Surface wave attenuation method and device

Abstract

The invention provides a surface wave attenuation method and device. The surface wave attenuation method comprises the following steps: constructing a multi-signal classification spatial spectrum corresponding to seismic data according to the pre-acquired seismic data of a target work area; picking up a frequency dispersion curve in the multiple signal classification spatial spectrum; and attenuating the surface wave of the target work area according to the frequency dispersion curve. According to the method, the characteristic that the surface wave has the frequency dispersion phenomenon is utilized, the frequency dispersion curve is picked up, the obtained frequency dispersion curve generates the forward modeling surface wave model, the forward modeling surface wave model is subtracted from the original single shot, and finally the purpose of surface wave attenuation is achieved. The technical defect that in the prior art, a manual picking mode is adopted for picking the surface wave frequency dispersion curve, and the labor cost is high is overcome. Moreover, the method can more accurately and effectively pick up a surface wave frequency dispersion curve, thereby more accurately generating a forward surface wave model, and achieving the purpose of attenuating seismic data shallow strong surface waves.

Description

Technical Field

[0001] The present application belongs to the technical field of oil and gas exploration, in particular to the technical field of oil and gas exploration seismic data processing, and specifically relates to a surface wave attenuation method and device. Background Art

[0002] In the simulated seismic records of earthquakes, a wave with a propagation speed slightly lower than the S wave speed and a strong amplitude was identified - the Rayleigh surface wave. Its main characteristics are:

[0003] 1. The derivative wave field generated by the interference of P waves and SV waves near the free surface has an elliptical particle motion trajectory;

[0004] 2. Strong energy, low frequency within 16HZ, speed 100-1000m / s;

[0005] 3. The intensity of energy is usually related to the lithology, depth, charge and surface seismic geological conditions;

[0006] 4. Dispersion occurs in inhomogeneous media.

[0007] A general pulse wave is composed of simple harmonic components of various frequencies, and each simple harmonic component maintains the same wave velocity and phase difference during propagation. However, if the seismic wave velocity is related to the seismic frequency, then after the seismic wave propagates for a certain period of time, the simple harmonic components of various frequencies will propagate at different distances due to different wave velocities, and the initial pulse wave will be dispersed into a series of waves. This phenomenon is called wave dispersion, and surface waves usually exhibit dispersion in seismic records, as shown in the attached figure. Figure 1 .

[0008] Surface wave attenuation refers to the phenomenon that the amplitude of surface waves in seismic waves gradually decreases during propagation. Surface wave attenuation is the loss of energy due to absorption and scattering of underground media. In seismic wave propagation, surface waves propagate along the surface of the earth, so they interact with the underground medium. This interaction causes the energy of the surface waves to gradually dissipate and disperse, causing the amplitude of the surface waves to gradually decrease or attenuate. The degree of surface wave attenuation depends on the properties of the underground medium and the frequency of the surface waves. High-frequency surface waves attenuate faster because the energy of the high-frequency part is more easily absorbed and scattered by the medium. On the contrary, low-frequency surface waves attenuate slower because low-frequency energy is more easily propagated in the underground medium.

[0009] The study of surface wave attenuation is very important for seismology and geophysics. By studying the attenuation characteristics of surface waves, we can understand the properties and structure of underground media, such as the attenuation capacity and scattering characteristics of underground rocks. This is of great significance for seismic exploration, earth structure research and earthquake disaster assessment. However, the methods for surface wave attenuation in the existing technology are not effective and accurate enough. Summary of the invention

[0010] The present invention belongs to the technical field of seismic data processing. One purpose of the present invention is to provide a more intelligent surface wave attenuation method for automatically picking up surface wave dispersion curves. The method can pick up surface wave dispersion curves more accurately and effectively, thereby more accurately forward modeling surface wave models to achieve the purpose of attenuating shallow strong surface waves in seismic data.

[0011] Another object of the present invention is to provide a surface roll attenuation device. Another object of the present invention is to provide an electronic device, the electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the steps of the surface roll attenuation method when executing the computer program. Another object of the present invention is to provide a readable medium, on which a computer program is stored, and when the computer program is executed by the processor, the steps of the surface roll attenuation method are implemented.

[0012] In order to solve the technical problems in the background technology of this application, the present invention provides the following technical solutions:

[0013] In a first aspect, the present invention provides a surface roll attenuation method, comprising:

[0014] Constructing a multiple signal classification spatial spectrum corresponding to the seismic data according to the pre-acquired seismic data of the target work area;

[0015] Picking up a dispersion curve in the multiple signal classification spatial spectrum;

[0016] The surface wave of the target working area is attenuated according to the dispersion curve.

[0017] In one embodiment of the present invention, the seismic data is time domain data, and the multiple signal classification spatial spectrum corresponding to the seismic data is constructed based on the pre-acquired seismic data of the target work area:

[0018] Performing a one-dimensional Fourier transform on the time domain data to convert the seismic data into frequency domain data;

[0019] Constructing a covariance matrix of the frequency domain data;

[0020] Performing eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​corresponding to the frequency domain data and eigenvectors corresponding to the eigenvalues;

[0021] The multiple signal classification spatial spectrum is constructed according to the eigenvalues ​​and the eigenvectors corresponding to the eigenvalues.

[0022] In one embodiment of the present invention, constructing the multiple signal classification spatial spectrum according to the eigenvalue and the eigenvector corresponding to the eigenvalue includes:

[0023] Calculate the power spectral density of the eigenvector in each direction;

[0024] The multiple signal classification spatial spectrum is constructed according to the eigenvalues ​​and the power spectrum density.

[0025] In one embodiment of the present invention, picking up a dispersion curve in the multiple signal classification spatial spectrum includes:

[0026] In the multiple signal classification spatial spectrum, the first-order surface wave and the second-order surface wave are subjected to excision constraints respectively to generate constraint results;

[0027] The dispersion curve of the first-order surface roll and the dispersion curve of the second-order surface roll are picked up in the constraint results.

[0028] In one embodiment of the present invention, the MSSC spatial spectrum includes a true signal term and a noise term.

[0029] In one embodiment of the present invention, attenuating the surface wave in the target working area according to the dispersion curve includes:

[0030] Generating a forward simulation surface wave model corresponding to the seismic data according to the dispersion curve;

[0031] The surface waves in the target work area are attenuated according to the seismic data and the forward simulation surface wave model.

