Pre-stack depth migration method and device based on multi-information coupling, equipment and medium

Abstract

The invention relates to the technical field of geophysical exploration seismic data processing, and discloses a pre-stack depth migration method based on multi-information coupling, and the method comprises the steps: generating a fusion range of a speed model according to a migration image corresponding to the pre-obtained speed model; generating a fusion weight of the speed model according to the fusion range, and calculating a fusion correction value of a high-frequency static correction value of the speed model according to the fusion weight; performing small smooth surface correction on the original seismic trace gather by using the fusion correction value to obtain a corrected seismic trace gather; and generating a fusion model of the velocity model, and performing depth migration processing on the corrected seismic gather by using the fusion model to obtain a pre-stack depth migration image of the corrected gather. The invention further provides a pre-stack depth migration device and equipment based on multi-information coupling and a medium. According to the invention, the migration imaging effect of pre-stack depth migration can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of processing geophysical exploration seismic data, and in particular to a pre-stack depth migration method, device, electronic equipment and medium based on multi-information coupling. Background Art

[0002] Currently in seismic data processing, simulated surface pre-stack depth migration processing based on small smooth surfaces is becoming the mainstream trend in depth domain processing. The advancement of this method is reflected in the use of accurate near-surface velocities to replace the medium and low-frequency tomographic static correction in conventional floating surface-based migration. When performing migration, this processing method takes into account the impact of velocity changes on the propagation angle on the near-surface ray path, which is more reasonable than the method of only performing vertical static correction based on floating surfaces.

[0003] However, the acquisition process often faces various limitations, resulting in incomplete first arrival information. For example, if there is a protected area in the work area, blasting cannot be performed, resulting in a lack of single shot data in the work area, which leads to incomplete near-bias data in the work area. In the location where the acquisition information is missing or the first arrival information is inaccurate, the near-surface velocity model inversion is inaccurate, and the effect of prestack depth migration is poor. Summary of the invention

[0004] In view of the above problems, embodiments of the present invention provide a pre-stack depth migration method, device, electronic device and medium based on multi-information coupling.

[0005] In a first aspect, an embodiment of the present invention provides a prestack depth migration method based on multi-information coupling, comprising:

[0006] Generating a fusion range of the velocity model according to the migration imaging corresponding to the pre-acquired velocity model;

[0007] Generate a fusion weight of the velocity model according to the fusion range, and calculate a fusion correction amount of a high-frequency static correction amount of the velocity model according to the fusion weight;

[0008] Performing small smoothing surface correction on the original seismic gathers by using the fusion correction amount to obtain a corrected seismic gathers;

[0009] A fusion model of the velocity model is generated, and depth migration processing is performed on the corrected seismic gathers using the fusion model to obtain a pre-stack depth migration image of the corrected gathers.

[0010] According to an embodiment of the present invention, generating a fusion range of the velocity model according to the migration imaging corresponding to the pre-acquired velocity model includes:

[0011] Acquire a velocity model of the original seismic gather, wherein the velocity model is a near-surface velocity model and a velocity model excluding the near-surface velocity model;

[0012] generating a near-surface migration image of the near-surface velocity model;

[0013] Generating the non-near-surface migration image without the near-surface velocity model;

[0014] The fusion range of the velocity model is generated by using the near surface migration imaging and the imaging without near surface migration.

[0015] According to an embodiment of the present invention, generating a fusion weight of the velocity model according to the fusion range includes:

[0016] Generate the regional range of the fusion range;

[0017] The area range is weighted in sections to obtain the weight of the area range excluding the near-surface velocity;

[0018] Generating the near-surface velocity weight of the regional range according to the near-surface velocity weight not including the near-surface velocity weight;

[0019] The weight excluding the near-surface velocity weight and the near-surface velocity weight are collected as a fusion weight of the velocity model.

[0020] According to an embodiment of the present invention, the step of calculating the fusion correction amount of the high-frequency static correction amount of the velocity model according to the fusion weight includes:

[0021] Calculating the high-frequency static correction amount corresponding to the velocity model, wherein the high-frequency static correction amount does not include the near-surface high-frequency static correction amount and the near-surface high-frequency static correction amount;

[0022] Generating a corresponding weight of the high-frequency static correction amount according to the fusion weight;

[0023] The high-frequency static correction amount is weightedly fused using the corresponding weights to obtain a fused correction amount of the high-frequency static correction amount.

[0024] According to an embodiment of the present invention, the method of performing small smoothing surface correction on the original seismic trace gathers by using the fusion correction amount to obtain the corrected seismic trace gathers includes:

[0025] The original sampling data on the original seismic trace gather are corrected one by one according to the fusion correction amount to obtain a corrected seismic trace gather.

