Model splicing methods and apparatus, electronic devices and readable storage media

Abstract

This application discloses a model stitching method and apparatus, electronic device, and readable storage medium, belonging to the technical field of data migration processing technology. The model stitching method includes: acquiring a near-surface velocity model and a mid-deep velocity model; acquiring a near-surface grid velocity information model based on the near-surface velocity model; acquiring a mid-deep grid velocity information model based on the mid-deep velocity model; acquiring the grid velocity difference based on the near-surface grid velocity information model and the mid-deep grid velocity information model; acquiring the fitting surface depth based on the grid velocity difference; and stitching the near-surface velocity model and the mid-deep velocity model based on the fitting surface depth. This application can effectively reduce stitching errors, improve the accuracy of the velocity field, achieve the fusion of two velocity models, and thus improve the accuracy of pre-stack depth migration processing.

Description

Technical Field

[0001] This application belongs to the field of data offset processing technology, specifically relating to a model splicing method and apparatus, electronic equipment and readable storage medium. Background Technology

[0002] In existing technologies, to improve the imaging accuracy of geological targets and the reliability of detailed descriptions of geological structures, pre-stack depth migration (PSM) processing is used in seismic data processing to meet the needs of high-precision seismic imaging in complex areas. PSM requires establishing a high-precision velocity field from shallow to deep layers. For the surface layer, a velocity model derived from first-arrival tomography under surface information constraints is typically used; for the intermediate and deep layers, velocity models are generally obtained from stratigraphic velocity information obtained from pre-stack time-migration profiles. A velocity field from shallow to deep is established by stitching the surface velocity model with the deep stratigraphic grid velocity model. However, the stitching methods in these technologies often introduce significant errors at the junction of the two velocity models, reducing the accuracy of the velocity field and negatively impacting PSM imaging processing. Summary of the Invention

[0003] The purpose of this application is to provide a model stitching method and apparatus, electronic device, and readable storage medium that can solve the problem of large errors occurring at the stitching point when stitching near-surface velocity models and mid-to-deep velocity models, reducing the accuracy of the velocity field and affecting subsequent pre-stack depth migration imaging processing.

[0004] In a first aspect, the technical solution of this application provides a model stitching method, including: obtaining a near-surface velocity model and a mid-deep velocity model; obtaining a near-surface grid velocity information model based on the near-surface velocity model; obtaining a mid-deep grid velocity information model based on the mid-deep velocity model; obtaining grid velocity differences based on the near-surface grid velocity information model and the mid-deep grid velocity information model; obtaining the fitting surface depth based on the grid velocity differences; and stitching the near-surface velocity model and the mid-deep velocity model based on the fitting surface depth.

[0005] This technical solution obtains the depth of the fitted surface by acquiring the mesh velocity difference, and then stitches the near-surface velocity model and the mid-deep velocity model according to the fitted surface depth. This can effectively reduce stitching errors, improve the accuracy of the velocity field, realize the fusion of the two velocity models, and thus improve the accuracy of pre-stack depth migration processing.

[0006] In addition, the technical solution provided by this invention may also have the following additional technical features:

[0007] In the above technical solution, obtaining the near-surface velocity model and the intermediate-deep velocity model specifically includes: using the first-arrival tomography inversion method under the constraint of surface information to obtain the near-surface velocity model; and using the pre-stack time migration profile velocity picking method or the grid tomography iteration method to obtain the depth domain velocity field model, which is the intermediate-deep velocity model.

[0008] This technical solution presents a method for obtaining near-surface velocity models and mid-deep velocity models, providing a foundation for the subsequent stitching of the two models.

[0009] In the above technical solution, the near-surface velocity model is used to obtain a near-surface grid velocity information model. Specifically, this includes: gridding the near-surface velocity model to obtain a near-surface grid velocity information model. In the gridding process, the size of the grid cell is determined according to the grid size in the tomographic inversion, and the maximum effective grid depth is determined according to the tomographic ray depth in the tomographic inversion.

[0010] In this technical solution, the near-surface velocity model is gridded to obtain a near-surface grid velocity information model, which facilitates the subsequent acquisition of the fitting interface depth.

[0011] In the above technical solution, the acquisition of the mid-deep grid velocity information model based on the mid-deep velocity model specifically includes: converting the depth domain velocity field model into a constrained tomographic inversion model format, resampling the grid, and obtaining the mid-deep grid velocity information model. The resampled grid has the same size as the grid cell in the near-surface velocity model gridding process.

[0012] In this technical solution, a medium-deep mesh velocity information model is obtained by format conversion and meshing of the medium-deep velocity model, which facilitates the subsequent acquisition of the fitting interface depth.

