A method, apparatus, device, and medium for imaging a geological structure

Abstract

This invention relates to the field of seismic exploration technology and discloses a geological structure imaging method, apparatus, equipment, and medium. It can acquire seismic wave velocity distribution data and quality factor distribution data corresponding to a target area, determine the viscoelastic vertical wavenumber based on the seismic wave velocity distribution data and quality factor distribution data, input the viscoelastic vertical wavenumber into a pre-constructed pressure wavefield determination model to determine the pressure wavefield distribution data corresponding to the target area; wherein, the pressure wavefield determination model is constructed based on the viscoelastic properties of the subsurface medium and a viscoelastic acoustic propagation operator based on matrix decomposition; and use the pressure wavefield distribution data to perform geological structure imaging of the target area, obtaining the geological structure imaging results of the target area. This invention can preserve wavefield information to a greater extent, improving the resolution of geological structure imaging and the imaging quality of deep subsurface areas.

Description

Technical Field

[0001] This invention relates to the field of seismic exploration technology, and in particular to a geological structure imaging method, apparatus, equipment, and medium. Background Technology

[0002] With the development of science and technology, seismic exploration technology has been continuously improved.

[0003] Related technologies involve conducting seismic exploration of a designated area, sending seismic waves to the geological structure of that area, and acquiring corresponding seismic wave data. The subsurface medium in the designated area is treated as having perfectly elastic properties, and migration imaging is performed based on the seismic wave data to obtain geological structure images. Geological structure imaging can provide detailed information about the subsurface geological structure, helping technicians understand the distribution, thickness, morphology, and interrelationships of strata. It can also be used to determine the location and characteristics of potential reservoirs and ore bodies, and to assess the reserves and recoverability of resources.

[0004] However, the resolution of geological structure imaging in related technologies is relatively low. Summary of the Invention

[0005] This invention provides a geological structure imaging method, apparatus, equipment, and medium to address the shortcomings of low resolution in geological structure imaging in related technologies and improve the resolution of geological structure imaging.

[0006] In a first aspect, the present invention provides a geological structure imaging method, comprising:

[0007] Obtain seismic wave velocity distribution data and quality factor distribution data for the target area;

[0008] The vertical wavenumber of viscoacoustic waves is determined based on the seismic wave velocity distribution data and the quality factor distribution data.

[0009] The viscoelastic vertical wavenumber is input into the constructed pressure wave field determination model to determine the pressure wave field distribution data corresponding to the target area; wherein, the pressure wave field determination model is constructed based on the viscoelastic properties of the subsurface medium and the viscoelastic propagation operator of matrix decomposition;

[0010] The geological structure of the target area is imaged using the pressure wave field distribution data, and the geological structure imaging results of the target area are obtained.

[0011] Optionally, the seismic wave velocity distribution data includes, assuming the subsurface medium of the target area is an acoustic medium, the propagation velocity data of seismic waves at different spatial locations during their propagation in the subsurface medium of the target area;

[0012] The quality factor distribution data includes quality factor data at different spatial locations during the propagation of seismic waves in the underground medium of the target area, assuming that the underground medium of the target area is an acoustic medium.

[0013] Optionally, determining the viscoelastic vertical wavenumber based on the seismic wave velocity distribution data and the quality factor distribution data includes:

[0014] Based on the seismic wave velocity distribution data, the acoustic wave velocity distribution data is determined; wherein, the acoustic wave velocity distribution data includes, assuming that the underground medium of the target area is an acoustic medium, the propagation velocity data of seismic waves at different spatial locations during the propagation of the underground medium of the target area;

[0015] The viscosity-acoustic parameters are determined based on the quality factor distribution data.

[0016] The vertical wavenumber of the adhesive acoustic wave is determined based on the adhesive acoustic wave velocity distribution data and the adhesive acoustic parameters.

[0017] Optionally, the process of constructing the pressure wave field determination model includes:

[0018] Considering that the underground medium in the target area is a viscous acoustic medium, a viscous acoustic wave equation for seismic wave propagation is established.

[0019] The Fourier transform of the viscous acoustic wave equation yields the corresponding frequency domain equation.

[0020] The frequency domain equation is transformed to obtain the viscous acoustic wave equation of the two-dimensional viscous acoustic medium in the frequency spatial domain;

[0021] Under the premise that the preset assumptions are true, the viscous acoustic wave equation of the two-dimensional viscous acoustic medium is decomposed to obtain the decomposed equation.

[0022] The viscosonic Helmholtz operator is determined based on the decomposed equations.

[0023] The eigenvalue decomposition of the viscosonic Helmholtz operator is performed to obtain the eigenvalue decomposition result;

[0024] The pressure wave field value solution equation is determined based on the eigenvalue decomposition results and used as the pressure wave field determination model.

[0025] Optionally, the viscous acoustic wave equation of the two-dimensional viscous acoustic medium is:

[0026]

[0027]

[0028]

[0029] in, For pressure field waves, These are the coordinates along the depth direction in the underground space. Sampling frequency, The speed at which seismic waves propagate in a viscous acoustic medium. The imaginary unit, For the reference frequency of the adhesive sound medium, For viscosity-sound parameters, These are the horizontal coordinates within the underground space. The speed at which seismic waves propagate in the acoustic medium. This is the quality factor.

