Method for improving imaging accuracy of deep reflection seismic data

Abstract

The application relates to a method for improving the imaging precision of deep reflection seismic data. The method comprises the following steps: performing first pre-stack depth migration on an initial depth domain layer velocity model and a common center point gather to obtain a first pre-stack depth migration profile and a pre-stack depth migration gather; picking a seed point by using the first pre-stack depth migration profile, picking residual curvature by using the pre-stack depth migration gather, performing velocity inversion on the initial depth domain layer velocity model according to the seed point and the residual curvature to obtain an optimized layer velocity model; performing second pre-stack depth migration on the optimized layer velocity model to obtain a second pre-stack depth migration profile; and if the migrated layer velocity converges, the second pre-stack depth migration profile is the imaging of the deep reflection seismic data. The scheme provided by the application can better improve the imaging precision of the deep rock structure and reveal the deep tectonic characteristics of the lithosphere.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of seismic data imaging, in particular to a method for improving imaging precision of deep reflection seismic data. BACKGROUND

[0002] Deep seismic reflection profile detection technology is one of the most effective technologies for detecting fine structure of the lithosphere recognized by the international geoscience community, and plays an important role in revealing crustal rock deformation and structural traces, regional tectonic events and important connections of the upper mantle of the lithosphere. For deep reflection seismic data, because the recording time is long, the seismic signal weakens with the increase of the detection depth, and the energy of the external background noise is independent of the recording depth, so the deep seismic reflection is actually a weak reflection, and the signal-to-noise ratio of the data is very low. In order to better explore the deep reflection data target area involving the imaging of the sedimentary basin basement, fault, internal crust and Moho surface and other super-buried deep strata, it is necessary to establish a reasonable velocity model of the entire crust from the shallow to the deep Moho, and to construct a fine rock structure image of the entire crust to the Moho based on the reasonable layer velocity model.

[0003] However, the current technical method for processing deep reflection seismic profile data is based on pre-stack time migration, but time migration ignores the lateral variation of velocity, which will cause a large error in the imaging precision of the rock structure.

[0004] Therefore, the present application aims to provide a method for improving the imaging precision of deep reflection seismic data, which can better improve the imaging precision of the deep rock structure and reveal the deep tectonic characteristics of the lithosphere. SUMMARY

[0005] In order to overcome the problems in the related art, the present application provides a method for improving the imaging precision of deep reflection seismic data, which can better improve the imaging precision of the deep rock structure and reveal the deep tectonic characteristics of the lithosphere.

[0006] The present application provides a method for improving the imaging precision of deep reflection seismic data, comprising:

[0007] S1: acquiring two-dimensional shot line data and navigation data, and performing pre-stack time migration pre-processing on the two-dimensional shot line data and the navigation data to obtain time domain root mean square velocity and common midpoint gather;

[0008] S2: performing constrained velocity inversion on the time domain root mean square velocity to obtain time domain layer velocity;

[0009] S3: iteratively processing the time domain layer velocity by using the pre-stack time migration to obtain a time domain layer velocity model;

[0010] S4: time-depth conversion is performed on the time domain layer velocity model to obtain a depth domain layer velocity model;

[0011] S5: the pre-stack time migration profile is converted to the depth domain, and parallel horizons are picked up in the depth profile Moho depth range;

[0012] S6: a depth domain constant velocity model below the parallel horizons is set, the depth domain constant velocity model and the depth domain layer velocity model are fused in velocity to obtain a depth domain initial layer velocity model;

[0013] S7: the depth domain initial layer velocity model and the common center point gather are first pre-stack depth migrated to obtain a first pre-stack depth migration profile and a pre-stack depth migration gather;

[0014] S8: a seed point is picked up using the first pre-stack depth migration profile, a residual curvature is picked up using the pre-stack depth migration gather, and a velocity inversion is performed on the depth domain initial layer velocity model according to the seed point and the residual curvature to obtain an optimized layer velocity model;

[0015] S9: a second pre-stack depth migration is performed on the optimized layer velocity model to obtain a second pre-stack depth migration profile, and if the migrated layer velocity converges, the second pre-stack depth migration profile is the depth reflection seismic data imaging.