[0013] In the above technical solution, the grid velocity difference is obtained based on the near-surface grid velocity information model and the mid-deep grid velocity information model. Specifically, this includes: matching the grids in the near-surface grid velocity information model with the grids in the mid-deep grid velocity information model; for the corresponding grids, subtracting the velocities of the two grids to obtain the grid velocity difference.

[0014] In this technical solution, the grid velocity difference is obtained by using the corresponding grids of the near-surface grid velocity information model and the mid-deep grid velocity information model, which facilitates the subsequent acquisition of the fitting interface depth through the grid velocity difference.

[0015] In the above technical solution, the depth of the fitting interface is obtained based on the grid velocity difference, specifically including: obtaining the first error of the grid velocity difference; obtaining the grid corresponding to the first error within a first numerical range; fitting the grid to obtain the depth of the fitting interface.

[0016] In this technical solution, selecting the grid corresponding to the first error within the first numerical range for fitting can minimize the dispersion of the grid distribution range, making it easier to fit.

[0017] In the above technical solution, the near-surface velocity model and the intermediate-deep velocity model are spliced ​​based on the fitting interface depth. Specifically, the fitting interface depth is used as the splicing surface to splice the near-surface velocity model and the intermediate-deep velocity model. Above the splicing surface, the constrained tomographic inversion velocity in the near-surface velocity model is used, and below the splicing surface, the depth domain velocity field velocity in the intermediate-deep velocity model is used.

[0018] In this technical solution, the near-surface velocity model and the mid-deep velocity model are stitched together using a stitching surface, which reduces the error at the stitching point, improves the accuracy of the velocity field, and thus enhances the accuracy of pre-stack depth migration processing.

[0019] Secondly, the technical solution of this application provides a model stitching device, including: a first acquisition module, a second acquisition module, a third acquisition module, a fourth acquisition module, a fifth acquisition module, and a stitching module; the first acquisition module is used to acquire a near-surface velocity model and a mid-deep velocity model; the second acquisition module is used to acquire a near-surface grid velocity information model based on the near-surface velocity model; the third acquisition module is used to acquire a mid-deep grid velocity information model based on the mid-deep velocity model; the fourth acquisition module is used to acquire the grid velocity difference based on the near-surface grid velocity information model and the mid-deep grid velocity information model; the fifth acquisition module is used to acquire the fitting surface depth based on the grid velocity difference; and the stitching module is used to stitch the near-surface velocity model and the mid-deep velocity model based on the fitting surface depth.

[0020] This technical solution obtains the depth of the fitted surface by acquiring the mesh velocity difference, and then stitches the near-surface velocity model and the mid-deep velocity model according to the fitted surface depth. This can effectively reduce stitching errors, improve the accuracy of the velocity field, realize the fusion of the two velocity models, and thus improve the accuracy of pre-stack depth migration processing.

[0021] Thirdly, the present application provides an electronic device, which includes a processor and a memory. The memory stores programs or instructions that can run on the processor. When the program or instructions are executed by the processor, they implement the steps of the model splicing method as described in the first aspect.

[0022] Fourthly, the technical solution of this application provides a readable storage medium on which a program or instruction is stored, and when the program or instruction is executed by a processor, it implements the steps of the model splicing method as described in the first aspect. Attached Figure Description

[0023] Figure 1One of the flowcharts of the model splicing method provided in the embodiments of this application is shown;

[0024] Figure 2 The second schematic diagram of the model splicing method provided in the embodiments of this application is shown;

[0025] Figure 3 The third schematic diagram of the model splicing method provided in the embodiments of this application is shown;

[0026] Figure 4 The fourth schematic flowchart of the model splicing method provided in the embodiments of this application is shown;

[0027] Figure 5 The fifth flowchart illustrates the model splicing method provided in this application embodiment;

[0028] Figure 6 The sixth illustration shows a flowchart of the model splicing method provided in the embodiments of this application;

[0029] Figure 7 The seventh flowchart illustrates the model splicing method provided in this application embodiment;

[0030] Figure 8 A schematic diagram of the splicing surface provided in an embodiment of this application is shown;

[0031] Figure 9 A structural block diagram of the model splicing device provided in an embodiment of this application is shown;

[0032] Figure 10 A structural block diagram of the electronic device provided in an embodiment of this application is shown;

[0033] Figure 11 A schematic diagram of the hardware structure of an electronic device according to an embodiment of this application is shown.

