A scholte wave dispersion curve adaptive inversion method and related device

Abstract

The one or more embodiments of the present disclosure provide a Scholte wave dispersion curve adaptive inversion method and a related device, the method comprising: obtaining and processing original seismic data of a target seabed to obtain a Scholte wave dispersion curve; constructing an initial shear wave velocity model based on the Scholte wave dispersion curve through linear inversion; and fitting and optimizing the Scholte wave dispersion curve based on the initial shear wave velocity model to obtain a two-dimensional shear wave velocity profile. Through the technical solution of the present disclosure, the problem that the traditional linear inversion method relies on an initial model is solved, the calculation efficiency is significantly improved, and the local extremum problem is avoided, thereby providing an automatic high-precision inversion solution suitable for batch data processing for seabed seismic exploration.

Description

Technical Field

[0001] This disclosure relates to the field of geophysical exploration technology, and in particular to an adaptive inversion method and related apparatus for Scholte wave dispersion curves. Background Technology

[0002] In fields such as marine geophysical exploration, underwater target detection, and seabed engineering geological assessment, accurately obtaining the mechanical properties and layered structure of shallow seabed sediments is crucial. Scholte waves, as surface waves propagating along the seabed interface, are extremely sensitive to parameters such as the shear wave velocity and layer thickness of the seabed medium. Therefore, inversion methods based on Scholte wave dispersion curves have become an effective means of exploring shallow seabed strata.

[0003] Traditional Scholte wave inversion methods often rely on global optimization algorithms (such as genetic algorithms and simulated annealing algorithms) to gradually approximate the observed dispersion curves by repeatedly performing forward modeling calculations and goodness-of-fit judgments on a pre-set stratigraphic model. However, such methods have significant limitations: First, the inversion process is prone to getting trapped in local optima, leading to inversion failure; second, global optimization algorithms are usually computationally expensive, require many iterations, and have slow convergence speeds, making it difficult to meet the needs of efficient processing in practical applications; furthermore, conventional methods lack the ability to adaptively adjust to changes in the dispersion curves under different geological conditions, exhibiting poor robustness.

[0004] It is worth noting that in the field of petroleum seismic exploration, the Dix formula, as a classic method, is widely used to directly estimate layer velocities from stacking velocities. Its core idea is to simplify the propagation relationship of seismic waves in layered media, transforming the complex inversion problem into an efficient direct calculation. Although the Dix formula has been maturely applied in reflection seismic studies, its theoretical framework differs significantly from the Scholte wave dispersion inversion problem in terms of mathematical and physical background. Currently, no technical solution has been found to successfully introduce Dix-type relationships into the Scholte wave dispersion curve inversion to solve the problems of initial model dependence and low computational efficiency in this inversion problem.

[0005] It should be noted that the above description of the technical background is only for the purpose of providing a clear and complete explanation of the technical solutions of the present invention and facilitating understanding by those skilled in the art. It should not be assumed that the above technical solutions are known to those skilled in the art simply because they have been described in the background section of this invention. Summary of the Invention

[0006] In view of this, the purpose of one or more embodiments of this disclosure is to propose an adaptive inversion method and related apparatus for Scholte wave dispersion curves based on Dix-type relationships, so as to solve the problems raised in the background art.

[0007] To achieve the above objectives, one or more embodiments of this disclosure provide an adaptive inversion method for Scholte wave dispersion curves, including:

[0008] The raw seismic data of the target seabed were acquired and processed to obtain the Scholte wave dispersion curve;

[0009] Based on the Scholte wave dispersion curve, an initial shear wave velocity model is constructed through linear inversion.

[0010] Based on the initial shear wave velocity model, the Scholte wave dispersion curve is fitted and optimized to obtain a two-dimensional shear wave velocity profile.

[0011] Furthermore, the automatic construction of the initial shear wave velocity model based on the Scholte wave dispersion curve through linear inversion specifically includes:

[0012] Based on the phase velocity data in the Scholte wave dispersion curve, a linear relationship between phase velocity and layer velocity is established.

[0013] Based on the wavelength data of the Scholte wave dispersion curve, a formation depth model is constructed, and the linear relationship between the phase velocity and the layer velocity is fitted by a power law to construct a priori velocity model.

[0014] An initial shear wave velocity model is constructed by regularizing the formation depth model and the prior velocity model.

[0015] Furthermore, the fitting and optimization of the Scholte wave dispersion curve based on the initial shear wave velocity model to obtain a two-dimensional shear wave velocity profile specifically includes:

[0016] Based on the initial shear wave velocity model and the Scholte wave dispersion curve, a linear inversion objective function is established and the model update direction is solved.

[0017] An adaptive damping factor is added to the model update direction to obtain the updated two-dimensional shear wave velocity profile.