[0016] In an embodiment, the parallel horizons are picked up in the depth profile Moho depth range, comprising:

[0017] A first parallel horizon is picked up at 9 km of the depth profile, and a second parallel horizon is picked up at 12 km of the depth profile.

[0018] In an embodiment, the depth domain constant velocity model below the parallel horizons is set, the depth domain constant velocity model and the depth domain layer velocity model are fused in velocity to obtain a depth domain initial layer velocity model, comprising:

[0019] A depth domain constant velocity model between the first parallel horizon and the second parallel horizon is set to obtain a first depth domain constant velocity model;

[0020] A depth domain constant velocity model below the second parallel horizon is set to obtain a second depth domain constant velocity model;

[0021] A depth domain layer velocity model above the first parallel horizon is extracted from the depth domain layer velocity model to obtain a first depth domain layer velocity model;

[0022] velocity-fuse the first depth domain interval velocity model, the first depth domain constant velocity model and the second depth domain constant velocity model to obtain a depth domain initial interval velocity model.

[0023] In one embodiment, the velocity-fusing the first depth domain interval velocity model, the first depth domain constant velocity model and the second depth domain constant velocity model to obtain a depth domain initial interval velocity model comprises:

[0024] fuse the first depth domain interval velocity model and the first depth domain constant velocity model to obtain a first fused interval velocity model;

[0025] extract a depth domain interval velocity model above the second parallel horizon from the first fused interval velocity model to obtain a second depth domain interval velocity model;

[0026] fuse the second depth domain interval velocity model and the second depth domain constant velocity model to obtain a second fused interval velocity model, and the second fused interval velocity model is the depth domain initial interval velocity model.

[0027] In one embodiment, the picking up residual curvature from the pre-stack depth migration gather comprises:

[0028] generating a depth domain residual velocity spectrum from the pre-stack depth migration gather;

[0029] picking up residual curvature on the depth domain residual velocity spectrum.

[0030] In one embodiment, the velocity-inverting the depth domain initial interval velocity model according to the seed point and the residual curvature to obtain an optimized interval velocity model comprises:

[0031] picking up a seed point on the first pre-stack depth migration profile, and shooting a ray path from the seed point to the surface, if the ray path satisfies from a shot point to a reflection point to a geophone, the seed point is a target seed point, and the ray path is a target ray path;

[0032] obtaining a dip angle field, the residual curvature and a migration velocity at the target seed point, and velocity-inverting the depth domain initial interval velocity model according to the dip angle field, the residual curvature, the migration velocity and the target ray path to generate an optimized interval velocity model.

[0033] In one embodiment, after the second pre-stack depth migration on the optimized interval velocity model, comprises:

[0034] If the migrated interval velocities do not converge, return to step S7 and substitute the migrated interval velocities into the initial depth domain interval velocity model until the migrated interval velocities converge.

[0035] In an embodiment, the pre-stack time migration of the two-dimensional shot-line data and the navigation data comprises:

[0036] Defining an observation system according to the two-dimensional shot-line data and the navigation data, and pre-stack denoising and multiple wave suppression of the two-dimensional shot-line data and the navigation data.

[0037] In an embodiment, the residual curvature picking on the depth domain residual velocity spectrum comprises:

[0038] Residual curvature picking of different resolutions on the depth domain residual velocity spectrum.

[0039] In an embodiment, the seed point picking on the first pre-stack depth migration profile comprises:

[0040] Seed point picking of different horizons on the first pre-stack depth migration profile.

[0041] The technical scheme provided in the application can include the following beneficial effects:

