A method, system, device and storage medium for velocity model fusion

Abstract

The application provides a velocity model fusion method, system, device and storage medium, the method comprising: selecting and extracting a fusion mosaic surface of a full-offset near-surface velocity model and a pre-stack deep velocity model, and performing horizon flattening correction on the full-offset near-surface velocity model and the pre-stack deep velocity model according to the fusion mosaic surface; performing gradual fusion processing from shallow to deep on the data of the full-offset near-surface velocity model and the pre-stack deep velocity model after flattening correction to obtain a process fusion mosaic velocity field; performing inverse correction according to the process fusion mosaic velocity field data, and performing pre-stack depth migration imaging processing according to the inverse correction mosaic velocity field; if the imaging quality does not meet the requirements, the above processing steps are performed again until the imaging quality meets the requirements. The application improves the depth migration imaging quality and reliability of seismic data.

Description

Technical Field

[0001] This invention relates to the field of seismic exploration technology for oil and gas, and more specifically, to a method, system, device, and storage medium for velocity model fusion. Background Technology

[0002] The fusion imaging of near-surface velocity models derived from tomographic inversion and initial velocity models from depth migration is of great significance. Previous seismic data processing often used fixed elevation depth surfaces or the top of high-velocity layers as fusion mosaic surfaces. However, due to the inconsistent stability of near-surface tomographic rays, the reliability of the models is often insufficient. Mechanically applying these methods for mosaicking can lead to inaccurate near-surface velocities, affecting shallow and mid-depth migration imaging. Furthermore, using the top of high-velocity layers as the fusion mosaic surface for fusion with the initial velocity model from depth migration can result in shallow fusion depths, failing to achieve the intended fusion goal. Moreover, since the fusion method involves direct data mosaicking and addition, mosaicking marks are easily left on the mosaic surface. Although subsequent smoothing of the velocity model can be performed laterally and vertically, this reduces the accuracy of the near-surface velocity model. Therefore, selecting a suitable fusion mosaic surface and performing reasonable, high-quality fusion mosaicking of the two velocity field data sets has become a challenging problem. Summary of the Invention

[0003] In view of this, the purpose of this invention is to propose a method, system, electronic device and computer-readable storage medium for velocity model fusion. This invention can improve the accuracy of shallow initial velocity models in pre-stack depth migration, and improve the problem of poor accuracy of shallow velocity models in shallow seismic data due to low inversion ray density, thereby ensuring the shallow imaging quality of the data, improving the imaging reliability of mid-deep data, and providing high-quality basic data for subsequent seismic data interpretation and application.

[0004] Based on the above objectives, one aspect of the present invention provides a method for velocity model fusion, comprising the following steps: selecting and extracting a fusion mosaic surface of a fully offset near-surface velocity model and a pre-stack deep-offset velocity model, and performing stratigraphic leveling correction on the fully offset near-surface velocity model and the pre-stack deep-offset velocity model according to the fusion mosaic surface; performing a gradual fusion process from shallow to deep on the leveled and corrected data of the fully offset near-surface velocity model and the pre-stack deep-offset velocity model to obtain a process fused mosaic velocity field; performing inverse correction on the process fused mosaic velocity field data according to the fusion mosaic surface, and performing pre-stack depth migration imaging processing on the inversely corrected velocity field and determining whether the imaging quality meets the requirements; if the imaging quality does not meet the requirements, repeating the above steps until the imaging quality meets the requirements.

[0005] In some implementations, the step of selecting and extracting the fusion mosaic surface of the full-offset near-surface velocity model and the pre-stack deep-offset velocity model includes: determining the inversion depth of the full-offset near-surface velocity model by using tomographic inversion of the ray density of the full-offset near-surface velocity model and the consistency between the full-offset near-surface velocity model and the micrologging velocity, and determining the fusion mosaic surface based on the inversion depth.

[0006] In some embodiments, the step of performing stratigraphic leveling correction on the full-offset near-surface velocity model and the pre-stack deep-offset velocity model according to the fused mosaic surface includes: placing the elevation value of the fused mosaic surface into the full-offset near-surface velocity model and the pre-stack deep-offset velocity model respectively as a correction value, and correcting the fused mosaic surface of the full-offset near-surface velocity model and the pre-stack deep-offset velocity model to the horizontal reference plane respectively.

[0007] In some embodiments, the step of placing the elevation values ​​of the fused mosaic surface into the full-offset near-surface velocity model and the pre-stack deep-offset velocity model as correction values ​​includes: determining the data correction amount for each trace in the fused mosaic surface based on the difference between the elevation values ​​of the horizontal reference surface and the elevation values ​​of each trace in the fused mosaic surface, and placing the data correction amount for each trace into the full-offset near-surface velocity model and the pre-stack deep-offset velocity model, respectively.