[0018] Furthermore, establishing a linear relationship between phase velocity and layer velocity based on the phase velocity data in the Scholte wave dispersion curve specifically includes:

[0019] Surface wave eigenfunctions satisfy:

[0020] (1)

[0021] in, ω is the angular frequency; k is the wave number; This is the integral term for "kinetic energy" related to the medium density and vertical displacement. This is the integral term for "potential energy" related to shear modulus and vertical displacement; For coupled integral terms; This is the integral term for "potential energy" related to the compressibility modulus and horizontal displacement;

[0022] By introducing the assumption of a homogeneous medium and an approximate solution method, the above nonlinear problem is transformed into a linear problem; for any frequency m Its phase velocity squared is expressed as:

[0023] (2)

[0024] Where h is the depth of the stratum or the depth of the stratigraphic interface; For a surface wave of the m-th frequency, its sensitivity to the shear wave velocity of the medium at the bottom boundary depth of the n-th layer. For a surface wave of the m-th frequency, its sensitivity to the shear wave velocity of the medium at the top boundary depth of the n-th layer. For kernel function The difference between the top and bottom boundaries of the nth layer represents the sensitivity of the wave of the mth frequency to the velocity of the nth layer, where z represents... and ; Shear wave velocity;

[0025] Combining equations (2) for all frequencies, a linear relationship is formed between the square of the phase velocity and the square of the shear velocity of the layer:

[0026] (3)

[0027] in, Phase velocity; Shear wave velocity; For the kernel function The kernel matrix is ​​obtained by difference calculation;

[0028] kernel matrix By depth sensitivity function The construction, specifically the expression, is:

[0029] (4)

[0030] Where z is the depth value; For wave number The sensitivity of the phase velocity of a surface wave to the change in shear wave velocity of the medium at depth z.

[0031] Furthermore, the construction of a formation depth model based on the wavelength data of the Scholte wave dispersion curve, and the power-law fitting of the linear relationship between the phase velocity and the layer velocity to construct a priori velocity model, specifically includes:

[0032] Determine the depth boundary based on the wavelength of the Scholte wave dispersion curve;

[0033] Based on the aforementioned depth boundaries, construct a formation depth model;

[0034] A power-law fit is performed on the linear relationship between the phase velocity and the layer velocity to construct a priori velocity model. .

[0035] Furthermore, the step of constructing an initial shear wave velocity model by regularizing the formation depth model and the prior velocity model specifically includes:

[0036] Select the data covariance matrix and model covariance matrix To introduce constraints:

[0037] Among them, the data covariance matrix For diagonal matrices:

[0038] (5)

[0039] Where I is the identity matrix, The standard deviation of the data;

[0040] Model covariance matrix The following form is used to introduce model smoothing constraints:

[0041] (6)

[0042] in, The standard deviation of the model represents the allowable deviation of the model from the prior velocity model. The degree; and These are the depths of the top of the i-th and j-th layers, respectively; It is a smooth distance;

[0043] Relate the above constraints to the matrix-vector relationship Combined, construct the augmented matrix equation:

[0044] (7)

[0045] in, Let be the square vector of the layer shear wave velocity to be determined; G is the squared phase velocity vector extracted from measured Scholte wave dispersion data; G is the kernel matrix. The data covariance matrix; This is the model covariance matrix, used to introduce model smoothness constraints; This is the data weight matrix; The model constraint weight matrix; The prior velocity model obtained by power-law fitting;

[0046] By scanning the model's standard deviation factor and related length factors These two parameters are used to solve the augmented matrix equation and obtain candidate models; each candidate model is evaluated based on the chi-square value. The objective function is evaluated to determine whether it simultaneously meets the requirements. ∈[1,1.5] and Candidate models with a value >0 are considered qualified models, and the average velocity profile of all qualified models is taken as the initial shear wave velocity model.

[0047] Chi-square value The objective function is:

[0048] (8)

[0049] in, Chi-square value; Let be the theoretical forward modeling operator, representing the shear wave velocity model based on the current nth iteration. The theoretical phase velocity value calculated through forward modeling of physical equations; is the inverse of the data covariance matrix; F is the degrees of freedom.

[0050] Furthermore, the step of establishing a linear inversion objective function and solving for the model update direction based on the initial shear wave velocity model and the Scholte wave dispersion curve specifically includes:

[0051] There is a nonlinear forward modeling relationship between the Scholte wave dispersion curve and the formation model parameters:

[0052] (9)

[0053] in, Frequency (Hz); For frequency The corresponding phase velocity; The transverse wave velocity vector; The longitudinal wave velocity vector; It is a density vector; This is the layer thickness vector;

[0054] In the initial shear wave velocity model Linearizing equation (9) using Taylor series expansion and applying matrix theory, we can obtain:

[0055] (10)

[0056] in, , representing the difference between the initial value and the model value; Initial transverse wave velocity The model response; Let be the vector of the shear wave velocity correction to be determined; for OK The Jacobian matrix of the column;

[0057] The shear wave velocity correction vector is solved using the damped least squares method. And define the objective function for the inversion problem:

[0058] (11)

[0059] in, for Vector length; It is the damping factor; Let be the vector of the transverse wave velocity correction to be determined; For weighted matrices, , It is a diagonal matrix; where,

[0060] (12)

[0061] in, Let be the vector of the shear wave velocity correction to be determined; , is the identity matrix, b is the data residual vector; U is the left singular vector matrix; V is the right singular vector matrix; It is a diagonal matrix, and the elements on its diagonal are called singular values; is the damping factor.

[0062] Furthermore, the step of adding an adaptive damping factor to the model update direction to obtain the updated two-dimensional shear wave velocity profile specifically includes:

[0063] The adaptive damping factor and the shear wave velocity correction vector Perform iterations to determine what makes the objective function... Minimize the damping factor ;

[0064] According to symmetric matrices Determine the initial value of the adaptive damping factor. The specific method is as follows:

[0065] (13)

[0066] in, The value range is 1 to 10;

[0067] If after the [number]th In the next iteration, the new solution cannot satisfy the objective function. If convergence occurs, increase the damping factor;

[0068] If after the [number]th The next iteration makes the objective function Convergence reduces the damping factor;

[0069] Based on the adaptive damping factor and the shear wave velocity correction vector and the prior velocity model Determine what makes the objective function Minimize the optimal step size;

[0070] First calculate And determine if it is less than ;

[0071] when When the new minimum point is approximated, the solution is taken as:

[0072] (14)

[0073] when The optimal step size for finding the minimum point is then determined using the parabolic method.