[0032] In one embodiment of the present invention, attenuating the surface wave of the target work area according to the seismic data and the forward simulation surface wave model includes:

[0033] parsing the seismic data to generate original single shot data of the target work area;

[0034] The forward simulation surface wave model is removed from the original single shot data to attenuate the surface wave in the target work area.

[0035] In a second aspect, the present invention provides a surface wave attenuation device, the device comprising:

[0036] A multiple signal classification spatial spectrum construction module is used to construct a multiple signal classification spatial spectrum corresponding to the seismic data according to the pre-acquired seismic data of the target work area;

[0037] A dispersion curve picking module, used to pick up the dispersion curve in the multiple signal classification spatial spectrum;

[0038] The surface roll attenuation module is used to attenuate the surface roll in the target working area according to the dispersion curve.

[0039] In one embodiment of the present invention, the seismic data is time domain data, and the multiple signal classification spatial spectrum construction module includes:

[0040] A seismic data conversion unit, configured to perform a one-dimensional Fourier transform on the time domain data to convert the seismic data into frequency domain data;

[0041] A covariance matrix construction unit, used to construct the covariance matrix of the frequency domain data;

[0042] An eigenvector acquisition unit, used for performing eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​corresponding to the frequency domain data and eigenvectors corresponding to the eigenvalues;

[0043] The multiple signal classification spatial spectrum construction unit is used to construct the multiple signal classification spatial spectrum according to the eigenvalue and the eigenvector corresponding to the eigenvalue.

[0044] In one embodiment of the present invention, the multiple signal classification spatial spectrum construction unit includes:

[0045] A power spectrum density calculation unit, used to calculate the power spectrum density of the eigenvector in each direction;

[0046] The multiple signal classification spatial spectrum construction subunit is used to construct the multiple signal classification spatial spectrum according to the eigenvalue and the power spectrum density.

[0047] In one embodiment of the present invention, the dispersion curve picking module includes:

[0048] A constraint result generating unit, used for performing excision constraints on the first-order surface wave and the second-order surface wave in the multiple signal classification spatial spectrum, so as to generate a constraint result;

[0049] A dispersion curve picking unit is used to pick up the dispersion curve of the first-order surface roll and the dispersion curve of the second-order surface roll in the constraint result.

[0050] In one embodiment of the present invention, the MSSC spatial spectrum includes a true signal term and a noise term.

[0051] In one embodiment of the present invention, the surface roll attenuation module includes:

[0052] A forward simulation surface wave model generating unit, used for generating a forward simulation surface wave model corresponding to the seismic data according to the dispersion curve;

[0053] A surface wave attenuation unit is used to attenuate the surface wave in the target work area according to the seismic data and the forward simulation surface wave model.

[0054] In one embodiment of the present invention, the surface wave attenuation unit comprises:

[0055] A seismic data analysis unit, used for analyzing the seismic data to generate original single shot data of the target work area;

[0056] The surface wave attenuation subunit is used to remove the forward simulation surface wave model from the original single shot data to attenuate the surface wave in the target work area.

[0057] In a third aspect, the present invention provides a computer program product, comprising a computer program / instruction, which, when executed by a processor, implements the steps of a surface roll attenuation method.

[0058] In a fourth aspect, the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of a surface roll attenuation method when executing the program.

[0059] In a fifth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of a surface roll attenuation method when executed by a processor.

[0060] From the above description, it can be seen that an embodiment of the present invention provides a surface roll attenuation method and device, and the corresponding surface roll attenuation method includes: first, constructing a multiple signal classification spatial spectrum corresponding to the seismic data based on the pre-acquired seismic data of the target work area; then, picking up the dispersion curve in the multiple signal classification spatial spectrum; finally, attenuating the surface roll of the target work area according to the dispersion curve.

[0061] The corresponding surface wave attenuation device includes: a multiple signal classification spatial spectrum construction module, which is used to construct a multiple signal classification spatial spectrum corresponding to the seismic data based on the seismic data of the target work area acquired in advance; a dispersion curve picking module, which is used to pick up the dispersion curve in the multiple signal classification spatial spectrum; and a surface wave attenuation module, which is used to attenuate the surface wave of the target work area according to the dispersion curve.

[0062] The surface wave attenuation method and device provided in the embodiment of the present invention utilizes the characteristic of the surface wave with dispersion phenomenon, and generates a forward simulation surface wave model by picking up the dispersion curve, and then subtracts the forward simulation surface wave model from the original single shot, so as to finally achieve the purpose of surface wave attenuation. This overcomes the technical pain point that the picking of the surface wave dispersion curve in the prior art is manually picked up, which consumes a large amount of manpower cost. In addition, the present invention can pick up the surface wave dispersion curve more accurately and effectively, thereby generating a forward surface wave model more accurately, and achieving the purpose of attenuating the shallow strong surface waves of the seismic data. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0064] Figure 1 It is a schematic diagram of a dispersive surface wave in the prior art;

[0065] Figure 2 A schematic diagram of a flow chart of a surface roll attenuation method in an embodiment of the present invention;

[0066] Figure 3 It is a flowchart diagram of step 100 of the surface roll attenuation method in an embodiment of the present invention;

[0067] Figure 4 It is a flowchart diagram of step 104 of the surface roll attenuation method in an embodiment of the present invention;

[0068] Figure 5 It is a flowchart diagram of step 200 of the surface roll attenuation method in an embodiment of the present invention;

[0069] Figure 6 It is a flowchart diagram of step 300 of the surface roll attenuation method in an embodiment of the present invention;

[0070] Figure 7 It is a flowchart diagram of step 302 of the surface roll attenuation method in an embodiment of the present invention;

[0071] Figure 8 It is a schematic flow chart of the surface wave attenuation method in a specific implementation manner of the present invention;

[0072] Fig. 9 It is a schematic diagram of a dispersion curve spectrum in a specific embodiment of the present invention;

[0073] Fig.10 Schematic diagram of the picking process of the first-order surface wave dispersion curve in a specific embodiment of the present invention Figure 1 ;

[0074] Fig.11 Schematic diagram of the picking process of the first-order surface wave dispersion curve in a specific embodiment of the present invention Figure 2 ;

