Step-by-step frequency expanding method and device, electronic equipment and storage medium

Abstract

The invention provides a step-by-step frequency expanding method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a pre-stack CRP gather; performing interpretive CRP gather superposition on the pre-stack CRP gather to obtain superposed seismic data; performing spectrum equalization processing of a scale domain on the superposed seismic data to obtain a reconstructed seismic image; carrying out smooth filtering processing on the reconstructed seismic image by adopting anisotropic diffusion filtering; selecting a scale range with a high signal-to-noise ratio in the reconstructed seismic image after smooth filtering processing to extrapolate other scale segments to obtain seismic data of a plurality of scale segments; and performing signal reconstruction on the seismic data of all the scale segments to obtain broadband seismic data. According to the method, the relative effective frequency band of seismic data can be remarkably widened, stratum details are well depicted, the resolution of the seismic data is effectively improved, and the method has high industrial practical value and application prospects in oil and gas seismic exploration.

Description

Technical Field

[0001] This application relates to the field of geophysical exploration, and more specifically to a stepwise frequency extension method, apparatus, electronic device, and storage medium. Background Technology

[0002] As oil and gas field exploration and development deepens, thin interbedded layers and small target bodies have become important components of upstream reservoir growth. High-quality seismic data plays an indispensable role in discovering these concealed oil and gas reservoirs. Therefore, improving seismic data resolution has always been a research hotspot for scholars both domestically and internationally. Currently, the most commonly used technique is deconvolution, which improves the temporal resolution of seismic data by compressing seismic wavelets. The Geophysical Analysis Group at MIT proposed deconvolution in the 1960s. Based on the Robinson model, it first obtains the inverse operator and then uses it to further obtain the reflection coefficient sequence.

[0003] Building upon this foundation, and through the continuous efforts and experimentation of those skilled in the art, various deconvolution methods have been proposed to meet different needs. Predictive deconvolution was pioneered in 1969. In 1975, Burger deconvolution was proposed, a method based on maximum entropy analysis. Homomorphic deconvolution circumvents the assumptions of minimum phase in seismic wavelets and white noise in reflection coefficients, allowing simultaneous extraction of both seismic wavelets and reflection coefficients. Minimum entropy deconvolution effectively enhances the characteristics of sharp pulses and improves the identification of "bright spots" in the profile. In the 1980s, maximum likelihood deconvolution emerged, effectively addressing the shortcomings of traditional deconvolution in achieving signal-to-noise separation. Subsequently, blind deconvolution appeared; compared to conventional deconvolution, which assumes many ideal conditions, blind deconvolution, without any assumptions, has greater practical value. In the late 1990s, with the in-depth research and application of wavelet analysis, deconvolution with resolution varying with wavelet scale was performed at various scales in the binary wavelet transform domain of seismic signals. Utilizing the correlation between deconvolution results at different resolutions and the attenuation characteristics of measurement noise with scale, high-resolution deconvolution results were approximated from low-resolution results. Gabor deconvolution, based on a non-steady-state seismic trace convolution model, performs deconvolution processing in the time-frequency domain, breaking through the traditional time-domain or frequency-domain deconvolution mode. Colored deconvolution considers the non-white noise characteristics of the actual reflection coefficient sequence, using the signal purity spectrum as the expected amplitude spectrum of the deconvolution output and performing colored compensation, further improving the resolution of seismic data.

[0004] Deconvolution techniques based on white noise reflection coefficients and known wavelet phase assumptions aim to compress wavelet data in the time domain, typically boosting high-frequency information. However, the high-frequency information of actual seismic signals is severely contaminated by noise. Boosting the effective high-frequency signal also increases noise, resulting in a low signal-to-noise ratio and discontinuities in the lateral phase axis of the processed seismic profile. Furthermore, the low-frequency information of the seismic signal is not expanded, leading to poor preservation of the relative amplitude relationship in the longitudinal direction; the relative bandwidth expansion of the processed seismic signal is limited.

