A True Surface Seismic-Geological Integrated Velocity Modeling and Pre-stack Depth Imaging Method

Abstract

This invention provides a true surface seismic-geological integrated velocity modeling and pre-stack depth imaging method, belonging to the field of seismic imaging technology. The method includes: obtaining a near-surface velocity model using first-arrival tomographic inversion and fusing it with a mid-deep velocity model obtained from a large floating surface of reflected waves to form an initial depth-migrated velocity field; optimizing the velocity model using reflected wave tomographic velocity inversion to construct a true surface isotropic velocity model; extracting azimuth and dip data from an isotropic migration seismic database, and then calculating various parameters of the anisotropic model and obtaining anisotropic velocities through anisotropic model analysis; and repeatedly iterating and updating the model using network tomographic inversion to obtain a multi-information-constrained anisotropic velocity model. This invention solves the problems of slow modeling speed, poor accuracy, poor imaging quality, and low precision in existing true surface seismic-geological velocity modeling and pre-stack depth imaging methods.

Description

Technical Field

[0001] This invention belongs to the field of seismic imaging technology, and particularly relates to a method for integrated velocity modeling of true surface seismic geology and pre-stack depth imaging. Background Technology

[0002] Multiple tectonic movements result in a complex surface and subsurface: the surface is undulating with large relative elevation differences, and the velocity and thickness of the low-velocity zone change rapidly; the subsurface structure is complex, leading to a complex wavefield, making it difficult to obtain high-quality seismic data with accurate and reliable imaging.

[0003] For seismic imaging studies in areas with complex geological structures and surface textures, the most effective method at present is pre-stack depth migration that approximates the "true" surface. The final imaging effect depends on two factors: first, the advancement and applicability of the migration algorithm; and second, the reasonable and accurate pre-stack depth migration velocity modeling that matches it.

[0004] Traditional migration velocity iteration processes aim to flatten the phase axis of reflected waves, i.e., using the residual delay (residual RMS velocity) of reflected waves to invert accurate migration velocities. However, in shallow layers, due to the low coverage number of reflected waves and the presence of a lot of noise, this method has significant limitations. Furthermore, the accuracy of shallow velocities in pre-stack depth migration directly affects the imaging quality of the underlying strata. Therefore, traditional migration velocity iteration processes result in poor imaging quality and low accuracy of the underlying strata.

[0005] Existing technology discloses a method for improving the accuracy of pre-stack depth migration imaging on undulating surfaces. This method involves smoothing the surface elevation surface and using the smoothed surface elevation as the pre-stack depth migration surface; using the first arrival time of the gun for tomographic inversion iteration to obtain a shallow velocity model, with the starting position of the shallow velocity model's journey coinciding with the pre-stack depth migration surface; calculating the low-frequency static correction corresponding to the surface low-velocity zone in the shallow velocity model; removing the low-frequency static correction from the migration input data; shifting the migration input data to the pre-stack depth migration surface in the time direction; obtaining a mid-deep velocity model; and fusing the shallow and mid-deep velocity models to obtain an initial pre-stack depth migration velocity model. However, this invention uses a weighted fusion method, which significantly reduces the accuracy of shallow velocities in pre-stack depth migration, resulting in poor imaging quality and very low precision. Summary of the Invention

[0006] To address the aforementioned shortcomings in existing technologies, this invention provides a true surface seismic-geological integrated velocity modeling and pre-stack depth imaging method, which solves the problems of slow modeling speed, poor accuracy, poor imaging quality, and low precision in existing true surface seismic-geological velocity modeling and pre-stack depth imaging methods.

[0007] To achieve the above objectives, the technical solution adopted by this invention is: a method for integrated velocity modeling and pre-stack depth imaging of true surface seismic geology, comprising the following steps:

[0008] S1. Collect seismic exploration observation data and isotropic migration seismic database of the study area, obtain the intermediate-deep velocity model, use first-arrival tomography inversion to obtain the near-surface velocity model, select the splicing surface and fusion surface, and based on the fusion surface, fuse the splicing surface into the near-surface velocity model and the intermediate-deep velocity model to form the initial depth migration velocity field.

[0009] S2. The near-surface velocity model and the intermediate-deep velocity model fused by splicing are fused to obtain a fused velocity model. The common center point gather is corrected, and the constraint framework and correction criteria are selected from multiple angles based on geological facies, structural style and well information. The fused velocity model is then corrected based on seismic data-driven, seismic-geological integration and multi-round deep fusion to obtain a large-scale floating surface velocity model of the common center point gather.

[0010] S3. Based on the isotropic migration seismic database, perform multi-scale grid tomography, use the common center point gather large-scale floating surface velocity model to model the true surface isotropic velocity model, and obtain the anisotropic parameter field.

[0011] S4. Based on seismic exploration observation data and isotropic migration seismic database, anisotropic parameter field is used to model the depth velocity model, and well location stratification is used to constrain the depth velocity model and eliminate errors. Multi-information constrained anisotropic velocity model is then modeled.

[0012] S5. Using the initial depth migration velocity field as the input for pre-stack depth migration, and employing a multi-information constrained anisotropic velocity model, a pre-defined pre-stack depth migration is performed to generate post-migration seismic imaging results.

[0013] The beneficial effects of this invention are as follows: In the process of integrated seismic-geological velocity modeling of the true surface, this invention continuously improves the accuracy of the depth domain velocity model through the deep fusion and processing interpretation of seismic data-driven and geological multi-information-guided methods, and the mutual feedback between seismic and geological multiple rounds, thereby improving the accuracy of the depth and attitude of geological targets, meeting the requirements of on-site tracking for the imaging accuracy of the target layer, and realizing high accuracy and high efficiency of true surface seismic-geological velocity modeling, as well as high quality and high precision pre-stack depth imaging.

[0014] Further, S1 includes the following steps:

[0015] S101. Collect seismic exploration observation data and isotropic migration seismic database of the study area, and perform small-scale smoothing on the surface elevation data to obtain the migration initiation surface. Based on the large floating surface of the reflected waves, model the intermediate-deep velocity model, and perform first-arrival tomography inversion to model the initial near-surface velocity model.

[0016] S102. Using the fusion technology of tomographic static correction and residual static correction, and combined with the offset starting surface, the initial near-surface velocity model is fine-tuned. In response to the preliminary static correction, the initial near-surface velocity model is constrained by micro-logging and densified equal-interval sampling is performed to obtain the optimized near-surface velocity model.

[0017] S103. Based on seismic exploration and observation data, select splicing surfaces and fusion surfaces, and based on the fusion surfaces, fuse the splicing surfaces into the optimized near-surface velocity model and the intermediate-deep velocity model to establish the initial depth migration velocity field.

[0018] Furthermore, step S101 includes the following steps:

[0019] S1011. Collect seismic exploration observation data and isotropic migration seismic database of the study area, and perform small-scale smoothing on the real surface elevation data in the seismic exploration observation data to obtain the migration starting surface.

[0020] S1012. Based on the large floating surface of the reflected wave, the mid-deep velocity model is modeled. Using the first arrival of the cannon with a preset offset, the first arrival tomographic inversion is performed to model the initial near-surface velocity model.

[0021] The beneficial effects of the above-mentioned further scheme are as follows: the present invention obtains the migration starting surface through small-scale smoothing processing, which realizes a more realistic reflection of the real near-surface wavefield characteristics, reduces the distortion of the wavefield during the depth migration process by time-domain static correction, and improves the accuracy of migration imaging; and by using the first-arrival tomographic inversion method to obtain the near-surface velocity model, it ensures that the velocity field of the low-deceleration zone with small thickness, low velocity, drastic changes and large time delay is reasonably applied during depth migration.

[0022] Furthermore, step S102 includes the following steps:

[0023] S1021. Using the fusion technique of tomographic static correction and residual static correction, and combined with the offset starting surface, the difference in near-surface lateral velocity in the initial near-surface velocity model is fine-tuned.

[0024] S1022. In response to the initial static correction, the first arrival wave tomographic static correction inversion is constrained by micro-logging to obtain a fine near-surface velocity model. The fine near-surface velocity model is then subjected to densified and equally spaced sampling to obtain an optimized near-surface velocity model.

