A method for extracting surface wave dispersion curves based on strain fields

Abstract

The present application provides a method for extracting surface wave dispersion curves based on strain fields, including the steps of: extracting strain data from seismic observation data; preprocessing the strain data according to the type of the seismic observation data to obtain strain components; converting the strain components from the time-space domain to the frequency-space domain through Fourier transform; converting the strain components from the frequency-space domain to the frequency-phase velocity domain through frequency-Bessel transform to obtain the surface wave dispersion spectrum of the strain components; and picking up the surface wave dispersion curves according to the surface wave dispersion spectrum. The present application fills the technical gap in extracting surface wave dispersion information from DAS data, provides a powerful tool for extracting surface wave dispersion information applicable to single-component or multi-component DAS data, has important theoretical significance and practical value for the development of surface wave dispersion imaging methods based on DAS observations, and provides ideas for extracting the dispersion information of surface waves or body waves such as Rayleigh waves, Scholte waves, and Love waves.

Description

Technical Field

[0001] The present invention relates to the technical field of surface wave dispersion imaging, and particularly relates to a method for extracting surface wave dispersion curves based on a strain field. Background Art

[0002] Surface wave dispersion imaging can detect the shear wave velocity structure of underground media by extracting and inverting surface wave phase velocity dispersion curves, and is one of the important methods in seismic imaging. It has been widely used in the research of shallow surface, regional and global scale imaging of the Earth's interior structure.

[0003] Distributed Acoustic Sensing (DAS) technology, also often referred to as distributed fiber optic vibration sensing technology in the field of seismology, is a newly developed dense array observation technology in recent years. It has the advantages of low observation cost, wide application range and strong repeatability, and has been rapidly applied in seismological research in different scenarios and scales. DAS technology is very suitable for the research of shallow surface structure imaging on land and under the sea. Compared with the data of seismic displacement (or velocity) field observed by traditional seismic observation instruments, DAS technology observes the response of seismic strain (or strain rate) field along the fiber axis.

[0004] At present, there is no clear understanding in theory on how to accurately extract surface wave dispersion curves from DAS data. The frequency-Bessel transform method has been proven to be more effective in extracting multi-mode surface wave dispersion curves from array seismic data of traditional seismic observations. However, there is currently no method proposed in theory from the perspective of seismic strain (or strain rate) field for extracting surface wave dispersion curves from single-component or multi-component DAS data.

[0005] In view of this, there is currently a technical gap in the method for extracting surface wave dispersion curves from DAS data from the perspective of the strain field, which needs to be further improved. Summary of the Invention

[0006] In view of this, the present invention proposes a method for extracting surface wave dispersion curves based on a strain field. From the perspective of the strain field, using Bessel functions, surface wave dispersion curves are extracted from single-component or multi-component DAS data, solving the technical problem in the prior art of lacking a method for extracting surface wave dispersion curves based on a strain field.

[0007] The first aspect of the present application provides a method for extracting surface wave dispersion curves based on a strain field, including the following steps:

[0008] Extract strain data from seismic observation data;

[0009] Preprocess the strain data according to the type of the seismic observation data to obtain strain components;

[0010] Convert the strain component from the time - space domain to the frequency - space domain through Fourier transform;

[0011] Convert the strain component from the frequency - space domain to the frequency - phase velocity domain through frequency - Bessel transform to obtain the surface wave dispersion spectrum of the strain component;

[0012] Pick up the surface wave dispersion curve according to the surface wave dispersion spectrum.

[0013] Specifically, the seismic observation data is single - component or multi - component DAS strain observation data; the types of the seismic observation data include one or more of active source data with known sources, active source data with unknown sources, and passive source data.

[0014] Specifically, the pre - processing of the strain data according to the type of the seismic observation data to obtain the strain component includes:

[0015] For the strain data extracted from the active source data with known sources, through de - convolution processing, convert the strain data into Green's function components, and the strain component is the Green's function component;

[0016] For the strain data extracted from the active source data with unknown sources, directly use it as the strain component;

[0017] For the strain data extracted from the passive source data, convert the strain data into empirical Green's function components, and the strain component is the empirical Green's function component.

[0018] Specifically, the conversion of the strain data into empirical Green's function components includes the following steps:

[0019] Perform background noise processing on the strain data to recover the empirical Green's function components;

[0020] Among them, the background noise processing includes single - channel pre - processing, cross - correlation processing of any two channels, and superposition processing of cross - correlation functions of noise in different time periods;

[0021] The single - channel pre - processing includes removing instrument response, data segmentation, de - meaning, de - trending, band - pass filtering, time - domain normalization, and spectral whitening.

