A method and apparatus for determining near-surface parameter models

Abstract

This invention provides a method and apparatus for determining near-surface parameter models, relating to the field of oil and gas exploration and development technology. The method includes: determining the exact solution of the complex scattering phase in VTI media using pseudo-acoustic equations based on the parameter model; obtaining an approximate solution of the complex scattering phase corresponding to preset parameters based on the exact solution of the complex scattering phase and using the Litov approximation; establishing a tomographic equation set based on the approximate solution of the complex scattering phase; and iteratively inverting the tomographic equation set using a first-order optimization iterative algorithm to obtain the near-surface parameter model. This invention can obtain a near-surface parameter model, improving the accuracy of near-surface modeling and thus improving the accuracy of seismic data migration imaging.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration and development technology, and more specifically, to a method and apparatus for determining near-surface parameter models. Background Technology

[0002] Conventional seismic data processing methods assume the Earth's medium is isotropic, attributing the effects of seismic anisotropy to velocity errors. However, as exploration targets become smaller, especially for tasks like reservoir lateral prediction, reservoir characterization, imaging of complex geological structures, and lithological imaging, higher resolution seismic data is required. Accurate imaging of fault planes and steeply dipping reflection interfaces is crucial to clearly understand the role of geological structures in hydrocarbon generation, storage, and caprock. Therefore, the demands on seismic data resolution and imaging quality are increasing, and the impact of seismic anisotropy in seismic data processing can no longer be ignored. In particular, reflections from mid-to-deep layers pass through the surface; inaccurate near-surface anisotropy parameters directly affect parameter modeling for the exploration area.

[0003] With the deepening of theoretical research and the enhancement of computer performance, wave equation travel-time tomography inversion has been increasingly widely used in isotropic media. However, in anisotropic media, the travel time and waveform of seismic signals can be severely affected by the anisotropy of the medium. Therefore, the inversion of anisotropic parameters is essential, especially in recent years with the promotion of "two-wide-one-high" seismic acquisition technology, resulting in an increasing amount of long / ultra-long offset and wide-azimuth acquisition data, thus highlighting the impact of anisotropy on the data, particularly transmitted wave data. Meanwhile, anisotropic parameter inversion is quite complex. To reduce the nonlinearity of the inversion, it is possible to consider using only the travel time information of transmitted waves for inversion. Generally, the travel-time sensitivity kernel function of wave equation travel-time inversion is mostly derived from the Born-Oppenheimer approximation (BO approximation, also known as the adiabatic approximation, hereinafter referred to as the Born approximation), but the Born approximation is based on the weak scattering approximation assumption, and its validity conditions are quite stringent. Compared to the Born approximation, the first-order Rytov approximation (hereinafter referred to as the Rytov approximation) can better describe the phase perturbation of forward-scattered waves caused by velocity perturbations, and does not require the assumption of small phase perturbation. Therefore, for simulation of the travel time of small-angle forward-scattered wave fields (transmission wave travel time), the Rytov approximation is more reasonable than the Born approximation.

[0004] Given the importance of near-surface parameter estimation for reservoir characterization, it is necessary to fully utilize transmitted wave travel time information for parameter inversion techniques based on the Rytov approximation in VTI media (transverse isotropy with a vertical axis of symmetry). Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a method and apparatus for determining near-surface parameter models, which can obtain near-surface parameter models, improve the accuracy of near-surface modeling, thereby improve the accuracy of seismic data migration imaging, provide technical support for full-depth domain modeling of complex surfaces, and meet the needs of piedmont exploration.

[0006] In a first aspect, the present invention provides a method for determining a near-surface parameter model, the method comprising:

[0007] The exact solution of the complex scattering phase in the VTI medium is determined by using the pseudo-acoustic equation based on the parametric model.

[0008] Based on the exact solution of the complex scattering phase, and using the Litov approximation, the approximate solution of the complex scattering phase corresponding to the preset parameters is obtained;

[0009] A set of tomographic equations is established based on the approximate solution of the complex scattering phase.

[0010] A first-order optimization iterative algorithm is used to iteratively invert the tomographic equations to obtain a near-surface parameter model; wherein, the near-surface parameter model includes the correspondence between velocity and preset parameters.

[0011] In one embodiment, the parameter model employs the determination of an exact solution for the complex scattering phase in the VTI medium based on the pseudoacoustic equation, including:

[0012] Determine the equation parameters corresponding to the pseudo-acoustic wave equation;

[0013] The update parameters corresponding to the VTI medium are determined based on the equation parameters.

[0014] Based on the updated parameters and the parameter model, an exact solution for the complex scattering phase in the VTI medium is generated.

[0015] In one embodiment, establishing a set of tomographic equations based on the approximate solution of the complex scattering phase includes:

[0016] Construct the travel-time sensitivity kernel function of the wave equation based on the approximate solution of the complex scattering phase;

[0017] A set of tomographic equations is constructed based on the travel-time sensitivity kernel function of the wave equation.