[0005] Following seismic data acquisition and conventional seismic data processing, the next stage is seismic interpretation. The proposed method of gradually widening the seismic band is an interpretative approach. Given the limitations of current manpower and resources, and the impossibility of performing routine, repeated seismic processing, this method outlines the seismic band widening process and methodology for the interpretation stage. Building upon previous conventional seismic processing, the method gradually widens the seismic band from pre-stack CRP gathers to post-stack gathers. This aims to maintain the effective band and signal-to-noise ratio of the original seismic signal while extrapolating low- and high-frequency information, thereby improving seismic resolution and meeting the requirements for identifying thin reservoirs and small target bodies or predicting seismic reservoirs during the interpretation stage. Summary of the Invention

[0006] In view of this, after seismic data acquisition and conventional seismic data processing, the seismic interpretation stage begins. This application proposes a progressive frequency extension method, apparatus, electronic equipment, and storage medium. This is an interpretative broadening method that, based on previous conventional seismic processing, progressively broadens the seismic frequency band from pre-stack CRP gathers to post-stack gathers. The aim is to extrapolate low-frequency and high-frequency information of the signal while maintaining the effective frequency band and signal-to-noise ratio of the original seismic signal, thereby improving seismic resolution and meeting the requirements for identifying thin reservoirs and small target bodies or predicting seismic reservoirs during the interpretation stage.

[0007] In a first aspect, embodiments of this application provide a progressive frequency extension method, which mainly includes the following steps:

[0008] Obtain the pre-stack CRP gather;

[0009] Interpretive CRP gathers are then stacked on the pre-stack CRP gathers to obtain stacked seismic data;

[0010] The superimposed seismic data is subjected to scale-domain spectral equalization to obtain a reconstructed seismic image;

[0011] Anisotropic diffusion filtering is used to smooth the reconstructed seismic image;

[0012] The scale range with high signal-to-noise ratio in the reconstructed seismic image after smoothing filtering is selected and extrapolated to other scale segments to obtain seismic data for multiple scale segments.

[0013] Signal reconstruction was performed on seismic data across all scales to obtain broadband seismic data.

[0014] In one possible implementation, the interpretation of CRP gather stacking on the pre-stack CRP gather specifically involves:

[0015] On the pre-stack CRP gather, select offset segments with stable waveform characteristics, reliable amplitude and phase of the target layer, and good signal-to-noise ratio for stacking.

[0016] In one possible implementation, the step of performing scale-domain spectral equalization on the stacked seismic data to obtain a reconstructed seismic image specifically involves:

[0017] Wavelet transform is performed on the stacked seismic data to obtain the scale domain signal;

[0018] For each of the scale domain signals, the amplitude envelope is calculated and smoothed to generate the gain curve at the corresponding scale.

[0019] Divide each scale domain signal by the gain curve at the corresponding scale to obtain the equalized scale signal;

[0020] All equalized scale signals are reconstructed using inverse wavelet transform to generate a reconstructed seismic image.

[0021] In one possible implementation, the anisotropic diffusion filter is calculated as follows:

[0022]

[0023] Where u(x,y,0) represents the reconstructed seismic image; div represents the divergence operator; D represents the diffusion tensor; S ρ ∠ represents the structure tensor; ∠ represents the gradient operator; t represents time; u represents seismic data; u0(x,y) represents the initial seismic data.

[0024] Structure tensor S ρ The calculation formula is as follows:

[0025]

[0026] Among them, s 11 s 12 s 13 s 22 s 23 s 33 Let G represent the elements of the structure tensor respectively. ρ Let represent the Gaussian function, σ represent the parameters of the Gaussian function, and x, y, z represent the three spatial directions;

[0027] The formula for calculating the diffusion tensor D is as follows:

[0028]

[0029] in,

[0030]

[0031] Where v1, v2, and v3 represent the eigenvectors of the structure tensor matrix, respectively; μ1, μ2, and μ3 represent the diffusion coefficients, respectively; d 11 d 22 d 33 d 12 d 13 d 23 Let λ1, λ2, and λ3 represent the elements of the diffusion tensor; α∈(0,1); λ1, λ2, and λ3 represent the eigenvalues ​​of the structure tensor matrix, respectively.

