A method, system, and electronic equipment for processing seismic data in the Loess Plateau region.

Abstract

This application discloses a method, system, and electronic equipment for processing seismic data in the Loess Plateau region. The method employs Fresnel tomography static correction for data inversion to obtain a static correction value. This static correction value is then applied to the seismic data volume to obtain a first seismic data volume after static correction. The first seismic data volume is then processed through surface wave denoising, linear dip filtering, and frequency division denoising to obtain a second seismic data volume after denoising. The second seismic data volume is then subjected to deconvolution operations on pre-stack shot gathers, common receiver gathers, and post-stack profiles to obtain the final seismic data. The method provided in this application can significantly improve the resolution and signal-to-noise ratio of low signal-to-noise ratio seismic data in the Loess Plateau region, while accurately reflecting the characteristics of underground structures and greatly improving the accuracy of profile imaging. This lays a solid foundation for subsequent detailed stratigraphic interpretation and the determination of fault relationships and levels.

Description

Technical Field

[0001] This application relates to the field of data processing, and in particular to a method, system, and electronic equipment for processing seismic data in the Loess Plateau region. Background Technology

[0002] In the processing of onshore seismic data, it is usually necessary to correct the seismic data to a unified reference plane, which is generally a horizontal plane. Seismic exploration and interpretation theories assume that the excitation point and receiver point are on the same horizontal plane and that the formation velocity is uniform. However, in reality, the ground is often uneven, the depth of each excitation point may vary, and the wave velocity in the low-velocity zone differs significantly from the wave velocity in the formation, which inevitably affects the shape of the measured time-distance curve. To eliminate these effects, the raw seismic data undergoes topographic correction, excitation depth correction, and low-velocity zone correction. These corrections are invariant across different seismic interfaces at the same observation point and are therefore collectively referred to as static correction. In a broader sense, static correction also includes phase correction and correction for instrument-related factors. With the development of digital processing technology, various automatic static correction methods and procedures have emerged.

[0003] Refraction static correction is, strictly speaking, also a model static correction method. However, its model building is different from traditional methods. It mainly obtains the refractive surface velocity and delay time by solving equations and then establishes a velocity model with the help of the surface velocity. Based on this, the static correction amount is calculated.

[0004] Currently, long-wavelength static correction in the Loess Plateau region can also be achieved through refraction static correction. However, due to the strong absorption and attenuation of seismic wavelets in the Loess Plateau region, the signal-to-noise ratio and frequency of seismic data are generally low. The single surface uniform deconvolution method cannot achieve zero-phase processing of the data, making it difficult to achieve the desired effect. Summary of the Invention

[0005] This application provides a method, system, and electronic equipment for processing seismic data in the Loess Plateau region, which can greatly improve the resolution and signal-to-noise ratio of low signal-to-noise ratio seismic data in the Loess Plateau region.

[0006] Firstly, this application provides a method for processing seismic data in the Loess Plateau region, the method comprising:

[0007] When the initial data is obtained, the data is inverted through Fresnel chromatography static correction to obtain the static correction amount;

[0008] The static correction amount is applied to the seismic data volume to obtain the first seismic data volume after static correction.

[0009] The first seismic data volume is processed by surface wave denoising, linear dip angle filtering and frequency division denoising to obtain the denoised second seismic data volume.

[0010] The second seismic data volume is deconvolved with pre-stack shot gathers, common receiver gathers, and post-stack profiles to obtain the final seismic data.

[0011] The method provided in this application can greatly improve the resolution and signal-to-noise ratio of low signal-to-noise ratio seismic data in the Loess Plateau region, while accurately reflecting the characteristics of underground structures and significantly improving the accuracy of profile imaging. This lays a solid foundation for subsequent fine interpretation of stratigraphy and determination of the relationships and levels of faults.

[0012] In an optional embodiment, the step of performing data inversion through Fresnel chromatography static correction to obtain the static correction amount includes:

[0013] An initial velocity model is established based on the initial data;

[0014] The initial velocity model is inverted to obtain the static correction value.

[0015] In one optional embodiment, the step of processing the first seismic data volume through surface wave denoising, linear dip filtering, and frequency division denoising to obtain a denoised second seismic data volume includes:

[0016] The first seismic data is filtered by a cross-shaped cone filter to obtain the first filtered seismic data.

[0017] The first filtered seismic data is filtered again by linear dip filtering to obtain the second filtered seismic data.

