Layered modeling apparatus and method for chromatographic model

Abstract

The application belongs to the technical field of geophysical exploration, and particularly discloses a layered modeling device and method for a tomographic model. The device comprises a tomographic inversion velocity module, a velocity difference calculation module, an elevation interface extraction module, a grid point surface layer velocity calculation module, a correction curve module and a layered model module. The method comprises the following steps: firstly, obtaining a grid velocity model through tomographic inversion; then, calculating the longitudinal velocity difference of the grid velocity model to obtain all of the demarcation grid points, extracting the elevation interface and obtaining the surface layer thickness; then, calculating the surface layer velocity at the grid points; and then, establishing the thickness correction curve and the velocity correction curve according to the previous results, and continuously correcting the thickness and the velocity to obtain the final layered model. The application can improve the accuracy of the isovelocity interface extraction, and provides control and verification data for the shallow surface layer velocity modeling of the static correction and the prestack depth migration imaging of the land oil seismic exploration data processing.

Description

Technical Field

[0001] The invention belongs to the technical field of geophysical exploration, and in particular relates to a layered modeling method and device for a tomographic model. Background Art

[0002] The primary method for finding and exploring for oil and natural gas involves three steps: seismic data acquisition, processing, and interpretation. Seismic data processing primarily provides data for interpretation and encompasses a wide range of processes, including static correction, denoising, deconvolution, dynamic correction, velocity analysis, stacking, and migration. Basic seismic theory assumes that the excitation and receiving points are on the same horizontal plane and that seismic wave velocities in the formations are uniform. However, the actual surface elevation varies, and a layer of weathered or unconsolidated sediments, known as the low-velocity layer, is commonly found near the surface. Due to varying degrees of weathering or consolidation, the thickness and velocity of the low-velocity layer vary significantly relative to the underlying strata. This underlying stratum is generally referred to as a high-velocity layer or refractive layer. This layer is separated from the low-velocity layer by an interface with a physical property variation of greater than 30%. These physical properties primarily refer to velocity, lithology, and water content. Furthermore, the seismic wave velocity is 30% higher than the maximum velocity of the low-velocity layer. Lateral variations in the low-velocity layer distort the hyperbolic shape of the reflection wave travel time in seismic exploration, affecting the accuracy of seismic imaging. Therefore, the distortion of the near-surface low-velocity layer on the reflection wave travel time must be eliminated during seismic data processing. There are two methods for eliminating reflection wave travel time distortion in seismic data processing. One method is to first strip the low-velocity layer and then fill it to a horizontal datum plane using the velocity of pre-weathered or consolidated rock. This is called static correction for time-domain imaging of seismic data. The other method is to directly use the velocity and thickness of the low-velocity layer as the near-surface velocity model, which is then integrated with the velocity models of shallow, medium, and deep layers to form the velocity model for depth-domain imaging and depth-domain migration imaging.

[0003] However, both time-domain statics and PSDM (prestack depth migration) require accurate low-velocity reduction thickness and velocity for shallow-surface velocity modeling, i.e., the location of the high-velocity layer top interface. Therefore, to improve the accuracy of statics and PSDM shallow-surface velocity modeling, the continuous velocity model obtained from first-arrival tomographic inversion must be converted into a layered velocity structure.

[0004] First-arrival tomographic inversion has become a major method for calculating static corrections and modeling shallow surface layers in recent years. The resulting velocity model has the characteristics of large depth and high reliability. However, the tomographic velocity model is a continuous model, and when calculating static corrections, it is necessary to find an interface between a low-velocity reduction layer and a high-velocity layer. The usual calculation method is to use a fixed velocity value in the tomographic velocity model to extract a constant velocity interface as the depth value of the interface, which is used as the bottom interface of the low-velocity reduction layer for the entire area. The velocity used when extracting the interface is generally based on the average velocity of the high-velocity layer at the surface survey point in the area or based on the high-velocity layer velocity value previously used by the calculator to extract similar areas. The advantage of this approach is simplicity; the disadvantage is that the high-velocity layer velocity used when extracting the constant velocity interface is subject to artificial intervention. Because the used velocity does not change, it cannot reflect the actual changes in the high-velocity top interface of a region. In particular, when different high-velocity layers exist in a region, the constant velocity interface cannot take both into account when extracting the constant velocity interface. Summary of the Invention

[0005] In order to solve the above-mentioned deficiencies in the known technology, the present invention aims to provide a layered modeling device for a tomographic model to solve the problem of inaccurate extraction of isokinetic interfaces.