[0030] Optionally, the adhesive acoustic Helmholtz operator is:

[0031]

[0032] in, For the aforementioned adhesive acoustic Helmholtz operator.

[0033] Optionally, the pressure wave field determination model is:

[0034]

[0035]

[0036] in, For depth step size, For wave field propagation operators, The vertical wavenumber of the viscous acoustic wave.

[0037] In a second aspect, the present invention provides a geological structure imaging device, comprising:

[0038] The acquisition unit is used to acquire seismic wave velocity distribution data and quality factor distribution data corresponding to the target area.

[0039] A determining unit is used to determine the viscoelastic vertical wavenumber based on the seismic wave velocity distribution data and the quality factor distribution data;

[0040] An input unit is used to input the viscoelastic vertical wavenumber into a pre-constructed pressure wave field determination model to determine the pressure wave field distribution data corresponding to the target area; wherein, the pressure wave field determination model is constructed based on the viscoelastic properties of the subsurface medium and a matrix-decomposed viscoelastic propagation operator;

[0041] An imaging unit is used to perform geological structure imaging of the target area using the pressure wave field distribution data, and to obtain the geological structure imaging results of the target area.

[0042] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the geological structure imaging method of the first aspect or any corresponding embodiment described above.

[0043] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to perform the geological structure imaging method of the first aspect or any corresponding embodiment thereof.

[0044] The geological structure imaging method, apparatus, equipment, and medium provided by this invention can acquire seismic wave velocity distribution data and quality factor distribution data corresponding to a target area, determine the viscoelastic vertical wavenumber based on the seismic wave velocity distribution data and quality factor distribution data, input the viscoelastic vertical wavenumber into a pre-constructed pressure wavefield determination model to determine the pressure wavefield distribution data corresponding to the target area; wherein, the pressure wavefield determination model is constructed based on the viscoelastic properties of the subsurface medium and a viscoelastic acoustic propagation operator based on matrix decomposition; and use the pressure wavefield distribution data to perform geological structure imaging of the target area to obtain the geological structure imaging results of the target area. This invention can preserve wavefield information to a greater extent, improve the resolution of geological structure imaging and the imaging quality of deep subsurface areas. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0046] Figure 1 A flowchart of a geological structure imaging method provided in an embodiment of the present invention;

[0047] Figure 2 A velocity model of a two-dimensional undulation model provided in an embodiment of the present invention;

[0048] Figure 3 A quality factor model for a two-dimensional fluctuation model provided in an embodiment of the present invention;

[0049] Figure 4 An embodiment of the present invention provides an imaging result of an underground structure generated based on acoustic data and an acoustic propagation operator based on matrix decomposition;

[0050] Figure 5An imaging result of an underground structure generated based on adhesive acoustic data and an adhesive acoustic propagation operator based on matrix decomposition, provided in an embodiment of the present invention;

[0051] Figure 6 An imaging result of an underground structure generated based on adhesive acoustic data and a sound wave propagation operator based on matrix decomposition, provided in an embodiment of the present invention;

[0052] Figure 7 A waveform comparison diagram provided for an embodiment of the present invention;

[0053] Figure 8 This is another waveform comparison diagram provided for an embodiment of the present invention;

[0054] Figure 9 This is another waveform comparison diagram provided in an embodiment of the present invention;

[0055] Figure 10 This is another waveform comparison diagram provided in an embodiment of the present invention;

[0056] Figure 11 This is a schematic diagram of a geological structure imaging device provided in an embodiment of the present invention;

[0057] Figure 12 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0059] The following is combined with Figures 1-10 The geological structure imaging method of the present invention is described.

[0060] like Figure 1 As shown, this embodiment proposes a first geological structure imaging method, which may include the following steps:

[0061] S101. Obtain the seismic wave velocity distribution data and quality factor distribution data corresponding to the target area.

[0062] The target area is a specific region where geological structure imaging is required. It is understood that this embodiment can perform geological structure imaging on the target area.

[0063] Optionally, the seismic wave velocity distribution data may include the propagation velocity data of seismic waves at different spatial locations during their propagation in the subsurface medium of the target area.

[0064] The quality factor distribution data includes the quality factor data at different spatial locations during the propagation of seismic waves in the underground medium of the target area.

[0065] Specifically, this embodiment can deploy seismic detectors and generate seismic wave sources in the target area to emit seismic waves towards the geological structure of the target area, recording the information of the seismic waves propagating underground, thereby achieving seismic exploration of the target area. Subsequently, this embodiment can use seismic instruments to acquire seismic wave data, including data such as the arrival time and amplitude of the seismic waves. This embodiment can preprocess the acquired seismic wave data, including noise removal, signal correction, and feature extraction, to improve data quality and usability.

