OBC and streamer data consistency processing method and system

Abstract

The present application relates to the field of geophysics, oil and gas exploration and development technology, and provides an OBC and streamer data consistency processing method and system, the method comprising: performing amplitude consistency processing on collected OBC data and streamer data; performing phase frequency consistency processing on the amplitude-consistent OBC data and streamer data; performing compressed sensing data reconstruction on the consistency-processed OBC data and streamer data to obtain reconstructed data; and obtaining CMP gathers using the obtained reconstructed data. According to the scheme of the present application, the present application can obtain the true seismic attribute difference caused by the change of part of oil, gas and water in the oil and gas reservoir, and normalize and correct the time-lapse seismic data of the non-oil and gas reservoir part, so that the profile is consistent as much as possible.

Description

Technical Field

[0001] The present invention relates to the technical fields of geophysics and oil and gas exploration and development, and in particular to a method and system for processing consistency between OBC and streamer data. Background Art

[0002] During the production process of oil and gas fields, seismic exploration is repeated at different stages of oil and gas reservoir development. The changes in seismic responses at different times over time can characterize the changes in fluid properties within the reservoir. Special time-lapse seismic processing, differential analysis and imaging, and computer visualization technology are used to describe the changes in physical parameters (porosity, saturation, pressure, temperature) within the reservoir, which is called time-lapse seismic (abbreviated as time-lapse seismic).

[0003] Many factors contribute to amplitude discrepancies between two seismic data sets over time. These include, in addition to changes in reservoir rock physical properties, inconsistent surface rock properties caused by groundwater level fluctuations, inconsistent ambient noise, inconsistent seismic sources, inconsistent acquisition instruments, and differences in observation systems. This necessitates significant effort in acquisition and processing for time-lapse seismic testing to minimize inconsistencies caused by various non-geological factors.

[0004] Time-lapse seismic data are collected and processed intermittently, making it difficult to ensure perfect consistency between two acquisitions. This necessitates that, in addition to meticulously optimizing acquisition to minimize inconsistencies caused by non-geological factors, time-lapse seismic monitoring also requires normalization of the data. The principle of time-lapse seismic normalization dictates that, under ideal conditions, seismic data collected at two different times should be consistent in the non-reservoir portion, where there is no change in fluid flow. Seismic signal variations in the reservoir portion are caused by pumping oil or injecting gas or water. The intermittent nature of real data also results in variations in seismic attributes such as arrival time, amplitude, velocity, frequency, and phase in the non-reservoir portion of the seismic profile. To capture the true differences in seismic attributes caused by changes in oil, gas, and water in the reservoir portion, normalization is performed on the non-reservoir time-lapse seismic data to maintain the most consistent profile possible.

[0005] Due to different acquisition methods, the two batches of data exhibit significant differences in seismic sources, cables, and recordings: differences in azimuth, coverage times, offsets, detectors, wavelets, and noise. These differences significantly impact the imaging quality, spectrum, and signal-to-noise ratio of the seismic data. Since time-lapse seismic research analyzes changes in parameters such as saturation and pressure caused by reservoir development from differences in seismic data signals, and thus analyzes the distribution of remaining oil, further research is needed on consistent processing of streamers and OBCs (submarine cables). This involves performing non-repetitive time-lapse seismic consistency processing on seismic data acquired at different times and by different methods to eliminate the effects of environmental and human factors on the seismic data, thus providing a reliable data foundation for subsequent time-lapse seismic research in the oil field. However, traditional approaches lack relevant research, making it impossible to address this issue with consistent processing of streamers and OBCs, eliminate the effects of environmental and human factors on the seismic data, and provide a reliable data foundation for subsequent time-lapse seismic research in the oil field. Summary of the Invention

[0006] The object of the present invention is to solve at least one technical problem in the background technology and to provide a method and system for processing consistency between OBC and streamer data.

[0007] To achieve the above object, the present invention provides a method for processing consistency between OBC and streamer data, comprising:

[0008] Perform amplitude consistency processing on the collected OBC data and streamer data;

[0009] Perform phase and frequency consistency processing on the OBC data and streamer data with consistent amplitude;

[0010] Performing compressed sensing data reconstruction on the OBC data and streamer data after consistency processing to obtain reconstructed data;

[0011] The reconstructed data are used to obtain CMP gathers.

