CN114791626A - Method for improving imaging precision of deep reflection seismic data - Google Patents

Abstract

The application relates to a method for improving imaging precision of deep reflection seismic data. The method comprises the following steps: performing first prestack depth migration on the depth domain initial layer velocity model and the common midpoint gather to obtain a first prestack depth migration profile and a prestack depth migration gather; picking up seed points by using a first prestack depth migration profile, picking up residual curvature by using a prestack depth migration gather, and performing velocity inversion on the depth domain initial layer velocity model according to the seed points and the residual curvature to obtain an optimized rear layer velocity model; and performing second pre-stack depth migration on the optimized interval velocity model to obtain a second pre-stack depth migration profile, wherein the second pre-stack depth migration profile is deep reflection seismic data imaging if the interval velocity after migration is converged. The scheme that this application provided can improve the rock deep structure imaging's precision better, announces rock pillar deep tectonic characteristics.

Description

Method for improving imaging precision of deep reflection seismic data

Technical Field

The 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

The deep seismic reflection profile detection technology is one of the most effective technologies for detecting the fine structure of the rock ring recognized in international geology, and plays an important role in disclosing deformation and structural traces of crust rock, structural events in a region and even important relation of a mantle on the rock ring. For deep reflection seismic data, because the recording time is long, seismic signals are weakened along with the increase of the detection depth, and the energy of external background noise is irrelevant to the recorded depth, deep seismic reflection is weak reflection actually, and the signal-to-noise ratio of the data is very low. In order to better find out that a deep reflection data target area relates to imaging of ultra-buried deep strata such as a sedimentary basin base, a section, the interior of a crust and a muo surface, a reasonable speed model of the whole crust from a shallow part to a deep muo surface needs to be established, and a fine rock structure image of the whole crust to the muo surface is established on the basis of the reasonable layer speed model.

However, the current technical method for processing deep reflection seismic section data is based on pre-stack time migration, but the time migration ignores the lateral change of velocity, and causes a large error to the accuracy of rock structure imaging.

Therefore, the method for improving the imaging precision of the deep reflection seismic data is provided, the imaging precision of the rock deep structure can be improved better, and the rock crib deep structure characteristics are disclosed.

Disclosure of Invention

In order to overcome the problems in the related art, the method for improving the imaging precision of the deep reflection seismic data can better improve the imaging precision of the rock deep structure and reveal the deep structure characteristics of the rock ring.

The application provides a method for improving imaging precision of deep reflection seismic data, which comprises the following steps:

s1: acquiring two-dimensional gun line data and navigation data, and performing pre-processing of pre-stack time migration on the two-dimensional gun line data and the navigation data to obtain a time domain root mean square speed and a common-center gather;

s2: carrying out constrained velocity inversion on the time domain root mean square velocity to obtain a time domain layer velocity;

s3: iterating the time domain layer speed by using the pre-stack time migration to obtain a time domain layer speed model;

s4: performing time-depth conversion on the time domain layer speed model to obtain a depth domain layer speed model;

s5: converting the cross section of the pre-stack time migration into a depth domain, and picking up parallel horizons in the depth range of a mohowl surface of the depth cross section;

s6: setting a depth domain constant velocity model below the parallel horizon, and carrying out 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;

s7: performing first prestack depth migration on the depth domain initial layer velocity model and the common midpoint gather to obtain a first prestack depth migration profile and a prestack depth migration gather;

s8: picking up a seed point by using the first prestack depth migration profile, picking up residual curvature by using the prestack depth migration gather, and performing speed inversion on the depth domain initial layer speed model according to the seed point and the residual curvature to obtain an optimized rear layer speed model;

s9: and performing second prestack depth migration on the optimized interval velocity model to obtain a second prestack depth migration profile, wherein the second prestack depth migration profile is deep reflection seismic data imaging if the migrated interval velocity is converged.

In one embodiment, the picking parallel horizons within a mojode depth range of a depth profile comprises:

the first parallel horizons are picked up at 9 km of the depth profile and the second parallel horizons are picked up at 12 km of the depth profile.