[0025] The beneficial effects of the above-mentioned further scheme are as follows: The present invention adopts the tomographic static correction and residual static correction fusion technology to realize the correction of the near-surface lateral velocity difference and make the near-surface velocity model meet the requirement of establishing a mid-deep velocity field on the common center point gather reference surface; and constrains the first arrival wave tomographic static correction inversion by micro-logging to make the obtained fine near-surface velocity model match the static correction, and realizes the lossless output of the optimized near-surface velocity model by using encrypted equal-interval sampling, laying a good foundation for subsequent velocity fusion.

[0026] Furthermore, step S103 includes the following steps:

[0027] S1031. Based on seismic exploration and observation data, the replacement velocity of the study area is used as the near-surface velocity fusion interface, the smooth surface of the replacement velocity of the study area is used as the splicing surface, and the smooth surface of the ray density interface is used as the fusion surface.

[0028] S1032. Based on the fusion surface, the splicing surface is fused into the optimized near-surface velocity model and the mid-deep velocity model to establish the initial depth migration velocity field for joint depth velocity modeling of first arrival wave and reflected wave.

[0029] The beneficial effects of the above-mentioned further scheme are as follows: This invention uses the replacement velocity of the study area as the basis for selecting the near-surface velocity fusion interface, and its replacement velocity smooth surface as the splicing surface; by using the ray density interface smooth surface as the fusion surface, the reliability of the near-surface inversion velocity above the fusion surface is improved, and the near-surface velocity above the fusion surface is made close to the seismic velocity below the fusion surface, and the anomalies in seismic travel time caused by the addition of the fusion surface are avoided; the method of fusing the near-surface velocity obtained by tomographic static correction inversion with the common center point gather floating surface velocity model reduces the impact of the near-surface low-velocity zone on the calculation of the travel time of the underlying strata.

[0030] Furthermore, S2 includes the following steps:

[0031] S201. The near-surface velocity model and the intermediate-deep velocity model fused by the splicing surface are fused, and the velocity between the splicing surface and the surface is replaced with the replacement velocity to establish an equivalent true surface velocity field and obtain the fused velocity model.

[0032] S202. Remove the tomographic static corrections obtained from the near-surface velocity model, revert the seismic exploration observation data to a near-real surface, and use the elevation or elevation smooth surface as the new floating surface. Move the common center point gather as a whole to the small smooth surface at the corresponding position of the new floating surface, and use travel time equivalence as the criterion, the splicing surface as the bottom, and the final datum surface as the top to obtain the travel times of the refined near-surface velocity model and the fused velocity model respectively.

[0033] S203. The travel time difference between the fine near-surface velocity model and the fused velocity model is used as a high-frequency quantity and applied to the seismic exploration observation data to correct the seismic exploration observation data. The residual static correction method of the seismic exploration observation data is used to calibrate the travel time error caused by the sampling of the fine near-surface velocity model.

[0034] S204. The seismic exploration observation data is calibrated from the common center point gather floating surface to the true surface corresponding to the true surface velocity field of the fused velocity model, and the fine near-surface velocity model is sampled at depth step and smoothed. The migration velocity is fused to the surface velocity of the near-true surface velocity field, and the seismic exploration observation data is corrected.

[0035] S205. Select a marker layer with a strong reflective interface as a constraint framework. Based on geological facies and structural style, pick N layers from shallow to deep to establish an initial structural model. Convert the root mean square velocity field of pre-stack time migration into layer velocity in the depth domain through constrained velocity inversion to form the initial background velocity of pre-stack depth migration. Under the constraint of a large set of geological layers, combine well information from multiple angles to normalize the well logging P-wave velocity curve, extract low-frequency components, and select the low-frequency information of well logging P-wave as the correction standard.

[0036] S206. Based on the constraint framework and correction criteria, a large-scale floating surface velocity field of the common center point gather is formed. The fused velocity model is then corrected based on seismic data-driven, seismic-geological integration, and multi-round deep fusion to obtain the large-scale floating surface velocity model of the common center point gather.

[0037] The beneficial effects of the above-mentioned further solutions are as follows: By adopting a conversion method between the true surface and small smooth surfaces, the velocity model obtained by this invention is based on the true surface. For small smooth surfaces above the surface, replacement velocity is used to fill them, and those below are directly removed. Transforming the true surface velocity model into the small smooth surface velocity model improves the accuracy requirements of layer imaging. By applying travel time difference as a high-frequency quantity to the data, errors caused by near-surface model processing, fusion, and migration surface changes are eliminated. Furthermore, by performing corresponding inverse correction or other time correction processing on the data after fusing the migration velocity with the near-surface velocity, the improvement effect of migration imaging is enhanced.

[0038] This invention calibrates seismic exploration observation data from a common center point gather floating surface to the true surface corresponding to the true surface velocity field of the fused velocity model. It then applies depth step sampling and smoothing to the near-surface velocity model, enabling the near-surface velocity model to effectively represent the near-surface wavefield and better integrate with the depth velocity model, thus improving the coupling effect between the near-surface model and the migration surface. Based on seismic data-driven and seismogeological integration, and performing multiple rounds of seismogeological corrections, the accuracy and coupling of the fused velocity model are continuously improved.

[0039] Furthermore, step S3 includes the following steps:

[0040] S301. Based on the isotropic migration seismic database, the dip volume, azimuth volume, and continuity factor volume are extracted from the migration volume. The reflection surface and direction are defined, the reflection points on the reflection surface are stored in the preset database, and the reflection points are used to perform ray tracing to the ground surface according to different opening angles and azimuth angles to obtain the automatic picking method of the migration volume.

[0041] S302. In the offset track set, the remaining delay is automatically picked up to obtain the automatic picking method of the remaining delay;

[0042] S303, an automatic picking method based on offset volume and residual delay, using a point-to-point feature operation mode based on anisotropic ray tracing to create a tomographic imaging matrix;

[0043] S304. Based on the tomographic imaging matrix, use small-scale high-resolution geological properties to constrain the along-layer grid tomography and obtain multi-scale grid tomographic imaging.

[0044] S305. Based on multi-scale grid tomography, high-resolution processing techniques are used to process the seismic data volume in the isotropic migration seismic database, and the seismic data volume is denoised. The dip volume, azimuth volume, and continuity factor volume of the seismic data volume after high-resolution processing and denoising are calculated.

[0045] S306. Based on the dip volume, azimuth volume and continuity factor volume of S305, ray tracing is performed to obtain the velocity change. Based on the velocity change, the seismic exploration observation data is fused with the large-scale floating surface velocity model of the common center point gather to form an initial true surface isotropic velocity field. The true surface isotropic velocity model is then modeled.

[0046] S307. Using well information constraints, the velocity error is used as the depth difference between the layer and the well location in the migration imaging to obtain the anisotropic vertical velocity field and the initial anisotropic parameter field. The anisotropic parameters in the initial anisotropic parameter field are iteratively updated by the anisotropic migration value. By obtaining the dip angle of the formation structure, the dip angle field and azimuth field reflecting the tilt symmetry axis are obtained.

[0047] S308. In response to establishing a true surface isotropic velocity model, based on dip and azimuth fields, and according to geological information and anisotropic parameters in seismic exploration observation data, interpolation extrapolation along the layers is performed in conjunction with the stratigraphic model to obtain the anisotropic parameter field.

[0048] Furthermore, S304 includes the following steps:

[0049] S3041. Based on the tomographic imaging matrix, ray tracing forward modeling is performed using the constructed layers and internal sub-layers as reflection points, and globally uniformly distributed points are used as the inversion grid. The least squares solution is obtained using the conjugate gradient method, and interpolation is performed under the constraints of the constructed model to update the depth velocity volume.

[0050] S3042. Solve the tomographic imaging matrix using different geological constraint methods to obtain multi-scale grid tomographic images;

[0051] The specific steps for solving the tomographic imaging matrix are as follows:

[0052] Using the common reflection point gathers generated by the pre-stack depth migration of the target line, the residual delay is defined as the residual delay value of the common receiving point gathers at the reflection point locations. The residual delay of the gathers is picked up using the common reflection point gathers, and the layer velocity of the formation is inverted step by step using the residual delay.

[0053] The beneficial effects of the above-mentioned further solutions are as follows: By removing the tomographic static correction amount obtained from the near-surface velocity model, the present invention achieves the regression of seismic exploration observation data to near the real surface; The present invention applies the travel time difference between the refined near-surface velocity model and the fused velocity model as a high-frequency quantity to the seismic exploration observation data, eliminating the errors caused by near-surface model processing, fusion, and migration surface changes.