[0026] According to an embodiment of the present invention, generating the fusion model of the velocity model includes:

[0027] Performing weight association on the fusion weights according to the model type of the velocity model to obtain an association weight of the velocity model;

[0028] The velocity models are fused using the association weights to obtain a fusion model of the velocity models.

[0029] According to an embodiment of the present invention, generating the fusion model of the velocity model includes:

[0030] The method of performing depth migration processing on the corrected seismic gathers by using the fusion model to obtain a prestack depth migration image of the corrected gathers includes:

[0031] generating an initial prestack migration gather according to the corrected seismic gather;

[0032] Performing prestack parameter configuration on the initial prestack migration gather to obtain a configured initial prestack migration gather;

[0033] interpolating the configured initial prestack migration gathers according to the fusion model to obtain target prestack migration gathers;

[0034] The target prestack migration gather is subjected to imaging processing to obtain a target imaging of the target prestack migration gather, and the target imaging is determined to be a prestack depth migration image of the correction gather.

[0035] In a second aspect, an embodiment of the present invention provides a prestack depth migration device based on multi-information coupling, characterized in that it includes:

[0036] A fusion range generating module, used for generating a fusion range of the velocity model according to the migration imaging corresponding to the pre-acquired velocity model;

[0037] A high-frequency static correction fusion module is used to generate a fusion weight of the velocity model according to the fusion range, and calculate a fusion correction amount of the high-frequency static correction amount of the velocity model according to the fusion weight;

[0038] A seismic gather correction module, used for performing small smoothing surface correction on the original seismic gather using the fused correction amount to obtain a corrected seismic gather;

[0039] The prestack depth migration module is used to generate a fusion model of the velocity model, and use the fusion model to perform depth migration processing on the corrected seismic gather to obtain a prestack depth migration image of the corrected gather.

[0040] In a third aspect, an embodiment of the present invention provides an electronic device, comprising:

[0041] processor;

[0042] a memory for storing instructions executable by the processor;

[0043] The processor is configured to execute the instructions to implement a pre-stack depth migration method based on multi-information coupling as described in the first aspect.

[0044] In a fourth aspect, an embodiment of the present invention provides a medium having a computer program stored thereon, which, when executed by a processor, implements a prestack depth migration method based on multi-information coupling as described in the first aspect.

[0045] Compared with the prior art, the above technical solution of the present invention has the following beneficial effects:

[0046] The embodiment of the present invention can obtain the contribution range of different velocity models at different depths by performing migration imaging in the velocity model, determine the boundary of the fusion range, fuse the velocity model using appropriate weights according to the contribution range of the velocity model at different depths, integrate the information of different velocity models, calculate the fusion correction amount of high-frequency static correction amounts of different velocity models through the fusion weights, so that the differences between different velocity models can be taken into account and fused into a more accurate high-frequency static correction amount, and the influence of the underground velocity model can be eliminated by applying the fusion correction amount to perform small smoothing surface correction on the original seismic trace gather, thereby improving the quality and accuracy of the seismic trace gather, and a more accurate velocity model can be obtained by fusing multiple velocity models, which integrates the advantages of different velocity models and ignores the near-surface velocity model that is inaccurately inverted, and at the same time couples the high-frequency static correction with the velocity model to realize the coupling of multiple information before migration, and can better describe the underground structure, and use the fusion model to perform depth migration processing, so that the seismic trace gather can be converted into a pre-stack depth migration image, and the quality and resolution of imaging can be further improved. Therefore, the pre-stack depth migration method, device, equipment and medium based on multi-information coupling proposed in the present invention can improve the migration imaging effect of pre-stack depth migration. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1 The flowchart of the prestack depth migration method based on multi-information coupling according to the first embodiment of the present invention is shown;

[0049] Figure 2aA velocity model diagram including near-surface velocity before fusion according to the first embodiment of the present invention is shown;

[0050] Figure 2b The fused velocity model diagram including the near-surface velocity of the first embodiment of the present invention is shown;

[0051] Figure 3a The figure shows the pre-stack depth migration profile before fusion of the first embodiment of the present invention;

[0052] Figure 3b The fused pre-stack depth migration profile of the first embodiment of the present invention is shown;

[0053] Figure 4 A functional module diagram of a prestack depth migration device based on multi-information coupling according to a third embodiment of the present invention is shown;

[0054] Figure 5 A schematic diagram of the composition structure of an electronic device for implementing the prestack depth migration method based on multi-information coupling according to the fourth embodiment of the present invention is shown. DETAILED DESCRIPTION

[0055] The present disclosure is further described below in conjunction with the embodiments shown in the accompanying drawings.

