Joint inversion method, device, equipment and medium for longitudinal wave velocity and transverse wave velocity

Abstract

The application discloses a method and device for jointly inverting longitudinal wave velocity and transverse wave velocity, equipment and a medium. The method comprises the following steps: determining first dispersion curve information and second dispersion curve information; determining a first elastic parameter model and taking the first elastic parameter model as a target model; determining third dispersion curve information and fourth dispersion curve information; updating the target model based on first dispersion curve residuals and second dispersion curve residuals to obtain a second elastic parameter model, wherein the first dispersion curve residuals are the difference between the third dispersion curve information and the first dispersion curve information, and the second dispersion curve residuals are the difference between the fourth dispersion curve information and the second dispersion curve information; taking the second elastic parameter model as a new target model and returning to the step of determining the third dispersion curve information and the fourth dispersion curve information. The scheme is based on target surface waves and target guided waves, and effectively realizes joint inversion of a longitudinal wave velocity model and a transverse wave velocity model.

Description

Technical Field

[0001] This invention relates to the field of seismic exploration technology, and in particular to a method, apparatus, equipment, and medium for the joint inversion of P-wave velocity and S-wave velocity. Background Technology

[0002] P-wave velocity and S-wave velocity are two key parameters describing the elastic properties of subsurface media. P-wave velocity models are used for reflection imaging, refraction tomography, lithological interpretation, and pore fluid analysis; S-wave velocity models are of great significance for near-surface structure investigation, engineering geological evaluation, shallow seismic hazard analysis, and estimation of geotechnical parameters.

[0003] In existing technologies, P-wave velocity models and S-wave velocity models are typically inverted independently. This leads to inconsistencies between the P-wave and S-wave velocity models in terms of structural location and interface morphology, making it difficult to obtain a stable P-wave / S-wave velocity ratio, which is detrimental to the interpretation of lithology, pore fluid, and engineering mechanical properties. Conventional full-waveform inversion methods can theoretically update both P-wave and S-wave velocities simultaneously, but they rely on complete waveform fitting, require high accuracy of the initial model, and are prone to local minima. Summary of the Invention

[0004] This invention provides a method, apparatus, equipment, and medium for the joint inversion of P-wave velocity and S-wave velocity, in order to solve the problem of the difficulty in simultaneously inverting the P-wave velocity and S-wave velocity of formations.

[0005] According to one aspect of the present invention, a joint inversion method for P-wave velocity and S-wave velocity is provided, the method comprising: The first dispersion curve information and the second dispersion curve information are determined. The first dispersion curve information is the dispersion curve information of the target surface wave determined based on the first seismic data. The second dispersion curve information is the dispersion curve information of the target guided wave determined based on the first seismic data. The first seismic data is obtained by collecting data from the target stratum. The target surface wave is the surface wave in the target stratum. The target guided wave is the guided wave formed by the interference superposition of the reflected and refracted waves of the longitudinal wave in the layered medium of the target stratum. A first elastic parameter model is determined and used as the target model. The first elastic parameter model includes the initial P-wave velocity model, S-wave velocity model, and density model of the target formation. The third and fourth dispersion curve information were determined. The third dispersion curve information is the dispersion curve information of the target surface wave determined based on the second seismic data. The fourth dispersion curve information is the dispersion curve information of the target guided wave determined based on the second seismic data. The second seismic data is obtained by forward modeling the elastic wave equation based on the target model. The target model is updated based on the residuals of the first and second dispersion curves to obtain the second elastic parameter model. The residual of the first dispersion curve is the difference between the information of the third dispersion curve and the information of the first dispersion curve, and the residual of the second dispersion curve is the difference between the information of the fourth dispersion curve and the information of the second dispersion curve. The second elastic parameter model is used as the new target model, and the steps to determine the third and fourth dispersion curve information are returned until the residuals of the first and second dispersion curves meet the preset conditions, or the number of iterations reaches the preset number. Then, the spatial distribution of P-wave velocity and S-wave velocity of the target stratum is described by the second elastic parameter model.

[0006] According to another aspect of the present invention, a joint inversion apparatus for longitudinal wave velocity and transverse wave velocity is provided, the apparatus comprising: The first determining module is used to determine the first dispersion curve information and the second dispersion curve information. The first dispersion curve information is the dispersion curve information of the target surface wave determined based on the first seismic data, and the second dispersion curve information is the dispersion curve information of the target guided wave determined based on the first seismic data. The first seismic data is obtained by collecting data from the target stratum. The target surface wave is the surface wave in the target stratum, and the target guided wave is the guided wave formed by the interference superposition of the reflected and refracted waves of the longitudinal wave in the layered medium of the target stratum. The second determining module is used to determine the first elastic parameter model and use the first elastic parameter model as the target model. The first elastic parameter model includes the initial P-wave velocity model, S-wave velocity model and density model of the target formation. The third determination module is used to determine the third dispersion curve information and the fourth dispersion curve information. The third dispersion curve information is the dispersion curve information of the target surface wave determined based on the second seismic data, and the fourth dispersion curve information is the dispersion curve information of the target guided wave determined based on the second seismic data. The second seismic data is obtained by forward modeling the elastic wave equation based on the target model. The first update module is used to update the target model based on the first dispersion curve residual and the second dispersion curve residual to obtain the second elastic parameter model. The first dispersion curve residual is the difference between the third dispersion curve information and the first dispersion curve information, and the second dispersion curve residual is the difference between the fourth dispersion curve information and the second dispersion curve information. The first execution module is used to take the second elastic parameter model as the new target model and return to the execution steps of determining the third and fourth dispersion curve information until the residuals of the first and second dispersion curves meet the preset conditions or the number of iterations reaches the preset number. Then, the spatial distribution of the P-wave velocity and S-wave velocity of the target stratum is described by the second elastic parameter model.