[0008] In some embodiments, the step of gradually fusing the data of the fully offset near-surface velocity model and the pre-stack deep offset velocity model from shallow to deep to obtain the process fused mosaic velocity field includes: calculating the scale factor of the fully offset near-surface velocity model and the first product of the velocity field of the fully offset near-surface velocity model, and calculating the scale factor of the pre-stack deep offset velocity model and the velocity field of the pre-stack deep offset velocity model; and using the sum of the first product and the second product as the mosaicked process velocity field.

[0009] In some embodiments, the step of gradually fusing the data of the fully offset near-surface velocity model and the pre-stack deep offset velocity model from shallow to deep to obtain the process fused mosaic velocity field includes: determining the scaling factor of the fully offset near-surface velocity model and the scaling factor of the pre-stack deep offset velocity model according to the depth of the fused mosaic surface, wherein the sum of the scaling factor of the fully offset near-surface velocity model and the scaling factor of the pre-stack deep offset velocity model is one.

[0010] In some embodiments, the method further includes unifying the surface and fill velocities of the full-offset near-surface velocity model and the pre-stack deep-offset velocity model.

[0011] In another aspect, this invention provides a velocity model fusion system, comprising: a correction module for selecting and extracting fusion mosaic surfaces of a full-offset near-surface velocity model and a pre-stack deep-offset velocity model, and performing stratigraphic leveling correction on the full-offset near-surface velocity model and the pre-stack deep-offset velocity model according to the fusion mosaic surfaces; a fusion module for performing gradual fusion processing from shallow to deep on the leveled and corrected data of the full-offset near-surface velocity model and the pre-stack deep-offset velocity model to obtain a process fused mosaic velocity field; an inverse correction module for performing inverse correction on the velocity field data according to the fusion mosaic surfaces, and performing pre-stack depth migration imaging processing on the inversely corrected velocity field and determining whether the imaging quality meets the requirements; and an execution module for repeating the above steps until the imaging quality meets the requirements if the imaging quality does not meet the requirements.

[0012] In another aspect of the present invention, an electronic device is provided, comprising: at least one processor; and a memory storing computer instructions executable on the processor, the instructions, when executed by the processor, implementing the steps of the method described above.

[0013] In another aspect, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method steps.

[0014] The present invention has the following beneficial technical effects: it can improve the accuracy of the shallow initial velocity model of pre-stack depth migration, improve the problem of poor accuracy of shallow velocity model due to low inversion ray density in shallow seismic data, thereby ensuring the shallow imaging quality of the data, improving the imaging reliability of medium and deep data, and providing high-quality basic data for subsequent seismic data interpretation and application. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained based on these drawings without creative effort.

[0016] Figure 1 A schematic diagram illustrating an embodiment of the velocity model fusion method provided by the present invention;

[0017] Figure 2 A block diagram of an embodiment of the velocity model fusion method provided by the present invention;

[0018] Figure 3A flowchart of an embodiment of the velocity model fusion method provided by the present invention;

[0019] Figure 4 This is a schematic diagram of the fusion tiling surface picking provided by the present invention;

[0020] Figure 5 This is a schematic diagram of layer flattening and correction processing using a fused mosaic surface as the interface provided by the present invention.

[0021] Figure 6 This is a comparison chart of the fusion results of conventional near-surface velocity models and the fusion results of this invention;

[0022] Figure 7 A comparison chart showing the fusion migration profile effect of a conventional near-surface velocity model and the fusion migration profile effect of this invention;

[0023] Figure 8 A schematic diagram of an embodiment of the velocity model fusion system provided by the present invention;

[0024] Figure 9 A schematic diagram of the hardware structure of an embodiment of the speed model fusion electronic device provided by the present invention;

[0025] Figure 10 A schematic diagram of an embodiment of a computer storage medium for speed model fusion provided by the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to specific examples and the accompanying drawings.

[0027] It should be noted that all uses of "first" and "second" in the embodiments of the present invention are for the purpose of distinguishing two entities or parameters with the same name but different names. It is clear that "first" and "second" are only for the convenience of expression and should not be construed as limiting the embodiments of the present invention. Subsequent embodiments will not explain this in detail.

[0028] In a first aspect, an embodiment of a velocity model fusion method is proposed. Figure 1 The diagram shown is a schematic representation of an embodiment of the velocity model fusion method provided by the present invention.

