A near-surface anisotropic first-arrival wave tomographic inversion modeling method and related equipment

Abstract

The present invention discloses a near-surface anisotropic first-arrival wave tomographic inversion modeling method and related equipment. The present invention is closer to the actual geological conditions and the propagation laws of seismic waves from the theoretical assumptions and actual inversion results, and provides a near-surface model that is more in line with the actual geological conditions for pre-stack depth migration. It advances the current "static correction" based on isotropy and pre-stack depth migration on an undulating surface to pre-stack depth migration on anisotropic undulating surfaces, thereby improving the imaging quality of seismic data in dual-complex exploration areas. This is also a huge technological advancement. By using it for pre-stack depth migration on undulating surfaces, better imaging effects than before are achieved. The present invention utilizes the characteristics of the first-arrival wave with a high signal-to-noise ratio and the fact that it propagates entirely near the surface, takes into account the near-surface anisotropy, and improves the accuracy and imaging quality of the imaging position of the depth migration.

Description

Technical Field

[0001] The present invention belongs to the field of oil and gas seismic exploration, and in particular relates to a near-surface anisotropic first-arrival wave tomography inversion modeling method and related equipment. Background Art

[0002] Figure 1 This is a near-surface topographic map of a certain exploration area. The strata and lithologies vary dramatically laterally. There are relatively flat exposed strata, but also a large number of strata with large dips that are nearly vertical, or even reversed. Directed bedding structures, especially when there are fracture fillings between the bedding layers, will exhibit anisotropy. The lithology parallel to the bedding direction is consistent, and the wave propagation velocity is high. However, the lithology perpendicular to the bedding direction varies (such as fracture fillings or thin interbeds), and the wave propagation velocity is low. The overall performance is anisotropy. Near-surface anisotropy distorts the ray propagation path, affecting the imaging effect and accuracy. Studying anisotropic near-surface velocity modeling methods is an important way to improve the imaging quality of seismic data in complex exploration areas.

[0003] Figure 2 It shows that when the stratum is tilted (black oblique line in the figure) and anisotropy exists, the propagation of the seismic wave front is shown as the blue arc in the figure, which shows that the propagation speed is higher along the stratum direction. When there is no anisotropy, the wave front is symmetrical, as shown by the red line in the figure. The imaging points under the two media are different, as shown by the red and blue arrows in the figure respectively.

[0004] The anisotropic nature of seismic wave propagation causes non-hyperbolic moveouts in CDP gathers, making them incapable of correction using standard normal moveout analysis. Whether using prestack time migration or prestack depth migration, ignoring the anisotropic properties of the overlying layer can lead to significant lateral deviations in the image position. This is particularly true for complex exploration areas. To achieve focused stack imaging, attempts are often made to flatten the common imaging point gathers. However, ignoring the effects of anisotropy can actually cause velocities to deviate from their correct values. When the overburden dips at 45 degrees, the lateral deviation is the product of the thickness of the overburden and the ratio of normal velocity to horizontal velocity. Lateral deviation error is related to the offset; larger offsets increase the deviation, leading to blurred stack images. Ray tracing studies have shown that strata near 20 or 65 degrees have a greater impact on image blur than steeper strata. If the angle of the floating formation is between 10 and 80 degrees, anisotropy must be considered. Otherwise, it will cause lateral deviation of the position and blurred imaging. Similarly, if anisotropy is ignored, the propagation speed will be greater than the actual speed, which will also cause the depth of the imaging point to be greater than the drilling result.

[0005] Figure 3Figures a and b show the results of isotropic (left) and anisotropic (right) prestack depth migration for a specific work area. The green dashed line represents the horizon that matches the target layer, and the red line represents the depth obtained through drilling. The target layer is approximately 2500 meters deep. The isotropic migration results in a 175-meter depth difference compared to the drilling depth, while the anisotropic migration error is only 15 meters. Within a single wavelength, the error in relative depth is less than 1%. This demonstrates that accounting for the anisotropic characteristics of the overburden significantly reduces the depth error.