[0056] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0057] The present invention proposes a prestack depth migration method based on multi-information coupling. Based on the small smooth surface theory and combined with the velocity model fusion method, a multi-information coupling migration processing flow is constructed to achieve more accurate imaging of seismic data in the depth domain. Compared with traditional methods, the imaging effect of prestack depth migration based on multi-information coupling is better and higher, and the resolution of underground structures is enhanced. It has great potential and application prospects in oil and gas exploration and development applications.

[0058] Embodiment 1

[0059] like Figure 1 As shown, the present invention proposes a prestack depth migration method based on multi-information coupling, comprising the following steps:

[0060] S1. Generate a fusion range of the velocity model according to the migration imaging corresponding to the pre-acquired velocity model.

[0061] In the embodiment of the present invention, the pre-acquired velocity model refers to two sets of velocity models, one set is a near-surface velocity model, and the other set is a model excluding the near-surface velocity model; the fusion range refers to a range that needs to be determined when the near-surface velocity model and the model excluding the near-surface velocity model are fused; the offset imaging refers to two sets of offset imaging, one set is the near-surface offset imaging corresponding to the near-surface velocity model, and the other set is the offset imaging excluding the near-surface velocity model corresponding to the model excluding the near-surface velocity model.

[0062] In the embodiment of the present invention, generating the fusion range of the velocity model according to the migration imaging corresponding to the pre-acquired velocity model includes:

[0063] Acquire a velocity model of the original seismic gather, wherein the velocity model is a near-surface velocity model and a velocity model excluding the near-surface velocity model;

[0064] generating a near-surface migration image of the near-surface velocity model;

[0065] Generating the non-near-surface migration image without the near-surface velocity model;

[0066] The fusion range of the velocity model is generated by using the near surface migration imaging and the imaging without near surface migration.

[0067] In detail, the original seismic track set refers to a set of unprocessed seismic data collected; the velocity model is a model used in geophysical exploration to describe the velocity distribution of underground media, and is used to explain and predict the velocity changes of seismic waves propagating underground.

[0068] In detail, the near-surface offset imaging for generating the near-surface velocity model is performed by utilizing the constructed near-surface velocity model to perform wavefield extrapolation on the seismic data, and performing offset processing on the seismic data subjected to wavefield extrapolation to obtain the near-surface offset imaging, and the near-surface offset imaging excluding the near-surface velocity model is generated by utilizing the constructed near-surface velocity model to perform wavefield extrapolation on the seismic data, and performing offset processing on the seismic data subjected to wavefield extrapolation to obtain the near-surface offset imaging excluding the near-surface velocity model.

[0069] In detail, generating the fusion range of the velocity model according to the migration imaging corresponding to the pre-acquired velocity model refers to comparing the imaging effect of the migration imaging corresponding to the pre-acquired velocity model, selecting a range A with a good migration effect that does not include the near-surface velocity, and expanding the range A outward by 1000 meters to obtain a range B, wherein the range A and the range B constitute a fusion range, and selecting the range with a better migration effect can be based on a variety of image processing and analysis methods, such as: structural similarity (SSIM) index, mean square error (MSE) index, etc. In addition, when selecting the range, it can be considered to add a certain threshold or rule to ensure the rationality and stability of the selected range.

[0070] Furthermore, in seismic data processing, two sets of velocity models are usually used for migration imaging, one set of velocity models includes near-surface velocity information, and the other set of velocity models does not include near-surface velocity information. This is because the acquisition of near-surface velocity is relatively complex, and the near-surface velocity changes rapidly, which has a greater impact on the imaging results. By performing corresponding processing and migration imaging on the two sets of velocity models, two sets of corresponding migration imaging effects can be obtained, and then the two sets of migration imaging effects are compared to observe which migration effect is better, that is, the image quality is higher and the imaging result is clearer. According to this comparison result, the range with better migration effect, that is, the range with better migration effect without near-surface velocity is selected as range A. Through such selection, it can be ensured that in the subsequent processing process, the migration effect of range A is used as a reference, and it is coupled with the velocity model including the near-surface velocity to improve the quality and accuracy of the migration imaging.

[0071] In detail, it is assumed that there are two velocity models: velocity model A and velocity model B, such as Figure 2a As shown in the figure, the velocity model A includes near-surface velocity information, while the velocity model B does not include near-surface velocity information. Now, an image of the underground structure is to be generated through migration imaging. First, migration imaging is performed using the velocity model A and the velocity model B, respectively, to obtain corresponding imaging effects A and B. Next, imaging effect A and imaging effect B are compared to observe which effect is better. Assume that after comparison, it is found that imaging effect B is not disturbed by the near-surface velocity, and the image is clearer and more accurate, while imaging effect A is affected by the near-surface velocity, and the image quality is poor. According to this comparison result, the range corresponding to imaging effect B is selected as range A. That is to say, in range A, the result of migration imaging using velocity model B is the best, which can more accurately reflect the underground structure, so range A is selected as the main reference range.