[0006] Another object of the present invention is to provide a layered modeling method for a tomographic model, which is implemented using the above-mentioned layered modeling device for a tomographic model. The method improves the shortcomings of conventional methods for extracting interface elevations such as high-speed layers in first-arrival tomographic inversion velocity models, improves the calculation accuracy of static correction values ​​and the accuracy of shallow layer modeling, and provides control and verification data for static correction in onshore oil seismic exploration data processing and shallow layer velocity modeling in pre-stack depth migration imaging.

[0007] To achieve the above object, the technical solutions adopted by the present invention are as follows:

[0008] A layered modeling device for a tomographic model, comprising:

[0009] Tomographic inversion velocity module, velocity difference calculation module, elevation interface extraction module, grid point surface velocity calculation module, correction curve module and layered model module;

[0010] The tomographic inversion velocity module is used to perform tomographic inversion and output the tomographic inversion model as a grid velocity model, which is further output to the velocity difference calculation module;

[0011] The velocity difference calculation module is used to calculate the longitudinal velocity difference of the grid velocity model and obtain the boundary grid points of all high-speed layers and low-speed reduction layers in the tomographic inversion velocity model of the entire work area, and further output them to the elevation interface extraction module;

[0012] The elevation interface extraction module is used to extract the boundary grid points between the final high-speed layer and the low-speed reduction layer, determine the final selected elevation interface, convert the relative elevation of the grid points in the elevation interface into grid elevation, obtain the surface thickness of the grid points in the elevation interface, and output them to the grid point surface velocity calculation module and the correction curve module respectively;

[0013] The grid point surface velocity calculation module calculates the surface velocity at the grid point according to the surface thickness of the grid point in the elevation interface and outputs it to the correction curve module;

[0014] The correction curve module establishes a thickness correction curve and a velocity correction curve based on the results obtained by the elevation interface extraction module and the grid point surface velocity calculation module, and outputs them to the layered model module;

[0015] The layered model module obtains the thickness correction formula and the velocity correction formula according to the thickness correction curve and the velocity correction curve, corrects the surface thickness and the surface velocity, and establishes the final layered model.

[0016] The present invention also discloses a layered modeling method for a tomographic model, which is implemented using the above-mentioned layered modeling device for a tomographic model. The method comprises the following steps performed in sequence:

[0017] S1. Prepare a tomographic inversion velocity model. Perform tomographic inversion based on the first arrivals picked up from the seismic data using the tomographic inversion velocity module. Output the obtained tomographic inversion model as a grid velocity model.

[0018] S2. Use the velocity difference calculation module to calculate the longitudinal velocity difference of the grid velocity model and obtain the boundary grid points of all high-speed layers and low-speed reduction layers in the tomographic inversion velocity model of the entire work area;

[0019] S3. Using the elevation interface extraction module, extract the final boundary grid point between the high-speed layer and the low-speed reduction layer from all boundary grid points between the high-speed layer and the low-speed reduction layer, determine the final selected elevation interface, convert the relative elevation of the grid points in the elevation interface into grid elevation, and obtain the thickness of the grid points in the elevation interface from the ground surface, that is, the surface thickness; based on the surface thickness, calculate the surface velocity at the grid point using the grid point surface velocity calculation module;

[0020] S4, repeating steps S1 to S3 to obtain the surface thickness and surface velocity at all grid points, and using the calibration curve module to establish a thickness calibration curve and a velocity calibration curve;

[0021] S5. According to the thickness correction curve and the speed correction curve, a thickness correction formula and a speed correction formula are obtained using a layered model module, and a final layered model is established after correction using the thickness correction formula and the speed correction formula;

[0022] The thickness correction formula obtained according to the thickness correction curve in step S4 is:

[0023] ,

[0024] in, is the thickness value before correction, is the thickness value after correction;

[0025] The speed correction formula obtained according to the speed correction curve is:

[0026] ,

[0027] in, is the speed value before correction, is the corrected speed value.

