A method, apparatus and equipment for multi-parameter Gaussian beam inversion imaging of acoustic VTI media.

Abstract

This application provides a multi-parameter Gaussian beam inversion imaging method, apparatus, and device for acoustic VTI media, belonging to the field of seismic data processing technology. Based on normal time-difference correction velocity parameters, non-elliptic parameters, and Thomsen parameters, the pressure wave field control equation in the acoustic VTI medium is parameterized to obtain a migration operator. Based on the migration operator, the acquired actual seismic data is migrated to obtain actual migration imaging results. Based on the migration operator, the difference between the forward-modeled seismic data containing and without point scatterers is migrated to obtain a point spread function. The point spread function is spatially convolved with the initial perturbation parameter model to obtain simulated migration imaging results. Based on the actual and simulated migration imaging results, a first model loss is determined. The initial perturbation parameter model is iterated with the goal of minimizing the first model loss, improving the stability of imaging and parameter estimation in weakly illuminated areas.

Description

Technical Field

[0001] This application relates to the field of seismic data processing technology, and in particular to a method, apparatus and equipment for multi-parameter Gaussian beam inversion imaging of acoustic VTI media. Background Technology

[0002] In geophysical exploration, achieving high-resolution imaging and accurate parameter estimation of seismic data is crucial for a deeper understanding of the Earth's internal structure and effective identification of subsurface resources. The anisotropic nature of the Earth's medium significantly influences the propagation paths and velocities of seismic waves, increasing the complexity of migration imaging and parameter estimation.

[0003] In related technologies, true amplitude imaging is fundamental to achieving high-resolution imaging and accurate parameter estimation. To ensure the accuracy of imaging results, the design of migration imaging algorithms must fully consider the anisotropy of the Earth's medium. Due to the inherent limitations of imaging systems, including the incompleteness of migration algorithms, Earth models, and observation systems, achieving true amplitude imaging faces significant challenges. However, traditional true amplitude migration methods, such as algorithms based on asymptotic inversion, typically rely on idealized assumptions, such as complete and continuous receiver coverage and high-frequency asymptotic conditions, to simplify the problem-solving process.

[0004] In cases of insufficient illumination of seismic data, the aforementioned asymptotic inversion method may become impractical because insufficient sampling of the dip angle at the output position cannot effectively handle footprint noise in the seismic data, resulting in unstable imaging results. Summary of the Invention

[0005] This application provides a method, apparatus, and device for multi-parameter Gaussian beam inversion imaging of acoustic VTI media, which improves the stability of imaging and parameter estimation in weakly illuminated regions. The technical solution is as follows:

[0006] On the one hand, a method for multi-parameter Gaussian beam inversion imaging of acoustic VTI media is provided, the method comprising:

[0007] Based on the normal time difference correction velocity parameter, non-elliptic parameter and Thomsen parameter, the pressure wave field control equation in acoustic VTI medium is parameterized to obtain the migration operator;

[0008] Based on the aforementioned migration operator, the acquired actual seismic data is processed by migration to obtain the actual migration imaging results;

[0009] Gaussian beam Born forward modeling is performed on the perturbation parameter model containing point scatterers and the perturbation parameter model without point scatterers, respectively, to obtain first simulated seismic data and second simulated seismic data. Based on the migration operator, the difference between the first simulated seismic data and the second simulated seismic data is processed to obtain a point spread function, which is used to represent the imaging response of the point scatterer.

[0010] Spatial convolution is performed between the point spread function and the initial perturbation parameter model to obtain simulated migration imaging results;

[0011] Based on the actual migration imaging results and the simulated migration imaging results, a first model loss is determined. With the goal of minimizing the first model loss, the initial perturbation parameter model is iterated to obtain a target perturbation parameter model. The first model loss is used to represent the difference between the actual migration imaging results and the simulated migration imaging results. The target perturbation parameter model is used to generate multi-parameter Gaussian beam inversion imaging results for acoustic VTI media.

[0012] In some embodiments, the parameterization of the pressure wave field control equation in the acoustic VTI medium based on the normal time difference correction velocity parameter, the non-elliptic parameter, and the Thomsen parameter to obtain the migration operator includes:

[0013] Based on the perturbation parameters of each parameter in the normal time difference correction velocity parameter, the non-elliptic parameter, and the Thomsen parameter, the perturbation parameter matrix is ​​determined;

[0014] Within the framework of Gaussian beam theory, based on the perturbation parameter matrix, the scattered pressure wave field in the acoustic VTI medium based on the first-order Born forward approximation is processed to obtain the forward operator;

[0015] The forward operator is transposed to obtain the offset operator.

[0016] In some embodiments, the process of processing the scattered pressure wave field in the acoustic VTI medium based on the first-order Born forward approximation within the framework of Gaussian beam theory, based on the perturbation parameter matrix, to obtain the forward modeling operator, includes:

[0017] Obtain the scattered pressure wave field in the acoustic VTI medium using the first-order Gaussian beam Born forward modeling approximation;

[0018] The forward operator is obtained by dividing the scattered pressure wave field by the perturbation parameter matrix.

[0019] In some embodiments, performing Gaussian beam Born forward modeling on the perturbation parameter model containing point scatterers and the perturbation parameter model not containing point scatterers to obtain first simulated seismic data and second simulated seismic data includes:

[0020] Gaussian beam Born forward modeling is performed on the perturbation parameter model containing point scatterers to obtain the first simulated seismic data, which is used to represent the propagation of sound waves in a medium containing point scatterers.

[0021] Gaussian beam Born forward modeling is performed on the perturbation parameter model that does not contain point scatterers to obtain the second simulated seismic data, which is used to represent the propagation of sound waves in a medium in the absence of point scatterers.

[0022] In the perturbation parameter model containing point scatterers, the point scatterers are uniformly distributed.

[0023] In some embodiments, the step of performing migration processing on the difference between the first simulated seismic data and the second simulated seismic data based on the migration operator to obtain a point spread function includes:

[0024] The second simulated seismic data is subtracted from the first simulated seismic data to obtain the seismic data difference, which is used to represent the point scatterer;

[0025] The point spread function is obtained by multiplying the migration operator with the difference in the seismic data.

[0026] In some embodiments, the seismic data difference is equal to the product between the forward modeling operator and the reference perturbation parameter model, the migration operator is the transpose of the forward modeling operator, and the reference perturbation parameter model is used to represent a uniformly distributed point scattering body in the subsurface;

[0027] The step of multiplying the migration operator with the difference in seismic data to obtain the point spread function includes:

[0028] The offset operator is multiplied by the forward operator to obtain the Hessian operator;

[0029] The point spread function is obtained by multiplying the Hessian operator with the reference perturbation parameter model.

[0030] In some embodiments, multiplying the Hessian operator with the reference perturbation parameter model to obtain the point spread function includes:

[0031] The Hessian operator is split to obtain a first submatrix and a second submatrix. The first submatrix is ​​a Hessian submatrix with parameters of the same type, and the second submatrix is ​​a Hessian submatrix with forward operators of different types of parameters coupled together through multiplication.

[0032] If the normal time difference correction velocity parameter, the non-elliptic parameter, and the Thomsen parameter have the same spatial distribution, then the product between the first submatrix and the reference perturbation parameter model is used as the point spread function.

[0033] In some embodiments, the method further includes:

[0034] A regularization term is added to the first model loss to obtain the second model loss, wherein the regularization term is used to represent the prior knowledge of the initial perturbation parameter model for natural images;

[0035] The step of iterating the initial perturbation parameter model to obtain the target perturbation parameter model with the objective of minimizing the loss of the first model includes:

[0036] With the goal of minimizing the loss of the second model, the initial perturbation parameter model is iterated to obtain the target perturbation parameter model.

[0037] On the other hand, a multi-parameter Gaussian beam inversion imaging device for acoustic VTI media is provided, the device comprising:

[0038] The first processing module is used to parameterize the pressure wave field control equation in the acoustic VTI medium based on the normal time difference correction velocity parameter, non-elliptic parameter and Thomsen parameter to obtain the offset operator.