[0029] like Figure 1 As shown, the embodiments of the present invention include the following steps:

[0030] S1. Select and extract the fusion mosaic surface of the full-offset near-surface velocity model and the pre-stack deep-offset velocity model, and perform stratigraphic leveling correction on the full-offset near-surface velocity model and the pre-stack deep-offset velocity model according to the fusion mosaic surface.

[0031] S2. The data of the full-offset near-surface velocity model and the pre-stack deep offset velocity model after flattening and correction are gradually fused from shallow to deep to obtain the process fused mosaic velocity field.

[0032] S3. Perform inverse correction on the velocity field data based on the fused mosaic surface, and perform pre-stack depth migration imaging processing based on the inversely corrected velocity field to determine whether the imaging quality meets the requirements.

[0033] S4. If the image quality does not meet the requirements, repeat the above steps until the image quality meets the requirements.

[0034] With the improvement of computing power and the updating of processing algorithms, pre-stack depth migration methods have evolved from conventional isotropic migration to anisotropic migration, and the migration reference surface is shifting from large smooth surface migration to pre-stack depth migration based on the "true" ground surface. However, regardless of the method, velocity modeling, i.e., the accurate acquisition of the velocity model, remains the core of pre-stack depth migration processing. Depth migration modeling is a complex iterative process, including the establishment of the initial model and the iterative optimization of the model.

[0035] The initial model should be established based on pre-stack time migration, combined with geological stratigraphy and well data to delineate and calibrate velocity control layers, and interpret the stratigraphic model in the time migration domain. However, the grid-driven inversion grids of current mainstream commercial migration software are relatively large, especially in shallow velocity models in steep and complex exploration areas. Due to the low number of shallow cover times, the ray density of reflected wave grid tomography inversion is low, often making it difficult to obtain an accurate initial velocity model. First-arrival tomography inversion based on micrologging constraints can effectively compensate for this deficiency. The approach to shallow surface velocity modeling for pre-stack depth migration is to use a larger migration distance (or even the full migration distance) for first-arrival inversion when inverting shallow surface models based on micrologging constraints, and to combine this with a small smooth surface from pre-stack depth migration for appropriate model smoothing. The specific approach involves finding a stable interface within the ray density volume obtained from the initial arrival tomography under drilling and logging velocity constraints. This interface serves as the mosaic interface for the shallow layer model. The near-surface velocity model obtained from this tomography is then embedded along the mosaic surface onto the high-velocity top interface of the initial velocity model. This solves the problem of low accuracy in shallow layer velocities without data-driven conditions.

[0036] In this embodiment of the invention, a high-precision small-area first-arrival tomographic inversion model constrained by micrologging is embedded into a pre-stack depth migration initial velocity model derived from large-area reflection tomographic inversion. The ray density of the tomographic inversion velocity model and its consistency with the micrologging velocity are used to determine the inversion depth that can be close to the surface model. The deeper fusion mosaic surface is extracted as much as possible, and the data is subjected to interface correction and flattening processing using this fusion mosaic surface. Then, the two sets of velocity data after flattening are gradually interpolated and fused mosaic according to the depth change. Finally, the fused mosaic velocity data is back-corrected to the normal migration reference surface, and this velocity field is used as the pre-stack depth migration initial velocity field.

[0037] The embodiments of the present invention improve the utilization rate of near-surface velocity models obtained by small-area tomography inversion, while reducing mosaic traces in the fusion of two sets of velocity field data, eliminating velocity anomalies, and improving the accuracy of shallow models in pre-stack depth migration, thereby improving the quality of pre-stack depth migration imaging.

[0038] Figure 2 A block diagram illustrating an embodiment of the velocity model fusion method provided by the present invention, as shown below. Figure 2 As shown, the embodiments of the present invention include: 1. Raw data preprocessing: unifying the near-surface model data obtained from well-constrained full-offset tomography with the reference surface and replacement filling velocity of the pre-stack depth-offset velocity model; 2. Extracting the fusion mosaic surface: determining the extractable near-surface tomography fusion thickness surface by combining the ray density obtained from full-offset tomography and the fit between the velocity model and the well; 3. Calibrating the velocity field data based on the fusion mosaic surface: flattening the data using the fusion mosaic surface as the interface; 4. Gradual processing of the flattened data from shallow to deep: fusion mosaicking the two sets of velocity volume data from shallow to deep by performing gradual interpolation, controlling the embedding ratio of the two sets of velocity fields by weighting coefficients; 5. Inverse calibration of the velocity field data based on the fusion mosaic surface; 6. Depth migration processing.

[0039] Figure 3 A flowchart illustrating an embodiment of the velocity model fusion method provided by the present invention, in conjunction with... Figure 3 The embodiments of the present invention will be described.