[0074] (15)

[0075] when The new minimum point approximate solution is taken as:

[0076] (16)

[0077] When the objective function The step size is gradually reduced using a binary search method. The values ​​are respectively ,in, , n The maximum number of binary search operations;

[0078] When a certain step size t satisfies When this happens, the new approximate solution for the minimum point is taken as:

[0079] (17)

[0080] If the objective function is obtained by using the bisection method to reduce the step size 7 times, If it still does not decrease, then it is considered yes The smallest point;

[0081] Find the new approximate solution at the minimum point Then, repeat the above steps iteratively to obtain... This continues until an updated two-dimensional shear wave velocity profile is obtained.

[0082] Based on the same inventive concept, this disclosure also provides an adaptive inversion system for Scholte wave dispersion curves, comprising:

[0083] The acquisition unit is used to acquire and process the raw seismic data of the target seabed to obtain the Scholte wave dispersion curve;

[0084] A construction unit is used to construct an initial shear wave velocity model based on the Scholte wave dispersion curve through linear inversion.

[0085] The optimization unit is used to fit and optimize the Scholte wave dispersion curve based on the initial shear wave velocity model to obtain a two-dimensional shear wave velocity profile.

[0086] Based on the same inventive concept, this disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements an adaptive inversion method for Scholte wave dispersion curves as described in any of the preceding claims.

[0087] As can be seen from the above, the Scholte wave dispersion curve adaptive inversion method provided by one or more embodiments of this disclosure obtains the Scholte wave dispersion curve by acquiring and processing the original seismic data of the target seabed; based on the Scholte wave dispersion curve, an initial shear wave velocity model is constructed through linear inversion; based on the initial shear wave velocity model, the Scholte wave dispersion curve is fitted and optimized to obtain a two-dimensional shear wave velocity profile. The technical solution of this disclosure realizes the automatic construction of the initial velocity model of the seabed strata; by introducing an adaptive damping factor to dynamically adjust the inversion iteration step size, the computational efficiency is significantly improved while ensuring the convergence of the algorithm, and an accurate two-dimensional shear wave velocity profile is obtained, which is convenient for subsequent high-precision processing of seismic data. It effectively solves the problems of traditional inversion methods being prone to getting trapped in local extrema and having low efficiency, and provides an automated high-precision inversion solution suitable for batch data processing for seabed seismic exploration.

[0088] The Scholte wave dispersion curve adaptive inversion system, electronic device, and computer-readable storage medium disclosed herein can all implement the steps of the aforementioned Scholte wave dispersion curve adaptive inversion method, and therefore also possess the beneficial effects of the aforementioned Scholte wave dispersion curve adaptive inversion method. Attached Figure Description

[0089] To more clearly illustrate the technical solutions in one or more embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only one or more embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0090] Figure 1 This is a flowchart illustrating an adaptive inversion method for Scholte wave dispersion curves according to one or more embodiments of this disclosure.

[0091] Figure 2 This is a schematic diagram of the structure of an adaptive inversion system for Scholte wave dispersion curves according to one or more embodiments of this disclosure;

[0092] Figure 3 This is a schematic diagram of the hardware structure of an electronic device according to one or more embodiments of this disclosure. Detailed Implementation

[0093] To make the objectives, technical solutions, and advantages of this disclosure clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0094] It should be noted that, unless otherwise defined, the technical or scientific terms used in one or more embodiments of this disclosure should have the ordinary meaning understood by one of ordinary skill in the art to which this disclosure pertains. The terms "first," "second," and similar words used in one or more embodiments of this disclosure do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0095] As described in the background section, the relevant technologies have the following problems: First, the inversion process heavily depends on the selection of the initial model. If the initial model deviates significantly from the actual situation, it is prone to getting trapped in local optima, leading to inversion failure. Second, global optimization algorithms are usually computationally expensive, have many iterations, and slow convergence speed, making it difficult to meet the needs of efficient processing in practical applications. Third, conventional methods lack the ability to adaptively adjust to the inversion process and have poor robustness to changes in dispersion curves under different geological conditions.

[0096] Scholte wave dispersion curve inversion is a highly nonlinear iterative optimization process involving multiple parameters, extrema, and modes, resulting in non-unique inversion results. Linear inversion methods heavily rely on the initial model, while nonlinear inversion methods suffer from low inversion efficiency and premature convergence, thus a better inversion method is still needed.

[0097] Therefore, there is an urgent need to construct a technical solution for adaptive inversion of Scholte wave dispersion curves, which can directly output a physically reliable initial model from the fundamental dispersion curve, and then adaptively invert and optimize it, dynamically adjusting the damping factor to accelerate convergence and avoid local extrema. From dispersion data to the final velocity profile, no manual intervention is required for the initial model or stratigraphic division, thereby significantly improving inversion efficiency, stability and accuracy to meet the urgent needs of modern marine exploration for real-time performance, high precision and strong robustness.

