Desert area seismic data processing near-surface velocity modeling method and storage medium

Abstract

The invention belongs to the technical field of oil-gas exploration seismic data processing, and particularly relates to a desert area seismic data processing near-surface velocity modeling method and a storage medium. The method comprises the following steps: establishing a desert area first near-surface velocity model based on micro-logging constraint; generating a second near-surface velocity model by using the ray density bottom interface horizon data; establishing a first depth domain initial interval velocity model based on time domain velocity time-depth conversion; generating a second depth domain initial interval velocity model by using the ray density bottom interface horizon data; establishing a third depth domain initial velocity model containing a near-surface velocity; the local angle domain processing picks up all-directional residual speed delay; and updating the third depth domain initial velocity model by using an omnibearing grid chromatography velocity inversion method. According to the method, the defects of a conventional near-surface velocity modeling method are overcome, and the precision of the near-surface velocity modeling in the desert area is effectively improved.

Description

Technical Field

[0001] The invention belongs to the technical field of oil and gas exploration seismic data processing, and in particular relates to a near-surface velocity modeling method and a storage medium for seismic data processing in desert areas. Background Art

[0002] Seismic wave velocity parameters run through the entire process of seismic data acquisition, processing and interpretation. In the process of seismic exploration, seismic wave velocity plays a vital role. Therefore, establishing an accurate velocity model has always been an issue worthy of in-depth study in seismic exploration.

[0003] In the processing of seismic data in desert areas, the accurate construction of the depth domain velocity model is the basis for achieving high-precision imaging. In the depth domain velocity modeling, the error accumulation effect of the near-surface velocity model will affect the reliability of the mid-deep velocity modeling and imaging results. For example, the prior art CN104536043B discloses a method and device for fusion of the depth domain overall velocity model, the method comprising: picking up the first arrival wave data in the seismic data; inverting the near-surface velocity model by the travel time tomography method according to the picked up first arrival wave data; performing time domain velocity analysis on the seismic data to obtain the root mean square velocity model; converting the root mean square velocity model into a depth domain layer velocity model; using the depth domain layer velocity model as the initial model, optimizing the initial model to obtain the mid-deep velocity model; fusing the near-surface velocity model and the mid-deep velocity model to obtain the depth domain overall velocity model; performing pre-stack depth migration on the depth domain overall velocity model.

[0004] The prior art CN104570102B discloses a method for fusing a near-surface velocity model with a mid-deep velocity model, and belongs to the field of seismic exploration data processing. The method comprises: (1) inputting a near-surface velocity model, an observation system, a velocity analysis datum, and a velocity function or seismic data obtained by velocity analysis; (2) calculating a CMP preferred velocity analysis datum according to the near-surface velocity model and the observation system; (3) performing velocity analysis relative to the preferred velocity analysis datum obtained in step (2) to obtain a velocity function representing the mid-deep velocity model; or using a velocity function obtained by velocity analysis relative to a non-preferred velocity analysis datum, the velocity function is corrected according to the difference between the velocity analysis datum and the preferred velocity analysis datum to obtain a velocity function representing the mid-deep velocity model; (4) fusing the near-surface velocity model with the mid-deep velocity model.

[0005] The above-mentioned existing technologies can obtain high-precision velocity models to a certain extent. However, they are limited by the inherent deficiencies of the beam-type three-dimensional seismic acquisition observation system. The amount of near-surface data obtained from field seismic data is limited. The conventional velocity modeling method based on reflection waves has large errors in the near-surface velocity characterization, which restricts the improvement of the velocity modeling accuracy and imaging quality in the depth domain of the entire layer system. Summary of the invention

[0006] In order to solve the above technical problems existing in the prior art, the present invention provides a near-surface velocity modeling method and storage medium for processing seismic data in desert areas, aiming to solve the shortcomings of improving the accuracy of conventional near-surface velocity modeling based on the near-surface characteristics of desert areas.