[0040] In some embodiments, the method further includes: unifying the surface and fill velocities of the full-offset near-surface velocity model and the pre-stack deep migration velocity model. Full-offset first-arrival tomographic inversion calculations for pre-stack depth migration velocity modeling are completed, generating standard SEGY format data. Based on this, and after thorough analysis of the original data, unified processing is performed on the tomographic inversion near-surface model data and the depth migration velocity model reference surface and replacement fill velocities.

[0041] Select and extract the fusion mosaic surface of the full-offset near-surface velocity model and the pre-stack deep-offset velocity model, and perform stratigraphic leveling correction on the full-offset near-surface velocity model and the pre-stack deep-offset velocity model according to the fusion mosaic surface.

[0042] In some embodiments, the step of selecting and extracting the fusion mosaic surface of the full-offset near-surface velocity model and the pre-stack deep-offset velocity model includes: determining the inversion depth of the full-offset near-surface velocity model by using tomographic inversion of the ray density of the full-offset near-surface velocity model and the consistency between the full-offset near-surface velocity model and the micrologging velocity, and determining the fusion mosaic surface based on the inversion depth. Figure 4 This is a schematic diagram of the fusion tiling surface picking provided by the present invention, as shown below. Figure 4 As shown, the reliable inversion depth is determined based on the inversion ray density of the near-surface model with tomographic static correction. The reliable mosaic thickness of the velocity model is selected by comprehensively considering the well-kissing conditions of the near-surface velocity model and drilling and logging data such as VSP, and the data mosaic surface of the shallow model fusion is extracted.

[0043] In some embodiments, the step of performing stratigraphic leveling correction on the full-offset near-surface velocity model and the pre-stack deep-offset velocity model according to the fused mosaic surface includes: placing the elevation value of the fused mosaic surface into the full-offset near-surface velocity model and the pre-stack deep-offset velocity model respectively as a correction value, and correcting the fused mosaic surface of the full-offset near-surface velocity model and the pre-stack deep-offset velocity model to the horizontal reference plane respectively. Figure 5 This is a schematic diagram of the layer flattening and correction process using the fused inlay surface as the interface provided by the present invention, as shown below. Figure 5 As shown, the elevation values ​​of the data mosaic surface are placed in the near-surface velocity model at the full offset using correction values, and a fixed surface (horizontal datum) is selected for data correction, thus correcting the near-surface model mosaic surface to the horizontal datum. Similarly, the elevation values ​​of the data mosaic surface are placed in the pre-stack depth migration initial velocity model using correction values, and the same fixed horizontal surface is selected for data correction, thus correcting the original depth migration velocity model mosaic surface to the same horizontal datum.

[0044] In some embodiments, the step of placing the elevation values ​​of the fused mosaic surface into the full-offset near-surface velocity model and the pre-stack deep-offset velocity model as correction values ​​includes: determining the data correction amount for each trace in the fused mosaic surface based on the difference between the elevation values ​​of the horizontal reference surface and the elevation values ​​of each trace, and placing the data correction amount for each trace into the full-offset near-surface velocity model and the pre-stack deep-offset velocity model, respectively. The formula for calculating the correction amount is as follows:

[0045] T i =TD -T fi

[0046] Among them, T i T represents the data correction amount for the i-th fused tessellation surface. D T represents the elevation value of the horizontal reference surface. fi This represents the elevation value of the mosaic surface fused from the i-th data channel.

[0047] The data from the fully offset near-surface velocity model and the pre-stack deep offset velocity model, after being flattened and corrected, are gradually fused from shallow to deep to obtain a fused mosaic surface.

[0048] In some embodiments, the step of gradually fusing the data of the fully offset near-surface velocity model and the pre-stack deep-offset velocity model from shallow to deep to obtain a fused mosaic surface includes: calculating the scale factor of the fully offset near-surface velocity model and the first product of the velocity field of the fully offset near-surface velocity model, and calculating the scale factor of the pre-stack deep-offset velocity model and the velocity field of the pre-stack deep-offset velocity model; and using the sum of the first product and the second product as the mosaicked velocity field, and obtaining the fused mosaic surface based on the mosaicked velocity field.

[0049] In some embodiments, the step of gradually fusing the data of the fully offset near-surface velocity model and the pre-stack deep-offset velocity model after flattening and correction from shallow to deep to obtain a fused mosaic surface includes: determining the scaling factor of the fully offset near-surface velocity model and the scaling factor of the pre-stack deep-offset velocity model according to the depth of the fused mosaic surface, and obtaining the fused mosaic surface based on the scaling factors, wherein the sum of the scaling factor of the fully offset near-surface velocity model and the scaling factor of the pre-stack deep-offset velocity model is one.