[0072] In other words, range A represents the area obtained by using a method without near-surface velocity information (velocity model B) for migration imaging, and the role of velocity model A is to provide a method containing near-surface velocity information for migration imaging. Although it may not be as effective as velocity model B in comparison, velocity model A is still important because in some cases, near-surface velocity information is necessary to correctly reflect the underground structure. It can help us deal with situations where the near-surface velocity changes quickly and is complex, but it may cause the imaging results to be interfered with or blurred by the near-surface velocity. Therefore, in practical applications, we may use velocity model A and velocity model B in combination: use the migration imaging results of velocity model B within range A to obtain more accurate underground structure information; use velocity model A in other areas to deal with situations where the near-surface velocity changes quickly.

[0073] S2. Generate a fusion weight of the velocity model according to the fusion range, and calculate a fusion correction amount of a high-frequency static correction amount of the velocity model according to the fusion weight.

[0074] In the embodiment of the present invention, the fusion weight is used to characterize the importance of the velocity model, and the fusion correction amount refers to a weighted value obtained by adding a weighted high-frequency correction amount corresponding to the near-surface velocity to a weighted high-frequency correction amount excluding the near-surface velocity.

[0075] In the embodiment of the present invention, generating the fusion weight of the velocity model according to the fusion range includes:

[0076] Generate the regional range of the fusion range;

[0077] The area range is weighted in sections to obtain the weight of the area range excluding the near-surface velocity;

[0078] Generating the near-surface velocity weight of the regional range according to the near-surface velocity weight not including the near-surface velocity weight;

[0079] The weight excluding the near-surface velocity weight and the near-surface velocity weight are collected as a fusion weight of the velocity model.

[0080] In detail, generating the regional range of the fusion range refers to dividing the fusion range into regions to obtain the regional range of the fusion range.

[0081] In detail, the segmented weighting of the area range refers to assigning different weights to the area range according to the area position of the area range.

[0082] Further, range A and range B are determined, wherein range A is a range for migration imaging using velocity model B, within range A, the weight is set to 1, indicating that velocity model B is fully adopted; in areas outside range B, the weight is set to 0, indicating that velocity model B is not adopted at all; in a 1000-meter transition area from range A to range B, the weight can be calculated in a linear decreasing manner, for example, in the transition area from range A to range B, the weight is decreased from 1 to 0 at equal intervals, and through the above steps, a near-surface velocity weight C excluding the near-surface velocity can be obtained, and the near-surface velocity weight C excluding the near-surface velocity indicates the degree of adoption of velocity model B in range A; finally, by subtracting the near-surface velocity weight C excluding the near-surface velocity from 1, a near-surface velocity weight D including the near-surface velocity model can be obtained, and the near-surface velocity weight D indicates the degree of adoption of velocity model A in range A, so that the near-surface velocity weight C excluding the near-surface velocity and the near-surface velocity weight D can be used to select appropriate velocity models according to different areas, and migration imaging can be performed, thereby obtaining a more accurate underground structure image.

[0083] In the embodiment of the present invention, the step of calculating the fusion correction amount of the high-frequency static correction amount of the velocity model according to the fusion weight includes:

[0084] Calculating the high-frequency static correction amount corresponding to the velocity model, wherein the high-frequency static correction amount does not include the near-surface high-frequency static correction amount and the near-surface high-frequency static correction amount;

[0085] Generating a corresponding weight of the high-frequency static correction amount according to the fusion weight;

[0086] The high-frequency static correction amount is weightedly fused using the corresponding weights to obtain a fused correction amount of the high-frequency static correction amount.

[0087] In detail, calculating the high-frequency static correction amount corresponding to the velocity model refers to calculating the high-frequency static correction amount excluding the near-surface velocity model in the velocity model, and calculating the near-surface high-frequency static correction amount corresponding to the near-surface velocity model in the velocity model.

[0088] In detail, the high-frequency static correction refers to the time offset of each sampling point in the seismic record. The time offset of each sampling point is determined by analyzing and comparing the seismic record, and the seismic record is corrected accordingly. The high-frequency static correction is usually expressed in the form of time shift, with the unit being milliseconds (ms). According to the specific situation of the seismic profile and the characteristics of the data, manual picking, automatic picking or other methods can be used to determine the high-frequency static correction.