[0007] To achieve the above object, the technical solution of the present invention is as follows:

[0008] A near-surface velocity modeling method for processing seismic data in desert areas, comprising:

[0009] S1. Input the micro-logging raw data obtained from the near-surface survey in seismic data acquisition, pick up the micro-logging first arrival time from the micro-logging raw data, and use the micro-logging first arrival wave tomographic velocity inversion to obtain the micro-logging interpretation result for characterizing the near-surface velocity-depth relationship;

[0010] S2, inputting seismic single shot data, picking up the seismic single shot first arrival time of the seismic single shot data, and performing first arrival wave tomographic inversion of the seismic single shot data under micro-logging constraints using the micro-logging interpretation result obtained in step S1, to obtain a first near-surface velocity model and a ray density model;

[0011] S3, picking up and outputting the ray density bottom interface horizon data of the ray density model in step S2, and generating a second near-surface velocity model using the ray density bottom interface horizon data and the first near-surface velocity model obtained in step S2;

[0012] S4, processing the seismic single shot data in step S2, performing gather extraction on the processed seismic single shot data to generate a CMP gather, performing velocity analysis on the CMP gather to obtain a time domain root mean square velocity model, inverting and time-depth conversion on the time domain root mean square velocity model to obtain a first depth domain initial layer velocity model;

[0013] S5, generating a second depth domain initial layer velocity model using the ray density bottom interface layer data obtained in step S3 and the first depth domain initial layer velocity model obtained in step S4;

[0014] S6. Add the second near-surface velocity model obtained in step S3 and the second depth-domain initial layer velocity model obtained in step S5 to obtain a third depth-domain initial velocity model including near-surface velocity.

[0015] Furthermore, after obtaining the third depth domain initial velocity model, the method further includes:

[0016] S7, inputting the seismic data CMP gathers in step S4 and the third depth domain initial velocity model obtained in step S6, performing local angle domain imaging processing, and obtaining a local angle domain imaging data volume and a local angle domain common reflection angle gathers;

[0017] S8, according to the ray density bottom interface layer data obtained in step S3, picking up the omnidirectional residual velocity delay above the ray density bottom interface layer in the local angle domain common reflection angle gather in step S7, and editing the picked up residual velocity delay;

[0018] S9. Based on the ray density bottom interface horizon data obtained in step S3 and the omnidirectional residual velocity delay edited in step S8, the third depth domain initial velocity model in step S6 is updated using an omnidirectional grid tomography velocity inversion method.

[0019] Furthermore, after updating the initial velocity model in the third depth domain, the method further includes:

[0020] S10, judging whether the third depth-domain velocity model meets the accuracy requirement, if not, repeating steps S7-S9 to update the third depth-domain initial velocity model.

[0021] Furthermore, step S1 specifically includes:

[0022] S11, picking up the micro-logging first arrival time in the micro-logging raw data;

[0023] S12, providing a first initial velocity model according to the near-surface prior information of the desert area;

[0024] S13, calculating the ray path and the first arrival time of micro-logging;

[0025] S14, calculating the difference between the picked micro-logging first arrival time and the calculated micro-logging first arrival time;

[0026] S15, updating the first initial velocity model in step S12 according to the upper and lower limits of the result velocity given by the near-surface prior information of the desert area, and obtaining a new first initial velocity model;

[0027] S16, judging whether the new first initial velocity model meets the accuracy requirement, if so, obtaining the micro-logging interpretation result according to the new first initial velocity model, if not, proceeding to step S13 to recalculate.

[0028] Furthermore, in step S13, the travel time linear interpolation ray tracing method is used to calculate the ray path and the micro-logging first arrival time.

[0029] Furthermore, in step S15, the first initial velocity model is updated using the SIRT algorithm or the SLQR algorithm.

[0030] Furthermore, step S2 specifically includes:

[0031] S21. Pick up the first arrival time of a seismic single shot from the seismic single shot data;

[0032] S22, providing a second initial velocity model according to the near-surface prior information of the desert area;

[0033] S23, using the travel time linear interpolation ray tracing method to calculate the ray path and the first arrival time of a single seismic shot;

[0034] S24, calculating the difference between the picked up seismic single shot first arrival time and the calculated seismic single shot first arrival time;

[0035] S25, according to the upper and lower limits of the result velocity given by the near-surface prior information of the desert area, the second initial velocity model in step S22 is updated using the SIRT algorithm or the SLQR algorithm to obtain a new second initial velocity model;

[0036] S26, judging whether the new second initial velocity model in step S25 meets the accuracy requirement, if so, obtaining the first near-surface velocity model and the ray density model according to the new second initial velocity model, if not, proceeding to step S23 to recalculate.

