A seismic data cross-domain cloud computing processing method and device

Abstract

The application discloses a kind of seismic data cross-domain cloud computing processing method and device, it is related to the field of digital basin data processing technology, which comprises the following steps: obtaining the seismic original data of target area, and selecting the data of survey line position in basin;Data adjustment is carried out on the data of survey line position in basin, to obtain adjusted data;The data is preprocessed after adjustment, to obtain preprocessed data;The preprocessed data is deconvolution processing, to obtain the data after deconvolution processing;Based on the data after deconvolution processing, replace trace head information, and carry out time difference correction, to obtain advantage data;The advantage data is fused, to obtain the seismic survey line profile across basin.The application can solve the problem of seismic data multi-source heterogeneity, uneven quality and survey line boundary effect in digital basin construction, and make full use of prestack data to ensure the smooth implementation of subsequent geological analysis and basin construction.

Description

Technical Field

[0001] This application relates to the field of digital basin data processing technology, and in particular to a cross-domain cloud computing processing method and apparatus for seismic data. Background Technology

[0002] In the construction of digital basins, the diversity and imaging quality of seismic data constrain the tectonic morphology of the basin. Seismic data is crucial in the geological cloud digital basin, and seismic results data is among the most accurate of various geophysical data. With the continuous improvement of exploration results over the years, a large amount of seismic results data has been generated. However, the original seismic data has different quality due to different construction years and some important parameters, such as observation systems, source types, and detector types. This affects the resolution, signal-to-noise ratio, and wave group characteristics of the seismic results data. Boundary effects at the connection points of different survey lines also lead to unclear geological structures and difficulty in tracing phase axes, which is not conducive to the comparison of stratigraphic relationships in the overall digital basin.

[0003] In previous digital basins, the problem of multi-source seismic data was usually solved by methods such as wavelet consistency processing after stacking. However, pre-stack seismic data contains a large amount of information on underground geological morphology, which makes it difficult to carry out subsequent basin construction techniques such as pre-stack migration, data inversion, and tectonic interpretation smoothly. Summary of the Invention

[0004] The purpose of this application is to provide a cross-domain cloud computing processing method and apparatus for seismic data, which can solve the problems of multi-source heterogeneity, uneven quality, and boundary effects of seismic data in the construction of digital basins, and make full use of pre-stack data to ensure the smooth implementation of subsequent geological analysis and basin construction.

[0005] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a cross-domain cloud computing processing method for earthquake data, the method comprising: Obtain raw seismic data for the target area and select data on the location of survey lines within the basin.

[0006] The data of the survey line positions in the basin are adjusted to obtain the adjusted data; the data adjustment includes: polarity adjustment, unification of the reference surface and redefinition of the observation system.

[0007] The adjusted data is preprocessed to obtain preprocessed data; the preprocessing includes: suppression of surface consistency anomaly amplitude, compensation of surface consistency amplitude, and static correction of surface consistency.

[0008] The preprocessed data is deconvolved to obtain deconvolved data; the deconvolution process includes: surface consistency deconvolution, prediction deconvolution, and multichannel statistical expectation deconvolution.

[0009] Based on the data processed by the deconvolution, the track heading information is replaced and time difference correction is performed to obtain the superior data.

[0010] The superior data are fused to obtain a seismic profile that passes through the basin.

[0011] In a second aspect, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the cross-domain cloud computing processing method for earthquake data described in the first aspect.

[0012] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the cross-domain cloud computing processing method for seismic data described in the first aspect.

[0013] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the cross-domain cloud computing processing method for earthquake data described in the first aspect.