[0054] This invention combines a novel depth-velocity modeling workflow with a layer-constrained, shallow-to-deep grid tomography inversion technique. It iteratively updates the velocity field and anisotropic parameter field, introducing the anisotropic parameter field at the beginning of the modeling process. For each well-location layer, anisotropic modeling is performed layer by layer. Throughout the modeling process, well-location layers consistently constrain the model, eliminating anisotropic errors between imaging and well-location layering. Therefore, the cumulative depth error of small-scale inter-layer iterations is small, accelerating the iteration convergence speed and improving the consistency between the obtained contiguous anisotropic results and well-location layering.

[0055] This invention is based on grid tomography technology, which is a powerful complement to layer-based tomography technology and solid model-based tomography technology. It aims to flatten each strong phase axis of each depth-offset CRP gather, regardless of whether the phase axis is at the layer position or between two layers, thus ensuring the imaging effect of areas with interlayer velocity anomalies or large interlayer velocity changes.

[0056] Furthermore, step S4 includes the following steps:

[0057] S401. Based on the seismic data volume in the isotropic migration seismic database, extract the azimuth and dip data volumes of the isotropic migration seismic data volume, and use anisotropy to model and establish a depth-velocity model.

[0058] S402. By calculating the parameters of the anisotropic parameter field, the anisotropic velocity is obtained. Using the mesh tomography inversion method, the constrained and error-eliminated depth velocity model is iterated multiple times to obtain the pre-stack depth migration anisotropic velocity field.

[0059] S403. Based on the pre-stack depth migration anisotropic velocity field, multi-scale fracture detection is performed on the seismic data volume to obtain discontinuous data volumes. According to the true surface isotropic velocity model and the interpreted horizon, combined with well information constraints, the relative coefficients of the velocity of each stratum discontinuity volume and the average stratum velocity are analyzed. The discontinuous data volume is then 3D sculpted to obtain a 3D discontinuous data volume. Combined with the relative coefficients, the stratum velocities are updated to obtain a refined velocity model. The refined velocity model is then repeatedly updated and reconstructed.

[0060] S404. In response to the update, a fine velocity model is constructed. Well location stratification is used to constrain the depth velocity model and eliminate the imaging and well location stratification errors caused by anisotropy layer by layer. The multi-information constrained anisotropic parameter field is obtained, and the multi-information constrained anisotropic velocity model is modeled.

[0061] The beneficial effects of the above-mentioned further scheme are as follows: The present invention updates the velocity model through repeated iterations of the network tomography inversion method to obtain the final pre-stack depth migration anisotropic velocity model. In the modeling process, the accuracy of the depth domain velocity model is continuously improved by the deep fusion and processing interpretation of seismic data-driven and geological multi-information-guided methods, and the mutual feedback between seismic and geological multiple rounds. This improves the accuracy of the depth and attitude of geological targets and meets the requirements of on-site tracking for the imaging accuracy of the target layer. Attached Figure Description

[0062] Figure 1 This is a flowchart of the method of the present invention.

[0063] Figure 2 This is a flowchart of the grid tomography process in this embodiment.

[0064] Figure 3 This is the first comparison diagram between the new pre-stack depth migration results and previous processing results in this embodiment.

[0065] Figure 4 This is a second comparison chart of the new pre-stack depth migration results and previous processing results in this embodiment.

[0066] Figure 5This is the third comparison chart between the new pre-stack depth migration results and previous processing results in this embodiment. Detailed Implementation

[0067] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0068] Before describing this embodiment, the following terms will be explained:

[0069] CMP: Common Centroid Gather;

[0070] CDP: Common Reflection Point Gather;

[0071] CRP: Common Receiving Point Collection;

[0072] PPF: Point Pair Feature;

[0073] RMS speed: Root Mean Square speed;

[0074] Kirchhoff pre-stack depth migration: Kirchhoff pre-stack depth migration;

[0075] TTI: Lateral isotropy of the tilted axis of symmetry;

[0076] Pencils database: A new database that stores the locations of reflection points;

[0077] Rayleigh Mode (RMO) refers to an interference phenomenon that occurs when seismic waves propagate through a subsurface medium. Due to the inhomogeneity of the subsurface medium, the seismic waves are scattered during propagation.

[0078] QC: Quality Control.

[0079] Example

[0080] In this embodiment, the idea and strategy for depth velocity modeling is as follows: the traditional migration velocity iteration process aims to flatten the phase axis of the reflected wave, that is, to use the residual delay (residual RMS velocity) of the reflected wave to invert the accurate migration velocity; however, in shallow layers, due to the low number of reflected wave coverage and the high noise development, this method has great limitations, and the accuracy of shallow velocity in pre-stack depth migration directly affects the imaging quality of the underlying strata.

[0081] Therefore, the depth velocity modeling strategy adopted in this invention is as follows: An accurate near-surface velocity field is obtained using first-arrival tomographic inversion, and then fused with the mid-deep velocity model obtained from the large floating surface of reflected waves to form an initial depth migration velocity field. Considering the influence of the near-surface low-velocity zone on the travel time calculation of the underlying strata, a method is adopted to fuse the near-surface velocity obtained by tomographic static correction inversion with the velocity model of the common center point gather floating surface. Based on this, the velocity model is optimized using reflected wave tomographic velocity inversion to construct a true surface isotropic pre-stack depth migration velocity field. The azimuth and dip data are extracted from the isotropic migration seismic database, and then the parameters of the anisotropic model are calculated and the anisotropic velocity is obtained after anisotropic model analysis. Based on this, the model is updated through repeated iterations using the network tomographic inversion method to obtain the final pre-stack depth migration anisotropic velocity model.

[0082] During the modeling process, the accuracy of the depth domain velocity model is continuously improved through the deep integration and processing of seismic data-driven and geological multi-information-guided methods, as well as the mutual feedback between seismic and geological multiple rounds. This improves the accuracy of the depth and orientation of geological targets, thereby meeting the requirements of on-site tracking for the imaging accuracy of the target layer.

[0083] In this embodiment, all velocity models are three-dimensional velocity bodies that vary with x, y, and z.

[0084] Based on this, such as Figure 1 As shown, this invention provides a method for integrated seismic and geological velocity modeling and pre-stack depth imaging of the true surface, the implementation of which is as follows:

[0085] S1. Collect seismic exploration observation data and isotropic migration seismic database for the study area to obtain the intermediate-deep velocity model. Use first-arrival tomography inversion to obtain the near-surface velocity model. Select the splicing surface and fusion surface, and based on the fusion surface, fuse the splicing surface into the near-surface velocity model and the intermediate-deep velocity model to form the initial depth migration velocity field. The specific steps are as follows:

[0086] S101. Collect seismic exploration observation data and isotropic migration seismic database for the study area, and perform small-scale smoothing on the surface elevation data to obtain the migration initiation surface. Based on the large floating surface of the reflected waves, model the intermediate-deep velocity model, and perform first-arrival tomography inversion to model the initial near-surface velocity model. The specific steps are as follows:

[0087] S1011. Collect seismic exploration observation data and isotropic migration seismic database of the study area, and perform small-scale smoothing on the real surface elevation data in the seismic exploration observation data to obtain the migration starting surface.

[0088] S1012. Based on the large floating surface of the reflected wave, the mid-deep velocity model is modeled. Using the first arrival of the cannon with a preset offset distance, the first arrival tomographic inversion is performed to model the initial near-surface velocity model.

[0089] In this embodiment, an offset surface is selected, specifically: the true surface elevation of the seismic exploration observation data collected in the study area is smoothed at a small scale to obtain the offset starting surface, also known as the small smooth offset surface, or simply the small smooth surface. Its purpose is to more realistically reflect the true near-surface wavefield characteristics, reduce the distortion of the wavefield during the depth migration process caused by time-domain static correction, thereby improving the accuracy of the migration imaging.

[0090] Small smooth surface migration is an approximation of the true Earth surface. Given the following advantages between conventional floating surfaces and the true Earth surface: small smooth surfaces are still extremely close to the true Earth surface, making it easy to achieve data transition and correction matching from floating surfaces to small smooth surfaces; and most commonly used migration algorithms are based on floating surfaces, such as the Kirchhoff algorithm, so using small smooth surface migration is in line with the applicability of the algorithm.