[0032] The elements d of the diffusion tensor 11 d 22 d 33 d 12 d 13 d 23 The expression is as follows:

[0033]

[0034] Among them, v 11 v 21 v 31 These represent the three elements of the first eigenvector of the structure matrix; v 12 v 22 v 32 These represent the three elements of the second eigenvector of the structure matrix; v 13 v 23 v 33 These represent the three elements of the third eigenvector of the structure matrix;

[0035] Discretizing formula (1) using formulas (2)-(5) yields the following anisotropic diffusion filter iterative formula:

[0036] u k+1 =u k +Δt·div(D▽u k (6)

[0037] Among them, u k and u k+1 These represent the filtering results of the reconstructed seismic image at kΔt and (k+1)Δt, respectively, where Δt is the diffusion time for one iteration;

[0038] Equation (6) can be transformed into discrete form as follows:

[0039]

[0040] In one possible implementation, the step of selecting a scale range with a high signal-to-noise ratio in the reconstructed seismic image after smoothing filtering and extrapolating to other scale segments to obtain seismic data for multiple scale segments is as follows:

[0041] Select the scale range with high signal-to-noise ratio in the reconstructed seismic image after smoothing and filtering to obtain the dominant scale signal;

[0042] The selected dominant scale signal is extrapolated to other scale segments to obtain the extrapolated scale signal;

[0043] The dominant scale signal and the extrapolated scale signal are combined and reconstructed into the time domain by inverse wavelet transform to generate seismic data in multiple scale segments.

[0044] In one possible implementation, extrapolating the selected dominant scale signal to other scale segments to obtain the extrapolated scale signal specifically involves:

[0045] The dominant scale signal is subjected to wavelet transform to obtain the dominant scale domain seismic signal, and the calculation formula is as follows:

[0046]

[0047] Where Wf(a,b) represents the dominant scale domain seismic signal, a represents the scale, b represents the time shift, and f(t) represents the dominant scale signal. The * denotes the wavelet basis function, * denotes complex conjugate, and t denotes time.

[0048] The Fourier transform of the dominant scale domain seismic signal is performed, and scale extrapolation is performed using the frequency shift property of the Fourier transform to obtain the extrapolated scale signal. The calculation formula is as follows:

[0049]

[0050] Where Wf(a′,b) represents the extrapolated scale signal, a′ represents the extrapolated scale, ω0 represents the frequency shift, j represents the imaginary number, and b represents the time.

[0051] In one possible implementation, the formula for calculating the signal reconstruction is:

[0052]

[0053] Where f′(t) represents the reconstructed broadband seismic data, The constants related to the wavelet are represented by Wf(a,b), which represents the dominant scale domain seismic signal, where a represents the scale and b represents the time shift. To reconstruct the wavelet.

[0054] Secondly, embodiments of this application provide a structural block diagram of a progressive frequency extension device, which mainly includes the following modules:

[0055] The data acquisition module is used to acquire pre-stack CRP gathers;

[0056] An interpretive CRP stacking module is used to perform interpretive CRP gather stacking on the pre-stack CRP gathers to obtain stacked seismic data;

[0057] The scale-domain spectral equalization module is used to perform scale-domain spectral equalization processing on the stacked seismic data to obtain a reconstructed seismic image.

[0058] A guided filter construction module is used to perform smoothing filtering on the reconstructed seismic image using anisotropic diffusion filtering;

[0059] The dominant scale extrapolation module is used to select the scale range with high signal-to-noise ratio in the reconstructed seismic image after smoothing and filtering, and extrapolate it to other scale segments to obtain seismic data of multiple scale segments.

[0060] The reconstruction module is used to reconstruct signals from seismic data across all scales to obtain broadband seismic data.

[0061] Thirdly, embodiments of this application provide an electronic device, including:

[0062] processor;

[0063] Memory;

[0064] And a computer program, wherein the computer program is stored in the memory, the computer program including instructions that, when executed by the processor, cause the electronic device to perform the method described in the first aspect.

[0065] Fourthly, embodiments of this application provide a computer-readable storage medium including a stored program, wherein the program, when running, controls the device where the computer-readable storage medium is located to execute the method described in the first aspect.