[0018] The strong amplitude noise of the second filtered seismic data is eliminated by frequency division denoising technology to obtain the second seismic data volume.

[0019] In one optional embodiment, the second seismic data volume is deconvolved with pre-stack shot gathers, common receiver gathers, and post-stack profiles to obtain the final seismic data, including:

[0020] The logarithmic amplitude spectrum and phase spectrum of each seismic trace in the second seismic data volume are calculated, and the power spectrum of the surface anomaly factor of each trace is calculated to solve for the surface anomaly inverse filter factor.

[0021] The spectrum of the wavelet is obtained by statistically analyzing the power spectrum and minimum phase spectrum of the second seismic data volume on the common receiver gather.

[0022] After stacking, the amplitude spectrum, phase spectrum, power spectrum, and spectrum of each wavelet are optimally mixed and deconvolved to obtain the final seismic data volume.

[0023] Secondly, this application provides a seismic data processing system for the Loess Plateau region, the system comprising:

[0024] The static correction processing module is used to perform data inversion through Fresnel chromatography static correction when the initial data is acquired, and obtain the static correction amount.

[0025] The processing module is used to load the static correction amount onto the seismic data volume to obtain a first seismic data volume after static correction; to process the first seismic data volume through surface wave denoising, linear dip filtering and frequency division denoising to obtain a second seismic data volume after denoising; and to perform deconvolution operations on the second seismic data volume on pre-stack shot gathers, common receiver point gathers and post-stack profiles to obtain the final seismic data.

[0026] In one optional embodiment, the static correction processing block is specifically used to establish an initial velocity model based on the initial data; and to perform inversion processing on the initial velocity model to obtain the static correction amount.

[0027] In one optional embodiment, the processing module is specifically used to filter the first seismic data using a cross-shaped cone filter to obtain first filtered seismic data; to filter the first filtered seismic data again using a linear dip filter to obtain second filtered seismic data; and to eliminate strong amplitude noise in the second filtered seismic data using a frequency division denoising technique to obtain the second seismic data volume.

[0028] In an optional embodiment, the processing module is specifically used to calculate the logarithmic amplitude spectrum and phase spectrum of each seismic trace in the second seismic data volume, calculate the power spectrum of the surface anomaly factor for each trace, and solve for the surface anomaly inverse filtering factor.

[0029] The spectrum of the wavelet is obtained by statistically analyzing the power spectrum and minimum phase spectrum of the second seismic data volume on the common receiver gather.

[0030] After stacking, the amplitude spectrum, phase spectrum, power spectrum, and spectrum of each wavelet are optimally mixed and deconvolved to obtain the final seismic data volume.

[0031] Thirdly, this application provides an electronic device, comprising:

[0032] Memory, used to store computer programs;

[0033] When the processor executes the computer program stored in the memory, it implements the steps of the above-described method for processing seismic data in the Loess Plateau region.

[0034] Fourthly, this application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described method for processing seismic data in the Loess Plateau region.

[0035] For the various aspects of the second to fourth aspects mentioned above, and the technical effects that each aspect may achieve, please refer to the above description of the technical effects that can be achieved for the first aspect or the various possible solutions in the first aspect, which will not be repeated here. Attached Figure Description

[0036] Figure 1 A flowchart of a seismic data processing method for the Loess Plateau region provided in this application;

[0037] Figure 2 A schematic diagram of the initial velocity model provided in this application;

[0038] Figure 3 A schematic diagram of the inversion velocity model provided in this application;

[0039] Figure 4 A schematic diagram of the static correction amount for chromatographic inversion provided in this application;

[0040] Figure 5 A comparison of single-shot images before and after static correction for refraction and static correction for tomographic inversion provided for this application;

[0041] Figure 6 Comparison of superimposed cross-sections of refractive static correction and tomographic inversion static correction provided for this application;

[0042] Figure 7 Comparison of records before and after comprehensive denoising provided for this application;

[0043] Figure 8 A comparison of the superposition profiles and spectra before (top) and after (bottom) of the three-step statistical wavelet deconvolution method provided in this application;

[0044] Figure 9 The three-step statistical wavelet deconvolution method provided in this application includes a comparison of the migration profiles and spectra before (top) and after (bottom) application of the method.

[0045] Figure 10 Comparison of conventional processing (top) and post-stack migration profiles for improving imaging accuracy of low signal-to-noise ratio seismic data in the Loess Plateau region (bottom) provided for this application.