[0007] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the joint inversion method of longitudinal wave velocity and transverse wave velocity according to any embodiment of the present invention.

[0008] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the joint inversion method of longitudinal wave velocity and transverse wave velocity according to any embodiment of the present invention.

[0009] The technical solution of this invention, through determining first and second dispersion curve information (the first and second dispersion curves being based on first seismic data to determine the dispersion curve of the target surface wave and the target guided wave, respectively, obtained by acquiring data from the target strata), determines a first elastic parameter model and uses it as the target model; and determines third and fourth dispersion curve information (the third and fourth dispersion curves being based on second seismic data to determine the dispersion curve of the target surface wave and the target guided wave, respectively, obtained by performing forward modeling of the elastic wave equation based on the target model using the second seismic data). This achieves the same elastic wave equation for both P-wave and S-wave velocity models. The forward modeling process is performed. Based on the residuals of the first and second dispersion curves, the target model is updated to obtain the second elastic parameter model. This reduces the nonlinearity of direct fitting of complex seismic waveforms, which helps to alleviate local minima and thus reduces the accuracy requirements of the initial model. The second elastic parameter model is used as the new target model, and the steps to determine the information of the third and fourth dispersion curves are returned until the residuals of the first and second dispersion curves meet the preset conditions or the number of iterations reaches the preset number. By iteratively updating the first elastic parameter model, the joint inversion of the P-wave velocity model and the S-wave velocity model is effectively realized, ensuring the consistency of the P-wave velocity structure and S-wave velocity structure of the target stratum. This enables the formation of an accurate P-wave and S-wave velocity ratio of the target stratum, which facilitates the accurate interpretation of the geological properties of the target stratum.

[0010] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A flowchart illustrating a method for jointly inverting longitudinal wave velocity and transverse wave velocity according to an embodiment of the present invention; Figure 2 A flowchart of another method for jointly inverting P-wave velocity and S-wave velocity provided in an embodiment of the present invention; Figure 3 A flowchart illustrating another method for jointly inverting P-wave velocity and S-wave velocity provided in an embodiment of the present invention; Figure 4 A schematic diagram of the structure of a joint inversion device for longitudinal wave velocity and transverse wave velocity provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of an electronic device that implements a joint inversion method for longitudinal wave velocity and transverse wave velocity, as provided in an embodiment of the present invention. Detailed Implementation

[0013] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0014] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0015] Figure 1This is a flowchart illustrating a joint inversion method for P-wave velocity and S-wave velocity provided in an embodiment of the present invention. This embodiment is applicable to the simultaneous inversion of P-wave velocity and S-wave velocity of formations. The method can be executed by a joint inversion device for P-wave velocity and S-wave velocity, which can be implemented in hardware and / or software and configured in an electronic device that implements the joint inversion method for P-wave velocity and S-wave velocity. Figure 1 As shown, the joint inversion method for P-wave velocity and S-wave velocity includes: S101. Determine the first dispersion curve information and the second dispersion curve information. The first dispersion curve information is the dispersion curve information of the target surface wave determined based on the first seismic data. The second dispersion curve information is the dispersion curve information of the target guided wave determined based on the first seismic data. The first seismic data is obtained by collecting data from the target stratum. The target surface wave is the surface wave in the target stratum. The target guided wave is the guided wave formed by the interference superposition of the reflected and refracted waves of the longitudinal wave in the layered medium of the target stratum.

[0016] Surface waves can refer to seismic waves that propagate along the interface when a body wave reaches the interface of a layered medium. Surface waves are mixed waves of multiple frequencies; in a layered medium, waves of different frequencies do not propagate at equal speeds, and these waves separate, forming surface wave dispersion. The dynamic properties of surface waves depend on shear waves, thus the dispersion characteristics of surface waves can be used for shear wave velocity structure inversion. Target guided waves can refer to P-guided waves in the target formation. The dispersion characteristics of P-guided waves can be used for P-wave velocity structure inversion. Dispersion curve information can be used to reflect the changes in phase velocity or wavenumber with frequency. For example, dispersion curve information can include phase velocity-frequency curve information and wavenumber-frequency curve information.

[0017] For details, please refer to Figure 2 Based on pre-set observation system parameters, seismic exploration of the target strata yields the first seismic data. The observation system parameters may include at least shot location, geophone location, trace spacing, shot spacing, sampling interval, and recording length. The first seismic data is observation data, including seismic data for target surface waves and target guided waves. Based on this first seismic data, the observed dispersion curves of the target surface waves and target guided waves are determined, thus obtaining the first dispersion curve information and the second dispersion curve information.

[0018] As an optional implementation of this invention, determining the first dispersion curve information and the second dispersion curve information includes the following steps A1-A2: Step A1: Determine the first waveform data and the second waveform data. The first waveform data is the waveform data of the target surface wave extracted from the first seismic data, and the second waveform data is the waveform data of the target guided wave extracted from the first seismic data.

[0019] Step A2: Determine the first dispersion curve information based on the first waveform data, and determine the second dispersion curve information based on the second waveform data.