[0012] According to one aspect of the present invention, performing amplitude consistency processing on the collected OBC data and streamer data includes:

[0013] Geometric diffusion compensation: Compensates for the longitudinal seismic wave energy difference caused by spherical diffusion factors, so that the amplitude value is only related to the reflection coefficient of the underground reflection interface;

[0014] Amplitude normalization: Perform amplitude normalization on the dragging data to unify it to the same energy level as the OBC data.

[0015] According to one aspect of the present invention, performing phase and frequency consistency processing on the OBC data and streamer data with consistent amplitudes includes:

[0016] Phase time difference adjustment: perform cross-correlation calculation on the OBC data and the streamer data in a sliding time window, calculate the correlation coefficient when the data have different time differences, and use the time difference corresponding to the maximum correlation coefficient as the phase time difference adjustment value;

[0017] Waveform matching: After adjusting the phase and time difference, matched filtering is performed on the dragged data and the OBC data at the same position after phase and time difference adjustment. The matched filter factor is obtained and applied to the gather to eliminate the waveform difference between the two caused by the excitation and reception factors, preparing for subsequent migration and multi-mode comparison.

[0018] According to one aspect of the present invention, the compressed sensing data reconstruction includes:

[0019] S1. Input irregular seismic data x representing OBC data as training samples and randomly select k atoms to form the initial dictionary D0;

[0020] S2. The initial dictionary D0 is converted into a sparse dictionary D using the K-SVD dictionary learning algorithm, and the sparse dictionary D is used to perform a sparse representation of the seismic data;

[0021] S3. Fix the sparse dictionary D and use the SR-ADMM algorithm to update and reconstruct the sparse seismic data to obtain a sparse matrix;

[0022] S4. Fix the sparse matrix and use sparse adaptive update to obtain the sparse dictionary Dk+1;

[0023] S5. Replace the seismic data obtained in step S4 with the seismic data obtained in this iteration, and repeat steps S3 and S4 until convergence;

[0024] S6. Output the iterated sparse matrix, calculate the irregular seismic data x, and realize seismic data reconstruction.

[0025] According to one aspect of the present invention, obtaining CMP gathers using the obtained reconstructed data includes:

[0026] Obtain OBC data CMP gathers using the obtained reconstructed OBC data;

[0027] The streamer data CMP gathers are obtained using the reconstructed streamer data.

[0028] Furthermore, to achieve the above-mentioned purpose, the present invention also provides an OBC and streamer data consistency processing system, comprising:

[0029] Amplitude consistency processing module, which performs amplitude consistency processing on the collected OBC data and streamer data;

[0030] Phase-frequency consistency processing module, which performs phase-frequency consistency processing on OBC data and streamer data with consistent amplitudes;

[0031] The data reconstruction module reconstructs the OBC data and streamer data after consistency processing through compressed sensing to obtain reconstructed data;

[0032] The CMP gather acquisition module uses the obtained reconstructed data to obtain CMP gathers.

[0033] Furthermore, to achieve the above-mentioned purpose, the present invention also provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the above-mentioned method for processing consistency between OBC and streamer data.

[0034] Furthermore, to achieve the above object, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned method for processing consistency between OBC and streamer data.

[0035] According to the solution of the present invention, in order to obtain the real seismic attribute differences caused by the changes in oil, gas and water in the oil and gas reservoir part, the time-lapse seismic data of the non-oil and gas reservoir part is normalized and corrected to keep the profile consistent as much as possible. The present invention proposes a consistency processing method for OBC and streamer data.

[0036] The present invention performs amplitude consistency processing on OBC data and streamer data, that is, uses geometric diffusion compensation and amplitude normalization processing to perform consistency processing on the amplitude of the data. Then, phase frequency consistency processing is performed on the processed data, that is, phase time difference adjustment and waveform matching are used to perform consistency processing on the phase spectra of the two data. The OBC and streamer data that have undergone consistency processing are then reconstructed based on compressed sensing. Finally, the CMP data set of the OBC data and streamer data is obtained from the reconstructed data. The present invention achieves a breakthrough in the consistency processing of streamers and OBCs, eliminates the influence of environmental and human factors on seismic data, and can provide a reliable data basis for subsequent time-lapse seismic research in oil fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A flowchart schematically illustrates a method for processing consistency between OBC and streamer data according to an embodiment of the present invention;

[0038] Figure 2 and Figure 3 They are respectively the superimposed cross-sectional views of the streamer and OBC before spherical diffusion compensation in Example 1;