[0042] After obtaining the depth domain interval velocity model through pre-stack time migration and time-depth conversion, the application converts the pre-stack time migration profile to the depth domain, picks parallel horizons in the depth range of the Moho surface of the depth profile and sets the constant velocity model of the depth domain below the parallel horizons, fuses the depth domain constant velocity model and the depth domain interval velocity model to obtain an initial depth domain interval velocity model, performs first pre-stack depth migration on the initial depth domain interval velocity model and the common center point gather to obtain a first pre-stack depth migration profile and a pre-stack depth migration gather, picks seed points using the first pre-stack depth migration profile, picks residual curvatures using the pre-stack depth migration gather, performs velocity inversion on the initial depth domain interval velocity model according to the seed points and the residual curvatures to obtain an optimized interval velocity model, performs second pre-stack depth migration on the optimized interval velocity model to obtain a second pre-stack depth migration profile, and if the migrated interval velocities converge, the second pre-stack depth migration profile is the imaging of the deep reflection seismic data. The application performs pre-stack depth migration twice after pre-stack time migration, greatly eliminates the error of pre-stack time migration in the lateral velocity, makes the interval velocity model more consistent with the actual stratum condition, and better improves the accuracy of the imaging of the deep structure of the rock and reveals the deep tectonic characteristics of the lithosphere.

[0043] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and cannot limit the application. Attached Figure Description

[0044] The above and other objects, features and advantages of this application will become more apparent from the more detailed description of exemplary embodiments thereof in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments thereof.

[0045] Figure 1 This is a flowchart illustrating Embodiment 1 of the method for improving the imaging accuracy of deep reflection seismic data as shown in the embodiments of this application;

[0046] Figure 2 This is a schematic diagram of the depth domain layer velocity model after time-depth conversion, as shown in an embodiment of this application;

[0047] Figure 3 This is a schematic diagram of the optimized layer velocity model shown in the embodiments of this application;

[0048] Figure 4 This is a schematic diagram of deep reflection seismic data imaging shown in an embodiment of this application;

[0049] Figure 5 This is a flowchart illustrating Embodiment 2 of the method for improving the imaging accuracy of deep reflection seismic data as shown in the embodiments of this application;

[0050] Figure 6 This is a schematic diagram of the velocity model of the first depth domain layer shown in an embodiment of this application;

[0051] Figure 7 This is a schematic diagram of a first depth domain constant velocity model shown in an embodiment of this application;

[0052] Figure 8 This is a schematic diagram of the velocity model of the first fusion layer shown in an embodiment of this application;

[0053] Figure 9 This is a schematic diagram of the second depth domain layer velocity model shown in an embodiment of this application;

[0054] Figure 10 This is a schematic diagram of the second depth domain constant velocity model shown in the embodiments of this application;

[0055] Figure 11 This is a schematic diagram of the initial layer velocity model in the depth domain shown in an embodiment of this application;

[0056] Figure 12 This is a flowchart illustrating Embodiment 3 of the method for improving the imaging accuracy of deep reflection seismic data as shown in the embodiments of this application;

[0057] Figure 13 These are schematic diagrams of residual curvature profiles at different resolutions shown in embodiments of this application;

[0058] Figure 14 FIG. 1 is a schematic diagram of seed point picking on a first pre-stack depth migration profile according to an embodiment of the present application. DETAILED DESCRIPTION

[0059] The preferred embodiments of the present application will be described in detail with reference to the drawings. Although the preferred embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided so that the present application can be more thorough and complete, and the scope of the present application can be accurately conveyed to those skilled in the art.

[0060] The terms used in the present application are merely for the purpose of describing specific embodiments and are not intended to limit the present application. The singular forms "a", "an" and "the" used in the present application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0061] It should be understood that although the terms "first", "second", "third", etc. can be used in the present application to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, the first information can also be referred to as the second information, and similarly, the second information can also be referred to as the first information without departing from the scope of the present application. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "multiple" is two or more, unless otherwise specifically limited.

[0062] The current technical method for processing deep reflection seismic profile data is based on pre-stack time migration, but time migration ignores the lateral variation of velocity, which can cause a large error in the accuracy of rock structure imaging.

[0063] To solve the above problems, the embodiments of the present application provide a method for improving the imaging accuracy of deep reflection seismic data, which can better improve the accuracy of rock deep structure imaging and reveal the deep tectonic characteristics of the lithosphere.

[0064] The technical solutions of the embodiments of the present application will be described in detail below with reference to the drawings.

[0065] Embodiment one

[0066] Figure 1 FIG. 1 is a schematic diagram of seed point picking on a first pre-stack depth migration profile according to an embodiment of the present application.