[0098] Based on some implementations of this disclosure, an adaptive inversion scheme for Scholte wave dispersion curves based on Dix-type relationships is provided. In this scheme, by extending the Dix-type relationship to surface wave dispersion inversion, power-law fitting and regularized scanning are used to automatically determine the number and thickness of stratigraphic layers, thus achieving automatic construction of the initial velocity model of the seafloor strata. By introducing an adaptive damping factor to dynamically adjust the inversion iteration step size, computational efficiency is significantly improved while ensuring algorithm convergence, resulting in an accurate two-dimensional shear wave velocity profile, facilitating subsequent high-precision processing of seismic data. This scheme eliminates the reliance on manually provided initial models, effectively overcoming the problems of traditional inversion methods being prone to local extrema and inefficiency. It significantly improves the accuracy and reliability of seafloor strata shear wave velocity profile inversion while increasing iteration efficiency by approximately 50%, and possesses the ability to automatically identify velocity inversion layers. This provides an automated, high-precision inversion solution suitable for batch data processing in seafloor seismic exploration.

[0099] refer to Figure 1 This disclosure discloses an adaptive inversion method for Scholte wave dispersion curves according to one or more embodiments, comprising the following steps:

[0100] Step S101: Acquire and process the raw seismic data of the target seabed to obtain the Scholte wave dispersion curve.

[0101] In this embodiment, raw seismic data of the target seabed is acquired using a marine seismic exploration system. The acquired raw seismic data is preprocessed to enhance the effective signal and suppress noise, providing high-quality data for subsequent dispersion analysis. Scholte wave dispersion curves are extracted from the preprocessed seismic records.

[0102] Step S102: Based on the Scholte wave dispersion curve, construct an initial shear wave velocity model through linear inversion.

[0103] In this embodiment, based on the Scholte wave dispersion curve, an initial shear wave velocity model is constructed through linear inversion, specifically including:

[0104] Step S1021: Based on the phase velocity data in the Scholte wave dispersion curve, establish a linear relationship between phase velocity and layer velocity, specifically as follows:

[0105] Scholte wave eigenfunctions satisfy:

[0106] (1)

[0107] in, ω is the angular frequency; k is the wave number; This is the integral term for "kinetic energy" related to the medium density and vertical displacement. This is the integral term for "potential energy" related to shear modulus and vertical displacement; For coupled integral terms; This is the integral term for "potential energy" related to the compressibility modulus and horizontal displacement. Phase velocity. It is determined by the elastic parameters of the underground medium (including shear wave velocity). β It is determined by (the frequency). The phase velocity of the Scholte wave observed at this frequency, with constant density, a Poisson's ratio of 0.25, and a shear velocity of [value missing]. It propagates in a homogeneous medium that is times larger than the original.

[0108] By introducing the assumption of a homogeneous medium and an approximate solution method, the aforementioned complex nonlinear problem is transformed into a linear problem. For any frequency... m Its phase velocity squared is expressed as:

[0109] (2)

[0110] Where h is the depth of the stratum or the depth of the stratigraphic interface; For a surface wave of the m-th frequency, its sensitivity to the shear wave velocity of the medium at the bottom boundary depth of the n-th layer. For a surface wave of the m-th frequency, its sensitivity to the shear wave velocity of the medium at the top boundary depth of the n-th layer. For kernel function The difference between the top and bottom boundaries of the nth layer represents the sensitivity of the wave of the mth frequency to the velocity of the nth layer, where z represents... and ; This represents the shear wave velocity.

[0111] Combining equations (2) for all frequencies, a linear relationship (matrix-vector relationship) is formed between the square of the phase velocity and the square of the shear velocity of the layer, i.e., the Dix-type relationship of the surface wave:

[0112] (3)

[0113] in, Phase velocity; Shear wave velocity; For the kernel function The kernel matrix obtained by the difference calculation relates the square of the shear velocity to the square of the phase velocity, forming a Dix-type relationship for surface waves.

[0114] kernel matrix By depth sensitivity function The construction, specifically the expression, is:

[0115] (4)

[0116] Where z is the depth value; For wave number The sensitivity of the phase velocity of a surface wave to the change in shear wave velocity of the medium at depth z.

[0117] Step S1022: Based on the wavelength data of the Scholte wave dispersion curve, construct a formation depth model, and perform power-law fitting on the linear relationship between the phase velocity and the layer velocity to construct a priori velocity model, specifically:

[0118] First, the depth boundaries are determined based on the wavelength data from the Scholte wave dispersion curve. The lower limit of the sensitive depth is the depth corresponding to the shortest wavelength, and the upper limit is the depth corresponding to the longest wavelength. The layered model is set up using a method of thin layers (fixed thickness) in the shallow part and exponentially increasing layer thickness in the deeper part.

[0119] Based on the aforementioned depth boundaries, construct a formation depth model;

[0120] Secondly, a power-law fit is performed on the linear relationship between the phase velocity and the layer velocity to construct a priori velocity model. Experiential relationships vs = cThe shear wave velocity estimate obtained by / 0.88 is mapped to its sensitive depth. (λ is the wavelength), and by interpolating through these scattered points, a smooth, depth-varying prior velocity model is constructed. This serves as a constraint for subsequent inversion.

[0121] Step S1023: By regularizing the formation depth model and the prior velocity model, an initial shear wave velocity model is constructed, specifically as follows:

[0122] The matrix-vector relationship is solved using a linear inversion method based on weighted damped least squares, and a regularization method is introduced.