[0050] Two sets of velocity model data, calibrated to the same reference plane, are gradually fused and mosaicked from shallow to deep. V3(d) = V1(d)*S1(di) + V2(d)*S2(di), where V3(d) represents the fused velocity field after mosaicking, S1(di) represents the scaling factor of the near-surface velocity field V1(d), and S2(di) represents the scaling factor of the near-surface velocity field V2(d). S1(di) + S2(di) = 1, S1(di) ≥ 0, and S2(di) ≥ 0. As the depth increases, S1(di) decreases monotonically, and S2(di) increases monotonically.

[0051] The velocity field data is inversely corrected based on the fused mosaic surface, and pre-stack depth migration imaging is performed based on the inversely corrected velocity field. Data corrections are removed according to the fused mosaic surface to restore the data to the unified reference surface of the work area.

[0052] If the imaging quality does not meet the requirements, the process is repeated to perform stratigraphic leveling correction and subsequent steps on the full-offset near-surface velocity model and the pre-stack deep-offset velocity model based on the fused mosaic surface until the imaging quality meets the requirements. The fused near-surface model migration velocity field is used as the new initial migration velocity field for pre-stack depth migration processing. If the imaging quality is good, iterative processing of mid-deep grid tomography inversion is performed; if the imaging quality is poor, further adjustments are made.

[0053] In this embodiment of the invention, the selection of the fusion mosaic surface is determined comprehensively based on the ray density inverted from the near-surface model and its consistency with micrologging velocities. The fusion mosaic surface is a manually picked continuous interpolation smooth surface, which not only follows the near-surface in-situ velocity patterns and structural morphology but also appropriately avoids points with large differences between the two sets of velocities. Then, the velocity field data is flattened along the fusion mosaic surface as the interface, and the two sets of velocity volumes are weighted and interpolated gradually with varying scaling factors from shallow to deep. This allows the high-precision near-surface velocity model to be uniformly and gradually embedded into the pre-stack depth migration velocity field, avoiding abnormal velocity jumps at the mosaic surface, maximizing the utilization of the near-surface velocity model, and the gradual fusion mosaic process is highly operable and modifiable, allowing processors to make real-time adjustments and optimizations based on the migration results. This processing method effectively improves the shallow imaging accuracy in seismic data, reduces shallow imaging errors, thereby further improving the imaging quality of medium-deep seismic data, reducing drilling risks for research targets, and providing reliable data for efficient exploration and development of oil and gas fields.

[0054] It should be noted that the steps in each embodiment of the above-mentioned velocity model fusion method can be interleaved, substituted, added, or deleted. Therefore, these reasonable permutations and combinations of the velocity model fusion method should also fall within the protection scope of this invention, and the protection scope of this invention should not be limited to the embodiments.

[0055] Based on the above objectives, a second aspect of this invention proposes a system for velocity model fusion. For example... Figure 8As shown, system 200 includes the following modules: a correction module, used to select and extract the fusion mosaic surface of the full-offset near-surface velocity model and the pre-stack deep-offset velocity model, and to perform stratigraphic leveling correction on the full-offset near-surface velocity model and the pre-stack deep-offset velocity model according to the fusion mosaic surface; a fusion module, used to perform gradual fusion processing from shallow to deep on the leveled and corrected data of the full-offset near-surface velocity model and the pre-stack deep-offset velocity model to obtain the fusion mosaic surface; an inverse correction module, used to perform inverse correction on the velocity field data according to the fusion mosaic surface, and to perform pre-stack depth migration imaging processing on the inversely corrected velocity field and determine whether the imaging quality meets the requirements; and an execution module, which, if the migration imaging quality does not meet the requirements, performs stratigraphic leveling correction on the full-offset near-surface velocity model and the pre-stack deep-offset velocity model according to the fusion mosaic surface and subsequent steps until the imaging quality meets the requirements.

[0056] In some embodiments, the correction module is further configured to: determine the inversion depth of the full-offset near-surface velocity model by using the ray density of the full-offset near-surface velocity model obtained by tomographic inversion and the degree of agreement between the full-offset near-surface velocity model and the micrologging velocity, and determine the fusion mosaic surface based on the inversion depth.

[0057] In some embodiments, the correction module is further configured to: place the elevation value of the fused mosaic surface into the full-offset near-surface velocity model and the pre-stack deep-offset velocity model respectively as correction values, and correct the fused mosaic surface of the full-offset near-surface velocity model and the pre-stack deep-offset velocity model to the horizontal reference plane respectively.