[0014] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a cross-domain cloud computing processing method and apparatus for seismic data. The method includes: acquiring raw seismic data of a target area and selecting data from the location of a seismic line in a basin; adjusting the data from the location of the seismic line in the basin to obtain adjusted data; the data adjustment includes: polarity adjustment, unifying the reference surface, and redefining the observation system; it can accurately locate the raw seismic data of the target seismic line in the basin, eliminate systematic deviations caused by differences in reference, polarity, and observation system, and achieve unified basic standards for multi-source data. The adjusted data is preprocessed to obtain preprocessed data; the preprocessing includes: suppressing surface consistency anomaly amplitude, compensating for surface consistency amplitude, and correcting for surface consistency static correction; it can suppress anomalous noise, compensate for amplitude energy, and correct for topographic effects, significantly improving the signal-to-noise ratio and waveform consistency of the data; the preprocessed data is deconvolved to obtain deconvolved data; the deconvolving process includes: surface consistency deconvolution, predictive deconvolution, and multichannel statistical expectation deconvolution; it can broaden the seismic data frequency band, compress wavelets, and improve vertical resolution, enhancing the ability to identify stratigraphic details. Based on the deconvolution-processed data, the lead information is replaced and time difference correction is performed to obtain superior data. This unifies data identification and eliminates time difference differences, improving data usability and lateral comparability. The superior data is then fused to obtain a seismic profile traversing the basin. This enables the effective integration of multi-source superior data, forming a continuous and complete basin seismic profile with weak boundary effects. This application effectively solves the problems of uneven quality, poor consistency, and boundary effects of multi-source seismic data by performing step-by-step regularization, noise reduction, quality improvement, and fusion processing on the original basin seismic data. It fully preserves and mines pre-stack data information, forming a high-quality continuous seismic profile, providing reliable data support for digital basin tectonic interpretation, stratigraphic correlation, and subsequent geological analysis. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating a cross-domain cloud computing processing method for earthquake data, provided as an embodiment of this application; Figure 2 A complete flowchart of a cross-domain cloud computing processing method for earthquake data is provided as an embodiment of this application; Figure 3 A schematic diagram of existing seismic data in a basin provided in an embodiment of this application; Figure 4A schematic diagram of seismic waves provided in an embodiment of this application; Figure 5 This is a schematic diagram showing the polarity before adjustment, provided in an embodiment of this application. Figure 6 This is a schematic diagram showing the polarity adjustment provided in an embodiment of this application; Figure 7 A schematic diagram of an auxiliary file for field seismic data provided in an embodiment of this application; Figure 8 A three-dimensional elevation map of a work area provided in an embodiment of this application; Figure 9 This is a schematic diagram of a single-shot record before abnormal amplitude provided in an embodiment of this application; Figure 10 A comparison image before and after superimposed profile compensation provided for an embodiment of this application; Figure 11 A plan view of an observation system provided in one embodiment of this application; Figure 12 An enlarged plan view of an observation system provided in an embodiment of this application; Figure 13 A schematic diagram of the superimposed cross-section of line 97548 and line 92548 provided in an embodiment of this application; Figure 14 A schematic diagram of three-dimensional interpolation with uniform cmp distance provided in an embodiment of this application; Figure 15 This is a schematic diagram illustrating the effect of suppressing surface uniformity anomaly amplitude in one embodiment of this application. Figure 16 A schematic diagram illustrating the effect of suppressing surface uniformity anomaly amplitude according to an embodiment of this application; Figure 17 This is a schematic diagram illustrating the effect of surface uniformity amplitude compensation before one embodiment of this application. Figure 18 A schematic diagram illustrating the effect of surface uniformity amplitude compensation according to an embodiment of this application; Figure 19 A schematic diagram illustrating the effect of residual static correction for surface consistency before and after an embodiment of this application; Figure 20 This is a schematic diagram of the effect before deconvolution provided in an embodiment of this application; Figure 21 This is a schematic diagram of the effect after deconvolution according to an embodiment of this application; Figure 22 This is a schematic diagram of the cross-sectional closure across a basin provided in one embodiment of this application; Figure 23 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] To address the diversity of seismic data in digital basins, this application first determines the reference surface for seismic data processing, as well as the processing flow and key parameters. Due to issues such as wavelet waveform, frequency, and phase caused by seismic data excitation and reception, surface consistency processing is used to correct the waveforms and time differences of different data wavelets. For combined deconvolution techniques with significant differences in seismic wavelets within the data, the wavelet characteristics in the basin are analyzed, and the desired wavelet is output to achieve consistency between wave groups. A unified CMP (Continuous Multiplication) surface element technique for 2D seismic data is employed, changing the data headers before and after stacking to address CMP consistency issues between different data sets.

[0019] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] In one exemplary embodiment, such as Figure 1 As shown, a cross-domain cloud computing processing method for seismic data is provided, which includes the following steps S1 to S6. Wherein: S1: Obtain raw seismic data for the target area and select data on the location of survey lines in the basin.

[0021] S2: Adjust the data of the survey line positions in the basin to obtain the adjusted data; the data adjustment includes: polarity adjustment, unification of the reference surface and redefinition of the observation system.

[0022] S3: Preprocess the adjusted data to obtain preprocessed data; the preprocessing includes: suppression of surface consistency anomaly amplitude, compensation of surface consistency amplitude, and static correction of surface consistency.

[0023] S4: Perform deconvolution processing on the preprocessed data to obtain deconvolution-processed data; the deconvolution processing includes: surface consistency deconvolution, prediction deconvolution and multichannel statistical expectation deconvolution.