[0123] Select the data covariance matrix and model covariance matrix To introduce constraints:

[0124] Among them, the data covariance matrix For diagonal matrices:

[0125] (5)

[0126] Where I is the identity matrix, This represents the standard deviation of the data. The purpose of this matrix is ​​to represent the standard deviation of the observed data. c 2. Weighting is applied to give higher weight to data points with high reliability, suppress the influence of noise, and ensure that the inversion focuses more on high-quality data.

[0127] Model covariance matrix The following form is used to introduce model smoothing constraints:

[0128] (6)

[0129] in, The standard deviation of the model represents the allowable deviation of the model from the prior velocity model. The degree; and These are the depths of the top of the i-th and j-th layers, respectively; It is the smoothing distance. The function of this matrix is ​​to enforce the smoothness and rationality of the model. Its exponential term ensures that the velocity values ​​of strata at similar depths are also similar, thereby avoiding violent oscillations that are not physical in nature in the inversion results.

[0130] Relate the above constraints to the matrix-vector relationship Combined, construct the augmented matrix equation:

[0131] (7)

[0132] in, Let be the square vector of the layer shear wave velocity to be determined; G is the squared phase velocity vector extracted from measured Scholte wave dispersion data; G is the kernel matrix. The data covariance matrix; This is the model covariance matrix, used to introduce model smoothness constraints; This is the data weight matrix; The model constraint weight matrix; The prior velocity model is obtained through power-law fitting.

[0133] By scanning the model's standard deviation factor and related length factors These two parameters are used to solve the augmented matrix equation and obtain a series of candidate models. Each candidate model is determined based on the chi-square value. The objective function (Equation 8) is evaluated to determine whether it simultaneously meets the requirements. ∈[1,1.5] and Candidate models with a velocity greater than 0 are considered qualified models. The average velocity profile of all qualified models is taken as the initial shear wave velocity model, also known as the vs profile.

[0134] Chi-square value The objective function is:

[0135] (8)

[0136] in, Chi-square value; Let be the theoretical forward modeling operator, representing the shear wave velocity model based on the current nth iteration. The theoretical phase velocity value calculated through forward modeling of physical equations; Let be the inverse of the data covariance matrix; F is the degrees of freedom. Normalizing the chi-square value according to the degrees of freedom makes it possible to set a unified, statistically significant criterion for goodness of fit (e.g., ...). This provides a key criterion for automating and objectifying the inversion process.

[0137] Step S103: Based on the initial shear wave velocity model, the Scholte wave dispersion curve is fitted and optimized to obtain a two-dimensional shear wave velocity profile.

[0138] In this embodiment, the initial shear wave velocity model is used as the starting point for iteration. The Scholte wave dispersion curve is fitted and optimized through adaptive linear inversion to obtain a two-dimensional shear wave velocity profile, which provides a reliable basis for evaluating the submarine stratigraphic structure and engineering geological characteristics.

[0139] In this embodiment, based on the initial shear wave velocity model, the Scholte wave dispersion curve is fitted and optimized to obtain a two-dimensional shear wave velocity profile, specifically including:

[0140] Step S1031: Based on the initial shear wave velocity model and the Scholte wave dispersion curve, establish a linear inversion objective function and solve for the model update direction, specifically as follows:

[0141] There is a nonlinear forward modeling relationship between the Scholte wave dispersion curve and the formation model parameters:

[0142] (9)

[0143] in, Frequency (Hz); For frequency The corresponding phase velocity; The transverse wave velocity vector; The longitudinal wave velocity vector; It is a density vector; This is the layer thickness vector.

[0144] Since formula (9) is a nonlinear function, in the initial shear wave velocity model Linearizing equation (9) using Taylor series expansion and applying matrix theory, we can obtain:

[0145] (10)

[0146] in, , representing the difference between the initial value and the model value; Initial transverse wave velocity The model response; Let be the vector of the shear wave velocity correction to be determined; for OK The Jacobian matrix of the column is the first-order partial derivative of with respect to the S-wave velocity.

[0147] Because the number of data points contained in the dispersion curve is much greater than the defined number of layers. The shear wave velocity correction vector is solved using the damped least squares method. And define the objective function for the inversion problem:

[0148] (11)

[0149] in, for Vector length; It is the damping factor; Let be the vector of the shear wave velocity correction to be determined; For weighted matrices, , It is a diagonal matrix.

[0150] (12)

[0151] in, Let be the vector of the shear wave velocity correction to be determined; , is the identity matrix, b is the data residual vector; U is the left singular vector matrix; V is the right singular vector matrix; It is a diagonal matrix, and the elements on its diagonal are called singular values; This refers to the damping factor. Marquardt (1963) pointed out that the damping factor... Controlled Direction and convergence rate, damping factor This also limits the model space. By adjusting the damping factor... It can improve the computation speed and ensure the stability of inversion convergence.

[0152] Step S1032: Add an adaptive damping factor to the model update direction to obtain the updated two-dimensional shear wave velocity profile, specifically:

[0153] As can be seen from equation (12), the model parameter correction amount and the damping factor Regarding, the best The solution must be as small as possible to obtain the maximum resolution, while also being as large as possible to ensure iterative convergence, making the solution stable and changing with the iteration process. Therefore, an adaptive damping factor modification method is used for inversion.