[0039] Figure 4 and Figure 5 They are respectively the superimposed cross-sectional views of the streamer and OBC after spherical diffusion compensation in Example 1;

[0040] Figure 6 and Figure 7 They are respectively the superimposed cross-sections of the streamer and OBC after amplitude normalization processing in Example 1;

[0041] Figure 8 is the RMS energy plane diagram of the streamer before amplitude normalization in Example 1;

[0042] Figure 9 and Figure 10 They are the RMS energy plane diagrams of the streamer and OBC data after amplitude normalization in Example 1;

[0043] Figure 11 Graphs of streamer data and OBC data before phase adjustment in Example 1;

[0044] Figure 12 Graph showing streamer data and OBC data after phase adjustment in Example 1;

[0045] Figure 13 The location diagram of the four quality control points of the streamer data and OBC data in Example 1;

[0046] Figure 14-17 They are respectively cross-correlation verification diagrams of the positions of the four quality control points of the streamer data and the OBC data after phase adjustment in Example 1;

[0047] Figure 18 The target layer wavelet and matching factor diagram of the drag and OBC data in Example 1;

[0048] Figure 19 This is the image before the drag wave matching in Example 1;

[0049] Figure 20 and Figure 21 They are respectively the images after dragging and OBC wavelet matching in Example 1;

[0050] Figure 22-24 This is a comparison diagram of the regularized OBC gathers of Example 1;

[0051] Figure 25-27 This is the OBC overlay regularization comparison diagram of Example 1. DETAILED DESCRIPTION

[0052] The present invention will now be discussed with reference to exemplary embodiments. It should be understood that the embodiments discussed are only for enabling those skilled in the art to better understand and implement the present invention, rather than implying any limitation on the scope of the present invention.

[0053] As used herein, the term "including" and variations thereof are to be interpreted as open-ended terms meaning "including, but not limited to." The term "based on" is to be interpreted as "based, at least in part, on." The terms "one embodiment" and "an embodiment" are to be interpreted as "at least one embodiment."

[0054] Figure 1 Schematically shows a flow chart of a method for processing consistency between OBC and streamer data according to an embodiment of the present invention. Figure 1 As shown, in this embodiment, the method for processing consistency between OBC and streamer data includes:

[0055] Perform amplitude consistency processing on the collected OBC data and streamer data;

[0056] Perform phase and frequency consistency processing on the OBC data and streamer data with consistent amplitude;

[0057] Performing compressed sensing data reconstruction on the OBC data and streamer data after consistency processing to obtain reconstructed data;

[0058] The reconstructed data are used to obtain CMP gathers.

[0059] Furthermore, according to one embodiment of the present invention, during the propagation of seismic waves, their energy gradually weakens due to spherical diffusion and stratum absorption attenuation. Therefore, it is necessary to compensate for the seismic amplitude and restore the true reflection amplitude characteristics of the underground strata. Due to the influence of differences in excitation and reception conditions on the amplitude of seismic wavelets, energy consistency processing is required. Therefore, amplitude consistency processing can be divided into:

[0060] 1. Geometric diffusion compensation:

[0061] Before performing prestack denoising and migration, we must first consider how to perform geometric diffusion compensation. This compensates for the vertical energy differences caused by spherical diffusion, maintaining an amplitude value that is solely related to the reflection coefficient of the subsurface reflection interface. The velocity function selected for this processing refers to the regional velocity of the work area, multiplying it by a percentage for scanning, and determining the velocity function for geometric diffusion compensation.

[0062] 2. Amplitude normalization processing:

[0063] Due to differences in acquisition age and source type, analysis of the raw data reveals exponential energy differences between the towed data and the OBC data. After geometric diffusion compensation, the towed data require amplitude normalization to bring them to the same energy level as the OBC data. The general principle of amplitude normalization is to normalize younger data toward older data, lower frequencies toward higher frequencies, and lower coverage times toward higher coverage times. This ensures that the data, after subsequent consistency processing, maintains amplitude, frequency, and fidelity.

[0064] Furthermore, according to one embodiment of the present invention, after performing amplitude consistency processing, it is necessary to perform phase and frequency consistency processing on the data. Phase and frequency consistency processing includes:

[0065] 1. Phase time difference adjustment:

[0066] Perform a sliding window cross-correlation calculation on the OBC data and the streamer data, and calculate the correlation coefficient for different time differences. The time difference corresponding to the maximum correlation coefficient is used as the value for the phase time difference adjustment.

