A surface wave data processing method, device, equipment and medium

Abstract

The present invention relates to a method, apparatus, device, and medium for processing surface roll data. The method comprises: converting raw seismic data within a target work area into multiple basis functions; modifying a pre-generated spatial model based on the multiple basis functions, wherein the spatial model is generated by the multiple basis functions; and processing the surface roll data in the raw seismic data based on the modified spatial model and a dispersion curve of the surface roll data in the raw seismic data to separate and obtain effective reflection signals from the raw seismic data. The method can effectively separate effective reflection signals from the raw seismic data under complex working conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas geophysical exploration, and in particular to a surface wave data processing method, device, equipment and medium. Background Art

[0002] Understandably, surface rolls are ubiquitous in seismic data collected in the field and are a significant factor limiting data quality. Surface rolls are a very common type of noise characterized by low frequency, low velocity, high energy, and dispersion and multimodality. To enhance the visibility of valid signals, such as reflected and refracted waves, effective suppression and elimination of surface roll noise is essential.

[0003] The strength of surface wave energy is related to the exciting lithology, exciting depth and surface seismic geological conditions. Especially when the amplitude of effective reflection signals in deep and ultra-deep layers is relatively weak, the high amplitude and strong dispersion of surface waves have a strong masking effect on the effective signals.

[0004] At present, with the complexity of field exploration terrain and the increase in the burial depth of underground target layers, the difficulty in distinguishing surface waves from effective reflection signals has become increasingly prominent. There is an urgent need for a technology that can effectively separate the effective reflection signals in the original seismic data under complex working conditions. Summary of the Invention

[0005] The present invention provides a method, device, equipment and medium for processing surface roll data to solve the problem that surface rolls and effective reflection signals are difficult to distinguish.

[0006] To achieve the above objectives, in a first aspect, the present invention provides a method for processing surface roll data, comprising:

[0007] Converting the original seismic data in the target work area into multiple basis functions;

[0008] modifying a pre-generated spatial model according to the plurality of basis functions, wherein the spatial model is generated by the plurality of basis functions;

[0009] Acquiring surface wave data from the original seismic data;

[0010] The surface wave data in the original seismic data are processed according to the modified spatial model and the dispersion curve of the surface wave data in the original seismic data to separate and obtain effective reflection signals of the original seismic data.

[0011] Preferably, the converting of the original seismic data in the target work area into a plurality of basis functions comprises:

[0012] converting the raw seismic data into a discrete time series;

[0013] In the potential energy-wave function domain of the original seismic data, the discrete time series is decomposed channel by channel to generate multiple basis functions, and the multiple basis functions include one or more of polynomial basis functions, Fourier basis functions, wavelet basis functions, Gaussian basis functions, B-spline basis functions, Laguerre basis functions, Chebyshev basis functions and orthogonal basis functions.

[0014] Preferably, before modifying the pre-generated spatial model according to the multiple basis functions and after converting the original seismic data in the target work area into the multiple basis functions, the method further includes:

[0015] Generating a loss function according to the multiple basis functions and the discrete time series; wherein the loss function is used to characterize the error between the multiple basis functions and the discrete time series;

[0016] The loss function is defined as:

[0017]

[0018] in δ is a tuning parameter, y i is the basis function, is the discrete time series;

[0019] The modifying of the pre-generated spatial model according to the plurality of basis functions comprises:

[0020] Correcting a pre-generated spatial model based on errors between the plurality of basis functions and the original seismic data;

[0021] The processing of the surface wave data according to the modified spatial model and the dispersion curve of the surface wave data in the original seismic data to separate and obtain effective reflection signals of the original seismic data includes:

[0022] Solving the loss function according to the modified spatial model to determine the amplitude, phase, and travel time of the spatial model;

[0023] performing bilateral filtering on the amplitude, the phase, and the travel time respectively;

[0024] The surface roll data in the original seismic data is processed according to the dispersion curve of the surface roll data in the original seismic data and the result of the bilateral filtering to separate and obtain the effective reflection signal of the original seismic data.