[0024] S5: Based on the data after deconvolution processing, replace the track heading information and perform time difference correction to obtain superior data.

[0025] S6: The superior data are fused to obtain a seismic profile traversing the basin. Implementing steps S1 to S6 above can unify and standardize the raw multi-source seismic data of the basin, reduce noise and improve quality, enhance resolution, and correct time differences. This effectively eliminates data quality inconsistencies and boundary effects caused by differences in acquisition parameters, reference surfaces, and observation systems, fully preserving and utilizing pre-stack data information. Ultimately, a continuous, clear, and highly consistent basin seismic profile is formed, providing high-quality and reliable data support for digital basin tectonic interpretation, stratigraphic correlation, and subsequent geological analysis.

[0026] As an optional implementation, in step S2, the data of the survey line positions in the basin are adjusted to obtain adjusted data, specifically including: S21: Adjust the polarity of the data of the survey line positions in the basin to obtain the polarity-adjusted data.

[0027] S22: Redefine the observation system based on the polarity-adjusted data to obtain the redefined data.

[0028] S23: Based on the redefined data, select a reference plane to obtain data for a unified reference plane.

[0029] As an optional implementation, in step S3, the adjusted data is preprocessed to obtain preprocessed data, specifically including: S31: The adjusted data is denoised by suppressing the surface uniformity anomaly amplitude to obtain the denoised data.

[0030] S32: Perform surface consistency amplitude compensation on the denoised data to obtain compensated data.

[0031] S33: Perform surface consistency static correction on the compensated data to obtain corrected data.

[0032] As an optional implementation, in step S4, the preprocessed data is deconvolved to obtain deconvolved data, specifically including: S41: Perform surface-consistent deconvolution on the preprocessed data to obtain a preliminary consistent seismic wavelet.

[0033] S42: Perform predictive deconvolution on the preliminarily consistent seismic wavelet to obtain the optimized seismic wavelet.

[0034] S43: Perform multichannel statistical expectation deconvolution on the optimized seismic wavelet, generate deconvolution factors through spectral analysis, spectral superposition, factor design, and factor application, and apply the deconvolution factors to each channel of the multi-source data to obtain the deconvolution-processed data.

[0035] In the multichannel statistical expectation deconvolution, the deconvolution factor is estimated by using the statistical average of the power spectrum or logarithmic power spectrum of each channel for the input CMP channel set.

[0036] As an optional implementation, in step S5, based on the data after deconvolution processing, the track heading information is replaced and time difference correction is performed to obtain superior data, specifically including: S51: The deconvolution-processed data is re-performed using a virtual three-dimensional meshing method, while retaining the original track head information. A three-dimensional observation system is defined on the two-dimensional data using a three-dimensional seismic data processing observation system, and the CMP distance of different survey lines is unified to obtain standardized track head information data.

[0037] S52: Perform wavelet time difference correction on the standardized data of the track head information, compare the time difference of the superimposed data of each survey line, and perform slight time difference correction on the part of the time difference less than or equal to the preset value to obtain the time difference corrected data.

[0038] S53: Perform pre-stack three-dimensional in-trace interpolation on the time difference corrected data, integrate redundant CMP points that are less than the preset CMP distance from the survey line, and interpolate and fill in missing CMP points that are greater than the preset CMP distance from the survey line to obtain interpolated data.

[0039] S54: Analyze the single-shot records and spectral information of the interpolated data, remove noise interference segments, retain segments with strong effective reflected signals and clear wave group characteristics, and obtain superior data.

[0040] As an optional implementation, in step S6, the superior data is fused to obtain a seismic profile traversing the basin, specifically including: S61: The advantageous data is fused, the three-dimensional grid information is removed and the original track head information is restored, and the advantageous data fragments of different survey lines are spliced ​​together to obtain the fused data.

[0041] S62: Perform non-surface consistency static correction on the fused data, optimize the model channel through bandpass filtering, and combine velocity analysis and automatic residual static correction through multiple iterations to eliminate residual static correction amount and obtain statically corrected data.

[0042] S63: Perform fine velocity analysis on the statically corrected data, and produce a velocity spectrum by reducing the scanning velocity increment, adjusting the calculation window length and sliding distance. Combine the geological model to analyze the longitudinal and lateral velocity variation patterns, and select appropriate shot-receiver distances and CMP combinations according to different sections to obtain a high-precision velocity field.

[0043] S64: Based on the high-precision velocity field, the velocity is corrected to obtain the corrected velocity data.

[0044] S65: Based on the high-precision velocity field, the velocity-corrected data is superimposed to generate a seismic profile that passes through the basin and has continuous and clear geological features.