[0075] Fig.12 Schematic diagram of the process of picking up the dispersion curve of the second-order surface wave after the first-order surface wave attenuates in a specific embodiment of the present invention Figure 1 ;

[0076] Fig.13Schematic diagram of the process of picking up the dispersion curve of the second-order surface wave after the first-order surface wave attenuates in a specific embodiment of the present invention Figure 2 ;

[0077] Fig.14 It is a schematic diagram of common detection line domain single shot data in a specific implementation manner of the present invention;

[0078] Fig.15 It is a schematic diagram of single shot data after first-order surface wave attenuation in a specific embodiment of the present invention;

[0079] Fig.16 It is a schematic diagram of single shot data after the attenuation of the first-order surface wave and the second-order surface wave in a specific embodiment of the present invention;

[0080] Fig.17 It is a first-order surface wave model simulated according to the dispersion curve in a specific embodiment of the present invention;

[0081] Fig.18 It is a second-order surface wave model simulated according to the dispersion curve in a specific embodiment of the present invention;

[0082] Fig.19 It is a block diagram of a surface wave attenuation device in a specific embodiment of the present invention;

[0083] Fig. 20 It is a schematic diagram of the structure of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0084] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0085] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented 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.

[0086] It should be noted that the terms "including" and "having" in the specification and claims of the present application and the above-mentioned drawings and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. In the absence of conflict, the embodiments in the present application and the features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0087] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of laws and regulations.

[0088] Embodiment 1:

[0089] The embodiment of the present invention provides a specific implementation of a surface roll attenuation method, see Figure 2 , specifically including the following:

[0090] Step 100: constructing a multiple signal classification spatial spectrum corresponding to the seismic data according to the pre-acquired seismic data of the target work area;

[0091] Step 200: Pick up a dispersion curve in the multiple signal classification spatial spectrum;

[0092] Step 300: Attenuate the surface wave in the target work area according to the dispersion curve.

[0093] From the above description, it can be seen that an embodiment of the present invention provides a surface roll attenuation method, including: first, constructing a multiple signal classification spatial spectrum corresponding to the seismic data based on the pre-acquired seismic data of the target work area; then, picking up the dispersion curve in the multiple signal classification spatial spectrum; finally, attenuating the surface roll of the target work area according to the dispersion curve.

[0094] The surface roll attenuation method provided by the embodiment of the present invention can achieve the same accurate results as manual picking while saving labor costs, thereby better utilizing the picked dispersion curve to simulate the surface roll model and achieve the effect of attenuating surface roll interference.

[0095] Embodiment 2:

[0096] It is understood that the Multiple Signal Classification (MUSIC) spatial spectrum in step 100 is a key concept in the MUSIC algorithm. It is used to estimate the direction of arrival (DOA) of the signal. The Multiple Signal Classification (MUSIC) spatial spectrum is a key concept in the MUSIC algorithm. It is used to estimate the direction of arrival (DOA) of the signal.

[0097] For step 200, a dispersion curve is a curve used to describe the relationship between the frequency and wave number of seismic waves propagating in a medium, which represents the relationship between the phase velocity or group velocity and the wave number at different frequencies.

[0098] It is understood that the waves propagating in the medium can be sound waves, seismic waves, electromagnetic waves, etc. Dispersion refers to the change in the speed of waves of different frequencies when they propagate in the medium. When the medium has dispersive properties, waves of different frequencies will propagate at different speeds. The dispersion curve shows the relationship between this frequency and speed.

[0099] Dispersion curves are usually obtained by analyzing or numerically simulating the characteristics of a medium and the propagation equation. It has important applications in seismic exploration, acoustics, optics, and other fields. By analyzing dispersion curves, we can understand the dispersion characteristics of a medium, including how the propagation velocity of a wave changes with frequency.

[0100] In seismic exploration, dispersion curves can be used to identify the properties and structure of underground media, such as the elastic parameters of rocks, the stratification of strata, etc. In acoustics and optics, dispersion curves can be used to analyze the acoustic or optical properties of materials, such as the speed of sound, refractive index, etc.

[0101] For step 300, surface wave attenuation refers to the phenomenon that the amplitude of the surface wave in the seismic wave gradually decreases during the propagation process. Surface wave attenuation is the loss of energy due to absorption and scattering of the underground medium. In the propagation of seismic waves, the surface wave propagates along the surface of the earth, so it will interact with the underground medium. This interaction will cause the energy of the surface wave to gradually dissipate and disperse, causing the amplitude of the surface wave to gradually decrease or attenuate.

[0102] The degree of surface wave attenuation depends on the nature of the underground medium and the frequency of the surface wave. High-frequency surface waves attenuate faster because the energy of the high-frequency part is more easily absorbed and scattered by the medium. On the contrary, low-frequency surface waves attenuate slower because low-frequency energy is more easily propagated in the underground medium.

[0103] By studying the attenuation characteristics of surface waves, we can understand the properties and structure of underground media, such as the attenuation capacity and scattering characteristics of underground rocks. This is of great significance for seismic exploration, earth structure research and earthquake hazard assessment.

[0104] In some embodiments of the present invention, the seismic data is time domain data. Figure 3 , step 100 comprises:

[0105] Step 101: performing a one-dimensional Fourier transform on the time domain data to convert the seismic data into frequency domain data;

[0106] Specifically, to convert time domain data into frequency domain data, a one-dimensional Fourier transform (1D Fourier Transform) can be used. The following are the steps to convert seismic data into frequency domain data:

[0107] Collect earthquake data: Obtain time series data of earthquake records.

[0108] Pre-process the seismic data: Filters can be applied to remove unwanted noise and interference.

[0109] Perform a Fourier Transform: Use a one-dimensional Fourier transform to convert time domain data to the frequency domain. The Fourier transform decomposes the time domain signal into a series of complex-valued spectral components.

[0110] Calculate spectrum amplitude: Extract spectrum amplitude information from Fourier transform results, which represents the energy or amplitude of different frequency components.

[0111] Calculate spectral phase: Extract spectral phase information from the Fourier transform result, which represents the relative phase difference of different frequency components.

[0112] In the frequency domain, the energy distribution, phase information, and frequency characteristics of different frequency components can be analyzed, which is very important for understanding the frequency characteristics of seismic signals, identifying the frequency response of underground structures, and processing and interpreting seismic data.