[0025] Preferably, the step of obtaining surface wave data from the original seismic data comprises:

[0026] Slicing the original seismic data to obtain fundamental surface wave data;

[0027] determining an effective frequency band range of the fundamental surface roll data;

[0028] Reconstructing the wavelength of the frequency-domain surface roll data corresponding to the fundamental surface roll data within the effective frequency band to obtain an amplitude field and a phase spectrum of the frequency-domain surface roll data;

[0029] Surface wave data of the original seismic data is determined according to the amplitude field and the phase spectrum.

[0030] Preferably, the pre-generated spatial model is corrected based on the errors between the plurality of basis functions and the original seismic data, comprising:

[0031] Determining respective coefficients of the plurality of basis functions in the spatial model according to the spatial model and errors between the plurality of basis functions and the original seismic data;

[0032] The spatial model is modified according to the respective coefficients.

[0033] To achieve the above-mentioned object, in a second aspect, the present invention relates to a surface wave data processing device, comprising:

[0034] The original seismic data conversion module is used to convert the original seismic data in the target work area into multiple basis functions;

[0035] A space model correction module is used to correct the pre-generated space model according to the multiple basis functions.

[0036] wherein the spatial model is generated by the plurality of basis functions;

[0037] A surface wave data acquisition module is used to acquire surface wave data from the original seismic data;

[0038] The surface wave data processing module is used to process the surface wave data in the original seismic data according to the modified spatial model and the dispersion curve of the surface wave data in the original seismic data, so as to separate and obtain the effective reflection signal of the original seismic data.

[0039] Preferably, the raw seismic data conversion module is specifically used to:

[0040] converting the raw seismic data into a discrete time series;

[0041] In the potential energy-wave function domain of the original seismic data, the discrete time series is decomposed channel by channel to generate multiple basis functions, and the multiple basis functions include one or more of polynomial basis functions, Fourier basis functions, wavelet basis functions, Gaussian basis functions, B-spline basis functions, Laguerre basis functions, Chebyshev basis functions and orthogonal basis functions.

[0042] Preferably, the method further comprises: a loss function generating unit, configured to generate a loss function based on the plurality of basis functions and the discrete time series; wherein the loss function is used to characterize the error between the plurality of basis functions and the discrete time series; and the loss function is defined as:

[0043]

[0044] in δ is a tuning parameter, y i is the basis function, is the discrete time series;

[0045] The spatial model correction module is specifically used to: correct the pre-generated spatial model according to the errors between the multiple basis functions and the original seismic data;

[0046] The surface roll data processing module is specifically configured to solve the loss function according to the modified spatial model to determine the amplitude, phase, and travel time of the spatial model;

[0047] performing bilateral filtering on the amplitude, the phase, and the travel time respectively;

[0048] The surface roll data in the original seismic data is processed according to the dispersion curve of the surface roll data in the original seismic data and the result of the bilateral filtering to separate and obtain the effective reflection signal of the original seismic data.

[0049] To achieve the above-mentioned purpose, in a third aspect, the present invention also relates to a computer device, comprising a memory, a processor and a computer program stored on the memory, characterized in that the processor executes the computer program to implement the steps of a surface wave data processing method as described in any one of claims 1 to 5.

[0050] To achieve the above-mentioned purpose, in a fourth aspect, the present invention further relates to a computer-readable storage medium, in which instructions are stored, and when the instructions are executed, the above-mentioned method for processing surface wave data is executed.

[0051] The present invention relates to a surface roll data processing method, device, equipment, and medium, which have the following beneficial effects compared to the prior art:

[0052] The present invention can effectively separate effective reflection signals, such as reflected and refracted waves, from raw seismic data under complex conditions. First, the raw seismic data within the target area is converted into multiple basis functions; each basis function has a different frequency width and phase distribution. Next, a pre-generated spatial model is modified based on these multiple basis functions. The spatial model is generated from these multiple basis functions. Constructing the spatial model using multiple signal segments from a discrete time series overcomes the noise introduced by existing techniques.