[0039] The offset module is used to perform offset processing on the acquired actual seismic data based on the offset operator to obtain the actual offset imaging result;

[0040] The second processing module is used to perform Gaussian beam Born forward modeling on the perturbation parameter model containing point scatterers and the perturbation parameter model without point scatterers, respectively, to obtain first simulated seismic data and second simulated seismic data. Based on the migration operator, the difference between the first simulated seismic data and the second simulated seismic data is processed to obtain a point spread function, which is used to represent the imaging response of the point scatterer.

[0041] The third processing module is used to perform spatial convolution on the point spread function and the initial perturbation parameter model to obtain simulated migration imaging results.

[0042] An iterative module is used to determine a first model loss based on the actual migration imaging results and the simulated migration imaging results, and to iterate the initial perturbation parameter model with the goal of minimizing the first model loss to obtain a target perturbation parameter model. The first model loss is used to represent the difference between the actual migration imaging results and the simulated migration imaging results. The target perturbation parameter model is used to generate acoustic VTI medium multi-parameter Gaussian beam inversion imaging results.

[0043] In some embodiments, the first processing module is configured to determine a perturbation parameter matrix based on the perturbation parameters of each of the normal time difference correction velocity parameters, the non-elliptic parameters, and the Thomsen parameters; within the framework of Gaussian beam theory, based on the perturbation parameter matrix, process the scattered pressure wave field in the acoustic VTI medium based on the first-order Born forward approximation to obtain a forward modeling operator; and transpose the forward modeling operator to obtain the offset operator.

[0044] In some embodiments, the first processing module is used to obtain the scattering pressure wave field of the first-order Gaussian beam Born forward modeling approximation in the acoustic VTI medium; and divide the scattering pressure wave field by the perturbation parameter matrix to obtain the forward modeling operator.

[0045] In some embodiments, the second processing module is configured to perform Gaussian beam Born forward modeling on a perturbation parameter model containing point scatterers to obtain the first simulated seismic data, the first simulated seismic data being used to represent the propagation of sound waves in a medium containing point scatterers; and to perform Gaussian beam Born forward modeling on a perturbation parameter model not containing point scatterers to obtain the second simulated seismic data, the second simulated seismic data being used to represent the propagation of sound waves in a medium without point scatterers; wherein the point scatterers are uniformly distributed in the perturbation parameter model containing point scatterers.

[0046] In some embodiments, the second processing module is configured to subtract the second simulated seismic data from the first simulated seismic data to obtain a seismic data difference, the seismic data difference being used to represent the point scatterer; and to multiply the offset operator with the seismic data difference to obtain the point spread function.

[0047] In some embodiments, the seismic data difference is equal to the product between the forward modeling operator and the reference perturbation parameter model, the migration operator is the transpose of the forward modeling operator, and the reference perturbation parameter model is used to represent a uniformly distributed point scattering body in the subsurface;

[0048] The second processing module is used to multiply the offset operator and the forward modeling operator to obtain the Hessian operator; and to multiply the Hessian operator and the reference perturbation parameter model to obtain the point spread function.

[0049] In some embodiments, the second processing module is used to decompose the Hessian operator to obtain a first submatrix and a second submatrix. The first submatrix is ​​a Hessian submatrix with parameters of the same type, and the second submatrix is ​​a Hessian submatrix with forward operators of different types of parameters multiplied and coupled together. If the normal time difference correction velocity parameter, the non-elliptic parameter, and the Thomsen parameter have the same spatial distribution, the product between the first submatrix and the reference perturbation parameter model is used as the point spread function.

[0050] In some embodiments, the apparatus further includes:

[0051] The fourth processing module is used to add a regularization term to the first model loss to obtain a second model loss. The regularization term is used to represent the prior knowledge of the initial perturbation parameter model for natural images.

[0052] The iterative module is used to iterate the initial perturbation parameter model with the goal of minimizing the loss of the second model, so as to obtain the target perturbation parameter model.

[0053] On the other hand, an electronic device is provided, comprising a processor and a memory, the memory being used to store at least one computer program, the at least one computer program being loaded and executed by the processor to implement the acoustic VTI medium multi-parameter Gaussian beam inversion imaging method in the embodiments of this application.

[0054] On the other hand, a computer-readable storage medium is provided, wherein at least one computer program is stored in the computer-readable storage medium, the at least one computer program being loaded and executed by a processor to implement the acoustic VTI medium multi-parameter Gaussian beam inversion imaging method as described in the embodiments of this application.

[0055] On the other hand, a computer program product is provided, including a computer program that, when executed by a processor, implements the acoustic VTI medium multi-parameter Gaussian beam inversion imaging method as described in the embodiments of this application.

[0056] This application provides a method for multi-parameter Gaussian beam inversion imaging of acoustic VTI media, which simultaneously processes multiple subsurface parameters such as normal time difference correction velocity parameters, non-elliptic parameters, and Thomsen parameters. This provides a more efficient and comprehensive solution for geophysical exploration. Specifically, by determining the migration operator through the aforementioned multiple subsurface parameters, the accuracy of the migration operator is ensured, so as to more accurately reflect the migration of seismic waves in the geology. Since the first simulated seismic data is obtained by forward modeling of a perturbation parameter model containing point scatterers, and the second simulated seismic data is obtained by forward modeling of a perturbation parameter model without point scatterers, the difference between the first and second simulated seismic data can accurately reflect the point scatterers in the geology. By migrating this difference through the migration operator, the imaging response of the point scatterers, i.e., the point spread function, can be accurately obtained. Then, by aiming to minimize the difference between the actual migration imaging results and the simulated migration imaging results, the parameters in the initial perturbation parameter model are adjusted so that the simulated seismic data inverted from the adjusted perturbation parameter model matches the observed actual seismic data as closely as possible, i.e., the simulation results are more accurate.

[0057] Compared to related techniques (inverse scattering migration inversion based on asymptotic analysis), this scheme improves the stability of imaging and parameter estimation in weakly illuminated regions. Furthermore, it demonstrates a significant advantage in improving the results for perturbations in medium parameters corresponding to non-elliptic parameters. Given that accurate recovery of non-elliptic parameters largely depends on large-angle (large offset) information, and the limitations of existing observation systems often lead to deep imaging being associated with small angles, this scheme overcomes this limitation, effectively recovering non-elliptic parameters and solving the imaging challenges of deep and boundary regions that are difficult to handle with traditional methods. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 This is a schematic diagram of an implementation environment provided according to an embodiment of this application;

[0060] Figure 2 This is a flowchart of a multi-parameter Gaussian beam inversion imaging method for acoustic VTI media provided in the embodiments of this application;

[0061] Figure 3 This is a Gullfaks model of an anisotropic VTI medium in the North Sea region provided according to an embodiment of this application;

[0062] Figure 4 This is a background model of the Gullfaks region in the North Sea region for anisotropic VTI media, provided according to an embodiment of this application.

[0063] Figure 5 This is a true perturbation parameter model of the Gullfaks model for anisotropic VTI media in the North Sea region, provided according to an embodiment of this application.

[0064] Figure 6 This is an actual migration result of the Gullfaks model for anisotropic VTI media in the North Sea region, provided according to an embodiment of this application;

[0065] Figure 7 This is a schematic diagram of the point spread function (PSF) of an anisotropic VTI medium in the North Sea region using the Gullfaks model, according to an embodiment of this application.

[0066] Figure 8 This is a schematic diagram of the imaging domain least squares migration result of a multi-parameter Gaussian beam inversion imaging method for acoustic VTI media based on point spread function, according to an embodiment of this application.