[0067] Figure 2 is a schematic diagram of a depth domain layer velocity model after time-depth conversion shown in an embodiment of the present application;

[0068] Figure 3 is a schematic diagram of an optimized layer velocity model shown in an embodiment of the present application;

[0069] Figure 4 is a schematic diagram of deep reflection seismic data imaging shown in an embodiment of the present application.

[0070] Referring to Figures 1-4 , the first embodiment of the method for improving the imaging accuracy of deep reflection seismic data in the embodiment of the present application comprises:

[0071] 101. Obtain two-dimensional shot line data and navigation data, and perform pre-stack time migration pre-processing on the two-dimensional shot line data and the navigation data to obtain time domain root mean square velocity and common midpoint gather;

[0072] The two-dimensional shot line data is two-dimensional seismic original data collected in the field, and the navigation data is data containing latitude and longitude coordinates and time information of the seismic data.

[0073] The pre-stack time migration pre-processing mainly includes defining an observation system, pre-stack denoising and multiple wave suppression.

[0074] Since the two-dimensional shot line data only records the seismic trace header and time information, and does not contain latitude and longitude coordinates, the observation system needs to be defined in subsequent processing, and the time information contained in the two-dimensional shot line data and the navigation data is matched to finally assign the latitude and longitude coordinate information in the navigation data to the trace header information of the two-dimensional shot line data.

[0075] The reception record of a single geophone is called a seismic trace, the collection of multiple seismic traces is simply referred to as a gather, and the common midpoint gather refers to the collection of seismic traces with the same center point of the geophone and the shot point in the observation system.

[0076] 102. Perform constrained velocity inversion on the time domain root mean square velocity to obtain a time domain layer velocity;

[0077] It should be noted that the time domain root mean square can be inverted using the constrained velocity inversion method, or other inversion methods can be used to invert the time domain root mean square, which is not limited here. In the embodiment, the time domain root mean square is inverted using the constrained velocity inversion method.

[0078] 103. Perform iteration on the time domain layer velocity using pre-stack time migration to obtain a time domain layer velocity model;

[0079] Iteration is the activity of repeating a feedback process, usually to approximate a desired goal or result. Each repetition of the process is called an "iteration", and the result of each iteration is used as the starting point for the next iteration, thus, the time domain velocity model obtained by using the iterative pre-stack time migration has convergence.

[0080] It should be noted that the pre-stack time migration can be curved ray pre-stack time migration or other types of pre-stack time migration, and the pre-stack time migration used in the embodiment is curved ray pre-stack time migration.

[0081] 104, time-depth conversion is performed on the time domain layer velocity model to obtain a depth domain layer velocity model;

[0082] Wherein, the depth recording length is 18 kilometers, the depth sampling interval is 3 meters, and the value range of the depth domain layer velocity model is 1488 meters / second to 5200 meters / second.

[0083] 105, converting the pre-stack time migration profile to the depth domain, and picking up the parallel horizon in the Moho depth range of the depth profile;

[0084] Moho is the interface between the crust and the mantle, and its depth is different in different regions. In the embodiment, it appears between 9 kilometers and 12 kilometers from the ground surface, so the parallel horizon is picked up between 9 kilometers and 12 kilometers in the depth profile.

[0085] 106, setting a depth domain constant velocity model below the parallel horizon, and performing velocity fusion on the depth domain constant velocity model and the depth domain layer velocity model to obtain a depth domain initial layer velocity model;

[0086] 107, performing first pre-stack depth migration on the depth domain initial layer velocity model and the common center point gather to obtain first pre-stack depth migration profile and pre-stack depth migration gather;

[0087] 108, picking up seed points using the first pre-stack depth migration profile, picking up residual curvatures using the pre-stack depth migration gather, and performing velocity inversion on the depth domain initial layer velocity model according to the seed points and the residual curvatures to obtain an optimized layer velocity model;

[0088] 109, performing second pre-stack depth migration on the optimized layer velocity model to obtain second pre-stack depth migration profile, and judging whether the migrated layer velocity converges;

[0089] Judging whether the migrated layer velocity converges means judging whether the migrated layer velocity approaches the preset value.