[0053] The surface wave data is processed based on the modified spatial model and the dispersion curve of the surface wave data in the original seismic data. It can be understood that through the above steps, the surface wave components are completely confined to a certain basis function. Finally, the amplitude, travel time, and phase of the surface wave components are suppressed. After reconstruction, the surface waves in the discrete time series are completely suppressed in terms of amplitude, travel time, and phase, thereby effectively separating effective signals such as reflected waves and refracted waves. The method of the present invention performs surface wave suppression on the original seismic data based on the basis function, ensuring that the surface waves are effectively suppressed while ensuring that the effective signals are well protected. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 The method flow of a surface wave data processing method in the first embodiment of the present invention is as follows: Figure 1 ;

[0055] Figure 2 The method flow of a surface wave data processing method in the first embodiment of the present invention is as follows: Figure 2 ;

[0056] Figure 3 The method flow of a surface wave data processing method in the first embodiment of the present invention is as follows: Figure 3 ;

[0057] Figure 4 The method flow of a surface wave data processing method in the first embodiment of the present invention is as follows: Figure 4 ;

[0058] Figure 5 The method flow of a surface wave data processing method in the first embodiment of the present invention is as follows: Figure 5 ;

[0059] Figure 6 The method flow of a surface wave data processing method in the first embodiment of the present invention is as follows: Figure 6 ;

[0060] Figure 7 This is a schematic diagram of the structure of a surface wave data processing device in the second embodiment of the present invention. Figure 1 ;

[0061] Figure 8This is a schematic diagram of the structure of a surface wave data processing device in the second embodiment of the present invention. Figure 2 . DETAILED DESCRIPTION

[0062] The present invention will be further described in detail below with reference to the accompanying drawings and examples. It will be understood that the specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions relevant to the present invention, not all structures.

[0063] Example 1

[0064] A surface wave data processing method, see Figure 1-6 , Figure 1 A flow chart of a surface roll data processing method provided in an embodiment of the present disclosure includes the following steps: S10 to S40.

[0065] S10: Convert the original seismic data in the target work area into multiple basis functions.

[0066] S20: Modifying a pre-generated spatial model according to a plurality of basis functions, wherein the spatial model is generated by the plurality of basis functions.

[0067] S30: Acquire surface wave data from the original seismic data.

[0068] S40: Processing the surface wave data in the original seismic data according to the modified spatial model and the dispersion curve of the surface wave data in the original seismic data to separate and obtain effective reflection signals of the original seismic data.

[0069] First, the original seismic data in the target work area is converted into multiple basis functions. Then, a pre-generated spatial model is corrected according to the multiple basis functions, wherein the spatial model is generated by the multiple basis functions. Finally, the surface wave data is processed according to the corrected spatial model and the dispersion curve of the surface wave data in the original seismic data.

[0070] In summary, the method provided in the embodiments of the present disclosure performs surface roll suppression on raw seismic data using basis functions, thereby ensuring both effective suppression of surface rolls and good protection of valid signals.

[0071] Preferably, the basis function includes one or more of a polynomial basis function, a Fourier basis function, a wavelet basis function, a Gaussian basis function, a B-spline basis function, a Laguerre basis function, a Chebyshev basis function and an orthogonal basis function.

[0072] It is understandable that step S20 is an iterative process, that is, iteratively generating basis functions, generating a spatial initial model, and updating the spatial initial model until the error meets the preset requirements. Figure 2As shown, preferably after S10 and before S20, it also includes: S50: generating a loss function based on multiple basis functions and discrete time series; wherein the loss function is used to characterize the error between the multiple basis functions and the discrete time series. Preferably, the loss function is defined as:

[0073]

[0074] in δ is a tuning parameter. This loss function combines the advantages of minimum square error and minimum absolute deviation, using a square term for small errors and an absolute value term for large errors. This reduces sensitivity to the original seismic data while maintaining sensitivity to small errors.

[0075] Regarding the dispersion curve of the surface wave data in step S40, this application has the following definition: When surface waves propagate in an inhomogeneous or stratified medium, waves of different frequencies may propagate at different velocities. This phenomenon is called dispersion. The dispersion curve reflects the relationship between wave velocity (or phase velocity or group velocity) and frequency.