[0067] Figure 9 This application provides an anisotropic VTI medium Gullfaks model for the North Sea region. Figure 5 and Figure 8 A schematic diagram of the single-channel results extracted from the results;

[0068] Figure 10 This is a schematic diagram of the structure of an acoustic VTI medium multi-parameter Gaussian beam inversion imaging device according to an embodiment of this application;

[0069] Figure 11 This is a structural block diagram of a terminal provided according to an embodiment of this application;

[0070] Figure 12 This is a structural block diagram of a server provided according to an embodiment of this application. Detailed Implementation

[0071] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0072] In this application, the terms "first," "second," etc., are used to distinguish identical or similar items with essentially the same function. It should be understood that there is no logical or temporal dependency between "first," "second," and "nth," nor are there any restrictions on quantity or execution order.

[0073] In this application, the term "at least one" means one or more, and "multiple" means two or more.

[0074] It should be noted that all information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals involved in this application have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, the earthquake data involved in this application was obtained with full authorization.

[0075] Figure 1 This is a schematic diagram of an implementation environment provided according to an embodiment of this application. See also: Figure 1 The implementation environment includes an electronic device, which can be provided as terminal 101, or as a combination of terminal 101 and server 102. If the electronic device is provided as terminal 101 and server 102, terminal 101 and server 102 can be connected via a wireless or wired network. In this embodiment, the electronic device is not specifically limited.

[0076] If the electronic device is provided as terminal 101, then terminal 101 performs acoustic VTI medium multi-parameter Gaussian beam inversion imaging on the seismic data based on the point spread function.

[0077] If the electronic devices are provided as terminal 101 and server 102, then terminal 101 sends seismic data to server 102, and server 102 performs acoustic VTI medium multi-parameter Gaussian beam inversion imaging on the seismic data. Terminal 101 has a target application installed, which is used to image the seismic data. Server 102 is the backend server for the target application, providing background services for it.

[0078] The terminal 101 can be at least one of the following: tablet computer, desktop computer, PC (Personal Computer) device, intelligent voice interaction device, etc. The server 102 can be at least one of the following: a single server, a server cluster consisting of multiple servers, a cloud server, a cloud computing platform, and a virtualization center.

[0079] Figure 2 This is a flowchart of a multi-parameter Gaussian beam inversion imaging method for acoustic VTI media according to an embodiment of this application. See also... Figure 2 In this embodiment, the method is described using an electronic device as an example. The acoustic VTI medium multi-parameter Gaussian beam inversion imaging method includes the following steps:

[0080] 201. Based on the normal time difference correction velocity parameter, non-elliptic parameter and Thomsen parameter, the electronic device parameterizes the pressure wave field control equation in the acoustic VTI medium to obtain the offset operator.

[0081] In this embodiment of the application, the normal moveout (NMO) velocity parameter v n This refers to the velocity used to correct the travel time of reflected waves during seismic data acquisition and processing. Normal time difference correction velocity parameters are typically achieved by performing normal time difference correction on seismic data through velocity scanning. This is accomplished by flattening the phase axis of the reflected waves and obtaining the stacking velocity or dynamic correction velocity; this application does not limit this method. The non-elliptic parameter η mainly reflects the propagation characteristics and spatial distribution features of seismic waves (sound waves). The Thomsen parameter δ is mainly used in the seismological field to describe the elastic properties of rocks, particularly their isotropic and anisotropic characteristics.

[0082] The governing equation for the pressure wave field in VTI (vertically transversely isotropic) acoustic waves can be found in Equation 1 below:

[0083]

[0084] Among them, v n The parameters used to represent normal time difference correction velocity are: η (for ellipticity correction), δ (for non-ellipticity correction), and P(x,s,ω). P(x,s,ω) represents the pressure wave field excited by a source at point s and received by a detector at x = (x,y,z) in Cartesian coordinates. ω represents the angular frequency. Used to represent the Laplace operator related to x and y. This is used to represent the second-order partial derivative terms related to z. The equation is parameterized by the normal time difference corrected velocity parameter, the non-elliptic parameter, and the Thomsen parameter.

[0085] Electronic devices can parameterize the control equations of the pressure wave field in a medium, constructing a forward modeling operator based on a multi-parameter acoustic VTI Gaussian beam Born, which includes normal time-difference correction velocity parameters, non-elliptic parameters, and Thomsen parameters, as well as its transpose (offset) operator. Specifically, the electronic devices determine the perturbation parameter matrix based on the perturbation parameters of each parameter in the normal time-difference correction velocity parameters, non-elliptic parameters, and Thomsen parameters. Then, within the framework of Gaussian beam theory, the electronic devices process the scattered pressure wave field in the acoustic VTI medium based on the first-order Born forward approximation, using the perturbation parameter matrix, to obtain the forward modeling operator. Finally, the electronic devices transpose the forward modeling operator to obtain the offset operator.

[0086] The perturbation parameter matrix includes the perturbation parameters corresponding to the normal time difference corrected velocity parameter, the non-elliptic parameter, and the Thomsen parameter. This matrix represents the changes in the medium within the formation, which affect the propagation characteristics of seismic waves (sound waves) within the medium. The electronic equipment defines these perturbation parameters using the following formula (Formula 2).

[0087] Formula 2:

[0088]

[0089] Among them, v n The parameter used to represent the normal time difference correction velocity parameter; η is used to represent the non-elliptic parameter; δ is used to represent the Thomsen parameter; the subscript 0 is used to represent the background value of the above parameters; f vn The disturbance parameter used to represent the normal time difference correction velocity parameter; f η The perturbation parameter used to represent non-elliptic parameters; f δ The perturbation parameters are used to represent the Thomsen parameters. Accordingly, the perturbation parameter matrix is ​​c1 as follows.

[0090]

[0091] Based on the above definitions and perturbation analysis, the electronic device can obtain the scattered pressure wave field based on the first-order Born approximation. Within the framework of Gaussian beam theory, the electronic device further processes the scattered pressure wave field based on the first-order Born approximation, thereby constructing a multi-parameter acoustic VTI Gaussian beam Born forward operator M, which includes normal time difference correction velocity parameters, non-elliptic parameters, and Thomsen parameters.

[0092] In calculating the aforementioned forward modeling operator, the electronic device acquires the scattered pressure wave field of the first-order Gaussian beam Born forward approximation in the acoustic VTI medium. Dividing the scattered pressure wave field by the perturbation parameter matrix yields the forward modeling operator. See Formula 3 below for details:

[0093]

[0094] Where ω represents the angular frequency; P1(r,s,ω) represents the scattered pressure wave field excited by the source at s and received by the detector at r; ω l Used to indicate the reference frequency of the Gaussian beam; w l Used to represent the effective half-width of the Gaussian beam reference; L is used to represent the beam center coordinates; p rh =(p rx ,p ry ) is used to represent the horizontal component of the initial slowness vector of the Gaussian beam at the receiver point; p rx and p ryThese are used to represent the components along the x-axis and y-axis, respectively; p sh =(p sx ,p sy ) is used to represent the horizontal component of the initial slowness vector of the Gaussian beam at the shot point; p sx and p sy These are used to represent the components along the x-axis and y-axis, respectively; p sz Used to represent the vertical component of the initial slowness vector of the Gaussian beam at the shot point;

[0095] Φ is a frequency-related term, which can be expanded as follows: In the formula, 'a' is a constant used to represent the bundle center spacing.

[0096] A r (x,L,s) is used to represent the full amplitude of two Gaussian beams that come from the shot point s and the receiver point L and pass through the imaging point x at the same time. Among them, A GB (x,L) represents the complex amplitude of the Gaussian beam from the receiving point L to the imaging point x, which can be obtained from dynamic ray tracing; A GB (x,s) represents the complex Gaussian beam amplitude from the shot point s to the imaging point x, which can also be obtained from dynamic ray tracing; v n0 (x) is used to represent the background value (background medium parameter) corresponding to the normal time difference correction velocity parameter at imaging point x.

[0097] τ T (x,L,s) represents the total travel time of two Gaussian beams originating from the shot point s and the receiver point L, and simultaneously passing through the imaging point x; τ T (x,L,s)=τ(x,L)+τ(x,s), where τ GB (x,L) represents the Gaussian beam retracing from the receiving point L to the imaging point x, which can be obtained from kinematic ray tracing; τ GB (x,s) is used to represent the Gaussian beam retracing time from the shot point s to the imaging point x, which can also be obtained from kinematic ray tracing.