[0028] As a limitation, the velocity model in step S1 is a data body in segy format or a data body in text format, and the velocity model includes velocity information and grid size information.

[0029] As a second limitation, when calculating the velocity difference between adjacent longitudinal points in step S2, the length of the grid velocity model is first defined as , width is , depth is , define the length direction of each grid as , width direction is , the longitudinal length is , then the number of grids in the length direction is , the number of grids in the width direction is , the number of vertical grids is ;

[0030] Then, each grid in the velocity model is represented as (i, j, k), and the value range of i is , the value range of j is , the value range of k is ;

[0031] Next, the velocity difference between adjacent longitudinal points is expressed as:

[0032] ,

[0033] ,

[0034] in, For the velocity of each grid, is the velocity difference between two vertically adjacent grids, is the enhanced value of the velocity difference between two adjacent grids;

[0035] If the point of the enhanced value of the velocity difference between two adjacent grids satisfies: and , then this point is the boundary grid point between the high-speed layer and the low-speed reduction layer, that is, the grid point that meets the conditions;

[0036] Finally, all grid points that meet the conditions are recorded.

[0037] As a third limitation, the method for extracting the boundary grid points between the final high-speed layer and the low-speed reduction layer in step S3 is: by comparing the existing surface survey point results within the first arrival data range used in the tomographic inversion, calibrating the elevation of the high-speed top interface, and determining the grid points corresponding to the final selected elevation interface;

[0038] The conversion formula for converting the relative elevation of the boundary grid point to the grid elevation is:

[0039] ,

[0040] Where E is the maximum elevation in the grid velocity model;

[0041] The surface thickness is:

[0042] ,

[0043] in, is the surface elevation corresponding to the grid point (i, j, k).

[0044] As a fifth limitation, the surface velocity at the grid point in step S3 is expressed as:

[0045] ;

[0046] ;

[0047] ;

[0048] in, is the time from the surface to the grid point (i, j, k), is the time of the grid point (i, j, k).

[0049] As a sixth limitation, the method for establishing the thickness correction curve in step S4 is:

[0050] a1) Establish the first set of forward models. The first set of forward models consists of multiple sets of forward models with two layers of shallow surface medium and different thickness of low velocity reduction layer. Record the surface thickness of low velocity reduction layer of the first set of forward models respectively. , where p represents the pth set;

[0051] a2) Perform forward simulation based on multiple sets of forward models with different thicknesses, pick the first arrival of seismic data, and obtain the tomographic inversion velocity model. Repeat steps S1 to S3 to obtain the grid elevation and surface thickness at all grid points. ;

[0052] a3) Compare the surface thickness obtained by inversion and the thickness of the surface layer of the low velocity reduction layer in the forward model The relationship between the thickness and the thickness is fitted using the least squares method to obtain the thickness correction curve:

[0053] ,

[0054] Among them, the superscript Indicates transposition, -1 indicates inversion, is the slope of the thickness calibration curve, is the intercept of the thickness calibration curve.