[0076] To extract the dispersion curve from surface wave data, this application provides the following steps: Select a time window containing surface waves, using the time difference between the direct wave arrival time and the surface wave arrival time. The dispersion curve is obtained by calculating the group velocity of different frequency components. For multiple recordings, the group velocity between stations can be calculated using the cross-correlation function. The frequency-time plot or cross-correlation function obtained through the above steps can be used to further extract the dispersion curve. Next, curve fitting or inversion techniques are used to extract the phase velocity and group velocity at different frequencies from the frequency-time plot.

[0077] Based on the above embodiments, Figure 3 As shown, for step S10, the original seismic data is represented by a set of relatively simple basis functions, specifically including: S11-S12.

[0078] S11: Convert the raw seismic data into discrete time series.

[0079] Specifically, determine the sampling frequency (in Hz) and the time window length (the sampling frequency determines the time resolution of the time series, and the time window length determines the length of the sequence.) Based on the sampling frequency and time window length, calculate the number of sampling points of the discrete time series. For example, if the sampling frequency is 100 Hz and the time window length is 10 seconds, the number of sampling points is 1000. Sample the continuous time raw seismic data at equal intervals to obtain a discrete time series. The interpolation method can be used to convert the raw data into discrete data points. Finally, save the sampled discrete data points into a one-dimensional array or vector as a discrete time series. This completes the conversion of seismic trace data to discrete time series.

[0080] S12: In the potential energy-wave function domain of the original seismic data, the discrete time series is decomposed trace by trace to generate multiple basis functions.

[0081] In this embodiment, first, the discrete time series is subjected to Fourier transform to obtain a frequency domain representation (so that the time domain series can be converted to the frequency domain). Then, the frequency domain signal is subjected to potential energy-wave function decomposition. Some commonly used basis functions can be used here, such as Morlet wavelet, Gabor wavelet, etc. By performing potential energy-wave function transform on the frequency domain signal, a time-frequency domain representation can be obtained. Each time-frequency unit corresponds to a basis function coefficient. By traversing all time-frequency units, a set of basis functions and their corresponding coefficients can be obtained. In this way, the original discrete time series can be decomposed into a linear combination of multiple basis functions. Each basis function represents a different time-frequency characteristic.

[0082] The beneficial effects of step S11 are: better characterization of the time-frequency characteristics of non-stationary signals, signal reconstruction and compression using a small number of basis functions, and feature extraction, providing an important basis for subsequent signal analysis and processing.

[0083] Additionally, the potential energy-wave function domain in step S12 involves concepts from quantum mechanics. In quantum mechanics, a wave function describes the quantum state of a particle, while potential energy describes the potential field in the particle's environment. The wave function domain can be understood as the domain of the wave function, i.e., the range of possible positions or states of a particle.

[0084] In quantum mechanics, the wave function is a complex function whose squared absolute value gives the probability density of a particle's occurrence at a given location and time. Potential energy is a real function that describes the potential field experienced by a particle at that location. The Schrödinger equation is one of the equations that describes the evolution of the wave function over time and has the following form:

[0085]

[0086] Where i is the imaginary unit, h is the reduced Planck constant, and m is the mass of the particle. is the second-order spatial derivative (the one-dimensional form of the Laplace operator), is a wave function that varies with position x and time t.

[0087] In the steady-state case, the time-independent Schrödinger equation can be written as:

[0088]

[0089] Here E is the energy of the particle.

[0090] Based on the above embodiments, see Figure 3, a surface wave data processing method, between S10 and S20 also includes: S50: generating a loss function according to multiple basis functions and discrete time series; wherein the loss function is used to characterize the error between the multiple basis functions and the discrete time series.

[0091] In addition, the loss function in S50 is also used to characterize the errors between multiple basis functions and the original seismic trace data.

[0092] Based on the above embodiments, see Figure 4 , S40 also includes: S41-S43.

[0093] S41: solving the loss function according to the modified spatial model to determine the amplitude, phase and travel time of the spatial model;

[0094] S42: performing bilateral filtering on the amplitude, phase and travel time respectively;

[0095] S43: Processing the surface wave data in the original seismic data according to the dispersion curve of the surface wave data in the original seismic data and the result of bilateral filtering to separate and obtain effective reflection signals of the original seismic data.