[0113] In addition, the present application has the following limitations on the one-dimensional Fourier transform in step 101:

[0114] One-dimensional Fourier transform (1D Fourier Transform) is a mathematical transformation method that converts a one-dimensional time domain signal into a frequency domain signal. It describes the frequency characteristics of a signal by decomposing the signal into a series of complex spectral components of sine and cosine functions.

[0115] The mathematical expression of one-dimensional Fourier transform is as follows:

[0116] F(k)=∑[f(n)*exp(-2πi*kn / N)]

[0117] Among them, F(k) represents the kth frequency component in the frequency domain, f(n) represents the nth sampling point in the time domain, exp is the natural exponential function, i is the imaginary unit, k represents the frequency index, and N represents the length of the signal.

[0118] The one-dimensional Fourier transform calculates the amplitude and phase information of a signal over a series of frequency components. The amplitude of a frequency component represents the energy or amplitude of the signal at the corresponding frequency, while the phase information represents the relative phase difference between different frequency components. Through the one-dimensional Fourier transform, the time domain signal can be converted into a frequency domain signal so that frequency analysis, filtering, spectrum estimation and other operations can be performed in the frequency domain. Frequency domain analysis can provide important information about the frequency characteristics of the signal, the energy distribution of the frequency components, and the phase information.

[0119] Step 102: constructing a covariance matrix of the frequency domain data;

[0120] Step 103: performing eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​corresponding to the frequency domain data and eigenvectors corresponding to the eigenvalues;

[0121] Step 104: construct the multiple signal classification spatial spectrum according to the eigenvalues ​​and the eigenvectors corresponding to the eigenvalues.

[0122] In step 102 to step 104, data containing the signal to be estimated is first collected, which is usually obtained through an array seismic data receiver. Next, the covariance matrix is ​​calculated using the collected data. The covariance matrix is ​​used to describe the statistical characteristics of the signal on the receiving array. The covariance matrix is ​​subjected to eigenvalue decomposition to obtain eigenvalues ​​and corresponding eigenvectors. The eigenvector represents the spatial distribution of the signal, while the eigenvalue represents the intensity of the signal in the corresponding direction. The spatial spectrum is constructed using the eigenvector. The spatial spectrum is a tool for analyzing the distribution of signals in different directions. A common construction method is to calculate the power spectral density in each direction in the eigenvector. Find the peak in the spatial spectrum and use the corresponding direction as the direction of arrival estimate (DOA) of the signal. The peak indicates that the signal has the highest intensity in that direction.

[0123] Furthermore, the correlation matrix between different frequency domain data at each time-frequency point is calculated. This can reflect the degree of correlation between frequency domain data. The correlation matrix is ​​subjected to eigenvalue decomposition to extract the main eigenvalues ​​and secondary eigenvalues. According to the ratio of the main and secondary eigenvalues, each time-frequency point is classified as a single frequency domain data source or a multi-frequency domain data source. Energy normalization is performed on each classification result. The classification results are mapped into a range to generate a 2DMSC spectrum. According to the delay differences of the frequency domain data sources in each frequency domain, their positions in 3D space are reconstructed. The position information is added to the MSC spectrum to form a 3DMSC spatial spectrum.

[0124] In some embodiments of the present invention, see Figure 4 , step 104 comprises:

[0125] Step 1041: Calculate the power spectrum density of the eigenvector in each direction;

[0126] Power Spectral Density (PSD) is a measure of the power density of a signal's frequency components. It is used to describe the energy distribution of a signal at different frequencies.

[0127] It is understood that in the time domain, a signal can be represented as a function of time. In the frequency domain, a signal can be represented as a function of frequency. Power spectral density can provide important information about the frequency characteristics of a signal by converting the signal from the time domain to the frequency domain. Power spectral density can be calculated by taking a Fourier transform of the signal. The Fourier transform converts the signal from the time domain to the frequency domain and provides the energy distribution of the signal at different frequencies.

[0128] Power spectral density is used to understand the frequency characteristics of a signal, extract the frequency components of interest, filter out noise, etc. Usually, power spectral density is expressed as power per unit frequency. The unit frequency can be Hz (Hertz) or rad / s (radian / second), and the unit of power spectral density is power / Hz or power / rad / s. There are many ways to calculate power spectral density, including the periodogram method, the Welch method, the autocorrelation method, etc.

[0129] Step 1042: construct the multiple signal classification spatial spectrum according to the eigenvalues ​​and the power spectrum density.

[0130] Specifically, the eigenvalue and power spectrum density data are combined to construct a classification spatial spectrum. The eigenvalue and power spectrum density data can be combined by splicing, superimposing, or combining them in a certain weighted manner. In this way, the eigenvalue and power spectrum density information of different signal samples can be integrated into a classification spatial spectrum with a higher dimension.

[0131] Preferably, the surface roll attenuation method further comprises:

[0132] Use the constructed classification space spectrum to classify new signals. For new signals, first extract their eigenvalues ​​and power spectrum density data. Then compare these data with the constructed classification space spectrum, and use distance metrics or classification algorithms (such as support vector machines, neural networks, etc.) to complete the classification task.

[0133] In some embodiments of the present invention, see Figure 5 , step 200 comprises:

[0134] Step 201: in the multiple signal classification spatial spectrum, performing excision constraints on the first-order surface wave and the second-order surface wave respectively to generate constraint results;

[0135] A first-order surface wave is a seismic wave that propagates along the surface of the earth. It is a surface wave whose vibration mainly occurs below the surface of the earth, forming an elliptical vibration path. It is the slowest wave mode among seismic waves, but has the largest amplitude and the widest energy propagation range. A first-order surface wave is mainly a transverse (perpendicular to the propagation direction) vibration.

[0136] Second-order surface waves are another type of seismic wave that propagates along the surface of the earth. It is a transverse (perpendicular to the propagation direction) vibration mode. Unlike first-order surface waves, its vibration occurs below the surface of the earth and the vibration path is a horizontal straight line. Second-order surface waves have a faster speed, a relatively small amplitude, and a relatively narrow energy propagation range.

[0137] Step 202: Pick up the dispersion curve of the first-order surface roll and the dispersion curve of the second-order surface roll in the constraint results.