[0018] In one embodiment, constructing a set of tomographic equations based on the travel-time sensitivity kernel function of the wave equation includes:

[0019] The simulated first arrival time is obtained by forward modeling based on the travel time sensitivity kernel function of the wave equation.

[0020] Determine the time difference between the simulated first arrival travel time and the first arrival travel time of the seismic data;

[0021] When the travel time difference is greater than a preset threshold, a set of tomographic equations is constructed based on the travel time sensitivity kernel function of the wave equation.

[0022] In one embodiment, the step of using a first-order optimization iterative algorithm to iteratively invert the tomographic equations to obtain a near-surface parameter model includes:

[0023] The steepest descent method in a first-order optimization iterative algorithm is used to perform iterative inversion processing on the tomographic equations.

[0024] By adjusting the magnitude of the parameters in the tomographic equations after iterative inversion, a near-surface parameter model is obtained.

[0025] Furthermore, it also includes:

[0026] The preset parameters are generated based on the equation parameters of the pseudo-acoustic wave equation.

[0027] In one embodiment, generating the preset parameters based on the equation parameters of the pseudo-acoustic wave equation includes:

[0028] The preset parameters are obtained by calculating and processing the parameters of the equation using first-order or second-order functions.

[0029] Furthermore, it also includes:

[0030] The parameter model is iteratively updated based on the near-surface parameter model.

[0031] Secondly, the present invention provides an apparatus for determining a near-surface parameter model, the apparatus comprising:

[0032] The complex scattering phase precise element is used to determine the precise solution of the complex scattering phase in the VTI medium based on the parametric model and the pseudo-acoustic equation.

[0033] A complex scattering phase approximation unit is used to calculate the complex scattering phase approximation solution corresponding to preset parameters based on the exact solution of the complex scattering phase and using the Litov approximation.

[0034] A tomography unit is used to establish a set of tomography equations based on the approximate solution of the complex scattering phase.

[0035] The iterative inversion unit is used to perform iterative inversion on the tomographic equations using a first-order optimization iterative algorithm to obtain a near-surface parameter model; wherein, the near-surface parameter model includes the correspondence between velocity and preset parameters.

[0036] In one embodiment, the complex scattering phase precision unit includes:

[0037] The pseudo-acoustic wave module is used to determine the equation parameters corresponding to the pseudo-acoustic wave equation;

[0038] The update module is used to determine the update parameters corresponding to the VTI medium based on the equation parameters;

[0039] The generation module is used to generate an exact solution of the complex scattering phase in the VTI medium based on the updated parameters and the parameter model.

[0040] In one embodiment, the chromatography unit includes:

[0041] The travel-time sensitivity kernel module is used to construct the travel-time sensitivity kernel function of the wave equation based on the approximate solution of the complex scattering phase.

[0042] A construction module is used to construct a set of tomographic equations based on the travel-time sensitivity kernel function of the wave equation.

[0043] In one embodiment, the construction module includes:

[0044] The forward modeling submodule is used to obtain the simulated first arrival travel time based on the travel time sensitivity kernel function of the wave equation through forward modeling.

[0045] The time difference submodule is used to determine the time difference between the simulated first arrival travel time and the first arrival travel time of the seismic data;

[0046] The judgment submodule is used to construct a set of tomographic equations based on the wave equation travel sensitivity kernel function when the travel time difference is greater than a preset threshold.

[0047] In one embodiment, the iterative inversion unit includes:

[0048] The iterative inversion module is used to perform iterative inversion processing on the tomographic equations using the steepest descent method in a first-order optimization iterative algorithm.

[0049] The adjustment module is used to adjust the magnitude of parameters in the tomographic equations after iterative inversion to obtain a near-surface parameter model.

[0050] Furthermore, it also includes:

[0051] The parameter unit is used to generate the preset parameters based on the equation parameters of the pseudo-acoustic wave equation.

[0052] In one embodiment, the parameter unit includes:

[0053] The function module is used to calculate and process the parameters of the equation using first-order or second-order functions to obtain the preset parameters.

[0054] Furthermore, it also includes:

[0055] The parameter model is iteratively updated based on the near-surface parameter model.

[0056] Thirdly, the present invention provides an electronic device, comprising: a processor, a memory, a communication interface, and a communication bus; wherein,

[0057] The processor, communication interface, and memory communicate with each other via a communication bus;

[0058] The processor is used to invoke computer instructions in memory to execute the steps of the method for determining the near-surface parameter model described above.

[0059] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions that, when executed, cause the computer to perform the steps of the method for determining the near-surface parameter model described above.

[0060] The present invention proposes a method and apparatus for determining near-surface parameter models. This method determines the exact solution of the complex scattering phase in VTI media based on the pseudo-acoustic equations; based on the exact solution of the complex scattering phase and using the Litov approximation, it obtains the approximate solution of the complex scattering phase corresponding to preset parameters; it establishes a set of tomographic equations based on the approximate solution of the complex scattering phase; and iteratively inverts the set of tomographic equations using a first-order optimization iterative algorithm to obtain the near-surface parameter model. This method can obtain a near-surface parameter model, improve the accuracy of near-surface modeling, and thus improve the accuracy of seismic data migration imaging. It provides technical support for full-depth domain modeling of complex surfaces and meets the needs of piedmont exploration.