[0096] In steps S41-S43, when the value of the loss function is less than the target value, the solution calculation is completed, and the surface roll components are suppressed in amplitude, travel time, and phase respectively using bilateral filtering operations. After the reconstruction, the surface roll is completely suppressed in terms of amplitude, travel time, and phase in the time series.

[0097] Based on the above embodiments, see Figure 5 , S30 of a surface wave data processing method also includes: S31-S34.

[0098] S31: Slicing the original seismic data to obtain fundamental surface wave data;

[0099] S32: determining the effective frequency band range of the fundamental surface wave data;

[0100] First, perform a Fourier transform on the fundamental surface roll data to obtain a frequency spectrum. This allows the frequency components contained in the data to be clearly identified. Next, calculate the signal-to-noise ratio of the fundamental surface roll data to determine the frequency band of the effective signal. Generally speaking, the frequency range with a high signal-to-noise ratio is the effective frequency band of the data. Preferably, the correlation of the fundamental surface roll data across different frequency bands is calculated to further determine the effective frequency band. The frequency range with a high correlation is the effective frequency band of the data. Based on the theoretical dispersion relationship of the fundamental surface roll and the geological conditions of the study area, the theoretical frequency band of the fundamental surface roll is predicted and compared and corrected with the above analysis results.

[0101] S33: reconstructing the wavelength of the frequency-domain surface roll data corresponding to the fundamental-order surface roll data within the effective frequency band to obtain the amplitude field and phase spectrum of the frequency-domain surface roll data;

[0102] The fundamental surface roll data in the time domain is converted to the frequency domain to obtain frequency domain surface roll data. Within the effective frequency band determined above, the frequency domain surface roll data is wavelength reconstructed (by recovering the wavelengths of different frequency components, the corresponding frequency domain amplitude field and phase spectrum can be obtained). From the wavelength reconstruction results, the amplitude field information of the frequency domain surface roll data is extracted (the amplitude field reflects the energy distribution characteristics of different frequency components). Similarly, the phase spectrum information of the frequency domain surface roll data is extracted from the wavelength reconstruction results (the phase spectrum reflects the phase changes of different frequency components).

[0103] S34: Determine surface wave data of the original seismic data according to the amplitude field and the phase spectrum.

[0104] First, the amplitude field and phase spectrum information of the frequency-domain surface-roll data is reconstructed back into the time domain via an inverse Fourier transform to obtain time-domain surface-roll data. The surface-roll data obtained from the time-domain reconstruction is then separated from the original seismic data. This can be done by using methods such as filtering and frequency-domain separation to extract the surface-roll portion from the original data. Based on the amplitude field information of the frequency-domain surface-roll data, the time-domain surface-roll data is amplitude-corrected to make it more consistent with the amplitude characteristics of the original seismic data. Similarly, based on the phase spectrum information of the frequency-domain surface-roll data, the phase characteristics of the time-domain surface-roll data are corrected to reflect the phase characteristics of the surface rolls in the original seismic data.

[0105] Based on the above embodiments, see Figure 6 , step S50 includes:

[0106] S51: determining respective coefficients of the plurality of basis functions in the spatial model according to the spatial model and errors between the plurality of basis functions and the original seismic trace data;

[0107] S52: Modify the spatial model according to the respective coefficients.

[0108] An embodiment of the present disclosure provides a method for processing surface wave data. First, the original seismic data in a target work area is converted into multiple basis functions. Then, a pre-generated spatial model is corrected according to the multiple basis functions, wherein the spatial model is generated by the multiple basis functions. Finally, the surface wave data is processed according to the corrected spatial model and the dispersion curve of the surface wave data in the original seismic data.

[0109] Example 2

[0110] A surface wave data processing device, such as Figure 7As shown, it includes: an original seismic data conversion module 61, a spatial model correction module 62, a surface wave data acquisition module 63 and a surface wave data processing module 64.

[0111] The original seismic data conversion module 61 is used to convert the original seismic data in the target work area into multiple basis functions;

[0112] a spatial model correction module 62 for correcting a pre-generated spatial model according to a plurality of basis functions, wherein the spatial model is generated by the plurality of basis functions;

[0113] A surface wave data acquisition module 63 is used to acquire surface wave data from the original seismic data;

[0114] The surface wave data processing module 64 is used to process the surface wave data in the original seismic data according to the modified spatial model and the dispersion curve of the surface wave data in the original seismic data, so as to separate and obtain effective reflection signals of the original seismic data.