[0089] In detail, the corresponding weight of the high-frequency static correction amount is generated according to the fusion weight because the corresponding weight of the high-frequency static correction amount varies with the model category of the velocity model. The corresponding weight refers to the near-surface high-frequency static correction amount corresponding to the near-surface velocity weight C, and the near-surface high-frequency static correction amount corresponding to the near-surface velocity weight D.

[0090] In detail, the weighted fusion of the high-frequency static correction amount using the corresponding weights refers to multiplying the near-surface high-frequency static correction amount corresponding to the near-surface velocity by the near-surface velocity weight D and multiplying the near-surface non-contained high-frequency static correction amount corresponding to the near-surface velocity by the near-surface velocity weight D, thereby obtaining a fused correction amount of the high-frequency static correction amount of the velocity model. This fused correction amount reflects the result of merging the near-surface velocity and the high-frequency static correction amount corresponding to the near-surface velocity and the high-frequency static correction amount excluding the near-surface velocity according to the fusion weight.

[0091] S3. Performing small smoothing surface correction on the original seismic gathers using the fusion correction amount to obtain corrected seismic gathers.

[0092] In the embodiment of the present invention, the method of performing small smoothing surface correction on the original seismic gathers by using the fusion correction amount to obtain the corrected seismic gathers includes:

[0093] The original sampling data on the original seismic trace gather are corrected one by one according to the fusion correction amount to obtain a corrected seismic trace gather.

[0094] In detail, the data correction of the original sampling data on the original seismic track gather one by one according to the fusion correction amount refers to adjusting the data on each seismic track according to the corresponding fusion correction amount, and for each seismic track, the value of each sampling point thereon is readjusted according to the corresponding fusion correction amount, which can be calculated using the following formula: Corrected value = original value + fusion correction amount. The corrected seismic track gather data can be mapped onto a small smooth surface, realizing the coupling of multiple information before migration. The corrected seismic track gather can more accurately reflect the underground structure, thereby improving the quality of seismic data processing.

[0095] S4, generating a fusion model of the velocity model, and performing depth migration processing on the corrected seismic gathers using the fusion model to obtain a pre-stack depth migration image of the corrected gathers.

[0096] In an embodiment of the present invention, generating the fusion model of the velocity model includes:

[0097] Performing weight association on the fusion weights according to the model type of the velocity model to obtain an association weight of the velocity model;

[0098] The velocity models are fused using the association weights to obtain a fusion model of the velocity models.

[0099] In detail, associating the fusion weights according to the model type of the velocity model refers to determining that the model excluding near-surface velocity is associated with the weight C excluding near-surface velocity, and the near-surface velocity model is associated with the near-surface velocity weight D.

[0100] In detail, the use of the associated weights to perform model fusion on the velocity model refers to summing the model excluding the near-surface velocity by the weight C excluding the near-surface velocity and the near-surface velocity model by the near-surface velocity weight D; Figure 2b As shown, the fusion model comprehensively considers the information including near-surface velocity and excluding near-surface velocity, so as to more accurately describe the underground structure. The fused velocity model including near-surface velocity ignores the near-surface velocity model with inaccurate inversion. This fusion model can be applied to seismic data processing, such as migration and imaging, to improve the accuracy and reliability of data processing.

[0101] In an embodiment of the present invention, the step of performing depth migration processing on the corrected seismic gathers by using the fusion model to obtain a prestack depth migration image of the corrected gathers includes:

[0102] generating an initial prestack migration gather according to the corrected seismic gather;

[0103] Performing prestack parameter configuration on the initial prestack migration gather to obtain a configured initial prestack migration gather;

[0104] interpolating the configured initial prestack migration gathers according to the fusion model to obtain target prestack migration gathers;

[0105] The target prestack migration gather is subjected to imaging processing to obtain a target imaging of the target prestack migration gather, and the target imaging is determined to be a prestack depth migration image of the correction gather.

[0106] In detail, generating the initial prestack migration gather according to the corrected seismic gather means that when performing prestack depth migration processing, it is necessary to create a two-dimensional array of the same size as the corrected seismic gather as the initial prestack migration gather. This two-dimensional array can usually be regarded as an image, and each element represents a point on a seismic record; initializing the prestack migration gather means setting all elements of the prestack migration gather to an initial value of 0 before starting the prestack depth migration. This is because depth migration is a stacking operation, and the reflected waves corresponding to each shot point position need to be superimposed on the correct position. By initializing the prestack migration gather to 0, a blank starting state is provided for the stacking operation.

[0107] In the embodiment of the present invention, the depth migration processing of the corrected seismic gathers by using the fusion model is suitable for velocity model fusion under complex geological conditions, and has a fast calculation speed but a strong assumption on wave propagation.