[0138] Specifically, by performing cutting constraints on the first-order surface wave dispersion curve and the second-order surface wave dispersion curve respectively, the dispersion curves of the surface wave are automatically picked up step by step.

[0139] In some embodiments of the present invention, the MSSC spatial spectrum includes a true signal term and a noise term.

[0140] The multiple signal classification spatial spectrum is composed of the true signal term and the noise term. Multiple mathematical operations are performed under the assumption that the noise is isotropic and random to obtain the final FK_MUSIC spectrum, thereby extracting the dispersion curve from the FK_MUSIC spectrum.

[0141] In some embodiments of the present invention, see Figure 6 , step 300 comprises:

[0142] Step 301: generating a forward simulation surface wave model corresponding to the seismic data according to the dispersion curve;

[0143] The forward simulation surface wave model is to simulate the process of seismic wave propagation in underground media by computer to obtain the response of surface wave mode in seismic exploration. Specifically, step 301 includes the following steps:

[0144] First, it is necessary to determine the model of the underground medium, including parameters such as velocity, density and attenuation of the formation. These parameters can be obtained through seismic exploration data, geological data and laboratory tests. Next, select a suitable wave equation according to the characteristics of the underground medium model. Commonly used wave equations include elastic wave equations (such as acoustic wave equations or elastic wave equations) and elastoplastic wave equations. The specific choice is determined according to actual needs and research purposes. Then the underground medium is discretized and divided into grids or cells. By discretizing the underground medium, the wave equation can be converted into a differential or finite element equation, which is convenient for computer numerical simulation.

[0145] In the simulation, it is necessary to set appropriate boundary conditions to simulate the actual situation. Common boundary conditions include free boundary conditions, absorbing boundary conditions and periodic boundary conditions. According to the characteristics of the wave equation and the underground medium, a suitable numerical method is selected for forward simulation. Common numerical methods include finite difference method, finite element method, boundary element method, etc. Finally, the wave equation is solved according to the dispersion curve by numerical methods to simulate the propagation process of seismic waves in the underground medium. The wave response within a specific frequency or time range can be simulated as needed.

[0146] According to the simulation results, we can get information about the propagation path, amplitude change, velocity and attenuation of surface waves, etc. These results can be used for interpretation of seismic exploration, earthquake risk assessment and earthquake engineering design.

[0147] Step 302: Attenuate the surface waves in the target work area according to the seismic data and the forward simulation surface wave model.

[0148] In some embodiments of the present invention, see Figure 7 , step 302 comprises:

[0149] Step 3021: parsing the seismic data to generate original single shot data of the target work area;

[0150] Specifically, first, it is necessary to obtain seismic data, which can be the original seismic records collected by seismic exploration instruments (such as seismographs), or seismic event data obtained by seismic monitoring stations. Then, the acquired seismic data is preprocessed, including removing noise, correcting instrument response, correcting time and gain, etc. These preprocessing steps are aimed at improving the quality and interpretability of the data. Data analysis is performed according to the characteristics of seismic data and actual needs. Analysis can include the following aspects:

[0151] Split the record: If the seismic data is recorded continuously, it needs to be split into individual shots. This involves determining the start and end times of the individual shots and stripping them from the continuous data.

[0152] Deconvolution: Seismic data may be affected by underground media and instrument response changes during recording. In order to obtain the original single-shot data, the data needs to be deconvolved to restore the original seismic waveform signal.

[0153] Data correction: During the analysis process, the data may need to be further corrected to correct amplitude and phase differences, remove instrument response, correct time delays, etc. In addition, the raw single-shot data obtained from the analysis can be further analyzed and processed. This may involve methods such as spectrum analysis, seismic imaging, and seismic inversion to extract underground information or solve specific problems. Based on the results of data analysis and processing, seismic waveforms, spectrum diagrams, seismic images, etc. can be generated to display and interpret underground structures, seismic event characteristics, etc.

[0154] Step 3022: Subtract the forward simulation surface wave model from the original single shot data to attenuate the surface wave in the target work area.

[0155] The forward simulated surface wave model is subtracted from the original single-shot seismic data to attenuate the surface wave to the greatest extent and protect the low-frequency components in the effective signal.

[0156] From the above description, it can be seen that an embodiment of the present invention provides a surface roll attenuation method, including: first, constructing a multiple signal classification spatial spectrum corresponding to the seismic data based on the pre-acquired seismic data of the target work area; then, picking up the dispersion curve in the multiple signal classification spatial spectrum; finally, attenuating the surface roll of the target work area according to the dispersion curve.

[0157] The present invention is based on the surface wave attenuation technology of forward modeling, the generation of FK_MUSIC spectrum and the construction of surface wave model. Specifically, the dispersion characteristics of surface waves are used, the surface waves are regarded as effective signals, and the dispersion curves of the original seismic data are picked up in the frequency domain. On this basis, the surface wave model is forward modeled, and finally the surface wave forward model is subtracted from the original seismic data, thereby attenuating the surface waves to the greatest extent and protecting the low-frequency components in the effective signals.

[0158] Embodiment three:

[0159] In a specific embodiment, the present invention also provides a specific embodiment of a surface roll attenuation method, see Figure 8 , specifically including the following steps.

[0160] S1: Acquire seismic data of the target work area;

[0161] Preferably, the seismic data is in SEG-Y format, which is a standard format for storing and exchanging seismic data. Its full name is Seismic Exploration Geophysics-Y format.

[0162] Seismic data in SEG-Y format is in binary format, usually including information such as seismic waveform data, detector location, acquisition parameters, etc. These seismic data are collected by seismic instruments on the ground or underwater, and are stored in SEG-Y format after processing. The SEG-Y format has many advantages, the most important of which is that it is a universal and extensible standard format that facilitates data exchange and sharing between different seismic instruments, software, and processing systems. In addition, the SEG-Y format can also store a variety of common data types, such as multi-channel seismic data, three-dimensional seismic data, etc.

[0163] S2: Convert seismic data from time domain to frequency domain using one-dimensional Fourier transform.

[0164] S3: Construct a MUSIC spectrum based on frequency domain seismic data.