[0020] For details, please refer to Figure 2 First waveform data can be obtained by extracting the waveform data of the target surface wave from the first seismic data based on the main energy window of the target surface wave, and second waveform data can be obtained by extracting the waveform data of the target guided wave from the first seismic data based on the arrival time window of the target guided wave. Furthermore, based on the vertical or radial components of the target surface wave in the first waveform data, the dispersion spectrum of the target surface wave can be calculated, and the dispersion curve information of the target surface wave can be obtained by extracting the dispersion curve information from the dispersion spectrum of the target surface wave. Similarly, based on the linear or curved phase axis characteristics of the target guided wave in the second waveform data, the dispersion spectrum of the target guided wave can be calculated, and the dispersion curve information of the target guided wave can be obtained by extracting the dispersion curve information from the dispersion spectrum of the target guided wave, thus obtaining the second dispersion curve information.

[0021] For example, the observed phase velocity curves and observed wavenumber curves corresponding to the target surface wave and the target guided wave, respectively, can be converted in the following ways: ; ; in, Information on the observed wavenumber curve of the target surface wave; Information on the observed phase velocity curve of the target surface wave; Information on the observed wavenumber curve of the target guided wave; Information on the observed phase velocity curve of the target guided wave; Indicates frequency.

[0022] S102. Determine the first elastic parameter model and use the first elastic parameter model as the target model. The first elastic parameter model includes the initial P-wave velocity model, S-wave velocity model and density model of the target formation.

[0023] Specifically, a first elastic parameter model can be pre-established based on prior data of the target formation, and this first elastic parameter model can be used as the target model. The prior data of the target formation can include at least well logging data, refraction tomography data, conventional velocity analysis data, and a one-dimensional velocity increment model of the target formation.

[0024] S103. Determine the third and fourth dispersion curve information. The third dispersion curve information is the dispersion curve information of the target surface wave determined based on the second seismic data. The fourth dispersion curve information is the dispersion curve information of the target guided wave determined based on the second seismic data. The second seismic data is obtained by forward modeling the elastic wave equation based on the target model.

[0025] Elastic wave equation forward modeling refers to the process of simulating the propagation of seismic waves in geology by numerically solving the elastic wave equation under the premise of known geological model, source excitation mode and boundary conditions, and finally obtaining predicted earthquake data.

[0026] For details, please refer to Figure 2 Based on the target model, the second seismic data can be obtained through forward modeling using two-dimensional or three-dimensional elastic wave equations. The second seismic data is simulated data, and the determination process for the third and fourth dispersion curves is similar to that for the first and second dispersion curves.

[0027] For example, the target model can be represented as The simulated phase velocity curves and simulated wavenumber curves corresponding to the target surface wave and the target guided wave, respectively, can be converted in the following ways: ; ; in, Information on the simulated wavenumber curve of the target surface wave; Information on the simulated phase velocity curve of the target surface wave; Information on the simulated wavenumber curve of the target guided wave; This represents the simulated phase velocity curve information of the target guided wave.

[0028] S104. The target model is updated based on the residuals of the first and second dispersion curves to obtain the second elastic parameter model. The residual of the first dispersion curve is the difference between the information of the third dispersion curve and the information of the first dispersion curve, and the residual of the second dispersion curve is the difference between the information of the fourth dispersion curve and the information of the second dispersion curve.

[0029] For details, please refer to Figure 2 The residual of the first dispersion curve can be obtained by calculating the difference between the information of the third dispersion curve and the information of the first dispersion curve, and the residual of the second dispersion curve can be obtained by calculating the difference between the information of the fourth dispersion curve and the information of the second dispersion curve. Furthermore, based on the residuals of the first and second dispersion curves, the second elastic parameter model can be obtained by simultaneously updating the P-wave velocity model and the S-wave velocity model in the target model. The density model in the target model can remain unchanged, or it can be determined based on the updated P-wave velocity model and the S-wave velocity model.

[0030] For example, the residuals of the first dispersion curve and the residuals of the second dispersion curve can be calculated in the following way: ; ; in, This represents the residual of the first dispersion curve; This represents the residual of the second dispersion curve.

[0031] S105. Take the second elastic parameter model as the new target model and return to the step of determining the third and fourth dispersion curve information until the residuals of the first and second dispersion curves meet the preset conditions, or the number of iterations reaches the preset number, and then describe the spatial distribution of the P-wave velocity and S-wave velocity of the target stratum through the second elastic parameter model.

[0032] For details, please refer to Figure 2 When the residuals of the first and second dispersion curves do not meet the preset conditions, and the number of iterations has not reached the preset number, the second elastic parameter model can be used as the new target model, and S103 and S104 can be repeated to obtain the new second elastic parameter model. When the residuals of the first and second dispersion curves meet the preset conditions, or after the number of iterations has reached the preset number, the spatial distribution of P-wave velocity and S-wave velocity of the target stratum can be described by the final second elastic parameter model, realizing the joint inversion of the P-wave velocity model and the S-wave velocity model. The preset conditions can be used to indicate that the sum of squares of the residuals of the first dispersion curve is less than a first preset threshold and the sum of squares of the residuals of the second dispersion curve is less than a second preset threshold.

[0033] As an optional embodiment of the present invention, the joint inversion method for P-wave velocity and S-wave velocity further includes: determining the joint inversion result, wherein the joint inversion result includes at least one of a first velocity model, a second velocity model, a third velocity model, a first fitting result, a second fitting result, and iterative update information of the target model, wherein the first velocity model is the S-wave velocity model in the second elastic parameter model, the second velocity model is the P-wave velocity model in the second elastic parameter model, the third velocity model is the P-wave / S-wave velocity ratio model determined based on the first velocity model and the second velocity model, the first fitting result is the fitting result of the dispersion curve information of the target surface wave during the iterative update of the target model, and the second fitting result is the fitting result of the dispersion curve information of the target guided wave during the iterative update of the target model.