[0091] Based on the large floating surface of the reflected wave, a mid-deep velocity model is modeled to obtain the mid-deep velocity model. In order to obtain a near-surface velocity model that is similar to the actual near-surface velocity structure, a first arrival of a cannon with a preset offset distance is used to perform first arrival tomography inversion and establish an initial near-surface velocity model.

[0092] S102. Using the fusion technique of tomographic static correction and residual static correction, and combined with the migration starting surface, the initial near-surface velocity model is fine-tuned. In response to the preliminary static correction, the initial near-surface velocity model is constrained by micro-logging, and densified equal-interval sampling is performed to obtain the optimized near-surface velocity model. The specific steps are as follows:

[0093] S1021. Using the fusion technique of tomographic static correction and residual static correction, and combined with the offset starting surface, the difference in near-surface lateral velocity in the initial near-surface velocity model is fine-tuned.

[0094] S1022. In response to the initial static correction, the first arrival wave tomographic static correction inversion is constrained by micro-logging to obtain a fine near-surface velocity model. The fine near-surface velocity model is then subjected to densified and equally spaced sampling to obtain an optimized near-surface velocity model.

[0095] In this embodiment, the near-surface lateral velocity difference in the initial near-surface velocity model is fine-tuned by using the fusion technique of tomographic static correction and residual static correction, combined with the offset starting surface.

[0096] In response to the initial static correction during fine-tuning using the fusion technique of tomographic static correction and residual static correction, a fine near-surface velocity model matching the static correction is obtained by inverting the tomographic static correction of the first arrival wave constrained by micro-logging. The fine near-surface velocity model is then subjected to densified equal-interval sampling, and the optimized near-surface velocity model is output.

[0097] S103. Based on seismic exploration observation data, select splicing surfaces and fusion surfaces, and based on the fusion surfaces, fuse the splicing surfaces into the optimized near-surface velocity model and the intermediate-deep velocity model to establish the initial depth migration velocity field. The specific steps are as follows:

[0098] S1031. Based on seismic exploration and observation data, the replacement velocity of the study area is used as the near-surface velocity fusion interface, the smooth surface of the replacement velocity of the study area is used as the splicing surface, and the smooth surface of the ray density interface is used as the fusion surface.

[0099] S1032. Based on the fusion surface, the splicing surface is fused into the optimized near-surface velocity model and the mid-deep velocity model to establish the initial depth migration velocity field for joint depth velocity modeling of first arrival wave and reflected wave.

[0100] In this embodiment, based on seismic exploration observation data, the replacement velocity of the study area is used as the near-surface velocity fusion interface, the smooth surface of the replacement velocity of the study area is used as the splicing surface, the smooth surface of the ray density interface is selected as the fusion surface, and based on the fusion surface, the splicing surface is fused into the optimized near-surface velocity model and the mid-deep velocity model to complete the initial depth migration velocity field for the joint depth velocity modeling of the first arrival wave and the reflected wave.

[0101] The selection requirements for the fusion surface are as follows: Since the near-surface inversion velocity above the fusion surface must be highly reliable, the ray density obtained from the reference tomographic inversion is used in practice. Therefore, the smooth surface of the ray density interface can be used as the reference location for selecting the fusion surface. The replacement velocity of the study area is used as the basis for selecting the near-surface velocity fusion interface, and its replacement velocity smooth surface is used as the splicing surface. The near-surface velocity above the fusion surface and the seismic velocity below the fusion surface should be as close as possible to avoid anomalies in seismic travel time caused by the addition of the fusion surface. After the fusion surface is selected, the processed near-surface velocity model and the conventional floating surface velocity model are well spliced, completing the initial depth migration velocity field for the joint depth velocity modeling of the first arrival and reflected waves.

[0102] S2. The near-surface velocity model and the intermediate-deep velocity model, which have been fused through splicing, are then fused to obtain a fused velocity model. The common center point gather is corrected, and constraint frames and correction criteria are selected from multiple perspectives based on geological facies, structural styles, and well information. The fused velocity model is then corrected based on seismic data-driven, seismic-geological integration, and multi-round deep fusion correction to obtain a large-scale floating surface velocity model of the common center point gather. The specific steps are as follows:

[0103] S201. The near-surface velocity model and the intermediate-deep velocity model fused by the splicing surface are fused, and the velocity between the splicing surface and the surface is replaced with the replacement velocity to establish an equivalent true surface velocity field and obtain the fused velocity model.

[0104] In this embodiment, the optimized near-surface velocity model and the mid-deep velocity model after splicing are fused. The fusion process is as follows: the velocity between the splicing surface and the surface is replaced with a replacement velocity to establish an equivalent true surface velocity field. This method has good adaptability in areas with thin and stable low-velocity zones. Through the fusion process, the fused velocity model is obtained.

[0105] Near-surface model fusion is the key and challenging aspect of the entire process, with the core issues being the selection of the fusion surface and data matching. A significant challenge lies in the coupling between the refined near-surface model and the migration process: depth migration requires discrete sampling (extended step size) of the velocity field in the x, y, and z directions, such as the commonly used 5m extended step size. For the near-surface model, this discrete sampling significantly reduces the accuracy of the velocity field, affecting the migration imaging results. This manifests primarily in the following ways: because the depth migration sampling step size is much larger than the spatial sampling scale of the inverted near-surface model, the depth migration velocity model cannot accurately represent near-surface characteristics, while the low-velocity zone near the surface is often thin and has the most significant impact on the true wavefield; furthermore, the wavefield variations under the refined near-surface velocity model are complex, and existing migration methods cannot effectively represent them, instead causing distortion in the migration imaging. The solution is to employ depth step size sampling and smoothing processing on the near-surface velocity model during the processing, ensuring that the near-surface velocity model can effectively represent the near-surface wavefield and better integrate with the depth velocity model.

[0106] S202. Remove the tomographic static corrections obtained from the near-surface velocity model, revert the seismic exploration observation data to a near-real surface, and use the elevation or elevation smooth surface as the new floating surface. Move the common center point gather as a whole to the small smooth surface at the corresponding position of the new floating surface, and use travel time equivalence as the criterion, the splicing surface as the bottom, and the final datum surface as the top to obtain the travel times of the refined near-surface velocity model and the fused velocity model respectively.

[0107] In this embodiment, to replace the conventional floating surface, the tomographic static correction amount obtained from the near-surface velocity model is removed, and the seismic exploration observation data is regressed to a state close to the real surface. However, because the seismic exploration observation data contains residual static correction amounts, it is not completely equivalent to the real surface. The elevation (real surface) or elevation smooth surface (small smooth surface) is used as the new floating surface, i.e., the low-frequency amount. The common center point gather is moved as a whole to the small smooth surface at the corresponding position of the new floating surface, that is, (the common center point gather is corrected to the real surface corresponding to the real surface velocity field in the fused velocity model). Based on the travel time equivalence criterion, with the splicing surface as the base and the final reference surface as the top, the travel times of the refined near-surface model and the fused velocity model are calculated respectively.

[0108] This invention employs a conversion method between the true ground surface and small smooth surfaces. The obtained velocity model is based on the true ground surface. Since there are small-scale differences between the ground surface elevation and the small smooth surface, the most reasonable processing method is to fill the top and cut the bottom. That is, for the small smooth surface above the ground surface, the velocity is replaced and filled; the one below is directly cut off. This can transform the true ground surface velocity model into the small smooth surface velocity model, thereby greatly improving the accuracy requirements of layer imaging.

[0109] S203. The travel time difference between the fine near-surface velocity model and the fused velocity model is used as a high-frequency quantity and applied to the seismic exploration observation data to correct the seismic exploration observation data. The residual static correction method of the seismic exploration observation data is used to calibrate the travel time error caused by the sampling of the fine near-surface velocity model.

[0110] In this embodiment, the travel time difference between the refined near-surface velocity model and the fused velocity model is used as a high-frequency quantity and applied to the seismic exploration observation data to eliminate errors caused by near-surface model processing, fusion, and migration surface changes, and to perform corresponding corrections on the seismic exploration observation data. Since the sampled refined near-surface velocity model will cause travel time errors, the residual static correction method of the seismic exploration observation data is used to calibrate the travel time errors caused by the sampling of the refined near-surface velocity model.