[0061] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0062] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the embodiments or the prior art will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0063] Figure 1This is a flowchart illustrating a method for determining near-surface parameter models provided by the present invention.

[0064] Figure 2 This is a flowchart illustrating a method for determining near-surface parameter models provided by the present invention.

[0065] Figure 3 This is a flowchart illustrating a method for determining near-surface parameter models provided by the present invention.

[0066] Figure 4 A schematic diagram of observation data of the VTI medium Hess model in the method for determining near-surface parameter models provided by the present invention;

[0067] in, Figure 4 (a) is the parameter v p0 Observational data for the corresponding VTI medium Hess model;

[0068] Figure 4 (b) in the figure represents the observation data of the VTI medium Hess model corresponding to parameter ε;

[0069] Figure 4 (c) in the figure represents the observation data of the VTI medium Hess model corresponding to parameter δ.

[0070] Figure 5 A schematic diagram of the initial parameter field in a method for determining near-surface parameter models provided by the present invention;

[0071] in, Figure 5 (a) in the text is v p0 The corresponding initial parameter field;

[0072] Figure 5 In the equation (b), the initial parameter field corresponding to ε is given.

[0073] Figure 6 A schematic diagram of the algorithm inversion results in the method for determining near-surface parameter models provided by the present invention;

[0074] in, Figure 6 (a) is v p0 The result obtained by iterative inversion;

[0075] (b) is the result obtained by ε-iteration inversion.

[0076] Figure 7 The image shows a comparison of the travel times of the first arrival wave in a method for determining near-surface parameter models provided by this invention.

[0077] Figure 8 This is a schematic diagram of the structure of a device for determining near-surface parameter models provided by the present invention.

[0078] Figure 9 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0079] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0080] This invention provides a method for determining a near-surface parameter model, see [link to relevant documentation]. Figure 1 As shown, it specifically includes the following content:

[0081] S101: Determine the exact solution of the complex scattering phase in the VTI medium using the pseudo-acoustic equation based on the parametric model;

[0082] In this step, the traditional coupled pseudoacoustic wave equation often assumes that the shear wave velocity is 0. When the parameter ε in the coupled pseudoacoustic wave equation is less than the parameter δ, the wave field propagation will be unstable. Through comparative analysis, the optimized pseudoacoustic wave equation is selected in this step.

[0083] In the specific implementation of this step, the pseudo-acoustic wave equation and its corresponding equation parameters are first determined. The pseudo-acoustic wave equation is as follows:

[0084]

[0085]

[0086] Among them, u H and u V The two components representing the wave field have the same kinematic characteristics; x and z are the X-axis and Z-axis, respectively. p0 ε is the vertical propagation velocity of the P-wave. ε is the P-wave anisotropy, a parameter that measures the intensity of the quasi-P-wave anisotropy. δ is the P-wave variation coefficient, which indicates the rate of change of the P-wave anisotropy in the vertical direction.

[0087] The equation parameters corresponding to the pseudo-acoustic wave equation are (v p0 ,ε,δ), where v p0Let ε be the vertical propagation velocity of the P-wave, ε be the P-wave anisotropy, which is a parameter that measures the intensity of the quasi-P-wave anisotropy, and δ be the P-wave variation coefficient, which indicates the rate of change of the P-wave anisotropy in the vertical direction.

[0088] It should be noted that the parameter α∈(0,1) is used to ensure the stability of wave field propagation when ε<δ.

[0089] Furthermore, in the frequency domain, the wave field in the background parameter field The wave field in the real parameter field is u = Ae i φ Then the complex scattering phase can be written as:

[0090] ψ=ln(u / u0)=ln(A / A0)+i(φ-φ0) (3)

[0091] Where ψ is the term to be solved, u and u0 are the wavelengths in the true parameter field and the background parameter field, respectively, A and A0 are the whole-amplitude phases in the true parameter field and the background parameter field, i is the imaginary part, and φ and φ0 are the phases in the true parameter field and the background parameter field, respectively.

[0092] The relationship between the total field and the background parameter field is as follows:

[0093] u=u0e ψ (4)

[0094] Secondly, the update parameters corresponding to the VTI medium are determined based on the equation parameters;

[0095] Redetermine with v p0 The update parameters m related to ε and δ v m ε m δ ,Right now m ε =1+2ε,m δ =1+2δ. Through m v m ε m δ To characterize the Thomsen parameterization.