[0066] Specifically, this embodiment can utilize the wave propagation time and distance information in the preprocessed seismic wave data to calculate the propagation velocity of seismic waves at various spatial locations in the subsurface medium through velocity analysis. The velocity analysis methods can include ray tracing and wave equations, among others.

[0067] Specifically, this embodiment can process the preprocessed seismic wave data based on amplitude attenuation, frequency change, or seismic wave propagation characteristics to obtain the quality factor of the seismic wave at various spatial locations in the underground medium.

[0068] S102. Determine the vertical wavenumber of viscous acoustic waves based on seismic wave velocity distribution data and quality factor distribution data.

[0069] Optionally, step S102 may include:

[0070] Based on the seismic wave velocity distribution data, the acoustic wave velocity distribution data is determined; the acoustic wave velocity distribution data includes the propagation velocity data of seismic waves at different spatial locations during the propagation of seismic waves in the underground medium of the target area, assuming that the underground medium of the target area is an acoustic medium.

[0071] Determine the viscosity-acoustic parameters based on the quality factor distribution data;

[0072] The vertical wavenumber of the adhesive acoustic wave is determined based on the adhesive acoustic wave velocity distribution data and adhesive acoustic parameters.

[0073] S103. Input the viscoelastic vertical wavenumber into the constructed pressure wave field determination model to determine the pressure wave field distribution data corresponding to the target area; wherein, the pressure wave field determination model is constructed based on the viscoelastic properties of the subsurface medium and the viscoelastic propagation operator of matrix decomposition.

[0074] The inventors of this invention discovered that related technologies neglect the viscoelastic information of the subsurface medium when imaging subsurface structures, treating the subsurface medium as an ideal medium for processing seismic wave data. This results in low resolution of subsurface structure imaging, and in severe cases, can even affect seismic interpretation. This embodiment no longer treats the subsurface medium as an ideal medium, but introduces viscoelastic and acoustic properties to fully grasp the medium information, constructing a wave equation formula for viscoelastic and acoustic media. This improves the resolution of subsurface structure imaging and preserves wavefield information to a greater extent, significantly improving the quality of deep subsurface imaging.

[0075] Specifically, in this embodiment, a pressure wave field determination model can be constructed first. Optionally, the process of constructing the pressure wave field determination model includes:

[0076] Considering that the underground medium in the target area is a viscous acoustic medium, a viscous acoustic wave equation for seismic wave propagation is established.

[0077] By performing a Fourier transform on the viscous acoustic wave equation, the corresponding frequency domain equation is obtained.

[0078] The frequency domain equations are transformed to obtain the viscous acoustic wave equations for two-dimensional viscous acoustic media in the frequency spatial domain.

[0079] Under the premise that the preset assumptions are true, the viscous acoustic wave equation of the two-dimensional viscous acoustic medium is decomposed to obtain the decomposed equation.

[0080] Determine the viscosonic Helmholtz operator based on the decomposed equations;

[0081] Eigenvalue decomposition is performed on the viscous acoustic Helmholtz operator to obtain the eigenvalue decomposition results;

[0082] The pressure wave field values ​​are determined based on the eigenvalue decomposition results, and the equations are used as the model for determining the pressure wave field.

[0083] The inventors of this invention have discovered that subsurface media are not ideally homogeneous, nor do they possess perfectly elastic properties. Therefore, in seismic exploration, seismic waves often experience energy attenuation and dispersion during propagation underground. This manifests in seismic records as decreased amplitude and waveform distortion. If these seismic records are used directly for migration without considering viscoelastic attenuation, the image quality and resolution will be reduced. In severe cases, this can lead to insufficient illumination in and below the attenuated region, resulting in incorrect migration depth. Therefore, attenuation compensation for strongly attenuated media regions is essential during migration imaging.

[0084] Specifically, this embodiment can consider the viscoelastic properties of the subsurface medium and establish a viscoelastic wave equation for seismic wave propagation:

[0085]

[0086]

[0087]

[0088] in, For pressure field waves, For time, For viscosity-sound parameters, This represents the propagation speed of seismic waves in a viscous acoustic medium. is the reference frequency for the adhesive sound medium, and is the set standard frequency value for the adhesive sound medium. For the Laplace operator. This represents the propagation speed of seismic waves in the acoustic medium. This is the quality factor.

[0089] Among them, when As it approaches infinity, The value is 0. At this point, the formula degenerates into the acoustic wave equation in related technologies.

[0090] Specifically, in this embodiment, formula (1) can be Fourier transformed to the frequency domain to obtain the corresponding frequency domain equation:

[0091]

[0092] in, It is the imaginary unit.

[0093] Subsequently, in this embodiment, equation (4) can be transformed to obtain the viscous acoustic wave equation of the two-dimensional viscous acoustic medium in the frequency spatial domain:

[0094]

[0095] in, These are the coordinates along the depth direction in the underground space. Sampling frequency, These are the horizontal coordinates within the underground space.

[0096] It should be noted that the acoustic wave equation in the relevant technology is:

[0097]

[0098] Comparing formula (5) and formula (6), the derived viscous sound wave equation achieves viscous sound compensation by introducing a viscous sound compensation term.