[0066] This application proposes a progressive frequency band extension method, which is an interpretative broadening approach. Based on previous conventional seismic processing, the seismic bandwidth is progressively broadened from pre-stack CRP gathers to post-stack gathers. While maintaining the original effective frequency band and signal-to-noise ratio of the seismic signal, low-frequency and high-frequency information of the signal is extrapolated, thereby increasing the relative bandwidth of the original signal. The processed seismic profile not only achieves improved vertical resolution and horizontal phase axis continuity, but also maintains the signal-to-noise ratio and preserves the original relative amplitude relationships well. This method has been applied to improve the resolution of onshore seismic data in my country, achieving significant results. Attached Figure Description

[0067] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0068] Figure 1 A flowchart illustrating a stepwise frequency extension method provided in an embodiment of this application;

[0069] Figure 2 The comparison diagram of seismic profiles before and after the gradual widening of the seismic frequency band provided in the embodiments of this application, wherein, Figure 2 (a) in the image represents the seismic profile before the seismic frequency band was gradually widened. Figure 2 (b) in the image shows the seismic profile after gradually widening the seismic frequency band;

[0070] Figure 3 A structural block diagram of a progressive frequency spreading device provided in an embodiment of this application;

[0071] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0072] To better understand the technical solution of this application, the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0073] It should be understood that the described embodiments are merely some, not all, of the embodiments in this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0074] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. The singular forms “a,” “the,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0075] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0076] A progressive frequency band extension method is proposed, starting from pre-stack CRP gathers and selecting a dominant offset range for stacking based on the characteristics of the target layer signal in the study area, providing high-quality stacked data for broadband seismic data processing. Subsequently, scale-domain spectral equalization processing and dominant scale signal extrapolation are performed to obtain broadband seismic data, improving the resolution of seismic data while maintaining the original signal-to-noise ratio. This method is applied to actual seismic data, proposing an effective processing flow for progressively widening the seismic frequency band.

[0077] See Figure 1 This is a flowchart illustrating a stepwise frequency extension method provided in an embodiment of this application. Figure 1 As shown, it mainly includes the following steps.

[0078] Step S1: Obtain the pre-stack CRP gather.

[0079] Step S2: Perform interpretive CRP gather stacking on the pre-stack CRP gathers to obtain stacked seismic data.

[0080] Full-stack seismic data, influenced by large-angle (or large-offset) data, struggles to distinguish subtle variations between reservoirs. This is because large-angle (or large-offset) data experiences stronger absorption and attenuation by the formation during seismic wave propagation, resulting in more severe dynamic correction stretching during data processing. Since full-stack data obscures some seismic wave information, it is difficult to reveal subtle changes. In contrast, interpretative stacking data combines seismic data within a specific angle or offset range, avoiding the cancellation of weak phase and phase inversion anomalies during stacking. It offers sufficient information and high resolution. Interpretive CRP gather stacking can be achieved by testing the stacking effect through offset grouping. The principle is that interpreters select offset segments with relatively stable waveform characteristics, reliable amplitude and phase of the target layer, and good signal-to-noise ratio from the pre-stack CRP gathers for stacking, obtaining high-quality stacked seismic data for broadband seismic data processing.

[0081] Step S3: Perform scale-domain spectral equalization on the stacked seismic data to obtain a reconstructed seismic image.

[0082] As is well known, the Fourier transform has inherent limitations, namely poor time-frequency locality, and its inability to analyze the local frequency characteristics of a signal. It does not consider the non-stationarity of actual seismic signals, and the number and width of bandpass filters are difficult to determine. If the bandpass filter width is too narrow, it may alter the amplitude spectrum characteristics of the reflection coefficient; if it is too wide, the spectral equalization effect will be poor. The wavelet transform overcomes these shortcomings. It ensures that the transform is reversible and can analyze the local frequency characteristics of the signal, making it a powerful tool for signal analysis. The wavelet transform transforms the signal to the scale domain, which not only allows for the analysis of the time-frequency local characteristics of non-stationary seismic signals, but also allows the scale-domain filter to automatically determine the octave bandwidth, preserving the characteristics of energy changes over time. The implementation steps of step S3 are as follows:

[0083] Step S31: Perform wavelet transform on the stacked seismic data to obtain the scale domain signal;

[0084] Step S32: Calculate the amplitude envelope for each scale domain signal and smooth it to generate the gain curve at the corresponding scale.

[0085] Step S33: Divide each scale domain signal by the gain curve at the corresponding scale to obtain the equalized scale signal;

[0086] Step S34: Perform inverse wavelet transform on all equalized scale signals to reconstruct the reconstructed seismic image.

[0087] Step S4: The reconstructed seismic image is smoothed using anisotropic diffusion filtering.