[0067] 2. Waveform matching:

[0068] After phase and time difference adjustment, matched filtering is performed on the same overlapped segments of the towed and OBC data after phase and time difference adjustment. The matched filter factors are then applied to the gathers to eliminate waveform differences between the two due to excitation and reception factors, preparing for subsequent migration and multi-mode comparison. This processing matches the low-frequency towed data to the OBC data, thereby preserving the data's frequency.

[0069] Furthermore, according to one embodiment of the present invention, in cable seismic acquisition, factors such as ocean currents cause irregularities in navigational trajectory and streamer feather angles, resulting in uneven offset, azimuth, and coverage times. In submarine cable data acquisition, the shot and receiver line spacings are relatively large, resulting in significant differences in the distribution patterns of shot detection points between submarine cables and streamers. These non-repeated acquisition observation systems result in the effect of non-geological factors on the seismic response during time-lapse seismic data processing, far exceeding the variation caused by reservoir geology. Therefore, it is necessary to reconstruct the seismic data to improve data regularity, significantly increasing the number of repeat shot detection points that can be matched between the two phases of seismic data, and thus increasing the feasibility of time-lapse seismic processing.

[0070] The emergence of compressed sensing theory has provided a new theoretical basis for sparse representation seismic data reconstruction methods, and a sparse representation compressed sensing seismic data reconstruction method has been developed. Compressed sensing means that under the premise of data sparseness and under the condition of being lower than the Nyquist sampling frequency, the original signal can be reconstructed through a nonlinear reconstruction algorithm. Compressed sensing seismic data reconstruction is mainly divided into a sparse representation stage and a reconstruction stage. The reconstruction stage is equivalent to a sparse optimization problem. This compressed sensing reconstruction will use the square regularized alternating direction method of multipliers (SR-ADMM) algorithm to solve the sparse optimization problem. The compressed sensing data reconstruction process is as follows:

[0071] S1. Input irregular seismic data x representing OBC data as training samples and randomly select k atoms to form the initial dictionary D0;

[0072] S2. The initial dictionary D0 is converted into a sparse dictionary D using the K-SVD dictionary learning algorithm, and the sparse dictionary D is used to perform a sparse representation of the seismic data;

[0073] S3. Fix the sparse dictionary D and use the SR-ADMM algorithm to update and reconstruct the sparse seismic data to obtain a sparse matrix;

[0074] S4. Fix the sparse matrix and adaptively update the sparse matrix to obtain the sparse dictionary Dk+1;

[0075] S5. Replace the seismic data obtained in step S4 with the seismic data obtained in this iteration, and repeat steps S3 and S4 until convergence;

[0076] S6. Output the iterated sparse matrix, calculate the irregular seismic data x, and realize seismic data reconstruction.

[0077] Furthermore, according to one embodiment of the present invention, obtaining CMP gathers using the obtained reconstructed data includes:

[0078] Obtain OBC data CMP gathers using the obtained reconstructed OBC data;

[0079] The streamer data CMP gathers are obtained using the reconstructed streamer data.

[0080] According to the above scheme of the present invention, in order to obtain the real seismic attribute differences caused by the changes in oil, gas and water in the oil and gas reservoir part, the time-lapse seismic data of the non-oil and gas reservoir part is normalized and corrected to keep the profile consistent as much as possible. The present invention proposes a consistency processing method for OBC and streamer data.

[0081] The present invention performs amplitude consistency processing on OBC data and streamer data, that is, uses geometric diffusion compensation and amplitude normalization processing to perform consistency processing on the amplitude of the data. Then, phase frequency consistency processing is performed on the processed data, that is, phase time difference adjustment and waveform matching are used to perform consistency processing on the phase spectra of the two data. The OBC and streamer data that have undergone consistency processing are then reconstructed based on compressed sensing. Finally, the CMP data set of the OBC data and streamer data is obtained from the reconstructed data. The present invention achieves a breakthrough in the consistency processing of streamers and OBCs, eliminates the influence of environmental and human factors on seismic data, and can provide a reliable data basis for subsequent time-lapse seismic research in oil fields.

[0082] Furthermore, to achieve the above-mentioned purpose, the present invention also provides an OBC and streamer data consistency processing system, comprising:

[0083] Amplitude consistency processing module, which performs amplitude consistency processing on the collected OBC data and streamer data;

[0084] Phase-frequency consistency processing module, which performs phase-frequency consistency processing on OBC data and streamer data with consistent amplitudes;

[0085] The data reconstruction module reconstructs the OBC data and streamer data after consistency processing through compressed sensing to obtain reconstructed data;

[0086] The CMP gather acquisition module uses the obtained reconstructed data to obtain CMP gathers.