[0099] Taking the derived acoustic wave equation for a two-dimensional viscous medium as an example, it is generally assumed that the depth direction is the main propagation direction of the seismic wave. When the seismic wave extends downward at certain depth intervals, the velocity of the medium only changes laterally within one depth step interval and does not change longitudinally. Based on this assumption, formula (5) can be decomposed as follows:

[0100]

[0101] Taking the following traveling wave propagation equation as an example, it can be written as:

[0102]

[0103] Therefore, the vertical wavenumber of viscous acoustics is defined as:

[0104]

[0105] The vertical wavenumber of sound waves based on the sound wave equation in related technologies is:

[0106]

[0107] By comparing the vertical wavenumber of the acoustic wave with that of the acoustic wave equation in related technologies, the vertical wavenumber of the acoustic wave defined in this embodiment achieves acoustic compensation by introducing an acoustic compensation term.

[0108] This embodiment can define the Helmholtz operator. L Written as:

[0109]

[0110] Combining the vertical wavenumber of sound waves and the Helmholtz operator from the sound wave equation in related technologies, we can obtain:

[0111]

[0112] At this point, the viscous acoustic Helmholtz operator based on viscous acoustic media It can be written as:

[0113]

[0114] in, For the adhesive sound Helmholtz operator.

[0115] Specifically, in this embodiment, parameters can be determined first based on seismic wave velocity distribution data and quality factor distribution data. and Then the parameters and Substituting into the formula, we obtain the viscous Helmholtz operator.

[0116] It should be noted that the adhesive Helmholtz operator is a symmetric matrix, and its eigenvalue decomposition is expressed as:

[0117]

[0118] In order to suppress evanescent waves during the calculation, positive eigenvalues ​​are retained while negative eigenvalues ​​are removed.

[0119] The extrapolation equation for the wave field along the depth can be written as:

[0120]

[0121] in, For depth step size, For wave field propagation operators.

[0122] Specifically, in this embodiment, the eigenvalue decomposition formula of the viscous acoustic Helmholtz operator can be substituted into the extrapolation equation of the wave field along the depth to obtain the pressure wave field at any depth.

[0123] Furthermore, substituting Euler's formula into the equation, the extrapolation equation of the wave field along the depth can be written in another form:

[0124]

[0125] Understandably, the pressure wave field determination model includes:

[0126]

[0127]

[0128] In this embodiment, by inputting the vertical wavenumber of the adhesive acoustic wave into the constructed pressure wave field determination model, the pressure wave field distribution data obtained by the calculation of the pressure wave field determination model can be obtained.

[0129] S104. Use pressure wave field distribution data to perform geological structure imaging of the target area and obtain the geological structure imaging results of the target area.

[0130] Specifically, this embodiment can perform migration imaging based on the pressure wave field distribution data after obtaining the pressure wave field distribution data, thereby obtaining geological structure imaging of the target area, improving the resolution of geological structure imaging, and preserving wave field information to a greater extent, thus significantly improving the quality of deep underground imaging.

[0131] The geological structure imaging method proposed in this embodiment can acquire seismic wave velocity distribution data and quality factor distribution data corresponding to the target area, and determine the viscoelastic vertical wavenumber based on the seismic wave velocity distribution data and quality factor distribution data. The viscoelastic vertical wavenumber is then input into a pre-constructed pressure wavefield determination model to determine the pressure wavefield distribution data corresponding to the target area. The pressure wavefield determination model is constructed based on the viscoelastic properties of the subsurface medium and a matrix decomposition viscoelastic propagation operator. The pressure wavefield distribution data is used to perform geological structure imaging of the target area, obtaining the geological structure imaging results for the target area. This embodiment can preserve wavefield information to a greater extent, improving the resolution of geological structure imaging and the imaging quality of deep subsurface areas.

[0132] To better illustrate the geological structure imaging method proposed in this embodiment, Example 1 is presented below, which applies the geological structure imaging method of this embodiment to a two-dimensional undulation model and explains the application process.

[0133] Example 1, Figure 2 and Figure 3 These are the velocity model and the corresponding quality factor model of the two-dimensional fluctuation model, respectively. Figure 2 and Figure 3 In this context, x represents the horizontal coordinate, and depth represents the depth coordinate. Figure 2 The color in the diagram represents the magnitude of the phase velocity; different colors correspond to different phase velocities. Phase velocity represents the speed at which seismic waves propagate in the acoustic medium. Figure 3 The color used in the model represents the quality factor, with different colors corresponding to different quality factor values. The dimensions of the two-dimensional undulation model are 1250 meters deep and 1000 meters horizontally. The two-dimensional undulation model contains multiple undulation interfaces, each with a uniform velocity. The minimum velocity of the model is 1500 meters per second, and the maximum velocity is 2300 meters per second. A total of 100 shots were recorded, with a shot spacing of 2 channels. Each shot was equipped with 60 geophones, with a channel spacing of 5 meters. The minimum shot-receiver distance was 0 meters, the sampling time interval was 0.0005 seconds, and the sampling length was 2 seconds. The velocity model grid spacing was 5 meters deep and 5 meters horizontally, with an extension depth of 1250 meters. The source used a Ricker wavelet with a dominant frequency of 20 Hz. The imaging results of the underground structure corresponding to the two-dimensional undulation model were determined based on relevant technologies and the geological structure imaging method of this embodiment.