[0006] Figure 4 This is a forward modeling study of anisotropy in a complex exploration area. The figure above is a near-surface velocity model established based on first-arrival wave tomographic inversion. The black arrows represent the angle field determined according to the near-surface topography map of the area. Here, anisotropy is set only near the surface (above the gray solid line in the figure), with Epsilon = 0.16 and Delta = 0.04. The deep velocity of the model comes from the geological interpretation results, and the anisotropy parameters are all set to zero. Therefore, the model has anisotropy only near the surface and no anisotropy at depth. Figure 5 The depth migration results with and without considering the near-surface anisotropy are shown respectively. It is not difficult to find that, when the near-surface anisotropy is ignored, the imaging effect is significantly worse for complex structures such as overthrust nappes.

[0007] Near-surface anisotropy is widely present in complex exploration areas, and shallow surface reflection waves have a short effective arrangement and are often suppressed by surface waves and oblique interference, resulting in a low signal-to-noise ratio. It is difficult to establish near-surface anisotropy properties based on reflection waves. Therefore, the influence of near-surface anisotropy is not considered in current actual data processing. Previous actual data and model forward modeling studies have shown that ignoring near-surface anisotropy affects the imaging position and imaging quality of depth migration. Summary of the Invention

[0008] The present invention provides a near-surface anisotropic first-arrival wave tomographic inversion modeling method, which solves the problem in the prior art of ignoring near-surface anisotropy and affecting the imaging position and imaging quality of depth migration.

[0009] To achieve the above object, the present invention provides the following technical solutions:

[0010] A near-surface anisotropic first-arrival wave tomographic inversion modeling method, comprising:

[0011] According to the field observation system SPS file and seismic data, the observation system is set up, the first arrival time is picked, and the initial velocity model is established;

[0012] The main shallow reflector layer is picked up based on the isotropic deep deflection profile, and the near-surface stratum azimuth and dip field model is established based on the main shallow reflector layer picked up based on the isotropic deep deflection profile;

[0013] The first arrival time is subjected to azimuthally isotropic first arrival wave tomographic inversion, and the near-surface Epsilon and Delta initial models are established based on the azimuthally isotropic first arrival wave tomographic inversion results.

[0014] The anisotropic first arrival travel time is forward modeled based on the near-surface formation azimuth and dip field model, initial velocity model, and near-surface Epsilon and Delta initial model to obtain the theoretical first arrival time and ray path.

[0015] Anisotropic first-arrival wave tomography inversion equation is established based on theoretical first-arrival time and ray path, and sparse inversion is performed on the anisotropic first-arrival wave tomography inversion equation in wavelet domain to obtain the inversion result.

[0016] The inversion results are evaluated. If the convergence conditions are met, the inversion results are output. If the convergence conditions are not met, the initial model is updated, including velocity, Epsilon and Delta, the first arrival time and ray path are recalculated, and the inversion is performed until the convergence conditions are met and the inversion results are output.

[0017] Preferably, the azimuthally isotropic first-arrival wave tomographic inversion results are specifically the velocity, main energy ray azimuth and inclination of each azimuth inversion.

[0018] Preferably, the initial velocity model is obtained based on a refracted wave delay time method.

[0019] Preferably, the theoretical first arrival time and the ray path are obtained by first obtaining the wavefront time equation of the anisotropic medium, which is:

[0020]

[0021] Where τ is the wavefront time, α is the phase angle, and v(α) is the phase velocity;

[0022] Thomsen obtained an approximate expression for the phase velocity under weak anisotropy conditions:

[0023] v(α)=v n (1+εsin 4 α+δsin 2 αcos 2 α)

[0024] v n is the velocity in the normal direction of the formation, δ is Delta, ε is Epsilon, and both δ and ε are Thomsen parameters;

[0025] Based on the approximate expression of phase velocity obtained by Thomsen under weak anisotropy conditions, the wavefront time equation of anisotropic media is solved to obtain the theoretical first arrival time and ray path.