[0098] W r (x,L,s) is used to represent the VTI medium scattering mode matrix of acoustic waves, as shown in Formula 4 below:

[0099]

[0100] in, Used to represent gradient; (m sx ,m sy ,m sz ) is used to represent the slowness vector at the gun point; m sx m sy msz These are used to represent the components of the slowness vector at the gun point in the x, y, and z Cartesian coordinate systems, respectively; (m Lx ,m Ly ,m Lz ) is used to represent the slowness vector of the receiving point; m Lx m Ly m Lz These are used to represent the components of the slowness vector of the receiving point in the x, y, and z directions of the Cartesian coordinate system, respectively.

[0101] Furthermore, the transpose (offset) operator M of the aforementioned Born forward operator M T The expression is given by the following formula five:

[0102]

[0103] The superscript * indicates the complex conjugate. The meanings of the other letters and symbols are the same as in Formula 4 above, and will not be repeated here. The electronic device can calculate the offset operator M using the above formula. T .

[0104] For example, Figure 3 This application provides an anisotropic VTI medium Gullfaks model for the North Sea region. Figure 3 (a) exemplarily illustrates the true vertical velocity v in the Gullfaks model of an anisotropic VTI medium in the North Sea region. p Electronic devices can be accessed via formulas. The vertical velocity v p Convert to normal time difference correction speed parameter v n . Figure 3 (b) in the example demonstrates the Thomsen parameter ε of the Gullfaks model for anisotropic VTI media in the North Sea region. Figure 3 (c) exemplarily illustrates the Thomsen parameter δ of the Gullfaks model for anisotropic VTI media in the North Sea region. Electronic devices are described by the formula... The anisotropic nonelliptic parameter η is obtained. Figure 4 This is a background model of the Gullfaks region in the North Sea region for anisotropic VTI media, provided according to an embodiment of this application. Figure 4 (a) exemplarily illustrates the vertical velocity v of the Gullfaks model in the North Sea region of an anisotropic VTI medium. p . Figure 4 (b) in the example shows the background model of the Gullfaks model in the North Sea region with anisotropic VTI media and the Thomsen parameter ε. Figure 4(a) in the example shows the background model of the Gullfaks model in the North Sea region with anisotropic VTI media and the Thomsen parameter δ. Figure 5 This is a true perturbation parameter model of the Gullfaks model for anisotropic VTI media in the North Sea region, provided according to an embodiment of this application. Figure 5 (a) exemplarily illustrates the medium parameter perturbation corresponding to the NMO velocity in the Gullfaks model in the North Sea region of anisotropic VTI medium. Model diagram. Figure 5 (b) exemplarily illustrates the medium parameter perturbation f corresponding to the non-elliptic parameters of the Gullfaks model in the North Sea region for anisotropic VTI media. η Model diagram. Figure 5 (c) exemplarily illustrates the medium parameter perturbation f corresponding to the Thomsen parameters of the Gullfaks model in the North Sea region for anisotropic VTI media. δ Model diagram.

[0105] 202. The electronic equipment uses an migration operator to perform migration processing on the acquired actual seismic data to obtain the actual migration imaging results.

[0106] In this embodiment of the application, the electronic device utilizes the offset operator M constructed in step 201. T The acquired actual seismic data is migrated to obtain the actual migrated imaging results. Electronic equipment can control the migration operator M. T The actual migration imaging result is obtained by multiplying the data with the actual seismic data. See Formula Six below for details:

[0107]

[0108] in, Used to represent the actual offset imaging results, M T Used to represent the offset operator, M = (M vn M η M δ ); P is used to represent actual earthquake data.

[0109] For example, Figure 6 This is the actual migration result of the Gullfaks model of an anisotropic VTI medium in the North Sea region, provided according to the embodiments of this application. Figure 6 (a) exemplarily illustrates the medium parameter perturbation f corresponding to the conventional migration NMO velocity in the Gullfaks model for anisotropic VTI media in the North Sea region. vn The inversion results. Figure 6(b) exemplarily illustrates the medium parameter perturbation f corresponding to the conventional migration non-elliptic parameters of the Gullfaks model in the North Sea region for anisotropic VTI media. η The inversion results. Figure 6 (c) in the figure exemplarily illustrates the medium parameter perturbation f corresponding to the conventional migration Thomsen parameters of the Gullfaks model in the North Sea region for anisotropic VTI media. δ The inversion results. Overall, the migration results are consistent with the true perturbation model ( Figure 5 Compared to the other two parameters, better imaging results were obtained, and the main features were well recovered. However, compared to the other two parameters, the results of perturbation of the medium parameter corresponding to the non-elliptic parameter were relatively poor, mainly because f η The recovery is mainly related to large angles (large offsets), and the observation system here is limited.

[0110] 203. The electronic device performs Gaussian beam Born forward modeling on the perturbation parameter model containing point scatterers and the perturbation parameter model without point scatterers, respectively, to obtain the first simulated seismic data and the second simulated seismic data. Based on the migration operator, the difference between the first simulated seismic data and the second simulated seismic data is processed to obtain the point spread function, which is used to represent the imaging response of the point scatterer.

[0111] In this embodiment, the electronic device performs Gaussian beam Born forward modeling on a perturbation parameter model containing point scatterers to obtain first simulated seismic data. The first simulated seismic data represents the propagation of sound waves (seismic waves) in a medium containing point scatterers. The electronic device then performs Gaussian beam Born forward modeling on a perturbation parameter model without point scatterers to obtain second simulated seismic data. The second simulated seismic data represents the propagation of sound waves in a medium without point scatterers. In the perturbation parameter model containing point scatterers, the point scatterers are uniformly distributed.

[0112] Then, the electronic device subtracts the second simulated seismic data from the first simulated seismic data to obtain the seismic data difference. This seismic data difference is used to represent the point scattering volume. The electronic device then multiplies the migration operator with the seismic data difference to obtain the point spread function, i.e., the multi-parameter PSFs. See Formula 7 below for details:

[0113]

[0114] in, Used to represent the reference disturbance parameter model, it is a representation of uniformly distributed point scatterers underground, equivalent to the disturbance parameter model containing point scatterers minus the disturbance parameter model not containing point scatterers; Used to represent the difference in seismic data, it depends primarily on the point scatterer; MT Used to represent the offset operator; f psf Used to represent the point spread function, it can be regarded as the offset result of the reference perturbation parameter model, providing the PSFs of the imaging system, which contains important information on how the system blurs the imaging results of point scatterers.

[0115] In some embodiments, the electronic device can introduce a new operator to calculate the point spread function based on Equation 7 above. This new operator is the Hessian operator. Accordingly, the electronic device multiplies the offset operator with the forward modeling operator to obtain the Hessian operator. Then, the electronic device multiplies the Hessian operator with the reference perturbation parameter model to obtain the point spread function.

[0116] Wherein, the Hessian operator H = M T M. Considering Then we have:

[0117]

[0118] The electronic device can then decompose the Hessian operator to obtain a first submatrix and a second submatrix. The first submatrix is ​​a Hessian submatrix with parameters of the same type, while the second submatrix is ​​a Hessian submatrix formed by multiplying and coupling forward operators with different types of parameters. If the normal time difference correction velocity parameter, the non-elliptic parameter, and the Thomsen parameter have the same spatial distribution, the electronic device uses the product of the first submatrix and the reference perturbation parameter model as the point spread function.