[0134] The final offset result is as follows Figure 4 , Figure 5 and Figure 6 As shown, Figure 4 , Figure 5 and Figure 6 In this context, 'x' represents the horizontal coordinate, 'depth' represents the depth coordinate, and 'grayscale intensity' represents the amplitude of the imaging result; the closer the color is to black, the larger the amplitude. Figure 4The results of underground structure imaging generated from acoustic data and acoustic propagation operators based on matrix decomposition (i.e., underground structure imaging results generated by related technologies). Figure 5 The image results of the subsurface structure generated based on the viscoelastic acoustic data and the viscoelastic acoustic propagation operator based on matrix decomposition (i.e., the subsurface structure imaging results generated by the geological structure imaging method proposed in this embodiment). Figure 6 The images show the subsurface structure imaging results generated based on viscous acoustic data and a matrix decomposition-based acoustic propagation operator (combining related technologies and the geological structure imaging method proposed in this embodiment). It can be seen that for the two-dimensional undulation model, both the viscous acoustic propagation operator based on matrix decomposition and the offset results based on the acoustic propagation operator based on matrix decomposition accurately reflect the actual situation of the strata.

[0135] However, the results obtained from viscosity data and the acoustic wave propagation operator based on matrix decomposition show that the imaging energy at depth is weak. To visually demonstrate the compensation effect of the viscosity propagation operator based on matrix decomposition on attenuated seismic waveforms, waveforms at horizontal coordinates of 400 and 800 meters can be compared. The waveform comparison at 400 meters can be found in [reference needed]. Figure 7 and Figure 8 , Figure 7 and Figure 8 The vertical axis, amplitude, represents the amplitude. Figure 7 The waveform comparison at x=400m (a) illustrates the effect of acoustic viscosity compensation (the blue line represents the acoustic wave data using the acoustic wave propagation operator based on matrix decomposition, and the red line represents the acoustic viscosity data using the acoustic viscosity propagation operator based on matrix decomposition). Figure 8 The diagram illustrates the results without compensation (the blue line represents acoustic data using a matrix factorization-based acoustic propagation operator, and the red line represents adhesive acoustic data using a matrix factorization-based acoustic propagation operator).

[0136] For a waveform comparison display over 800 meters, please refer to [link / reference]. Figure 9 and Figure 10 . Figure 9 The waveform comparison at x=800m (a) illustrates the effect of acoustic viscosity compensation (the blue line represents the acoustic wave data using the acoustic wave propagation operator based on matrix decomposition, and the red line represents the acoustic viscosity data using the acoustic viscosity propagation operator based on matrix decomposition). Figure 10 The diagram illustrates the results without compensation (the blue line represents acoustic data using a matrix factorization-based acoustic propagation operator, and the red line represents adhesive acoustic data using a matrix factorization-based acoustic propagation operator).

[0137] It can be seen that the waveform of the viscoelastic propagation operator based on matrix decomposition is very close to that of the acoustic wave propagation operator based on matrix decomposition, which means that the viscoelastic propagation operator based on matrix decomposition has a good compensation effect on the attenuated seismic waveform. Meanwhile, processing the viscoelastic data using the acoustic wave propagation operator based on matrix decomposition in related technologies results in a significant decrease in the resolution of the migration results. Failure to use the viscoelastic propagation operator based on matrix decomposition for compensation leads to severe distortion of the seismic wave waveform information. Further analysis... Figure 8 and Figure 10 The waveform analysis revealed that the results obtained using the matrix decomposition-based acoustic propagation operator for viscoelastic data exhibited not only amplitude attenuation but also phase dispersion, both caused by the viscoelasticity of the formation. Therefore, the migration results lacked accuracy. This indicates that the matrix decomposition-based viscoelastic propagation operator has a positive effect on compensating for attenuated seismic waveforms. Even when considering the viscosity of the medium, the corresponding matrix decomposition-based viscoelastic propagation operator must be used to obtain accurate migration results.

[0138] This embodiment can be further validated using the Marmousi model. Combining the Marmousi model's velocity model and quality factor, the Marmousi model has dimensions of 1875 meters in depth and 12500 meters in the horizontal direction, encompassing complex structures such as faults and synclines. The model's minimum velocity is 1500 m / s, and its maximum velocity is 5500 m / s. A total of 120 shots were recorded, with 8 channels between shots. Each shot had 240 geophones with an 8-meter channel spacing, a minimum shot-receiver distance of 0 meters, a sampling interval of 0.0005 s, and a sampling length of 3 s. The velocity model grid spacing is 8 meters in depth and 8 meters in the horizontal direction, with an extension depth of 1875 meters. The seismic source uses a Ricker wavelet with a dominant frequency of 20 Hz. The subsurface structure imaging results corresponding to the two-dimensional undulation model are determined based on relevant technologies and the geological structure imaging method of this embodiment.