[0058] In some embodiments, the correction module is further configured to: determine the data correction amount for each trace in the fused mosaic surface based on the difference between the elevation value of the horizontal reference surface and the elevation value of each trace in the fused mosaic surface, and place the data correction amount for each trace into the full offset near-surface velocity model and the pre-stack deep offset velocity model, respectively.

[0059] In some embodiments, the fusion module is further configured to: calculate the scale factor of the fully offset near-surface velocity model and the first product of the velocity field of the fully offset near-surface velocity model, and calculate the scale factor of the pre-stack deep offset velocity model and the second product of the velocity field of the pre-stack deep offset velocity model; and use the sum of the first product and the second product as the mosaicked velocity field.

[0060] In some embodiments, the fusion module is further configured to: determine the scaling factor of the fully offset near-surface velocity model and the scaling factor of the pre-stack deep offset velocity model based on the depth of the fusion mosaic surface, and obtain the fusion mosaic surface based on the scaling factors, wherein the sum of the scaling factor of the fully offset near-surface velocity model and the scaling factor of the pre-stack deep offset velocity model is one.

[0061] In some embodiments, the system further includes a unification module for unifying the surface and fill velocities of the full-offset near-surface velocity model and the pre-stack deep-offset velocity model.

[0062] Based on the above objectives, a third aspect of the present invention provides an electronic device, comprising: at least one processor; and a memory storing computer instructions executable by the processor to perform the following steps: S1, selecting and extracting a fusion mosaic surface of a full-offset near-surface velocity model and a pre-stack deep-offset velocity model, and performing stratigraphic leveling correction on the full-offset near-surface velocity model and the pre-stack deep-offset velocity model according to the fusion mosaic surface; S2, performing a gradual fusion process from shallow to deep on the leveled and corrected data of the full-offset near-surface velocity model and the pre-stack deep-offset velocity model to obtain a process fusion mosaic velocity field; S3, performing inverse correction on the velocity field data according to the fusion mosaic surface to obtain a final fusion mosaic velocity field, and performing pre-stack depth migration imaging processing and determining whether the imaging quality meets the requirements according to the inversely corrected velocity field; and S4, if the migration imaging quality does not meet the requirements, repeating the above steps until the imaging quality meets the requirements.

[0063] In some embodiments, the step of selecting and extracting the fusion mosaic surface of the full-offset near-surface velocity model and the pre-stack deep-offset velocity model includes: determining the inversion depth of the full-offset near-surface velocity model by using tomographic inversion of the ray density of the full-offset near-surface velocity model and the consistency between the full-offset near-surface velocity model and the micrologging velocity, and determining the fusion mosaic surface based on the inversion depth.

[0064] In some embodiments, the step of performing stratigraphic leveling correction on the full-offset near-surface velocity model and the pre-stack deep-offset velocity model according to the fused mosaic surface includes: placing the elevation value of the fused mosaic surface into the full-offset near-surface velocity model and the pre-stack deep-offset velocity model respectively as a correction value, and correcting the fused mosaic surface of the full-offset near-surface velocity model and the pre-stack deep-offset velocity model to the horizontal reference plane respectively.

[0065] In some embodiments, the step of placing the elevation values ​​of the fused mosaic surface into the full-offset near-surface velocity model and the pre-stack deep-offset velocity model as correction values ​​includes: determining the data correction amount for each trace in the fused mosaic surface based on the difference between the elevation values ​​of the horizontal reference surface and the elevation values ​​of each trace in the fused mosaic surface, and placing the data correction amount for each trace into the full-offset near-surface velocity model and the pre-stack deep-offset velocity model, respectively.

[0066] In some embodiments, the step of gradually fusing the data of the fully offset near-surface velocity model and the pre-stack deep offset velocity model from shallow to deep to obtain the fused mosaic velocity field includes: calculating the scale factor of the fully offset near-surface velocity model and the first product of the velocity field of the fully offset near-surface velocity model, and calculating the scale factor of the pre-stack deep offset velocity model and the velocity field of the pre-stack deep offset velocity model; and using the sum of the first product and the second product as the mosaicked velocity field.

[0067] In some embodiments, the step of gradually fusing the data of the fully offset near-surface velocity model and the pre-stack deep-offset velocity model from shallow to deep to obtain a fused mosaic velocity field includes: determining the scaling factor of the fully offset near-surface velocity model and the scaling factor of the pre-stack deep-offset velocity model according to the depth of the fused mosaic surface, and obtaining the fused mosaic surface based on the scaling factor, wherein the sum of the scaling factor of the fully offset near-surface velocity model and the scaling factor of the pre-stack deep-offset velocity model is one.