In one embodiment, the 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 horizon velocity model to obtain a depth-domain initial horizon velocity model includes:

setting a depth domain constant speed model between the first parallel layer and the second parallel layer to obtain a first depth domain constant speed model;

setting a depth domain constant velocity model below the second parallel layer to obtain a second depth domain constant velocity model;

extracting a depth domain layer velocity model above the first parallel layer from the depth domain layer velocity model to obtain a first depth domain layer velocity model;

and carrying out speed fusion on the first depth domain layer speed model, the first depth domain constant speed model and the second depth domain constant speed model to obtain a depth domain initial layer speed model.

In one embodiment, the velocity fusing the first depth domain layer velocity model, the first depth domain constant velocity model, and the second depth domain constant velocity model to obtain a depth domain initial layer velocity model includes:

fusing the first depth domain layer velocity model and the first depth domain constant velocity model to obtain a first fused layer velocity model;

extracting a depth domain layer velocity model above the second parallel layer from the first fusion layer velocity model to obtain a second depth domain layer velocity model;

and fusing the second depth domain layer velocity model and the second depth domain constant velocity model to obtain a second fusion layer velocity model, wherein the second fusion layer velocity model is the depth domain initial layer velocity model.

In one embodiment, said picking residual curvature with said prestack depth offset gather comprises:

generating a depth domain residual velocity spectrum using the prestack depth migration gather;

residual curvature pickup is performed on the depth domain residual velocity spectrum.

In one embodiment, the performing velocity inversion on the depth domain initial layer velocity model according to the seed point and the residual curvature to obtain an optimized layer velocity model includes:

picking up seed points on the first prestack depth migration profile, and shooting ray paths from the seed points to the ground surface, wherein if the ray paths meet the conditions from a shot point to a reflection point to a wave detection point, the seed points are target seed points, and the ray paths are target ray paths;

and acquiring an inclination angle field, the residual curvature and the offset speed at the target seed point, and performing speed inversion on the depth domain initial layer speed model by using the inclination angle field, the residual curvature, the offset speed and the target ray path to generate an optimized rear layer speed model.

In one embodiment, after the performing the second pre-stack depth migration on the optimized post-layer velocity model, the method includes:

if the layer velocity after the deviation does not converge, the step S7 is returned to, and the layer velocity after the deviation is substituted into the depth domain initial layer velocity model until the layer velocity after the deviation converges.

In one embodiment, the pre-processing of the two-dimensional gun line data and the navigation data by pre-stack time migration includes:

defining an observation system according to the two-dimensional gun line data and the navigation data, and performing pre-stack denoising and multiple suppression on the two-dimensional gun line data and the navigation data.

In one embodiment, the residual curvature picking on the depth domain residual velocity spectrum comprises:

residual curvature pickup is performed at different resolutions on the depth domain residual velocity spectrum.

In one embodiment, the performing seed point picking on the first prestack depth migration profile comprises:

and picking up seed points of different layers on the first prestack depth migration profile.

The technical scheme provided by the application can comprise the following beneficial effects:

the method comprises the steps of obtaining a depth domain layer velocity model through prestack time migration and time-depth conversion, converting a profile of the prestack time migration to a depth domain, picking up a parallel layer in a depth range of a mohuo surface of the depth profile, setting a depth domain constant velocity model below the parallel layer, 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, performing first prestack depth migration on the depth domain initial layer velocity model and a common-center-point gather to obtain a first prestack depth migration profile and a prestack depth migration gather, picking up seed points by using the first prestack depth migration profile, picking up residual curvature by using the prestack depth migration gather, performing velocity inversion on the depth domain initial layer velocity model according to the seed points and the residual curvature to obtain an optimized rear layer velocity model, and performing second prestack depth migration on the optimized rear layer velocity model, and obtaining a second prestack depth migration section, wherein the second prestack depth migration section is a deep reflection seismic data image if the layer velocity after migration is converged. According to the method, the pre-stack time migration is performed twice and then the pre-stack depth migration is performed twice, so that the error of the pre-stack time migration in the transverse speed is greatly eliminated, the stratum speed model is more in line with the actual stratum condition, the imaging precision of the rock deep structure is better improved, and the deep structure characteristics of the rock ring are revealed.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.

Drawings

The foregoing and other objects, features and advantages of the application will be apparent from the following more particular descriptions of exemplary embodiments of the application as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts throughout the application.