[0090] 110, if the migrated layer velocity converges, the second pre-stack depth migration profile is the imaging of the deep reflection seismic data;

[0091] If the migrated interval velocities do not converge, return to step 107 and substitute the migrated interval velocities into the initial depth domain interval velocity model until the migrated interval velocities converge.

[0092] After the inversion of step 108, a residual velocity field is formed, with a value range of approximately -150 m / s to 150 m / s, reflecting the increase or decrease of the interval velocity of each iteration relative to the interval velocity of the previous iteration.

[0093] The following beneficial effects can be obtained from the above embodiment one:

[0094] After obtaining the depth domain interval velocity model through the pre-stack time migration and time-depth conversion in this embodiment, the pre-stack time migration profile is converted to the depth domain, parallel horizons are picked up in the depth range of the Moho surface of the depth profile and a constant velocity model in the depth domain below the parallel horizons is set, the depth domain constant velocity model and the depth domain interval velocity model are fused in velocity, the depth domain initial interval velocity model is obtained, the depth domain initial interval velocity model and the common center point gather are subjected to first pre-stack depth migration, the first pre-stack depth migration profile and the pre-stack depth migration gather are obtained, the seed points are picked up using the first pre-stack depth migration profile, the residual curvatures are picked up using the pre-stack depth migration gather, the depth domain initial interval velocity model is subjected to velocity inversion according to the seed points and the residual curvatures, the optimized interval velocity model is obtained, the second pre-stack depth migration is performed on the optimized interval velocity model, the second pre-stack depth migration profile is obtained, and if the migrated interval velocities converge, the second pre-stack depth migration profile is the imaging of the deep reflection seismic data. This embodiment performs pre-stack depth migration twice after pre-stack time migration, greatly eliminates the error of pre-stack time migration in the lateral velocity, makes the interval velocity model more consistent with the actual stratum condition, and better improves the precision of the rock deep structure imaging and reveals the deep tectonic characteristics of the lithosphere.

[0095] Embodiment two

[0096] In actual application, on the basis of embodiment one, this embodiment specifically introduces how to fuse the depth domain constant velocity model and the depth domain interval velocity model in velocity to obtain the depth domain initial interval velocity model.

[0097] Figure 5 is a flowchart of the method for improving the imaging precision of deep reflection seismic data according to the embodiment two of the method for improving the imaging precision of deep reflection seismic data according to the embodiment one of the present application;

[0098] Figure 6 is a schematic diagram of the first depth domain interval velocity model according to the embodiment one of the present application;

[0099] Figure 7 is a schematic diagram of the first depth domain constant velocity model according to the embodiment one of the present application;

[0100] Figure 8 is a first fusion layer velocity model schematic diagram shown in the embodiment of the present application;

[0101] Figure 9 is a second depth domain layer velocity model schematic diagram shown in the embodiment of the present application;

[0102] Figure 10 is a second depth domain constant velocity model schematic diagram shown in the embodiment of the present application;

[0103] Figure 11 is a depth domain initial layer velocity model schematic diagram shown in the embodiment of the present application.

[0104] Referring to Figures 5-11 , the embodiment two of the method for improving the imaging precision of deep reflection seismic data in the embodiment of the present application comprises:

[0105] 201, picking up a first parallel horizon at 9 km of the depth profile and a second parallel horizon at 12 km of the depth profile;

[0106] Since the Moho surface appears between 9 km and 12 km from the surface in the embodiment, the parallel horizons are picked up at 9 km and 12 km respectively to cover the entire Moho surface range.

[0107] 202, setting a depth domain constant velocity model between the first parallel horizon and the second parallel horizon to obtain a first depth domain constant velocity model;

[0108] The sedimentary structure of the deep reflection seismic data can be divided into five layers, from shallow to deep, in turn: seawater, Cenozoic, Mesozoic, Moho surface and lithospheric mantle. In the target area adjacent to the sedimentary structure of the work area, the seawater velocity range is 1480 m / s to 1520 m / s, the Cenozoic layer velocity range is 1600 m / s to 3200 m / s, the Cenozoic bottom to the Moho interface layer velocity range is 3500 m / s to 6000 m / s, and the Moho interface and deeper layer velocity is greater than 7000 m / s, so the layer velocity of the depth domain constant velocity model between the first parallel horizon and the second parallel horizon is given as 6200 m / s, and the layer velocity of the velocity model above the first parallel horizon is set to zero.