[0088] The reconstructed seismic image, after scale-domain spectral equalization, undergoes smoothing filtering to further improve its signal-to-noise ratio. In this embodiment, anisotropic diffusion filtering is used for smoothing. The reconstructed seismic image is used as the initial condition, and the diffused image is obtained by solving a partial differential equation with respect to time. In the diffusion equation, local structural information (faults, pinch-outs, etc.) is obtained by introducing a structural tensor. Based on this structural information, the diffusion tensor is designed, i.e., different diffusion coefficients are used in different directions, thereby achieving the effect of denoising while preserving edges.

[0089] The calculation formula for the anisotropic diffusion filter is as follows:

[0090]

[0091] Where u(x,y,0) represents the reconstructed seismic image; div represents the divergence operator; D represents the diffusion tensor, whose elements are designed based on the local structural information of the seismic image extracted from the structure tensor; S ρ ∠ represents the structure tensor; ∠ represents the gradient operator; t represents time; u represents seismic data; u0(x,y) represents the initial seismic data.

[0092] Structure tensor S ρ It is to reconstruct the first-order partial differential information of the seismic image. x u y Another form of expression provides matrix field information, such that each point in the reconstructed seismic image corresponds to a 3×3 (in the case of three dimensions) real symmetric matrix, and its calculation formula is as follows:

[0093]

[0094] Among them, s 11 s 12 s 13 s 22 s 23 s 33 Let G represent the elements of the structure tensor respectively. ρ Let represent the Gaussian function, σ represent the parameters of the Gaussian function, and x, y, z represent the three spatial directions. This is related to the Gaussian function G... ρ Convolution can take surrounding information into account, avoiding the drawback of canceling each other out when the edges are oriented in opposite directions.

[0095] Diffusion tensor D used with structure tensor S ρ For the same eigenvector, diffusion coefficients μ1, μ2, and μ3 are designed based on the local features extracted from the structure tensor. These coefficients independently control the diffusion behavior in the v1, v2, and v3 directions, respectively, thereby reducing noise while enhancing edges. v1, v2, and v3 are the eigenvectors of the structure tensor matrix, obtained by solving for eigenvalues ​​and eigenvectors. In this embodiment, the calculation formula for the diffusion tensor D is as follows:

[0096]

[0097] in,

[0098]

[0099] Where v1, v2, and v3 represent the eigenvectors of the structure tensor matrix, respectively; μ1, μ2, and μ3 represent the diffusion coefficients, respectively; d 11 d 22 d 33 d 12 d 13d 23 These represent the elements of the diffusion tensor; α∈(0,1), which is usually a very small positive number, and in this embodiment, α=0.001 is preferred; λ1, λ2, and λ3 represent the eigenvalues ​​of the structure tensor matrix, respectively.

[0100] As can be seen from the above formulas, in this embodiment, along the v1 direction, which is parallel to the gradient or the direction with the fastest rate of change, the diffusion coefficient is very small, and the edges are protected. Along the v2 direction, the diffusion coefficient is adjusted according to coherence: the greater the coherence, the closer the diffusion coefficient is to 1 (the maximum diffusion coefficient). Conversely, if there is no significant coherence in a local area of ​​the image, this may be the end of reflection in the seismic image. In this case, μ2 = α, and the diffusion will be very slow, thus protecting geological structures such as faults and pinch-outs.

[0101] According to formula (3), the elements d of the diffusion tensor can be calculated. 11 d 22 d 33 d 12 d 13 d 23 The expression is as follows:

[0102]

[0103] Among them, v 11 v 21 v 31 These represent the three elements of the first eigenvector of the structure matrix; v 12 v 22 v 32 These represent the three elements of the second eigenvector of the structure matrix; v 13 v 23 v 33 These represent the three elements of the third eigenvector of the structure matrix.

[0104] Discretizing formula (1) using formulas (2)-(5) yields the following anisotropic diffusion filter iterative formula:

[0105] u k+1 =u k +Δt·div(D▽u k (6)

[0106] Among them, u k and u k+1The values ​​represent the filtering results of the reconstructed seismic image at kΔt and (k+1)Δt, respectively, where Δt is the diffusion time for one iteration (0.1 ≤ Δt ≤ 0.2 yields the best results). As can be seen from the above, the anisotropic diffusion filtering effect in this embodiment is achieved through repeated iterations, which raises the issue of controlling the number of iterations. Too few iterations will not yield satisfactory results; while too many iterations may result in over-smoothing.