[0119] Accordingly, the Hessian operator H can be rewritten as the sum of the following two submatrices:

[0120]

[0121] Here, the first term is the first submatrix, which is a diagonal matrix; the second term is the second submatrix. Since the elements in the second submatrix depend on both parameters, when imaging one parameter, the structure of the other parameter will also be revealed, easily causing crosstalk. Accordingly, Formula 7 can be further expanded as follows:

[0122]

[0123] To simplify the problem, we assume that the normal time difference correction velocity parameter, the non-elliptic parameter, and the Thomsen parameter have the same spatial distribution, and correspondingly, we can ignore the crosstalk term, thus we have:

[0124]

[0125] For example, Figure 7 This is a schematic diagram of the point diffusion function (PSF) of an anisotropic VTI medium in the Gullfaks model in the Beihai region, provided according to an embodiment of this application. Figure 7 (a) exemplarily illustrates the velocity perturbation f of the NMO in the Gullfaks model for anisotropic VTI media in the North Sea region. vn The corresponding point spread function (PSF) diagram. Figure 7 (b) exemplarily illustrates the non-elliptic parameter perturbation f in the Gullfaks model for anisotropic VTI media in the North Sea region. η The corresponding point spread function (PSF) diagram. Figure 7 (c) exemplarily illustrates the Thomsen parameter perturbation f in the Gullfaks model for anisotropic VTI media in the North Sea region. δ A schematic diagram of the corresponding point spread function (PSF). (From...) Figure 7 It can be seen that the NMO velocity v n The point spread function (PSF) related to the anisotropy parameter δ is well focused and exhibits a normal shape. However, the PSF corresponding to the non-elliptic parameter η is distorted, with a diffuse tail.

[0126] 204. The electronic device performs spatial convolution of the point spread function and the initial perturbation parameter model to obtain simulated migration imaging results.

[0127] In this embodiment of the application, the electronic device can use the following formula eight to apply the point spread function f psf With the initial disturbance parameter model Perform spatial convolution to determine the simulated migration imaging results.

[0128] Formula 8:

[0129]

[0130] Here, x′ represents the spatial coordinates of a point scatterer with unit intensity; since PSFs are calculated at specific predetermined model locations, they need to be interpolated during the convolution process. A linear interpolation method in the spatial domain is used here.

[0131] 205. Based on the actual migration imaging results and the simulated migration imaging results, the electronic equipment determines the first model loss. With the goal of minimizing the first model loss, it iterates the initial perturbation parameter model to obtain the target perturbation parameter model. The first model loss is used to represent the difference between the actual migration imaging results and the simulated migration imaging results. The target perturbation parameter model is used to generate the acoustic VTI medium multi-parameter Gaussian beam inversion imaging results.

[0132] In this embodiment, the electronic device can define an objective function in the imaging domain to help identify the perturbation parameter model. The optimal estimate is obtained. This objective function is used to calculate the difference between the actual and simulated migration imaging results, yielding the first model loss. The electronic device fine-tunes the initial perturbation parameter model with the objective of minimizing this first model loss. This continues until the first model loss meets a certain condition. For example, if the first model loss is less than a preset value, the adjustment of the perturbation parameter model is stopped. This yields the target perturbation parameter model.

[0133] In the process of updating the perturbation parameter model, the electronic equipment can be updated according to Equation 8. The advantage of this method is that it does not require forward simulation and migration in each iteration, which greatly reduces the computational burden of obtaining the inverted perturbation parameter model. As the perturbation parameter model is continuously updated, the simulated seismic data inverted from the perturbation parameter model matches the observed actual seismic data as closely as possible, that is, the simulation results are more accurate.

[0134] However, due to the various noises contained in the offset images and the potential limitations of the observation system, directly solving the inversion problem to obtain accurate results can be very difficult. To enhance robustness, incorporating prior knowledge into the inverse problem is helpful. Here, a natural image prior is used. To achieve this, the following objective function is given, incorporating a regularization term reflecting the natural image prior. Accordingly, the electronic device adds a regularization term to the first model loss to obtain a second model loss. The regularization term represents the prior knowledge of the initial perturbation parameter model with respect to the natural image. Then, the electronic device iterates over the initial perturbation parameter model with the objective of minimizing the second model loss to obtain the target perturbation parameter model. See Equation Nine below for details:

[0135]

[0136] in, Used to represent simulated migration imaging results; Used to represent the actual offset imaging results; Used to represent the loss of the first model; `w` represents the regularization term; `w` represents the regularization parameter, used to control the balance between model complexity and data fit; `g` represents the regularization term. i,k The prior filter used to represent the vectorization applied to the image; ρ is used to represent the sparsity of the derivative used to quantize the image. Used to represent the initial perturbation parameter model; * is used to represent the convolution operation.

[0137] In some embodiments, this application selects a characteristic function with a heavy-tailed distribution, such as ρ(z) = |z|0.8 This function is advantageous because it concentrates its derivative on a small number of pixels of the image, thereby achieving clearer delineation at edges and reducing noise levels.

[0138] The reason for employing a sparse prior in the above method is that natural images often exhibit sparse characteristics under the action of derivative filters. This differs from a Gaussian prior, which tends to assume a more uniform distribution of image derivatives, potentially leading to over-smoothing in the reconstructed image. Due to the introduction of a sparse prior, the optimization problem becomes non-convex, potentially containing multiple local minima. To address this challenge, this invention employs an Iteratively Reweighted Least Squares (IRLS) algorithm. This algorithm effectively handles non-convex optimization problems by adjusting the weights (i.e., w) of each term in the objective function (Equation 9) based on the current solution in each iteration. In each iteration, the algorithm updates the weights and solves a weighted least squares problem until convergence is achieved, ultimately yielding a solution that satisfies both data fidelity and regularization requirements.

[0139] For example, Figure 8 This is a schematic diagram of the least squares migration result in the imaging domain of a multi-parameter Gaussian beam inversion imaging method for acoustic VTI media based on point spread function, according to an embodiment of this application. Figure 8 (a) exemplarily illustrates a multi-parameter Gaussian beam inversion imaging method for acoustic VTI media based on point spread function, using NMO velocity perturbations. The corresponding least squares offset result. Figure 8 (b) exemplarily demonstrates a non-elliptic parameter perturbation f in a multi-parameter Gaussian beam inversion imaging method for acoustic VTI media based on point spread function. η The corresponding least squares offset result. Figure 8 (c) exemplarily illustrates a multi-parameter Gaussian beam inversion imaging method for acoustic VTI media based on point spread function, with Thomsen parameter perturbation f. δ The corresponding least-squares offset result. (From...) Figure 7 and Figure 8 As can be seen from the comparison, after least-squares migration, the phase axes are more continuous, there are fewer artifacts, the amplitude balance is better, and the resolution is higher, especially for non-elliptic parameter perturbations f. η Its imaging quality has been significantly improved.

[0140] To more clearly illustrate the advantages of this scheme, single-channel inversion results are extracted and compared along a specific spatial location. For example, Figure 9 This application provides an anisotropic VTI medium Gullfaks model for the North Sea region. Figure 5 and Figure 8A schematic diagram of the single-channel results extracted from the results. Figure 9 (a) exemplarily illustrates the velocity perturbation f of the NMO in the Gullfaks model for anisotropic VTI media in the North Sea region. vn The corresponding single-channel amplitude. Figure 9 (b) exemplarily illustrates the non-elliptic parameter perturbation f in the Gullfaks model for anisotropic VTI media in the North Sea region. η The corresponding single-channel amplitude. Figure 9 (c) exemplarily illustrates the Thomsen parameter perturbation f in the Gullfaks model for anisotropic VTI media in the North Sea region. δ The corresponding single-channel amplitude. Comparison shows that the inverted values ​​and the true values ​​exhibit good consistency in amplitude and phase, thus demonstrating the effectiveness of the method provided by this invention.

[0141] This application provides a method for multi-parameter Gaussian beam inversion imaging of acoustic VTI media, which simultaneously processes multiple subsurface parameters such as normal time difference correction velocity parameters, non-elliptic parameters, and Thomsen parameters. This provides a more efficient and comprehensive solution for geophysical exploration. Specifically, by determining the migration operator through the aforementioned multiple subsurface parameters, the accuracy of the migration operator is ensured, so as to more accurately reflect the migration of seismic waves in the geology. Since the first simulated seismic data is obtained by forward modeling of a perturbation parameter model containing point scatterers, and the second simulated seismic data is obtained by forward modeling of a perturbation parameter model without point scatterers, the difference between the first and second simulated seismic data can accurately reflect the point scatterers in the geology. By migrating this difference through the migration operator, the imaging response of the point scatterers, i.e., the point spread function, can be accurately obtained. Then, by aiming to minimize the difference between the actual migration imaging results and the simulated migration imaging results, the parameters in the initial perturbation parameter model are adjusted so that the simulated seismic data inverted from the adjusted perturbation parameter model matches the observed actual seismic data as closely as possible, i.e., the simulation results are more accurate.