[0111] Since the residual static correction serves as a supplement to the near-surface model error and is processed independently of the near-surface model fusion, it is applied and retained in this invention.

[0112] S204. The seismic exploration observation data is calibrated from the common center point gather floating surface to the true surface corresponding to the true surface velocity field of the fused velocity model, and the fine near-surface velocity model is sampled at depth step and smoothed. The migration velocity is fused to the surface velocity of the near-true surface velocity field, and the seismic exploration observation data is corrected.

[0113] In this embodiment, when calibrating the seismic exploration observation data from the common center point gather floating surface to the true surface corresponding to the true surface velocity field of the fused velocity model, data matching is required to fuse the migration velocity into the surface velocity of the near-true surface velocity field and perform corresponding inverse correction or other time correction processing on the seismic exploration observation data.

[0114] This invention calibrates seismic exploration observation data from the CMP surface to the corresponding true surface velocity field on the true surface. It employs depth step sampling and smoothing processing on the near-surface velocity model, enabling the near-surface velocity model to effectively represent the near-surface wave field and better integrate with the depth velocity model, thereby achieving better fusion between the near-surface model and the offset surface.

[0115] S205. Select a marker layer with a strong reflective interface as a constraint framework. Based on geological facies and structural style, pick N layers from shallow to deep to establish an initial structural model. Convert the root mean square velocity field of pre-stack time migration into layer velocity in the depth domain through constrained velocity inversion to form the initial background velocity of pre-stack depth migration. Under the constraint of a large set of geological layers, combine well information from multiple angles to normalize the well logging P-wave velocity curve, extract low-frequency components, and select the low-frequency information of well logging P-wave as the correction standard.

[0116] S206. Based on the constraint framework and correction criteria, a large-scale floating surface velocity field of the common center point gather is formed. The fused velocity model is then corrected based on seismic data-driven, seismic-geological integration, and multi-round deep fusion to obtain the large-scale floating surface velocity model of the common center point gather.

[0117] In this embodiment, the process of constructing the CMP large-scale smooth floating surface velocity model is as follows: select the marker layer of the geologically significant strong reflective interface as the constraint framework, pick N layers from shallow to deep, establish the initial structural model, construct the stratigraphic structure framework through seismic data and geological facies division, the initial structural model is the constraint framework for velocity field modeling, and convert the pre-stack time migration root mean square velocity field into the layer velocity in the depth domain through the constraint velocity inversion method to form the initial background velocity of pre-stack depth migration;

[0118] Under the constraints of a large set of geological strata, the velocity field is corrected using well logging curves. Combined with multi-angle well information, the well logging P-wave velocity curves are normalized, and the low-frequency components in the normalized well logging P-wave velocity curves are extracted. The low-frequency information of well logging P-waves is selected as the correction standard to constrain seismic velocities, forming a large-scale floating surface velocity field of CMP. Based on seismic data-driven, seismic-geological integration, and multi-round deep fusion, the fused velocity model is corrected, and a large-scale floating surface velocity model with common center point gathers is obtained. The framework constraint and correction are not completed at one time. Well data will increase as the project is implemented. Therefore, the multi-round fusion is based on the well data and is updated multiple times. The selection and use of the constraint framework and correction standard are also carried out multiple times.

[0119] S3. Based on the isotropic migration seismic database, perform multi-scale grid tomography. Using the large-scale floating surface velocity model of the common center point gather, model the true surface isotropic velocity model and obtain the anisotropic parameter field. The specific steps are as follows:

[0120] S301. Based on the isotropic migration seismic database, the dip volume, azimuth volume, and continuity factor volume are extracted from the migration volume. The reflection surface and direction are defined, the reflection points on the reflection surface are stored in the preset database, and the reflection points are used to perform ray tracing to the ground surface according to different opening angles and azimuth angles to obtain the automatic picking method of the migration volume.

[0121] S302. In the offset track set, the remaining delay is automatically picked up to obtain the automatic picking method of the remaining delay;

[0122] S303, an automatic picking method based on offset volume and residual delay, uses a point-to-point feature operation mode based on anisotropic ray tracing to create a tomographic imaging matrix.

[0123] In this embodiment, to establish a detailed structural model, a near-surface fused small-scale velocity model is used as the initial model for the depth domain. Under the constraints of the geological structural model, the velocity field characteristics are gradually characterized through multi-scale grid tomography. First, large-scale grid tomography is used across the entire area, and the velocity information of structural strata and inter-strata is updated gradually through multiple iterations. After obtaining relatively accurate velocities, small-scale grid tomography and multi-attribute constraints can be used to enhance the tomographic imaging accuracy of large velocity volume models and complex geological bodies.

[0124] To facilitate subsequent tomographic imaging using CRP gathers obtained after pre-set Kirchhoff pre-stack depth migration to update subsurface velocities, this tomographic technique presents significant challenges. The tomographic equation linearly links the travel time error along the reflected rays with the updated subsurface velocity model parameters. The rays trace from dense subsurface reflection points to the surface based on different reflection angles and azimuths. In fact, this invention determines the travel time error from the unflattened co-image gathers in the offset or angular domains. The goal of tomographic imaging is to find an optimal update parameter to minimize the travel time error globally using a constrained least-squares method. This yields a model that closely approximates the subsurface structure, enabling a new round of migration and ultimately resulting in an ideal migrated output gather.

[0125] In this embodiment, to enhance the tomographic accuracy of large velocity volume models and complex geological bodies, grid tomography supports multi-azimuth update, anisotropic parameter update, and automatic picking of each reflection layer and residual delay. All calculations in this method (including data allocation, automatic picking, and matrix and solution matrix formation during grid tomography) employ PPF mode to ensure efficient operation despite the large computational load.

[0126] Layer-based and solid model-based tomography techniques primarily consider the average layer velocity across a large set of layers, exhibiting an averaging effect on inter-layer velocities. While suitable for most intra-layer phase axes, they struggle to guarantee velocity accuracy in areas with inter-layer velocity anomalies or significant inter-layer velocity variations, resulting in velocities that are either too high or too low. Grid-based tomography, on the other hand, serves as a powerful complement to layer-based and solid model-based tomography techniques. It aims to flatten each strong phase axis of each depth-migrated CRP gather, regardless of whether the phase axis is located within a layer or between two layers.

[0127] In this embodiment, as Figure 2 As shown, based on the isotropic migration seismic database, automatic extraction is performed in the migration volume to extract the dip volume, azimuth volume, and continuity factor volume. The reflection surface and direction are defined, the reflection surface is automatically picked up, the reflection points on the reflection surface are stored in the preset database, and ray tracing is performed on the ground surface using the reflection points according to different opening angles and azimuth angles to achieve automatic picking and imaging. Figure 2 The FASTVEL / VEVLNAN automatic / manual residual speed analysis represents three speed data extraction methods: automatic, semi-automatic, and manual.

[0128] In the offset track set, the remaining delay is automatically picked up; the remaining delay is automatically picked up in the previously defined new library point, and the remaining delay data supports two methods: vector and per-track remaining delay value support, and multi-directional remaining delay is fully supported by the system.

[0129] Based on automatic pickup imaging and automatic pickup of residual delay, a point-to-point feature operation mode based on anisotropic ray tracing is adopted to create a tomographic imaging matrix. The PPF operation mode adopted improves the operation efficiency of each node on the cluster.

[0130] S304. Based on the tomographic imaging matrix, constrain the along-layer grid tomography using small-scale high-resolution geological properties to obtain multi-scale grid tomographic images. The specific steps are as follows:

[0131] S3041. Based on the tomographic imaging matrix, ray tracing forward modeling is performed using the constructed layers and internal sub-layers as reflection points, and globally uniformly distributed points are used as the inversion grid. The least squares solution is obtained using the conjugate gradient method, and interpolation is performed under the constraints of the constructed model to update the depth velocity volume.

[0132] S3042. Solve the tomographic imaging matrix using different geological constraint methods to obtain multi-scale grid tomographic images;

[0133] The specific steps for solving the tomographic imaging matrix are as follows:

[0134] Using the common reflection point gathers generated by the pre-stack depth migration of the target line, the residual delay is defined as the residual delay value of the common receiving point gathers at the reflection point locations. The residual delay of the gathers is picked up using the common reflection point gathers, and the layer velocity of the formation is inverted step by step using the residual delay.