FIG. 1 is a schematic flowchart illustrating a first embodiment of a method for improving imaging accuracy of deep reflection seismic data according to an embodiment of the present application;

FIG. 2 is a schematic diagram of a depth domain layer velocity model after time-depth conversion according to an embodiment of the present application;

FIG. 3 is a schematic diagram of an optimized layer velocity model according to an embodiment of the present application;

FIG. 4 is a schematic diagram illustrating deep reflection seismic data imaging according to an embodiment of the present application;

FIG. 5 is a schematic flow chart of a second embodiment of a method for improving imaging accuracy of deep reflection seismic data according to an embodiment of the present application;

FIG. 6 is a schematic diagram of a first depth domain layer velocity model shown in an embodiment of the present application;

FIG. 7 is a diagram illustrating a first depth-domain constant velocity model according to an embodiment of the present application;

FIG. 8 is a diagram illustrating a first fusion layer velocity model according to an embodiment of the present application;

FIG. 9 is a diagram illustrating a second depth domain layer velocity model according to an embodiment of the present application;

FIG. 10 is a diagram illustrating a second depth-domain constant velocity model according to an embodiment of the present application;

FIG. 11 is a diagram illustrating a depth-domain initial layer velocity model according to an embodiment of the present application;

FIG. 12 is a schematic flowchart of a third embodiment of a method for improving imaging accuracy of deep reflection seismic data according to an embodiment of the present application;

FIG. 13 is a schematic view of a residual curvature profile of different resolution as shown in an embodiment of the present application;

fig. 14 is a schematic diagram of seed point picking on a first prestack depth migration profile according to an embodiment of the present disclosure.

Detailed Description

Preferred embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While the preferred embodiments of the present application are shown in the drawings, it should be understood that the present application may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

It should be understood that although the terms "first," "second," "third," etc. may be used herein to describe various information, these information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present application. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present application, "a plurality" means two or more unless specifically limited otherwise.

At present, technical methods for processing deep reflection seismic section data are all based on pre-stack time migration, but the time migration ignores the transverse change of the velocity, and large errors can be caused to the accuracy of rock structure imaging.

In view of the above problems, the embodiments of the present application provide a method for improving imaging accuracy of deep reflection seismic data, which can better improve imaging accuracy of a rock deep structure and reveal deep structural features of a rock collar.

The technical solutions of the embodiments of the present application are described in detail below with reference to the accompanying drawings.

Example one

FIG. 1 is a schematic flow chart illustrating a first embodiment of a method for improving imaging accuracy of deep reflection seismic data according to an embodiment of the present disclosure;

FIG. 2 is a schematic diagram of a depth domain layer velocity model after time-depth conversion according to an embodiment of the present application;

FIG. 3 is a schematic diagram of an optimized layer velocity model according to an embodiment of the present application;

FIG. 4 is a schematic diagram of deep reflection seismic data imaging as shown in an embodiment of the application.

Referring to fig. 1-4, an embodiment of the method for improving the imaging accuracy of deep reflection seismic data in the embodiment of the present application includes:

101. acquiring two-dimensional gun line data and navigation data, and performing pre-stack time migration preprocessing on the two-dimensional gun line data and the navigation data to obtain a time domain root-mean-square velocity and a common-center gather;

the two-dimensional gun line data is two-dimensional seismic original data acquired in the field, and the navigation data is data containing longitude and latitude coordinates and time information of seismic data.

The pre-processing of the prestack time migration mainly comprises defining an observation system, prestack denoising and multiple suppression.

Because only the seismic channel head and the time information are recorded in the two-dimensional gun line data and the longitude and latitude coordinates are not included, an observation system is defined in the subsequent processing, the two-dimensional gun line number and the time information included in the navigation data are matched, and finally the longitude and latitude coordinate information in the navigation data is assigned to the channel head information of the two-dimensional gun line data.

The receiving record of a single geophone is called a seismic channel, a set of a plurality of seismic channels is called a channel set for short, and a common-center channel set refers to a set of seismic channels with the same central point of the geophone and an excitation point in an observation system.

102. Carrying out constrained velocity inversion on the time domain root-mean-square velocity to obtain a time domain layer velocity;

it should be noted that the time domain root mean square may be inverted by using a constrained velocity inversion method, or the time domain root mean square may also be inverted by using another inversion method, which is not limited here.