[0046] Figure 11 A schematic diagram of the structure of a seismic data processing system for the Loess Plateau region provided in this application;

[0047] Figure 12 This is a schematic diagram of the structure of an electronic device provided in this application. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The specific operational methods in the method embodiments can also be applied to the device embodiments or system embodiments. It should be noted that in the description of this application, "multiple" is understood as "at least two". "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. A connected to B can represent: A and B directly connected, and A and B connected through C. Furthermore, in the description of this application, terms such as "first" and "second" are used only for distinguishing the purpose of description and should not be construed as indicating or implying relative importance or order.

[0049] Currently, in the processing of onshore seismic data, it is common practice to correct seismic data to a unified reference surface, which is generally a horizontal plane. Seismic exploration and interpretation theories assume that the excitation and reception points are on the same horizontal plane and that the formation velocity is uniform. However, in reality, the ground is often uneven, the depths of different excitation points may vary, and the wave velocities in the low-velocity zone differ significantly from those in the formation, inevitably affecting the shape of the measured time-distance curve. To eliminate these effects, topographic correction, excitation depth correction, and low-velocity zone correction are performed on the raw seismic data. These corrections are invariant across different seismic interfaces at the same observation point and are therefore collectively referred to as static correction. In a broader sense, static correction also includes phase correction and correction for instrument-related factors. With the development of digital processing technology, various automatic static correction methods and procedures have emerged.

[0050] Refraction static correction is, strictly speaking, also a model static correction method. However, its model building is different from traditional methods. It mainly obtains the refractive surface velocity and delay time by solving equations and then establishes a velocity model with the help of the surface velocity. Based on this, the static correction amount is calculated.

[0051] Currently, long-wavelength static correction in the Loess Plateau region can also be achieved through refraction static correction. However, due to the strong absorption and attenuation of seismic wavelets in the Loess Plateau region, the signal-to-noise ratio and frequency of seismic data are generally low. The single surface uniform deconvolution method cannot achieve zero-phase processing of the data, making it difficult to achieve the desired effect.

[0052] To address the aforementioned issues, this application provides a method for processing seismic data in the Loess Plateau region. This method employs Fresnel tomography static correction for data inversion to obtain a static correction value. This static correction value is then applied to the seismic data volume to obtain a statically corrected first seismic data volume. The first seismic data volume is then processed using surface wave denoising, linear dip filtering, and frequency-division denoising to obtain a denoised second seismic data volume. Finally, the second seismic data volume is deconvolved with pre-stack shot gathers, common receiver gathers, and post-stack profiles to obtain the final seismic data. The method provided in this application significantly improves the resolution and signal-to-noise ratio of low signal-to-noise ratio seismic data in the Loess Plateau region, while accurately reflecting the characteristics of underground structures and greatly improving the accuracy of profile imaging. This lays a solid foundation for subsequent detailed stratigraphic interpretation and the determination of fault relationships and levels.

[0053] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0054] like Figure 1 The figure shown is a seismic data processing method for the Loess Plateau region provided in an embodiment of this application. The specific implementation process of this method is as follows:

[0055] S1, when the initial data is obtained, the data is inverted through Fresnel chromatography static correction to obtain the static correction amount;

[0056] Specifically, the initial data is first subjected to Fresnel zone tomography static correction. Fresnel zone tomography static correction is a crucial processing step for the Loess Plateau region, and its accuracy is closely related to the continuity of the reflected wave phase axis and the accurate repositioning of geological stratigraphic shifts in imaging. The method involves fully utilizing first-arrival information to establish an initial velocity model, such as... Figure 2 The initial velocity model diagram is shown below.

[0057] Based on the obtained initial velocity model, an inversion process is performed, specifically inverting the thickness and velocity of the low-velocity zone. The resulting inverted velocity model is as follows: Figure 3 As shown, the accurate static correction is then calculated, and the inversion static correction obtained through tomographic inversion is as follows: Figure 4 As shown.

[0058] S2, apply the static correction to the seismic data volume to obtain the first seismic data volume after static correction;

[0059] After obtaining the static correction, the static correction is applied to the seismic data volume to obtain the first seismic data volume after static correction.

[0060] Specifically, the obtained tomographic inversion static correction is applied to the seismic data volume to obtain the tomographic inversion statically corrected seismic data volume. For example... Figure 5 , Figure 6The comparison of single-shot and stacked profiles before and after applying different static correction methods shows that tomographic static correction not only solves the problem of uneven near-surface velocity, but also solves the problem of long-wavelength static correction, and truly reflects the underground structural characteristics of monoclinic strata, which has obvious advantages.