[0022] The strain data includes one or more of radial normal strain, radial shear strain, tangential normal strain, and vertical normal strain.

[0023] Specifically, after extracting the strain data from the seismic observation data, it further includes:

[0024] Synthesize the strain field record according to multiple channels of the strain data;

[0025] Specifically, the strain field recording includes a radially positive strain recording excited vertically, a vertically positive strain recording excited vertically, a tangentially positive strain recording excited vertically, a radially positive strain recording excited radially, a vertically positive strain recording excited radially, a tangentially positive strain recording excited radially, and a radially tangential strain recording excited tangentially.

[0026] Specifically, picking up the surface wave dispersion curve according to the surface wave dispersion spectrum includes the following steps:

[0027] Identifying the dispersion energy of the strain component from the actual dispersion spectrum;

[0028] Determining the frequency and the surface wave phase velocity value corresponding to the frequency according to the position of the extreme value in the frequency-dispersion energy in the frequency-phase velocity domain;

[0029] Picking up the surface wave dispersion curve of the strain component according to multiple groups of the frequencies and the corresponding surface wave phase velocity values.

[0030] Specifically, the surface wave dispersion curve includes a Rayleigh wave dispersion curve and a Love wave dispersion curve.

[0031] The second aspect of the present application provides a system for extracting a surface wave dispersion curve based on a strain field, and the system includes:

[0032] A data extraction module for extracting strain data from seismic observation data;

[0033] A preprocessing module for preprocessing the strain data according to the type of the seismic observation data to obtain strain components;

[0034] A time-frequency conversion module for converting the strain component from the time-space domain to the frequency-space domain through Fourier transform;

[0035] A Bessel conversion module for converting the strain component from the frequency-space domain to the frequency-phase velocity domain through frequency-Bessel transform to obtain the surface wave dispersion spectrum of the strain component;

[0036] A picking module for picking up the surface wave dispersion curve according to the surface wave dispersion spectrum.

[0037] A method for extracting surface wave dispersion curves based on a strain field according to the present invention has the following beneficial effects compared with the prior art: The present application provides a method for extracting surface wave dispersion curves based on a strain field, including the steps of: extracting strain data from seismic observation data; preprocessing the strain data according to the type of the seismic observation data to obtain strain components; converting the strain components from the time-space domain to the frequency-space domain through Fourier transform; converting the strain components from the frequency-space domain to the frequency-phase velocity domain through frequency-Bessel transform to obtain the surface wave dispersion spectrum of the strain components; and picking up the surface wave dispersion curve according to the surface wave dispersion spectrum. The present application proposes different frequency-Bessel transform formulas for active source data with known sources, active source data with unknown sources, and passive source data from the perspective of the strain field for single-component or multi-component DAS data to extract the surface wave dispersion spectrum, and then pick up the surface wave dispersion curve, filling the technical gap in extracting surface wave dispersion information from DAS data, providing a powerful tool for extracting surface wave dispersion information applicable to single-component or multi-component DAS data, having important theoretical significance and practical value for the development of surface wave dispersion imaging methods based on DAS observations, and providing ideas for extracting the dispersion information of surface waves or body waves such as Rayleigh waves, Scholte waves, and Love waves. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0039] Figure 1 It is a schematic flow chart of a method for extracting surface wave dispersion curves based on a strain field for an embodiment.

[0040] Figure 2 It is a schematic diagram of a synthetic record of strain fields of different strain components for an embodiment.

[0041] Figure 3 It is a theoretical surface wave dispersion spectrum diagram of different strain components for an embodiment.

[0042] Figure 4 It is an actual surface wave dispersion spectrum diagram of different strain components for an embodiment.

[0043] Figure 5 It is an approximate actual surface wave dispersion spectrum diagram of different strain components for an embodiment.

[0044] Figure 6 It is a schematic diagram of a system for extracting surface wave dispersion curves based on a strain field for an embodiment. Detailed implementation manners

[0045] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0046] It should be understood that the terms used in the specification of the embodiments of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the embodiments of the present invention. As used in the specification of the embodiments of the present invention and the appended claims, unless otherwise clearly specified in the context, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0047] The present invention provides a method for extracting surface wave dispersion curves based on a strain field. As Figure 1 shown, it is a schematic flowchart of a method for extracting surface wave dispersion curves based on a strain field in the present application, which specifically includes the following steps:

[0048] S1. Extract strain data from seismic observation data.

[0049] Specifically, the strain data includes one or more of radial normal strain, radial shear strain, tangential normal strain, and vertical normal strain.

[0050] Specifically, after step S1, it further includes synthesizing a strain field record according to multi-channel strain data.