103. Iterating the time domain layer speed by using pre-stack time migration to obtain a time domain layer speed model;

an iteration is an activity of a repetitive feedback process, usually with the purpose of approximating a desired goal or result. Each iteration of the process is called an "iteration", and the result obtained from each iteration is used as the initial value of the next iteration, so that the time domain velocity model obtained after the iteration is performed on the time domain layer velocity by using the prestack time migration has convergence.

It should be noted that the prestack time migration may be a curved-ray prestack time migration, or may be other types of prestack time migration, and the prestack time migration used in the present embodiment is a curved-ray prestack time migration.

104. Performing time-depth conversion on the time domain layer velocity model to obtain a depth domain layer velocity model;

wherein the depth record 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.

105. Converting the cross section of the pre-stack time migration into a depth domain, and picking up parallel layers in the depth range of a mohowl surface of the depth cross section;

the mohuo surface is the interface dividing the crust and mantle, and its depth varies from region to region, in this example it occurs between 9 km to 12 km from the surface of the earth, thus picking up parallel horizons between 9 km to 12 km in the depth profile.

106. Setting a depth domain constant speed model below the parallel horizon, and carrying out speed fusion on the depth domain constant speed model and the depth domain horizon speed model to obtain a depth domain initial horizon speed model;

107. performing first prestack depth migration on the depth domain initial layer velocity model and the common midpoint gather to obtain a first prestack depth migration profile and a prestack depth migration gather;

108. picking up seed points by using a first pre-stack depth migration profile, picking up residual curvature by using a pre-stack depth migration gather, and performing velocity inversion on the initial layer velocity model of the depth domain according to the seed points and the residual curvature to obtain an optimized rear layer velocity model;

109. performing second prestack depth migration on the optimized layer velocity model to obtain a second prestack depth migration profile, and judging whether the layer velocity after migration is converged;

and judging whether the layer speed after the deviation is converged or not, namely judging whether the layer speed after the deviation approaches a preset value or not.

110. If the layer velocity after the migration converges, the second prestack depth migration profile is deep reflection seismic data imaging;

if the layer velocity after the shift is not converged, the procedure returns to step 107, and the layer velocity after the shift is substituted into the initial layer velocity model in the depth domain until the layer velocity after the shift is converged.

After the inversion in step 108, a residual velocity field is formed, and the range of the value range is approximately-150 m/s to 150 m/s, which reflects the increase and decrease of the layer velocity of each iteration relative to the layer velocity of the previous iteration.

The following advantageous effects can be obtained from the first embodiment:

in the embodiment, after a depth domain layer velocity model is obtained through prestack time migration and time-depth conversion, a profile of the prestack time migration is converted into a depth domain, a parallel layer is picked up in a depth range of a moho surface of the depth profile, a depth domain constant velocity model below the parallel layer is set, the depth domain constant velocity model and the depth domain layer velocity model are subjected to velocity fusion to obtain a depth domain initial layer velocity model, the depth domain initial layer velocity model and a common center point gather are subjected to first prestack depth migration to obtain a first prestack depth migration profile and a prestack depth migration gather, a seed point is picked up by the first prestack depth migration profile, residual curvature is picked up by the prestack depth migration gather, the depth domain initial layer velocity model is subjected to velocity inversion according to the seed point and the residual curvature to obtain an optimized layer velocity model, and a second prestack depth migration is performed on the optimized layer velocity model, and obtaining a second prestack depth migration section, wherein the second prestack depth migration section is a deep reflection seismic data image if the layer velocity after migration is converged. According to the method, the pre-stack time migration is carried out twice and then the pre-stack depth migration is carried out twice, so that the error of the pre-stack time migration in the transverse speed is greatly eliminated, the stratum speed model is more in line with the actual stratum condition, the imaging precision of the rock deep structure is better improved, and the deep structure characteristics of the rock ring are revealed.

Example two

In practical applications, on the basis of the first embodiment, the first embodiment describes how to fuse the depth-domain constant velocity model and the depth-domain layer velocity model to obtain the depth-domain initial layer velocity model.