[0034] The first velocity model describes the spatial distribution of shear wave velocity in the target formation. The second velocity model describes the spatial distribution of p-wave velocity in the target formation. The third velocity model describes the spatial distribution of the p-wave to shear wave velocity ratio in the target formation. During the iterative update of the target model, the model continuously changes, and the corresponding third and fourth dispersion curves also change, so that the third dispersion curve gradually approaches the first dispersion curve, and the fourth dispersion curve gradually approaches the second dispersion curve. Therefore, the first fitting result reflects the process of the third dispersion curve gradually approaching the first dispersion curve, and the second fitting result reflects the process of the fourth dispersion curve gradually approaching the second dispersion curve.

[0035] In existing technologies, surface wave dispersion inversion methods mainly invert the shear wave velocity structure and usually cannot stably obtain the longitudinal wave velocity structure; P-guided wave dispersion inversion methods mainly invert the longitudinal wave velocity structure and usually do not use shear wave velocity as the main inversion target; if one parameter is inverted first and then used as a fixed background for inverting another parameter, the error of the previous stage will be propagated to the next stage.

[0036] The technical solution of this invention, through determining first and second dispersion curve information (the first and second dispersion curves being based on first seismic data to determine the dispersion curve of the target surface wave and the target guided wave, respectively, obtained by acquiring data from the target strata), determines a first elastic parameter model and uses it as the target model; and determines third and fourth dispersion curve information (the third and fourth dispersion curves being based on second seismic data to determine the dispersion curve of the target surface wave and the target guided wave, respectively, obtained by performing forward modeling of the elastic wave equation based on the target model using the second seismic data). This achieves the same elastic wave equation for both P-wave and S-wave velocity models. The forward modeling process is performed. Based on the residuals of the first and second dispersion curves, the target model is updated to obtain the second elastic parameter model. This reduces the nonlinearity of direct fitting of complex seismic waveforms, which helps to alleviate local minima and thus reduces the accuracy requirements of the initial model. The second elastic parameter model is used as the new target model, and the steps to determine the information of the third and fourth dispersion curves are returned until the residuals of the first and second dispersion curves meet the preset conditions or the number of iterations reaches the preset number. By iteratively updating the first elastic parameter model, the joint inversion of the P-wave velocity model and the S-wave velocity model is effectively realized, ensuring the consistency of the P-wave velocity structure and S-wave velocity structure of the target stratum. This enables the formation of an accurate P-wave and S-wave velocity ratio of the target stratum, which facilitates the accurate interpretation of the geological properties of the target stratum.

[0037] Figure 3 A flowchart illustrating another method for jointly inverting P-wave velocity and S-wave velocity provided in an embodiment of the present invention. Figure 3 As shown, the joint inversion method for P-wave velocity and S-wave velocity includes: S201. Determine the first dispersion curve information and the second dispersion curve information. The first dispersion curve information is the dispersion curve information of the target surface wave determined based on the first seismic data. The second dispersion curve information is the dispersion curve information of the target guided wave determined based on the first seismic data. The first seismic data is obtained by collecting data from the target stratum. The target surface wave is the surface wave in the target stratum. The target guided wave is the guided wave formed by the interference superposition of the reflected and refracted waves of the longitudinal wave in the layered medium of the target stratum.

[0038] S202. Determine the first elastic parameter model and use the first elastic parameter model as the target model. The first elastic parameter model includes the initial P-wave velocity model, S-wave velocity model and density model of the target formation.

[0039] S203. Determine the third and fourth dispersion curve information. The third dispersion curve information is the dispersion curve information of the target surface wave determined based on the second seismic data. The fourth dispersion curve information is the dispersion curve information of the target guided wave determined based on the second seismic data. The second seismic data is obtained by forward modeling the elastic wave equation based on the target model.

[0040] S204. Determine the first gradient and the second gradient based on the residuals of the first and second dispersion curves. The first gradient is used to indicate the update amount and update direction of the shear wave velocity, and the second gradient is used to indicate the update amount and update direction of the longitudinal wave velocity.

[0041] For details, please refer to Figure 2 A joint objective function can be pre-constructed based on the dispersion curve residuals of the target surface wave and the target guided wave. The dispersion curve residual of the target surface wave can be expressed as half the sum of the squares of the dispersion curve residuals of the target surface wave; similarly, the dispersion curve residual of the target guided wave can also be expressed as half the sum of the squares of the dispersion curve residuals of the target guided wave. Furthermore, based on the first and second dispersion curve residuals, the first and second gradients can be obtained by solving the joint objective function.

[0042] As an optional implementation of this invention, determining the first gradient and the second gradient based on the residuals of the first and second dispersion curves includes the following steps B1-B3: Step B1: Determine the third gradient and the fourth gradient. The third gradient is the inversion gradient of the shear wave velocity determined based on the first associated source. The first associated source is a virtual seismic associated source determined based on the residual of the first dispersion curve. The fourth gradient is the inversion gradient of the p-wave velocity determined based on the second associated source. The second associated source is a virtual seismic associated source determined based on the residual of the second dispersion curve.