[0051] Specifically, the strain field record includes a radial normal strain record excited vertically, a vertical normal strain record excited vertically, a tangential normal strain record excited vertically, a radial normal strain record excited radially, a vertical normal strain record excited radially, a tangential normal strain record excited radially, and a radial shear strain record excited tangentially. Preferably, the seismic observation data in the present application is single-component or multi-component distributed fiber optic acoustic sensor strain observation data. Distributed Acoustic Sensing (DAS) technology is also often referred to as distributed fiber optic vibration sensing technology in the field of seismology. In practical applications, the optical cable is usually laid horizontally during ground observation and vertically during well observation. Multi-component observation can also be achieved through laying methods such as helical winding of the optical cable. The optical cable is usually provided with multiple observation channels at certain intervals, and multi-channel seismic observation data can be obtained, generating multiple strain field records in different strain directions. Preferably, the strain field record contains 150 channels with a channel spacing of 2 meters.

[0052] Preferably, the strain observation directions are positive, tangential and vertical. Therefore, the strain data includes radial positive strain ε rr , radial shear strain ε rθ , tangential normal strain ε θθ , vertical normal strain ε zz One or more of wherein r, θ and z represent coordinate variables in radial, tangential and vertical directions (i.e., radius, azimuth and depth), respectively.

[0053] like Figure 2 FIG. 1 is a schematic diagram of synthetic recording of strain fields of different strain components in an embodiment of the present invention. The strain field recording includes 150 traces with a trace spacing of 2 meters. Figure 2 In each figure, the horizontal axis is the offset (Offset) and the vertical axis is the time (Time), showing the synthetic record of strain data of different components in the time-space domain. Figure 2 (a) is the vertical normal strain record ε of radial excitation zz,R ; Figure 2 (b) is the vertical normal strain record ε of vertical excitation zz,Z ; Figure 2 (c) is the radial normal strain record ε of radial excitation rr,R ; Figure 2 (d) is the radial normal strain record ε of vertical excitation rr,Z ; Figure 2 (e) is the radially excited tangential normal strain record ε θθ,R ; Figure 2 (f) is the tangential normal strain record ε of the vertical excitation θθ，Z ; Figure 2 (g) is the radial shear strain record ε of the tangential excitation rθ,T ; The subscripts R, T, and Z represent the vertical, tangential, and radial directions of earthquake source excitation, respectively.

[0054] The present invention provides a method for extracting surface wave dispersion curves based on strain fields, further comprising the step of: S2, preprocessing the strain data according to the type of the seismic observation data to obtain strain components.

[0055] Specifically, the seismic observation data is single-component or multi-component DAS strain observation data; the types of the seismic observation data include one or more of active source data of known seismic sources, active source data of unknown seismic sources, and passive source data. Preferably, the passive source data is background noise data.

[0056] Specifically, step S2 includes the following steps:

[0057] S21. For the strain data extracted from the active source data of the known seismic source, through deconvolution processing, convert the strain data into Green's function components, and the strain components are the Green's function components;

[0058] Or S22. For the strain data extracted from the active source data of the unknown seismic source, directly use it as the strain component;

[0059] Or S23. For the strain data extracted from the passive source data, convert the strain data into empirical Green's function components, and the strain components are the empirical Green's function components.

[0060] Due to different data types, the subsequent processing procedures of the frequency-Bessel ratio transformation are also different. Before performing the Fourier transform, the strain data is processed according to different data sources.

[0061] A method for extracting surface wave dispersion curves based on the strain field in this application further includes the step: S3. Through the Fourier transform, convert the strain component from the time-space domain to the frequency-space domain.

[0062] Specifically, the formula used in the Fourier transform is where t represents the time variable, f(t) represents any time-domain data, F(ω) represents the spectrum corresponding to f(t), and ω represents the angular frequency. The Fourier transform can also be expressed as F(ω) = FFT[f(t)], and FFT[·] represents the Fourier transform function.

[0063] Specifically, the passive source data needs to be processed for background noise to recover the empirical Green's function. Therefore, in step S23. For the strain data extracted from the passive source data, convert the strain data into empirical Green's function components, the following steps are further included:

[0064] Perform background noise processing on the strain data to recover the empirical Green's function components.

[0065] Specifically, the background noise processing includes single-channel preprocessing, cross-correlation processing of any two channels, and superposition processing of cross-correlation functions of noise in different time periods;

[0066] Specifically, the single-channel preprocessing includes removing instrument response, data segmentation, removing the mean, removing trends, band-pass filtering, time-domain normalization, and spectral whitening.