[0037] Furthermore, step S3 specifically includes:

[0038] S31, picking up the ray density bottom interface layer data in the ray density model;

[0039] S32. According to the ray density bottom interface layer data picked up in step S31, the first near-surface velocity model is cut off, and the portion above the ray density bottom interface layer is retained as the second near-surface velocity model.

[0040] Furthermore, processing the seismic single shot data in step S2 specifically includes performing static correction, noise suppression, amplitude compensation, and deconvolution processing on the seismic single shot data in step S2.

[0041] Furthermore, step S4 specifically includes:

[0042] S41. Perform static correction on the seismic single shot data to eliminate the influence of surface elevation and near-surface velocity reduction zone on the travel time path of seismic wave propagation;

[0043] S42, performing noise suppression processing on the single shot record after static correction to improve the signal-to-noise ratio of the seismic single shot data;

[0044] S43, performing amplitude compensation processing on the denoised single shot record to eliminate the spatial inconsistency problem of seismic wave amplitude;

[0045] S44, deconvolution processing is performed on the single shot record after amplitude compensation, and the vertical and horizontal resolutions of the seismic data are improved by compressing the seismic wavelet;

[0046] S45, extracting the single shot data after static correction, noise suppression, amplitude compensation and deconvolution processing to generate CMP gathers;

[0047] S46, perform velocity analysis on the CMP gathers to obtain a time domain root mean square velocity model;

[0048] S47, inverting the time domain root mean square velocity model using constrained velocity inversion to obtain a time domain layer velocity model;

[0049] S48, performing time-depth conversion on the time-domain layer velocity model to obtain a first depth-domain initial velocity model.

[0050] Furthermore, step S5 specifically includes:

[0051] S51, inputting the ray density bottom interface layer data of the ray density model;

[0052] S52. According to the ray density bottom interface layer data picked up in step S51, the first depth domain initial layer velocity model is cut off, and the portion below the ray density bottom interface layer is retained as the second depth domain initial layer velocity model.

[0053] Further, step S6 specifically includes:

[0054] S61, inputting a second near-surface velocity model;

[0055] S62, inputting the initial layer velocity model of the second depth domain;

[0056] S63, adding the second near-surface velocity model and the second depth-domain initial layer velocity model to obtain a third depth-domain initial velocity model including the near-surface velocity.

[0057] Further, step S7 specifically includes:

[0058] S71, input seismic data CMP gathers;

[0059] S72, inputting the initial layer velocity model of the third depth domain;

[0060] S73. Using the CMP gathers and the initial layer velocity model in the third depth domain, key parameters are selected to perform local angle domain imaging processing to obtain a local angle domain imaging data volume and a local angle domain common reflection angle gathers.

[0061] Furthermore, the key parameters include offset aperture, scanning step size, number of coverages, and cutoff frequency.

[0062] Furthermore, step S8 specifically includes:

[0063] S81. Input the ray density bottom interface layer data;

[0064] S82, inputting a local angle domain imaging data volume and a local angle domain common reflection angle gather;

[0065] S83, calculating the seismic attributes of the local angle domain imaging data volume;

[0066] S84, performing noise suppression and removal processing on the local angle domain common reflection angle gather;

[0067] S85, using the seismic attribute picking processing of the local angle domain imaging data volume to obtain the omnidirectional residual velocity delay above the ray density bottom interface layer in the local angle domain common reflection angle gather;

[0068] S86, full range residual speed delay of editing pick.

[0069] Further, step S9 specifically includes:

[0070] S91, input the bottom interface layer data of the ray density model data;

[0071] S92, input the edited full range remaining speed delay;

[0072] S93, using the omnidirectional residual velocity delay above the ray density bottom interface layer, establishing an omnidirectional tomographic velocity inversion matrix above the ray density bottom interface layer;

[0073] S94. Based on the ray density bottom interface layer data and the omnidirectional residual velocity delay above the ray density bottom interface layer, solve the omnidirectional tomographic velocity inversion matrix above the ray density bottom interface layer to obtain an updated initial velocity model of the third depth domain including the near-surface velocity.