[0045] like Figure 2 As shown, the cross-domain cloud computing processing method for earthquake data provided in this embodiment includes the following: 1. Based on the basin construction requirements, delineate the basin area and collect raw seismic data for the target area from our institute's data room or the National Earthquake Archives. If data from the same location is available, select data with higher seismic quality. Figure 3 The data was stitched together after overlaying existing seismic data in the basin, resulting in significant stitching artifacts. The original data spans a large number of years and varies in quality. Some data is due to long storage time and magnetic tape adhesion, while others are not universally applicable, necessitating data screening.

[0046] 2. In the digital basin, seismic data is selected according to requirements. After polarity adjustment, since the measurement information such as the position coordinates of all shot points and receiver points are stored in the trace head, the observation system is defined according to the observation records; the travel time needs to be corrected to a unified reference surface.

[0047] 2.1 Regarding polarity: Generally speaking, in seismic data processing and seismic profile interpretation, a negative polarity wavelet whose main lobe jumps to the left is considered positive polarity. For data that is not positive polarity, the polarity header is multiplied by negative one and applied to the data to achieve data polarity consistency.

[0048] like Figure 4 As shown, the seismic waves received by the geophone are as follows: when the excitation point is excited, the receiving point (geophone) will receive the seismic waves from the underground reflecting layer. In the left figure, the first wave point of the seismic wave jumps to the left, and in the right figure, the seismic wave jumps to the right. The black lines represent wave crests. The horizontal axis represents the location of the survey line (parallel to the ground), and the vertical axis represents time (the time it takes for the seismic wave to propagate to the reflecting layer). Figure 5 and Figure 6 This is a single-shot waveform record, adjusted to positive polarity.

[0049] 2.2 Redefining the observation system.

[0050] Because seismic data acquisition years, instrument models, observation systems, and construction directions vary within the basin, data from the same location may be collected in different years. Therefore, a serial number should be added when defining the observation system to distinguish them, such as... Figure 7 The number 13 in the data is the shot number, which becomes 10000013 when defining the observation system (generally 8 digits in data processing) for differentiation in subsequent seismic data processing.

[0051] Figure 7This is an auxiliary file for field seismic data (SPS file), mainly used to mark the location of waveforms (seismic data). An SPS file generally consists of three parts: the excitation point file (S), the receiver point file (R), and the relationship file between the two (X). This image shows the X file. The red box records trace header information, such as: recording format SEG; E1 represents the explosive source; 12Kg represents the explosive weight; 1.3 meters represents the explosive well depth; the last line represents the measurement date, etc. The blue box mainly records the location information of each trace (one waveform). The first line, X, indicates it's the X file; 2007 represents the survey line number; 13 represents the excitation point number (the shot number corresponds to the information in the S file); 1 represents the first received trace; 720 represents the 720th received trace; 1460 represents the point number received in the first trace (corresponding to the information in the R file); and 2179 represents the point number received in the 720th trace (corresponding to the information in the R file).

[0052] 2.3 Unified reference surface.

[0053] A reference surface is used in seismic data processing. Due to variations in surface elevation in terrestrial seismic data, including mountainous and hilly areas, detectors in valleys may receive signals earlier or later than those on mountaintops. This can lead to misalignment of signals from the same underground reflection point, resulting in blurred images. Since the excitation and reception are not on the same plane, a reference surface is selected, on which all data is corrected. Generally, the elevation of the highest point is chosen.

[0054] like Figure 8 As shown, the plane represents the XY coordinates of the work area, and the colored portion displays the elevation. When establishing a seismic profile in the basin, the collected seismic data must be processed in a unified manner, with standardized parameters and a unified reference surface, to form the basis for connecting multiple seismic data into a large seismic profile.

[0055] 3. Suppression of surface uniformity anomalous amplitude, compensation of surface uniformity amplitude, and static correction of surface uniformity.

[0056] Surface uniformity decomposition and component analysis are key technologies in seismic data processing, primarily used to address seismic signal distortion caused by complex surface conditions (such as variations in low-velocity zones and differences in excitation and reception conditions). The core principle is to decompose the seismic signal into four categories of surface-related factors: shot point, receiver point, offset, and common midpoint, and then correct each category separately to eliminate amplitude, phase, and wavelet distortions caused by surface inhomogeneities, thereby making the data more spatially consistent.

[0057] 3.1 Suppression of surface uniformity anomaly amplitude.

[0058] With the development of seismic data acquisition in recent years, the anomalous amplitude of newly acquired data is not obvious. However, to establish a large seismic profile in the basin, some old data is involved. Therefore, surface uniform anomalous amplitude suppression is adopted to eliminate noise and improve the signal-to-noise ratio.