[0067] A method for extracting surface wave dispersion curves based on the strain field in this application further includes the step: S4. Through the frequency-Bessel transform, convert the strain component from the frequency-space domain to the frequency-phase velocity domain to obtain the surface wave dispersion spectrum of the strain component.

[0068] Optionally, since the frequency-phase velocity domain can be replaced by the frequency-wavenumber domain, that is, a method for extracting surface wave dispersion curves based on a strain field in the present application further includes the step of converting the strain component from the frequency-space domain to the frequency-wavenumber domain through a frequency-Bessel transform to obtain the surface wave dispersion spectrum of the strain component.

[0069] Specifically, different frequency-Bessel transforms are adopted for different types of active source data with known sources, active source data with unknown sources, and passive source data.

[0070] Specifically, the origin of the frequency-Bessel transform method in the present application is based on the cylindrical coordinate system and the generalized reflection and transmission coefficient method.

[0071] First, based on the relationship between the strain field and the displacement field and the expression of the displacement field, establish the expressions of 4 strain data (radial normal strain, radial shear strain, tangential normal strain, vertical normal strain) in the frequency domain under the cylindrical coordinate system:

[0072]

[0073] Among them, r, θ, and z respectively represent the coordinate variables in the radial, tangential, and vertical directions (i.e., radius, azimuth angle, and depth); ω represents the angular frequency; F(ω) represents the source time function in the frequency domain; the unit direction vector of a single force point source in the cylindrical coordinate system is expressed as m = (m R (θ), m T (θ), m Z ); R, T, and Z respectively represent the directions of source excitation (R is radial, T is tangential, Z is vertical); G ξ,ζ (ξ = rr, θθ, rθ, zz and ζ = R, T, Z) represents the Green's function of the strain field in different excitation and different receiving directions. For example, G rr,Z represents the Green's function of the radial normal strain excited by a vertical source.

[0074] Here, for different types of active source data with known sources, active source data with unknown sources, and passive source data, the above-mentioned strain components are different. For the strain data extracted from the active source data with known sources, through deconvolution processing, the strain data is converted into Green's function components, and the strain component is the Green's function component; for the strain data extracted from the active source data with unknown sources, it is directly used as the strain component; for the strain data extracted from the passive source data, the strain data is converted into empirical Green's function components, and the strain component is the empirical Green's function component.

[0075] For the strain data extracted from the active source data of a known seismic source, the correlation relationship between the strain data and the corresponding Green's function components and source time function is established through the expression of the above formula (1).

[0076] Using the generalized reflection-transmission coefficient method, the expressions of the Green's function of the strain field for different components in the above formula (1) can be obtained, as shown in the following formulas (2) to (8):

[0077]

[0078]

[0079]

[0080]

[0081]

[0082]

[0083]

[0084] Among them, k represents the wave number, J 0 (kr) represents the Bessel function of the first kind of order zero, J 1 (kr) represents the Bessel function of the first kind of order one, J 2 (kr) represents the Bessel function of the first kind of order two; [g PS,0 1 、[g PS,0 2 、[g PS,1 1 ,[g PS,1 2 and g SH,1 are the integral kernels in the wave number integral of the Green's function.

[0085] Using the orthogonality of the Bessel function and the combination of different Green's function components, the above formulas (2) to (8) are transformed to obtain the following formulas (9) to (13):

[0086]

[0087]

[0088]

[0089]

[0090] ​​​​

[0091] Through the above formulas (9) to (13), the expressions on both sides of the equal sign can be transformed respectively to obtain the expression of the theoretical dispersion spectrum and the frequency-Bessel transform formula of the actual dispersion spectrum.

[0092] Specifically, take the imaginary part (i.e., operate Im(·)) on both sides of formulas (9) to (13), and define the right-side expression as the theoretical dispersion spectrum S of real values R01 、S R02 、S R11 、S R12 and S L , that is:

[0093]

[0094]

[0095]

[0096]

[0097]

[0098] where the subscripts R and L represent Rayleigh wave (Rayleigh wave or Raleigh wave), Love wave (Love wave or Love wave), and the subscripts 0, 1, 2 are used to distinguish different variables.

[0099] As Figure 3 shown, it is the theoretical surface wave dispersion spectrum diagram obtained by forward calculation using the generalized reflection and transmission coefficient method in an embodiment of the present application. Among them, the abscissa in each figure is frequency, and the ordinate is phase velocity. Figure 3 (a) of R01 is the theoretical Rayleigh wave dispersion spectrum S calculated by formula (14); Figure 3 (b) of R02 is the theoretical Rayleigh wave dispersion spectrum S calculated by formula (15); Figure 3 (c) of R11 is the theoretical Rayleigh wave dispersion spectrum S calculated by formula (16); Figure 3 (d) of R12 is the theoretical Rayleigh wave dispersion spectrum S calculated by formula (17); Figure 3 (e) of L is the theoretical Love wave dispersion spectrum S calculated by formula (18).