[0107] During the iteration process, the differential is replaced by the available difference, and formula (6) is transformed into the following discrete form:

[0108]

[0109] Step S5 involves selecting the scale range with a high signal-to-noise ratio in the reconstructed seismic image after smoothing and filtering, and extrapolating this range to other scale segments to obtain seismic data for multiple scale segments. Specifically:

[0110] Step S5.1: Select the scale range with high signal-to-noise ratio in the reconstructed seismic image after smoothing and filtering to obtain the dominant scale signal;

[0111] Step S5.2 involves extrapolating the selected dominant scale signal to other scale segments to obtain the extrapolated scale signal; specifically:

[0112] Step S5.2.1: Perform wavelet transform on the dominant scale signal to obtain the dominant scale domain seismic signal. The calculation formula is as follows:

[0113]

[0114] Where Wf(a,b) represents the dominant scale domain seismic signal, a represents the scale, b represents the time shift, and f(t) represents the dominant scale signal. The * denotes the wavelet basis function, * denotes complex conjugate, and t denotes time.

[0115] Step S5.2.2: Perform a Fourier transform on the dominant scale domain seismic signal, and use the frequency shift property of the Fourier transform to perform scale extrapolation to obtain the extrapolated scale signal. The calculation formula is as follows:

[0116]

[0117] Where Wf(a′,b) represents the extrapolated scale signal, a′ represents the extrapolated scale, ω0 represents the frequency shift, j represents the imaginary number, and b represents the time.

[0118] Step S5.3: The dominant scale signal and the extrapolated scale signal are combined and reconstructed into the time domain by inverse wavelet transform to generate seismic data of multiple scale segments.

[0119] Step S6: Reconstruct the signals from the seismic data across all scales to obtain broadband seismic data.

[0120] The calculation formula for signal reconstruction is as follows:

[0121]

[0122] Where f′(t) represents the reconstructed broadband seismic data, The constants related to the wavelet are represented by Wf(a,b), which represents the dominant scale domain seismic signal, where a represents the scale and b represents the time shift. To reconstruct the wavelet.

[0123] The progressive widening seismic bandwidth method disclosed in this embodiment is an interpretative widening method. Based on previous conventional seismic processing, the seismic bandwidth is progressively widened from pre-stack CRP gathers to post-stack gathers. While maintaining the original effective bandwidth and signal-to-noise ratio of the seismic signal, low-frequency and high-frequency information of the signal is extrapolated, thereby increasing the relative bandwidth of the original signal. The processed seismic profile not only has improved vertical resolution and horizontal phase axis continuity, but also maintains the signal-to-noise ratio and preserves the original relative amplitude relationship well. This method has been applied to improve the resolution of onshore seismic data in my country, achieving significant results.

[0124] To verify the technical effectiveness of the method, a progressive frequency extension method from this embodiment was applied to progressively broaden the seismic frequency band of some land seismic data. See also... Figure 2 This is a comparison diagram of seismic profiles before and after the gradual widening of the seismic frequency band provided in the embodiments of this application, wherein... Figure 2 (a) in the image represents the seismic profile before the seismic frequency band was gradually widened. Figure 2 (b) in the image shows the seismic profile after gradually widening the seismic frequency band. For example... Figure 2 As shown, the seismic resolution is significantly improved after gradually widening the seismic frequency band. The structural features and energy relationships are consistent before and after processing, the signal-to-noise ratio is high, the lateral continuity is good, and small layers can be tracked.

[0125] Corresponding to the above embodiments, this application also provides a progressive frequency extension device.

[0126] See Figure 3 This is a structural block diagram of a progressive frequency spreading device provided in an embodiment of this application. Figure 3 As shown, it mainly includes the following modules:

[0127] Data acquisition module 301 is used to acquire pre-stack CRP gathers;

[0128] Interpretive CRP stacking module 302 is used to perform interpretive CRP stacking on the pre-stack CRP gathers to obtain stacked seismic data;

[0129] The scale-domain spectral equalization module 303 is used to perform scale-domain spectral equalization processing on the stacked seismic data to obtain a reconstructed seismic image.

[0130] The guided filter construction module 304 is used to perform smoothing filtering on the reconstructed seismic image using anisotropic diffusion filtering.

[0131] The dominant scale extrapolation module 305 is used to select the scale range with high signal-to-noise ratio in the reconstructed seismic image after smoothing and filtering, and extrapolate other scale segments to obtain seismic data of multiple scale segments.