[0055] As a seventh limitation, the method for establishing the speed correction curve in step S4 is:

[0056] b1) Establish a second set of forward models. The second set of forward models consists of multiple sets of forward models with different low-velocity reduction layers in the shallow layer, with two layers of media. The low-velocity reduction layer velocities of the second set of forward models are recorded respectively. ;

[0057] b2) Based on the established multiple forward models with different velocities of low-velocity layers, forward simulation is performed, and the first arrival of the seismic data is picked to obtain the tomographic inversion velocity model. Steps S1 to S3 are repeated to obtain the model inversion surface velocity at all grid points. ;

[0058] b3) Comparison of surface velocities obtained from inversion and the forward model low velocity layer velocity The relationship between the two is fitted using the least squares method to obtain the speed correction curve:

[0059] ,

[0060] in, is the slope of the speed correction curve, is the intercept of the velocity correction curve.

[0061] Due to the adoption of the above technical solution, the present invention has the following beneficial effects compared with the prior art:

[0062] (1) The device of the present invention solves the problems that the high-speed layer velocity used in extracting the constant-speed interface is subject to human intervention, the constant speed used cannot reflect the real high-speed top interface changes in a region, and when there are different high-speed layer velocities in a region, the constant-speed interface cannot be taken into account when extracting the constant-speed interface;

[0063] (2) The method of the present invention solves the problem of human intervention in the high-speed layer velocity used in extracting the constant velocity interface by adopting a new method for extracting the high-speed layer top interface elevation. The velocity used can more realistically reflect the actual high-speed top interface changes in a region. At the same time, when there are different high-speed layer velocities in a region, the high-speed layer velocity can be effectively extracted.

[0064] (3) The method of the present invention effectively improves the calculation accuracy of static correction and shallow layer modeling, and provides control and verification data for static correction in onshore oil seismic exploration data processing and shallow layer velocity modeling in prestack depth migration imaging.

[0065] In summary, the present invention can improve the accuracy of isovelocity interface extraction, thereby improving the accuracy of static correction calculation and shallow layer modeling, and providing control and verification data for static correction in onshore oil seismic exploration data processing and shallow layer velocity modeling in prestack depth migration imaging. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] The present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments.

[0067] Figure 1 This is a principle block diagram of embodiment 1 of the present invention;

[0068] Figure 2 This is a continuous velocity model diagram of Example 2 of the present invention;

[0069] Figure 3 This is a longitudinal velocity difference curve diagram of Example 2 of the present invention;

[0070] Figure 4 This is a thickness calibration curve diagram of Example 2 of the present invention;

[0071] Figure 5 This is a speed correction curve diagram of Example 2 of the present invention. DETAILED DESCRIPTION

[0072] In order to better explain the present invention and facilitate understanding, preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings through specific implementation methods.

[0073] Example 1 A layered modeling device for a tomographic model

[0074] like Figure 1As shown, this embodiment includes: a tomographic inversion velocity module, a velocity difference calculation module, an elevation interface extraction module, a grid point surface velocity calculation module, a correction curve module and a layered model module.

[0075] The tomographic inversion velocity module is used to perform tomographic inversion and output the tomographic inversion model as a grid velocity model, which is further output to the velocity difference calculation module; the velocity difference calculation module is used to calculate the longitudinal velocity difference of the grid velocity model and obtain the boundary grid points of all high-speed layers and low-speed reduction layers in the tomographic inversion velocity model of the entire work area, which are further output to the elevation interface extraction module; the elevation interface extraction module is used to extract the boundary grid points of the final high-speed layer and low-speed reduction layer, determine the final selected elevation interface, convert the relative elevation of the grid points in the elevation interface into grid elevation, and obtain the grid point table in the elevation interface. Layer thickness is calculated and output to the grid point surface velocity calculation module and the correction curve module respectively; the grid point surface velocity calculation module calculates the surface velocity at the grid point according to the surface thickness of the grid point in the elevation interface, and outputs it to the correction curve module; the correction curve module establishes a thickness correction curve and a velocity correction curve based on the results obtained by the elevation interface extraction module and the grid point surface velocity calculation module, and outputs them to the layered model module; the layered model module obtains the thickness correction formula and the velocity correction formula based on the thickness correction curve and the velocity correction curve, corrects the surface thickness and surface velocity, and establishes the final layered model.