[0109] 203, setting a depth domain constant velocity model below the second parallel horizon to obtain a second depth domain constant velocity model;

[0110] As above, the layer velocity of the depth domain constant velocity model below the second parallel horizon is given as 8000 m / s, and the layer velocity of the velocity model above the second parallel horizon is set to zero.

[0111] In actual application, there is no strict time sequence relationship between step 202 and step 203, that is, either step can be executed first, which is not limited here.

[0112] 204、extracting a depth domain interval velocity model above the first parallel horizon from the depth domain interval velocity model to obtain a first depth domain interval velocity model;

[0113] The velocity range of the first depth domain interval velocity model is 0 m / s to 5200 m / s, and the interval velocity of the velocity model below the first parallel horizon is set to zero.

[0114] 205、fusing the first depth domain interval velocity model and the first depth domain constant velocity model to obtain a first fused interval velocity model;

[0115] After fusion, smoothing is performed, and the value range of the first fused interval velocity model is 1490 m / s to 6200 m / s.

[0116] 206、extracting a depth domain interval velocity model above the second parallel horizon from the first fused interval velocity model to obtain a second depth domain interval velocity model;

[0117] The velocity range of the second depth domain interval velocity model is 0 m / s to 6200 m / s, and the interval velocity of the velocity model below the second parallel horizon is set to zero.

[0118] 207、fusing the second depth domain interval velocity model and the second depth domain constant velocity model to obtain a second fused interval velocity model, which is the depth domain initial interval velocity model.

[0119] After fusion, smoothing is performed, and the value range of the second fused interval velocity model, i.e., the depth domain initial interval velocity model, is 1498 m / s to 7800 m / s.

[0120] The following beneficial effects can be obtained from the above embodiment two:

[0121] In this embodiment, the first depth domain interval velocity model and the first depth domain constant velocity model are first fused to obtain a first fused interval velocity model, then a depth domain interval velocity model above the second parallel horizon is extracted from the first fused interval velocity model to obtain a second depth domain interval velocity model, and finally the second depth domain interval velocity model and the second depth domain constant velocity model are fused to obtain a depth domain initial interval velocity model. Through the fusion-extraction-re-fusion mode, the obtained depth domain initial interval velocity model is more accurate and reliable.

[0122] Embodiment three

[0123] In actual application, on the basis of the above embodiments, this embodiment details how to obtain an optimized interval velocity model.

[0124] Figure 12is a flowchart of a third embodiment of the method for improving imaging precision of deep reflection seismic data shown in the embodiments of the present application;

[0125] Figure 13 is a schematic diagram of residual curvature profiles with different resolutions shown in the embodiments of the present application;

[0126] Figure 14 is a schematic diagram of seed point picking on a first pre-stack depth migration profile shown in the embodiments of the present application.

[0127] Referring to Figures 3-4 that is, Figures 12-14 (wherein, Figure 12 the resolutions and the residual curvatures in the residual curvature profiles from top to bottom are sequentially improved), the third embodiment of the method for improving imaging precision of deep reflection seismic data in the embodiments of the present application includes:

[0128] 301. generating a residual velocity spectrum in a depth domain by using a pre-stack depth migration gather;

[0129] 302. picking residual curvatures on the residual velocity spectrum in the depth domain;

[0130] wherein, the picked residual curvatures are residual curvatures with different resolutions and different values, which can be selected according to specific needs, and in the embodiments, the selected residual curvatures are 3, 9 and 16 respectively.

[0131] 303. picking a seed point on a first pre-stack depth migration profile, and shooting a ray path from the seed point to the surface to determine whether the ray path satisfies from a shot point to a reflection point to a receiver;

[0132] The seed point is a point on different horizons of the first pre-stack depth migration profile, and is on a strongly continuous stratum.

[0133] 304. if the ray path satisfies from the shot point to the reflection point to the receiver, the seed point is a target seed point, and the ray path is a target ray path;

[0134] wherein, the shot point, the reflection point and the receiver are all set in advance in a target area.