[0154] To select a suitable damping factor, the adaptive damping factor and the shear wave velocity correction vector are... Perform iterations to determine what makes the objective function... Minimize the damping factor λ;

[0155] According to symmetric matrices Determine the initial value of the adaptive damping factor. The specific method is as follows:

[0156] (13)

[0157] in, The value range is 1 to 10.

[0158] If after the [number]th In the next iteration, the new solution cannot satisfy the objective function. If convergence occurs, it indicates that the current iteration step size is too large. In this case, the damping factor should be increased, typically by a factor of 1.5.

[0159] Conversely, if after the first If the objective function converges after a second iteration, it indicates that the correction step size can be increased, thus reducing the damping factor, which can generally be reduced by 1 / 2.

[0160] In determining the direction of model correction After that, it is not used directly to modify parameters. Instead, it is viewed as a search direction, looking for ways to achieve the objective function. Minimize the optimal step size.

[0161] Based on the adaptive damping factor and the shear wave velocity correction vector and the prior velocity model Determine what makes the objective function Minimize the optimal step size;

[0162] First calculate And determine if it is less than ;

[0163] when When the new minimum point is approximated, the solution is taken as:

[0164] (14)

[0165] when The optimal step size for finding the minimum point is then determined using the parabolic method.

[0166] (15)

[0167] when The new minimum point approximate solution is taken as:

[0168] (16)

[0169] When the objective function The step size is gradually reduced using a binary search method. The values ​​are respectively ,in, , n The maximum number of binary search operations;

[0170] When a certain step size t satisfies When this happens, the new approximate solution for the minimum point is taken as:

[0171] (17)

[0172] If the objective function is obtained by using the bisection method to reduce the step size 7 times, If it still does not decrease, then it is considered yes The smallest point;

[0173] Find the new approximate solution at the minimum point Then, repeat the above steps iteratively to obtain... This continues until an updated two-dimensional shear wave velocity profile is obtained.

[0174] In this embodiment, linear inversion methods heavily rely on the initial model, while nonlinear inversion methods suffer from low efficiency and premature convergence, introducing significant uncertainty into the inversion process. This disclosure establishes a Dix-type relationship for surface waves and employs power-law fitting to construct a depth model, enabling the direct acquisition of a relatively accurate velocity model from the fundamental dispersion curve. This solves the problem of linear inversion methods heavily relying on and requiring manually provided initial models. The velocity model obtained using Dix-type inversion exhibits significantly improved accuracy compared to previous methods and possesses the ability to identify velocity inversion layers. The entire process eliminates the need for manual layer or initial model settings, making it suitable for batch data processing. The introduction of an adaptive damping factor reduces the number of inversion iterations by approximately 50%, significantly improving computational efficiency.

[0175] It is understandable that this method can be executed by any device, equipment, platform, or cluster of devices with computing and processing capabilities.

[0176] It should be noted that the methods of one or more embodiments of this disclosure can be executed by a single device, such as a computer or server. The methods of this embodiment can also be applied in a distributed scenario, where multiple devices cooperate to complete the process. In such a distributed scenario, one of these devices may execute only one or more steps of the methods of one or more embodiments of this disclosure, and the multiple devices will interact with each other to complete the method described.

[0177] It should be noted that the above description pertains to specific embodiments of this disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than those shown in the embodiments and may still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0178] Based on the same inventive concept, corresponding to any of the above-described embodiments, this disclosure also provides an adaptive inversion system for Scholte wave dispersion curves. For example... Figure 2 As shown, the above system includes:

[0179] Acquisition unit 201 is used to acquire and process the raw seismic data of the target seabed to obtain the Scholte wave dispersion curve;

[0180] Construction unit 202 is used to automatically construct an initial shear wave velocity model based on the Scholte wave dispersion curve through linear inversion;

[0181] The optimization unit 203 is used to fit and optimize the Scholte wave dispersion curve based on the initial shear wave velocity model to obtain the optimal two-dimensional shear wave velocity profile.

[0182] For ease of description, the above system is described by dividing it into various modules based on their functions. Of course, when implementing one or more embodiments of this disclosure, the functions of each module can be implemented in one or more software and / or hardware.

[0183] The system described above is used to implement the corresponding methods in the foregoing embodiments and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0184] Figure 3 This embodiment illustrates a more specific hardware structure of an electronic device, which may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0185] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this disclosure.

[0186] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this disclosure are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0187] The input / output interface 1030 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components within the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.

[0188] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0189] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0190] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this disclosure, and not necessarily all the components shown in the figures.

[0191] The electronic devices described above are used to implement the corresponding methods in the foregoing embodiments and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0192] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this disclosure (including the claims) is limited to these examples; within the framework of this disclosure, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of one or more embodiments of this disclosure as described above, which are not provided in detail for the sake of brevity.

[0193] Additionally, to simplify the description and discussion, and to avoid obscuring one or more embodiments of this disclosure, the provided drawings may or may not show well-known power / ground connections to integrated circuit (IC) chips and other components. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring one or more embodiments of this disclosure, and this also takes into account the fact that the details of implementation of these block diagram systems are highly dependent on the platform on which one or more embodiments of this disclosure will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuitry) are set forth to describe exemplary embodiments of this disclosure, it will be apparent to those skilled in the art that one or more embodiments of this disclosure may be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0194] Although this disclosure has been described in conjunction with specific embodiments thereof, many substitutions, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description.