[0135] In this embodiment, small-scale high-resolution mesh tomography modeling is used to mainly control the abnormal changes in velocity caused by fractures. Based on the tomographic imaging matrix, ray tracing forward modeling is completed using the constructed layers and internal sub-layers as reflection points, and globally uniformly distributed points are used as the inversion mesh. The least squares solution is obtained using the conjugate gradient method, and interpolation is performed under the constraints of the constructed model to update the depth velocity volume.

[0136] Different geological constraint methods are used to solve the tomographic imaging matrix to obtain multi-scale grid tomographic imaging. The specific solution process is as follows: using the common reflection point gather generated by the pre-stack depth migration of the target line, the residual delay is defined as the residual delay value of the common receiving point gather at the reflection point location. The residual delay of the gather is picked up using the common reflection point gather, and the layer velocity of the strata is inverted layer by layer using the residual delay.

[0137] S305. Based on multi-scale grid tomography, high-resolution processing techniques are used to process the seismic data volume in the isotropic migration seismic database, and the seismic data volume is denoised. The dip volume, azimuth volume, and continuity factor volume of the seismic data volume after high-resolution processing and denoising are calculated.

[0138] S306. Based on the dip volume, azimuth volume, and continuity factor volume of S305, ray tracing is performed to obtain the velocity change. Based on the velocity change, the seismic exploration observation data is fused with the large-scale floating surface velocity model of the common center point gather to form an initial true surface isotropic velocity field. Modeling is then performed to obtain the true surface isotropic velocity model.

[0139] In this embodiment, based on the multi-scale grid tomography method, high-resolution processing technology is used to process the seismic data volume in the isotropic migration seismic database, and the seismic data volume is denoised. Then, the dip volume, azimuth volume, and continuity factor volume of the seismic data volume after high-resolution processing and denoising are calculated. Ray tracing is performed based on these values ​​to record and characterize the detailed changes in velocity. Based on the detailed changes in velocity, the seismic exploration observation data is fused with the large-scale floating surface velocity model of the common center point gather to form an initial true surface isotropic velocity field. The true surface isotropic velocity model is then modeled.

[0140] S307. Using well information constraints, the velocity error is used as the depth difference between the layer and the well location in the migration imaging to obtain the anisotropic vertical velocity field and the initial anisotropic parameter field. The anisotropic parameters in the initial anisotropic parameter field are iteratively updated by the anisotropic migration value. By obtaining the dip angle of the formation structure, the dip angle field and azimuth field reflecting the tilt symmetry axis are obtained.

[0141] S308. In response to establishing a true surface isotropic velocity model, based on dip and azimuth fields, and according to geological information and anisotropic parameters in seismic exploration observation data, interpolation extrapolation along the layers is performed in conjunction with the stratigraphic model to obtain the anisotropic parameter field.

[0142] In this embodiment, after establishing the isotropic velocity model, the invention incorporates well information constraints. Based on the depth difference between the stratigraphic level and the well location in the migration imaging, the velocity error is converted to obtain the anisotropic vertical velocity field and the anisotropic parameter field. Then, the anisotropic parameters in the anisotropic parameter field are iteratively updated using the anisotropic migration value. Finally, by calculating the dip angle of the formation structure, the dip angle field and azimuth field reflecting the tilt symmetry axis are obtained. When establishing the isotropic velocity model, geological information and anisotropic parameters obtained from the drilled wells are also combined with the stratigraphic model to perform interpolation extrapolation along the stratigraphic level, finally obtaining the anisotropic parameter field.

[0143] S4. Based on seismic exploration observation data and isotropic migration seismic database, an anisotropic parameter field is used to model the depth-velocity model. Well location stratification is then used to constrain the depth-velocity model and eliminate errors. The multi-information constrained anisotropic velocity model is then modeled, with the specific steps as follows:

[0144] S401. Based on the seismic data volume in the isotropic migration seismic database, extract the azimuth and dip data volumes of the isotropic migration seismic data volume, and use anisotropy to model and establish a depth-velocity model.

[0145] S402. By calculating the parameters of the anisotropic parameter field, the anisotropic velocity is obtained. Using the mesh tomography inversion method, the constrained and error-eliminated depth velocity model is iterated multiple times to obtain the pre-stack depth migration anisotropic velocity field.

[0146] In this embodiment, although the isotropic velocity modeling stage references the well velocity for layer velocity constraints, local non-ideal fits still exist when investigating the well-seismic velocity consistency of the isotropic results. This is because the well velocity constraints in the isotropic migration stage are only based on the principle of flattening common imaging points, which reduces the range of multiple solutions for travel time errors. However, using a simple isotropic algorithm for migration imaging cannot truly solve the anisotropy problem. Anisotropy is widespread in nature, and to obtain pre-stack depth migration results that more closely approximate the actual subsurface structure, anisotropic velocity modeling is needed to meet the accuracy requirements of current exploration and mining. Especially for this invention, which requires depth-domain seismic results to perform horizontal well steering prediction for key target layers such as multi-peak groups, improve the box drilling encounter rate, and meet the requirements of drilling-while-tracking, higher demands are placed on pre-stack depth imaging.

[0147] Based on the seismic data volume in the isotropic migration seismic database, the azimuth and dip data volumes of the isotropic migration seismic data volume are extracted, and anisotropic modeling analysis is performed to establish a depth velocity model. Various parameters of the anisotropic parameter field are calculated and anisotropic velocities are obtained. The model is then updated after multiple iterations using the grid tomography inversion method to obtain the pre-stack depth migration anisotropic velocity field.

[0148] In this embodiment, the currently common depth-velocity modeling process is as follows: First, an isotropic velocity model is established. During this modeling stage, migration is performed according to the isotropic assumption, considering only the focusing of the same phase axis and not the matching between the imaging layer and the well location layer. Even if well information constraints are added, they are generally limited to using the low-frequency components of the acoustic logging velocity to constrain the large velocity trends of each layer. Second, after the isotropic velocity field is established, the depth difference between the migrated imaging layer and the well location layer is converted into a velocity error to obtain the anisotropic vertical velocity field (Vp0) and the anisotropic parameter field. Then, the anisotropic parameter delta is adjusted through anisotropic migration. ) and epsilon ( The process is iteratively updated; finally, by calculating the dip angle of the stratigraphic structure, the dip field and azimuth field reflecting the TTI symmetry axis are obtained.

[0149] However, this process typically suffers from the following problems: a lack of well location constraints during the isotropic velocity modeling stage; and significant discrepancies between imaging depth and well location stratification when anisotropy is pronounced in the region, leading to substantial errors when calculating Vp0. To address these issues, a new depth-velocity modeling workflow and a grid tomography inversion technique with shallow-to-deep stratification constraints are employed. Specifically, the velocity field and anisotropic parameter field are iteratively updated alternately. Anisotropic parameter fields are introduced at the beginning of the modeling process, and anisotropic modeling is performed layer by layer for each well location stratification. Throughout the modeling process, well location stratification is consistently used to constrain the model, eliminating anisotropic errors between imaging and well location stratification. Therefore, the cumulative depth error of small-loop iterations between each set of layers is small, and iteration convergence is faster, resulting in contiguous anisotropic results that typically show high agreement with well stratification.

[0150] S403. Based on the pre-stack depth migration anisotropic velocity field, multi-scale fracture detection is performed on the seismic data volume to obtain discontinuous data volumes. According to the true surface isotropic velocity model and the interpreted horizon, combined with well information constraints, the relative coefficients of the velocity of each stratum discontinuity volume and the average stratum velocity are analyzed. The discontinuous data volume is then 3D sculpted to obtain a 3D discontinuous data volume. Combined with the relative coefficients, the stratum velocities are updated to obtain a refined velocity model. The refined velocity model is then repeatedly updated and reconstructed.

[0151] S404. In response to the update, a fine velocity model is constructed. Well location stratification is used to constrain the depth velocity model and eliminate the imaging and well location stratification errors caused by anisotropy layer by layer. The multi-information constrained anisotropic parameter field is obtained, and the multi-information constrained anisotropic velocity model is modeled.