[0165] Specifically, a parameterized FK domain MUSIC spectrum is generated through a high-precision MUSIC algorithm (the final FK_MUSIC spectrum is obtained through multiple mathematical operations, and the dispersion curve is extracted from it). First, the data covariance matrix is ​​obtained from the data; the covariance matrix is ​​eigen-decomposed; the number of signal sources is determined by the eigenvalues ​​of the covariance matrix; the signal subspace and noise subspace are determined; the spectrum peak is searched according to the signal parameter range; the angle corresponding to the maximum point is found to be the signal incident direction.

[0166] Furthermore, the DOA mathematical model of the narrowband far-field signal is:

[0167] X(t)=A(θ)s(t))+N(t)

[0168] The covariance matrix of the array matrix is:

[0169] R=E[XX H ]=AE[SS H ]A H +σ 2 I=AR S A H +σ 2 I

[0170] Since the signal and noise are independent of each other, the data covariance matrix can be decomposed into signal and noise. S A H For the signal part,

[0171] Performing eigendecomposition on R, we have:

[0172]

[0173] The first term Us in the formula is the signal subspace spanned by the eigenvectors corresponding to the large eigenvalues, and the second term Un is the noise subspace spanned by the eigenvectors corresponding to the small eigenvalues ​​of the noise.

[0174] Since the signal and noise are independent under ideal conditions, the signal subspace and the noise subspace are orthogonal to each other, and the steering vector in the signal subspace is also orthogonal to the noise subspace:

[0175] a H (θ)U N =0

[0176] Considering that the actual received data matrix is ​​of finite length, the maximum likelihood estimate of the data covariance matrix is:

[0177]

[0178] right The noise subspace eigenvector matrix can be calculated by eigendecomposition: Due to the presence of noise, α H (θ) and They are not completely orthogonal, so DOA is implemented by minimum optimization search, namely:

[0179]

[0180] Therefore, the spectrum estimation formula of the MUSIC algorithm is:

[0181]

[0182] S4: Extracting dispersion curves from MUSIC spectra.

[0183] Preferably, the frequency and phase velocity relationship curve is extracted to extract the dispersion curve. Fig. 9 The dispersion curves in the figure are obtained by manual picking. In this step, the dispersion curves of each order of surface waves are automatically picked up step by step by performing cutting constraints on the dispersion curves of each order of surface waves. The specific performance is shown in the attached figure. Fig.10 as well as Fig.11 , Attachment Fig.12 as well as Fig.13 , through the attachment Fig.10 The first-order surface wave in the dispersion curve spectrum is cut off and constrained, that is, the upper dotted line is picked up, and the lower dotted line is picked down, so that only the first-order surface wave dispersion curve spectrum between the two dotted lines is left, and then the attached dispersion curve is automatically picked. Fig.11The first-order surface wave dispersion curve picked up on the right is accurate. The second-order surface wave dispersion curve is picked up in the same way. The picking result is as follows: Fig.12 as well as Fig.13 shown.

[0184] S5: Generate a surface wave forward model in the time domain based on the dispersion curve.

[0185] Specifically, the dispersion curve is first inverted to determine the surface wave velocity parameters, and then a first-order surface wave forward modeling-time domain surface wave model and a second-order surface wave forward modeling-time domain surface wave model are generated according to the surface wave velocity parameters.

[0186] S6: Attenuate the surface waves in the target work area according to the surface wave forward modeling-time domain surface wave model.

[0187] The purpose of surface wave attenuation is finally achieved by subtracting the first-order surface wave forward modeling-time domain surface wave model and the second-order surface wave forward modeling-time domain surface wave model from the original single-shot data.

[0188] Attached Figures 14 to 16 This is a schematic diagram of the common detection line domain single shot data and the single shot data after attenuation of the first and second order surface waves. Fig.17 18 is a first-order surface wave forward modeling-time domain surface wave model and a second-order surface wave forward modeling-time domain surface wave model simulated according to the dispersion curve. Figures 14 to 16 , Attachment Fig.17 as well as Fig.18 From the surface wave models of single shots and forward simulation after attenuating the first-order and second-order surface waves, it can be seen that the surface wave attenuation method of automatically picking up the surface wave dispersion curve can better simulate the accurate surface wave model without damaging the effective wave, and at the same time achieve the purpose of surface wave attenuation.

[0189] From the above description, it can be seen that an embodiment of the present invention provides a surface roll attenuation method, including: first, constructing a multiple signal classification spatial spectrum corresponding to the seismic data based on the pre-acquired seismic data of the target work area; then, picking up the dispersion curve in the multiple signal classification spatial spectrum; finally, attenuating the surface roll of the target work area according to the dispersion curve.

[0190] The present invention is based on the surface wave attenuation technology of forward modeling, the generation of FK_MUSIC spectrum and the construction of surface wave model. Specifically, the dispersion characteristics of surface waves are used, the surface waves are regarded as effective signals, and the dispersion curves of the original seismic data are picked up in the frequency domain. On this basis, the surface wave model is forward modeled, and finally the surface wave forward model is subtracted from the original seismic data, thereby attenuating the surface waves to the greatest extent and protecting the low-frequency components in the effective signals.

[0191] Embodiment 4:

[0192] Based on the same inventive concept, the embodiments of the present application also provide a surface wave attenuation device, which can be used to implement the method described in the above embodiments, such as the following embodiments. Since the principle of solving the problem by the surface wave attenuation device is similar to that of the surface wave attenuation method, the implementation of the surface wave attenuation device can refer to the implementation of the surface wave attenuation method, and the repeated parts will not be repeated. As used below, the term "unit" or "module" can be a combination of software and / or hardware that implements a predetermined function. Although the system described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0193] The embodiment of the present invention provides a specific implementation of a surface wave attenuation device capable of implementing a surface wave attenuation method, see Fig.19 , the surface wave attenuation device comprises:

[0194] A multiple signal classification spatial spectrum construction module 10 is used to construct a multiple signal classification spatial spectrum corresponding to the seismic data according to the pre-acquired seismic data of the target work area;

[0195] A dispersion curve picking module 20, used to pick up the dispersion curve in the multiple signal classification spatial spectrum;

[0196] The surface roll attenuation module 30 is used to attenuate the surface roll in the target work area according to the dispersion curve.

