Adaptive anti-q filtering method and system based on time-frequency signal-to-noise ratio

Abstract

This application provides an adaptive inverse Q-filtering method and system based on time-frequency signal-to-noise ratio (SNR), belonging to the field of oil and gas exploration and development technology. The method includes: acquiring seismic data to be compensated; calculating the synchronous compression transform domain time spectrum of the seismic data to be compensated; calculating the time-frequency SNR of the seismic data to be compensated based on the synchronous compression transform domain time spectrum; calculating the statistical characteristics of the time-frequency SNR of the seismic data to be compensated and mapping them to a preset regularization parameter range to obtain adaptive regularization parameters; and performing inverse Q-filtering on the seismic data to be compensated based on the adaptive regularization parameters to obtain adaptively compensated seismic data, thus completing the adaptive inverse Q-filtering method. This application can solve the technical problems of noise amplification and insufficient effective signal compensation caused by improper selection of regularization parameters in existing technologies.

Description

Technical Field

[0001] This application belongs to the field of oil and gas exploration and development technology, specifically relating to an adaptive inverse Q filtering method and system based on time-frequency signal-to-noise ratio. Background Technology

[0002] With the continuous advancement of oil and gas exploration and development technologies, the focus of exploration has gradually shifted to deep and ultra-deep oil and gas reservoirs. However, due to the complex geological structures of deep reservoirs and the significant attenuation of seismic waves during propagation, seismic reflection signals from deep oil and gas reservoirs are often weak and difficult to identify. Effective compensation and recovery of weak signals from deep reservoirs can help achieve accurate identification and location of deep oil and gas reservoirs, thereby significantly improving the accuracy of deep oil and gas exploration.

[0003] The inverse Q-filtering technique, developed based on wavefield continuation theory, enhances the energy of weak reflections from deep layers by compensating for the absorption and attenuation of seismic waves. This not only increases the dominant frequency of seismic data but also broadens the effective bandwidth, thereby significantly improving the resolution of the seismic data. A complete inverse Q-filtering process includes amplitude compensation and phase correction. Phase correction is an unconditionally stable process, while the amplitude compensation operator is an exponential function of frequency and time. This exponential relationship can lead to instability in the amplitude compensation operator, especially under conditions of strong noise at depth.

[0004] Mathematically, the inverse Q-filter algorithm can be solved within the framework of an inverse problem, and stable amplitude compensation results can be obtained by introducing regularization constraints. However, in the regularization-constrained inverse Q-filter algorithm, the choice of regularization parameter has a crucial impact on the quality of the results. While a smaller regularization parameter can adequately compensate for deep weak signals, it can easily amplify noise; conversely, a larger regularization parameter can effectively suppress noise, but it can lead to insufficient compensation for deep weak signals. Therefore, when performing inverse Q-filtering, an adaptive regularization parameter adjustment strategy needs to be introduced to dynamically adjust the regularization parameter according to the characteristics of the seismic data, thereby achieving adequate compensation for deep weak signals while effectively suppressing noise amplification. Summary of the Invention

[0005] This application addresses the technical problems of noise amplification and insufficient effective signal compensation caused by improper selection of regularization parameters in existing technologies by providing an adaptive inverse Q filtering method and system based on time-frequency signal-to-noise ratio.

[0006] To achieve the above objectives, this application adopts the following technical solution: The first aspect is an adaptive inverse Q filtering method based on time-frequency signal-to-noise ratio, which includes the following steps: Acquire the seismic data to be compensated, and calculate the synchronous compression transform domain time spectrum of the seismic data to be compensated; Calculate the time-frequency signal-to-noise ratio of the seismic data to be compensated based on the synchronous compression transform domain time spectrum; The statistical characteristics of the time-frequency signal-to-noise ratio of the earthquake data to be compensated are calculated and mapped to a preset regularization parameter range to obtain adaptive regularization parameters; The adaptive inverse Q-filter is applied to the seismic data to be compensated based on the adaptive regularization parameters to obtain adaptively compensated seismic data, thus completing the adaptive inverse Q-filtering method.