[0034] in, Figures 8 to 11 The correspondence between the reference numerals and component names in the attached drawings is as follows:

[0035] 100: Model splicing device; 110: First acquisition module; 120: Second acquisition module; 130: Third acquisition module; 140: Fourth acquisition module; 150: Fifth acquisition module; 160: Splicing module; 1000: Electronic device; 1002: Processor; 1004: Memory; 1100: Electronic device; 1101: Radio frequency unit; 1102: Network module; 1103: Audio output unit; 1104: Input unit; 1104 1: Graphics processor; 11042: Microphone; 1105: Sensor; 1106: Display unit; 11061: Display panel; 1107: User input unit; 11071: Touch panel; 11072: Other input devices; 1108: Interface unit; 1109: Memory; 1110: Processor; A: Surface; B: High-speed top interface; C: Grid conforming to relative error range requirements; D: Meshing surface; E: Maximum depth of tomographic rays. Detailed Implementation

[0036] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0037] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0038] The following is in conjunction with the appendix Figures 1 to 11 The present application provides a detailed description of the model splicing method and apparatus, electronic device and readable storage medium provided in the embodiments of this application through specific implementations and application scenarios.

[0039] This application provides a model stitching method. Figure 1 This document illustrates one of the flowcharts of the model splicing method provided in an embodiment of this application, such as... Figure 1 As shown, the model splicing methods include:

[0040] Step 102: Obtain the near-surface velocity model and the mid-deep velocity model.

[0041] Step 104: Based on the near-surface velocity model, obtain the near-surface grid velocity information model.

[0042] Step 106: Based on the medium-deep velocity model, obtain the medium-deep mesh velocity information model.

[0043] Step 108: Obtain the grid velocity difference based on the near-surface grid velocity information model and the mid-deep grid velocity information model.

[0044] Step 110: Obtain the depth of the fitted surface based on the grid velocity difference.

[0045] Step 112: Based on the depth of the fitted surface, the near-surface velocity model and the mid-deep velocity model are stitched together.

[0046] Pre-stack depth migration (PSM) is used in seismic data processing to meet the requirements of high-precision seismic imaging in complex areas. PSM requires establishing a high-precision velocity field from shallow to deep layers, with velocity models including near-surface and intermediate-deep velocity models. The velocity field from shallow to deep layers is established by stitching together the near-surface and intermediate-deep velocity models.

[0047] Understandably, near-surface velocity models and mid-deep velocity models are affected by factors such as modeling methods, tectonic depth, and lateral velocity variations. Conventional methods are somewhat blind when stitching these models together, making it difficult to find suitable stitching positions. Large differences in velocity at the stitching points can easily lead to significant errors, affecting the pre-stack depth migration results, especially in complex plateau regions.

[0048] This embodiment provides a method for generating a splicing surface between shallow and deep velocity models between the high-velocity top and the tomographic ray depth. By obtaining the mesh velocity difference, the depth of the fitting surface is obtained, and then the near-surface velocity model and the mid-deep velocity model are spliced ​​according to the depth of the fitting surface. This can effectively reduce splicing errors, improve the accuracy of the velocity field, realize the fusion of the two velocity models, and thus improve the accuracy of pre-stack depth migration processing.

[0049] This embodiment is suitable for establishing high-precision velocity fields in pre-stack depth migration processing. It is simple and effective, improves the accuracy of shallow, medium and deep velocity modeling, avoids errors caused by stitching velocity models established by different methods, and effectively improves the effect of pre-stack depth migration processing. It can be applied to pre-stack depth migration imaging processing in the extremely low signal-to-noise ratio area of ​​the Qinghai-Tibet Plateau in my country and other complex tectonic areas in China, effectively improving imaging quality and showing broad application prospects.

[0050] In some embodiments of this application, Figure 2 This illustrates a second flowchart of the model splicing method provided in an embodiment of this application, as shown below. Figure 2As shown, the near-surface velocity model and the intermediate-deep velocity model are obtained, specifically including:

[0051] Step 202: The near-surface velocity model is obtained by using the first-arrival tomographic inversion method under the constraint of surface information.

[0052] Step 204: Use the pre-stack time migration profile velocity picking method or the mesh tomography iteration method to obtain the depth domain velocity field model, which is the mid-deep layer velocity model.

[0053] In this embodiment, a near-surface velocity model is obtained through an initial arrival tomography inversion method constrained by surface information. During the inversion process, more distant initial arrival information or all offsets are utilized to obtain reliable velocity information for deeper layers below the high-velocity top, and the depth of the high-velocity interface and the maximum depth of the tomographic rays are also obtained.

[0054] It can be understood that the high-velocity top is the bottom interface of the low-velocity zone at the surface. The depth of the high-velocity interface and the maximum depth of the tomographic rays can be marked on the tomographic inversion velocity model and ray model.

[0055] In this embodiment, a depth-domain velocity field is established from shallow to deep using either a pre-stack time-migration profile velocity picking method or a mesh tomography iteration method. The shallow velocity model at this point is relatively inaccurate.

[0056] This embodiment presents a method for obtaining near-surface velocity models and mid-deep velocity models, providing a foundation for the subsequent stitching of the two models.