[0076] Example 2 A layered modeling method for a tomographic model

[0077] This embodiment provides a layered modeling method for a tomographic model, which is implemented using the apparatus described in Example 1. This embodiment includes the following steps, performed in sequence:

[0078] S1. Prepare a tomographic inversion velocity model. Perform tomographic inversion based on the first arrivals picked up from the seismic data using the tomographic inversion velocity module. Output the obtained tomographic inversion model as a grid velocity model. The velocity model is a data volume in segy format or text format. The velocity model includes velocity information and grid size information.

[0079] S2. Use the velocity difference calculation module to calculate the longitudinal velocity difference of the grid velocity model and obtain the boundary grid points of all high-speed layers and low-velocity reduction layers in the tomographic inversion velocity model of the entire work area.

[0080] When calculating the velocity difference between adjacent longitudinal points, first define the length of the grid velocity model as , width is , depth is , define the length direction of each grid as , width direction is , the longitudinal length is , then the number of grids in the length direction is , the number of grids in the width direction is , the number of vertical grids is ;

[0081] Then, each grid in the velocity model is represented as (i, j, k), and the value range of i is , the value range of j is , the value range of k is ;

[0082] Next, the velocity difference between adjacent longitudinal points is expressed as:

[0083] ,

[0084] ,

[0085] in, For the velocity of each grid, is the velocity difference between two vertically adjacent grids, is the enhanced value of the velocity difference between two adjacent grids;

[0086] If the point of the enhanced value of the velocity difference between two adjacent grids satisfies: and , then this point is the boundary grid point between the high-speed layer and the low-speed reduction layer, that is, the grid point that meets the conditions;

[0087] Finally, all grid points that meet the conditions are recorded.

[0088] S3. Since there is more than one grid in a column that meets the conditions in step S2, it is necessary to use the elevation interface extraction module to extract the final boundary grid points of the high-speed layer and the low-speed reduction layer from all the boundary grid points of the high-speed layer and the low-speed reduction layer. At the same time, since the position of the boundary grid point is the relative position in the tomographic inversion model, it cannot be used for modeling without elevation information. Therefore, it is necessary to determine the final selected elevation interface, convert the relative elevation of the grid points in the elevation interface into grid elevation, and obtain the thickness of the grid points in the elevation interface from the ground surface, that is, the surface thickness.

[0089] The method for extracting the boundary grid points between the high-speed layer and the low-speed reduction layer is as follows: by comparing the existing surface survey points within the first arrival data range used in the tomographic inversion, calibrating the elevation of the high-speed top interface, and determining the grid points corresponding to the final selected elevation interface;

[0090] The conversion formula for converting the relative elevation of the boundary grid point to the grid elevation is:

[0091] ,

[0092] Where E is the maximum elevation in the grid velocity model;

[0093] The surface thickness is:

[0094] ,

[0095] in, is the surface elevation corresponding to the grid point (i, j, k).

[0096] According to the surface thickness, the surface velocity at the grid point is calculated by the grid point surface velocity calculation module;

[0097] The surface velocity at the grid point is expressed as:

[0098] ;

[0099] ;

[0100] ;

[0101] in, is the time from the surface to the grid point (i, j, k), is the time of the grid point (i, j, k).

[0102] S4. Repeat steps S1 to S3 to obtain the surface thickness and surface velocity at all grid points, and use the correction curve module to establish a thickness correction curve and a velocity correction curve.

[0103] The method for establishing a thickness calibration curve comprises the following steps performed in sequence:

[0104] a1) Establish the first set of forward models. The first set of forward models consists of multiple sets of forward models with two layers of shallow surface medium and different thickness of low velocity reduction layer. Record the surface thickness of low velocity reduction layer of the first set of forward models respectively. , where the subscript p represents the pth set, p ≥ 1;

[0105] In the first set of forward models in this embodiment, ten sets of forward models with low velocity reduction layer thickness values ​​ranging from 20m to 40m were selected. The velocity of the low velocity reduction layer in each forward model was 750m / s and the velocity of the high velocity layer was 2000m / s.