[0115] In some embodiments, the raw seismic data conversion module 61 is specifically configured to:

[0116] Convert raw seismic data into discrete time series;

[0117] In the potential energy-wave function domain of the original seismic data, the discrete time series is decomposed trace by trace to generate multiple basis functions.

[0118] In some embodiments, as Figure 8 As shown, it also includes: a loss function generating unit 65, which is used to: generate a loss function according to multiple basis functions and discrete time series; wherein the loss function is used to characterize the error between the multiple basis functions and the discrete time series;

[0119] The spatial model correction module 62 is specifically used to correct the pre-generated spatial model according to the errors between the multiple basis functions and the original seismic data;

[0120] The surface wave data processing module 64 is specifically configured to:

[0121] Solve the loss function based on the modified spatial model to determine the amplitude, phase and travel time of the spatial model;

[0122] Perform bilateral filtering on amplitude, phase and travel time respectively;

[0123] The surface wave data in the original seismic data is processed according to the dispersion curve of the surface wave data in the original seismic data and the result of bilateral filtering to separate and obtain the effective reflection signal of the original seismic data.

[0124] The implementation process, method and effect of the surface roll data processing device of this embodiment are the same as those of the surface roll data processing method described in the first embodiment, and will not be described in detail here.

[0125] Example 3

[0126] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the above embodiment.

[0127] Example 4

[0128] The present invention relates to a computer-readable storage medium, which stores instructions. When the instructions are executed, a method for processing surface roll data in embodiment 1 is executed. The execution process and effect thereof are the same as those of the method for processing surface roll data described in embodiment 1, and are not described in detail here.

[0129] The processor may include, but is not limited to, one or more processors or microprocessors. Each processor may be implemented by an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components to execute the methods in the above embodiments.

[0130] The computer-readable storage medium may be implemented by any type of volatile or non-volatile storage device or a combination thereof. The computer-readable storage medium may include, but is not limited to, for example, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, computer storage media (such as hard disks, floppy disks, solid-state drives, removable disks, CD-ROMs, DVD-ROMs, Blu-ray discs, etc.).

[0131] The computer-readable storage medium may also store at least one computer-executable program / instruction, such as a computer-readable instruction. Computer-readable storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Computer-readable storage media may include, for example, read-only memory (ROM), a hard disk, a flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above may be performed.

[0132] In addition, the computer device may also include (but is not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (eg, keyboard, mouse, speaker, etc.).

[0133] The processor can communicate with external devices via an I / O bus via a wired or wireless network.

[0134] In one embodiment, the at least one computer executable instruction may also be compiled into or constitute a software product / computer program product, wherein one or more computer executable instructions are executed by a processor to perform the various functions and / or method steps in the embodiments described in the present technology.

[0135] In the embodiments provided in the present disclosure, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a portion of code, and the above-mentioned module, program segment or a portion of code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or can be implemented using a combination of dedicated hardware and computer instructions.

[0136] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0137] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for processing surface roll data, characterized in that: include: Converting the original seismic data in the target work area into multiple basis functions; modifying a pre-generated spatial model according to the plurality of basis functions, wherein the spatial model is generated by the plurality of basis functions; Acquiring surface wave data from the original seismic data; The surface wave data in the original seismic data are processed according to the modified spatial model and the dispersion curve of the surface wave data in the original seismic data to separate and obtain effective reflection signals of the original seismic data.

2. The method for processing surface wave data according to claim 1, wherein: The conversion of the original seismic data in the target work area into a plurality of basis functions includes: converting the raw seismic data into a discrete time series; In the potential energy-wave function domain of the original seismic data, the discrete time series is decomposed channel by channel to generate multiple basis functions, and the multiple basis functions include one or more of polynomial basis functions, Fourier basis functions, wavelet basis functions, Gaussian basis functions, B-spline basis functions, Laguerre basis functions, Chebyshev basis functions and orthogonal basis functions.