[0068] In some implementations, the steps further include: unifying the surface and fill velocities of the full-offset near-surface velocity model and the pre-stack deep-offset velocity model.

[0069] like Figure 9 The figure shown is a hardware structure diagram of an embodiment of the electronic device for speed model fusion provided by the present invention.

[0070] For example Figure 9 Taking the device shown as an example, the device includes a processor 301 and a memory 302.

[0071] Processor 301 and memory 302 can be connected via a bus or other means. Figure 9 Taking the example of a connection between China and Israel via a bus.

[0072] The memory 302, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as the program instructions / modules corresponding to the speed model fusion method in this embodiment. The processor 301 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions, and modules stored in the memory 302, thereby realizing the speed model fusion method.

[0073] Memory 302 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created using the speed model fusion method, etc. Furthermore, memory 302 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, memory 302 may optionally include memory remotely located relative to processor 301, and these remote memories may be connected to the local module via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0074] The computer instructions 303 corresponding to one or more velocity model fusion methods are stored in memory 302. When executed by processor 301, the velocity model fusion method in any of the above method embodiments is executed.

[0075] Any embodiment of an electronic device that performs the above-described speed model fusion method can achieve the same or similar effects as any of the aforementioned method embodiments.

[0076] The present invention also provides a computer-readable storage medium storing a computer program of a method for performing speed model fusion when executed by a processor.

[0077] like Figure 10 The diagram shown is a schematic representation of an embodiment of the computer storage medium for the above-described speed model fusion provided by the present invention. Figure 10 Taking the computer storage medium shown as an example, the computer-readable storage medium 401 stores a computer program 402 that, when executed by a processor, performs the above method.

[0078] Finally, it should be noted that those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program for the speed model fusion method can be stored in a computer-readable storage medium. When executed, the program can include the processes of the embodiments of the above methods. The storage medium for the program can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc. The above computer program embodiments can achieve the same or similar effects as any of the corresponding foregoing method embodiments.

[0079] The above are exemplary embodiments disclosed in this invention. However, it should be noted that various changes and modifications can be made without departing from the scope of the embodiments of this invention as defined by the claims. The functions, steps, and / or actions of the methods according to the disclosed embodiments described herein do not need to be performed in any particular order. Furthermore, although the elements disclosed in the embodiments of this invention may be described or claimed individually, they may be understood as multiple unless explicitly limited to a singular number.

[0080] It should be understood that, as used herein, the singular form “a” is intended to include the plural form as well, unless the context clearly supports an exception. It should also be understood that, as used herein, “and / or” refers to any and all possible combinations of one or more of the associated listed items.

[0081] The embodiment numbers disclosed in the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0082] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0083] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the invention (including the claims) is limited to these examples. Within the framework of the invention, technical features of the above embodiments or different embodiments can be combined, and many other variations of different aspects of the invention exist, which are not provided in the details for the sake of brevity. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the invention should be included within the protection scope of the invention.

Claims

1. A method for velocity model fusion, characterized in that, Includes the following steps: Select and extract the fusion mosaic surface of the full-offset near-surface velocity model and the pre-stack deep-offset velocity model, and perform stratigraphic leveling correction on the full-offset near-surface velocity model and the pre-stack deep-offset velocity model according to the fusion mosaic surface; The data of the full-offset near-surface velocity model and the pre-stack deep offset velocity model, after being flattened and corrected, are gradually fused from shallow to deep to obtain the process fused mosaic velocity field. The process fusion mosaicking velocity field is inversely corrected based on the fusion mosaicking surface, and pre-stack depth migration imaging processing is performed based on the inversely corrected velocity field to determine whether the imaging quality meets the requirements. If the image quality does not meet the requirements, repeat the above steps until the image quality meets the requirements. The step of performing stratigraphic leveling correction on the full-offset near-surface velocity model and the pre-stack deep-off velocity model based on the fused mosaic surface includes: The elevation values ​​of the fused mosaic surface are placed in the full-offset near-surface velocity model and the pre-stack deep-offset velocity model respectively as correction values, and the fused mosaic surfaces of the full-offset near-surface velocity model and the pre-stack deep-offset velocity model are corrected to the horizontal reference plane respectively. The step of placing the elevation values ​​of the fused mosaic surface into the full-offset near-surface velocity model and the pre-stack deep offset velocity model respectively using correction values ​​includes: The data correction amount for each trace in the fused mosaic surface is determined based on the difference between the elevation value of the horizontal reference surface and the elevation value of each trace in the fused mosaic surface, and the data correction amount for each trace is placed in the full offset near-surface velocity model and the pre-stack deep offset velocity model, respectively. The step of performing a gradual fusion process from shallow to deep on the data of the flattened and corrected full-off near-surface velocity model and the pre-stack deep-off velocity model to obtain the process fused mosaic velocity field includes: Calculate the scale factor of the fully offset near-surface velocity model and the first product of the velocity field of the fully offset near-surface velocity model; and calculate the scale factor of the pre-stack deep offset velocity model and the second product of the velocity field of the pre-stack deep offset velocity model; and The sum of the first product and the second product is used as the mosaicked velocity field, and the process fusion mosaicked velocity field is obtained based on the mosaicked velocity field. The scaling factors of the full offset near-surface velocity model and the pre-stack deep offset velocity model are determined based on the depth of the fused mosaic surface. Among them, the scaling factor corresponding to the full offset near-surface velocity model decreases with increasing depth, while the scaling factor corresponding to the pre-stack deep offset velocity model increases with increasing depth.