[0152] In this embodiment, multi-information constrained anisotropic modeling is performed. Based on the pre-stack depth migration anisotropic velocity field, multi-scale fracture detection is performed on the seismic data volume to obtain discontinuous data volumes. According to the true surface isotropic velocity model and the interpreted horizon, combined with well information constraints, the relative coefficients of the velocities of each stratum discontinuity and the average stratum velocity are analyzed. The discontinuous data volumes are then 3D sculpted to obtain 3D discontinuous data volumes. Combined with the relative coefficients, the velocities of each stratum are updated to obtain a refined velocity model. The refined velocity model is then repeatedly updated and constructed. In response to the updated and constructed refined velocity model, well location stratification is used to constrain the depth velocity model and eliminate the imaging and well location stratification errors caused by anisotropy layer by layer to obtain a multi-information constrained anisotropic parameter field. The multi-information constrained anisotropic velocity model is then modeled to obtain the multi-information constrained anisotropic velocity model.

[0153] The expression for the multi-information constrained anisotropic velocity model is as follows:

[0154] ;

[0155] in, This represents a multi-information constrained anisotropic velocity model. This represents an isotropic velocity model. and Both represent anisotropic parameters. Indicates the angle of incidence. Indicates the observation range. Indicates the orientation of the crack's axis of symmetry.

[0156] S5. Using the initial depth migration velocity field as the input for pre-stack depth migration, and employing a multi-information constrained anisotropic velocity model, a pre-defined pre-stack depth migration is performed to generate post-migration seismic imaging results.

[0157] In this embodiment, the preset pre-stack depth migration adopts Kirchhoff pre-stack depth migration. The initial depth migration velocity field is used as the input of pre-stack depth migration. The Kirchhoff pre-stack depth migration is performed using a multi-information constrained anisotropic velocity model, and finally the migrated seismic imaging results are formed.

[0158] like Figure 3 , Figure 4 ,as well as Figure 5 As shown, a large-scale pre-stack depth migration data volume with clear stratigraphic and fault imaging, relatively reasonable structural attitude, and good well-seismic matching effect was formed;

[0159] Figure 3 , Figure 4 ,as well as Figure 5In the image, the left side shows an existing seismic imaging result, while the right side shows a migrated seismic imaging result generated by the present invention. The present invention solves the problems of slow modeling speed, poor accuracy, poor imaging quality, and low precision in existing true surface seismic geological velocity modeling and pre-stack depth imaging.

[0160] In this embodiment, the advantages of the present invention are as follows:

[0161] I. This invention first establishes a detailed geological structure model, using the velocity field of a small, smooth surface after near-surface fusion as the initial model for the depth domain. Under the constraints of the geological structure model, the velocity field characteristics are gradually characterized through multi-scale grid tomography. First, large-scale grid tomography is used across the entire area for multiple iterations to gradually update the velocity information of structural strata and inter-strata. After obtaining relatively accurate velocities, small-scale grid tomography and multi-attribute constraints can be used to enhance the tomographic imaging accuracy of large-scale velocity volume models and complex geological bodies.

[0162] Conventional methods, such as tomography based on interpreting the layers and tomography based on solid models, mainly consider the average layer velocity of a large set of layers. They have an averaging effect on the layer velocity between layers and are suitable for most phase axes within a layer. However, they are difficult to guarantee for areas with abnormal interlayer velocities or large interlayer velocity variations, resulting in velocities that are either too high or too low.

[0163] The present invention, based on grid tomography technology, is a powerful complement to layer-based tomography technology and solid model-based tomography technology. It aims to flatten each strong seismic reflection phase axis of each depth-migrated CRP gather, regardless of whether the phase axis is at the layer position or between two layers.

[0164] To enhance the accuracy of tomographic imaging of large-scale velocity volume models and velocities in complex geological bodies, a small-grid tomography technique with multi-attribute constraints is required. Before extracting structural attributes, a combined denoising technique is used to improve the signal-to-noise ratio and enhance the stability of the velocity field.

[0165] Although the isotropic velocity modeling stage referenced well velocities for layer-by-layer velocity constraints, a well-seismic velocity consistency survey of the isotropic results revealed local discrepancies in the migration results. This is because the well velocity constraints in the isotropic migration stage are based solely on the principle of flattening common imaging points, which reduces the range of multiple solutions for travel time errors. However, using a simple isotropic algorithm for migration imaging cannot truly solve the anisotropy problem. Anisotropy is widespread in nature, and to obtain pre-stack depth migration results that more closely approximate the actual subsurface structure, anisotropic velocity modeling is necessary to meet the current accuracy requirements of exploration and production. Especially for situations requiring depth-domain seismic results to predict horizontal well guidance for the main target layers and improve the box drilling rate, pre-stack depth imaging must meet even higher requirements.

[0166] II. The depth-velocity modeling process commonly used in the seismic processing industry currently has the following problems: In the isotropic velocity modeling stage, there is a lack of well location constraints; when the anisotropic phenomenon in the region is obvious, the imaging depth will have a large layering error with the well location, and the error will also be large when converting to obtain the anisotropic vertical velocity field (Vp0).

[0167] To address the aforementioned issues, this invention utilizes seismic exploration observation data to guide a multi-information-constrained anisotropic modeling process. The conventional process is adapted into a new depth-velocity modeling workflow and a layer-constrained, shallow-to-deep grid tomography inversion technique. Specifically, the velocity field and anisotropic parameter field are iteratively updated alternately. The anisotropic parameter field is introduced at the beginning of the modeling process, and anisotropic modeling is performed layer by layer for each well location layer. During modeling, geological information and anisotropic parameters obtained from existing wells are fully utilized, combined with the layer model for layer-by-layer interpolation and extrapolation to obtain a complete anisotropic parameter field. Thus, throughout the entire modeling process, well location layers consistently constrain the model, eliminating anisotropic errors in imaging and well location layering layer by layer. Therefore, the cumulative depth error of small-cycle iterations between each set of formations is small, and the iteration convergence is faster. The final contiguous anisotropic results typically show better agreement with well-layer data. Practical applications have shown that this invention has achieved excellent results. The new depth offset profile is significantly improved compared to the previous results, and the drilling rate of horizontal well trajectories revealed by actual drilling has also been greatly improved.

Claims

1. A true surface seismic geology integrated velocity modeling and prestack depth imaging method, characterized in that, The method comprises the following steps: S1, collecting seismic exploration observation data and isotropic migration seismic database of a research area, obtaining a middle-deep layer velocity model, using first arrival tomographic inversion to obtain a near-surface velocity model, selecting a splicing surface and a fusion surface, and fusing the splicing surface to the near-surface velocity model and the middle-deep layer velocity model based on the fusion surface to form an initial depth migration velocity field; S2, performing fusion processing on the near-surface velocity model and the middle-deep layer velocity model fused by the splicing surface to obtain a fused velocity model, correcting common midpoint gathers, and based on geologic facies, structural style and well information, selecting a constraint framework and a correction standard in multiple angles to perform seismic data driven, seismic-geologic integrated and multi-round depth fusion correction on the fused velocity model to obtain a common midpoint gather large-scale floating surface velocity model; S3, performing multi-scale grid tomographic imaging according to the isotropic migration seismic database, using the common midpoint gather large-scale floating surface velocity model to model a true-surface isotropic velocity model, and obtaining an anisotropic parameter field; S4, using the anisotropic parameter field to model according to the seismic exploration observation data and the isotropic migration seismic database to obtain a depth velocity model, and using well position layering to constrain and eliminate errors of the depth velocity model, and modeling a multi-information constraint anisotropic velocity model; S5, using the multi-information constraint anisotropic velocity model to perform preset prestack depth migration on the initial depth migration velocity field as an input of the prestack depth migration to form post-migration seismic imaging results.

2. The true surface seismic geology integrated velocity modeling and prestack depth imaging method of claim 1, wherein, The S1 comprises the following steps: S101, collecting seismic exploration observation data and isotropic migration seismic database of a research area, and performing small-scale smoothing processing on surface elevation data to obtain a migration starting surface, modeling a middle-deep layer velocity model according to a large floating surface of reflected waves, and performing first arrival tomographic inversion to model an initial near-surface velocity model; S102, using tomographic static correction and residual static correction fusion technology, and combining the migration starting surface to fine-tune the initial near-surface velocity model, responding to preliminary static correction, constraining the initial near-surface velocity model by micro-logging, and performing encryption interval sampling to obtain an optimized near-surface velocity model; S103, selecting a splicing surface and a fusion surface according to the seismic exploration observation data, and fusing the splicing surface to the optimized near-surface velocity model and the middle-deep layer velocity model based on the fusion surface to establish an initial depth migration velocity field.