[0108] Furthermore, when performing depth migration processing, at each shot point position and time point, the energy in the correction gather is superimposed on the corresponding position of the prestack migration gather according to a certain coefficient. In this way, after the superposition operation of multiple shot points and time points, the final prestack migration gather reflects the underground reflector information.

[0109] In detail, when the prestack parameter configuration of the initial prestack migration gather is performed, the parameters that need to be set include but are not limited to: the prestack time window range, the prestack time sampling interval, the reflection coefficient calculation step size of each shot point, the offset distance calculation step size and the prestack migration gather output image size, etc., wherein the prestack time window range is used to define the size and range of the time window for prestack migration calculation at each time point; the prestack time sampling interval represents the time interval of sampling on the time axis, which determines the time resolution of the prestack migration gather; the reflection coefficient calculation step size of each shot point is used to calculate the reflection coefficient at each shot point position, and the step size determines the spatial resolution of the reflection coefficient; the offset distance calculation step size is used to calculate the offset distance at each shot point position, and the step size determines the spatial resolution of the offset distance; the prestack migration gather output image size defines the size and resolution of the output image of the prestack migration gather.

[0110] In detail, the interpolation processing of the configured initial pre-stack migration gather according to the fusion model to obtain the target pre-stack migration gather refers to looping through each shot point, calculating the offset distance of each shot point, looping through each time point in the pre-stack time window, calculating the pre-stack migration coefficient at each time point, performing pre-stack migration at the time point, and for each time point and offset distance, performing interpolation calculation according to the pre-stack migration coefficient, velocity model and data, and adding the interpolation result of the position to the corresponding position of the initial pre-stack migration gather.

[0111] Further, Figure 3a As shown, Figure 3a The prestack depth migration profile before fusion is Figure 2a The prestack depth migration result corresponding to the velocity model containing the near-surface velocity before fusion; Figure 3b As shown, Figure 3b The fused prestack depth migration profile is Figure 2b The prestack depth migration result corresponding to the fused velocity model map containing near-surface velocity, where Figure 3a compared to, Figure 3b The profile event axis is more continuous, false breaks are eliminated, the imaging is focused, the signal-to-noise ratio is higher, and the imaging is more reasonable.

[0112] Furthermore, the pre-stack migration gathers can be imaged using a stacking operation or a multiplication operation, wherein the stacking operation is a commonly used pre-stack depth migration imaging method, which obtains the value of the pre-stack depth migration image at the same position by superimposing the values ​​of multiple pre-stack migration gathers at the same position, thereby enhancing the reflection signal and suppressing noise. The stacking operation usually sums or averages the gathers at different shot points and receiving points at the same position; the multiplication operation is another commonly used pre-stack depth migration imaging method, which obtains the pre-stack depth migration image by analytically processing the pre-stack migration gathers and the wave equation. The multiplication operation takes into account the velocity model of the underground medium and simulates the propagation path of the seismic wave in the underground, thereby more accurately returning the reflected energy to the correct depth position.

[0113] Embodiment 2

[0114] In order to understand the present invention more clearly, a second embodiment is used below to further explain the situation in which the present invention uses the fusion model to perform depth migration processing on the corrected seismic gather to obtain a pre-stack depth migration image of the corrected gather.

[0115] In the embodiment of the present invention, the depth migration processing is performed on the corrected seismic gathers by using the fusion model to obtain the prestack depth migration image of the corrected gathers, including:

[0116] constructing an acoustic wave equation of the fusion model and generating a simulated seismic record of the acoustic wave equation;

[0117] Using the corrected seismic gathers to perform correction processing on the simulated seismic record to obtain a corrected simulated seismic record;

[0118] A wavefield continuation calculation is performed on the corrected simulated seismic record, and a prestack depth migration image of the corrected gather is generated according to the calculation result of the wavefield continuation calculation.

[0119] In detail, the acoustic wave equation for constructing the fusion model refers to constructing the acoustic wave equation according to the velocity model and density model of the underground medium, and the acoustic wave equation describes the propagation process of seismic waves underground; the simulated seismic record for generating the acoustic wave equation refers to discretizing and solving the acoustic wave equation using a finite difference method, a finite element method or other numerical methods to obtain a simulated seismic record, and the simulated seismic record can be used to represent the reflection and refraction conditions underground; the correction processing of the simulated seismic record using the corrected seismic trace set refers to applying the amplitude and arrival time information of the corrected seismic trace set as initial conditions to the simulated seismic record, so that the simulated record includes the correction effect; the wave field continuation calculation of the corrected simulated seismic record refers to performing a wave field continuation calculation based on the simulated seismic record, that is, gradually updating the state of the wave field through time stepping, and calculating a new wave field state in each time step according to the previous wave field state and the acoustic wave equation, and according to the result of the wave field continuation calculation, a pre-stack depth migration image can be obtained, which reflects the reflection and refraction conditions underground and provides more accurate underground structure information.