2. The method for velocity model fusion according to claim 1, characterized in that, The steps of selecting and extracting the fusion mosaic surface of the full-offset near-surface velocity model and the pre-stack deep offset velocity model include: The inversion depth of the full-offset near-surface velocity model is determined by using the ray density of the full-offset near-surface velocity model and the consistency between the full-offset near-surface velocity model and the micro-logging velocity, and the fusion mosaic surface is determined based on the inversion depth.

3. The method for velocity model fusion according to claim 1, characterized in that, The sum of the scaling factor of the fully offset near-surface velocity model and the scaling factor of the pre-stack deep offset velocity model is one.

4. The method for velocity model fusion according to claim 1, characterized in that, The method further includes: The surface and filling velocities of the fully offset near-surface velocity model and the pre-stack deep offset velocity model are unified.

5. A system for velocity model fusion, characterized in that, include: The correction module is used to select and extract the fusion mosaic surface of the full-offset near-surface velocity model and the pre-stack deep-offset velocity model, and to perform stratigraphic leveling correction on the full-offset near-surface velocity model and the pre-stack deep-offset velocity model according to the fusion mosaic surface. The step of performing stratigraphic leveling correction on the full-offset near-surface velocity model and the pre-stack deep-offset velocity model according to the fusion mosaic surface includes: placing the elevation values ​​of the fusion mosaic surface into the full-offset near-surface velocity model and the pre-stack deep-offset velocity model respectively as correction values, and respectively... The fusion mosaic surface of the full-offset near-surface velocity model and the pre-stack deep-offset velocity model is corrected to a horizontal reference surface; the step of placing the elevation values ​​of the fusion mosaic surface into the full-offset near-surface velocity model and the pre-stack deep-offset velocity model respectively as correction values ​​includes: determining the data correction amount of each trace in the fusion mosaic surface based on the difference between the elevation value of the horizontal reference surface and the elevation value of each trace, and placing the data correction amount of each trace into the full-offset near-surface velocity model and the pre-stack deep-offset velocity model respectively; The fusion module is used to perform a gradual fusion process from shallow to deep on the data of the full-offset near-surface velocity model and the pre-stack deep-offset velocity model to obtain a process fused mosaic velocity field. The inverse correction module is used to perform inverse correction on the velocity field data of the fusion mosaicking process based on the fusion mosaicking surface, and to perform pre-stack depth migration imaging processing based on the inverse-corrected velocity field and determine whether the imaging quality meets the requirements; and The execution module is used to repeat the above steps until the image quality meets the requirements if the image quality does not meet the requirements. The step of performing a gradual fusion process from shallow to deep on the data of the flattened and corrected full-off near-surface velocity model and the pre-stack deep-off velocity model to obtain the process fused mosaic velocity field includes: Calculate the scale factor of the fully offset near-surface velocity model and the first product of the velocity field of the fully offset near-surface velocity model; and calculate the scale factor of the pre-stack deep offset velocity model and the second product of the velocity field of the pre-stack deep offset velocity model; and The sum of the first product and the second product is used as the mosaicked velocity field, and the process fusion mosaicked velocity field is obtained based on the mosaicked velocity field. The scaling factors of the full offset near-surface velocity model and the pre-stack deep offset velocity model are determined based on the depth of the fused mosaic surface. Among them, the scaling factor corresponding to the full offset near-surface velocity model decreases with increasing depth, while the scaling factor corresponding to the pre-stack deep offset velocity model increases with increasing depth.

6. An electronic device, characterized in that, include: At least one processor; as well as A memory storing computer instructions executable on the processor, which, when executed by the processor, implement the steps of the method according to any one of claims 1-4.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-4.

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