[0106] a2) Perform forward simulation based on multiple sets of forward models with different thicknesses, pick the first arrival of seismic data, and obtain the tomographic inversion velocity model. Repeat steps S1 to S3 to obtain the grid elevation and surface thickness at all grid points. ;

[0107] a3) Compare the surface thickness obtained by inversion and the thickness of the surface layer of the low velocity reduction layer in the forward model The relationship between the thickness and the thickness is fitted using the least squares method to obtain the thickness correction curve:

[0108] ,

[0109] Among them, the superscript Indicates transposition, -1 indicates inversion, is the slope of the thickness calibration curve, is the intercept of the thickness calibration curve;

[0110] The thickness correction formula obtained from the thickness correction curve is:

[0111] ,

[0112] in, is the thickness value before correction, is the thickness value after correction.

[0113] The method for establishing a speed correction curve comprises the following steps performed in sequence:

[0114] b1) Establish a second set of forward models. The second set of forward models consists of multiple sets of forward models with different low-velocity reduction layers in the shallow layer, with two layers of media. The low-velocity reduction layer velocities of the second set of forward models are recorded respectively. ;

[0115] In the second set of forward models in this embodiment, three forward models with low velocity reduction layer thicknesses of 50m, 100m, and 150m and ten forward models with low velocity reduction layer velocities were selected, for a total of thirty sets of forward models. The velocity values ​​of the low velocity reduction layer were selected in the range of 500m / s to 1700m / s; the velocity of the high velocity layer was 2000m / s.

[0116] b2) Based on the established multiple forward models with different velocities of low-velocity layers, forward simulation is performed, and the first arrival of the seismic data is picked to obtain the tomographic inversion velocity model. Steps S1 to S3 are repeated to obtain the model inversion surface velocity at all grid points. ;

[0117] b3) Comparison of surface velocities obtained from inversion and the forward model low velocity layer velocity The relationship between the two is fitted using the least squares method to obtain the speed correction curve:

[0118] ,

[0119] in, is the slope of the speed correction curve, is the intercept of the velocity correction curve;

[0120] The speed correction formula obtained from the speed correction curve is:

[0121] ,

[0122] in, is the speed value before correction, is the corrected speed value.

[0123] S5. According to the thickness correction curve and the speed correction curve, a thickness correction formula and a speed correction formula are obtained using a layered model module, and a final layered model is established after correction using the thickness correction formula and the speed correction formula.

[0124] Figure 2 The continuous velocity model diagram obtained by the tomographic inversion in step S1, the horizontal axis corresponds to the length of the continuous velocity model, the vertical axis corresponds to the depth of the continuous velocity model, the unit is m, Figure 2 On the right side are the speeds corresponding to the colors at different depths, in m / s.

[0125] Figure 3 The difference curve is obtained by calculating the velocity model difference in step S2. The horizontal axis represents the number of tracks and the vertical axis represents the depth. When calibrating the elevation of the high-speed top interface, the survey point results within the data range are compared with the Figure 3 The curve with the highest degree of overlap corresponds to the curve in , and the curve with the highest degree of overlap is selected as the elevation of the high-speed top interface.

[0126] Figure 4 This is the thickness correction curve established in step S4. The horizontal axis is the surface thickness of the inversion model, and the vertical axis is the surface thickness of the forward model. To ensure the accuracy of the experimental results, this embodiment performs multiple calculations on each model in the first group of forward models. The same forward model thickness obtains multiple inversion thickness data. The figure selects the points corresponding to the inversion model thickness where the inversion thickness is stable within a certain range.

[0127] Figure 5 This is the velocity correction curve established in step S4. The horizontal axis is the surface velocity of the inversion model, and the vertical axis is the velocity of the low-velocity reduction layer of the forward model. In order to ensure the accuracy of the experimental results, this embodiment performs multiple calculations on each model in the second group of forward models. The velocity of the low-velocity reduction layer of the same forward model is used to obtain multiple inverted low-velocity reduction layer velocity data. The figure selects the points corresponding to the inversion model low-velocity reduction layer velocity where the inverted low-velocity reduction layer is stable within a certain range.