[0074] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned near-surface velocity modeling method for processing seismic data in desert areas is implemented.

[0075] Compared with the prior art, the present invention has the following beneficial effects:

[0076] The method for near-surface velocity modeling in desert area seismic data processing provided by the present invention combines the micro-logging constrained first-arrival wave tomographic velocity inversion in the desert area with the local angle domain omnidirectional grid tomographic velocity inversion, which solves the shortcomings of conventional near-surface velocity modeling methods and effectively improves the accuracy of near-surface velocity modeling in desert areas. It is widely used in improving the accuracy of velocity modeling and imaging effect in the depth domain of the whole layer system in desert area seismic data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 The figure is a flow chart of the method of the present invention.

[0078] Figure 2 Schematic diagram of micro-logging data and first arrival picking in an embodiment of the present invention.

[0079] Figure 3 Schematic diagram of a first near-surface velocity model in an embodiment of the present invention.

[0080] Figure 4 Schematic diagram of ray density model data in an embodiment of the present invention.

[0081] Figure 5 Schematic diagram of a second near-surface velocity model in an embodiment of the present invention.

[0082] Figure 6 This is the initial layer velocity model in the first depth domain in an embodiment of the present invention.

[0083] Figure 7 This is the initial layer velocity model in the second depth domain in an embodiment of the present invention.

[0084] Figure 8 This is the initial layer velocity model in the third depth domain in an embodiment of the present invention.

[0085] Fig. 9 It is a schematic diagram of the local angle domain common reflection angle gather and omnidirectional residual velocity delay picking in an embodiment of the present invention.

[0086] Fig.10 It is a schematic diagram of the final initial layer velocity model in the third depth domain in an embodiment of the present invention. DETAILED DESCRIPTION

[0087] The technical solution of the present invention will be clearly described below in conjunction with the accompanying drawings. Obviously, the described embodiments are not all embodiments of the present invention, and all other embodiments obtained by ordinary technicians in the field without making creative work are within the protection scope of the present invention.

[0088] It should be noted that, unless otherwise specifically stated, the relative arrangements of components and steps, and numerical expressions set forth in these embodiments should not be construed as limiting the scope of the present invention.

[0089] The following description of the exemplary embodiments is merely illustrative and is not intended to limit the present invention and its application or use in any sense. Techniques, methods and devices known to ordinary technicians in the relevant field may not be discussed in detail here, but where applicable, these techniques, methods and devices should be considered as part of this specification.

[0090] The present invention provides a near-surface velocity modeling method for processing seismic data in desert areas, such as Figure 1 As shown, including:

[0091] S1. Input the micro-logging raw data obtained from the near-surface survey in seismic data acquisition, pick up the micro-logging first arrival time from the micro-logging raw data, and use the micro-logging first arrival wave tomographic velocity inversion to obtain the micro-logging interpretation result for characterizing the near-surface velocity-depth relationship; specifically including:

[0092] S11. Pick up the micro-logging first arrival time in the micro-logging raw data, such as Figure 2 As shown;

[0093] S12, providing a first initial velocity model according to the near-surface prior information of the desert area;

[0094] S13, using LTI (travel time linear interpolation) ray tracing method to calculate ray paths and micro-logging first arrival times;

[0095] S14, calculating the difference between the picked micro-logging first arrival time and the calculated micro-logging first arrival time;

[0096] S15, according to the upper and lower limits of the result velocity given by the near-surface prior information of the desert area, the first initial velocity model in step S12 is updated by using the SIRT algorithm or the SLQR algorithm to obtain a new first initial velocity model;

[0097] S16, judging whether the new first initial velocity model meets the accuracy requirement, if so, obtaining the micro-logging interpretation result according to the new first initial velocity model, if not, proceeding to step S13 to recalculate.