[0043] Step B2: Determine the fifth and sixth gradients. The fifth gradient is the derivative of the model constraint term with respect to the shear wave velocity, and the sixth gradient is the derivative of the model constraint term with respect to the p-wave velocity. The model constraint terms are determined based on the target model.

[0044] Step B3: Determine the first gradient based on the third and fifth gradients, and determine the second gradient based on the fourth and sixth gradients.

[0045] For details, please refer to Figure 2 The first data term is obtained by substituting the residual of the first dispersion curve into the residual term of the dispersion curve of the target surface wave. This first data term is then used as the first adjoint source. By solving the elastic wave equation and propagating the wave field in the reverse direction, the third gradient can be output through coupling calculation between the forward and reverse adjoint wave fields. Similarly, the second data term is obtained by substituting the residual of the second dispersion curve into the residual term of the dispersion curve of the target guided wave. This second data term is then used as the second adjoint source. By solving the elastic wave equation and propagating the wave field in the reverse direction, the fourth gradient can be output through coupling calculation between the forward and reverse adjoint wave fields.

[0046] The joint objective function also includes model constraint terms. These terms can be constructed based on the gradients of the P-wave and S-wave velocity fields of the target model to enhance the structural consistency of the P-wave and S-wave velocities. The P-wave velocity field gradient reflects the spatial rate of change of the P-wave velocity, and the S-wave velocity field gradient reflects the spatial rate of change of the S-wave velocity. Furthermore, the fifth gradient can be obtained by calculating the derivative of the model constraint terms with respect to the S-wave velocity, and the sixth gradient can be obtained by calculating the derivative of the model constraint terms with respect to the P-wave velocity. Finally, the first gradient is obtained by weighted summation of the third and fifth gradients, and the second gradient is obtained by weighted summation of the fourth and sixth gradients.

[0047] For example, the joint objective function can be represented as follows: ; ; ; ; in, Describe the joint objective function; Represents the residual term of the dispersion curve of the target surface wave; Represents the residual term of the dispersion curve of the target guided wave; Represents model constraint terms; This represents the gradient of the P-wave velocity field of the target model; This represents the gradient of the shear wave velocity field of the target model; and Indicates the weighting coefficient. , , This represents the first elastic parameter model.

[0048] For example, the first gradient and the second gradient can be determined in the following way: ,or ; ,or ; in, Indicates the first gradient; Indicates the second gradient; Indicates the third gradient; Indicates the fourth gradient; Indicates the fifth gradient; Indicates the sixth gradient; and This represents the cross-adjustment coefficient.

[0049] As an optional embodiment of the present invention, after determining the first gradient and the second gradient based on the first dispersion curve residual and the second dispersion curve residual, the method further includes: performing target processing on the first gradient and the second gradient respectively, wherein the target processing includes at least one of illumination compensation processing, normalization processing, spatial smoothing processing and boundary constraint processing.

[0050] Illumination compensation processing can be used to compensate for illumination inhomogeneities caused by the observation system, geological structures, or wavefield propagation paths. Spatial smoothing processing can suppress noise or disturbances by performing spatial domain low-pass filtering on the first and second gradients. Boundary constraint processing can be used to limit the first and second gradients from exceeding a preset gradient range. Target processing is performed on the first and second gradients separately to improve inversion stability and resolution, and accelerate convergence.

[0051] S205. Update the shear wave velocity model in the target model based on the first gradient, and update the longitudinal wave velocity model in the target model based on the second gradient to obtain the second elastic parameter model.

[0052] Specifically, the shear wave velocity model in the target model is updated based on the first gradient, and the p-wave velocity model in the target model is updated based on the second gradient. This achieves synchronous updates of the shear wave velocity model and the p-wave velocity model in the same iteration, instead of inverting one parameter first and then fixing that parameter to invert another parameter, thus reducing error propagation.

[0053] As an optional implementation of this invention, updating the shear wave velocity model in the target model based on a first gradient and updating the longitudinal wave velocity model in the target model based on a second gradient includes: determining a first step length and a second step length, wherein the first step length is the gradient step length of the shear wave velocity and the second step length is the gradient step length of the longitudinal wave velocity; updating the shear wave velocity model in the target model based on the first step length and the first gradient; and updating the longitudinal wave velocity model in the target model based on the second step length and the second gradient.

[0054] Specifically, the first step length and the second step length can be determined using independent line search or joint line search. Then, the shear wave velocity model in the target model can be updated based on the product of the first step length and the first gradient, and the p-wave velocity model in the target model can be updated based on the product of the second step length and the second gradient.

[0055] For example, the update of the target model can be represented as follows: ; ; in, Indicates the length of the first step; This indicates the second step length.

[0056] S206. Take the second elastic parameter model as the new target model and return to the step of determining the third and fourth dispersion curve information until the residuals of the first and second dispersion curves meet the preset conditions, or the number of iterations reaches the preset number, and then describe the spatial distribution of the P-wave velocity and S-wave velocity of the target stratum through the second elastic parameter model.

[0057] For example, when the decrease ratio of the joint objective function is less than the third preset threshold, the spatial distribution of P-wave velocity and S-wave velocity of the target stratum can also be described by the second elastic parameter model.