[0120] In the embodiment of the present invention, using the fusion model to perform depth migration processing on the corrected seismic gathers can provide more accurate results under more sophisticated geological conditions, but the calculation complexity is higher.

[0121] Embodiment 3

[0122] like Figure 4 As shown, this embodiment also provides a functional module diagram of a pre-stack depth migration device based on multi-information coupling.

[0123] The prestack depth migration device 100 based on multi-information coupling described in this embodiment can be installed in an electronic device. According to the functions implemented, the prestack depth migration device 100 based on multi-information coupling can include a fusion range generation module 101, a high-frequency static correction amount fusion module 102, a seismic trace correction module 103 and a prestack depth migration module 104. The module described in the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0124] In this embodiment, the functions of each module / unit are as follows:

[0125] The fusion range generating module 101 is used to generate the fusion range of the velocity model according to the migration imaging corresponding to the pre-acquired velocity model;

[0126] The high-frequency static correction fusion module 102 is used to generate a fusion weight of the velocity model according to the fusion range, and calculate a fusion correction amount of the high-frequency static correction of the velocity model according to the fusion weight;

[0127] The seismic gather correction module 103 is used to perform small smoothing surface correction on the original seismic gathers using the fused correction amount to obtain a corrected seismic gather;

[0128] The prestack depth migration module 104 is used to generate a fusion model of the velocity model, and perform depth migration processing on the corrected seismic gathers using the fusion model to obtain a prestack depth migration image of the corrected gathers.

[0129] In detail, each module described in the user portrait-based product recommendation device 100 described in the embodiment of the present invention adopts the same technical means as the user portrait-based product recommendation method described in Example 1 and Example 2 when used, and can produce the same technical effects, which will not be repeated here.

[0130] Embodiment 4

[0131] like Figure 5 As shown, this embodiment also provides a computer electronic device, which may include a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a product recommendation program based on user portraits.

[0132] In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 10 is the control core (ControlUnit) of the electronic device, and uses various interfaces and lines to connect the various components of the entire electronic device, and executes or executes programs or modules stored in the memory 11 (for example, executing a product recommendation program based on user portraits, etc.), and calls the data stored in the memory 11 to execute various functions of the electronic device and process data.

[0133] The memory 11 includes at least one type of medium, including flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of an electronic device, such as a mobile hard disk of the electronic device. In other embodiments, the memory 11 can also be an external storage device of an electronic device, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Further, the memory 11 can also include both an internal storage unit of the electronic device and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device, such as the code of a product recommendation program based on user portraits, but can also be used to temporarily store data that has been output or is to be output.

[0134] The communication bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0135] The communication interface 13 is used for communication between the above-mentioned electronic device and other electronic devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)), and optionally, the user interface may also be a standard wired interface, a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device and to display a visual user interface.

[0136] The figure only shows an electronic device with components. Those skilled in the art will understand that the structure shown in the figure does not constitute a limitation on the electronic device, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0137] For example, although not shown, the electronic device may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0138] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0139] The product recommendation program based on user portrait stored in the memory 11 of the electronic device is a combination of multiple instructions, and when running in the processor 10, it can achieve:

[0140] Generating a fusion range of the velocity model according to the migration imaging corresponding to the pre-acquired velocity model;

[0141] Generate a fusion weight of the velocity model according to the fusion range, and calculate a fusion correction amount of a high-frequency static correction amount of the velocity model according to the fusion weight;

[0142] Performing small smoothing surface correction on the original seismic gathers by using the fusion correction amount to obtain a corrected seismic gathers;

[0143] A fusion model of the velocity model is generated, and depth migration processing is performed on the corrected seismic gathers using the fusion model to obtain a pre-stack depth migration image of the corrected gathers.

[0144] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, which will not be repeated here.

[0145] Furthermore, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a medium. The medium can be volatile or non-volatile. For example, the medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0146] Embodiment 5

[0147] This embodiment provides a medium storing a computer program. When the computer program is executed by a processor, the steps of the prestack depth migration method based on multi-information coupling as described above are implemented.

[0148] These program codes can also be loaded onto a computer or other programmable data processing device so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions executed on the computer or other programmable device for implementing the process. Figure 1 The steps of a specified function in a process or multiple processes.

[0149] The medium includes permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, modules of programs or other data. Examples of media can include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0150] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. When the terms "include" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or their combinations.

[0151] It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of operation in sequences other than those illustrated or described herein.