[0139] The final migration results show that, for the Marmousi model, the migration results of the matrix decomposition-based viscoelastic propagation operator are basically consistent with those of the related technique's matrix decomposition-based acoustic propagation operator. Both accurately reflect the actual situation of the subsurface medium with clear details. Furthermore, the migration results of the matrix decomposition-based viscoelastic propagation operator show significantly better imaging results in complex structures than the related technique's matrix decomposition-based acoustic propagation operator. However, the results obtained using the matrix decomposition-based acoustic propagation operator with viscoelastic data show weaker imaging energy and significantly reduced resolution in deep complex structures. To intuitively reflect the compensation effect of the matrix decomposition-based viscoelastic propagation operator on attenuated seismic waveforms, this embodiment selects waveforms at horizontal coordinates of 4800, 5600, and 6400 m for comparison. The comparison results confirm that for complex models, the waveform of the matrix decomposition-based viscoelastic propagation operator is also close to that of the matrix decomposition-based acoustic propagation operator, indicating that this matrix decomposition-based viscoelastic propagation operator has practical value and a certain degree of accuracy. Meanwhile, processing the acoustic data using a matrix decomposition-based acoustic propagation operator in related technologies resulted in a significant decrease in resolution, making deep information almost invisible. The failure to use the matrix decomposition-based acoustic propagation operator for compensation led to severe distortion of the seismic wave waveform, rendering the migration results inaccurate. This is consistent with the conclusions drawn from the two-dimensional undulation model.

[0140] Finally, when this embodiment is applied to actual seismic data, it can be combined with the velocity model and quality factor model corresponding to the actual data. The model dimensions are a depth of 8605 meters and a horizontal length of 15850 meters. The shot gather records a total of 445 shots, each with 360 geophones, a sampling interval of 0.002 s, a sampling length of 5.004 s, and a velocity model grid spacing of [missing information]. The extension depth is 8605m. The seismic source uses a Ricker wavelet with a dominant frequency of 14Hz. Based on relevant technologies and the geological structure imaging method of this embodiment, the imaging results of the subsurface structure corresponding to the two-dimensional undulation model are determined using actual data. According to the final migration results, it can be determined that for actual seismic data, both the viscous acoustic propagation operator based on matrix decomposition and the acoustic propagation operator based on matrix decomposition reflect the actual situation of the subsurface medium. The results obtained by the acoustic propagation operator based on matrix decomposition are worse than those obtained by the viscous acoustic propagation operator based on matrix decomposition. Overall, the resolution of the acoustic propagation operator based on matrix decomposition is also significantly lower than that of the viscous acoustic propagation operator based on matrix decomposition. This indicates that the geological structure imaging method proposed in this embodiment has certain practical value and can, to some extent, obtain migration results comparable to or even better locally than the acoustic propagation operator based on matrix decomposition in relevant technologies.

[0141] It should be noted that related techniques, such as migration imaging based on matrix decomposition of acoustic propagation operators, suffer from low resolution due to neglecting the viscoelastic information of the subsurface medium, which can even negatively impact seismic interpretation. This embodiment, based on the acoustic wave equation, no longer treats the subsurface medium as an ideal medium but introduces viscoelastic properties to fully grasp the medium's information, constructing a wave equation formula for viscoelastic media. Numerical experiments using two-dimensional undulation models and Marmousi models demonstrate that this method can improve the resolution of migration imaging. The matrix decomposition-based viscoelastic propagation operator proposed in this embodiment can effectively obtain imaging results comparable to those of related techniques using acoustic wave equations. Application to actual seismic data affirms the beneficial effects of this geological structure imaging method, further confirming the practical value of the matrix decomposition-based viscoelastic propagation operator. It can effectively improve the resolution of subsurface imaging, preserve wavefield information to a greater extent, and significantly improve the quality of deep subsurface imaging. This geological structure imaging method represents a development of wave equation migration theory, expanding the application value of fractional wave equations.

[0142] like Figure 11 As shown, this embodiment proposes a geological structure imaging device, which may include:

[0143] Acquisition unit 101 is used to acquire seismic wave velocity distribution data and quality factor distribution data corresponding to the target area;

[0144] Unit 102 is used to determine the viscoelastic vertical wavenumber based on seismic wave velocity distribution data and quality factor distribution data.

[0145] Input unit 103 is used to input the viscoelastic vertical wavenumber into the constructed pressure wave field determination model to determine the pressure wave field distribution data corresponding to the target area; wherein, the pressure wave field determination model is constructed based on the viscoelastic properties of the subsurface medium and the viscoelastic propagation operator of matrix decomposition;

[0146] Imaging unit 104 is used to perform geological structure imaging of the target area using pressure wave field distribution data, and obtain the geological structure imaging results of the target area.

[0147] It should be noted that the processing procedures of the acquisition unit 101, the determination unit 102, the input unit 103, and the imaging unit 104, and the beneficial effects thereof, can be referred to respectively. Figure 1 Steps S101 to S104 in the process will not be described again.