[0142] Compared to related techniques (inverse scattering migration inversion based on asymptotic analysis), this scheme improves the stability of imaging and parameter estimation in weakly illuminated regions. Furthermore, it demonstrates a significant advantage in improving the results for perturbations in medium parameters corresponding to non-elliptic parameters. Given that accurate recovery of non-elliptic parameters largely depends on large-angle (large offset) information, and the limitations of existing observation systems often lead to deep imaging being associated with small angles, this scheme overcomes this limitation, effectively recovering non-elliptic parameters and solving the imaging challenges of deep and boundary regions that are difficult to handle with traditional methods.

[0143] Figure 10This is a schematic diagram of a multi-parameter Gaussian beam inversion imaging device for acoustic VTI media according to an embodiment of this application. This device is used to perform the steps of the aforementioned multi-parameter Gaussian beam inversion imaging method for acoustic VTI media. See [link to relevant documentation]. Figure 10 The acoustic VTI medium multi-parameter Gaussian beam inversion imaging device includes:

[0144] The first processing module 1001 is used to parameterize the control equation of the pressure wave field in the acoustic VTI medium based on the normal time difference correction velocity parameter, non-elliptic parameter and Thomsen parameter to obtain the offset operator.

[0145] The migration module 1002 is used to perform migration processing on the acquired actual seismic data based on the migration operator to obtain the actual migration imaging result;

[0146] The second processing module 1003 is used to perform Gaussian beam Born forward modeling on the perturbation parameter model containing point scatterers and the perturbation parameter model without point scatterers, respectively, to obtain first simulated seismic data and second simulated seismic data. Based on the migration operator, the difference between the first simulated seismic data and the second simulated seismic data is processed to obtain the point spread function. The point spread function is used to represent the imaging response of the point scatterer.

[0147] The third processing module 1004 is used to perform spatial convolution on the point spread function and the initial disturbance parameter model to obtain simulated migration imaging results.

[0148] The iteration module 1005 is used to determine the first model loss based on the actual migration imaging results and the simulated migration imaging results. With the goal of minimizing the first model loss, the initial perturbation parameter model is iterated to obtain the target perturbation parameter model. The first model loss is used to represent the difference between the actual migration imaging results and the simulated migration imaging results. The target perturbation parameter model is used to generate the acoustic VTI medium multi-parameter Gaussian beam inversion imaging results.

[0149] In some embodiments, the first processing module 1001 is used to determine a perturbation parameter matrix based on the perturbation parameters of each parameter among the normal time difference correction velocity parameter, non-elliptic parameter, and Thomsen parameter; within the framework of Gaussian beam theory, based on the perturbation parameter matrix, the scattered pressure wave field in the acoustic VTI medium based on the first-order Born forward approximation is processed to obtain a forward modeling operator; and the forward modeling operator is transposed to obtain a shift operator.

[0150] In some embodiments, the first processing module 1001 is used to obtain the scattered pressure wave field of the first-order Gaussian beam Born forward approximation in the acoustic VTI medium; and divide the scattered pressure wave field by the perturbation parameter matrix to obtain the forward operator.

[0151] In some embodiments, the second processing module 1003 is used to perform Gaussian beam Born forward modeling on a perturbation parameter model containing point scatterers to obtain first simulated seismic data, which represents the propagation of sound waves in a medium containing point scatterers; and to perform Gaussian beam Born forward modeling on a perturbation parameter model not containing point scatterers to obtain second simulated seismic data, which represents the propagation of sound waves in a medium without point scatterers; wherein the point scatterers are uniformly distributed in the perturbation parameter model containing point scatterers.

[0152] In some embodiments, the second processing module 1003 is used to subtract the second simulated seismic data from the first simulated seismic data to obtain a seismic data difference, which is used to represent a point scattering body; and to multiply the offset operator with the seismic data difference to obtain a point spread function.

[0153] In some embodiments, the seismic data difference is equal to the product between the forward modeling operator and the reference perturbation parameter model, where the migration operator is the transpose of the forward modeling operator, and the reference perturbation parameter model is used to represent a uniformly distributed point scattering body in the subsurface.

[0154] The second processing module 1003 is used to multiply the offset operator and the forward modeling operator to obtain the Hessian operator; and to multiply the Hessian operator and the reference perturbation parameter model to obtain the point spread function.

[0155] In some embodiments, the second processing module 1003 is used to decompose the Hessian operator to obtain a first submatrix and a second submatrix. The first submatrix is ​​a Hessian submatrix with parameters of the same type, and the second submatrix is ​​a Hessian submatrix with forward operators of different types of parameters multiplied and coupled together. If the normal time difference correction velocity parameter, the non-elliptic parameter, and the Thomsen parameter have the same spatial distribution, the product between the first submatrix and the reference perturbation parameter model is used as the point spread function.

[0156] In some embodiments, the apparatus further includes:

[0157] The fourth processing module is used to add a regularization term to the first model loss to obtain the second model loss. The regularization term is used to represent the prior knowledge of the initial perturbation parameter model for natural images.

[0158] The iteration module 1005 is used to iterate the initial perturbation parameter model with the goal of minimizing the second model loss, so as to obtain the target perturbation parameter model.

[0159] This application provides a multi-parameter Gaussian beam inversion imaging device for acoustic VTI media, which simultaneously processes multiple subsurface parameters such as normal time difference correction velocity parameters, non-elliptic parameters, and Thomsen parameters. This provides a more efficient and comprehensive solution for geophysical exploration. Specifically, by determining the migration operator through the aforementioned multiple subsurface parameters, the accuracy of the migration operator is ensured, so as to more accurately reflect the migration of seismic waves in the geology. Since the first simulated seismic data is obtained by forward modeling of a perturbation parameter model containing point scatterers, and the second simulated seismic data is obtained by forward modeling of a perturbation parameter model without point scatterers, the difference between the first and second simulated seismic data can accurately reflect the point scatterers in the geology. By migrating this difference through the migration operator, the imaging response of the point scatterers, i.e., the point spread function, can be accurately obtained. Then, by aiming to minimize the difference between the actual migration imaging results and the simulated migration imaging results, the parameters in the initial perturbation parameter model are adjusted so that the simulated seismic data inverted by the adjusted perturbation parameter model matches the observed actual seismic data as closely as possible, i.e., the simulation results are more accurate.

[0160] Compared to related techniques (inverse scattering migration inversion based on asymptotic analysis), this scheme improves the stability of imaging and parameter estimation in weakly illuminated regions. Furthermore, it demonstrates a significant advantage in improving the results for perturbations in medium parameters corresponding to non-elliptic parameters. Given that accurate recovery of non-elliptic parameters largely depends on large-angle (large offset) information, and the limitations of existing observation systems often lead to deep imaging being associated with small angles, this scheme overcomes this limitation, effectively recovering non-elliptic parameters and solving the imaging challenges of deep and boundary regions that are difficult to handle with traditional methods.

[0161] It should be noted that the logging data generation device provided in the above embodiments is only illustrated by the division of the above functional modules when running the application. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the logging data generation device and the logging data generation method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0162] In the embodiments of this application, the electronic device can be configured as a terminal or a server. When the electronic device is configured as a terminal, the terminal can act as the execution subject to implement the technical solution provided in the embodiments of this application. When the electronic device is configured as a server, the server can act as the execution subject to implement the technical solution provided in the embodiments of this application. Alternatively, the technical solution provided in this application can be implemented through the interaction between the terminal and the server. The embodiments of this application do not limit this.