[0100] Specifically, in actual calculation, there is a range limit for the offset. When the radial offset range is [r min , rmax Under the condition of [], for the active source data with known source information, the strain data is converted into Green's function components, and then the Green's function components are transformed from the time - space domain to the frequency - space domain through Fourier transform, and then the Green's function components are transformed from the frequency - space domain to the frequency - wavenumber domain or the frequency - phase velocity domain through frequency - Bessel transform to obtain the surface wave dispersion spectrum of the Green's function components. From the left side of formulas (9) - (13), the actual dispersion spectrum corresponding to the Green's function component G is defined as D, specifically including D R01 、D R02 、D R11 、D R12 and D L , and is calculated using the following formulas:

[0101]

[0102]

[0103]

[0104]

[0105]

[0106] Formulas (19) - (23) are the exact frequency - Bessel transform formulas corresponding to the Green's function. Among them, since it can be seen from formulas (4) and (5) that G θθ,R and G θθ,Z decay rapidly with the increase of the offset r (including the factor 1 / r), so their integral contributions to formulas (19), (21) and (23) are very small. When G θθ,R and G θθ,Z are ignored, the approximate frequency - Bessel transform formulas corresponding to formulas (19), (21) and (23) are:

[0107]

[0108]

[0109]

[0110] where the subscript a represents approximate.

[0111] Such as Figure 4 and Figure 5As shown, it is the active source data for known seismic source information, and the actual surface wave dispersion spectrogram extracted by the frequency-Bessel transform formula and the approximate frequency-Bessel ratio transform formula of an embodiment of this solution. Among them, the abscissa in each figure is the frequency, and the ordinate is the phase velocity. Figure 4 (a) of Figure 4 is the actual dispersion spectrum D of Rayleigh waves extracted from the Green's function components corresponding to the radial and tangential normal strains excited vertically calculated by formula (19) R01 ; Figure 4 (b) of Figure 4 is the actual dispersion spectrum D of Rayleigh waves extracted from the Green's function components corresponding to the vertical normal strain excited vertically calculated by formula (20) R02 ; Figure 4 (c) of Figure 4 is the actual dispersion spectrum D of Rayleigh waves extracted from the Green's function components corresponding to the radial and tangential normal strains excited radially calculated by formula (21) R11 ; Figure 4 (d) of Figure 4 is the actual dispersion spectrum D of Rayleigh waves extracted from the Green's function components corresponding to the vertical normal strain excited radially calculated by formula (22) R12 ; Figure 4 (e) of Figure 4 is the theoretical dispersion spectrum D of Love waves extracted from the Green's function components corresponding to the radial shear strain excited tangentially and the tangential normal strain excited radially calculated by formula (23) L ; Figure 5 (a) of Figure 5 is the dispersion spectrum D of Rayleigh waves extracted from the Green's function components corresponding to the radial normal strain excited vertically calculated by formula (24) aR01 ; Figure 5 (b) of Figure 5 is the actual surface wave dispersion spectrum D of Rayleigh waves extracted from the Green's function components corresponding to the radial normal strain excited radially calculated by formula (25) aR11 ; Figure 5 (c) of Figure 5 is the dispersion spectrum result D of Love waves extracted from the Green's function components corresponding to the radial shear strain excited tangentially calculated by formula (26) aL ;

[0112] A method for extracting surface wave dispersion curves based on the strain field in this application further includes the step: S5. Pick up the surface wave dispersion curve according to the surface wave dispersion spectrogram.

[0113] Specifically, step S5 includes:

[0114] S51. Identify the dispersion energy of the strain component from the actual dispersion spectrum;

[0115] S52. Determine the frequency and the surface wave phase velocity value corresponding to the frequency according to the position of the extreme value in the dispersion energy in the frequency-phase velocity domain;

[0116] S53. Pick up the surface wave dispersion curve of the strain component according to multiple groups of the frequencies and the corresponding surface wave phase velocity values.

[0117] Specifically, in step S52, the position in the frequency-phase velocity domain refers to the position in the coordinate axis with the frequency as the abscissa and the phase velocity as the ordinate.

[0118] Specifically, since the frequency-phase velocity domain can be equivalently replaced by the frequency-wavenumber domain, step S52 can also be replaced by: determining the frequency and the surface wave value corresponding to the frequency according to the position of the extreme value in the frequency-wavenumber domain in the dispersion energy. Step S53 can also be replaced by: picking up the surface wave dispersion curve of the strain component according to multiple groups of the frequencies and the corresponding surface wave values.