[0197] In one embodiment of the present invention, the seismic data is time domain data, and the multiple signal classification spatial spectrum construction module includes:

[0198] A seismic data conversion unit, configured to perform a one-dimensional Fourier transform on the time domain data to convert the seismic data into frequency domain data;

[0199] A covariance matrix construction unit, used to construct the covariance matrix of the frequency domain data;

[0200] An eigenvector acquisition unit, used for performing eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​corresponding to the frequency domain data and eigenvectors corresponding to the eigenvalues;

[0201] The multiple signal classification spatial spectrum construction unit is used to construct the multiple signal classification spatial spectrum according to the eigenvalue and the eigenvector corresponding to the eigenvalue.

[0202] In one embodiment of the present invention, the multiple signal classification spatial spectrum construction unit includes:

[0203] A power spectrum density calculation unit, used to calculate the power spectrum density of the eigenvector in each direction;

[0204] The multiple signal classification spatial spectrum construction subunit is used to construct the multiple signal classification spatial spectrum according to the eigenvalue and the power spectrum density.

[0205] In one embodiment of the present invention, the dispersion curve picking module includes:

[0206] A constraint result generating unit, used for performing excision constraints on the first-order surface wave and the second-order surface wave in the multiple signal classification spatial spectrum, so as to generate a constraint result;

[0207] A dispersion curve picking unit is used to pick up the dispersion curve of the first-order surface roll and the dispersion curve of the second-order surface roll in the constraint result.

[0208] In one embodiment of the present invention, the MSSC spatial spectrum includes a true signal term and a noise term.

[0209] In one embodiment of the present invention, the surface roll attenuation module includes:

[0210] A forward simulation surface wave model generating unit, used for generating a forward simulation surface wave model corresponding to the seismic data according to the dispersion curve;

[0211] A surface wave attenuation unit is used to attenuate the surface wave in the target work area according to the seismic data and the forward simulation surface wave model.

[0212] In one embodiment of the present invention, the surface wave attenuation unit comprises:

[0213] A seismic data analysis unit, used for analyzing the seismic data to generate original single shot data of the target work area;

[0214] The surface wave attenuation subunit is used to remove the forward simulation surface wave model from the original single shot data to attenuate the surface wave in the target work area.

[0215] From the above description, it can be seen that an embodiment of the present invention provides a surface roll attenuation device, including: a multiple signal classification spatial spectrum construction module, used to construct a multiple signal classification spatial spectrum corresponding to the seismic data based on the pre-acquired seismic data of the target work area; a dispersion curve picking module, used to pick up the dispersion curve in the multiple signal classification spatial spectrum; a surface roll attenuation module, used to attenuate the surface roll of the target work area according to the dispersion curve.

[0216] The surface wave attenuation device provided in the embodiment of the present invention utilizes the characteristic of the surface wave dispersion phenomenon, picks up the dispersion curve, generates a forward simulation surface wave model by the obtained dispersion curve, and then subtracts the forward simulation surface wave model from the original single shot, thereby finally achieving the purpose of surface wave attenuation. This overcomes the technical pain point that the surface wave dispersion curve is picked up manually in the prior art, which consumes a large amount of manpower cost.

[0217] Embodiment five:

[0218] The embodiment of the present application also provides a specific implementation of an electronic device capable of implementing all steps in the surface wave attenuation method in the above embodiment, see Fig. 20 , electronic equipment specifically includes the following:

[0219] Processor (processor) 1201, memory (memory) 1202, communication interface (CommunicationsInterface) 1203 and bus 1204;

[0220] The processor 1201, the memory 1202, and the communication interface 1203 communicate with each other through the bus 1204; the communication interface 1203 is used to realize information transmission between the server device and the client device and other related devices;

[0221] The processor 1201 is used to call the computer program in the memory 1202. When the processor executes the computer program, all steps in the surface roll attenuation method in the above embodiment are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0222] Constructing a multiple signal classification spatial spectrum corresponding to the seismic data according to the pre-acquired seismic data of the target work area;

[0223] Picking up a dispersion curve in the multiple signal classification spatial spectrum;

[0224] The surface wave of the target working area is attenuated according to the dispersion curve.

[0225] In one embodiment, the seismic data is time domain data, and the multiple signal classification spatial spectrum corresponding to the seismic data is constructed based on the pre-acquired seismic data of the target work area:

[0226] Performing a one-dimensional Fourier transform on the time domain data to convert the seismic data into frequency domain data;

[0227] Constructing a covariance matrix of the frequency domain data;

[0228] Performing eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​corresponding to the frequency domain data and eigenvectors corresponding to the eigenvalues;

[0229] The multiple signal classification spatial spectrum is constructed according to the eigenvalues ​​and the eigenvectors corresponding to the eigenvalues.

[0230] In one embodiment, constructing the multiple signal classification spatial spectrum according to the eigenvalue and the eigenvector corresponding to the eigenvalue includes:

[0231] Calculate the power spectral density of the eigenvector in each direction;

[0232] The multiple signal classification spatial spectrum is constructed according to the eigenvalues ​​and the power spectrum density.

[0233] In one embodiment, picking up a dispersion curve in the multiple signal classification spatial spectrum includes:

[0234] In the multiple signal classification spatial spectrum, the first-order surface wave and the second-order surface wave are subjected to excision constraints respectively to generate constraint results;

[0235] The dispersion curve of the first-order surface roll and the dispersion curve of the second-order surface roll are picked up in the constraint results.

[0236] In one embodiment, the MSSC spatial spectrum includes a true signal term and a noise term.

[0237] In one embodiment, attenuating the surface wave in the target working area according to the dispersion curve includes:

[0238] Generating a forward simulation surface wave model corresponding to the seismic data according to the dispersion curve;

[0239] The surface waves in the target work area are attenuated according to the seismic data and the forward simulation surface wave model.

[0240] In one embodiment, attenuating the surface wave in the target work area according to the seismic data and the forward simulation surface wave model includes:

[0241] parsing the seismic data to generate original single shot data of the target work area;

[0242] The forward simulation surface wave model is removed from the original single shot data to attenuate the surface wave in the target work area.

[0243] Embodiment six:

[0244] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps of the surface roll attenuation method in the above embodiments. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, all the steps of the surface roll attenuation method in the above embodiments are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0245] Constructing a multiple signal classification spatial spectrum corresponding to the seismic data according to the pre-acquired seismic data of the target work area;

[0246] Picking up a dispersion curve in the multiple signal classification spatial spectrum;

[0247] The surface wave of the target working area is attenuated according to the dispersion curve.