3. The method for processing surface wave data according to claim 2, wherein: Before modifying the pre-generated spatial model according to the multiple basis functions and after converting the original seismic data in the target work area into the multiple basis functions, the method further includes: Generating a loss function according to the multiple basis functions and the discrete time series; wherein the loss function is used to characterize the error between the multiple basis functions and the discrete time series; The loss function is defined as: in δ is a tuning parameter, y i is the basis function, is the discrete time series; The modifying of the pre-generated spatial model according to the plurality of basis functions comprises: Correcting a pre-generated spatial model based on errors between the plurality of basis functions and the original seismic data; The processing of the surface wave data according to the modified spatial model and the dispersion curve of the surface wave data in the original seismic data to separate and obtain effective reflection signals of the original seismic data includes: Solving the loss function according to the modified spatial model to determine the amplitude, phase, and travel time of the spatial model; performing bilateral filtering on the amplitude, the phase, and the travel time respectively; The surface roll data in the original seismic data is processed according to the dispersion curve of the surface roll data in the original seismic data and the result of the bilateral filtering to separate and obtain the effective reflection signal of the original seismic data.

4. A surface wave data processing method according to any one of claims 1 to 3, characterized in that: The obtaining of surface wave data from the original seismic data comprises: Slicing the original seismic data to obtain fundamental surface wave data; determining an effective frequency band range of the fundamental surface roll data; Reconstructing the wavelength of the frequency-domain surface roll data corresponding to the fundamental surface roll data within the effective frequency band to obtain an amplitude field and a phase spectrum of the frequency-domain surface roll data; Surface wave data of the original seismic data is determined according to the amplitude field and the phase spectrum.

5. The method for processing surface wave data according to claim 3, wherein: The method of correcting the pre-generated spatial model based on the errors between the plurality of basis functions and the original seismic data comprises: Determining respective coefficients of the plurality of basis functions in the spatial model according to the spatial model and errors between the plurality of basis functions and the original seismic data; The spatial model is modified according to the respective coefficients.

6. A surface wave data processing device, characterized in that: include: The original seismic data conversion module is used to convert the original seismic data in the target work area into multiple basis functions; a space model correction module, configured to correct a pre-generated space model according to the plurality of basis functions, wherein the space model is generated by the plurality of basis functions; A surface wave data acquisition module is used to acquire surface wave data from the original seismic data; The surface wave data processing module is used to process the surface wave data in the original seismic data according to the modified spatial model and the dispersion curve of the surface wave data in the original seismic data, so as to separate and obtain the effective reflection signal of the original seismic data.

7. The surface wave data processing device according to claim 6, characterized in that: The original seismic data conversion module is specifically used to: converting the raw seismic data into a discrete time series; In the potential energy-wave function domain of the original seismic data, the discrete time series is decomposed channel by channel to generate multiple basis functions, and the multiple basis functions include one or more of polynomial basis functions, Fourier basis functions, wavelet basis functions, Gaussian basis functions, B-spline basis functions, Laguerre basis functions, Chebyshev basis functions and orthogonal basis functions.

8. The surface wave data processing device according to claim 7, characterized in that: Also includes: A loss function generating unit is configured to generate a loss function based on the multiple basis functions and the discrete time series; wherein the loss function is used to characterize the error between the multiple basis functions and the discrete time series; and the loss function is defined as: in δ is a tuning parameter, y i is the basis function, is the discrete time series; The spatial model correction module is specifically used to: correct the pre-generated spatial model according to the errors between the multiple basis functions and the original seismic data; The surface roll data processing module is specifically configured to solve the loss function according to the modified spatial model to determine the amplitude, phase, and travel time of the spatial model; performing bilateral filtering on the amplitude, the phase, and the travel time respectively; The surface roll data in the original seismic data is processed according to the dispersion curve of the surface roll data in the original seismic data and the result of the bilateral filtering to separate and obtain the effective reflection signal of the original seismic data.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory, wherein: The processor executes the computer program to implement the steps of the surface wave data processing method according to any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that: The storage medium stores instructions, which, when executed, execute a surface wave data processing method according to any one of claims 1 to 5.

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