[0007] In some implementations, calculating the synchronous compression transform domain time spectrum of the seismic data to be compensated includes: Read the time variable and trace number of the seismic data to be compensated; Continuous wavelet transform is performed on each channel of the seismic data to be compensated to obtain the wavelet transform time spectrum of a single channel of the seismic data to be compensated. The phase change rate of the wavelet transform time spectrum is calculated along the direction of the time variable to obtain the instantaneous frequency estimate. Based on the time variables and number of traces of the seismic data to be compensated, and according to the wavelet transform time spectrum and instantaneous frequency estimate, the synchronous compression transform domain time spectrum is calculated trace by trace.

[0008] In some implementations, calculating the time-frequency signal-to-noise ratio of the seismic data to be compensated includes: Based on the synchronous compression transform domain time spectrum, calculate the average instantaneous power spectrum of the seismic data to be compensated and the average instantaneous power spectrum of the effective signal; The time-frequency signal-to-noise ratio of the seismic data to be compensated is calculated based on the average instantaneous power spectrum of the seismic data to be compensated and the average instantaneous power spectrum of the effective signal.

[0009] In some implementations, the step of calculating the statistical characteristics of the time-frequency signal-to-noise ratio of the seismic data to be compensated and mapping them to a preset regularization parameter range to obtain adaptive regularization parameters includes: The empirical cumulative distribution function for calculating the time-frequency signal-to-noise ratio of the seismic data to be compensated is used. Based on the empirical cumulative distribution function and according to the preset regularization parameter range, the adaptive regularization parameter is calculated.

[0010] In some implementations, performing inverse Q-filtering on the seismic data to be compensated based on the adaptive regularization parameter includes: Construct and solve The objective function of the inverse problem of attenuation compensation with norm regularization constraints yields the column vector of time-spectrum coefficients in the synchronous compression transform domain of the compensated seismic data. When the objective function reaches the preset iteration stopping condition, the column vector of the synchronous compression transform domain time-frequency coefficients of the compensated seismic data is rearranged into a two-dimensional time-frequency matrix, and an inverse synchronous compression transform is performed to obtain adaptively compensated seismic data.

[0011] In some implementations, the construction and solution The objective function of the decay compensation inverse problem of norm regularization constraints includes: The The objective function of the decay compensation inverse problem of norm regularization constraint The norm term is approximately differentiable, and the solution is obtained using the iterative reweighted least squares method. In each iteration of the iterative reweighted least squares method, the weighted diagonal matrix is ​​updated based on the synchronous compression transform domain time-spectrum coefficient column vector of the compensated seismic data from the previous iteration, and the synchronous compression transform domain time-spectrum coefficient column vector of the compensated seismic data for the current iteration is calculated based on the updated weighted diagonal matrix, until the preset iteration stop condition is met.

[0012] Secondly, an adaptive inverse Q filtering system based on time-frequency signal-to-noise ratio includes: The synchronous compression transform domain time spectrum calculation module is used to acquire the seismic data to be compensated and calculate the synchronous compression transform domain time spectrum of the seismic data to be compensated. The time-frequency signal-to-noise ratio calculation module is used to calculate the time-frequency signal-to-noise ratio of the seismic data to be compensated based on the time spectrum of the synchronous compression transform domain. The adaptive regularization parameter calculation module is used to calculate the statistical characteristics of the time-frequency signal-to-noise ratio of the seismic data to be compensated, and map it to a preset regularization parameter range to obtain the adaptive regularization parameter. The adaptive inverse Q filtering module is used to perform inverse Q filtering on the seismic data to be compensated according to the adaptive regularization parameters to obtain adaptive compensated seismic data, thus completing the adaptive inverse Q filtering method.

[0013] Thirdly, a computer device includes: a processor and a computer-readable storage medium; A processor, adapted to execute computer programs; A computer-readable storage medium storing a computer program, which, when executed by the processor, implements the adaptive inverse Q filtering method based on time-frequency signal-to-noise ratio.

[0014] Fourthly, a computer-readable storage medium storing a computer program adapted to be loaded by a processor and executed by the aforementioned adaptive inverse Q-filtering method based on time-frequency signal-to-noise ratio.