[0057] In some embodiments of this application, Figure 3 The third schematic diagram of the model splicing method provided in the embodiments of this application is shown. Figure 3 As shown, the model for obtaining near-surface grid velocity information based on the near-surface velocity model specifically includes:

[0058] Step 302: Grid the near-surface velocity model to obtain a near-surface grid velocity information model.

[0059] In the meshing process, the size of the mesh cells is determined based on the mesh size in the tomographic inversion, and the maximum effective mesh depth is determined based on the tomographic ray depth in the tomographic inversion.

[0060] In this embodiment, the near-surface velocity model obtained through constrained tomography inversion is first meshed, with the size of the mesh cells matching the mesh size from the tomography inversion. The maximum effective mesh depth is defined by the ray depth from the tomography inversion, thus achieving the meshed output of the near-surface velocity model and obtaining the near-surface mesh velocity information model.

[0061] In this embodiment, the near-surface velocity model is gridded to obtain a near-surface grid velocity information model, which facilitates the subsequent acquisition of the fitting interface depth.

[0062] In some embodiments of this application, Figure 4 The fourth flowchart of the model splicing method provided in this application embodiment is shown. Figure 4 As shown, the model for obtaining velocity information of medium-deep meshes based on the medium-deep velocity model specifically includes:

[0063] Step 402: Convert the depth domain velocity field model into a constrained tomographic inversion model format, resample the mesh, and obtain a mid-deep mesh velocity information model.

[0064] In this process, the resampled grid has the same size as the grid cells in the near-surface velocity model.

[0065] In this embodiment, firstly, the deep domain velocity field model is converted into a tomographic inversion model format. Then, the grid is resampled using grids of the same size as the grid cells in the near-surface velocity model gridding process to obtain a deep grid velocity information model.

[0066] In this embodiment, a medium-deep mesh velocity information model is obtained by format conversion and meshing of the medium-deep velocity model, which facilitates the subsequent acquisition of the fitting interface depth.

[0067] In some embodiments of this application, Figure 5 The fifth schematic diagram of the model splicing method provided in the embodiments of this application is shown. Figure 5 As shown, based on the near-surface grid velocity information model and the mid-deep grid velocity information model, the grid velocity difference is obtained, specifically including:

[0068] Step 502: Match the grids in the near-surface grid velocity information model with the grids in the mid-deep grid velocity information model.

[0069] Step 504: For the corresponding grid, subtract the velocities of the two grids to obtain the grid velocity difference.

[0070] In this embodiment, the grid in the near-surface grid velocity information model is found to be the corresponding grid in the intermediate-deep grid velocity information model. The velocity of the grid in the near-surface grid velocity information model is the shallow velocity of the grid, and the corresponding grid in the intermediate-deep grid velocity information model is the deep velocity of the grid. The grid velocities of the two corresponding grids are subtracted to obtain the grid velocity difference.

[0071] In this embodiment, the grid velocity difference is obtained by using the grids corresponding to the near-surface grid velocity information model and the mid-deep grid velocity information model, which facilitates the subsequent acquisition of the fitting interface depth through the grid velocity difference.

[0072] In some embodiments of this application, Figure 6 The sixth schematic diagram of the model splicing method provided in the embodiments of this application is shown. Figure 6 As shown, the depth of the fitted interface is obtained based on the grid velocity difference, specifically including:

[0073] Step 602: Obtain the first error of the grid velocity difference.

[0074] Step 604: Obtain the grid corresponding to the first error within the first numerical range.

[0075] Step 606: Fit the mesh to obtain the depth of the fitted interface.

[0076] In this embodiment, firstly, the first error of the mesh velocity difference, i.e., the relative error of the mesh velocity difference, is obtained:

[0077]

[0078] Where Z represents the first error of the mesh velocity difference, v2 represents the deep mesh velocity, and v1 represents the shallow mesh velocity.

[0079] Furthermore, a first data range for the first error is preset, the grid corresponding to the first error within the first range is obtained, the grid is fitted with a curve and surface, and the depth of the fitted interface is output.

[0080] Furthermore, the corresponding mesh velocities of the two mesh velocity models are subtracted, that is, the mesh velocity difference between the high-speed top velocity and the maximum ray depth is selected from the meshes within the first numerical range of the relative error. For example, the first numerical range can be a number interval or a single value, or the first minimum error value can be taken.

[0081] Furthermore, the mesh range between the high-speed top velocity and the maximum ray depth mentioned above refers to the range of shallow and deep models during pre-stack depth offset modeling and splicing.

[0082] In this embodiment, selecting the grid corresponding to the first error within the first numerical range for fitting can minimize the dispersion of the grid distribution range, making it easier to fit.