[0119] Specifically, in step S52, the position in the frequency-wavenumber domain refers to the position in the coordinate axis with the frequency as the abscissa and the wavenumber as the ordinate.

[0120] Figure 3 , Figure 4 and Figure 5 In the surface wave dispersion spectrograms of, the strip-shaped colored part is the distribution diagram of the surface wave dispersion curve, reflecting the distribution of the surface wave dispersion energy.

[0121] Preferably, after step S5, it further includes verifying the accuracy of the actual dispersion spectrum and the surface wave dispersion curve, and verifying through a double-verification method.

[0122] The theoretical dispersion spectrum is used to verify the accuracy of the actual dispersion spectrum. As Figure 3 shown, it is the theoretical surface wave dispersion spectrogram obtained by forward calculation using the generalized reflection and transmission coefficient method in an embodiment of the present solution. As Figure 4 shown, it is the actual surface wave dispersion spectrogram extracted from the corresponding strain field Green's function component by the frequency-Bessel transform method in an embodiment of the present solution for the active source data with known source information. As Figure 5 shown, it is the actual surface wave dispersion spectrogram extracted from the corresponding strain field Green's function component by the approximate frequency-Bessel transform method in an embodiment of the present solution for the active source data with known source information. Figure 4 and Figure 5 The curve formed by the black solid dots in is the theoretical surface wave dispersion curve obtained by forward calculation using the solution of the prior art. Figure 3 , Figure 4 and Figure 5 In the surface wave dispersion spectrograms of, the strip-shaped colored part is the distribution diagram of the surface wave dispersion curve, reflecting the distribution of the surface wave dispersion energy. Figure 4 (a) to (e) ofFigure 3 (a) - (e) of Figure 5 (a) - (c) of Figure 3 (a), (c), (e) of Figure 1 correspond one - to - one, based on the same strain data. It can be seen from the figure that the actual surface - wave dispersion spectrum of this solution is highly consistent with the theoretical surface - wave dispersion spectrum

[0123] In addition to the active - source data for known source information above, this application can also use different frequency - Bessel transform formulas for passive - source data (such as background - noise data) and active - source data with unknown source information to extract the surface - wave dispersion spectrum, and then pick up the surface - wave dispersion curve. Among them, the steps of picking up the surface - wave dispersion curve from the surface - wave dispersion spectrum are the same as the processing process of active - source data with known source information, as shown in step S5 specifically.

[0124] Specifically, for the strain data extracted from passive - source data, it is necessary to convert the strain data into empirical Green's function components, and use the empirical Green's function components as strain components to obtain the surface - wave dispersion spectrum through Fourier transform and frequency - Bessel transform.

[0125] Passive - source data is usually mainly background - noise observation data, and it is necessary to recover the empirical Green's function (also often called the noise cross - correlation function) through background - noise processing.

[0126] Specifically, based on the relationship between the Green's function and the empirical Green's function, that is, the empirical Green's function C is proportional to the imaginary part of the Green's function G (i.e., C ξ,ζ = A·Im(G ξ,ζ ))), modifying formulas (19) - (26), the frequency - Bessel transform formula for extracting the surface - wave dispersion curve from passive - source data can be obtained:

[0127]

[0128]

[0129]

[0130]

[0131]

[0132]

[0133]

[0134]

[0135] Among them, the operation Re(·) represents taking the real part of a complex number.

[0136] For the active source data of an unknown seismic source, that is, the active source data of a single-force point source, the strain data can be directly used as the strain component, and the surface wave dispersion spectrum can be obtained through Fourier transform and frequency-Bessel transform. Therefore, by modifying the above formulas (19) to (26), the frequency-Bessel transform formula for extracting the surface wave dispersion curve from the active source data of an unknown seismic source is obtained:

[0137]

[0138]

[0139]

[0140]

[0141]

[0142]

[0143]

[0144]

[0145] Among them, the operation |·| represents taking the absolute value of a real number or the modulus of a complex number.

[0146] Specifically, for the passive source data (such as ambient noise data) and the active source data of an unknown seismic source, the actual dispersion spectrum is obtained through Fourier transform and frequency-Bessel transform. The specific process is the same as the above steps for the active source data of a known seismic source.

[0147] In summary, the present application can process different types of multi-component DAS data, including the active source data of a known seismic source, the active source data of an unknown seismic source, and the passive source data, with wider practicability.