[0059] Assuming surface uniformity, surface uniformity anomalous amplitude suppression is achieved by picking anomalous amplitudes within a given time window, then decomposing them into surface uniformity shot point, receiver point, and CMP point terms to solve for the scaling factor, and finally applying it within the corresponding time window to eliminate anomalous amplitudes. Surface uniformity amplitude compensation eliminates the energy variations of seismic waves caused by the surface in space. Surface uniformity residual amplitude compensation further eliminates energy differences between different survey lines. Surface uniformity residual correction eliminates residual time differences between seismic traces.

[0060] like Figure 9 The image shown is a single-shot record acquired in the field. Each channel represents the received seismic waveform. The horizontal axis represents the ground location of the receiving channel, and the vertical axis represents the time it takes for the seismic wave to travel underground (1000ms). The black portion in the middle represents abnormal amplitudes that affect the imaging of seismic data and is defined as noise.

[0061] The picking window is selected based on the time range of abnormal amplitudes of a single shot and applied to the entire data. The suitability of the selected parameters is determined by overlaying profiles or removing abnormal amplitudes.

[0062] 3.2 Surface uniformity amplitude compensation.

[0063] Eliminating energy variations in seismic waves caused by surface factors in space is fundamental to subsequent survey line linking. The application of seismic wave amplitude compensation is a crucial consideration in the process; effective surface-consistent amplitude compensation forms the basis for establishing a digital basin.

[0064] In seismic data processing, spherical diffusion compensation (geometric diffusion) is generally used. Its main direction of action is longitudinal (which varies with depth / time). It can compensate for the energy reduction caused by the divergence of seismic waves as the propagation distance increases. Usually, spherical diffusion compensation is performed first, followed by surface consistency compensation.

[0065] However, this application uses surface uniform amplitude compensation, which mainly acts in the lateral direction (which varies with spatial location), and can eliminate the energy inconsistency between the shot point and the receiver point caused by surface differences, focusing on solving spatial "non-geological factor" interference.

[0066] like Figure 10 As shown, the horizontal axis represents the firing point of a shot and the receiving point of a receiver, and the vertical axis represents time. The left side shows the single-shot record before compensation, the middle side shows the single-shot record after compensation at the spherical plaza, and the right side shows the single-shot record after compensation for surface uniform amplitude. The left image shows the single-shot record before compensation, and the right image shows the single-shot record after compensation. The effective reflection layer can be seen at a deeper level.

[0067] 3.3 Surface consistency static correction.

[0068] In the field, some geophones are planted on mountaintops, some in valleys, and others are covered with soft soil (low-velocity zones). This can cause waves reflected from the same stratum to arrive at different geophones at different times. The solution is to eliminate the time difference caused by surface undulations and changes in low-velocity zones. 4. Surface-consistent deconvolution, predictive deconvolution, and multichannel statistical deconvolution.

[0069] Deconvolution improves temporal resolution and provides true subsurface reflections by compressing the fundamental seismic wavelet in the seismic record, suppressing mixed echoes and short-period multiples.

[0070] 4.1 Surface-consistent defolding.

[0071] First, surface-consistent deconvolution is used, assuming that the wavelet depends only on the positions of the shot point and receiver point and is independent of the reflection path between strata, thus initially improving the consistency of the seismic wavelet.

[0072] Information will be provided for subsequent well location determination.

[0073] 4.2 Predicting deconvolution.

[0074] By analyzing the stratigraphic characteristics of the basin (combining the analysis of the lithological characteristics of the stratigraphy in the region with the collected seismic geological data, such as volcanic rocks, shale, and structural features), combining the geological tasks (such as whether the target layer is deep or shallow, and whether the interpretation focuses on lithofacies or structural interpretation), combining well logging data, and testing parameters such as distance and operator length, the signal-to-noise ratio of seismic data is further improved by applying predictive deconvolution.

[0075] 4.3 Desired wavelet deconvolution.

[0076] In seismic data processing, because the desired wavelet deconvolution is based on the seismic wave itself, it is mostly used to improve the resolution of stacked data in order to ensure the true reflection of the seismic wavelet. In the construction of digital basins, it effectively suppresses random noise and increases the lateral continuity of the signal to characterize large-scale sedimentary bodies or structures. When applying desired wavelet deconvolution, multichannel statistical calculation of the deconvolution factor is often used.

