the imaging result module is specifically configured to determine a data residual of the inversion observation data and the forward simulated local plane wave data in a multi-wave local plane wave domain, and use a two-norm of the data residual as the target function.
In this embodiment, the imaging result module is further specifically configured to perform offset on the data residual in the objective function, perform iterative solution by using a conjugate gradient algorithm according to the offset of the data residual, obtain a solution corresponding to the minimum value of the objective function, and use the solution as the elastic wave imaging result.
Based on the same application concept as the local plane wave domain elastic wave imaging method, the invention also provides the local plane wave domain elastic wave imaging device. The principle of the local plane wave domain elastic wave imaging device for solving the problems is similar to that of a local plane wave domain elastic wave imaging method, so that the implementation of the local plane wave domain elastic wave imaging device can refer to the implementation of the local plane wave domain elastic wave imaging method, and repeated parts are not repeated.
According to the invention, under a least square offset frame, the elastic wave imaging section is updated in an iterative manner, so that the imaging amplitude approaches to the real reflectivity of the stratum, the purpose of amplitude-preserving imaging is achieved, the imaging resolution is improved along with the increase of the iteration times, and crosstalk noise is suppressed.
The invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method when executing the program.
The present invention also provides a computer-readable storage medium storing a computer program for executing the above method.
As shown in fig. 10, the electronic device 600 may further include: communication module 110, input unit 120, audio processing unit 130, display 160, power supply 170. It is noted that the electronic device 600 does not necessarily include all of the components shown in FIG. 10; furthermore, the electronic device 600 may also comprise components not shown in fig. 10, which may be referred to in the prior art.
As shown in fig. 10, the central processor 100, sometimes referred to as a controller or operational control, may include a microprocessor or other processor device and/or logic device, the central processor 100 receiving input and controlling the operation of the various components of the electronic device 600.
The memory 140 may be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information relating to the failure may be stored, and a program for executing the information may be stored. And the central processing unit 100 may execute the program stored in the memory 140 to realize information storage or processing, etc.
The input unit 120 provides input to the cpu 100. The input unit 120 is, for example, a key or a touch input device. The power supply 170 is used to provide power to the electronic device 600. The display 160 is used to display an object to be displayed, such as an image or a character. The display may be, for example, an LCD display, but is not limited thereto.
The memory 140 may be a solid state memory such as Read Only Memory (ROM), Random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes called an EPROM or the like. The memory 140 may also be some other type of device. Memory 140 includes buffer memory 141 (sometimes referred to as a buffer). The memory 140 may include an application/function storage section 142, and the application/function storage section 142 is used to store application programs and function programs or a flow for executing the operation of the electronic device 600 by the central processing unit 100.
The memory 140 may also include a data store 143, the data store 143 for storing data, such as contacts, digital data, pictures, sounds, and/or any other data used by the electronic device. The driver storage portion 144 of the memory 140 may include various drivers of the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging application, address book application, etc.).
The communication module 110 is a transmitter/receiver 110 that transmits and receives signals via an antenna 111. The communication module (transmitter/receiver) 110 is coupled to the central processor 100 to provide an input signal and receive an output signal, which may be the same as in the case of a conventional mobile communication terminal.
Based on different communication technologies, a plurality of communication modules 110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, may be provided in the same electronic device. The communication module (transmitter/receiver) 110 is also coupled to a speaker 131 and a microphone 132 via an audio processor 130 to provide audio output via the speaker 131 and receive audio input from the microphone 132 to implement general telecommunications functions. Audio processor 130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, an audio processor 130 is also coupled to the central processor 100, so that recording on the local can be enabled through a microphone 132, and so that sound stored on the local can be played through a speaker 131.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. A local plane wave domain elastic wave imaging method is characterized by comprising the following steps:
obtaining the reflection rate of the elastic wave according to a speed disturbance formula;
inputting a smooth elastic wave velocity model and an elastic wave reflectivity model by utilizing the reflection rate of the elastic wave according to a Born approximation theory, calculating to obtain multi-mode local plane wave data on which the elastic wave Gaussian beam Born is forward, and taking the multi-mode local plane wave data on which the elastic wave Gaussian beam Born is forward as inversion observation data;
using a Born forward simulation of elastic wave Gaussian beam superposition to obtain forward simulation local plane wave data;
and constructing an objective function in a multi-wave type local plane wave domain according to the inversion observation data and the forward simulation local plane wave data, performing iterative solution on the objective function by using a conjugate gradient algorithm, and taking a solution corresponding to a minimum value of the objective function as an elastic wave imaging result.
2. The method of claim 1, wherein the forward modeling of Born with elastic wave gaussian beam superposition to obtain forward modeled local plane wave data comprises:
determining a seismic wave field expression based on the Born forward modeling of the elastic wave Gaussian beam stacking by utilizing the Born forward modeling of the elastic wave Gaussian beam stacking;
and obtaining forward simulation local plane wave data according to the seismic wave field expression.
3. The method of claim 1, wherein constructing an objective function in a multi-mode local plane wave domain from the inverted observation data and the forward simulated local plane wave data comprises:
and in a multi-wave type local plane wave domain, determining data residual errors of the inversion observation data and the forward simulation local plane wave data, and taking the two norms of the data residual errors as the target function.
4. The method according to claim 3, wherein the iterative solution of the objective function by using the conjugate gradient algorithm, and the using the solution corresponding to the minimum value of the objective function as the elastic wave imaging result comprises:
and offsetting the data residual in the target function, and carrying out iterative solution by using a conjugate gradient algorithm according to the offset of the data residual to obtain a solution corresponding to the minimum value of the target function, wherein the solution is used as an elastic wave imaging result.
5. A local plane wave domain elastic wave imaging apparatus, characterized in that the apparatus comprises:
the reflectivity module is used for obtaining the reflectivity of the elastic wave according to a speed disturbance formula;
the observation data module is used for inputting a smooth elastic wave velocity model and an elastic wave reflectivity model according to a Born approximation theory by utilizing the reflection rate of the elastic wave, calculating to obtain multi-wave type local plane wave data on which the elastic wave Gaussian beam Born is forward, and taking the multi-wave type local plane wave data on which the elastic wave Gaussian beam Born is forward as inversion observation data;
the simulation data module is used for utilizing the Born forward simulation of the elastic wave Gaussian beam superposition to obtain forward simulation local plane wave data;
and the imaging result module is used for constructing an objective function in a multi-wave type local plane wave domain according to the inversion observation data and the forward simulation local plane wave data, performing iterative solution on the objective function by using a conjugate gradient algorithm, and taking a solution corresponding to a minimum value of the objective function as an elastic wave imaging result.
6. The apparatus of claim 5, wherein the analog data module comprises:
the seismic wave field unit is used for determining a seismic wave field expression based on the Born forward modeling of the elastic wave Gaussian beam stacking by utilizing the Born forward modeling of the elastic wave Gaussian beam stacking;
and the simulation data unit is used for obtaining forward simulation local plane wave data according to the seismic wave field expression.
7. The apparatus of claim 5, wherein the imaging result module is specifically configured to determine a data residual of the inverse observed data and the forward simulated local plane wave data in a multi-mode local plane wave domain, and use a two-norm of the data residual as the objective function.
8. The apparatus according to claim 7, wherein the imaging result module is further configured to shift a data residual in the objective function, perform iterative solution by using a conjugate gradient algorithm according to the shift of the data residual, obtain a solution corresponding to a minimum value of the objective function, and use the solution as the elastic wave imaging result.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 4 when executing the program.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1 to 4.
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