[0148] Preferably, the surface wave dispersion curve includes the Rayleigh wave dispersion curve and the Love wave dispersion curve.

[0149] Preferably, the first-kind Bessel function J ν (kr) in the frequency-Bessel transform formula can be replaced by the third-kind Bessel function (i.e., the Hankel function), which can effectively avoid possible spatial aliasing.

[0150] Preferably, for the strain rate field data, after being converted into strain field data, the method for extracting the surface wave dispersion curve based on the strain field in the present application can also be used.

[0151] Preferably, since the Scholte wave propagating along the seabed surface is similar to the Rayleigh wave propagating on the land surface in theory and application, the above frequency-Bessel transform formula applicable to the Rayleigh wave is also applicable to extracting the Scholte wave dispersion information. Preferably, the method for extracting the surface wave dispersion curve provided in the present application can also be applied to extracting the body wave dispersion curve.

[0152] The second aspect of the present application lies in providing a system for extracting the surface wave dispersion curve based on the strain field, as Figure 6 shown, is a schematic diagram of a system for extracting the surface wave dispersion curve based on the strain field in the present application. The system includes:

[0153] A data extraction module 601, configured to extract strain data from seismic observation data;

[0154] A preprocessing module 602, configured to preprocess the strain data according to the type of the seismic observation data to obtain strain components;

[0155] A time-frequency conversion module 603, configured to convert the strain components from the time-space domain to the frequency-space domain through Fourier transform;

[0156] A Bessel conversion module 604, configured to convert the strain components from the frequency-space domain to the frequency-phase velocity domain through frequency-Bessel transform to obtain the surface wave dispersion spectrum of the strain components;

[0157] A picking module 605, configured to pick up the surface wave dispersion curve according to the surface wave dispersion spectrum.

[0158] Specifically, the Bessel conversion module 604 can also be configured to convert the strain components from the frequency-space domain to the frequency-wavenumber domain through frequency-Bessel transform to obtain the surface wave dispersion spectrum of the strain components;

[0159] The system further includes a front-end DAS observation device 600. The DAS observation device mainly consists of two parts. One is a demodulator, including an optical system and a signal acquisition system; the other is an optical fiber for sensing. The optical fiber in the DAS observation device is horizontally arranged, vertically arranged, spirally arranged, etc., and is used to obtain single-component or multi-component DAS data and send it to the data extraction module.

[0160] The third aspect of the present application lies in providing a terminal, comprising: a memory, a processor, and a program of a method for extracting surface wave dispersion curves based on a strain field stored on the memory and executable on the processor. When the program of the method for extracting surface wave dispersion curves based on a strain field is executed by the processor, the steps of a method for extracting surface wave dispersion curves based on a strain field are implemented.

[0161] The fourth aspect of the present application lies in providing a computer-readable storage medium storing a program of a method for extracting surface wave dispersion curves based on a strain field. When the program of the method for extracting surface wave dispersion curves based on a strain field is executed by a processor, the steps of a method for extracting surface wave dispersion curves based on a strain field are implemented.

[0162] In summary, the present application innovatively provides a method for extracting surface wave dispersion curves based on a strain field, including the steps of: extracting strain data from seismic observation data; preprocessing the strain data according to the type of the seismic observation data to obtain strain components; converting the strain components from the time-space domain to the frequency-space domain through Fourier transform; converting the strain components from the frequency-space domain to the frequency-phase velocity domain through frequency-Bessel transform to obtain the surface wave dispersion spectrum of the strain components; and picking up the surface wave dispersion curve according to the surface wave dispersion spectrum. The present application proposes different frequency-Bessel transform formulas for active source data with known sources, active source data with unknown sources, and passive source data respectively from the perspective of the strain field for single-component or multi-component DAS data to extract the surface wave dispersion spectrum, and then pick up the surface wave dispersion curve, filling the technical gap in extracting surface wave dispersion information from DAS data, providing a powerful tool for extracting surface wave dispersion information applicable to single-component or multi-component DAS data, having important theoretical significance and practical value for the development of surface wave dispersion imaging methods based on DAS observations, and providing ideas for extracting the dispersion information of surface waves or body waves such as Rayleigh waves, Scholte waves, and Love waves.