[0077] Since a single seismic survey line comprises seismic data from multiple years, traditional methods for data stitching, which typically involve using multiple shaping factors and sequentially shaping wavelets between adjacent data points, are practically difficult to implement. Therefore, a more efficient approach is to calculate the similarity of multiple data wavelets using a multi-channel statistical expectation deconvolution method. This expectation deconvolution is achieved through spectral analysis, spectral stacking (statistical averaging), factor design, and factor application. For the input CMP gather, a deconvolution factor is estimated using the statistical average of the power spectrum or logarithmic power spectrum of each channel. This factor is then applied to each channel in the multi-source data to achieve consistency in the data wavelets.

[0078] set up for n Seismic data at any given time (time series); The input is the power spectrum of a specific channel in the cmp channel set.

[0079] ; in, Power spectrum; M The size of the time window; T The sampling interval for seismic data; for n Earthquake data at any given time; j The imaginary unit; f The frequency of the seismic waves; Pi is the mathematical constant of a circle.

[0080] After obtaining the average power spectrum of a given CMP, the deconvolution factor of that CMP is designed using the factor design module. The factor design formula is: ; ; in, It is the deconvolution factor; To determine the amplitude spectrum of the desired output wavelet; The phase spectrum of the desired output wavelet; The average amplitude spectrum obtained after superposition of spectra; The minimum phase spectrum is calculated from the adjusted average amplitude spectrum; ω is the angular frequency of the seismic wave; t This represents the propagation time of the seismic waves.

[0081] For wavelet consistency issues that are not well addressed by multi-channel deconvolution, wavelet shaping techniques are used at individual splicing points.

[0082] 5. Replace trackhead information, perform slight time difference correction and fusion of advantageous data, and perform pre-stack interpolation.

[0083] After resolving issues related to amplitude domain, time domain, and vertical resolution to achieve clearer profile imaging, multi-data stitching is performed. Due to the diversity of seismic data, different acquisition parameters, different acquisition directions, and differences in observation systems, the CMP distance varies along each two-dimensional survey line. The CMP mesh unification technique uses a virtual 3D method to define a 3D mesh, and then uses methods such as enlarging or interpolating to unify the mesh to obtain a unified CMP distance gather.

[0084] 5.1 3D Mesh Definition.

[0085] Using a 3D seismic data processing observation system, a 3D observation system is defined on top of the 2D data, unifying the CMP distance. The observation system plan view is shown below. Figure 11As shown, when magnified... Figure 12 As shown, the red dots represent the firing points (shot points), and the blue dots represent the receiving points (detector points), defined by the CMP mesh. For example, there are CMP distances of 25 meters, 20 meters, and 50 meters. The CMP distances along the entire survey line are investigated, and a suitable CMP distance is selected through experimentation. The 25-meter CMP distance is defined using a 3D mesh. 50 yuan.

[0086] 5.2 Adjust time difference correction and integrate advantageous data.

[0087] First, overlay the various survey lines and compare the time difference. If it is less than or equal to 10ms, perform a slight time difference correction, conduct splicing point tests, remove redundant trackhead information, and integrate advantageous data.

[0088] At the splicing point, investigate single-shot records and spectrum information, and integrate advantageous data.

[0089] like Figure 13 As shown, this is an overlay profile after integrating the superior data. The data to the left of the red line is from 1997, and the data to the right is from 1992. The cmp distance for the 1997 data is 10m, and the cmp distance for the 1992 data is 25m.

[0090] 5.3 Pre-stack interpolation.

[0091] For small CMP distance survey lines, such as Figure 13 In the unified CMP distance plan, two CMP points will appear in a single cell at a CMP distance of 20m from the ground. For survey lines with large CMP distances, some cells may not have any CMP points. Therefore, pre-stack 3D trace interpolation is used.

[0092] like Figure 14 As shown, the horizontal axis is cmp and the vertical axis is time. The left side is before unified cmp, the middle side shows the situation where cmp is missing after unified cmp, and the right side shows the result after 3D interpolation.

[0093] 6. Static calibration and precise speed control.

[0094] 6.1 Non-surface consistent static correction.

[0095] Automatic residual static correction (ARPC) calculates time differences through cross-correlation of model traces, then decomposes them into shot point, receiver point, offset, and structural terms, applying these correction components to the seismic data. Therefore, the quality of the model traces significantly impacts the effectiveness of ARPC. To address this, we employ bandpass filtering to highlight the advantageous frequency bands of the signal and perform modifying processing on the model traces to improve their quality. Simultaneously, through multiple iterations of velocity analysis and ARPC, the stacking velocity accuracy is improved, and the residual static correction is gradually eliminated, thereby enhancing the signal-to-noise ratio of the data.

[0096] 6.2 Detailed speed analysis.