[0195] This disclosure includes one or more embodiments intended to cover all such substitutions, modifications, and variations falling within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. An adaptive inversion method for Scholte wave dispersion curves, characterized in that, include: The raw seismic data of the target seabed were acquired and processed to obtain the Scholte wave dispersion curve; Based on the Scholte wave dispersion curve, an initial shear wave velocity model is constructed through linear inversion. Based on the initial shear wave velocity model, the Scholte wave dispersion curve is fitted and optimized to obtain a two-dimensional shear wave velocity profile. The step of constructing an initial shear wave velocity model based on the Scholte wave dispersion curve through linear inversion includes: Based on the phase velocity data in the Scholte wave dispersion curve, a linear relationship between phase velocity and layer velocity is established. Based on the wavelength data of the Scholte wave dispersion curve, a formation depth model is constructed, and the linear relationship between the phase velocity and the layer velocity is fitted by a power law to construct a priori velocity model. An initial shear wave velocity model is constructed by regularizing the formation depth model and the prior velocity model. The step of establishing a linear relationship between phase velocity and layer velocity based on the phase velocity data in the Scholte wave dispersion curve includes: The establishment of a linear relationship between phase velocity and layer velocity based on the phase velocity data in the Scholte wave dispersion curve specifically includes: Surface wave eigenfunctions satisfy: （1） in, ω is the angular frequency; k is the wave number; This is the integral term for "kinetic energy" related to the medium density and vertical displacement. This is the integral term for "potential energy" related to shear modulus and vertical displacement; For coupled integral terms; This is the integral term for "potential energy" related to the compressibility modulus and horizontal displacement; By introducing the assumption of a homogeneous medium and an approximate solution method, the above nonlinear problem is transformed into a linear problem; for any frequency m Its phase velocity squared is expressed as: （2） Where h is the depth of the stratum or the depth of the stratigraphic interface; For a surface wave of the m-th frequency, its sensitivity to the shear wave velocity of the medium at the bottom boundary depth of the n-th layer. For a surface wave of the m-th frequency, its sensitivity to the shear wave velocity of the medium at the top boundary depth of the n-th layer. For kernel function The difference between the top and bottom boundaries of the nth layer represents the sensitivity of the wave of the mth frequency to the velocity of the nth layer, where z represents... and ; Shear wave velocity; Combining equations (2) for all frequencies, a linear relationship is formed between the square of the phase velocity and the square of the shear velocity of the layer: （3） in, Phase velocity; Shear wave velocity; For the kernel function The kernel matrix is ​​obtained by difference calculation; kernel matrix By depth sensitivity function The construction, specifically the expression, is: （4） Where z is the depth value; For wave number The sensitivity of the phase velocity of a surface wave to the change in shear wave velocity of the medium at depth z.

2. The method according to claim 1, characterized in that, The process of fitting and optimizing the Scholte wave dispersion curve based on the initial shear wave velocity model to obtain a two-dimensional shear wave velocity profile specifically includes: Based on the initial shear wave velocity model and the Scholte wave dispersion curve, a linear inversion objective function is established and the model update direction is solved. An adaptive damping factor is added to the model update direction to obtain the updated two-dimensional shear wave velocity profile.

3. The method according to claim 1, characterized in that, Based on the wavelength data of the Scholte wave dispersion curve, a formation depth model is constructed, and a power-law fitting is performed on the linear relationship between the phase velocity and the layer velocity to construct a priori velocity model, specifically including: Depth boundaries are determined based on wavelength data from the Scholte wave dispersion curve; Based on the aforementioned depth boundaries, construct a formation depth model; A power-law fit is performed on the linear relationship between the phase velocity and the layer velocity to construct a priori velocity model. .

4. The method according to claim 1, characterized in that, The step of constructing an initial shear wave velocity model by regularizing the formation depth model and the prior velocity model specifically includes: Select the data covariance matrix and model covariance matrix To introduce constraints: Among them, the data covariance matrix For diagonal matrices: （5） Where I is the identity matrix, The standard deviation of the data; Model covariance matrix The following form is used to introduce model smoothing constraints: （6） in, The standard deviation of the model represents the allowable deviation of the model from the prior velocity model. The degree; and These are the depths of the top of the i-th and j-th layers, respectively; It is a smooth distance; Relate the above constraints to the matrix-vector relationship Combined, construct the augmented matrix equation: （7） in, Let be the square vector of the layer shear wave velocity to be determined; G is the squared phase velocity vector extracted from measured Scholte wave dispersion data; G is the kernel matrix. The data covariance matrix; This is the model covariance matrix, used to introduce model smoothness constraints; This is the data weight matrix; The model constraint weight matrix; The prior velocity model obtained by power-law fitting; By scanning the model's standard deviation factor and related length factors These two parameters are used to solve the augmented matrix equation and obtain candidate models; each candidate model is evaluated based on the chi-square value. The objective function is evaluated to determine whether it simultaneously meets the requirements. ∈[1,1.5] and Candidate models with a value >0 are considered qualified models, and the average velocity profile of all qualified models is taken as the initial shear wave velocity model. Chi-square value The objective function is: （8） in, Chi-square value; Let be the theoretical forward modeling operator, representing the shear wave velocity model based on the current nth iteration. The theoretical phase velocity value calculated through forward modeling of physical equations; is the inverse of the data covariance matrix; F is the degrees of freedom.