[0135] 305. if the ray path does not satisfy from the shot point to the reflection point to the receiver, the seed point is not a target seed point, and is ignored;

[0136] 306. obtaining a dip field, a residual curvature and a migration velocity at the target seed point, and using the dip field, the residual curvature, the migration velocity and the target ray path to perform velocity inversion on an initial layer velocity model in the depth domain to generate an optimized layer velocity model.

[0137] The number of iterations is different according to actual conditions, in the embodiment, after three iterations of tomographic velocity inversion, the velocities of the Cenozoic and Mesozoic are more convergent and stable, the Moho velocity and the lithospheric mantle velocity tend to be stable and convergent, and the value range of the optimized layer velocity model is 1400m / s to 7780m / s.

[0138] Taking the residual curvature analysis as the basic principle and the gather flattening as the main principle in the migration velocity analysis, the imaging depth error in the common imaging point gather is used to provide information for the velocity updating, the residual curvature, the dip field, the migration velocity and the target ray path at the target seed point are fully used in the iteration process to perform the velocity inversion, complete one iteration of the initial layer velocity model in the depth domain, and the convergence degree is high and the efficiency is fast in one iteration.

[0139] In addition, in order to control the overall structural trend, small residual curvature is selected for tomography in the early iteration, and large residual curvature is gradually selected for tomography in the later iteration, so as to better control the details, and at the same time, low-resolution residual curvature is usually selected to control the general trend of the structure in the early iteration, and high-resolution residual curvature is selected to finely depict the details of the structure in the later iteration.

[0140] The following beneficial effects can be obtained from the above embodiment three:

[0141] In the embodiment, the residual curvature is picked up from the prestack depth migration gather, the seed point is picked up on the first prestack depth migration profile, the target ray path is extracted from the target seed point, the dip field, the residual curvature, the migration velocity and the target ray path of the target seed point are used for velocity inversion of the initial layer velocity model in the depth domain, and the optimized layer velocity model is generated, so that the convergence degree and the efficiency can be improved.

[0142] The scheme of the present application has been described in detail above with reference to the drawings. In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments. It should be known by those skilled in the art that the actions and modules involved in the specification are not necessarily required by the present application. In addition, it can be understood that the steps in the method of the embodiments of the present application can be adjusted, combined and reduced in sequence according to actual needs, and the modules in the device of the embodiments of the present application can be combined, divided and reduced according to actual needs.

[0143] Having described various embodiments of the application, it is to be understood that the above description is meant not to limit and not to encompass all of the possible embodiments. Many modifications and variations of this application can be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. It is intended that the scope of the application be defined by the scope of the patent and by the claims as allowed by the patent office, which can include adaptations based on the description, equivalents, and / or substitutions of elements individually or collectively to the entire disclosure.