FIG. 5 is a schematic flowchart illustrating a second embodiment of a method for improving imaging accuracy of deep reflection seismic data according to an embodiment of the present application;

FIG. 6 is a diagram illustrating a first depth domain layer velocity model according to an embodiment of the present application;

FIG. 7 is a diagram illustrating a first depth-domain constant velocity model according to an embodiment of the present application;

FIG. 8 is a schematic diagram of a first fusion layer velocity model shown in an embodiment of the present application;

FIG. 9 is a diagram illustrating a second depth-domain layer velocity model according to an embodiment of the present application;

FIG. 10 is a diagram illustrating a second depth-domain constant velocity model according to an embodiment of the present application;

fig. 11 is a schematic diagram of a depth-domain initial layer velocity model according to an embodiment of the present application.

Referring to fig. 5-11, an embodiment of the method for improving the imaging accuracy of deep reflection seismic data in the embodiment of the present application includes:

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;

since the morehole face appears between 9 and 12 kilometers from the earth's surface in this embodiment, parallel horizons are picked up at 9 and 12 kilometers, respectively, to cover the entire morehole face range.

202. Setting a depth domain constant speed model between a first parallel layer and a second parallel layer to obtain a first depth domain constant speed model;

the deposition structure of the deep reflection seismic data can be divided into five layers from shallow to deep: seawater, neonatal, mesogenic, mojoo-surface and mantle of rocky ring. In the deposition structure of the work area adjacent to the target area in this embodiment, the sea water velocity range is 1480 m/s to 1520 m/s, the new generation layer velocity range is 1600 m/s to 3200 m/s, the new generation bottom to 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 region constant velocity model between the first parallel layer and the second parallel layer is 6200m/s, and the layer velocity of the velocity model above the first parallel layer is set to be zero.

203. Setting a depth domain constant velocity model below a second parallel layer to obtain a second depth domain constant velocity model;

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

In practical applications, there is no strict timing relationship between step 202 and step 203, that is, either step can be executed simultaneously or any step can be executed first, which is not limited herein.

204. Extracting a depth domain layer velocity model above a first parallel layer from the depth domain layer velocity model to obtain a first depth domain layer velocity model;

the first depth domain layer velocity model has a velocity range of 0m/s to 5200 m/s and the velocity model is set to zero layer velocities below the first parallel layer.

205. Fusing the first depth domain layer velocity model and the first depth domain constant velocity model to obtain a first fused layer velocity model;

after the fusion, smoothing is performed, and the range of the first fusion layer velocity model is 1490 m/s to 6200 m/s.

206. Extracting a depth domain layer velocity model above a second parallel layer from the first fusion layer velocity model to obtain a second depth domain layer velocity model;

the second depth-domain interval velocity model has a velocity range of 0m/s to 6200m/s, and the interval velocity of the velocity model below the second parallel layer is set to be zero.

207. And fusing the second depth domain layer velocity model and the second depth domain constant velocity model to obtain a second fusion layer velocity model, wherein the second fusion layer velocity model is the depth domain initial layer velocity model.

And performing smoothing after the fusion, wherein the range of the second fusion layer velocity model, namely the range of the depth domain initial layer velocity model, is 1498 m/s to 7800 m/s.

The following advantageous effects can be obtained from the second embodiment:

in this embodiment, a first depth domain layer velocity model and a first depth domain constant velocity model are fused to obtain a first fusion layer velocity model, a depth domain layer velocity model above a second parallel horizon is extracted from the first fusion layer velocity model to obtain a second depth domain layer velocity model, and finally, the second depth domain layer velocity model and the second depth domain constant velocity model are fused to obtain a depth domain initial layer velocity model.

EXAMPLE III

In practical applications, on the basis of the above embodiments, this embodiment gives a detailed description of how to obtain an optimized back-layer velocity model.

FIG. 12 is a schematic flowchart of a third embodiment of a method for improving imaging accuracy of deep reflection seismic data according to an embodiment of the present application;

FIG. 13 is a schematic diagram illustrating a residual curvature profile at different resolutions according to an embodiment of the present application;

fig. 14 is a schematic diagram illustrating seed point picking on a first prestack depth migration profile according to an embodiment of the application.