5. The method according to claim 2, characterized in that, The process of establishing a linear inversion objective function and solving for the model update direction based on the initial shear wave velocity model and the Scholte wave dispersion curve specifically includes: There is a nonlinear forward modeling relationship between the Scholte wave dispersion curve and the formation model parameters: （9） in, Frequency (Hz); For frequency The corresponding phase velocity; The transverse wave velocity vector; The longitudinal wave velocity vector; It is a density vector; This is the layer thickness vector; In the initial shear wave velocity model Linearizing equation (9) using Taylor series expansion and applying matrix theory, we can obtain: （10） in, , representing the difference between the initial value and the model value; Initial transverse wave velocity The model response; Let be the vector of the shear wave velocity correction to be determined; for m OK The Jacobian matrix of the column; The shear wave velocity correction vector is solved using the damped least squares method. And define the objective function for the inversion problem: （11） in, for Vector length; It is the damping factor; Let be the vector of the shear wave velocity correction to be determined; For weighted matrices, , It is a diagonal matrix; where, （12） in, Let be the vector of the shear wave velocity correction to be determined; , is the identity matrix, b is the data residual vector; U is the left singular vector matrix; V is the right singular vector matrix; It is a diagonal matrix, and the elements on its diagonal are called singular values; is the damping factor.

6. The method according to claim 5, characterized in that, The step of adding an adaptive damping factor to the model update direction to obtain the updated two-dimensional shear wave velocity profile specifically includes: The adaptive damping factor and the shear wave velocity correction vector Perform iterations to determine what makes the objective function... Minimize the damping factor ; According to symmetric matrices Determine the initial value of the adaptive damping factor. The specific method is as follows: (13) in, The value range is 1 to 10; If after the [number]th In the next iteration, the new solution cannot satisfy the objective function. If convergence occurs, increase the damping factor; If after the [number]th The next iteration makes the objective function Convergence reduces the damping factor; Based on the adaptive damping factor and the shear wave velocity correction vector and prior velocity model Determine what makes the objective function Minimize the optimal step size; First calculate And determine if it is less than ; when When the new minimum point is approximated, the solution is taken as: （14） when The optimal step size for finding the minimum point is then determined using the parabolic method. （15） when The new minimum point approximate solution is taken as: （16） When the objective function The step size is gradually reduced using a binary search method. The values ​​are respectively ,in, , n The maximum number of binary search operations; When a certain step size t satisfies When this happens, the new approximate solution for the minimum point is taken as: （17） If the objective function is obtained by using the bisection method to reduce the step size 7 times, If it still does not decrease, then it is considered yes The smallest point; Find the new approximate solution at the minimum point Then, repeat the above steps iteratively to obtain... This continues until an updated two-dimensional shear wave velocity profile is obtained.

7. An adaptive inversion system for Scholte wave dispersion curves, characterized in that, include: The acquisition unit is used to acquire and process the raw seismic data of the target seabed to obtain the Scholte wave dispersion curve; A construction unit is used to construct an initial shear wave velocity model based on the Scholte wave dispersion curve through linear inversion. The step of constructing an initial shear wave velocity model based on the Scholte wave dispersion curve through linear inversion includes: Based on the phase velocity data in the Scholte wave dispersion curve, a linear relationship between phase velocity and layer velocity is established. Based on the wavelength data of the Scholte wave dispersion curve, a formation depth model is constructed, and the linear relationship between the phase velocity and the layer velocity is fitted by a power law to construct a priori velocity model. An initial shear wave velocity model is constructed by regularizing the formation depth model and the prior velocity model. The step of establishing a linear relationship between phase velocity and layer velocity based on the phase velocity data in the Scholte wave dispersion curve includes: The establishment of a linear relationship between phase velocity and layer velocity based on the phase velocity data in the Scholte wave dispersion curve specifically includes: Surface wave eigenfunctions satisfy: （1） in, ω is the angular frequency; k is the wave number; This is the integral term for "kinetic energy" related to the medium density and vertical displacement. This is the integral term for "potential energy" related to shear modulus and vertical displacement; For coupled integral terms; This is the integral term for "potential energy" related to the compressibility modulus and horizontal displacement; By introducing the assumption of a homogeneous medium and an approximate solution method, the above nonlinear problem is transformed into a linear problem; for any frequency m Its phase velocity squared is expressed as: （2） Where h is the depth of the stratum or the depth of the stratigraphic interface; For a surface wave of the m-th frequency, its sensitivity to the shear wave velocity of the medium at the bottom boundary depth of the n-th layer. For a surface wave of the m-th frequency, its sensitivity to the shear wave velocity of the medium at the top boundary depth of the n-th layer. For kernel function The difference between the top and bottom boundaries of the nth layer represents the sensitivity of the wave of the mth frequency to the velocity of the nth layer, where z represents... and ; Shear wave velocity; Combining equations (2) for all frequencies, a linear relationship is formed between the square of the phase velocity and the square of the shear velocity of the layer: （3） in, Phase velocity; Shear wave velocity; For the kernel function The kernel matrix is ​​obtained by difference calculation; kernel matrix By depth sensitivity function The construction, specifically the expression, is: （4） Where z is the depth value; For wave number The sensitivity of the phase velocity of a surface wave to the change in shear wave velocity of the medium at depth z. The optimization unit is used to fit and optimize the Scholte wave dispersion curve based on the initial shear wave velocity model to obtain a two-dimensional shear wave velocity profile.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executed by the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 6.

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