[0132] The reconstruction module 306 is used to reconstruct signals from seismic data across all scales to obtain broadband seismic data.

[0133] It should be noted that the specific content involved in the embodiments of this application can be found in the description of the above method embodiments, and will not be repeated here for the sake of brevity.

[0134] Corresponding to the above embodiments, this application also provides an electronic device.

[0135] See Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 4 As shown, the electronic device 400 may include a processor 401, a memory 402, and a communication unit 403. These components communicate via one or more buses. Those skilled in the art will understand that the electronic device structure shown in the figures does not constitute a limitation on the embodiments of this application. It may be a bus topology or a star topology, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0136] The communication unit 403 is used to establish a communication channel, thereby enabling the electronic device to communicate with other devices.

[0137] The processor 401 serves as the control center of the electronic device, connecting various parts of the device via various interfaces and lines. It executes software programs and / or modules stored in the memory 402, and calls data stored in the memory to perform various functions and / or process data. The processor can be composed of integrated circuits (ICs), such as a single packaged IC or multiple packaged ICs with the same or different functions connected together. For example, the processor 401 may consist only of a central processing unit (CPU). In this embodiment, the CPU may have a single processing core or include multiple processing cores.

[0138] Memory 402 is used to store the execution instructions of processor 401. Memory 402 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0139] When the execution instructions in memory 402 are executed by processor 401, the electronic device 400 is able to perform some or all of the steps in the above method embodiments.

[0140] Corresponding to the above embodiments, this application also provides a computer-readable storage medium, wherein the computer-readable storage medium may store a program, wherein when the program runs, it can control the device where the computer-readable storage medium is located to execute some or all of the steps in the above method embodiments. Specifically, the computer-readable storage medium may be a magnetic disk, an optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0141] Corresponding to the above embodiments, this application also provides a computer program product containing executable instructions that, when executed on a computer, cause the computer to perform some or all of the steps in the above method embodiments.

[0142] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, the simultaneous existence of A and B, or the existence of B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, and c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0143] Those skilled in the art will recognize that the units and algorithm steps described in the embodiments disclosed herein can be implemented using electronic hardware, computer software, or a combination of electronic hardware and software. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0144] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0145] In the several embodiments provided in this application, any function, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0146] The above description is merely a specific embodiment of this application. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the protection scope of this application. The protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A stepwise frequency extension method, characterized in that, The main steps include: Obtain the pre-stack CRP gather; Interpretive CRP gathers are then stacked on the pre-stack CRP gathers to obtain stacked seismic data; The superimposed seismic data is subjected to scale-domain spectral equalization to obtain a reconstructed seismic image; Anisotropic diffusion filtering is used to smooth the reconstructed seismic image; The scale range with high signal-to-noise ratio in the reconstructed seismic image after smoothing filtering is selected and extrapolated to other scale segments to obtain seismic data for multiple scale segments. Signal reconstruction was performed on seismic data across all scales to obtain broadband seismic data.

2. The stepwise frequency extension method according to claim 1, characterized in that, The interpretation of CRP gather stacking on the pre-stack CRP gathers specifically involves: On the pre-stack CRP gather, select offset segments with stable waveform characteristics, reliable amplitude and phase of the target layer, and good signal-to-noise ratio for stacking.

3. The stepwise frequency extension method according to claim 1, characterized in that, The process of performing scale-domain spectral equalization on the stacked seismic data to obtain the reconstructed seismic image specifically involves: Wavelet transform is performed on the stacked seismic data to obtain the scale domain signal; For each of the scale domain signals, the amplitude envelope is calculated and smoothed to generate the gain curve at the corresponding scale. Divide each scale domain signal by the gain curve at the corresponding scale to obtain the equalized scale signal; All equalized scale signals are reconstructed using inverse wavelet transform to generate a reconstructed seismic image.