[0026] Preferably, the anisotropic first-arrival wave tomographic inversion equation is established based on the theoretical first-arrival time and the ray path. Specifically, based on the previously determined ray path and theoretical first-arrival time, the length of the ray in the grid, the theoretical pickup time, and the calculated travel time difference are obtained to establish the anisotropic first-arrival wave tomographic inversion equation, which includes three unknowns: the formation normal velocity, Epsilon, and Delta.

[0027] Preferably, the wavelet domain sparse equations are solved by an LSQR algorithm.

[0028] Preferably, the output inversion results include normal velocity field, Epsilon field and Delta field.

[0029] A near-surface anisotropic first-arrival wave tomographic inversion modeling system, comprising:

[0030] The first model building module: According to the field observation system SPS file and seismic data, the observation system is set up, the first arrival time is picked, and the initial velocity model is established;

[0031] The second model building module: based on the isotropic deep deflection profile, the main shallow surface reflection layer is picked up, and the near-surface stratum azimuth and dip field model is established based on the main shallow surface reflection layer picked up by the isotropic deep deflection profile;

[0032] The third model building module: performs azimuthally isotropic first-arrival wave tomographic inversion on the first-arrival time, and establishes the near-surface Epsilon and Delta initial models based on the azimuthally isotropic first-arrival wave tomographic inversion results;

[0033] Forward modeling module: forward modeling of anisotropic first arrival travel time based on near-surface formation azimuth and dip field models, initial velocity models, and near-surface Epsilon and Delta initial models to obtain theoretical first arrival time and ray path;

[0034] Solution module: establishes anisotropic first-arrival wave tomography inversion equations based on theoretical first-arrival time and ray path, and performs sparse inversion in the wavelet domain;

[0035] Evaluation module: Evaluate the inversion results. If the convergence conditions are met, the inversion results are output. If the convergence conditions are not met, the initial model is updated, including velocity, Epsilon and Delta, the first arrival time and ray path are recalculated, and the inversion is performed until the convergence conditions are met and the inversion results are output.

[0036] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the near-surface anisotropic first-arrival wave tomography inversion modeling method are implemented.

[0037] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a near-surface anisotropic first-arrival wave tomography inversion modeling method.

[0038] Compared with the existing technology, the present invention has the following beneficial effects: the present invention provides a near-surface anisotropic first-arrival wave tomographic inversion modeling method. The present invention is closer to the actual geological conditions and the propagation law of seismic waves in terms of theoretical assumptions and actual inversion results, and provides a near-surface model that is more in line with the actual geological conditions for pre-stack depth migration. It advances the current "static correction" based on isotropy and pre-stack depth migration of undulating surfaces to anisotropic pre-stack depth migration of undulating surfaces, thereby improving the imaging quality of seismic data in dual-complex exploration areas. This is also a huge technological advancement. By being used for pre-stack depth migration of undulating surfaces, better imaging effects than before have been achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A typical near-surface topography map of the frontal zone of a certain region;

[0040] Figure 2 Schematic diagram of seismic wave front in anisotropic medium;

[0041] Figure 3 This is the second example of the effect of anisotropy on imaging effects;

[0042] Figure 4 It is a theoretical model of near-surface anisotropy;

[0043] Figure 5 Depth migration results with (left) and without (right) near-surface anisotropy considered;

[0044] Figure 6 The first arrival time curve (top) and elevation (bottom);

[0045] Figure 7 It is a dip field established based on previous isotropic deep deflection profiles. The shallow surface structure is high and steep, and the lateral changes are drastic.

[0046] Figure 8 The velocity field is obtained by tomographic inversion of isotropic first-arrival waves using the actual first-arrival time.

[0047] Figure 9 The velocity field is obtained by performing anisotropic first-arrival wave tomographic inversion using the actual first-arrival time.

[0048] Figure 10 Delta field from tomographic inversion of anisotropic first-arrival waves.

[0049] Figure 11 Epsilon field of anisotropic first-arrival wave tomographic inversion.

[0050] Figure 12 Based on the depth migration results of the near-surface model using isotropic and anisotropic first-arrival wave tomography inversion, the continuity of the steeply dipping strata on the left flank is strengthened, and the imaging quality of the depression between the two mountains is significantly improved.