[0087] Furthermore, according to one embodiment of the present invention, during the propagation of seismic waves, their energy gradually weakens due to spherical diffusion and stratum absorption attenuation. Therefore, it is necessary to compensate for the seismic amplitude and restore the true reflection amplitude characteristics of the underground strata. Due to the influence of differences in excitation and reception conditions on the amplitude of seismic wavelets, energy consistency processing is required. Therefore, amplitude consistency processing can be divided into:

[0088] 1. Geometric diffusion compensation:

[0089] Before performing prestack denoising and migration, we must first consider how to perform geometric diffusion compensation. This compensates for the vertical energy differences caused by spherical diffusion, maintaining an amplitude value that is solely related to the reflection coefficient of the subsurface reflection interface. The velocity function selected for this processing refers to the regional velocity of the work area, multiplying it by a percentage for scanning, and determining the velocity function for geometric diffusion compensation.

[0090] 2. Amplitude normalization processing:

[0091] Due to differences in acquisition age and source type, analysis of the raw data reveals exponential energy differences between the towed data and the OBC data. After geometric diffusion compensation, the towed data require amplitude normalization to bring them to the same energy level as the OBC data. The general principle of amplitude normalization is to normalize younger data toward older data, lower frequencies toward higher frequencies, and lower coverage times toward higher coverage times. This ensures that the data, after subsequent consistency processing, maintains amplitude, frequency, and fidelity.

[0092] Furthermore, according to one embodiment of the present invention, after performing amplitude consistency processing, it is necessary to perform phase and frequency consistency processing on the data. Phase and frequency consistency processing includes:

[0093] 1. Phase time difference adjustment:

[0094] Perform a sliding window cross-correlation calculation on the OBC data and the streamer data, and calculate the correlation coefficient for different time differences. The time difference corresponding to the maximum correlation coefficient is used as the value for the phase time difference adjustment.

[0095] 2. Waveform matching:

[0096] After phase and time difference adjustment, matched filtering is performed on the same overlapped segments of the towed and OBC data after phase and time difference adjustment. The matched filter factors are then applied to the gathers to eliminate waveform differences between the two due to excitation and reception factors, preparing for subsequent migration and multi-mode comparison. This processing matches the low-frequency towed data to the OBC data, thereby preserving the data's frequency.

[0097] Furthermore, according to one embodiment of the present invention, in cable seismic acquisition, factors such as ocean currents cause irregularities in navigational trajectory and streamer feather angles, resulting in uneven offset, azimuth, and coverage times. In submarine cable data acquisition, the shot and receiver line spacings are relatively large, resulting in significant differences in the distribution patterns of shot detection points between submarine cables and streamers. These non-repeated acquisition observation systems result in the effect of non-geological factors on the seismic response during time-lapse seismic data processing, far exceeding the variation caused by reservoir geology. Therefore, it is necessary to reconstruct the seismic data to improve data regularity, significantly increasing the number of repeat shot detection points that can be matched between the two phases of seismic data, and thus increasing the feasibility of time-lapse seismic processing.

[0098] The emergence of compressed sensing theory has provided a new theoretical basis for sparse representation seismic data reconstruction methods, and a sparse representation compressed sensing seismic data reconstruction method has been developed. Compressed sensing means that under the premise of data sparseness and under the condition of being lower than the Nyquist sampling frequency, the original signal can be reconstructed through a nonlinear reconstruction algorithm. Compressed sensing seismic data reconstruction is mainly divided into a sparse representation stage and a reconstruction stage. The reconstruction stage is equivalent to a sparse optimization problem. This compressed sensing reconstruction will use the square regularized alternating direction method of multipliers (SR-ADMM) algorithm to solve the sparse optimization problem. The compressed sensing data reconstruction process is as follows:

[0099] S1. Input irregular seismic data x representing OBC data as training samples and randomly select k atoms to form the initial dictionary D0;

[0100] S2. The initial dictionary D0 is converted into a sparse dictionary D using the K-SVD dictionary learning algorithm, and the sparse dictionary D is used to perform a sparse representation of the seismic data;

[0101] S3. Fix the sparse dictionary D and use the SR-ADMM algorithm to update and reconstruct the sparse seismic data to obtain a sparse matrix;

[0102] S4. Fix the sparse matrix and adaptively update the sparse matrix to obtain the sparse dictionary Dk+1;

[0103] S5. Replace the seismic data obtained in step S4 with the seismic data obtained in this iteration, and repeat steps S3 and S4 until convergence;

[0104] S6. Output the iterated sparse matrix, calculate the irregular seismic data x, and realize seismic data reconstruction.