[0163] Figure 11 A structural block diagram of a terminal 1100 provided according to an embodiment of this application. The terminal 1100 can be a portable mobile terminal, such as a smartphone, tablet computer, MP3 player (Moving Picture Experts Group Audio Layer III), MP4 player (Moving Picture Experts Group Audio Layer IV), laptop computer, or desktop computer. The terminal 1100 may also be referred to as a user device, portable terminal, laptop terminal, desktop terminal, or other names.

[0164] Typically, terminal 1100 includes a processor 1101 and a memory 1102.

[0165] Processor 1101 may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. Processor 1101 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). Processor 1101 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 1101 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content required to be displayed on the screen. In some embodiments, processor 1101 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0166] The memory 1102 may include one or more computer-readable storage media, which may be non-transitory. The memory 1102 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory 1102 are used to store at least one computer program, which is executed by the processor 1101 to implement the acoustic VTI medium multi-parameter Gaussian beam inversion imaging method provided in the method embodiments of this application.

[0167] In some embodiments, the terminal 1100 may also optionally include a peripheral device interface 1103 and at least one peripheral device. The processor 1101, memory 1102, and peripheral device interface 1103 can be connected via a bus or signal line. Each peripheral device can be connected to the peripheral device interface 1103 via a bus, signal line, or circuit board. Specifically, the peripheral device includes at least one of the following: a radio frequency circuit 1104, a display screen 1105, a camera assembly 1106, an audio circuit 1107, and a power supply 1108.

[0168] Peripheral device interface 1103 can be used to connect at least one I / O (Input / Output) related peripheral device to processor 1101 and memory 1102. In some embodiments, processor 1101, memory 1102 and peripheral device interface 1103 are integrated on the same chip or circuit board; in some other embodiments, any one or two of processor 1101, memory 1102 and peripheral device interface 1103 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0169] The radio frequency (RF) circuit 1104 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The RF circuit 1104 communicates with communication networks and other communication devices via electromagnetic signals. The RF circuit 1104 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals back into electrical signals. In some embodiments, the RF circuit 1104 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, etc. The RF circuit 1104 can communicate with other terminals via at least one wireless communication protocol. This wireless communication protocol includes, but is not limited to: the World Wide Web, metropolitan area networks, intranets, various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and / or WiFi (Wireless Fidelity) networks. In some embodiments, the RF circuit 1104 may also include circuitry related to NFC (Near Field Communication), which is not limited in this application.

[0170] Display screen 1105 is used to display a UI (User Interface). This UI may include graphics, text, icons, videos, and any combination thereof. When display screen 1105 is a touch display screen, it also has the ability to collect touch signals on or above its surface. These touch signals can be input as control signals to processor 1101 for processing. In this case, display screen 1105 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, there may be one display screen 1105, disposed on the front panel of terminal 1100; in other embodiments, there may be at least two display screens, disposed on different surfaces of terminal 1100 or in a folded design; in still other embodiments, display screen 1105 may be a flexible display screen, disposed on a curved or folded surface of terminal 1100. Furthermore, display screen 1105 may be configured as a non-rectangular, irregular shape, i.e., a non-rectangular screen. The display screen 1105 can be made of materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

[0171] The camera assembly 1106 is used to acquire images or videos. In some embodiments, the camera assembly 1106 includes a front-facing camera and a rear-facing camera. Typically, the front-facing camera is located on the front panel of the terminal, and the rear-facing camera is located on the back of the terminal. In some embodiments, there are at least two rear-facing cameras, which are any one of a main camera, a depth-sensing camera, a wide-angle camera, and a telephoto camera, to achieve background blurring by fusion of the main camera and the depth-sensing camera, panoramic shooting by fusion of the main camera and the wide-angle camera, VR (Virtual Reality) shooting, or other fusion shooting functions. In some embodiments, the camera assembly 1106 may also include a flash. The flash can be a single-color temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm light flash and a cool light flash, which can be used for light compensation at different color temperatures.

[0172] The audio circuit 1107 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, converting the sound waves into electrical signals that are input to the processor 1101 for processing, or input to the radio frequency circuit 1104 for voice communication. For stereo sound acquisition or noise reduction purposes, multiple microphones may be used, each positioned at a different location on the terminal 1100. The microphone may also be an array microphone or an omnidirectional microphone. The speaker is used to convert electrical signals from the processor 1101 or the radio frequency circuit 1104 into sound waves. The speaker may be a conventional diaphragm speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can convert electrical signals not only into audible sound waves but also into inaudible sound waves for purposes such as distance measurement. In some embodiments, the audio circuit 1107 may also include a headphone jack.

[0173] Power supply 1108 is used to power the various components in terminal 1100. Power supply 1108 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When power supply 1108 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery that is charged via a wired line, and a wireless rechargeable battery is a battery that is charged via a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0174] In some embodiments, the terminal 1100 further includes one or more sensors 1109. The one or more sensors 1109 include, but are not limited to: an acceleration sensor 1110, a gyroscope sensor 1111, a pressure sensor 1112, an optical sensor 1113, and a proximity sensor 1114.

[0175] Accelerometer 1110 can detect the magnitude of acceleration along the three coordinate axes of a coordinate system established with terminal 1100. For example, accelerometer 1110 can be used to detect the components of gravitational acceleration along the three coordinate axes. Processor 1101 can control display screen 1105 to display the user interface in either a landscape or portrait view based on the gravitational acceleration signal acquired by accelerometer 1110. Accelerometer 1110 can also be used for games or for acquiring user motion data.

[0176] The gyroscope sensor 1111 can detect the orientation and rotation angle of the terminal 1100. The gyroscope sensor 1111 can work in conjunction with the accelerometer sensor 1110 to collect the user's 3D movements on the terminal 1100. Based on the data collected by the gyroscope sensor 1111, the processor 1101 can perform the following functions: motion sensing (e.g., changing the UI based on the user's tilt), image stabilization during shooting, game control, and inertial navigation.

[0177] The pressure sensor 1112 can be disposed on the side bezel of the terminal 1100 and / or on the lower layer of the display screen 1105. When the pressure sensor 1112 is disposed on the side bezel of the terminal 1100, it can detect the user's grip signal on the terminal 1100, and the processor 1101 can perform left / right hand recognition or quick operation based on the grip signal collected by the pressure sensor 1112. When the pressure sensor 1112 is disposed on the lower layer of the display screen 1105, the processor 1101 can control the operable controls on the UI interface based on the user's pressure operation on the display screen 1105. The operable controls include at least one of button controls, scroll bar controls, icon controls, and menu controls.

[0178] An optical sensor 1113 is used to collect ambient light intensity. In one embodiment, the processor 1101 can control the display brightness of the display screen 1105 based on the ambient light intensity collected by the optical sensor 1113. Specifically, when the ambient light intensity is high, the display brightness of the display screen 1105 is increased; when the ambient light intensity is low, the display brightness of the display screen 1105 is decreased. In another embodiment, the processor 1101 can also dynamically adjust the shooting parameters of the camera assembly 1106 based on the ambient light intensity collected by the optical sensor 1113.

[0179] The proximity sensor 1114, also known as a distance sensor, is typically located on the front panel of the terminal 1100. The proximity sensor 1114 is used to detect the distance between the user and the front of the terminal 1100. In one embodiment, when the proximity sensor 1114 detects that the distance between the user and the front of the terminal 1100 is gradually decreasing, the processor 1101 controls the display screen 1105 to switch from a screen-on state to a screen-off state; when the proximity sensor 1114 detects that the distance between the user and the front of the terminal 1100 is gradually increasing, the processor 1101 controls the display screen 1105 to switch from a screen-off state to a screen-on state.

[0180] Those skilled in the art will understand that Figure 11 The structure shown does not constitute a limitation on terminal 1100 and may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0181] Figure 12 This is a structural block diagram of a server according to an embodiment of this application. The server 1200 can vary considerably due to different configurations or performance. It may include one or more Central Processing Units (CPUs) 1201 and one or more memories 1202. The memory 1202 stores at least one computer program, which is loaded and executed by the processor 1201 to implement the acoustic VTI medium multi-parameter Gaussian beam inversion imaging method provided in the above-described method embodiments. Of course, the server 1200 may also have wired or wireless network interfaces, a keyboard, and input / output interfaces for input and output. The server 1200 may also include other components for implementing device functions, which will not be elaborated here.