[0051] Figure 13 The flowchart of near-surface modeling using anisotropic first-arrival tomographic inversion includes the following steps: setting up the observation and picking the first arrival; picking the main shallow layers of the isotropic deep-deflected profile and establishing the near-surface dip and azimuth fields; obtaining the initial values ​​of the anisotropic parameters Epsilon and Delta through azimuth inversion; obtaining the first-arrival traveltime and ray path through forward modeling of the anisotropic model; establishing the tomographic inversion equation group; and establishing and solving the sparse equation group in the wavelet domain.

[0052] Figure 14 An example of the observation system and quality control is shown. The displayed linear correction box (red line in the figure) is consistent with the trend of the first arrival data, indicating that the observation is correct.

[0053] Figure 15 An example of first arrival picking is shown. The red dot in the figure is the picked first arrival time, which coincides with the first arrival wavelet, indicating that the picking is correct.

[0054] Figure 16 It shows how to pick reflection horizons and build shallow surface dip and azimuth fields on an isotropic deep deflection profile. Figure 16 The solid lines of different colors on the section in (a) represent the different picked horizons; Figure 16 (b) Displaying the layers picked up from different sections in three-dimensional space, the spatial distribution of each geological layer can be clearly seen; Figure 16 (c) is the calculation of the dip field based on the picked horizon, with the dip angle range from 0° to 90°; Figure 16 (d) is the azimuth field calculated based on the picked horizon.

[0055] Figure 17 To illustrate the azimuth, the seismic traces were intercepted according to the angle between the shot-detection line and the detection point direction. Here, the data sets were divided into 6 groups: (-30°-30°), (0°-60°), (30°-90°), (60°-120°), (90°-150°), and (120°-180°).

[0056] Figure 18is the velocity field obtained by inversion in six directions;

[0057] Figure 19 It is the ray density distribution map obtained by inversion in six directions;

[0058] Figure 20 It is the azimuth distribution diagram of the main energy rays obtained by inversion in six azimuths;

[0059] Figure 21 It is the main energy ray inclination distribution map obtained by inversion in six azimuths. The main energy is the main part of all rays in each grid;

[0060] Figure 22 is the anisotropic normal velocity distribution map calculated based on the azimuth inversion results;

[0061] Figure 23 is the Delta field distribution map obtained from the azimuth inversion results;

[0062] Figure 24 is the Epsilon field distribution map obtained from the azimuth inversion results;

[0063] Figure 25 This is an example of anisotropic first arrival travel time forward modeling. A single-layer near-surface anisotropic model is designed, where the inclination angle is 18°, the azimuth angle is 20°, Epsilon is 0.25, and Delta is 0.1. (a) is the gradually varying normal velocity field, (b) is the anisotropic wavefront time field, and (c) is a schematic diagram of ray tracing time shift.

[0064] Figure 26 The present invention provides a near-surface anisotropic first-arrival wave tomographic inversion modeling system. DETAILED DESCRIPTION

[0065] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0066] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort shall fall within the scope of protection of the present invention.

[0067] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0068] In the description of the embodiments of the present invention, it should be noted that if the terms "upper," "lower," "horizontal," "inner," etc. appear, the orientation or positional relationship indicated is based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the inventive product is typically placed when in use. These terms are merely for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or component referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limitations on the present invention. In addition, the terms "first," "second," etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0069] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0070] In the description of the embodiments of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "disposed," "installed," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to internal connections between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0071] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0072] like Figure 13 As shown, a near-surface anisotropic first-arrival wave tomographic inversion modeling system includes the following steps:

[0073] S1: Seismic data is placed in the observation system and the first arrival is picked. According to the given field observation system SPS file and seismic data, the observation system is placed, quality control and inspection are carried out, and then the first arrival time is picked accurately.

[0074] S2: Pick the main shallow surface layers of the isotropic depth migration section. The anisotropic processing is based on the isotropic processing results. The early isotropic deep deviation section can be used to more accurately obtain the angle field information of the stratum interface. First, pick the layers on each inline section, and each layer forms a surface in space.