[0015] Fifthly, a computer program product comprising a computer program that, when executed by a processor, implements the adaptive inverse Q filtering method based on time-frequency signal-to-noise ratio.

[0016] Compared with the prior art, this application has the following beneficial effects: This application provides an adaptive inverse Q-filtering method based on time-frequency signal-to-noise ratio (SNR). It acquires the seismic data to be compensated and calculates its synchronous compression transform domain time spectrum, thereby calculating the time-frequency SNR. The statistical characteristics of the time-frequency SNR are then mapped to a preset regularization parameter range to obtain adaptive regularization parameters. Finally, inverse Q-filtering is performed based on these adaptive regularization parameters to obtain adaptively compensated seismic data. Using the time-frequency SNR of the seismic signal as prior information, the regularization parameters are adaptively adjusted, effectively solving the problems of noise amplification and insufficient effective signal compensation caused by improper selection of regularization parameters in existing regularization-constrained inverse Q-filtering algorithms. By adaptively determining the regularization parameters through statistical characteristics, the subjectivity and uncertainty of manual parameter selection are avoided. This allows the regularization parameters to automatically adjust according to the SNR distribution of the data itself. In regions with low time-frequency SNR, a larger regularization parameter is allocated to suppress noise amplification during compensation; while in regions with high time-frequency SNR, a smaller regularization parameter is allocated to fully compensate the effective signal. This achieves a dynamic balance between signal compensation and noise suppression, effectively improving the compensation accuracy of deep weak signals and the resolution of seismic data. This differentiated compensation strategy better meets the actual compensation needs of seismic data, effectively solving the problems of noise amplification and insufficient signal compensation caused by improper parameter selection in traditional inverse Q-filter algorithms based on regularization constraints. It achieves higher accuracy amplitude recovery and better fidelity, which is beneficial for subsequent parameter inversion and reservoir interpretation.

[0017] Furthermore, by calculating the synchronous squeeze transform domain time spectrum based on time variables and channel number, and according to the wavelet transform time spectrum and instantaneous frequency estimates, the energy in the original wavelet transform time spectrum can be rearranged according to the instantaneous frequency. Thus, a time-frequency representation with focused energy and higher resolution is obtained, providing a more reliable data foundation for the subsequent accurate calculation of time-frequency signal-to-noise ratio.

[0018] Furthermore, the time-frequency signal-to-noise ratio (SNR) is calculated by using the average instantaneous power spectrum of the seismic data to be compensated and the average instantaneous power spectrum of the effective signal. This allows the SNR at each time-frequency point to be quantified, thus enabling a fine characterization of the distribution differences between signal and noise. This provides a fine-grained basis for the adaptive allocation of subsequent regularization parameters, thereby enabling differentiated processing for different time-frequency regions during the subsequent compensation process and improving the fidelity of amplitude recovery.

[0019] Furthermore, by calculating the empirical cumulative distribution function of the time-frequency signal-to-noise ratio (SNR) of the seismic data to be compensated, the original time-frequency SNR values ​​are converted into uniformly distributed sorted indices, eliminating the influence of the absolute value of the original SNR. This makes subsequent mapping depend only on the relative sorting position of the SNR. By calculating adaptive regularization parameters based on the empirical cumulative distribution function and according to the preset regularization parameter range, low SNR regions (corresponding to positions with smaller empirical cumulative distribution function values) automatically obtain larger regularization parameters close to the upper limit of the range to suppress noise, while high SNR regions (corresponding to positions with larger empirical cumulative distribution function values) automatically obtain smaller regularization parameters close to the lower limit of the range to fully compensate the signal. Therefore, fully adaptive differential compensation without the need for manual thresholding is achieved.