[0061] S3, the first seismic data volume is processed by surface wave denoising, linear dip angle filtering and frequency division denoising to obtain the denoised second seismic data volume;

[0062] The first seismic data is filtered using a cross-shaped cone filter to obtain first filtered seismic data; the first filtered seismic data is then filtered again using a linear dip filter to obtain second filtered seismic data; finally, strong amplitude noise in the second filtered seismic data is eliminated using frequency division denoising technology to obtain the second seismic data volume.

[0063] Specifically, after static correction and restoration of the true form of effective reflections and noise, pre-stack denoising is performed. Pre-stack denoising is a crucial step in seismic data processing, especially in low signal-to-noise ratio regions, as it affects the accuracy of data velocity analysis and the quality of the final image. Therefore, targeted denoising is necessary before deconvolution. Different denoising methods are applied for different types of interference waves, employing a comprehensive multi-domain, step-by-step, fidelity-preserving denoising technique. First, in the surface wave region, a cross-shaped cone filter technique is used to identify and suppress surface waves, resulting in a surface wave-free record. Then, linear tilt filtering effectively removes refraction interference. Finally, frequency division denoising is used to eliminate strong amplitude noise, yielding a comprehensively denoised record. The records before and after comprehensive denoising are shown below. Figure 7 As shown, the recording after comprehensive denoising effectively removes noise without altering the reflection amplitude characteristics of the effective signal, providing high-fidelity and amplitude-preserving processing results for subsequent comprehensive studies.

[0064] S4. Perform deconvolution operations on the pre-stack shot gather, common receiver gather, and post-stack profile of the second seismic data volume to obtain the final seismic data.

[0065] The amplitude spectrum and phase spectrum of each seismic trace in the second seismic data volume are calculated, and the power spectrum of the anomaly factor corresponding to each seismic trace is calculated. The spectrum of the wavelet is obtained by statistically deconvolving the second seismic data volume. The amplitude spectrum, phase spectrum, power spectrum and spectrum of each wavelet are optimally mixed and deconvolved to obtain the final seismic data.

[0066] Specifically, after obtaining the denoised records, a three-step statistical wavelet deconvolution method is used to comprehensively improve the longitudinal resolution of seismic data, effectively eliminate the filtering effect of the ground on the reflected wavelet, compensate for high-frequency losses, and restore the reflection coefficient characteristics of the seismic wavelet. The first step involves performing a Fourier transform on the pre-stack shot gathers, calculating the logarithmic amplitude spectrum and phase spectrum for each trace, and calculating the power spectrum of the surface anomaly factor for each trace through iterative calculations. The surface anomaly inverse filtering factor is solved using the Levinson recursion method, and the seismic traces are convolved with the inverse filtering factor. The second step uses a multi-channel statistical wavelet deconvolution method on the common receiver point gathers. To obtain accurate wavelets while simultaneously eliminating interference noise, a multi-channel weighted averaging method is used to obtain their power spectrum, thereby obtaining the minimum phase spectrum, and then the wavelet spectrum. Finally, this wavelet is used for deconvolution operations. The first two steps of deconvolution effectively compressed the seismic wavelet, significantly improving the resolution of the stacked profile and increasing the dominant frequency from 25Hz to 40Hz. Simultaneously, it maintained a high signal-to-noise ratio and lateral consistency of the wavelet, greatly enhancing data fidelity. Figure 8 As shown. The third step is to apply optimal hybrid phase deconvolution to the post-stack profile. Because optimal hybrid phase deconvolution considers the spatiotemporal variations of the wavelet and performs multi-gathering and time-window processing, the high-frequency components of the seismic traces after hybrid phase deconvolution processing increase, and the phase axis becomes thinner. From Figure 9 The comparison of migration profiles and spectra before and after the application of hybrid phase deconvolution shows that the seismic profile after hybrid phase deconvolution improves the energy of high-frequency signals, expands the bandwidth, enriches the frequency components, and improves seismic resolution.

[0067] In summary, compared with conventional processing, the method of this invention yields a more reasonable wave group relationship in the reflected waves and a clearer structural response, while maintaining the signal-to-noise ratio. This is beneficial for determining the location of breakpoints and tracing and comparing stratigraphic layers. Figure 10 As shown.