[0097] To obtain a high-precision velocity field, we conducted numerous experimental studies during the velocity acquisition process. For low signal-to-noise ratio data, energy clusters in the mid- and deep velocity spectra are generally difficult to concentrate. Therefore, repeated stacking experiments and constant-rate and variable-rate scanning methods were used to determine the velocities, and the velocity variation patterns were understood according to geological models. In velocity spectrum fabrication, reducing the scan velocity increment, decreasing the calculation window length, and the sliding distance can improve the accuracy of velocity analysis. Different shot-receiver distances and the number of CMP combinations were used for different sections to improve the quality of the velocity spectrum. During velocity interpretation, not only the vertical variation of velocity should be considered, but also the lateral variation. Furthermore, the selection of cut parameters is crucial; when determining cut parameters, timely spatial variation based on the formation's variation patterns is necessary.

[0098] In summary, this application has the following advantages: Digital basins unify the reference surface, data flow, and data processing parameters for multi-source seismic data, thereby eliminating large closure errors and significant differences in wave group characteristics. This is beneficial for stratigraphic correlation and sequence division within digital basins.

[0099] Surface consistency seismic data processing methods include surface consistency anomalous amplitude suppression and surface consistency amplitude compensation, which eliminate the compensation for seismic wave energy absorption and attenuation caused by the surface. Surface consistency residual static correction eliminates static correction time differences caused by surface variations, improves the continuity of seismic wave phase axes, facilitates stratigraphic tracking, and enhances the accuracy of seismic data interpretation.

[0100] The effects of suppressing the anomalous amplitude of surface uniformity are as follows: Figure 15 and Figure 16 As shown, the horizontal coordinate of the superimposed profile is cmp, and there are labels at the top and bottom of the image.

[0101] The effects before and after surface uniformity amplitude compensation are as follows: Figure 17 and Figure 18 As shown, the time slice image is a parallel ground image. The horizontal axis of the 3D seismic data represents the data line number, and the vertical axis represents the point number. It can be seen from the image that the amplitude is more uniform at the same time. The amplitude energy is improved in the 1000ms time slice, which can better show the underground structure.

[0102] The effects of residual static correction for surface uniformity before and after are as follows: Figure 19 As shown, the horizontal axis of the superimposed profile is cmp number, and the vertical axis is time, thus improving the continuity of the in-phase axis.

[0103] Surface-consistent deconvolution effectively suppresses seismic wavelets and short-period multiples, eliminating reverberation. Predictive deconvolution further compresses the wavelets, improving resolution. To further eliminate the influence of multi-source data, multichannel statistical expectation deconvolution is employed, ensuring consistent wavegroup characteristics between adjacent data points, reducing phase differences between adjacent data points, and clarifying basin tectonic features.

[0104] like Figure 20 and Figure 21 As shown, the horizontal axis of the superimposed profile is the cmp number, and the vertical axis is time; the horizontal axis of the frequency spectrum is frequency, and the vertical axis is amplitude to improve resolution; the wavelet autocorrelation convolution has the horizontal axis line number and the vertical axis phase, with wavelet main lobe compression and side lobe suppression.

[0105] The unified CMP (Cross-Minute Motion) pixel technique for 2D seismic data solves the problems of no intersection between adjacent data points and different CMP distances for the same seismic line. The profile closure situation across the basin is as follows: Figure 22 As shown.

[0106] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 23 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs in the non-volatile storage media to run. The database stores raw seismic data for the target area. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a cross-domain cloud computing processing method for seismic data.

[0107] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0108] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0109] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0110] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0111] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for seismic data cross-domain cloud computing processing, characterized in that, The cross-domain cloud computing processing method for earthquake data includes: Obtain raw seismic data for the target area and select data on the location of survey lines within the basin; The data of the survey line locations in the basin are adjusted to obtain the adjusted data; the data adjustment includes: polarity adjustment, unification of the reference surface, and redefinition of the observation system; The adjusted data is preprocessed to obtain preprocessed data; the preprocessing includes: suppression of surface consistency anomaly amplitude, compensation of surface consistency amplitude, and static correction of surface consistency. The preprocessed data is deconvolved to obtain deconvolved data; the deconvolution process includes: surface consistency deconvolution, prediction deconvolution, and multichannel statistical expectation deconvolution; Based on the data after deconvolution, the track start information is replaced and time difference correction is performed to obtain the superior data; The superior data are fused to obtain a seismic profile that passes through the basin.