[0098] S2, inputting seismic single shot data, picking up the seismic single shot first arrival time of the seismic single shot data, using the micro-well logging interpretation result obtained in step S1 to perform first arrival wave tomographic inversion of the seismic single shot data under micro-well logging constraints, and obtaining a first near-surface velocity model and ray density model; specifically comprising:

[0099] S21. Pick up the first arrival time of a seismic single shot from the seismic single shot data;

[0100] S22, providing a second initial velocity model according to the near-surface prior information of the desert area;

[0101] S23, using LTI (travel time linear interpolation) ray tracing method to calculate the ray path and the first arrival time of a seismic single shot;

[0102] S24, calculating the difference between the picked up seismic single shot first arrival time and the calculated seismic single shot first arrival time;

[0103] S25, according to the upper and lower limits of the result velocity given by the near-surface prior information of the desert area, the second initial velocity model in step S22 is updated using the SIRT algorithm or the SLQR algorithm to obtain a new second initial velocity model;

[0104] S26, judging whether the new second initial velocity model in step S25 meets the accuracy requirement, if so, obtaining the first near-surface velocity model and the ray density model according to the new second initial velocity model, the first near-surface velocity model is as follows: Figure 3 As shown, the ray density model is Figure 4 If not, proceed to step S23 to recalculate.

[0105] S3, picking up and outputting the ray density bottom interface horizon data of the ray density model in step S2, and generating a second near-surface velocity model using the ray density bottom interface horizon data and the first near-surface velocity model obtained in step S2; specifically comprising:

[0106] S31, picking up the ray density bottom interface layer data in the ray density model;

[0107] S32, according to the ray density bottom interface layer data picked up in step S31, the first near-surface velocity model is cut off, and the part above the ray density bottom interface layer is retained as the second near-surface velocity model, such as Figure 5 shown.

[0108] S4, performing static correction, noise suppression, amplitude compensation, and deconvolution processing on the seismic single shot data in step S2, performing gather extraction on the processed seismic single shot data to generate a CMP (Common Middle Point) gather, performing velocity analysis on the CMP gather to obtain a time domain root mean square velocity model, and performing inversion and time-depth conversion on the time domain root mean square velocity model to obtain a first depth domain initial layer velocity model; specifically comprising:

[0109] S41. Perform static correction on the seismic single shot data to eliminate the influence of surface elevation and near-surface velocity reduction zone on the travel time path of seismic wave propagation;

[0110] S42, performing noise suppression processing on the single shot record after static correction to improve the signal-to-noise ratio of the seismic single shot data;

[0111] S43, performing amplitude compensation processing on the denoised single shot record to eliminate the spatial inconsistency problem of seismic wave amplitude;

[0112] S44, deconvolution processing is performed on the single shot record after amplitude compensation, and the vertical and horizontal resolutions of the seismic data are improved by compressing the seismic wavelet;

[0113] S45, extracting the single shot data after static correction, noise suppression, amplitude compensation and deconvolution processing to generate CMP gathers;

[0114] S46, perform velocity analysis on CMP (Common Middle Point) gathers to obtain the time domain RMS velocity model;

[0115] S47, using CVI (Constrained Velocity Inversion) to invert the time domain root mean square velocity model to obtain a time domain layer velocity model;

[0116] S48, performing time-depth conversion on the time-domain layer velocity model to obtain a first depth-domain initial velocity model, such as Figure 6 shown.

[0117] S5, using the ray density bottom interface horizon data obtained in step S3 and the first depth domain initial layer velocity model obtained in step S4 to generate a second depth domain initial layer velocity model; specifically comprising:

[0118] S51, inputting the ray density bottom interface layer data of the ray density model;

[0119] S52, according to the ray density bottom interface layer data picked up in step S51, the first depth domain initial layer velocity model is cut off, and the part below the ray density bottom interface layer is retained as the second depth domain initial layer velocity model, such as Figure 7 shown.

[0120] S6, adding the second near-surface velocity model obtained in step S3 and the second depth-domain initial layer velocity model obtained in step S5 to obtain a third depth-domain initial velocity model including near-surface velocity; specifically comprising:

[0121] S61, inputting a second near-surface velocity model;

[0122] S62, inputting the initial layer velocity model of the second depth domain;

[0123] S63, adding the second near-surface velocity model and the second depth domain initial layer velocity model to obtain a third depth domain initial velocity model including the near-surface velocity, such as Figure 8 shown.