Claims

1. A method for improving imaging accuracy of deep reflection seismic data, characterized in that: S1: obtaining two-dimensional shot line data and navigation data, and performing pre-stack time migration preprocessing on the two-dimensional shot line data and the navigation data to obtain time domain root mean square velocity and common midpoint gather; S2: performing constrained velocity inversion on the time domain root mean square velocity to obtain time domain interval velocity; S3: substituting the time domain interval velocity into a pre-stack time migration process to generate a preliminary imaging profile, iteratively adjusting the time domain interval velocity by analyzing the quality of the preliminary imaging profile until the quality of the preliminary imaging profile meets a preset standard to obtain a time domain interval velocity model; S4: performing time-depth conversion on the time domain interval velocity model to obtain a depth domain interval velocity model; S5: converting the profile of the pre-stack time migration to the depth domain, and picking parallel horizons in the depth range of the Moho surface of the depth profile; S6: setting a depth domain constant velocity model below the parallel horizons, and performing velocity fusion on the depth domain constant velocity model and the depth domain interval velocity model to obtain a depth domain initial interval velocity model; S7: performing first pre-stack depth migration on the depth domain initial interval velocity model and the common midpoint gather to obtain first pre-stack depth migration profile and pre-stack depth migration gather; S8: picking seed points using the first pre-stack depth migration profile, picking residual curvatures using the pre-stack depth migration gather, and performing velocity inversion on the depth domain initial interval velocity model according to the seed points and the residual curvatures to obtain an optimized interval velocity model; S9: performing second pre-stack depth migration on the optimized interval velocity model to obtain second pre-stack depth migration profile, and if the migrated interval velocity converges, the second pre-stack depth migration profile is the imaging of deep reflection seismic data. The picking of parallel horizons in the depth range of the Moho surface of the depth profile comprises: picking a first parallel horizon at 9 km of the depth profile, and picking a second parallel horizon at 12 km of the depth profile. The setting of the depth domain constant velocity model below the parallel horizons, the velocity fusion of the depth domain constant velocity model and the depth domain interval velocity model, and the obtaining of the depth domain initial interval velocity model comprise: setting a depth domain constant velocity model between the first parallel horizon and the second parallel horizon to obtain a first depth domain constant velocity model; setting a depth domain constant velocity model below the second parallel horizon to obtain a second depth domain constant velocity model; extracting a depth domain interval velocity model above the first parallel horizon from the depth domain interval velocity model to obtain a first depth domain interval velocity model; and performing velocity fusion on the first depth domain interval velocity model, the first depth domain constant velocity model, and the second depth domain constant velocity model to obtain the depth domain initial interval velocity model. The velocity fusion of the first depth domain interval velocity model, the first depth domain constant velocity model, and the second depth domain constant velocity model to obtain the depth domain initial interval velocity model comprises: performing fusion on the first depth domain interval velocity model and the first depth domain constant velocity model to obtain a first fused interval velocity model; performing fusion on the first fused interval velocity model and the second depth domain constant velocity model to obtain a second fused interval velocity model; and performing fusion on the second fused interval velocity model and the depth domain interval velocity model above the first parallel horizon to obtain the depth domain initial interval velocity model. ​ ​ ​ ​ ​ ​ 2. The method for improving the imaging accuracy of deep reflection seismic data according to claim 1, characterized in that, ​ ​ 3. The method for improving the imaging accuracy of deep reflection seismic data according to claim 2, characterized in that, ​ ​ ​ ​ ​ 4. The method for improving the imaging accuracy of deep reflection seismic data according to claim 3, characterized in that, ​ ​ extracting a second depth domain layer velocity model above the second parallel horizon in the first fusion layer velocity model; fusing the second depth domain layer velocity model and the second depth domain constant velocity model to obtain a second fusion layer velocity model, and the second fusion layer velocity model is the initial depth domain layer velocity model.

5. The method of claim 1, wherein, The method further comprises: generating a depth domain residual velocity spectrum by using the pre-stack depth migration gather; picking up residual curvature on the depth domain residual velocity spectrum.

6. The method for improving the imaging accuracy of deep reflection seismic data according to claim 5, characterized in that, The method further comprises: picking up seed points on the first pre-stack depth migration profile, and shooting a ray path from the seed points to the surface, and if the ray path meets the requirement from a shot point to a reflection point to a receiver, the seed point is a target seed point and the ray path is a target ray path; obtaining an angle field, the residual curvature and a migration velocity at the target seed point, and performing velocity inversion on the initial depth domain layer velocity model by using the angle field, the residual curvature, the migration velocity and the target ray path to generate an optimized layer velocity model.

7. The method of claim 1, wherein, The method further comprises: if the migrated layer velocity does not converge, returning to step S7 and substituting the migrated layer velocity into the initial depth domain layer velocity model until the migrated layer velocity converges.

8. The method for improving the imaging accuracy of deep reflection seismic data according to claim 1, characterized in that, The method further comprises: defining an observation system according to the two-dimensional shot line data and the navigation data, and performing pre-stack denoising and multiple wave suppression on the two-dimensional shot line data and the navigation data.

9. The method for improving the imaging accuracy of deep reflection seismic data according to claim 5, characterized in that, The method further comprises: performing residual curvature picking up on the depth domain residual velocity spectrum in different resolutions.

10. The method for improving the imaging accuracy of deep reflection seismic data according to claim 6, characterized in that, The method further comprises: performing seed point picking up on the first pre-stack depth migration profile in different horizons.

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