[0075] S3: Based on the multiple geological layer "surfaces" picked up in the previous step, calculate the normal direction of each grid point according to the following formula. Then, calculate the inclination and azimuth. The angle between the normal direction and the Z axis of the model is defined as the inclination, and the angle between the normal direction and the north direction of the geodetic coordinate (i.e., the Y coordinate) is defined as the azimuth.

[0076] Suppose P0(x0,y0,z0) is a known point on the stratum. To obtain the dip and azimuth of the stratum at point P0, take another n known points P1(x1,y1,z1), P2(x2,y2,z2), ..., P0 at different directions near P0. n (x n ,y n ,z n ), find the value of P0, P1, ..., P n The plane where the sum of the squares of the distances is the smallest is used as the dip and azimuth of the formation at point P0. The calculation steps are as follows:

[0077] (1) Input P0, P1, ..., P n The coordinates of the point are x0, y0, z0; x1, y1, z1; ...; x n ,y n ,z n .

[0078] (2) Calculate a according to the following formula ij (i=1,2,3;j=1,2,3),d j (j=1,2,3)

[0079]

[0080]

[0081]

[0082] (3) Calculate D, D1, and D2 according to the following formulas.

[0083]

[0084]

[0085]

[0086] (4) Calculate c1 and c2 according to the following formula.

[0087] c1=D1 / D, c2=D2 / D.

[0088] (5) Calculate the azimuth of the formation at point P0 by the following formula

[0089]

[0090]

[0091] (6) Press to calculate the dip angle θ of the formation at point P0.

[0092]

[0093] S4: The first arrival time is divided into azimuths, and azimuthally isotropic first arrival wave tomographic inversion is performed to obtain the velocity, main energy ray azimuth and inclination of each azimuth inversion result.

[0094] S5: Due to the influence of anisotropy, the results of different azimuthal inversions vary. This paper uses these differences to backcalculate Epsilon and Delta. Theoretically, the azimuthal inversion still uses isotropic tomographic inversion, which does not conform to actual geological conditions. Therefore, it is used as the initial field for the anisotropic attribute parameters Epsilon and Delta.

[0095] (1) Assume that there is a ray I passing through a near-surface imaging unit, and the inclination angle θ of this ray is known. i (i=1,2,L,I), azimuth and the group velocity V on each ray i (i=1,2,L,I), the corresponding group slowness is Assume that the imaging unit is a TTI medium, and the inclination and orientation of its symmetry axis are θ0 and The angles between each ray and the axis of symmetry (i.e., group angles) are φ i (i=1,2,L,I). Establish the following system of equations with respect to the parameters a1, a2 and a3.

[0096]

[0097] in,

[0098]

[0099] (2) Use the least squares method to solve the above equations to obtain the parameters a1, a2 and a3.

[0100] (3) Solving the following equations, we can obtain the velocity V in the direction of the symmetry axis. P⊥ And Thomsen parameters Epsilon and Delta.

[0101]

[0102] S6: Under the anisotropic model, the propagation velocity of seismic waves varies depending on the angle between the propagation velocity and the normal direction of the formation. The velocity here refers to the propagation velocity perpendicular to the interface, that is, the propagation velocity in the direction of the interface normal. Here, the initial velocity model is still established based on the refracted wave delay time method.

[0103] S7: Forward modeling of anisotropic first arrival travel time to obtain the theoretical first arrival time.

[0104] The wavefront time equation in anisotropic media is:

[0105]

[0106] Where τ is the wavefront time, α is the phase angle, and v(α) is the phase velocity. Thomsen obtained an approximate expression for the phase velocity under weak anisotropy conditions:

[0107] v(α)=v n (1+εsin 4 α+δsin 2 αcos 2 α)

[0108] v n is the formation normal velocity, δ and ε are Thomsen parameters.

[0109] Solve equation (1) to obtain the first arrival time and ray path.

[0110] S8: Based on the previously determined ray path and first arrival time, the length of the ray in the grid Δl and the difference between the picking time and the calculated travel time Δt are obtained, and the anisotropic first arrival wave tomography inversion equation is established, which contains three unknowns: the formation normal velocity, Epsilon, and Delta.