[0124] S7, inputting the seismic data CMP gathers in step S4 and the third depth domain initial velocity model obtained in step S6, performing local angle domain imaging processing, and obtaining a local angle domain imaging data volume and a local angle domain common reflection angle gather; specifically comprising:

[0125] S71, input seismic data CMP gathers;

[0126] S72, inputting the initial layer velocity model of the third depth domain;

[0127] S73, using the CMP gather and the initial layer velocity model in the third depth domain, select key parameters to perform local angle domain imaging processing to obtain a local angle domain imaging data volume and a local angle domain common reflection angle gather, wherein the key parameters include offset aperture, scanning step length, number of coverages, and cutoff frequency.

[0128] S8, according to the ray density bottom interface layer data obtained in step S3, picking up the omnidirectional residual velocity delay above the ray density bottom interface layer in the local angle domain common reflection angle gather in step S7, and editing the picked up residual velocity delay; specifically comprising:

[0129] S81. Input the ray density bottom interface layer data;

[0130] S82, inputting a local angle domain imaging data volume and a local angle domain common reflection angle gather;

[0131] S83, calculating the seismic attributes such as dip, azimuth, continuity, etc. of the local angle domain imaging data volume;

[0132] S84, perform optimization processing such as noise suppression and removal on the local angle domain common reflection angle gather to improve the quality of the local angle domain common reflection angle gather;

[0133] S85, using the dip, azimuth, continuity and other seismic attributes of the local angle domain imaging data body to pick and process the omnidirectional residual velocity delay above the ray density bottom interface layer in the local angle domain common reflection angle gather, such as Fig. 9 As shown;

[0134] S86. Edit and optimize the full range of remaining speed delays picked up based on evaluation criteria such as data thresholds and correlation indicators.

[0135] S9, according to the ray density bottom interface horizon data obtained in step S3 and the omnidirectional residual velocity delay edited in step S8, using the omnidirectional grid tomography velocity inversion method, updating the third depth domain initial velocity model in step S6; specifically comprising:

[0136] S91, input the bottom interface layer data of the ray density model data;

[0137] S92, input the full range residual velocity delay above the edited ray density bottom interface layer;

[0138] S93, using the omnidirectional residual velocity delay above the ray density bottom interface layer, establishing an omnidirectional tomographic velocity inversion matrix above the ray density bottom interface layer;

[0139] S94, based on the ray density bottom interface layer data and the omnidirectional residual velocity delay above the ray density bottom interface layer, solve the omnidirectional tomographic velocity inversion matrix above the ray density bottom interface layer, and obtain the updated initial velocity model of the third depth domain containing the near-surface velocity, such as Fig.10 shown.

[0140] S10, judging whether the third depth-domain velocity model meets the accuracy requirement, if not, repeating steps S7-S9 to update the third depth-domain initial velocity model.

[0141] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned near-surface velocity modeling method for processing seismic data in desert areas is implemented.

[0142] The above specific implementation methods 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 examples, a person 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 scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A near-surface velocity modeling method for seismic data processing in desert areas, It is characterized in that include: S1. Input the micro-logging raw data obtained from the near-surface survey in seismic data acquisition, pick up the micro-logging first arrival time from the micro-logging raw data, and use the micro-logging first arrival wave tomographic velocity inversion to obtain the micro-logging interpretation result for characterizing the near-surface velocity-depth relationship; S2, inputting seismic single shot data, picking up the seismic single shot first arrival time of the seismic single shot data, and performing first arrival wave tomographic inversion of the seismic single shot data under micro-logging constraints using the micro-logging interpretation result obtained in step S1, to obtain a first near-surface velocity model and a ray density model; S3, picking up and outputting the ray density bottom interface horizon data of the ray density model in step S2, and generating a second near-surface velocity model using the ray density bottom interface horizon data and the first near-surface velocity model obtained in step S2; S4, processing the seismic single shot data in step S2, performing gather extraction on the processed seismic single shot data to generate a CMP gather, performing velocity analysis on the CMP gather to obtain a time domain root mean square velocity model, inverting and time-depth conversion on the time domain root mean square velocity model to obtain a first depth domain initial layer velocity model; S5, generating a second depth domain initial layer velocity model using the ray density bottom interface layer data obtained in step S3 and the first depth domain initial layer velocity model obtained in step S4; S6. Add the second near-surface velocity model obtained in step S3 and the second depth-domain initial layer velocity model obtained in step S5 to obtain a third depth-domain initial velocity model including near-surface velocity.