[0083] In some embodiments of this application, Figure 7 The seventh flowchart of the model splicing method provided in this application embodiment is shown, as follows: Figure 7As shown, based on the fitting interface depth, the near-surface velocity model and the mid-to-deep velocity model are stitched together, specifically including:

[0084] Step 702: Using the fitting interface depth as the splicing surface, the near-surface velocity model and the intermediate-deep velocity model are spliced ​​together. Above the splicing surface, the constrained tomographic inversion velocity in the near-surface velocity model is used, and below the splicing surface, the depth domain velocity field velocity in the intermediate-deep velocity model is used.

[0085] Understandably, the fitting interface depth is where the velocities of the near-surface velocity model and the mid-deep velocity model of the two models are closest. Using this as the splicing surface, there will not be a significant difference in the velocities above and below it. Above the fitting interface, the velocity is obtained by constrained tomography inversion, i.e., the near-surface velocity model, while below the fitting interface, the velocity field model in the depth domain is used, thus achieving a natural transition between the two velocity models.

[0086] In this embodiment, the near-surface velocity model and the mid-deep velocity model are stitched together using a stitching surface to reduce errors at the stitching point, improve the accuracy of the velocity field, and thus enhance the accuracy of pre-stack depth migration processing. Specific implementation examples:

[0088] The Qinghai-Tibet Plateau is rich in oil and gas resources and is an important strategic energy reserve for China. Oil and gas development areas are generally located at altitudes of 2800-5000 meters, with relative elevation differences reaching hundreds to thousands of meters. Intense geological movements have resulted in complex stratigraphic structures, leading to severe energy attenuation of seismic waves during propagation, significant static correction problems in seismic data, and low signal-to-noise ratio and resolution. Overall, the signal-to-noise ratio of seismic data is extremely low. With the challenges of the national energy strategy, the country has increased its efforts in oil and gas exploration and development in the complex plateau region. Two-dimensional seismic exploration has been conducted in the Qiangtang Basin, and large-scale three-dimensional seismic acquisition has been carried out in the complex mountainous terrain of the Qaidam Basin. Seismic exploration technology for the complex Qinghai-Tibet Plateau region has made significant progress. In particular, to improve the imaging accuracy of geological targets and the reliability of detailed descriptions of geological structures, pre-stack depth migration processing technology is applied in seismic data processing to meet the needs of high-precision seismic imaging in complex areas. Pre-stack depth migration (PSM) processing requires establishing a high-precision velocity field from shallow to deep layers. The surface layer typically uses a velocity model derived from first-arrival tomography under surface information constraints; the intermediate and deep layers generally use velocity models obtained from pre-stack time-migrating profiles. A velocity field from shallow to deep is established by stitching together the surface velocity model and the deep stratigraphic grid velocity model. Traditional stitching methods often introduce errors at the junction of the two velocity models, reducing the accuracy of the velocity field and negatively impacting PSM imaging processing.

[0089] Near-surface velocity models are generally derived from first-arrival tomography velocity inversion models constrained by surface information. Mid- and deep-seated velocity models are typically obtained through pre-stack time-migrating profiles, grid tomography, and other methods. The two velocity models are influenced by modeling methods, structural depth, and lateral velocity variations. Conventional methods for stitching these models are often indiscriminate, making it difficult to find suitable stitching points. Large differences in velocity at the stitching point can easily lead to significant errors, affecting the effectiveness of pre-stack depth migration, especially in complex plateau regions.

[0090] The model stitching method provided in this embodiment generates a shallow and deep velocity model stitching surface between the high-velocity top and the tomographic ray depth, effectively reducing stitching errors, achieving the fusion of two velocity models, and improving the pre-stack depth migration processing effect. Specifically, it includes:

[0091] (1) Obtain the near-surface velocity model (shallow layer).

[0092] First-arrival tomographic inversion under surface information constraints yields a near-surface velocity model. During the inversion, first-arrival information from further offsets (or all offsets) is utilized to obtain reliable velocity information for deeper layers below the high-velocity top. The depth of the high-velocity interface and the maximum depth of the tomographic rays are also obtained.

[0093] It is understandable that the high-speed top is the bottom interface of the surface low-speed reduction zone;

[0094] Understandably, the high-speed interface depth and the maximum depth of the tomographic ray can be marked on the tomographic inversion velocity model and ray model, and can be achieved using professional tomographic software.

[0095] (2) Obtain the mid-to-deep velocity model.

[0096] A depth-domain velocity field model is established by using methods such as velocity picking from pre-stack time-migrated profiles and mesh tomography iteration. At this point, the shallow velocity model is relatively inaccurate.

[0097] Understandably, obtaining mid-to-deep velocity models can be achieved in specialized processing systems.

[0098] (3) Obtain near-surface grid velocity information model.