[0152] In the several embodiments provided by the present invention, it should be understood that the disclosed electronic device, apparatus and method can be implemented in other ways. For example, the apparatus embodiment described above is only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0153] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0154] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0155] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0156] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0157] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0158] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the system claim can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

[0159] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A prestack depth migration method based on multi-information coupling, characterized in that: The method comprises: Generating a fusion range of the velocity model according to the migration imaging corresponding to the pre-acquired velocity model; Generate a fusion weight of the velocity model according to the fusion range, and calculate a fusion correction amount of a high-frequency static correction amount of the velocity model according to the fusion weight; Performing small smoothing surface correction on the original seismic gathers by using the fusion correction amount to obtain a corrected seismic gathers; A fusion model of the velocity model is generated, and depth migration processing is performed on the corrected seismic gathers using the fusion model to obtain a pre-stack depth migration image of the corrected gathers.

2. The prestack depth migration method based on multi-information coupling according to claim 1, characterized in that: The generating the fusion range of the velocity model according to the migration imaging corresponding to the pre-acquired velocity model includes: Acquire a velocity model of the original seismic gather, wherein the velocity model is a near-surface velocity model and a velocity model excluding the near-surface velocity model; generating a near-surface migration image of the near-surface velocity model; Generating the non-near-surface migration image without the near-surface velocity model; The fusion range of the velocity model is generated by using the near surface migration imaging and the imaging without near surface migration.

3. The prestack depth migration method based on multi-information coupling according to claim 1, characterized in that: Generating the fusion weight of the velocity model according to the fusion range includes: Generate the regional range of the fusion range; The area range is weighted in sections to obtain the weight of the area range excluding the near-surface velocity; Generating the near-surface velocity weight of the regional range according to the near-surface velocity weight not including the near-surface velocity weight; The weight excluding the near-surface velocity weight and the near-surface velocity weight are collected as a fusion weight of the velocity model.

4. The prestack depth migration method based on multi-information coupling according to claim 1, characterized in that: The step of calculating the fusion correction amount of the high-frequency static correction amount of the velocity model according to the fusion weight includes: Calculating the high-frequency static correction amount corresponding to the velocity model, wherein the high-frequency static correction amount does not include the near-surface high-frequency static correction amount and the near-surface high-frequency static correction amount; Generating a corresponding weight of the high-frequency static correction amount according to the fusion weight; The high-frequency static correction amount is weightedly fused using the corresponding weights to obtain a fused correction amount of the high-frequency static correction amount.

5. The prestack depth migration method based on multi-information coupling according to claim 1, characterized in that: The method of using the fusion correction amount to perform small smoothing surface correction on the original seismic gathers to obtain corrected seismic gathers includes: The original sampling data on the original seismic trace gather are corrected one by one according to the fusion correction amount to obtain a corrected seismic trace gather.

6. The prestack depth migration method based on multi-information coupling according to claim 1, characterized in that: The generating of the fusion model of the velocity model comprises: Performing weight association on the fusion weights according to the model type of the velocity model to obtain an association weight of the velocity model; The velocity models are fused using the association weights to obtain a fusion model of the velocity models.

7. The prestack depth migration method based on multi-information coupling according to any one of claims 1 to 6, characterized in that: The method of performing depth migration processing on the corrected seismic gathers by using the fusion model to obtain a prestack depth migration image of the corrected gathers includes: generating an initial prestack migration gather according to the corrected seismic gather; Performing prestack parameter configuration on the initial prestack migration gather to obtain a configured initial prestack migration gather; interpolating the configured initial prestack migration gathers according to the fusion model to obtain target prestack migration gathers; The target prestack migration gather is subjected to imaging processing to obtain a target imaging of the target prestack migration gather, and the target imaging is determined to be a prestack depth migration image of the correction gather.

8. A prestack depth migration device based on multi-information coupling, characterized in that: The device comprises: A fusion range generating module, used for generating a fusion range of the velocity model according to the migration imaging corresponding to the pre-acquired velocity model; A high-frequency static correction fusion module is used to generate a fusion weight of the velocity model according to the fusion range, and calculate a fusion correction amount of the high-frequency static correction amount of the velocity model according to the fusion weight; A seismic gather correction module, used for performing small smoothing surface correction on the original seismic gather using the fused correction amount to obtain a corrected seismic gather; The prestack depth migration module is used to generate a fusion model of the velocity model, and use the fusion model to perform depth migration processing on the corrected seismic gather to obtain a prestack depth migration image of the corrected gather.

9. An electronic device, comprising: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the prestack depth migration method based on multi-information coupling as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the program is executed by a processor, the prestack depth migration method based on multi-information coupling as claimed in any one of claims 1 to 7 is implemented.

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