2. The near-surface velocity modeling method for desert area seismic data processing according to claim 1, It is characterized in that After obtaining the initial velocity model in the third depth domain, the following steps are also included: S7, inputting the seismic data CMP gathers in step S4 and the third depth domain initial velocity model obtained in step S6, performing local angle domain imaging processing, and obtaining a local angle domain imaging data volume and a local angle domain common reflection angle gathers; S8, according to the ray density bottom interface layer data obtained in step S3, picking up the omnidirectional residual velocity delay above the ray density bottom interface layer in the local angle domain common reflection angle gather in step S7, and editing the picked up residual velocity delay; S9. Based on the ray density bottom interface horizon data obtained in step S3 and the omnidirectional residual velocity delay edited in step S8, the third depth domain initial velocity model in step S6 is updated using an omnidirectional grid tomography velocity inversion method.

3. The near-surface velocity modeling method for desert area seismic data processing according to claim 2, It is characterized in that After the initial velocity model in the third depth domain is updated, it also includes: S10, judging whether the third depth-domain velocity model meets the accuracy requirement, if not, repeating steps S7-S9 to update the third depth-domain initial velocity model.

4. The near-surface velocity modeling method for desert area seismic data processing according to claim 1, It is characterized in that Step S1 specifically includes: S11, picking up the micro-logging first arrival time in the micro-logging raw data; S12, providing a first initial velocity model according to the near-surface prior information of the desert area; S13, calculating the ray path and the first arrival time of micro-logging; S14, calculating the difference between the picked micro-logging first arrival time and the calculated micro-logging first arrival time; S15, updating the first initial velocity model in step S12 according to the upper and lower limits of the result velocity given by the near-surface prior information of the desert area, and obtaining a new first initial velocity model; S16, judging whether the new first initial velocity model meets the accuracy requirement, if so, obtaining the micro-logging interpretation result according to the new first initial velocity model, if not, proceeding to step S13 to recalculate.

5. The near-surface velocity modeling method for desert area seismic data processing according to claim 4, It is characterized in that In step S13, the travel time linear interpolation ray tracing method is used to calculate the ray path and the micro-logging first arrival time.

6. The near-surface velocity modeling method for desert area seismic data processing according to claim 1, It is characterized in that Step S2 specifically includes: S21. Pick up the first arrival time of a seismic single shot from the seismic single shot data; S22, providing a second initial velocity model according to the near-surface prior information of the desert area; S23, using the travel time linear interpolation ray tracing method to calculate the ray path and the first arrival time of a single seismic shot; S24, calculating the difference between the picked up seismic single shot first arrival time and the calculated seismic single shot first arrival time; S25, according to the upper and lower limits of the result velocity given by the near-surface prior information of the desert area, the second initial velocity model in step S22 is updated using the SIRT algorithm or the SLQR algorithm to obtain a new second initial velocity model; S26, judging whether the new second initial velocity model in step S25 meets the accuracy requirement, if so, obtaining the first near-surface velocity model and the ray density model according to the new second initial velocity model, if not, proceeding to step S23 to recalculate.

7. The near-surface velocity modeling method for desert area seismic data processing according to claim 1, It is characterized in that Step S3 specifically includes: S31, picking up the ray density bottom interface layer data in the ray density model; S32. According to the ray density bottom interface layer data picked up in step S31, the first near-surface velocity model is cut off, and the portion above the ray density bottom interface layer is retained as the second near-surface velocity model.

8. The near-surface velocity modeling method for desert area seismic data processing according to claim 1, It is characterized in that The processing of the seismic single shot data in step S2 in step S4 specifically includes performing static correction, noise suppression, amplitude compensation, and deconvolution processing on the seismic single shot data in step S2.