[0096] Finally, based on the updated parameters and parameter model, an exact solution for the complex scattering phase in the VTI medium is generated. According to the complex and detailed derivation (substituting the updated parameters into the optimized pseudoacoustic equation), the corresponding parameter m in the VTI medium... v m ε m δ The exact solution for the complex scattering phase can be written as:

[0097]

[0098]

[0099]

[0100] Where, Δm v , Δm v , Δm v Indicates anisotropic parameter perturbation; G H0 and G V0 For the background parameter field from the receiver point x r Green's functions of the wavefield components at the underground scattering point x; u H0 Then it is the background parameter field from the source point x s The H-component wave field at the underground scattering point x. H0x and u H0xx Indicate u H0 The first and second spatial derivatives with respect to the x-direction. ψ x and ψ z Let ψ represent the spatial first-order derivatives of the complex scattering phase ψ with respect to the x and z directions. xx Let ψ denote the spatial second derivative of the complex scattering phase ψ with respect to the x-direction.

[0101] S102: Based on the exact solution of the complex scattering phase and using the Litov approximation, obtain the approximate solution of the complex scattering phase corresponding to the preset parameters;

[0102] In this step, the exact solution of the complex scattering phase in the VTI medium can be obtained according to step S101. Obviously, both the left and right sides of formulas (5), (6) and (7) contain the term ψ to be solved. According to the Rytov approximation, which is more suitable for describing the forward-scattered wave field, the derivative of the term ψ with respect to the spatial direction is small. Therefore, the corresponding parameter m in the VTI medium is small. v m ε m δ The approximate solution for the complex scattering phase is:

[0103]

[0104]

[0105]

[0106] Where, Δm v , Δm v , Δm v Indicates anisotropic parameter perturbation; x s and x r These are the coordinates of the seismic source and the receiver, respectively; G H0 (x,ω;x r ) and G V0 (x,ω;x r () represents the background parameter field from the receiver point x rGreen's functions of the wavefield components at the underground scattering point x; u H0 (x,ω;x s Then, the background parameter field originating from the seismic source point x... s The H-component wave field at the underground scattering point x. H0xx (x,ω;x s ) represents u H0 The spatial second derivative with respect to the x-direction.

[0107] In this step, the corresponding parameter m in the VTI medium v m ε m δ The preset parameters are generated based on the equation parameters of the pseudo-acoustic wave equation. First-order or second-order functions are used to calculate and process the equation parameters to obtain the preset parameters, specifically: m ε =1+2ε,m δ =1+2δ.

[0108] S103: Establish a set of tomographic equations based on the approximate solution of the complex scattering phase;

[0109] In this step, the travel time sensitivity kernel function of the wave equation, which is expressed in the time-space domain according to the approximate solution of the complex scattering phase, is used to construct a tomographic equation set between the travel time residual, the sensitivity kernel function, and the model perturbation.

[0110] It should be noted that the phase perturbation ΔΦ of the wave field is the imaginary part of the complex scattering phase, that is:

[0111] ΔΦ(ω)=Imψ(ω) (11)

[0112] Based on this, the approximate relationship between the seismic signal phase perturbation ΔΦ(ω) and the single-frequency travel time perturbation can be written as:

[0113] ΔΦ(ω)=ωΔt(ω) (12)

[0114] Combining formulas (8), (11), and (12), the single-frequency harmonic travel time disturbance caused by the velocity disturbance can be obtained:

[0115]

[0116] Similarly, the single-frequency harmonic travel time disturbances caused by ε and δ disturbances are written as follows:

[0117]

[0118]

[0119] In this step, the travel-time sensitivity kernel function of the wave equation is constructed based on the approximate solution of the complex scattering phase, specifically including:

[0120] With parameter m v Taking the square of the slowness as an example, determine the time-sensitivity kernel function of the frequency and time domain wave equations for velocity. Let the relationship between the single-frequency harmonic time-travel disturbance and the band-limited signal time-travel disturbance be:

[0121]

[0122] Where W(ω) is a single-frequency weighting function, and the weighting function is selected to be related to the energy spectrum of the seismic wavelet.

[0123]

[0124] The superscript * represents the complex conjugate.

[0125] Combining formulas (13), (16), and (17), the time-of-flight disturbance of the band-limited signal is:

[0126]

[0127] The integral kernel in equation (18) above can be expressed as:

[0128]

[0129] The above equation is the frequency domain equation derived based on the Rytov approximation for the Δm caused by the velocity disturbance. v Finite-frequency time-sensitivity kernel function.

[0130] Formula (19) is the kernel function of the frequency domain kernel time-sensitivity. Using the Bashwal equation, formula (19) can be represented in the time domain as:

[0131]

[0132] Formula (20) is the finite-frequency travel time sensitivity kernel function related to velocity perturbation, derived in the time-space domain based on the Rytov approximation.

[0133] Where λ0(x,t;x r ) is the accompanying wave field of counter-time propagation:

[0134]

[0135] in, This indicates the time first and second derivatives with respect to the wave field.