[0058] The technical solution of this invention involves determining first and second dispersion curve information. The first dispersion curve information is the dispersion curve information of the target surface wave determined based on first seismic data, and the second dispersion curve information is the dispersion curve information of the target guided wave determined based on the first seismic data. A first elastic parameter model is determined and used as the target model. Third and fourth dispersion curve information are determined. The third dispersion curve information is the dispersion curve information of the target surface wave determined based on second seismic data, and the fourth dispersion curve information is the dispersion curve information of the target guided wave determined based on the second seismic data. A first gradient and a second gradient are determined based on the residuals of the first and second dispersion curves. The first gradient indicates the update amount and update direction of the shear wave velocity, and the second gradient indicates the update amount and update direction of the p-wave velocity. The shear wave velocity model in the target model is updated based on the first gradient, and the longitudinal wave velocity model in the target model is updated based on the second gradient to obtain the second elastic parameter model. This realizes the simultaneous updating of the shear wave velocity model and the longitudinal wave velocity model in the same iteration based on the residuals of the first and second dispersion curves and the joint objective function. Instead of inverting one parameter first and then fixing that parameter to invert the other parameter, the second elastic parameter model is used as the new target model, and the steps to determine the information of the third and fourth dispersion curves are returned until the residuals of the first and second dispersion curves meet the preset conditions or the number of iterations reaches the preset number. By iteratively updating the first elastic parameter model, the joint inversion of the longitudinal wave velocity model and the shear wave velocity model is effectively realized.

[0059] Figure 4 This is a schematic diagram of a joint inversion device for P-wave velocity and S-wave velocity provided in an embodiment of the present invention. This embodiment of the present invention is applicable to the simultaneous inversion of P-wave velocity and S-wave velocity of formations, and the device can be implemented in hardware and / or software. Figure 4 As shown, the joint inversion device for P-wave velocity and S-wave velocity includes: The first determining module 301 is used to determine the first dispersion curve information and the second dispersion curve information. The first dispersion curve information is the dispersion curve information of the target surface wave determined based on the first seismic data, and the second dispersion curve information is the dispersion curve information of the target guided wave determined based on the first seismic data. The first seismic data is obtained by collecting data from the target stratum. The target surface wave is the surface wave in the target stratum, and the target guided wave is the guided wave formed by the interference superposition of the reflected wave and the refracted wave of the longitudinal wave in the layered medium of the target stratum. The second determining module 302 is used to determine the first elastic parameter model and use the first elastic parameter model as the target model. The first elastic parameter model includes the initial P-wave velocity model, S-wave velocity model and density model of the target formation. The third determining module 303 is used to determine the third dispersion curve information and the fourth dispersion curve information. The third dispersion curve information is the dispersion curve information of the target surface wave determined based on the second seismic data, and the fourth dispersion curve information is the dispersion curve information of the target guided wave determined based on the second seismic data. The second seismic data is obtained by forward modeling the elastic wave equation based on the target model. The first update module 304 is used to update the target model based on the first dispersion curve residual and the second dispersion curve residual to obtain the second elastic parameter model. The first dispersion curve residual is the difference between the third dispersion curve information and the first dispersion curve information, and the second dispersion curve residual is the difference between the fourth dispersion curve information and the second dispersion curve information. The first execution module 305 is used to take the second elastic parameter model as the new target model and return to the execution of the steps to determine the third dispersion curve information and the fourth dispersion curve information until the residuals of the first dispersion curve and the second dispersion curve meet the preset conditions, or the number of iterations reaches the preset number, and then describes the spatial distribution of the P-wave velocity and S-wave velocity of the target stratum through the second elastic parameter model.

[0060] Based on any of the above optional technical solutions, optionally, the first update module 304 includes: a fourth determining unit and a second update unit. The fourth determining unit is used to determine a first gradient and a second gradient based on the residuals of the first and second dispersion curves. The first gradient indicates the update amount and direction of the shear wave velocity, and the second gradient indicates the update amount and direction of the longitudinal wave velocity. The second update unit is used to update the shear wave velocity model in the target model based on the first gradient and to update the longitudinal wave velocity model in the target model based on the second gradient, thereby obtaining a second elastic parameter model.

[0061] Based on any of the above optional technical solutions, optionally, the fourth determining unit includes: a fifth determining sub-unit, a sixth determining sub-unit, and a seventh determining sub-unit. The fifth determining sub-unit is used to determine the third gradient and the fourth gradient. The third gradient is the inversion gradient of the shear wave velocity determined based on the first accompanying source. The first accompanying source is a virtual seismic accompanying source determined based on the residual of the first dispersion curve. The fourth gradient is the inversion gradient of the P-wave velocity determined based on the second accompanying source. The second accompanying source is a virtual seismic accompanying source determined based on the residual of the second dispersion curve. The sixth determining sub-unit is used to determine the fifth gradient and the sixth gradient. The fifth gradient is the derivative of the model constraint term with respect to the shear wave velocity, and the sixth gradient is the derivative of the model constraint term with respect to the P-wave velocity. The model constraint term is determined based on the target model. The seventh determining sub-unit is used to determine the first gradient based on the third gradient and the fifth gradient, and to determine the second gradient based on the fourth gradient and the sixth gradient.

[0062] Based on any of the above optional technical solutions, optionally, the second update unit is specifically used to determine the first step length and the second step length, where the first step length is the gradient step length of the shear wave velocity and the second step length is the gradient step length of the longitudinal wave velocity; update the shear wave velocity model in the target model based on the first step length and the first gradient; and update the longitudinal wave velocity model in the target model based on the second step length and the second gradient.

[0063] Based on any of the above-mentioned optional technical solutions, the first update module 304 may optionally further include: a first processing unit. The first processing unit is configured to perform target processing on the first gradient and the second gradient respectively after determining the first gradient and the second gradient based on the first dispersion curve residual and the second dispersion curve residual. The target processing includes at least one of illumination compensation processing, normalization processing, spatial smoothing processing, and boundary constraint processing.