[0099] The near-surface model derived from constrained tomographic inversion is meshed, with mesh cells matched to the tomographic inversion mesh size. The maximum effective mesh depth is defined by the ray depth from the tomographic inversion.

[0100] Understandably, the gridded model and output method can be implemented by professional tomography software.

[0101] (4) Obtain the velocity information model of the medium-deep grid.

[0102] The established depth domain velocity field model is converted into a constrained tomographic inversion model format, and the mesh is resampled. The resampled mesh has the same size as the mesh of the constrained tomographic inversion model, and the mesh velocity model is output.

[0103] Understandably, resampling the mesh can be achieved using specialized software;

[0104] Understandably, converting to a constrained tomographic inversion model format can be achieved using specialized software.

[0105] (5) Velocity model mesh difference sorting and fitting.

[0106] Subtract the corresponding mesh velocities from the two mesh velocity models, select the mesh with the smallest relative error in the mesh velocity difference between the high-speed top velocity and the maximum ray depth range, and then fit the mesh to a curve (surface) to generate a fitted surface (line). For example... Figure 8 As shown, the firing point is used for excitation, and the receiving point is used for data reception. A represents the ground surface, B represents the high-velocity top interface, C represents the grid that meets the requirements of the relative error value range, D represents the splicing surface, and E represents the maximum depth of the tomographic rays. It can be seen that the gray square in the figure is the grid C that meets the requirements of the relative error value range between the high-velocity top velocity and the maximum ray depth. The splicing surface D (i.e. the fitting surface) can be obtained from the grid C.

[0107] It is understandable that the above-mentioned high-speed top velocity and maximum ray depth range are the range of shallow and deep models during pre-stack depth migration modeling and splicing.

[0108] It is understandable that the relative error of the mesh velocity difference is calculated using the following formula:

[0109]

[0110] Where Z represents the relative error of the mesh velocity difference (i.e., the first error), v2 represents the deep mesh velocity, and v1 represents the shallow mesh velocity.

[0111] Understandably, the minimum relative error can be set to a range to minimize the dispersion of the grid distribution range, thus facilitating curve fitting.

[0112] Understandably, curve (surface) fitting using meshes is achieved through specialized software.

[0113] (6) Output of the fitted surface.

[0114] The fitting interface depth is where the two sets of velocities are closest. Using this as the splicing surface, the velocities above and below it will not have a significant difference. The velocity above the interface is obtained by constrained tomography inversion, and the velocity below the interface is obtained by the depth domain velocity field.

[0115] The method described in this embodiment has been explored and applied in the repeated processing of old survey lines in the Long'e'ni area of ​​the Qiangtang Basin and the 3D seismic depth migration processing in the Ganchaigou area of ​​the Qaidam Basin. The Long'e'ni area of ​​the Qiangtang Basin has well-developed shallow folds and extremely low signal-to-noise ratios in its seismic data. The application of this embodiment improves the accuracy of pre-stack depth migration processing for shallow folds and small faults. In the 3D pre-stack depth migration processing of the Ganchaigou area, the application of this embodiment improves the accuracy of the "double-complex" surface model in this region, enhances the imaging accuracy of structural parts and slope areas, and eliminates some well-seismic discrepancies.

[0116] The model stitching method provided in this application can be executed by a model stitching device. This application uses a model stitching device executing the model stitching method as an example to illustrate the model stitching device provided in this application.

[0117] In some embodiments of this application, a model splicing device is provided. Figure 9 A structural block diagram of the model splicing device provided in an embodiment of this application is shown, as follows: Figure 9 As shown, the model splicing device 100 includes a first acquisition module 110, a second acquisition module 120, a third acquisition module 130, a fourth acquisition module 140, a fifth acquisition module 150, and a splicing module 160.

[0118] The first acquisition module 110 is used to acquire the near-surface velocity model and the intermediate-deep velocity model.

[0119] The second acquisition module 120 is used to acquire near-surface grid velocity information model based on the near-surface velocity model.

[0120] The third acquisition module 130 is used to acquire the velocity information model of the medium-deep grid based on the medium-deep velocity model.

[0121] The fourth acquisition module 140 is used to acquire the grid velocity difference based on the near-surface grid velocity information model and the mid-deep grid velocity information model.

[0122] The fifth acquisition module 150 is used to obtain the depth of the fitted surface based on the grid velocity difference.

[0123] The stitching module 160 is used to stitch together the near-surface velocity model and the intermediate-deep velocity model based on the depth of the fitted surface.

[0124] This embodiment obtains the depth of the fitted surface by acquiring the mesh velocity difference, and then stitches the near-surface velocity model and the mid-deep velocity model according to the fitted surface depth. This can effectively reduce stitching errors, improve the accuracy of the velocity field, realize the fusion of the two velocity models, and thus improve the accuracy of pre-stack depth migration processing.