[0105] Furthermore, according to one embodiment of the present invention, obtaining CMP gathers using the obtained reconstructed data includes:

[0106] Obtain OBC data CMP gathers using the obtained reconstructed OBC data;

[0107] The streamer data CMP gathers are obtained using the reconstructed streamer data.

[0108] According to the above scheme of the present invention, in order to obtain the real seismic attribute differences caused by the changes in oil, gas and water in the oil and gas reservoir part, the time-lapse seismic data of the non-oil and gas reservoir part is normalized and corrected to keep the profile consistent as much as possible. The present invention proposes a consistency processing method for OBC and streamer data.

[0109] The present invention performs amplitude consistency processing on OBC data and streamer data, that is, uses geometric diffusion compensation and amplitude normalization processing to perform consistency processing on the amplitude of the data. Then, phase frequency consistency processing is performed on the processed data, that is, phase time difference adjustment and waveform matching are used to perform consistency processing on the phase spectra of the two data. The OBC and streamer data that have undergone consistency processing are then reconstructed based on compressed sensing. Finally, the CMP data set of the OBC data and streamer data is obtained from the reconstructed data. The present invention achieves a breakthrough in the consistency processing of streamers and OBCs, eliminates the influence of environmental and human factors on seismic data, and can provide a reliable data basis for subsequent time-lapse seismic research in oil fields.

[0110] Furthermore, to achieve the above objectives, the present invention also provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the OBC and streamer data consistency processing method as described above is implemented.

[0111] Furthermore, to achieve the above object, the present invention also provides a computer-readable storage medium storing a computer program, which implements the above-mentioned method for processing consistency between OBC and streamer data when executed by a processor.

[0112] Furthermore, based on the above solution, the technical solution of the present invention is verified in the form of a specific embodiment in combination with the accompanying drawings.

[0113] Example 1

[0114] will be as Figure 2 and Figure 3 The original streamer data and OBC data shown in the figure are diffusely compensated to obtain the following Figure 4 and Figure 5 The data shown are after diffusion compensation.

[0115] The data after collective diffusion compensation were amplitude normalized to obtain the following Figure 6 and Figure 7 The results shown. Figures 8-10 Shown are the RMS energy plane plots of the data before and after amplitude normalization.

[0116] Points with relatively high signal-to-noise ratios of the two sets of data were selected for cross-correlation, and it was found that there was a fixed time difference of about 8ms between the two sets of data. The phase time difference of the towed data was adjusted to ensure that the reflection time of the towed data was consistent with that of the explosive source data at the overlapping position after consistency processing. Figure 11 The data shown are before phase adjustment; Figure 12 Shown are the data after phase adjustment. Figure 13-17 The figure shows the cross-correlation verification of the quality control points of the two data sets after phase adjustment. Comparing the cross-correlation before and after phase adjustment, the processed OBC data and streamer data are more consistent, resolving the time difference between the two data sets.

[0117] Using the above phase-adjusted data, the target layer wavelet is obtained by superposition, and the following is obtained: Figure 18 The matched filter factor is shown in . And the factor is applied to the gather of streamer data, and the following is obtained: Figure 19-21 The results are shown in the figure. The data processed above is reconstructed by compressed sensing data, and the following is obtained: Figure 22-24 The results are shown in Figure 2. The data reconstructed by the data gather stacking is obtained as follows: Figure 25-27 results.

[0118] Those skilled in the art will appreciate that the modules and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented using electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0119] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and equipment can refer to the corresponding processes in the aforementioned method implementation methods and will not be repeated here.

[0120] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is merely a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0121] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the objectives of the embodiments of the present invention.

[0122] In addition, each functional module in the embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0123] If the functions are implemented as software modules and sold or used as standalone products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the energy-saving signal transmission / reception method according to various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, ROM, RAM, a magnetic disk, or an optical disk.

[0124] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the inventive concept. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this application.