[0248] In one embodiment, the seismic data is time domain data, and the multiple signal classification spatial spectrum corresponding to the seismic data is constructed based on the pre-acquired seismic data of the target work area:

[0249] Performing a one-dimensional Fourier transform on the time domain data to convert the seismic data into frequency domain data;

[0250] Constructing a covariance matrix of the frequency domain data;

[0251] Performing eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​corresponding to the frequency domain data and eigenvectors corresponding to the eigenvalues;

[0252] The multiple signal classification spatial spectrum is constructed according to the eigenvalues ​​and the eigenvectors corresponding to the eigenvalues.

[0253] In one embodiment, constructing the multiple signal classification spatial spectrum according to the eigenvalue and the eigenvector corresponding to the eigenvalue includes:

[0254] Calculate the power spectral density of the eigenvector in each direction;

[0255] The multiple signal classification spatial spectrum is constructed according to the eigenvalues ​​and the power spectrum density.

[0256] In one embodiment, picking up a dispersion curve in the multiple signal classification spatial spectrum includes:

[0257] In the multiple signal classification spatial spectrum, the first-order surface wave and the second-order surface wave are subjected to excision constraints respectively to generate constraint results;

[0258] The dispersion curve of the first-order surface roll and the dispersion curve of the second-order surface roll are picked up in the constraint results.

[0259] In one embodiment, the MSSC spatial spectrum includes a true signal term and a noise term.

[0260] In one embodiment, attenuating the surface wave in the target working area according to the dispersion curve includes:

[0261] Generating a forward simulation surface wave model corresponding to the seismic data according to the dispersion curve;

[0262] The surface waves in the target work area are attenuated according to the seismic data and the forward simulation surface wave model.

[0263] In one embodiment, attenuating the surface wave in the target work area according to the seismic data and the forward simulation surface wave model includes:

[0264] parsing the seismic data to generate original single shot data of the target work area;

[0265] The forward simulation surface wave model is removed from the original single shot data to attenuate the surface wave in the target work area.

[0266] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the hardware + program embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0267] The above is a description of a specific embodiment of the present specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0268] Although the present application provides method operation steps such as embodiments or flow charts, more or fewer operation steps may be included based on conventional or non-creative labor. The order of steps listed in the embodiments is only one way of executing the order of many steps and does not represent the only execution order. When the actual device or client product is executed, it can be executed in the order of the method shown in the embodiments or the drawings or in parallel (for example, in a parallel processor or multi-threaded processing environment).

[0269] For the convenience of description, the above devices are described in various modules according to their functions. Of course, when implementing the embodiments of this specification, the functions of each module can be implemented in the same or more software and / or hardware, or the module implementing the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0270] Those skilled in the art also know that, in addition to implementing the controller in a purely computer-readable program code, the controller can be made to implement the same function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, such a controller can be considered as a hardware component, and the devices for implementing various functions included therein can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software modules for implementing the method and structures within the hardware component.

[0271] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0272] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0273] Each embodiment in this specification is described in a progressive manner, and the same and similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. In the description of this specification, the description of the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of this specification. In this specification, the schematic representation of the above terms does not necessarily target the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, in the absence of contradiction, a person skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0274] The above is only an example of the embodiment of the present specification and is not intended to limit the embodiment of the present specification. For those skilled in the art, the embodiment of the present specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiment of the present specification shall be included in the scope of the claims of the embodiment of the present specification.

Claims

1. A surface roll attenuation method, characterized in that: include: Constructing a multiple signal classification spatial spectrum corresponding to the seismic data according to the pre-acquired seismic data of the target work area; Picking up a dispersion curve in the multiple signal classification spatial spectrum; The surface wave of the target working area is attenuated according to the dispersion curve.

2. The surface roll attenuation method according to claim 1, characterized in that: The seismic data is time domain data, and the multiple signal classification spatial spectrum corresponding to the seismic data is constructed based on the pre-acquired seismic data of the target work area: Performing a one-dimensional Fourier transform on the time domain data to convert the seismic data into frequency domain data; Constructing a covariance matrix of the frequency domain data; Performing eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​corresponding to the frequency domain data and eigenvectors corresponding to the eigenvalues; The multiple signal classification spatial spectrum is constructed according to the eigenvalues ​​and the eigenvectors corresponding to the eigenvalues.

3. The surface wave attenuation method according to claim 2, characterized in that: Constructing the multiple signal classification spatial spectrum according to the eigenvalue and the eigenvector corresponding to the eigenvalue, comprising: Calculate the power spectral density of the eigenvector in each direction; The multiple signal classification spatial spectrum is constructed according to the eigenvalues ​​and the power spectrum density.

4. The surface wave attenuation method according to claim 1, characterized in that: Picking up a dispersion curve in the multiple signal classification spatial spectrum, comprising: In the multiple signal classification spatial spectrum, the first-order surface wave and the second-order surface wave are subjected to excision constraints respectively to generate constraint results; The dispersion curve of the first-order surface roll and the dispersion curve of the second-order surface roll are picked up in the constraint results.

5. The surface wave attenuation method according to claim 1, characterized in that: The MSSC spatial spectrum includes a true signal term and a noise term.

6. The surface wave attenuation method according to any one of claims 1 to 5, characterized in that: Attenuating the surface wave in the target work area according to the dispersion curve, including: Generating a forward simulation surface wave model corresponding to the seismic data according to the dispersion curve; The surface waves in the target work area are attenuated according to the seismic data and the forward simulation surface wave model.

7. The surface roll attenuation method according to claim 6, characterized in that: Attenuating the surface wave in the target work area according to the seismic data and the forward simulation surface wave model, including: parsing the seismic data to generate original single shot data of the target work area; The forward simulation surface wave model is removed from the original single shot data to attenuate the surface wave in the target work area.

8. A surface wave attenuation device, characterized in that: include: A multiple signal classification spatial spectrum construction module is used to construct a multiple signal classification spatial spectrum corresponding to the seismic data according to the pre-acquired seismic data of the target work area; A dispersion curve picking module, used to pick up the dispersion curve in the multiple signal classification spatial spectrum; The surface roll attenuation module is used to attenuate the surface roll in the target working area according to the dispersion curve.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the surface roll attenuation method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the surface roll attenuation method according to any one of claims 1 to 7 are implemented.

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