3. The true surface seismic geology integrated velocity modeling and prestack depth imaging method of claim 2, wherein, The S101 comprises the following steps: S1011, collecting seismic exploration observation data and isotropic migration seismic database of a research area, and performing small-scale smoothing processing on true-surface elevation data in the seismic exploration observation data to obtain a migration starting surface; S1012, modeling a middle-deep layer velocity model according to a large floating surface of reflected waves, using a large offset first arrival of a preset migration to perform first arrival tomographic inversion, and modeling an initial near-surface velocity model.

4. The true surface seismic geology integrated velocity modeling and prestack depth imaging method of claim 2, wherein, The S102 comprises the following steps: S1021, fine the near-surface lateral velocity difference in the initial near-surface velocity model by using the tomographic static correction and residual static correction fusion technology and in combination with the offset starting surface; S1022, in response to the preliminary static correction, obtain a fine near-surface velocity model by constraining the first-arrival wave tomographic static correction inversion by micro-logging, and perform interval sampling encryption on the fine near-surface velocity model to obtain an optimized near-surface velocity model.

5. The true surface seismic geology integrated velocity modeling and prestack depth imaging method of claim 2, wherein, The S103 includes the following steps: S1031, according to the seismic exploration observation data, use the replacement velocity of the research area as the near-surface velocity fusion interface, use the replacement velocity smoothing surface of the research area as the splicing surface, and use the ray density interface smoothing surface as the fusion surface; S1032, based on the fusion surface, fuse the splicing surface into the optimized near-surface velocity model and the middle-deep layer velocity model to establish an initial depth migration velocity field of the first-arrival wave and the reflected wave combined depth velocity modeling.

6. The true surface seismic geology integrated velocity modeling and prestack depth imaging method of claim 4, wherein, The S2 includes the following steps: S201, perform fusion processing on the near-surface velocity model and the middle-deep layer velocity model fused by the splicing surface, replace the velocity between the splicing surface and the ground with the replacement velocity, establish an equivalent true near-surface velocity field, and obtain a fused velocity model; S202, remove the tomographic static correction quantity obtained by the near-surface velocity model, regress the seismic exploration observation data to the near true ground, use the elevation or the elevation smoothing surface as a new floating surface, move the common offset gather as a whole to the small smoothing surface corresponding to the position of the new floating surface, use the travel time as the equivalent criterion, use the splicing surface as the bottom, and use the final reference surface as the top, and respectively obtain the travel time of the fine near-surface velocity model and the fused velocity model; S203, use the travel time difference of the fine near-surface velocity model and the fused velocity model as a high-frequency quantity, apply it to the seismic exploration observation data, correct the seismic exploration observation data, and use the residual static correction mode of the seismic exploration observation data to calibrate the travel time error caused by the sampling of the fine near-surface velocity model; S204, calibrate the seismic exploration observation data from the common offset gather floating surface to the true ground surface corresponding to the true near-surface velocity field of the fused velocity model, perform depth step sampling and smoothing processing on the fine near-surface velocity model, fuse the migration velocity to the ground velocity of the near true near-surface velocity field, and perform correction processing on the seismic exploration observation data; S205, select a marker layer with a strong reflection interface as a constraint framework, pick up N horizons from shallow to deep based on the geological facies and structural style, establish an initial structural model, convert the pre-stack time migration root mean square velocity field into a layer velocity in the depth domain by a constrained velocity inversion method to form an initial background velocity of pre-stack depth migration, normalize the logging P-wave velocity curve under the constraint of a large set of geological horizons, extract the low-frequency component, and select the low-frequency information of the logging P-wave as a correction standard; S206, form a common offset gather large-scale floating surface velocity field according to the constraint framework and the correction standard, and correct the fused velocity model based on the seismic data driving, the seismic-geological integration, and the multi-round depth fusion to obtain a common offset gather large-scale floating surface velocity model.

7. The true surface seismic geology integrated velocity modeling and prestack depth imaging method of claim 1, wherein, The S3 comprises the following steps: S301, according to the isotropic migration seismic database, extracting the dip angle volume, the azimuth angle volume and the continuity factor volume in the migration volume, defining the reflection surface and the direction, storing the reflection points on the reflection surface in a preset database, and using the reflection points to perform ray tracing to the ground surface according to different opening angles and azimuth angles, to obtain an automatic picking mode of the migration volume; S302, in the migration trace set, automatically picking the residual delay to obtain an automatic picking mode of the residual delay; S303, based on the automatic picking modes of the migration volume and the residual delay, using a point pair feature operation mode based on an anisotropic ray tracing to create a tomographic imaging matrix; S304, according to the tomographic imaging matrix, using small-scale high-resolution geological attributes to constrain the tomography along the layer grid to obtain multi-scale grid tomographic imaging; S305, according to the multi-scale grid tomographic imaging, using a high-resolution processing technology to process the seismic data volume in the isotropic migration seismic database, and performing denoising processing on the seismic data volume, and calculating the dip angle volume, the azimuth angle volume and the continuity factor volume of the seismic data volume after the high-resolution processing and the denoising processing; S306, according to the dip angle volume, the azimuth angle volume and the continuity factor volume of S305, performing ray tracing to obtain a velocity variation, and based on the velocity variation, fusing the seismic exploration observation data and the common center point trace set large-scale floating surface velocity model to form an initial true ground isotropic velocity field, and modeling to obtain a true ground isotropic velocity model; S307, using well information constraint, taking the velocity error as the depth difference of the layer position and the well position of the migration imaging to obtain an anisotropic vertical velocity field and an initial anisotropic parameter field, and iteratively updating the anisotropic parameters in the initial anisotropic parameter field through the anisotropic migration value, and obtaining a dip angle field and an azimuth angle field reflecting the tilt symmetry axis through the calculation of the structural dip angle of the stratum; S308, in response to establishing the true ground isotropic velocity model, based on the dip angle field and the azimuth angle field, according to the geological information and the anisotropic parameters in the seismic exploration observation data, combining the layer position model to perform interpolation extrapolation along the layer to obtain the anisotropic parameter field.

8. The true surface seismic geology integrated velocity modeling and prestack depth imaging method of claim 7, wherein, The S304 comprises the following steps: S3041, according to the tomographic imaging matrix, using the structural layer position and the internal small layer as reflection points to perform ray tracing forward, using globally uniformly distributed points as inversion grids, using the conjugate gradient method to obtain the least square solution, performing interpolation under the constraint of the structural model, and updating the depth velocity volume; S3042, using different geological constraint methods to solve the tomographic imaging matrix to obtain multi-scale grid tomographic imaging; The tomographic imaging matrix solving step specifically comprises: Using the common reflection point trace set generated by the target line prestack depth migration, defining the residual delay as the residual delay value of the common acceptance point trace set of the reflection point position, using the common reflection point trace set to pick the residual delay of the trace set, and using the residual delay to gradually and layer by layer invert the layer velocity of the stratum.

9. The true surface seismic geology integrated velocity modeling and prestack depth imaging method of claim 1, wherein, The S4 comprises the following steps: S401, according to the seismic data body in the isotropic migration seismic database, extract the azimuth and inclination data body of the isotropic migration seismic data body, and model using anisotropy to establish a depth velocity model; S402, by calculating the parameters of the anisotropy parameter field, obtaining anisotropy velocity, using grid tomography inversion method, multiple iterations are carried out on the depth velocity model which is constrained and error-eliminated, and a prestack depth migration anisotropy velocity field is obtained; S403, based on the prestack depth migration anisotropy velocity field, multi-scale fracture detection is carried out on the seismic data body to obtain a discontinuity data body, according to the true surface isotropic velocity model and the interpreted horizon, combined with well information constraint, the relative coefficient of the velocity and the average velocity of each formation discontinuity body is analyzed, the discontinuity data body is three-dimensionally carved to obtain a three-dimensional discontinuity data body, and combined with the relative coefficient, the velocity of each formation is updated to obtain a fine velocity model, and the fine velocity model is repeatedly updated and constructed; S404, in response to updating and constructing the fine velocity model, using well site stratification, the depth velocity model is constrained and the error of imaging and well site stratification caused by anisotropy is eliminated layer by layer to obtain a multi-information constraint anisotropy parameter field, and a multi-information constraint anisotropy velocity model is modeled.

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