[0125] It should be understood that the size of the serial numbers of each step in the content of the invention and the implementation methods of the present invention does not absolutely mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the implementation methods of the present invention.

Claims

1. A method for processing consistency between OBC and streamer data, characterized in that: include: Perform amplitude consistency processing on the collected OBC data and streamer data; Perform phase and frequency consistency processing on the OBC data and streamer data with consistent amplitude; Performing compressed sensing data reconstruction on the OBC data and streamer data after consistency processing to obtain reconstructed data; Obtain CMP gathers using the obtained reconstructed data; The amplitude consistency processing of the collected OBC data and streamer data includes: Geometric diffusion compensation: Compensates for the longitudinal seismic wave energy difference caused by spherical diffusion factors, so that the amplitude value is only related to the reflection coefficient of the underground reflection interface; Amplitude normalization: Perform amplitude normalization on the dragging data to unify it to the same energy level as the OBC data; The performing phase and frequency consistency processing on the OBC data and streamer data with consistent amplitudes includes: Phase time difference adjustment: perform cross-correlation calculation on the OBC data and the streamer data in a sliding time window, calculate the correlation coefficient when the data have different time differences, and use the time difference corresponding to the maximum correlation coefficient as the phase time difference adjustment value; Waveform matching: After adjusting the phase and time difference, matched filtering is performed on the dragged data and the OBC data at the same position after phase and time difference adjustment. The matched filter factor is obtained and applied to the gather to eliminate the waveform difference between the two caused by the excitation and reception factors, preparing for subsequent migration and multi-mode comparison.

2. The method for processing consistency between OBC and streamer data according to claim 1, characterized in that: The compressed sensing data reconstruction includes: S1. Input irregular seismic data x representing OBC data as training samples and randomly select k atoms to form the initial dictionary D0; S2. The initial dictionary D0 is converted into a sparse dictionary D using the K-SVD dictionary learning algorithm, and the sparse dictionary D is used to perform a sparse representation of the seismic data; S3. Fix the sparse dictionary D and use the SR-ADMM algorithm to update and reconstruct the sparse seismic data to obtain a new sparse matrix; S4. Fix the sparse matrix and adaptively update the sparse matrix to obtain the sparse dictionary Dk+1; S5. Replace the seismic data obtained in step S4 with the seismic data obtained in this iteration, and repeat steps S3 and S4 until convergence; S6. Output the iterated sparse matrix, calculate the irregular seismic data x, and realize seismic data reconstruction.

3. The method for processing consistency between OBC and streamer data according to claim 1 or 2, characterized in that: The method of obtaining CMP gathers using the obtained reconstructed data includes: Obtain OBC data CMP gathers using the obtained reconstructed OBC data; The streamer data CMP gathers are obtained using the reconstructed streamer data. 4.OBC and streamer data consistency processing system, characterized by: include: Amplitude consistency processing module, which performs amplitude consistency processing on the collected OBC data and streamer data; Phase-frequency consistency processing module, which performs phase-frequency consistency processing on OBC data and streamer data with consistent amplitudes; The data reconstruction module reconstructs the OBC data and streamer data after consistency processing through compressed sensing to obtain reconstructed data; CMP gather acquisition module, which uses the obtained reconstructed data to obtain CMP gathers; The amplitude consistency processing of the collected OBC data and streamer data includes: Geometric diffusion compensation: Compensates for the longitudinal seismic wave energy difference caused by spherical diffusion factors, so that the amplitude value is only related to the reflection coefficient of the underground reflection interface; Amplitude normalization: Perform amplitude normalization on the dragging data to unify it to the same energy level as the OBC data; The performing phase and frequency consistency processing on the OBC data and streamer data with consistent amplitudes includes: Phase time difference adjustment: perform cross-correlation calculation on the OBC data and the streamer data in a sliding time window, calculate the correlation coefficient when the data have different time differences, and use the time difference corresponding to the maximum correlation coefficient as the phase time difference adjustment value; Waveform matching: After adjusting the phase and time difference, matched filtering is performed on the dragged data and the OBC data at the same position after phase and time difference adjustment. The matched filter factor is obtained and applied to the gather to eliminate the waveform difference between the two caused by the excitation and reception factors, preparing for subsequent migration and multi-mode comparison.

5. An electronic device, characterized in that The method comprises a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the method for processing consistency between OBC and streamer data according to any one of claims 1 to 3 is implemented.

6. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for processing consistency between OBC and streamer data according to any one of claims 1 to 3 is implemented.

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