[0136] Similarly, the time-domain travel sensitivity kernel function related to ε and δ perturbations can be derived:

[0137]

[0138]

[0139] In formula (23), λ1(x,t; x r ) is related to the wave field u V The associated wavefield has the same form as equation (21). Equations (20), (22), and (23) are the time-space travel-time sensitivity kernel functions related to Thomsen parameter perturbations, derived based on the Rytov approximation and using the seismic wavelet normalized energy spectrum as the weighting function. All three equations have a unified expression form for any shot-detection pair (x r ,x s The calculation of the kernel function in anisotropic media requires two forward propagations of the wave field (calculating u). H (x,t;x s ) and u H (x,t;x s And two wavefield reverse-time propagations (calculating λ0(x,t; x)). r ) and λ1(x,t;x r The cross-correlation imaging process is basically the same as that of reverse time migration (RTM).

[0140] The specific implementation of constructing the tomographic equation system based on the travel-time sensitivity kernel function of the wave equation in this step is described in [reference needed]. Figure 2 Specifically, it includes:

[0141] S201: The simulated first arrival time is obtained by forward modeling based on the travel time sensitivity kernel function of the wave equation;

[0142] S202: Determine the time difference between the simulated first arrival time and the first arrival time of the seismic data;

[0143] S203: When the travel time difference is greater than a preset threshold, construct a set of tomographic equations based on the travel time sensitivity kernel function of the wave equation.

[0144] In this embodiment, the simulated first arrival travel time of seismic waves can be obtained through forward modeling using the wave equation travel time sensitivity kernel function. The first arrival travel time is then obtained from actual seismic data. The accuracy of the wave equation travel time sensitivity kernel function in simulating the first arrival travel time is determined based on the simulated first arrival travel time and the actual first arrival travel time. Specifically, the travel time difference between the simulated first arrival travel time and the first arrival travel time in the seismic data is assessed. If this travel time difference is less than or equal to a preset threshold, the wave equation travel time sensitivity kernel function can be considered to accurately simulate the first arrival travel time. If the travel time difference is greater than the preset threshold, a tomographic equation set is constructed based on the wave equation travel time sensitivity kernel function. This allows for subsequent optimization of the tomographic equation set, and the parameter model for constructing the wave equation travel time sensitivity kernel function is iteratively updated based on the optimized tomographic equation set.

[0145] The linear equations for determining the near-surface parameter model in this embodiment can be expressed as:

[0146]

[0147] In the formula, (K mv K mε )and They can be abbreviated as: and δt represents the travel time residual of the transmitted wave.

[0148] S104: The near-surface parameter model is obtained by iteratively inverting the tomographic equations using a first-order optimization iterative algorithm; wherein, the near-surface parameter model includes the correspondence between velocity and preset parameters.

[0149] In this step, the steepest descent method in the first-order optimization iterative algorithm is needed to perform iterative inversion processing on the tomographic equations. Specifically, the steepest descent method is used to solve formula (24), and its iterative format can be written as:

[0150]

[0151] Where, α k This is the iteration step size.

[0152] because and Besides the difference in wave path morphology in different propagation directions, the magnitudes of the two parameters also differ significantly, causing ill-conditioned equation (24) and affecting the convergence efficiency of multi-parameter joint inversion. Therefore, by adjusting the magnitudes of the parameters in the tomographic equations after iterative inversion, specifically by introducing a preconditioning operator P to adjust the magnitudes between the multiple parameters and accelerate convergence, equation (25) is modified as follows:

[0153]

[0154] The above equation represents the near-surface parameter model obtained by iteratively inverting the tomographic equations using a first-order optimization iterative algorithm. Combining equations (20) and (22), the joint inversion gradient of the VTI medium can be expressed as the superposition of multi-shot gradients:

[0155]

[0156] The method for determining near-surface parameter models provided in this invention involves: determining the exact solution of the complex scattering phase in the VTI medium based on the pseudo-acoustic equation; obtaining the approximate solution of the complex scattering phase corresponding to preset parameters based on the exact solution of the complex scattering phase and using the Litov approximation; establishing a tomographic equation set based on the approximate solution of the complex scattering phase; and using a first-order optimization iterative algorithm to iteratively invert the tomographic equation set to obtain the near-surface parameter model. This method can obtain a near-surface parameter model, improving the accuracy of near-surface modeling and thus improving the accuracy of seismic data migration imaging. Furthermore, it fully utilizes the transmitted wave information in seismic data, establishing a near-surface parameter model that balances theoretical and practical aspects, improving the accuracy of the near-surface model, and thus improving the accuracy of seismic data migration imaging. This provides technical support for full-depth domain modeling of complex surfaces and meets the needs of piedmont exploration.

[0157] As described above, a high-precision near-surface VTI anisotropy model is established. By selecting appropriate pseudo-acoustic equations and introducing the Rytov approximation to construct the time-space travel-time sensitivity kernel function of the VTI medium wave equation, not only are the constraints of the weak scattering assumption overcome and the accuracy of the linearized forward problem of the wave equation improved, but the tomographic equations can also be solved efficiently, thereby constructing a refined near-surface model.

[0158] Based on the above embodiments, after step S104 of the above embodiments, the method further includes:

[0159] The parameter model is iteratively updated based on the near-surface parameter model.