[0068] The method of this invention can greatly improve the resolution and signal-to-noise ratio of low signal-to-noise ratio seismic data in the Loess Plateau region, while accurately reflecting the characteristics of underground structures and significantly improving the accuracy of profile imaging. This lays a solid foundation for subsequent fine interpretation of stratigraphy and determination of the interrelationships and levels of faults.

[0069] Based on the same inventive concept, this application also provides a seismic data processing system for the Loess Plateau region, such as... Figure 11 As shown, the system includes:

[0070] The static correction processing module 101 is used to perform data inversion through Fresnel chromatography static correction when the initial data is acquired, and obtain the static correction amount.

[0071] Processing module 102 is used to load the static correction amount onto the seismic data volume to obtain a first seismic data volume after static correction; process the first seismic data volume through surface wave denoising, linear dip filtering and frequency division denoising to obtain a second seismic data volume after denoising; and perform deconvolution operation on the second seismic data volume on pre-stack shot gathers, common receiver point gathers and post-stack profiles to obtain the final seismic data.

[0072] In one optional embodiment, the static correction processing module 101 is specifically used to establish an initial velocity model based on the initial data; and to perform inversion processing on the initial velocity model to obtain the static correction amount.

[0073] In an optional embodiment, the processing module 102 is specifically used to filter the first seismic data using a cross-shaped cone filter to obtain first filtered seismic data; to filter the first filtered seismic data again using a linear dip angle filter to obtain second filtered seismic data; and to eliminate strong amplitude noise in the second filtered seismic data using frequency division denoising technology to obtain the second seismic data volume.

[0074] In an optional embodiment, the processing module 102 is specifically used to calculate the logarithmic amplitude spectrum and phase spectrum of each seismic trace in the second seismic data volume, calculate the power spectrum of the surface anomaly factor of each trace, and solve for the surface anomaly inverse filtering factor.

[0075] The spectrum of the wavelet is obtained by statistically analyzing the power spectrum and minimum phase spectrum of the second seismic data volume on the common receiver gather.

[0076] After stacking, the amplitude spectrum, phase spectrum, power spectrum, and spectrum of each wavelet are optimally mixed and deconvolved to obtain the final seismic data volume.

[0077] Based on the same inventive concept, this application also provides an electronic device that can realize the functions of the aforementioned seismic data processing system for the Loess Plateau region. (Refer to...) Figure 12 The electronic device includes:

[0078] At least one processor 1101 and a memory 1102 connected to at least one processor 1101. In this embodiment, the specific connection medium between the processor 1101 and the memory 1102 is not limited. Figure 12 The example shown is the connection between processor 1101 and memory 1102 via bus 1100. Bus 1100 is... Figure 12 The connections between other components are shown in thick lines only and are not intended to be limiting. Bus 1100 can be divided into address bus, data bus, control bus, etc., for ease of representation. Figure 12The term 1101 is represented by a single thick line, but this does not imply that there is only one bus or one type of bus. Alternatively, the processor 1101 may also be referred to as a controller; there is no restriction on the name.

[0079] In this embodiment, the memory 1102 stores instructions executable by at least one processor 1101. By executing the instructions stored in the memory 1102, the at least one processor 1101 can perform the aforementioned method for processing seismic data in the Loess Plateau region. The processor 1101 can implement... Figure 10 The system shown illustrates the functions of each module.

[0080] The processor 1101 is the control center of the device. It can connect to various parts of the control device through various interfaces and lines. By running or executing instructions stored in memory 1102 and calling data stored in memory 1102, the processor can perform various functions and process data, thereby monitoring the device as a whole.

[0081] In one possible design, processor 1101 may include one or more processing units. Processor 1101 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may also not be integrated into processor 1101. In some embodiments, processor 1101 and memory 1102 may be implemented on the same chip; in some embodiments, they may also be implemented on separate chips.

[0082] The processor 1101 can be a general-purpose processor, such as a central processing unit (CPU), digital signal processor, application-specific integrated circuit, field-programmable gate array or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method for processing seismic data in the Loess Plateau region disclosed in the embodiments of this application can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0083] Memory 1102, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. Memory 1102 may include at least one type of storage medium, such as flash memory, hard disk, multimedia card, card-type memory, random access memory (RAM), static random access memory (SRAM), programmable read-only memory (PROM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), magnetic storage, magnetic disk, optical disk, etc. Memory 1102 can be any other medium capable of carrying or storing desired program code in the form of instructions or data structures that can be accessed by a computer, but is not limited thereto. In the embodiments of this application, memory 1102 can also be a circuit or any other device capable of implementing storage functions for storing program instructions and / or data.