9. The near-surface velocity modeling method for desert area seismic data processing according to claim 8, It is characterized in that Step S4 specifically includes: S41. Perform static correction on the seismic single shot data to eliminate the influence of surface elevation and near-surface velocity reduction zone on the travel time path of seismic wave propagation; S42, performing noise suppression processing on the single shot record after static correction to improve the signal-to-noise ratio of the seismic single shot data; S43, performing amplitude compensation processing on the denoised single shot record to eliminate the spatial inconsistency problem of seismic wave amplitude; S44, deconvolution processing is performed on the single shot record after amplitude compensation, and the vertical and horizontal resolutions of the seismic data are improved by compressing the seismic wavelet; S45, extracting the single shot data after static correction, noise suppression, amplitude compensation and deconvolution processing to generate CMP gathers; S46, perform velocity analysis on the CMP gathers to obtain a time domain root mean square velocity model; S47, inverting the time domain root mean square velocity model using constrained velocity inversion to obtain a time domain layer velocity model; S48, performing time-depth conversion on the time-domain layer velocity model to obtain a first depth-domain initial velocity model.

10. The near-surface velocity modeling method for desert area seismic data processing according to claim 1, It is characterized in that Step S5 specifically includes: S51, inputting the ray density bottom interface layer data of the ray density model; S52. According to the ray density bottom interface layer data picked up in step S51, the first depth domain initial layer velocity model is cut off, and the portion below the ray density bottom interface layer is retained as the second depth domain initial layer velocity model.

11. The method for near-surface velocity modeling for seismic data processing in desert areas according to claim 1, It is characterized in that Step S6 specifically includes: S61, inputting a second near-surface velocity model; S62, inputting the initial layer velocity model of the second depth domain; S63, adding the second near-surface velocity model and the second depth-domain initial layer velocity model to obtain a third depth-domain initial velocity model including the near-surface velocity.

12. The near-surface velocity modeling method for desert area seismic data processing according to claim 2, It is characterized in that Step S7 specifically includes: S71, input seismic data CMP gathers; S72, inputting the initial layer velocity model of the third depth domain; S73. Using the CMP gathers and the initial layer velocity model in the third depth domain, key parameters are selected to perform local angle domain imaging processing to obtain a local angle domain imaging data volume and a local angle domain common reflection angle gathers.

13. The near-surface velocity modeling method for desert area seismic data processing according to claim 12, It is characterized in that The key parameters include offset aperture, scanning step length, number of coverages, and cutoff frequency.

14. The near-surface velocity modeling method for desert area seismic data processing according to claim 2, It is characterized in that Step S8 specifically includes: S81. Input the ray density bottom interface layer data; S82, inputting a local angle domain imaging data volume and a local angle domain common reflection angle gather; S83, calculating the seismic attributes of the local angle domain imaging data volume; S84, performing noise suppression and removal processing on the local angle domain common reflection angle gather; S85, using the seismic attribute picking processing of the local angle domain imaging data volume to obtain the omnidirectional residual velocity delay above the ray density bottom interface layer in the local angle domain common reflection angle gather; S86, full range residual speed delay of editing pick.

15. The near-surface velocity modeling method for desert area seismic data processing according to claim 2, It is characterized in that Step S9 specifically includes: S91, input the bottom interface layer data of the ray density model data; S92, input the edited full range remaining speed delay; S93, using the omnidirectional residual velocity delay above the ray density bottom interface layer, establishing an omnidirectional tomographic velocity inversion matrix above the ray density bottom interface layer; S94. Based on the ray density bottom interface layer data and the omnidirectional residual velocity delay above the ray density bottom interface layer, solve the omnidirectional tomographic velocity inversion matrix above the ray density bottom interface layer to obtain an updated initial velocity model of the third depth domain including the near-surface velocity.

16. A computer-readable storage medium having a computer program stored thereon, It is characterized in that When the computer program is executed by a processor, the method for near-surface velocity modeling for processing seismic data in desert areas as described in any one of claims 1 to 15 is implemented.

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