[0160] Specifically, in the above implementation, if the travel time difference exceeds a preset threshold, a tomographic equation set is constructed based on the travel time sensitivity kernel function of the wave equation. After optimizing this tomographic equation set, the parameter model for constructing the travel time sensitivity kernel function of the wave equation is iteratively updated based on the optimized tomographic equation set.

[0161] To further illustrate this solution, the present invention provides a detailed embodiment of the entire process for determining a near-surface parameter model, see [link to embodiment]. Figure 3 Specifically, it includes the following:

[0162] Get the given initial parameter model v 0 ;

[0163] It should be noted that the velocity model v in the parametric model needs to be obtained. 0 .

[0164] Based on this initial velocity model v 0 By combining the robust pseudoacoustic equation Rytov approximation, the approximate solution of the complex scattering phase in the VTI medium is calculated;

[0165] Understandably, wave field propagation becomes unstable when the parameter ε in the pseudoacoustic equation is less than the parameter δ. Through comparative analysis, this step selects a robust pseudoacoustic equation, where the parameter ε is greater than the parameter δ; specifically, it optimizes the pseudoacoustic equation.

[0166] Based on the approximate solution of the complex scattering phase obtained above, a forward modeling simulation of the first arrival travel time t using the travel time sensitivity kernel function of the time-space wave equation is constructed. k cal .

[0167] Calculate the time difference t between the first arrival time and the orthogonal first arrival time. k res .

[0168] It is understandable that the picked first arrival travel time is obtained from the seismic data. obs The forward modeling of the first arrival travel time is obtained by forward modeling the travel time sensitivity kernel function of the time-space domain wave equation, which simulates the first arrival travel time t. k cal .

[0169] Determine if the time difference t is satisfied k res Small enough, that is, to determine the time difference t k res Is it greater than the preset threshold?

[0170] Wherein, in the time difference t k res If the value is less than or equal to a preset threshold, the simulated first arrival time t obtained through forward modeling can be output. k cal In the time difference t k res If the error exceeds a preset threshold, a set of tomographic equations is constructed between the travel time residual, the sensitivity kernel function, and the model perturbation, and a first-order optimization algorithm is used to solve the set of tomographic equations.

[0171] The update rate model v is obtained by solving the torsion equations. k+1 =v 0 +v k The update speed model v k+1 This serves as a new initial velocity, thereby enabling iterative updates to the initial parameter model.

[0172] To further illustrate this solution, the present invention provides a specific embodiment of a method for determining near-surface parameter models, which specifically includes the following:

[0173] Here, the right-hand side of a 2D Hess model is used, along with an observation system that uses surface excitation and surface reception, to illustrate the effects of the invention. For example... Figure 4As shown, ▽ represents the location of the detector point, and △ represents the location of the seismic source. Figure 4 (a) in the text is v p0 Observational data for the corresponding VTI medium Hess model; Figure 4 (b) in the figure represents the observation data of the VTI medium Hess model corresponding to ε; Figure 4 (c) represents the observation data of the VTI medium Hess model corresponding to δ; where v p0 Let ε be the vertical propagation velocity of the P-wave, ε be the P-wave anisotropy, which is a parameter that measures the intensity of the quasi-P-wave anisotropy, and δ be the P-wave variation coefficient, which indicates the rate of change of the P-wave anisotropy in the vertical direction.

[0174] Figure 4 The model shown has the following dimensions: lengths of 4km and 2km in the x and z directions, respectively, with grid spacing dx = 0.005km and dz = 0.005km. A surface observation system was designed: the seismic source and detectors are placed on the surface, and the initial shot point is located at (s...). x ,s z The range is (0.1km, 0.0km), with a shot spacing of 0.1km, for a total of 49 shots; each shot corresponds to 1001 geophones evenly distributed on the surface, with a geophone spacing of 0.005km, and the starting position (r) x ,r z = (0.0km, 0.0km). Similarly, Ricker wavelet with a dominant frequency of 15Hz is used as the simulated seismic source. In the real model, formulas (1) and (2) are used to simulate earthquake data, and the first arrival travel time is picked as the observation data.

[0175] Initial parameters are as follows Figure 5 The initial parameter field is shown. Where, Figure 5 (a) in the text is v p0 The corresponding initial parameter field; Figure 5 In the diagram, (b) represents the initial parameter field corresponding to ε. The δ parameter remains unchanged from the true parameter, and v... p0 And ε are modeled using a gradient that increases linearly with depth: ε init =0.0~0.22. Iterative inversion was performed using formulas (20), (22), and (25) given in this invention, and the final result is as follows: Figure 6 The algorithm inversion results shown are as follows, where, Figure 6 (a) is v p0 (a) is the result obtained by iterative inversion; (b) is the result obtained by ε-iterative inversion. Since v p0 The sensitivity of v to travel time differs between ε and v in different directions, leading to differences in the sensitivity of v to travel time. p0 The inversion results are superior to ε. To quantitatively demonstrate that the inversion results include low wavenumber background components from the true model, such as... Figure 7 The diagram shows a comparison of first arrival travel times under different parameter fields (the dotted line, dashed line, and solid line in the diagram represent the first arrival travel times of the initial model, the actual model, and the inversion result of the algorithm of this invention, respectively). Figure 7 The simulated first arrival travel time diagram for shot number 25 (with the epicenter located in the middle and a maximum offset of 2.5 km) shows that the first arrival travel time predicted by the algorithm of this invention is basically consistent with the observed travel time, indicating that in areas with good illumination (shallow layers), the Rytov approximate wave theory travel time tomography of VTI media can obtain good inversion results.