[0148] Optionally, the seismic wave velocity distribution data may include the propagation velocity data of seismic waves at different spatial locations during their propagation in the subsurface medium of the target area, assuming that the subsurface medium of the target area is an acoustic medium.

[0149] The quality factor distribution data includes quality factor data at different spatial locations during the propagation of seismic waves in the underground medium of the target area, assuming that the underground medium of the target area is an acoustic medium.

[0150] Optionally, the determining unit 102 is also used for:

[0151] Based on the seismic wave velocity distribution data, the acoustic wave velocity distribution data is determined; the acoustic wave velocity distribution data includes the propagation velocity data of seismic waves at different spatial locations during the propagation of seismic waves in the underground medium of the target area, assuming that the underground medium of the target area is an acoustic medium.

[0152] Determine the viscosity-acoustic parameters based on the quality factor distribution data;

[0153] The vertical wavenumber of the adhesive acoustic wave is determined based on the adhesive acoustic wave velocity distribution data and adhesive acoustic parameters.

[0154] Optionally, construct a pressure wave field determination model, set as follows:

[0155] Considering that the underground medium in the target area is a viscous acoustic medium, a viscous acoustic wave equation for seismic wave propagation is established.

[0156] By performing a Fourier transform on the viscous acoustic wave equation, the corresponding frequency domain equation is obtained.

[0157] The frequency domain equations are transformed to obtain the viscous acoustic wave equations for two-dimensional viscous acoustic media in the frequency spatial domain.

[0158] Under the premise that the preset assumptions are true, the viscous acoustic wave equation of the two-dimensional viscous acoustic medium is decomposed to obtain the decomposed equation.

[0159] Determine the viscosonic Helmholtz operator based on the decomposed equations;

[0160] Eigenvalue decomposition is performed on the viscous acoustic Helmholtz operator to obtain the eigenvalue decomposition results;

[0161] The pressure wave field values ​​are determined based on the eigenvalue decomposition results, and the equations are used as the model for determining the pressure wave field.

[0162] Optionally, the viscous acoustic wave equation for a two-dimensional viscous acoustic medium is:

[0163]

[0164]

[0165]

[0166] in, For pressure field waves, These are the coordinates along the depth direction in the underground space. Sampling frequency, The speed at which seismic waves propagate in a viscous acoustic medium. The imaginary unit, For the reference frequency of the adhesive sound medium, For viscosity-sound parameters, These are the horizontal coordinates within the underground space. The speed at which seismic waves propagate in the acoustic medium. This is the quality factor.

[0167] Optionally, the adhesive sound Helmholtz operator is:

[0168]

[0169] in, For the adhesive sound Helmholtz operator.

[0170] Optionally, the pressure wave field determination model is as follows:

[0171]

[0172]

[0173] in, For depth step size, For wave field propagation operators, The vertical wavenumber of the viscous acoustic wave.

[0174] The geological structure imaging device proposed in this embodiment can acquire seismic wave velocity distribution data and quality factor distribution data corresponding to the target area, and determine the viscoelastic vertical wavenumber based on the seismic wave velocity distribution data and quality factor distribution data. The viscoelastic vertical wavenumber is then input into a pre-constructed pressure wavefield determination model to determine the pressure wavefield distribution data corresponding to the target area. The pressure wavefield determination model is constructed based on the viscoelastic properties of the subsurface medium and a matrix decomposition viscoelastic propagation operator. The pressure wavefield distribution data is used to perform geological structure imaging of the target area, obtaining the geological structure imaging results for the target area. This embodiment can preserve wavefield information to a greater extent, improving the resolution of geological structure imaging and the imaging quality of deep subsurface areas.

[0175] In this embodiment, the geological structure imaging device is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0176] This invention also provides a computer device having the above-described features. Figure 11 The geological structure imaging device shown.

[0177] Please see Figure 12 The present invention provides a schematic diagram of the structure of a computer device according to an optional embodiment. The computer device includes one or more processors 10, a memory 20, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are interconnected via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 12 Take a processor 10 as an example.

[0178] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0179] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0180] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function. The data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, which can be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0181] Memory 20 may include volatile memory, such as random access memory. Memory may also include non-volatile memory, such as flash memory, hard disk, or solid-state drive. Memory 20 may also include combinations of the above types of memory.