Claims

1. A layered modeling device for a tomographic model, characterized in that: The device comprises: a tomographic inversion velocity module, a velocity difference calculation module, an elevation interface extraction module, a grid point surface velocity calculation module, a correction curve module and a layered model module; The tomographic inversion velocity module is used to perform tomographic inversion and output the tomographic inversion model as a grid velocity model, which is further output to the velocity difference calculation module; The velocity difference calculation module is used to calculate the longitudinal velocity difference of the grid velocity model and obtain the boundary grid points of all high-speed layers and low-speed reduction layers in the tomographic inversion velocity model of the entire work area, and further output them to the elevation interface extraction module; The elevation interface extraction module is used to extract the boundary grid points between the final high-speed layer and the low-speed reduction layer, determine the final selected elevation interface, convert the relative elevation of the grid points in the elevation interface into grid elevation, obtain the surface thickness of the grid points in the elevation interface, and output them to the grid point surface velocity calculation module and the correction curve module respectively; The grid point surface velocity calculation module calculates the surface velocity at the grid point according to the surface thickness of the grid point in the elevation interface and outputs it to the correction curve module; The correction curve module establishes a thickness correction curve and a velocity correction curve based on the results obtained by the elevation interface extraction module and the grid point surface velocity calculation module, and outputs them to the layered model module; The layered model module obtains the thickness correction formula and the velocity correction formula according to the thickness correction curve and the velocity correction curve, corrects the surface thickness and the surface velocity, and establishes the final layered model.

2. A method for layered modeling of a tomographic model, implemented using the device for layered modeling of a tomographic model according to claim 1, the method comprising the following steps performed in sequence: S1. Prepare a tomographic inversion velocity model. Perform tomographic inversion based on the first arrivals picked up from the seismic data using the tomographic inversion velocity module. Output the obtained tomographic inversion model as a grid velocity model. S2. Use the velocity difference calculation module to calculate the longitudinal velocity difference of the grid velocity model and obtain the boundary grid points of all high-speed layers and low-speed reduction layers in the tomographic inversion velocity model of the entire work area; S3. Using the elevation interface extraction module, extract the final boundary grid point between the high-speed layer and the low-speed reduction layer from all boundary grid points between the high-speed layer and the low-speed reduction layer, determine the final selected elevation interface, convert the relative elevation of the grid points in the elevation interface into grid elevation, and obtain the thickness of the grid points in the elevation interface from the ground surface, that is, the surface thickness; based on the surface thickness, calculate the surface velocity at the grid point using the grid point surface velocity calculation module; S4, repeating steps S1 to S3 to obtain the surface thickness and surface velocity at all grid points, and using the calibration curve module to establish a thickness calibration curve and a velocity calibration curve; S5. According to the thickness correction curve and the speed correction curve, a thickness correction formula and a speed correction formula are obtained using a layered model module, and a final layered model is established after correction using the thickness correction formula and the speed correction formula; The thickness correction formula obtained according to the thickness correction curve in step S4 is: y1=0.811987x1-10.916624, Among them, x1 is the thickness value before correction, and y1 is the thickness value after correction; The speed correction formula obtained according to the speed correction curve is: y2=1.35066x2-471.75570, Among them, x2 is the speed value before correction, and y2 is the speed value after correction.

3. The layered modeling method of a tomographic model according to claim 2, characterized in that: The velocity model in step S1 is a data body in segy format or a data body in text format, and the velocity model includes velocity information and grid size information.