[0176] Numerical experimental results show that the algorithm of this invention can capture the low wavenumber components of the VTI medium parameter model well and establish a relatively accurate near-surface (shallow) anisotropy model.

[0177] This invention provides a specific implementation of a near-surface parameter model determination device capable of realizing all the contents of the near-surface parameter model determination method, see [link to specific implementation details]. Figure 8 The device for determining the near-surface parameter model specifically includes the following components:

[0178] The complex scattering phase precision unit 10 is used to determine the complex scattering phase precision solution in the VTI medium based on the parametric model and the pseudo-acoustic equation.

[0179] The complex scattering phase approximation unit 20 is used to calculate the complex scattering phase approximation solution corresponding to the preset parameters based on the exact solution of the complex scattering phase and using the Litov approximation.

[0180] Tomography unit 30 is used to establish a set of tomographic equations based on the approximate solution of the complex scattering phase;

[0181] The iterative inversion unit 40 is used to perform iterative inversion on the tomographic equations using a first-order optimization iterative algorithm to obtain a near-surface parameter model; wherein, the near-surface parameter model includes the correspondence between velocity and preset parameters.

[0182] The complex scattering phase precision unit 10 includes:

[0183] The pseudo-acoustic wave module is used to determine the equation parameters corresponding to the pseudo-acoustic wave equation;

[0184] The update module is used to determine the update parameters corresponding to the VTI medium based on the equation parameters;

[0185] The generation module is used to generate an exact solution of the complex scattering phase in the VTI medium based on the updated parameters.

[0186] The chromatography unit 30 includes:

[0187] The travel-time sensitivity kernel module is used to construct the travel-time sensitivity kernel function of the wave equation based on the approximate solution of the complex scattering phase.

[0188] A construction module is used to construct a set of tomographic equations based on the travel-time sensitivity kernel function of the wave equation.

[0189] The iterative inversion unit 40 includes:

[0190] The iterative inversion module is used to perform iterative inversion processing on the tomographic equations using the steepest descent method in a first-order optimization iterative algorithm.

[0191] The adjustment module is used to adjust the magnitude of parameters in the tomographic equations after iterative inversion to obtain a near-surface parameter model.

[0192] The embodiments of the near-surface parameter model determination device provided by the present invention can be used to execute the processing flow of the near-surface parameter model determination method in the above embodiments. Its functions will not be repeated here, but can be referred to the detailed description of the above method embodiments.

[0193] As described above, the near-surface parameter model determination device provided in this embodiment of the invention determines the exact solution of the complex scattering phase in the VTI medium using pseudo-acoustic equations; based on the exact solution of the complex scattering phase and using the Litov approximation, it obtains the approximate solution of the complex scattering phase corresponding to preset parameters; it establishes a set of tomographic equations based on the approximate solution of the complex scattering phase; and iteratively inverts the set of tomographic equations using a first-order optimization iterative algorithm to obtain the near-surface parameter model. This device can obtain a near-surface parameter model, improving the accuracy of near-surface modeling and thus improving the accuracy of seismic data migration imaging. Furthermore, it fully utilizes the transmitted wave information in seismic data, establishing a near-surface parameter model that balances theoretical and practical aspects, improving the accuracy of the near-surface model, and thus improving the accuracy of seismic data migration imaging. This provides technical support for full-depth domain modeling of complex surfaces and meets the needs of piedmont exploration.

[0194] This invention provides an embodiment of an electronic device for implementing all or part of the methods for determining the near-surface parameter model, see [link to embodiment]. Figure 9 The electronic device specifically includes the following:

[0195] The system includes a processor 810, a communications interface 820, a memory 830, and a communication bus 840. The processor 810, communications interface 820, and memory 830 communicate with each other via the communication bus 840. The processor 810 can call computer instructions stored in the memory 830 to execute the following methods:

[0196] Determine the exact solution of the complex scattering phase in the VTI medium based on the pseudoacoustic equation;

[0197] Based on the exact solution of the complex scattering phase, and using the Litov approximation, the approximate solution of the complex scattering phase corresponding to the preset parameters is obtained;

[0198] A set of tomographic equations is established based on the approximate solution of the complex scattering phase.

[0199] A first-order optimization iterative algorithm is used to iteratively invert the tomographic equations to obtain a near-surface parameter model.