Referring to fig. 3-4 and fig. 12-14 (in which the resolution and the residual curvature in fig. 12 are sequentially increased from top to bottom), the third embodiment of the method for improving the imaging accuracy of deep reflection seismic data in the embodiment of the present application includes:

301. generating a depth domain residual velocity spectrum by using the prestack depth migration gather;

302. performing residual curvature picking on the depth domain residual velocity spectrum;

the picked residual curvatures are residual curvatures with different resolutions and different values, and can be selected according to specific needs, and in this embodiment, the selected residual curvatures are 3, 9, and 16, respectively.

303. Picking up seed points on the first prestack depth migration profile, shooting a ray path from the seed points to the ground surface, and judging whether the ray path meets the requirements from a shot point to a reflection point to a demodulator probe;

the seed points are points of different horizons on the first prestack depth migration profile and are on a stratum with strong continuity.

304. If the ray path meets the condition from the shot point to the reflection point to the wave detection point, the seed point is a target seed point, and the ray path is a target ray path;

wherein, the shot point, the reflection point and the wave detection point are all set in advance in the target area.

305. If the ray path does not meet the conditions from the shot point to the reflection point to the wave detection point, the seed point is not a target seed point and is ignored;

306. and obtaining an inclination angle field, residual curvature and offset speed at the target seed point, and performing speed inversion on the initial layer speed model in the depth domain by using the inclination angle field, the residual curvature, the offset speed and a target ray path to generate an optimized rear layer speed model.

The inversion times are different according to actual conditions, in the embodiment, after three rounds of chromatographic velocity inversion iterations, the new-living and middle-living speeds are more stable in convergence, the Mohuo surface speed and the rock mantle speed tend to be stable in convergence, and the value range of the optimized back-layer velocity model is 1400 m/s to 7780 m/s.

The method has the advantages that residual curvature analysis is used as a basic principle, track gather leveling is used as a main principle in migration velocity analysis, imaging depth errors in a common imaging point track gather are used for providing information for velocity updating, seismic data information is fully utilized in an iteration process, residual curvature, an inclination angle field, migration velocity and a target ray path at a target seed point are used for carrying out velocity inversion, a round of iteration of a depth domain initial layer velocity model is completed, one-time iteration convergence degree is high, and efficiency is high.

In addition, in order to control the whole construction trend, a small residual curvature is selected for chromatography in the initial stage of iteration, a large residual curvature is selected for chromatography gradually in the middle and later stages of iteration so as to control details better, meanwhile, the low-resolution residual curvature is selected to control the rough trend of the construction firstly through the presented residual curvature section in the initial stage of iteration, and then the high-resolution residual curvature is selected to finely depict the details of the construction.

The following beneficial effects can be obtained from the third embodiment:

in the embodiment, the residual curvature is picked up through the prestack depth migration gather, the seed point is picked up on the first prestack depth migration profile, the target ray path is extracted through the target seed point, the velocity inversion is performed on the depth domain initial layer velocity model by using the dip angle field, the residual curvature, the migration velocity and the target ray path of the target seed point, the optimized layer velocity model is generated, and the convergence degree and the efficiency can be improved.

The aspects of the present application have been described in detail hereinabove with reference to the accompanying drawings. In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments. Those skilled in the art should also appreciate that acts and modules referred to in the specification are not necessarily required in the present application. In addition, it can be understood that the steps in the method of the embodiment of the present application may be sequentially adjusted, combined, and deleted according to actual needs, and the modules in the device of the embodiment of the present application may be combined, divided, and deleted according to actual needs.

The foregoing description of the embodiments of the present application has been presented for purposes of illustration and description and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims (10)

1. A method for improving imaging precision of deep reflection seismic data is characterized in that:

s1: acquiring two-dimensional gun line data and navigation data, and performing pre-processing of pre-stack time migration on the two-dimensional gun line data and the navigation data to obtain a time domain root mean square speed and a common-center gather;

s2: carrying out constrained velocity inversion on the time domain root mean square velocity to obtain a time domain layer velocity;

s3: iterating the time domain layer speed by using the pre-stack time migration to obtain a time domain layer speed model;

s4: performing time-depth conversion on the time domain layer speed model to obtain a depth domain layer speed model;

s5: converting the cross section of the pre-stack time migration into a depth domain, and picking up parallel horizons in the depth range of the Mohuo surface of the depth cross section;

s6: setting a depth domain constant velocity model below the parallel horizon, and carrying out 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;

s7: performing first prestack depth migration on the depth domain initial layer velocity model and the common midpoint gather to obtain a first prestack depth migration profile and a prestack depth migration gather;

s8: picking up a seed point by using the first pre-stack depth migration profile, picking up residual curvature by using the pre-stack depth migration gather, and performing speed inversion on the depth domain initial layer speed model according to the seed point and the residual curvature to obtain an optimized layer speed model;

s9: and performing second pre-stack depth migration on the optimized interval velocity model to obtain a second pre-stack depth migration profile, wherein the second pre-stack depth migration profile is deep reflection seismic data imaging if the interval velocity after migration is converged.