[0125] The model splicing device 100 provided in this application embodiment can implement all the processes of the above-described model splicing method embodiment and achieve the same technical effect. To avoid repetition, it will not be described again here.

[0126] The model splicing device in this application embodiment can be an electronic device or a component within an electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal or other devices besides a terminal. For example, the electronic device can be a mobile phone, tablet computer, laptop computer, PDA, in-vehicle electronic device, mobile internet device (MID), augmented reality (AR) / virtual reality (VR) device, robot, wearable device, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc. It can also be a server, network attached storage (NAS), personal computer (PC), television (TV), ATM, or self-service machine, etc. This application embodiment does not specifically limit the device.

[0127] The model splicing device in this application embodiment can be a device with an operating system. This operating system can be Android, iOS, or other possible operating systems; this application embodiment does not specifically limit it.

[0128] The model splicing device provided in this application embodiment can realize all the processes implemented in the above method embodiment, and will not be described again here to avoid repetition.

[0129] Optionally, such as Figure 10 As shown, this application embodiment also provides an electronic device 1000, which includes a processor 1002 and a memory 1004. The memory 1004 stores a program or instructions that can run on the processor 1002. When the program or instructions are executed by the processor 1002, they implement the various steps of the above method embodiments and can achieve the same technical effect. To avoid repetition, they will not be described again here.

[0130] It should be noted that the electronic devices in the embodiments of this application include the aforementioned mobile electronic devices and non-mobile electronic devices.

[0131] Figure 11 A schematic diagram of the hardware structure of an electronic device to implement an embodiment of this application.

[0132] The electronic device 1100 includes, but is not limited to, components such as: radio frequency unit 1101, network module 1102, audio output unit 1103, input unit 1104, sensor 1105, display unit 1106, user input unit 1107, interface unit 1108, memory 1109, and processor 1110.

[0133] Those skilled in the art will understand that the electronic device 1100 may also include a power supply (such as a battery) for supplying power to various components. The power supply may be logically connected to the processor 1110 through a power management system, thereby enabling functions such as managing charging, discharging, and power consumption through the power management system. Figure 11 The electronic device structure shown does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown, or combine certain components, or have different component arrangements, which will not be elaborated here.

[0134] The processor 1110 is used to acquire near-surface velocity models and mid-to-deep velocity models.

[0135] Processor 1110 is used to obtain near-surface grid velocity information model based on near-surface velocity model.

[0136] Processor 1110 is used to obtain mid-deep mesh velocity information model based on mid-deep velocity model.

[0137] Processor 1110 is used to obtain the grid velocity difference based on the near-surface grid velocity information model and the medium-deep grid velocity information model.

[0138] Processor 1110 is used to obtain the depth of the fitted surface based on the grid velocity difference.

[0139] Processor 1110 is used to stitch together near-surface velocity models and mid-to-deep velocity models based on the depth of the fitted surface.

[0140] This embodiment obtains the depth of the fitted surface by acquiring the mesh velocity difference, and then stitches the near-surface velocity model and the mid-deep velocity model according to the fitted surface depth. This can effectively reduce stitching errors, improve the accuracy of the velocity field, realize the fusion of the two velocity models, and thus improve the accuracy of pre-stack depth migration processing.

[0141] The processor 1110 provided in this application embodiment can implement each process of the above model splicing method embodiment and achieve the same technical effect. To avoid repetition, it will not be described again here.

[0142] It should be understood that, in this embodiment, the input unit 1104 may include a graphics processing unit (GPU) 11041 and a microphone 11042. The GPU 11041 processes image data of still images or videos obtained by an image capture device (such as a camera) in video capture mode or image capture mode. The display unit 1106 may include a display panel 11061, which may be configured in the form of a liquid crystal display, an organic light-emitting diode, or the like. The user input unit 1107 includes at least one of a touch panel 11071 and other input devices 11072. The touch panel 11071 is also called a touch screen. The touch panel 11071 may include a touch detection device and a touch controller. Other input devices 11072 may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, power buttons, etc.), trackballs, mice, and joysticks, which will not be described in detail here.

[0143] The memory 1109 can be used to store software programs and various data. The memory 1109 may primarily include a first storage area for storing programs or instructions and a second storage area for storing data. The first storage area may store the operating system, application programs or instructions required for at least one function (such as sound playback, image playback, etc.). Furthermore, the memory 1109 may include volatile memory or non-volatile memory, or both. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus RAM (DRRAM). The memory 1109 in this embodiment includes, but is not limited to, these and any other suitable types of memory.

[0144] Processor 1110 may include one or more processing units; optionally, processor 1110 integrates an application processor and a modem processor, wherein the application processor mainly handles operations involving the operating system, user interface, and applications, and the modem processor mainly handles wireless communication signals, such as a baseband processor. It is understood that the aforementioned modem processor may also not be integrated into processor 1110.