[0182] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0183] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0184] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A geological structure imaging method, characterized in that, include: Obtain seismic wave velocity distribution data and quality factor distribution data for the target area; The vertical wavenumber of viscoacoustic waves is determined based on the seismic wave velocity distribution data and the quality factor distribution data. The viscoelastic vertical wavenumber is input into the constructed pressure wave field determination model to determine the pressure wave field distribution data corresponding to the target area; wherein, the pressure wave field determination model is constructed based on the viscoelastic properties of the subsurface medium and the viscoelastic propagation operator of matrix decomposition; The geological structure of the target area is imaged using the pressure wave field distribution data to obtain the geological structure imaging results of the target area. The process of constructing the pressure wave field determination model includes: Considering that the underground medium in the target area is a viscous acoustic medium, a viscous acoustic wave equation for seismic wave propagation is established. The Fourier transform of the viscous acoustic wave equation yields the corresponding frequency domain equation. The frequency domain equation is transformed to obtain the viscous acoustic wave equation of the two-dimensional viscous acoustic medium in the frequency spatial domain; Under the premise that the preset assumptions are met, the acoustic wave equation of the two-dimensional acoustic medium is decomposed to obtain the decomposed equation; wherein, the preset assumptions are that when the seismic wave extends downward at a certain depth interval, the velocity of the medium only changes laterally and not longitudinally within a depth step interval. The viscosonic Helmholtz operator is determined based on the decomposed equations. The eigenvalue decomposition of the viscosonic Helmholtz operator is performed to obtain the eigenvalue decomposition result; The pressure wave field value solution equation is determined based on the eigenvalue decomposition results and used as the pressure wave field determination model. The step of determining the pressure wave field value solution equation based on the eigenvalue decomposition result includes: Substitute the eigenvalue decomposition results into the extrapolation equation of the wave field along the depth to obtain the solution equation for the pressure wave field value.

2. The method according to claim 1, characterized in that, The seismic wave velocity distribution data includes, assuming that the underground medium of the target area is an acoustic medium, the propagation velocity data of seismic waves at different spatial locations during the propagation of the underground medium of the target area. The quality factor distribution data includes quality factor data at different spatial locations during the propagation of seismic waves in the underground medium of the target area, assuming that the underground medium of the target area is an acoustic medium.

3. The method of claim 1, wherein, The determination of the viscosity vertical wavenumber based on the seismic wave velocity distribution data and the quality factor distribution data includes: Based on the seismic wave velocity distribution data, the acoustic wave velocity distribution data is determined; wherein, the acoustic wave velocity distribution data includes, assuming that the underground medium of the target area is an acoustic medium, the propagation velocity data of seismic waves at different spatial locations during the propagation of the underground medium of the target area; The viscosity-acoustic parameters are determined based on the quality factor distribution data. The vertical wavenumber of the adhesive acoustic wave is determined based on the adhesive acoustic wave velocity distribution data and the adhesive acoustic parameters.

4. The method of claim 1, wherein, The viscous acoustic wave equation of the two-dimensional viscous acoustic medium is: in, For pressure field waves, These are the coordinates along the depth direction in the underground space. Sampling frequency, The speed at which seismic waves propagate in a viscous acoustic medium. The imaginary unit, For the reference frequency of the adhesive sound medium, For viscosity-sound parameters, These are the horizontal coordinates within the underground space. The speed at which seismic waves propagate in the acoustic medium. This is the quality factor.

5. The method of claim 4, wherein, The adhesive acoustic Helmholtz operator is: wherein is the viscous Helmholtz operator.

6. The method of claim 5, wherein, The pressure wave field determination model is as follows: wherein, is a depth step, is a wavefield propagation operator, is a viscously anisotropic vertical wavenumber.

7. A geological structure imaging device, characterized in that, include: The acquisition unit is used to acquire seismic wave velocity distribution data and quality factor distribution data corresponding to the target area. A determining unit is used to determine the viscoelastic vertical wavenumber based on the seismic wave velocity distribution data and the quality factor distribution data; An input unit is used to input the viscoelastic vertical wavenumber into a pre-constructed pressure wave field determination model to determine the pressure wave field distribution data corresponding to the target area; wherein, the pressure wave field determination model is constructed based on the viscoelastic properties of the subsurface medium and a matrix-decomposed viscoelastic propagation operator; An imaging unit is used to perform geological structure imaging of the target area using the pressure wave field distribution data, and to obtain the geological structure imaging results of the target area. The pressure wave field determination model is constructed as follows: Considering that the underground medium in the target area is a viscous acoustic medium, a viscous acoustic wave equation for seismic wave propagation is established. The Fourier transform of the viscous acoustic wave equation yields the corresponding frequency domain equation. The frequency domain equation is transformed to obtain the viscous acoustic wave equation of the two-dimensional viscous acoustic medium in the frequency spatial domain; Under the premise that the preset assumptions are met, the acoustic wave equation of the two-dimensional acoustic medium is decomposed to obtain the decomposed equation; wherein, the preset assumptions are that when the seismic wave extends downward at a certain depth interval, the velocity of the medium only changes laterally and not longitudinally within a depth step interval. The viscosonic Helmholtz operator is determined based on the decomposed equations. The eigenvalue decomposition of the viscosonic Helmholtz operator is performed to obtain the eigenvalue decomposition result; The pressure wave field value solution equation is determined based on the eigenvalue decomposition results and used as the pressure wave field determination model. The equation for determining the pressure wave field value based on the eigenvalue decomposition result is set as follows: Substitute the eigenvalue decomposition results into the extrapolation equation of the wave field along the depth to obtain the solution equation for the pressure wave field value.

8. A computer device, comprising: include: A memory and a processor are communicatively connected, the memory stores computer instructions, and the processor executes the geological structure imaging method according to any one of claims 1 to 7 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the geological structure imaging method according to any one of claims 1 to 7.

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