[0182] This application also provides a computer-readable storage medium storing at least one computer program. This computer program is loaded and executed by a processor of an electronic device to implement the operations performed by the electronic device in the acoustic VTI medium multi-parameter Gaussian beam inversion imaging method of the above embodiments. For example, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device, etc.

[0183] This application also provides a computer program product, including a computer program stored in a computer-readable storage medium. An electronic device's processor reads the computer program from the computer-readable storage medium and executes the computer program, causing the electronic device to perform the acoustic VTI medium multi-parameter Gaussian beam inversion imaging method provided in the various optional implementations described above.

[0184] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware, or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0185] The above are merely optional embodiments of this application and are not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for multi-parameter Gaussian beam inversion imaging of acoustic VTI media, characterized in that, The method includes: Based on the normal time difference correction velocity parameter, non-elliptic parameter and Thomsen parameter, the pressure wave field control equation in acoustic VTI medium is parameterized to obtain the migration operator; Based on the aforementioned migration operator, the acquired actual seismic data is processed by migration to obtain the actual migration imaging results; Gaussian beam Born forward modeling is performed on the perturbation parameter model containing point scatterers and the perturbation parameter model without point scatterers, respectively, to obtain first simulated seismic data and second simulated seismic data. Based on the migration operator, the difference between the first simulated seismic data and the second simulated seismic data is processed to obtain a point spread function, which is used to represent the imaging response of the point scatterer. Spatial convolution is performed between the point spread function and the initial perturbation parameter model to obtain simulated migration imaging results; Based on the actual migration imaging results and the simulated migration imaging results, a first model loss is determined. With the goal of minimizing the first model loss, the initial perturbation parameter model is iterated to obtain a target perturbation parameter model. The first model loss is used to represent the difference between the actual migration imaging results and the simulated migration imaging results. The target perturbation parameter model is used to generate multi-parameter Gaussian beam inversion imaging results for acoustic VTI media.

2. The acoustic VTI medium multi-parameter Gaussian beam inversion imaging method according to claim 1, characterized in that, The pressure wave field control equation in the acoustic VTI medium is parameterized based on the normal time difference correction velocity parameter, non-elliptic parameter, and Thomsen parameter to obtain the migration operator, including: Based on the perturbation parameters of each parameter in the normal time difference correction velocity parameter, the non-elliptic parameter, and the Thomsen parameter, the perturbation parameter matrix is ​​determined; Within the framework of Gaussian beam theory, based on the perturbation parameter matrix, the scattered pressure wave field in the acoustic VTI medium based on the first-order Born forward approximation is processed to obtain the forward operator; The forward operator is transposed to obtain the offset operator.

3. The acoustic VTI medium multi-parameter Gaussian beam inversion imaging method according to claim 2, characterized in that, Within the framework of Gaussian beam theory, based on the perturbation parameter matrix, the scattered pressure wave field in the acoustic VTI medium based on the first-order Born forward approximation is processed to obtain a forward modeling operator, including: Obtain the scattered pressure wave field in the acoustic VTI medium using the first-order Gaussian beam Born forward modeling approximation; The forward operator is obtained by dividing the scattered pressure wave field by the perturbation parameter matrix.

4. The acoustic VTI medium multi-parameter Gaussian beam inversion imaging method according to claim 1, characterized in that, The perturbation parameter models containing point scatterers and those without point scatterers are respectively subjected to Gaussian beam Born forward modeling to obtain first simulated seismic data and second simulated seismic data, including: Gaussian beam Born forward modeling is performed on the perturbation parameter model containing point scatterers to obtain the first simulated seismic data, which is used to represent the propagation of sound waves in a medium containing point scatterers. Gaussian beam Born forward modeling is performed on the perturbation parameter model that does not contain point scatterers to obtain the second simulated seismic data, which is used to represent the propagation of sound waves in a medium in the absence of point scatterers. In the perturbation parameter model containing point scatterers, the point scatterers are uniformly distributed.

5. The acoustic VTI medium multi-parameter Gaussian beam inversion imaging method according to claim 1, characterized in that, The step of performing migration processing on the difference between the first simulated seismic data and the second simulated seismic data based on the migration operator to obtain the point spread function includes: The second simulated seismic data is subtracted from the first simulated seismic data to obtain the seismic data difference, which is used to represent the point scatterer; The point spread function is obtained by multiplying the migration operator with the difference in the seismic data.

6. The acoustic VTI medium multi-parameter Gaussian beam inversion imaging method according to claim 5, characterized in that, The seismic data difference is equal to the product between the forward modeling operator and the reference perturbation parameter model. The migration operator is the transpose of the forward modeling operator. The reference perturbation parameter model is used to represent a uniformly distributed point scattering body in the subsurface. The step of multiplying the migration operator with the difference in seismic data to obtain the point spread function includes: The offset operator is multiplied by the forward operator to obtain the Hessian operator; The point spread function is obtained by multiplying the Hessian operator with the reference perturbation parameter model.

7. The acoustic VTI medium multi-parameter Gaussian beam inversion imaging method according to claim 6, characterized in that, The step of multiplying the Hessian operator with the reference perturbation parameter model to obtain the point spread function includes: The Hessian operator is split to obtain a first submatrix and a second submatrix. The first submatrix is ​​a Hessian submatrix with parameters of the same type, and the second submatrix is ​​a Hessian submatrix with forward operators of different types of parameters coupled together through multiplication. If the normal time difference correction velocity parameter, the non-elliptic parameter, and the Thomsen parameter have the same spatial distribution, then the product between the first submatrix and the reference perturbation parameter model is used as the point spread function.

8. The acoustic VTI medium multi-parameter Gaussian beam inversion imaging method according to claim 1, characterized in that, The method further includes: A regularization term is added to the first model loss to obtain the second model loss, wherein the regularization term is used to represent the prior knowledge of the initial perturbation parameter model for natural images; The step of iterating the initial perturbation parameter model to obtain the target perturbation parameter model with the objective of minimizing the loss of the first model includes: With the goal of minimizing the loss of the second model, the initial perturbation parameter model is iterated to obtain the target perturbation parameter model.

9. A multi-parameter Gaussian beam inversion imaging device for acoustic VTI media, characterized in that, The device includes: The first processing module is used to parameterize the pressure wave field control equation in the acoustic VTI medium based on the normal time difference correction velocity parameter, non-elliptic parameter and Thomsen parameter to obtain the offset operator. The offset module is used to perform offset processing on the acquired actual seismic data based on the offset operator to obtain the actual offset imaging result; The second processing module is used to perform Gaussian beam Born forward modeling on the perturbation parameter model containing point scatterers and the perturbation parameter model without point scatterers, respectively, to obtain first simulated seismic data and second simulated seismic data. Based on the migration operator, the difference between the first simulated seismic data and the second simulated seismic data is processed to obtain a point spread function, which is used to represent the imaging response of the point scatterer. The third processing module is used to perform spatial convolution on the point spread function and the initial perturbation parameter model to obtain simulated migration imaging results. An iterative module is used to determine a first model loss based on the actual migration imaging result and the simulated migration imaging result, and to iterate the initial perturbation parameter model with the goal of minimizing the first model loss to obtain a target perturbation parameter model. The first model loss is used to represent the difference between the actual migration imaging result and the simulated migration imaging result. The target perturbation parameter model is used to generate acoustic VTI medium multi-parameter Gaussian beam inversion imaging results.

10. An electronic device, characterized in that, The electronic device includes a processor and a memory, the memory being used to store at least one computer program, the at least one computer program being loaded by the processor and executed as described in any one of claims 1 to 8, the acoustic VTI medium multi-parameter Gaussian beam inversion imaging method.

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