2. The method for seismic data cross-domain cloud computing processing according to claim 1, characterized in that, The data of the survey line positions in the basin are adjusted to obtain the adjusted data, specifically including: The polarity of the data at the survey line locations in the basin is adjusted to obtain polarity-adjusted data; The observation system is redefined based on the polarity-adjusted data to obtain the redefined data. Based on the redefined data, a reference plane is selected to obtain data for a unified reference plane.

3. The method of claim 1, wherein, The adjusted data is preprocessed to obtain preprocessed data, specifically including: The adjusted data was denoised by suppressing the surface uniformity anomaly amplitude to obtain the denoised data. The denoised data is subjected to surface consistency amplitude compensation to obtain compensated data; The compensated data is subjected to surface consistency static correction to obtain the corrected data.

4. The seismic data cloud computing processing method of claim 1, wherein, The preprocessed data is then deconvolved to obtain deconvolved data, specifically including: The preprocessed data is subjected to surface-consistent deconvolution to obtain a preliminary consistent seismic wavelet; The preliminarily consistent seismic wavelet is subjected to predictive deconvolution to obtain the optimized seismic wavelet; The optimized seismic wavelet is subjected to multichannel statistical expectation deconvolution. Deconvolution factors are generated through spectral analysis, spectral superposition, factor design, and factor application. The deconvolution factors are then applied to each channel of the multi-source data to obtain the deconvolution-processed data.

5. The seismic data cross-domain cloud computing processing method of claim 4, wherein, In the multichannel statistical expectation deconvolution, the deconvolution factor is estimated by using the statistical average of the power spectrum or logarithmic power spectrum of each channel for the input CMP channel set. The formula for calculating the power spectrum is: ； wherein, is a power spectrum; M is a size of a time window; T is a sampling interval of seismic data; is n is seismic data at a time instant; j is an imaginary unit; f is a frequency of a seismic wave; is a circle constant; The expression for the deconvolution factor is: ； ； wherein is the deconvolution factor; is the amplitude spectrum of the desired output wavelet; is the phase spectrum of the desired output wavelet; is the average amplitude spectrum after the spectrum is stacked; is the minimum phase spectrum calculated from the modified average amplitude spectrum; is the angular frequency of the seismic wave; t is the travel time of the seismic wave.

6. The seismic data cloud computing processing method of claim 1, wherein, Based on the data processed by the deconvolution, the track heading information is replaced, and time difference correction is performed to obtain superior data, specifically including: The deconvolution-processed data is re-performed using a virtual 3D meshing method, while retaining the original track head information. A 3D seismic data processing and observation system is used to define a 3D observation system on the 2D data, unify the CMP distance of different survey lines, and obtain standardized track head information data. The standardized data of the trackhead information is corrected by wavelet time difference correction. The time difference of the superimposed data of each survey line is compared. The part with time difference less than or equal to the preset value is slightly corrected to obtain the time difference corrected data. The time difference corrected data is subjected to pre-stack three-dimensional in-trace interpolation. Redundant CMP points with a distance less than the preset CMP distance from the survey line are integrated, and missing CMP points with a distance greater than the preset CMP distance from the survey line are interpolated and filled to obtain interpolated data. By analyzing the single-shot records and spectral information of the interpolated data, noise interference segments are removed, and segments with strong effective reflected signals and clear wave group characteristics are retained to obtain superior data.

7. The cross-domain cloud computing processing method for seismic data according to claim 1, characterized in that, The superior data are fused to obtain a seismic profile traversing the basin, specifically including: The advantageous data are fused, the three-dimensional grid information is removed and the original track head information is restored, and the advantageous data fragments of different survey lines are spliced ​​together to obtain the fused data; The fused data is subjected to non-surface consistency static correction. The model channel is optimized by bandpass filtering. Combined with velocity analysis and automatic residual static correction, the residual static correction amount is eliminated through multiple iterations to obtain the statically corrected data. A fine velocity analysis was performed on the statically corrected data. A velocity spectrum was generated by reducing the scanning velocity increment, adjusting the calculation window length and sliding distance. The longitudinal and lateral velocity variation patterns were analyzed in conjunction with the geological model. The appropriate shot-receiver distance and the number of CMP combinations were selected according to different sections to obtain a high-precision velocity field. Based on the high-precision velocity field, the velocity is corrected to obtain the corrected velocity data. Based on the high-precision velocity field, the velocity-corrected data is superimposed to generate a seismic profile that passes through the basin and has continuous and clear geological features.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that the processor executes the computer program to implement the cross-domain cloud computing processing method for seismic data according to any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the cross-domain cloud computing processing method for earthquake data as described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the cross-domain cloud computing processing method for earthquake data as described in any one of claims 1-7.

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