4. The layered modeling method of a tomographic model according to claim 3, characterized in that: When calculating the velocity difference between adjacent longitudinal points in step S2, first define the length of the grid velocity model as L x , width is L y , depth is H, define each grid length direction as △x, width direction as △y, vertical length as △z, then the number of grids in the length direction is L x / △x, the number of grids in the width direction is L y / △y, the number of vertical grids is H / △z; Then, each grid in the velocity model is represented as (i, j, k), and the value range of i is The value range of j is The value range of k is Next, the velocity difference between adjacent longitudinal points is expressed as: △v1 i,j+1,k =v i,j+i,k -v i,j,k , Where v is the velocity of each grid, △v1 is the velocity difference between two adjacent grids in the longitudinal direction, and △v2 is the enhancement value of the velocity difference between two adjacent grids; If the point of the enhanced value of the velocity difference between two adjacent grids satisfies: i,j+1,k >△v2 i,j,k And △v2 i,j+1,k >△v2 i,j+2,k , then this point is the boundary grid point between the high-speed layer and the low-speed reduction layer, that is, the grid point that meets the conditions; Finally, all grid points that meet the conditions are recorded.

5. The layered modeling method of a tomographic model according to claim 4, characterized in that: The method for extracting the boundary grid points between the final high-speed layer and the low-speed reduction layer in step S3 is: by comparing the existing surface survey point results within the initial arrival data range used in the tomographic inversion, calibrating the high-speed top interface elevation, and determining the grid points corresponding to the final selected elevation interface; The conversion formula for converting the relative elevation of the boundary grid point to the grid elevation is: he i,j,k =Ej*△z, Where E is the maximum elevation in the grid velocity model; The surface thickness is: h i,j,k =F i,j,k -he i,j,k , Among them, F i,j,k is the surface elevation corresponding to the grid point (i, j, k).

6. The layered modeling method of a tomographic model according to claim 5, characterized in that: The surface velocity at the grid point in step S3 is expressed as: V i,j,k =h i,j,k / t i,j,k ; t' i,j,k =△z / v i,j,k ; Among them, t i,j,k is the time from the surface to the grid point (i, j, k), t' i,j,k is the time of the grid point (i, j, k).

7. The layered modeling method of a tomographic model according to claim 6, characterized in that: The method for establishing the thickness calibration curve in step S4 is: a1) Establishing a first set of forward models, which consists of multiple sets of forward models with two shallow layers of medium and low velocity reduction layers of different thicknesses, and recording the surface thickness h' of the low velocity reduction layer in the first set of forward models respectively. i,j,k(p) , where p represents the pth set; a2) Perform forward simulation based on the established multiple sets of forward models with different thicknesses, perform first arrival picking on the seismic data, obtain the tomographic inversion velocity model, and repeat steps S1 to S3 to obtain the grid elevation and surface thickness h at all grid points. i,j,k(p) ; a3) Compare the surface thickness h obtained by inversion i,j,k(p) And the forward model low velocity reduction layer surface thickness h' i,j,k(p) The relationship between the thickness and the thickness is fitted using the least squares method to obtain the thickness correction curve: Wherein, the superscript T indicates transposition, -1 indicates inversion, k1 is the slope of the thickness correction curve, and c1 is the intercept of the thickness correction curve.

8. The layered modeling method of a tomographic model according to claim 6, characterized in that: The method for establishing the speed correction curve in step S4 is: b1) Establishing a second set of forward models, which consists of multiple sets of forward models with different low-velocity reduction layers in two layers of shallow surface media, and recording the low-velocity reduction layer velocity V' of the second set of forward models respectively. i,j,k(p) ; b2) Based on the established multiple forward models with different velocities of low-velocity layers, forward simulation is performed, and first arrival picking is performed on the seismic data to obtain a tomographic inversion velocity model. Steps S1 to S3 are repeated to obtain the model inversion surface velocity V at all grid points. i,j,k(p) ; b3) Comparison of the surface velocity V obtained by inversion i,j,k(p) and the forward model low velocity layer velocity V' i，j，k(p) The relationship between the two is fitted using the least squares method to obtain the speed correction curve: Wherein, k2 is the slope of the speed correction curve, and c2 is the intercept of the speed correction curve.

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