[0145] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described model splicing method embodiments and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0146] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0147] This application also provides a chip, which includes a processor and a communication interface. The communication interface and the processor are coupled. The processor is used to run programs or instructions to implement the various processes of the above-described model splicing method embodiments and achieve the same technical effect. To avoid repetition, it will not be described again here.

[0148] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0149] This application provides a computer program product, which is stored in a storage medium and executed by at least one processor to implement the various processes of the above-described model splicing method embodiments, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0150] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0151] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application.

[0152] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A model stitching method, characterized by, include: Obtain near-surface velocity models and mid-to-deep-layer velocity models; Based on the near-surface velocity model, obtain a near-surface grid velocity information model; Based on the aforementioned mid-deep velocity model, a mid-deep mesh velocity information model is obtained; Based on the near-surface grid velocity information model and the mid-deep grid velocity information model, the grid velocity difference is obtained; Based on the mesh velocity difference, the depth of the fitted surface is obtained; Based on the depth of the fitted surface, the near-surface velocity model and the intermediate-deep velocity model are stitched together; The process of obtaining the fitting interface depth based on the grid velocity difference specifically includes: A first numerical range of the first error is preset, and the first error of the grid velocity difference is obtained; Obtain the grid corresponding to the first error within the first numerical range; The mesh is fitted to obtain the depth of the fitted interface.

2. The model stitching method of claim 1, wherein, The acquisition of the near-surface velocity model and the intermediate-deep velocity model specifically includes: The near-surface velocity model is obtained by using the first-arrival tomographic inversion method constrained by surface information. The depth domain velocity field model is obtained by using the pre-stack time migration profile velocity picking method or the mesh tomography iteration method. The depth domain velocity field model is the mid-deep layer velocity model.

3. The model stitching method of claim 2, wherein, The model for obtaining near-surface grid velocity information based on the near-surface velocity model specifically includes: The near-surface velocity model is gridded to obtain the near-surface grid velocity information model. The size of the grid cells during the gridding process is determined according to the grid size in the tomographic inversion, and the maximum effective grid depth is determined according to the tomographic ray depth in the tomographic inversion.

4. The model stitching method of claim 2, wherein, The model for obtaining mid-to-deep mesh velocity information based on the aforementioned mid-to-deep velocity model specifically includes: The depth domain velocity field model is converted into a constrained tomographic inversion model format, and the grid is resampled to obtain the mid-deep grid velocity information model. The resampled grid has the same size as the grid cell in the near-surface velocity model gridding process.

5. The model stitching method of claim 1, wherein, The step of obtaining the grid velocity difference based on the near-surface grid velocity information model and the mid-deep grid velocity information model specifically includes: The grids in the near-surface grid velocity information model are correlated with the grids in the intermediate-deep grid velocity information model; For a given grid, the velocities of the two grids are subtracted to obtain the grid velocity difference.

6. The model stitching method according to any one of claims 1 to 5, characterized in that, The step of stitching together the near-surface velocity model and the mid-to-deep velocity model based on the fitting interface depth specifically includes: Using the depth of the fitting interface as the splicing surface, the near-surface velocity model and the intermediate-deep velocity model are spliced ​​together. Above the splicing surface, the constrained tomographic inversion velocity in the near-surface velocity model is used, and below the splicing surface, the depth domain velocity field velocity in the intermediate-deep velocity model is used.

7. A model stitching device, characterized by, include: The first acquisition module is used to acquire near-surface velocity models and mid-deep velocity models; The second acquisition module is used to acquire a near-surface grid velocity information model based on the near-surface velocity model. The third acquisition module is used to acquire the mid-deep mesh velocity information model based on the mid-deep velocity model. The fourth acquisition module is used to acquire the grid velocity difference based on the near-surface grid velocity information model and the mid-deep grid velocity information model; The fifth acquisition module is used to acquire the depth of the fitted surface based on the grid velocity difference. The process of obtaining the fitting interface depth based on the grid velocity difference specifically includes: A first numerical range of the first error is preset, and the first error of the grid velocity difference is obtained; Obtain the grid corresponding to the first error within the first numerical range; The mesh is fitted to obtain the depth of the fitted interface. The stitching module is used to stitch the near-surface velocity model and the intermediate-deep velocity model based on the depth of the fitted surface.

8. An electronic device, comprising: include: A memory that stores programs or instructions; A processor for executing the program or instructions to implement the model stitching steps as described in any one of claims 1 to 6.

9. A readable storage medium having a program or instructions stored thereon, characterized in that, When the program or instructions are executed by the processor, they implement the steps of the model stitching method as described in any one of claims 1 to 6.

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