[0163] The above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for extracting surface wave dispersion curves based on strain field, characterized in that: The steps include: Extract strain data from earthquake observation data; Preprocessing the strain data according to the type of the seismic observation data to obtain strain components; Converting the strain components from the time-space domain to the frequency-space domain by Fourier transformation; The strain component is converted from the frequency-space domain to the frequency-phase velocity domain by frequency-Bessel transformation to obtain the surface wave dispersion spectrum of the strain component; picking up a surface wave dispersion curve according to the surface wave dispersion spectrum; The seismic observation data is single-component or multi-component distributed optical fiber acoustic wave induction deformation observation data; The strain data includes one or more of radial normal strain, radial shear strain, tangential normal strain, and vertical normal strain; The frequency-Bessel transform is based on a cylindrical coordinate system and a generalized reflection-transmission coefficient method, and includes: based on the relationship between the strain field and the displacement field and the expression of the displacement field, establishing an expression of the strain data in the frequency domain in the cylindrical coordinate system, and transforming to obtain different types of surface wave dispersion spectra.

2. The method for extracting surface wave dispersion curve based on strain field according to claim 1, characterized in that: The types of the seismic observation data include one or more of active source data of known seismic sources, active source data of unknown seismic sources, and passive source data.

3. The method for extracting surface wave dispersion curve based on strain field according to claim 2, characterized in that: The preprocessing of the strain data according to the type of the seismic observation data to obtain strain components includes: For the strain data extracted from the active source data of the known seismic source, convert the strain data into Green's function components through deconvolution processing, wherein the strain components are the Green's function components; Directly using the strain data extracted from the active source data of the unknown seismic source as the strain component; For the strain data extracted from the passive source data, the strain data is converted into empirical Green's function components, and the strain components are the empirical Green's function components.

4. The method for extracting surface wave dispersion curve based on strain field according to claim 3, characterized in that: The step of converting the strain data into empirical Green's function components comprises the following steps: performing background noise processing on the strain data to restore the empirical Green's function component; Wherein, the background noise processing includes single-channel preprocessing, any two-channel cross-correlation processing and superposition processing of noise cross-correlation functions in different time periods; The single channel preprocessing includes removing instrument response, data segmentation, removing mean, removing trend, bandpass filtering, time domain normalization and spectrum whitening.

5. The method for extracting surface wave dispersion curve based on strain field according to claim 1, characterized in that: The step of extracting strain data from seismic observation data further includes: Synthesize strain field records based on the multiple channels of strain data; The strain field records include radial positive strain records of vertical excitation, vertical positive strain records of vertical excitation, tangential positive strain records of vertical excitation, radial positive strain records of radial excitation, vertical positive strain records of radial excitation, tangential positive strain records of radial excitation, and radial shear strain records of tangential excitation.

6. The method for extracting surface wave dispersion curve based on strain field according to claim 1, characterized in that: The step of picking up a surface wave dispersion curve according to the surface wave dispersion spectrum comprises the following steps: identifying the dispersion energy of the strain component from the actual dispersion spectrum; Determine the frequency and the surface wave phase velocity value corresponding to the frequency according to the position of the extreme value in the dispersion energy in the frequency-phase velocity domain; The surface wave dispersion curve of the strain component is picked up according to a plurality of sets of the frequencies and the corresponding surface wave phase velocity values.

7. The method for extracting surface wave dispersion curve based on strain field according to claim 6, characterized in that: The surface wave dispersion curve includes a Rayleigh wave dispersion curve and a Love wave dispersion curve.

8. A system for extracting surface wave dispersion curves based on strain field, characterized in that: The system comprises: A data extraction module, used to extract strain data from seismic observation data; A preprocessing module, used for preprocessing the strain data according to the type of the seismic observation data to obtain strain components; A time-frequency conversion module, used for converting the strain component from the time-space domain to the frequency-space domain through Fourier transformation; A Bessel conversion module, used for converting the strain component from the frequency-space domain to the frequency-phase velocity domain through frequency-Bessel transformation to obtain the surface wave dispersion spectrum of the strain component; A picking module, used for picking up a surface wave dispersion curve according to the surface wave dispersion spectrum; Wherein, the seismic observation data is single-component or multi-component distributed optical fiber acoustic wave induction deformation observation data; The strain data includes one or more of radial normal strain, radial shear strain, tangential normal strain, and vertical normal strain; The frequency-Bessel transform is based on a cylindrical coordinate system and a generalized reflection-transmission coefficient method, and includes: based on the relationship between the strain field and the displacement field and the expression of the displacement field, establishing an expression of the strain data in the frequency domain in the cylindrical coordinate system, and transforming to obtain different types of surface wave dispersion spectra.

9. A terminal, characterized in that: The terminal includes: a memory, a processor, and a program of a method for extracting a surface wave dispersion curve based on a strain field stored in the memory and executable on the processor. When the program of a method for extracting a surface wave dispersion curve based on a strain field is executed by the processor, the steps of a method for extracting a surface wave dispersion curve based on a strain field as described in any one of claims 1 to 7 are implemented.

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