[0200] This invention provides a computer-readable storage medium for implementing all or part of the content of the method embodiment for determining the near-surface parameter model. The computer-readable storage medium stores computer instructions, which, when executed, cause the computer to perform all the steps of the method for determining the near-surface parameter model in the above embodiment. For example, when the processor executes the computer instructions, it implements the following steps:

[0201] Determine the exact solution of the complex scattering phase in the VTI medium based on the pseudoacoustic equation;

[0202] Based on the exact solution of the complex scattering phase, and using the Litov approximation, the approximate solution of the complex scattering phase corresponding to the preset parameters is obtained;

[0203] A set of tomographic equations is established based on the approximate solution of the complex scattering phase.

[0204] A first-order optimization iterative algorithm is used to iteratively invert the tomographic equations to obtain a near-surface parameter model.

[0205] While this invention provides the method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive labor. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only possible execution order. In actual device or client product execution, the methods shown in the embodiments or drawings can be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment).

[0206] In the embodiments provided by this invention, it should be understood that the disclosed methods and apparatus can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0207] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0208] In addition, the functional units in the embodiments provided by the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0209] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0210] In this document, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Those skilled in the art will understand the specific meaning of these terms in this invention according to the specific circumstances.

[0211] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention is not limited to any single aspect, nor to any single embodiment, nor to any combination and / or substitution of these aspects and / or embodiments. Furthermore, each aspect and / or embodiment of the present invention can be used alone or in combination with one or more other aspects and / or embodiments thereof.

[0212] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. All should be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for determining a near-surface parameter model, characterized in that, include: The exact solution of the complex scattering phase in the VTI medium is determined by using the pseudo-acoustic equation based on the parametric model. Based on the exact solution of the complex scattering phase and using the Litov approximation, the approximate solution of the complex scattering phase corresponding to the preset parameters is obtained. The preset parameters are obtained by calculating and processing the equation parameters of the pseudo-acoustic wave equation using first-order or second-order functions. A set of tomographic equations is established based on the approximate solution of the complex scattering phase. A first-order optimization iterative algorithm is used to iteratively invert the tomographic equations to obtain a near-surface parameter model; wherein, the near-surface parameter model includes the correspondence between velocity and preset parameters.

2. The method for determining the near-surface parameter model according to claim 1, characterized in that, The process of determining the exact solution of the complex scattering phase in the VTI medium using the pseudo-acoustic equation based on the parametric model includes: Determine the equation parameters corresponding to the pseudo-acoustic wave equation; The update parameters corresponding to the VTI medium are determined based on the equation parameters. Based on the updated parameters and the parameter model, an exact solution for the complex scattering phase in the VTI medium is generated.

3. The method for determining the near-surface parameter model according to claim 1, characterized in that, The step of establishing a set of tomographic equations based on the approximate solution of the complex scattering phase includes: Construct the travel-time sensitivity kernel function of the wave equation based on the approximate solution of the complex scattering phase; A set of tomographic equations is constructed based on the travel-time sensitivity kernel function of the wave equation.

4. The method for determining the near-surface parameter model according to claim 3, characterized in that, The construction of the tomographic equation system based on the travel-time sensitivity kernel function of the wave equation includes: The simulated first arrival time is obtained by forward modeling based on the travel time sensitivity kernel function of the wave equation. Determine the time difference between the simulated first arrival time and the first arrival time of the seismic data; When the travel time difference is greater than a preset threshold, a set of tomographic equations is constructed based on the travel time sensitivity kernel function of the wave equation.

5. The method for determining the near-surface parameter model according to claim 1, characterized in that, The step of using a first-order optimization iterative algorithm to iteratively invert the tomographic equations to obtain a near-surface parameter model includes: The steepest descent method in a first-order optimization iterative algorithm is used to perform iterative inversion processing on the tomographic equations. By adjusting the magnitude of the parameters in the tomographic equations after iterative inversion, a near-surface parameter model is obtained.

6. The method for determining the near-surface parameter model according to claim 1, characterized in that, Also includes: The parameter model is iteratively updated based on the near-surface parameter model.

7. A device for determining near-surface parameter models, characterized in that, include: The complex scattering phase precise element is used to determine the precise solution of the complex scattering phase in the VTI medium based on the parametric model and the pseudo-acoustic equation. A complex scattering phase approximation unit is used to obtain a complex scattering phase approximation solution corresponding to preset parameters based on the exact solution of the complex scattering phase and using the Litov approximation. The preset parameters are obtained by calculating the equation parameters of the pseudo-acoustic wave equation using a first-order or second-order function. A tomography unit is used to establish a set of tomography equations based on the approximate solution of the complex scattering phase. The iterative inversion unit is used to perform iterative inversion on the tomographic equations using a first-order optimization iterative algorithm to obtain a near-surface parameter model; wherein, the near-surface parameter model includes the correspondence between velocity and preset parameters.

8. An electronic device, characterized in that, include: Processor, memory, communication interface, and communication bus; among which, The processor, communication interface, and memory communicate with each other via a communication bus; The processor is used to invoke computer instructions in memory to perform the steps of the method for determining the near-surface parameter model according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed, cause the computer to perform the steps of the method for determining the near-surface parameter model according to any one of claims 1-6.

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