[0111]

[0112] in:

[0113]

[0114] Where subscript i is the ray number and j is the grid number.

[0115] S9: The sparse inversion equation group in the wavelet domain is established, and the tomographic inversion equation is subjected to wavelet transform to form a sparse equation group.

[0116] Let I, J, K be the number of grids in the z, y and x directions. Any ray satisfies the following equation:

[0117]

[0118] For example, if the grid slowness Δs is subjected to orthogonal wavelet transform in the x and y directions, it can be decomposed into the smoothness coefficient x and the detail coefficient d. Since the detail coefficient is approximately 0, Δs can be approximately expressed by x as follows:

[0119]

[0120] where h x and h y For the reconstruction filter in the x and y directions, replace Δs in the equation with the above formula to obtain the following equation with the wavelet coefficient x as the unknown number

[0121]

[0122]

[0123] In the above formula, the dimension of the wavelet coefficient x is N=I*J*K / 4, which is one fourth of the dimension I*J*K of Δs. The coefficient Δl of the original equation is decomposed by wavelet to obtain the coefficient of the new equation. The unknown number of the new equation is one tenth of the original equation.

[0124] S10: LSQR least squares QR decomposition to solve the inverse equation.

[0125] Because the equations used in tomographic inversion are often ill-posed and lack exact solutions, only optimal solutions can be sought. Different optimization methods can lead to significant differences in the resulting solutions. The LSQR algorithm is commonly used in conventional tomographic inversion because of its small memory footprint, moderate speed, fast convergence, and stable results.

[0126] The LSQR algorithm is an algorithm for computing large sparse linear systems, proposed by Paige and Saunders in 1982. The method primarily solves the following linear system (A is an m*n matrix, m>n) while minimizing the second-order residual norm:

[0127] Ax=b

[0128]

[0129] in

[0130] A={c ij} M×N

[0131] b=[Δt1,Δt2,...,Δt M ] T

[0132] x=[x1,x2,...,x N ] T

[0133] For traditional least squares problems, the solution to the equation can be directly obtained. However, for large sparse matrices, it is often an ill-posed problem (overdetermined or underdetermined system of equations). Therefore, the LSQR method does not directly solve x, but adheres to the principle of least squares and seeks the optimal solution in the space limited by the normal equation.

[0134] S11: There are two evaluation methods for inversion results: one is that the iterative convergence difference is less than a given threshold, and the other is that the convergence difference of the last few iterations tends to be stable.

[0135] Based on the velocity, epsilon, and delta fields generated by each iterative inversion, anisotropic first-arrival traveltimes are forward-modeled to obtain theoretical synthetic times. The mean square error between this set of times and the actual first-arrival times is called the iterative convergence error. When the iterative convergence error is less than a given threshold, the inversion result is considered reliable and output. Because the iterative convergence error varies across different work areas, it is difficult to assign a reasonable threshold in actual production. Another evaluation method is to consider the iterative convergence error stable, that is, if the iterative convergence error no longer decreases with increasing iterations, the inversion result is considered reasonable and output.

[0136] S12: If the iterative convergence conditions are met, the inversion results are output, including the normal velocity field, Epsilon field, and Delta field. If the iterative convergence conditions are not met, the model is updated and S6 to S10 are repeated until the convergence conditions are met.

[0137] The present invention is closer to the actual geological conditions and the propagation laws of seismic waves in terms of theoretical assumptions and actual inversion results, providing a near-surface model that is more in line with the actual geological conditions for pre-stack depth migration. It advances the current "static correction" and pre-stack depth migration based on isotropy and undulating surfaces to pre-stack depth migration based on anisotropy and undulating surfaces, thereby improving the imaging quality of seismic data in dual-complex exploration areas. This is also a huge technological advancement. By using it for pre-stack depth migration on undulating surfaces, better imaging effects have been achieved than before. The application effect on a two-dimensional survey line in a mountainous area in Xiqiu is demonstrated here.