2. The method for improving imaging accuracy of deep reflection seismic data of claim 1, wherein picking parallel horizons within a mohojolt depth range of a depth profile comprises:

the first parallel horizons are picked up at 9 km of the depth profile and the second parallel horizons are picked up at 12 km of the depth profile.

3. The method for improving imaging accuracy of deep reflection seismic data according to claim 2, wherein the setting of the depth domain constant velocity model below the parallel horizon and the velocity fusion of the depth domain constant velocity model and the depth domain interval velocity model to obtain the depth domain initial interval velocity model comprises:

setting a depth domain constant velocity model between the first parallel layer and the second parallel layer to obtain a first depth domain constant velocity model;

setting a depth domain constant velocity model below the second parallel layer to obtain a second depth domain constant velocity model;

extracting a depth domain layer velocity model above the first parallel layer from the depth domain layer velocity model to obtain a first depth domain layer velocity model;

and carrying out speed fusion on the first depth domain layer speed model, the first depth domain constant speed model and the second depth domain constant speed model to obtain a depth domain initial layer speed model.

4. The method of claim 3, wherein 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 a depth domain initial interval velocity model comprises:

fusing the first depth domain layer velocity model and the first depth domain constant velocity model to obtain a first fused layer velocity model;

extracting a depth domain layer velocity model above the second parallel layer from the first fusion layer velocity model to obtain a second depth domain layer velocity model;

and fusing the second depth domain layer velocity model and the second depth domain constant velocity model to obtain a second fusion layer velocity model, wherein the second fusion layer velocity model is the depth domain initial layer velocity model.

5. The method of improving imaging accuracy of deep reflection seismic data of claim 1, wherein said picking residual curvatures using said prestack depth migration gathers comprises:

generating a depth domain residual velocity spectrum by using the pre-stack depth migration gather;

residual curvature pickup is performed on the depth domain residual velocity spectrum.

6. The method of claim 5, wherein the performing velocity inversion on the depth domain initial interval velocity model according to the seed points and the residual curvatures to obtain an optimized post-interval velocity model comprises:

picking up seed points on the first prestack depth migration profile, and shooting ray paths from the seed points to the ground surface, wherein if the ray paths meet the conditions from a shot point to a reflection point to a wave detection point, the seed points are target seed points, and the ray paths are target ray paths;

and acquiring an inclination angle field, the residual curvature and the offset speed at the target seed point, and performing speed inversion on the depth domain initial layer speed model by using the inclination angle field, the residual curvature, the offset speed and the target ray path to generate an optimized rear layer speed model.

7. The method for improving imaging accuracy of deep reflection seismic data of claim 1, wherein after performing the second prestack depth migration on the optimized back interval velocity model, the method comprises:

if the layer velocity after the shift does not converge, returning to step S7, and substituting the layer velocity after the shift into the depth domain initial layer velocity model until the layer velocity after the shift converges.

8. The method of improving imaging accuracy of deep reflection seismic data according to claim 1, wherein the pre-processing of the two-dimensional shot line data and the navigation data by pre-stack time migration comprises:

defining an observation system according to the two-dimensional gun line data and the navigation data, and performing pre-stack denoising and multiple suppression on the two-dimensional gun line data and the navigation data.

9. The method of improving imaging accuracy of deep reflection seismic data according to claim 5, wherein said performing residual curvature picking on said depth domain residual velocity spectrum comprises:

residual curvature pickup is performed at different resolutions on the depth domain residual velocity spectrum.

10. The method of improving imaging accuracy of deep reflection seismic data of claim 6, wherein said performing a seed point pick on said first prestack depth migration profile comprises:

and picking up seed points of different layers on the first prestack depth migration profile.

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