[0064] Based on any of the above-mentioned optional technical solutions, optionally, the first determining module 301 includes: an eighth determining unit and a ninth determining unit. The eighth determining unit is used to determine first waveform data and second waveform data, wherein the first waveform data is waveform data of a target surface wave extracted from the first seismic data, and the second waveform data is waveform data of a target guided wave extracted from the first seismic data; the ninth determining unit is used to determine first dispersion curve information based on the first waveform data, and to determine second dispersion curve information based on the second waveform data.

[0065] Based on any of the above optional technical solutions, optionally, the joint inversion device for P-wave velocity and S-wave velocity includes: a tenth determining module. The tenth determining module is used to determine the joint inversion result, which includes at least one of a first velocity model, a second velocity model, a third velocity model, a first fitting result, a second fitting result, and iterative update information of the target model. The first velocity model is the S-wave velocity model in the second elastic parameter model; the second velocity model is the P-wave velocity model in the second elastic parameter model; the third velocity model is the P-wave / S-wave velocity ratio model determined based on the first and second velocity models; the first fitting result is the fitting result of the dispersion curve information of the target surface wave during the iterative update of the target model; and the second fitting result is the fitting result of the dispersion curve information of the target guided wave during the iterative update of the target model.

[0066] The technical solution of this invention involves determining first and second dispersion curve information through a first determining module 301. The first dispersion curve information is the dispersion curve information of the target surface wave determined based on first seismic data, and the second dispersion curve information is the dispersion curve information of the target guided wave determined based on the first seismic data. The first seismic data is obtained by collecting data from the target strata. A second determining module 302 determines a first elastic parameter model and uses this model as the target model. A third determining module 303 determines third and fourth dispersion curve information. The third dispersion curve information is the dispersion curve information of the target surface wave determined based on the second seismic data, and the fourth dispersion curve information is the dispersion curve information of the target guided wave determined based on the second seismic data. The second seismic data is obtained by forward modeling the elastic wave equation based on the target model, thus achieving the same elastic wave equation for both P-wave velocity models and S-wave velocity models. The velocity model is forward modeled. The first update module 304 updates the target model based on the residuals of the first and second dispersion curves to obtain the second elastic parameter model. This reduces the nonlinearity of directly fitting complex seismic waveforms, helps alleviate local minima, and thus lowers the accuracy requirements of the initial model. The first execution module 305 uses the second elastic parameter model as the new target model and returns to the steps of determining the information of the third and fourth dispersion curves until the residuals of the first and second dispersion curves meet preset conditions or the number of iterations reaches a preset number. Through iterative updates of the first elastic parameter model, the joint inversion of the P-wave velocity model and the S-wave velocity model is effectively achieved, ensuring the consistency of the P-wave velocity structure and S-wave velocity structure of the target stratum. This allows for the accurate P-wave / S-wave velocity ratio of the target stratum, facilitating precise interpretation of the geological properties of the target stratum.

[0067] The joint inversion device for P-wave velocity and S-wave velocity provided in this embodiment of the invention can execute the joint inversion method for P-wave velocity and S-wave velocity provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0068] Figure 5 This is a schematic diagram of an electronic device for implementing a joint inversion method for P-wave velocity and S-wave velocity, provided as an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0069] like Figure 5 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from storage unit 18. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0070] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0071] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the joint inversion method of P-wave velocity and S-wave velocity.

[0072] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication unit 19, or installed from storage unit 18, or installed from ROM 12. When the computer program is executed by processor 11, it performs the functions defined in the methods of the embodiments of the present invention.

[0073] In some embodiments, the joint inversion method for P-wave and S-wave velocities can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the joint inversion method for P-wave and S-wave velocities described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the joint inversion method for P-wave and S-wave velocities by any other suitable means (e.g., by means of firmware).

[0074] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transferring data and instructions to the storage system, the at least one input device, and the at least one output device.

[0075] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0076] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0077] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0078] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0079] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0080] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0081] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for jointly inverting longitudinal wave velocity and transverse wave velocity, characterized in that, The method includes: First dispersion curve information and second dispersion curve information are determined. The first dispersion curve information is the dispersion curve information of the target surface wave determined based on the first seismic data. The second dispersion curve information is the dispersion curve information of the target guided wave determined based on the first seismic data. The first seismic data is obtained by collecting data from the target stratum. The target surface wave is the surface wave in the target stratum. The target guided wave is the guided wave formed by the interference superposition of reflected and refracted waves of the longitudinal wave in the layered medium of the target stratum. A first elastic parameter model is determined and used as the target model. The first elastic parameter model includes the initial P-wave velocity model, S-wave velocity model and density model of the target formation. The third dispersion curve information and the fourth dispersion curve information are determined. The third dispersion curve information is the dispersion curve information of the target surface wave determined based on the second seismic data. The fourth dispersion curve information is the dispersion curve information of the target guided wave determined based on the second seismic data. The second seismic data is obtained by forward modeling the elastic wave equation based on the target model. The target model is updated based on the residuals of the first and second dispersion curves to obtain the second elastic parameter model. The residual of the first dispersion curve is the difference between the information of the third dispersion curve and the information of the first dispersion curve. The residual of the second dispersion curve is the difference between the information of the fourth dispersion curve and the information of the second dispersion curve. The second elastic parameter model is used as the new target model, and the steps to determine the third and fourth dispersion curve information are returned until the residuals of the first and second dispersion curves meet the preset conditions, or the number of iterations reaches the preset number. Then, the spatial distribution of the P-wave velocity and S-wave velocity of the target stratum is described by the second elastic parameter model.