[0138] Figure 6 It is the first arrival time and topography of the two-dimensional survey line, with an elevation difference of 700 meters; Figure 7 It is a dip field established based on previous isotropic deep deviated profiles, with high and steep shallow surface structures and dramatic lateral variations; Figure 8 and Figure 9 These are the velocity models obtained by isotropic and anisotropic tomographic inversion. It is not difficult to find that the shallow surface velocity law obtained by anisotropic inversion is more reasonable. Figure 10 and 11 are the anisotropic attribute parameters Epsilon and Delta values ​​obtained by inversion; Figure 12The results of pre-stack depth migration obtained by using the velocity models of isotropic inversion and anisotropic inversion respectively are demonstrated. The continuity of the steeply dipped strata on the left flank is enhanced, and the imaging quality of the depression between the two mountains is significantly improved.

[0139] like Figure 26 The present invention further provides a near-surface anisotropic first-arrival wave tomographic inversion modeling system, comprising:

[0140] The first model building module: According to the field observation system SPS file and seismic data, the observation system is set up, the first arrival time is picked, and the initial velocity model is established;

[0141] The second model building module: based on the isotropic deep deflection profile, the main shallow surface reflection layer is picked up, and the near-surface stratum azimuth and dip field model is established based on the main shallow surface reflection layer picked up by the isotropic deep deflection profile;

[0142] The third model building module: performs azimuthally isotropic first-arrival wave tomographic inversion on the first-arrival time, and establishes the near-surface Epsilon and Delta initial models based on the azimuthally isotropic first-arrival wave tomographic inversion results;

[0143] Forward modeling module: forward modeling of anisotropic first arrival travel time based on near-surface formation azimuth and dip field models, initial velocity models, and near-surface Epsilon and Delta initial models to obtain theoretical first arrival time and ray path;

[0144] Solution module: establishes anisotropic first-arrival wave tomography inversion equations based on theoretical first-arrival time and ray path, performs wavelet transform on the anisotropic first-arrival wave tomography inversion equations to obtain a sparse equation system in the wavelet domain, and then solves the equation system;

[0145] Evaluation module: Evaluate the inversion results. If the convergence conditions are met, the inversion results are output. If the convergence conditions are not met, the initial model (including velocity, Epsilon and Delta) is updated, the first arrival time and ray path are recalculated, and the inversion is performed until the convergence conditions are met and the inversion results are output.

[0146] An embodiment of the present invention provides a terminal device. The terminal device of this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of each of the aforementioned method embodiments are implemented. Alternatively, when the processor executes the computer program, the functions of each module / unit in each of the aforementioned device embodiments are implemented.

[0147] The computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to accomplish the present invention.

[0148] The terminal device may be a computing device such as a desktop computer, a notebook computer, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0149] The processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0150] The memory may be used to store the computer programs and / or modules, and the processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory.

[0151] If the module / unit integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0152] Although the embodiments of the present invention have been described above with reference to the accompanying drawings, the present invention is not limited to the above-mentioned specific embodiments and application fields. The above-mentioned specific embodiments are merely illustrative and instructive, and are not restrictive. A person skilled in the art, guided by the description, may devise various forms without departing from the scope of protection of the claims of the present invention, all of which fall within the scope of protection of the present invention.

Claims

1. A near-surface anisotropic first-arrival wave tomographic inversion modeling method, characterized in that: include: According to the field observation system SPS file and seismic data, the observation system is set up, the first arrival time is picked, and the initial velocity model is established; The main shallow reflector layer is picked up based on the isotropic deep deflection profile, and the near-surface stratum azimuth and dip field model is established based on the main shallow reflector layer picked up based on the isotropic deep deflection profile; The first arrival time is subjected to azimuthally isotropic first arrival wave tomographic inversion, and the near-surface Epsilon and Delta initial models are established based on the azimuthally isotropic first arrival wave tomographic inversion results. The anisotropic first arrival travel time is forward modeled based on the near-surface formation azimuth and dip field model, initial velocity model, and near-surface Epsilon and Delta initial model to obtain the theoretical first arrival time and ray path. Anisotropic first-arrival wave tomography inversion equation is established based on theoretical first-arrival time and ray path, and sparse inversion is performed on the anisotropic first-arrival wave tomography inversion equation in wavelet domain to obtain the inversion result. The inversion results are evaluated. If the convergence conditions are met, the inversion results are output. If the convergence conditions are not met, the initial model is updated, including velocity, Epsilon and Delta, the first arrival time and ray path are recalculated, and the inversion is performed until the convergence conditions are met and the inversion results are output.