[0084] By designing and programming the processor 1101, the code corresponding to the seismic data processing method for the Loess Plateau region described in the aforementioned embodiments can be embedded into the chip, thereby enabling the chip to execute the code during operation. Figure 1 The illustrated embodiment describes the steps of a seismic data processing method for the Loess Plateau region. How to design and program the processor 1101 is a technique well-known to those skilled in the art and will not be elaborated upon here.

[0085] Based on the same inventive concept, this application also provides a storage medium storing computer instructions that, when executed on a computer, cause the computer to perform a method for processing seismic data in the Loess Plateau region as described above.

[0086] In some possible implementations, various aspects of the seismic data processing method for the Loess Plateau region provided in this application can also be implemented in the form of a program product, which includes program code. When the program product is run on a device, the program code is used to cause the control device to perform the steps in the seismic data processing method for the Loess Plateau region according to the various exemplary embodiments of this application described above.

[0087] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0088] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0089] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0090] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0091] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for processing seismic data in the Loess Plateau region, characterized in that, The method includes: When the initial data is obtained, the data is inverted through Fresnel chromatography static correction to obtain the static correction amount; The static correction amount is applied to the seismic data volume to obtain the first seismic data volume after static correction. The first seismic data volume is processed by surface wave denoising, linear dip filtering, and frequency division denoising to obtain a denoised second seismic data volume. The process includes: filtering the first seismic data using a cross-shaped cone filter to obtain first filtered seismic data; filtering the first filtered seismic data again using linear dip filtering to obtain second filtered seismic data; and eliminating strong amplitude noise in the second filtered seismic data using frequency division denoising technology to obtain the second seismic data volume. The second seismic data volume is deconvolved with pre-stack shot gathers, common receiver gathers, and post-stack profiles to obtain the final seismic data. This includes: calculating the logarithmic amplitude spectrum and phase spectrum of each seismic trace in the second seismic data volume, calculating the power spectrum of the surface anomaly factor for each trace, and solving for the surface anomaly inverse filtering factor; obtaining the wavelet spectrum by statistically analyzing the power spectrum and minimum phase spectrum of the second seismic data volume on the common receiver gathers; and performing optimal hybrid deconvolution on the amplitude spectrum, phase spectrum, power spectrum, and spectrum of each wavelet after stacking to obtain the final seismic data volume.

2. The method as described in claim 1, characterized in that, The data inversion through Fresnel chromatography static correction yields the static correction amount, including: An initial velocity model is established based on the initial data; The initial velocity model is inverted to obtain the static correction value.

3. A seismic data processing system for the Loess Plateau region, characterized in that, The system includes: The static correction processing module is used to perform data inversion through Fresnel chromatography static correction when the initial data is acquired, and obtain the static correction amount. The processing module is used to load the static correction amount onto the seismic data volume to obtain a first seismic data volume after static correction; to process the first seismic data volume through surface wave denoising, linear dip filtering and frequency division denoising to obtain a second seismic data volume after denoising; and to perform deconvolution operations on the second seismic data volume on pre-stack shot gathers, common receiver point gathers and post-stack profiles to obtain the final seismic data. Specifically, the processing module is used to filter the first seismic data using a cross-shaped cone filter to obtain first filtered seismic data; to filter the first filtered seismic data again using a linear dip filter to obtain second filtered seismic data; and to eliminate strong amplitude noise in the second filtered seismic data using frequency division denoising technology to obtain the second seismic data volume. Specifically, the processing module is used to calculate the logarithmic amplitude spectrum and phase spectrum of each seismic trace in the second seismic data volume, calculate the power spectrum of the surface anomaly factor of each trace, and solve for the surface anomaly inverse filtering factor; obtain the spectrum of the wavelet by statistically analyzing the power spectrum and minimum phase spectrum of the second seismic data volume on the common receiver point gather; and perform optimal hybrid deconvolution on the amplitude spectrum, phase spectrum, power spectrum and spectrum of each wavelet after stacking to obtain the final seismic data volume.

4. The system as described in claim 3, characterized in that, The static correction processing module is specifically used to establish an initial velocity model based on the initial data; and to perform inversion processing on the initial velocity model to obtain the static correction amount.

5. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, when executing a computer program stored in the memory, implements the steps of the method according to claim 1 or 2.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method described in claim 1 or 2.

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