2. The method according to claim 1, characterized in that, The target model is updated based on the residuals of the first and second dispersion curves to obtain a second elastic parameter model, including: The first gradient and the second gradient are determined based on the residuals of the first and second dispersion curves. The first gradient is used to indicate the update amount and update direction of the shear wave velocity, and the second gradient is used to indicate the update amount and update direction of the longitudinal wave velocity. The transverse wave velocity model in the target model is updated based on the first gradient, and the longitudinal wave velocity model in the target model is updated based on the second gradient to obtain the second elastic parameter model.

3. The method according to claim 2, characterized in that, The first gradient and the second gradient are determined based on the residuals of the first and second dispersion curves, including: The third gradient and the fourth gradient are determined. The third gradient is the inversion gradient of the shear wave velocity based on the first associated source, which is a virtual seismic associated source determined based on the residual of the first dispersion curve. The fourth gradient is the inversion gradient of the p-wave velocity based on the second associated source, which is a virtual seismic associated source determined based on the residual of the second dispersion curve. The fifth and sixth gradients are determined, wherein the fifth gradient is the derivative of the model constraint term with respect to the shear wave velocity, and the sixth gradient is the derivative of the model constraint term with respect to the p-wave velocity. The model constraint terms are determined based on the target model. The first gradient is determined based on the third gradient and the fifth gradient, and the second gradient is determined based on the fourth gradient and the sixth gradient.

4. The method according to claim 2, characterized in that, The process involves updating the shear wave velocity model in the target model based on the first gradient and updating the p-wave velocity model in the target model based on the second gradient, including: Determine the first step length and the second step length, wherein the first step length is the gradient step length of the transverse wave velocity and the second step length is the gradient step length of the longitudinal wave velocity; The shear wave velocity model in the target model is updated based on the first step length and the first gradient; The longitudinal wave velocity model in the target model is updated based on the second step size and the second gradient.

5. The method according to claim 2, characterized in that, After determining the first gradient and the second gradient based on the residuals of the first and second dispersion curves, the process also includes: The first gradient and the second gradient are respectively subjected to target processing, which includes at least one of illumination compensation processing, normalization processing, spatial smoothing processing and boundary constraint processing.

6. The method according to claim 1, characterized in that, Determine the information of the first dispersion curve and the second dispersion curve, including: First waveform data and second waveform data are determined, wherein the first waveform data is the waveform data of the target surface wave extracted from the first seismic data, and the second waveform data is the waveform data of the target guided wave extracted from the first seismic data; The first dispersion curve information is determined based on the first waveform data, and the second dispersion curve information is determined based on the second waveform data.

7. The method according to claim 1, characterized in that, The method further includes: The joint inversion results are determined, which include at least one of the following: a first velocity model, a second velocity model, a third velocity model, a first fitting result, a second fitting result, and iterative update information of the target model. The first velocity model is the shear wave velocity model in the second elastic parameter model, the second velocity model is the longitudinal wave velocity model in the second elastic parameter model, the third velocity model is the longitudinal wave / semi-wave velocity ratio model determined based on the first velocity model and the second velocity model, the first fitting result is the fitting result of the dispersion curve information of the target surface wave during the iterative update of the target model, and the second fitting result is the fitting result of the dispersion curve information of the target guided wave during the iterative update of the target model.

8. A joint inversion device for longitudinal wave velocity and transverse wave velocity, characterized in that, The device includes: The first determining module is used to determine the first dispersion curve information and the second dispersion curve information. The first dispersion curve information is the dispersion curve information of the target surface wave determined based on the first seismic data, and the second dispersion curve information is the dispersion curve information of the target guided wave determined based on the first seismic data. The first seismic data is obtained by collecting data from the target stratum. The target surface wave is a surface wave in the target stratum, and the target guided wave is a guided wave formed by the interference superposition of reflected and refracted waves of the longitudinal wave in the layered medium of the target stratum. The second determining module is used to determine the first elastic parameter model and use the first elastic parameter model as the target model. The first elastic parameter model includes the initial P-wave velocity model, S-wave velocity model and density model of the target formation. The third determining module is used to determine the third dispersion curve information and the fourth dispersion curve information. The third dispersion curve information is the dispersion curve information of the target surface wave determined based on the second seismic data, and the fourth dispersion curve information is the dispersion curve information of the target guided wave determined based on the second seismic data. The second seismic data is obtained by forward modeling the elastic wave equation based on the target model. The first update module is used to update the target model based on the first dispersion curve residual and the second dispersion curve residual to obtain a second elastic parameter model. The first dispersion curve residual is the difference between the third dispersion curve information and the first dispersion curve information, and the second dispersion curve residual is the difference between the fourth dispersion curve information and the second dispersion curve information. The first execution module is used to take the second elastic parameter model as the new target model and return to the execution of the steps to determine the third dispersion curve information and the fourth dispersion curve information until the residuals of the first dispersion curve and the second dispersion curve meet the preset conditions, or the number of iterations reaches the preset number, and then describes the spatial distribution of the P-wave velocity and S-wave velocity of the target stratum through the second elastic parameter model.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the joint inversion method of P-wave velocity and S-wave velocity as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the joint inversion method for longitudinal wave velocity and transverse wave velocity as described in any one of claims 1-7.

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