2. A near-surface anisotropic first-arrival wave tomographic inversion modeling method according to claim 1, characterized in that: The results of azimuthally isotropic first-arrival tomographic inversion are specifically the velocity, main energy ray azimuth and dip of each azimuth inversion.

3. The near-surface anisotropic first-arrival wave tomographic inversion modeling method according to claim 1, characterized in that: The initial velocity model is obtained based on the refracted wave delay time method.

4. The near-surface anisotropic first-arrival wave tomographic inversion modeling method according to claim 1, characterized in that: The theoretical first arrival time and ray path are obtained by first obtaining the wavefront time equation of the anisotropic medium. The wavefront time equation of the anisotropic medium is: Where τ is the wavefront time, α is the phase angle, and v(α) is the phase velocity; Thomsen obtained an approximate expression for the phase velocity under weak anisotropy conditions: v(a)=v n (1+sin 4 α+δsin 2 acos 2 a) v n is the velocity in the normal direction of the formation, δ is Delta, ε is Epsilon, and both δ and ε are Thomsen parameters; Based on the approximate expression of phase velocity obtained by Thomsen under weak anisotropy conditions, the wavefront time equation of anisotropic media is solved to obtain the theoretical first arrival time and ray path.

5. The near-surface anisotropic first-arrival wave tomographic inversion modeling method according to claim 1, characterized in that: The anisotropic first-arrival wave tomographic inversion equation is established based on the theoretical first-arrival time and ray path. Specifically, based on the previously determined ray path and theoretical first-arrival time, the length of the ray on the grid, the theoretical picking time, and the calculated travel time difference are obtained to establish the anisotropic first-arrival wave tomographic inversion equation, which includes three unknowns: the formation normal velocity, Epsilon, and Delta.

6. The near-surface anisotropic first-arrival wave tomographic inversion modeling method according to claim 1, characterized in that: The wavelet domain sparse equations are solved by the LSQR algorithm.

7. The near-surface anisotropic first-arrival wave tomographic inversion modeling method according to claim 1, characterized in that: The output inversion results include normal velocity field, Epsilon field and Delta field.

8. A near-surface anisotropic first-arrival wave tomographic inversion modeling system, characterized by: include: The first model building module: According to the field observation system SPS file and seismic data, the observation system is set up, the first arrival time is picked, and the initial velocity model is established; The second model building module: based on the isotropic deep deflection profile, the main shallow surface reflection layer is picked up, and the near-surface stratum azimuth and dip field model is established based on the main shallow surface reflection layer picked up by the isotropic deep deflection profile; The third model building module: performs azimuthally isotropic first-arrival wave tomographic inversion on the first-arrival time, and establishes the near-surface Epsilon and Delta initial models based on the azimuthally isotropic first-arrival wave tomographic inversion results; Forward modeling module: forward modeling of anisotropic first arrival travel time based on near-surface formation azimuth and dip field models, initial velocity models, and near-surface Epsilon and Delta initial models to obtain theoretical first arrival time and ray path; Solution module: establishes anisotropic first-arrival wave tomography inversion equations based on theoretical first-arrival time and ray path, and performs sparse inversion in the wavelet domain; Evaluation module: Evaluate the inversion results. If the convergence conditions are met, the inversion results are output. If the convergence conditions are not met, the initial model is updated, including velocity, Epsilon and Delta, the first arrival time and ray path are recalculated, and the inversion is performed until the convergence conditions are met and the inversion results are output.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the near-surface anisotropic first-arrival wave tomographic inversion modeling method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the near-surface anisotropic first-arrival wave tomographic inversion modeling method as claimed in any one of claims 1 to 7 are implemented.

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