4. The stepwise frequency extension method according to claim 1, characterized in that, The calculation formula for the anisotropic diffusion filter is as follows: Where u(x,y,0) represents the reconstructed seismic image; div represents the divergence operator; D represents the diffusion tensor; S ρ Represents the structure tensor; The gradient operator is represented by t; time is represented by t; seismic data is represented by u; and initial seismic data is represented by u0(x,y). Structure tensor S ρ The calculation formula is as follows: Among them, s 11 s 12 s 13 s 22 s 23 s 33 Let G represent the elements of the structure tensor respectively. ρ Let represent the Gaussian function, σ represent the parameters of the Gaussian function, and x, y, z represent the three spatial directions; The formula for calculating the diffusion tensor D is as follows: in, Where v1, v2, and v3 represent the eigenvectors of the structure tensor matrix, respectively; μ1, μ2, and μ3 represent the diffusion coefficients, respectively; d 11 d 22 d 33 d 12 d 13 d 23 Let λ1, λ2, and λ3 represent the elements of the diffusion tensor; α∈(0,1); λ1, λ2, and λ3 represent the eigenvalues ​​of the structure tensor matrix, respectively. The elements d of the diffusion tensor 11 d 22 d 33 d 12 d 13 d 23 The expression is as follows: Among them, v 11 v 21 v 31 These represent the three elements of the first eigenvector of the structure matrix; v 12 v 22 v 32 These represent the three elements of the second eigenvector of the structure matrix; v 13 v 23 v 33 These represent the three elements of the third eigenvector of the structure matrix; Discretizing formula (1) using formulas (2)-(5) yields the following anisotropic diffusion filter iterative formula: Among them, u k and u k+1 These represent the filtering results of the reconstructed seismic image at kΔt and (k+1)Δt, respectively, where Δt is the diffusion time for one iteration; Equation (6) can be transformed into discrete form as follows:

5. The stepwise frequency extension method according to claim 1, characterized in that, The process involves selecting scales with high signal-to-noise ratios from the reconstructed seismic image after smoothing and filtering, extrapolating these scales to other scale segments to obtain seismic data across multiple scale segments. Specifically: Select the scale range with high signal-to-noise ratio in the reconstructed seismic image after smoothing and filtering to obtain the dominant scale signal; The selected dominant scale signal is extrapolated to other scale segments to obtain the extrapolated scale signal; The dominant scale signal and the extrapolated scale signal are combined and reconstructed into the time domain by inverse wavelet transform to generate seismic data in multiple scale segments.

6. The stepwise frequency extension method according to claim 5, characterized in that, The process of extrapolating the selected dominant scale signal to other scale segments to obtain the extrapolated scale signal is as follows: The dominant scale signal is subjected to wavelet transform to obtain the dominant scale domain seismic signal, and the calculation formula is as follows: Where Wf(a,b) represents the dominant scale domain seismic signal, a represents the scale, b represents the time shift, and f(t) represents the dominant scale signal. The * denotes the wavelet basis function, * denotes complex conjugate, and t denotes time. The Fourier transform of the dominant scale domain seismic signal is performed, and scale extrapolation is performed using the frequency shift property of the Fourier transform to obtain the extrapolated scale signal. The calculation formula is as follows: Where Wf(a′,b) represents the extrapolated scale signal, a′ represents the extrapolated scale, ω0 represents the frequency shift, j represents the imaginary number, and b represents the time.

7. The stepwise frequency extension method according to claim 1, characterized in that, The calculation formula for signal reconstruction is as follows: Where f′(t) represents the reconstructed broadband seismic data, Let Wf(a,b) represent the wavelet-related constants, where a represents the scale and b represents the time shift. To reconstruct the wavelet.

8. A structural block diagram of a progressive frequency extension device, characterized in that, It mainly includes the following modules: The data acquisition module is used to acquire pre-stack CRP gathers; An interpretive CRP stacking module is used to perform interpretive CRP gather stacking on the pre-stack CRP gathers to obtain stacked seismic data; The scale-domain spectral equalization module is used to perform scale-domain spectral equalization processing on the stacked seismic data to obtain a reconstructed seismic image. A guided filter construction module is used to perform smoothing filtering on the reconstructed seismic image using anisotropic diffusion filtering; The dominant scale extrapolation module is used to select the scale range with high signal-to-noise ratio in the reconstructed seismic image after smoothing and filtering, and extrapolate it to other scale segments to obtain seismic data of multiple scale segments. The reconstruction module is used to reconstruct signals from seismic data across all scales to obtain broadband seismic data.

9. An electronic device, characterized in that, include: processor; Memory; And a computer program, wherein the computer program is stored in the memory, the computer program including instructions that, when executed by the processor, cause the electronic device to perform the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method according to any one of claims 1 to 7.

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