[0020] Furthermore, by The objective function of the decay compensation inverse problem of norm regularization constraint The norm term is approximately differentiable, which overcomes the problem. The non-differentiability of the norm at zero makes it difficult to solve using gradient-based algorithms. This paper employs an iterative reweighted least squares method, updating the weighted diagonal matrix in each iteration based on the column vector of the synchronous compression transform domain time-frequency coefficients of the compensated seismic data from the previous iteration. This allows the weights to dynamically adjust as the solution improves. The compensation column vector of the current iteration is calculated based on the updated weighted diagonal matrix until a preset iteration stopping condition is met, ensuring stable convergence to a high-quality solution. Furthermore, by combining adaptive regularization parameters, refined compensation with differentiated regularization intensities at different time-frequency points is achieved, enhancing the stability and accuracy of the method. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 The noisy attenuated synthetic seismic data used in the embodiments of this application; Figure 2 This refers to the time-frequency signal-to-noise ratio of the noisy attenuated synthesized seismic data when the quality factor Q=60 in the embodiments of this application. Figure 3 The values ​​of the adaptive regularization parameters for the noisy attenuation synthetic seismic data in the embodiments of this application are used. Figure 4This is the adaptive attenuation compensation result for the noisy attenuation synthetic seismic data in the embodiments of this application; Figure 5 The actual seismic data used in the embodiments of this application; Figure 6 The compensation result is based on actual earthquake data in the embodiments of this application; Figure 7 A schematic diagram illustrating the technical principle of the adaptive inverse Q filtering method based on time-frequency signal-to-noise ratio provided in this application embodiment; Figure 8 A flowchart illustrating the adaptive inverse Q filtering method based on time-frequency signal-to-noise ratio provided in this application embodiment; Figure 9 A structural diagram of an adaptive inverse Q filter system based on time-frequency signal-to-noise ratio provided in an embodiment of this application; Figure 10 A schematic diagram of a computer device provided in an embodiment of this application. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] like Figure 7 and Figure 8 As shown, this application provides an adaptive inverse Q filtering method based on time-frequency signal-to-noise ratio, including the following steps: S100, acquire the seismic data to be compensated, and calculate the synchronous compression transform domain time spectrum of the seismic data to be compensated; As one embodiment, calculating the synchronous compression transform domain time spectrum of the seismic data to be compensated includes: S110, Read earthquake data to be compensated ,in, and These are the time variable and trace number of the seismic data to be compensated, respectively; S120, Calculate the synchronous compression transform domain time spectrum of the seismic data to be compensated, channel by channel, according to the synchronous compression transform forward transformation calculation formula. The formula for calculating the positive transformation of synchronous extrusion transformation is as follows:

[0025] in, and Representing scale and time variables respectively. For angular frequency variables, Single-channel seismic data to be compensated Wavelet transform frequency spectrum This is the estimated instantaneous frequency.

[0026] Compared with traditional time-frequency transformation methods such as short-time Fourier transform, S-transform, and wavelet transform, synchronous squeezing transform can suppress the diffusion of time-frequency energy and obtain a more sparse and highly localized time-frequency representation, thereby achieving accurate attenuation compensation for non-stationary seismic signals.

[0027] S200, calculate the time-frequency signal-to-noise ratio of the seismic data to be compensated based on the time spectrum of the synchronous compression transform domain; As one embodiment, calculating the time-frequency signal-to-noise ratio of the seismic data to be compensated includes: S210, calculate the average instantaneous power spectrum of the seismic data to be compensated using the following formula. :

[0028] The average instantaneous power spectrum of the effective signal is calculated using the following formula. :

[0029] in, For the first The synchronous compression transform domain time spectrum of the seismic data to be compensated For the first The conjugate of the synchronous compression transform domain time spectrum of the seismic data to be compensated. M The number of channels in the earthquake data to be compensated.

[0030] S220, calculate the time-frequency signal-to-noise ratio of the seismic data to be compensated using the following formula. :

[0031] S300, calculate the statistical characteristics of the time-frequency signal-to-noise ratio of the earthquake data to be compensated, and map it to a preset regularization parameter range to obtain adaptive regularization parameters; As one embodiment, the statistical characteristics of the time-frequency signal-to-noise ratio of the seismic data to be compensated are calculated and mapped to a preset regularization parameter range to obtain adaptive regularization parameters, including: S310, Based on the time-frequency signal-to-noise ratio of the earthquake data to be compensated, the empirical cumulative distribution function is calculated using the following formula:

[0032] in, This is the ascending index value of the time-frequency signal-to-noise ratio of the earthquake data to be compensated. The number of time-frequency signal-to-noise ratio data points of the earthquake data to be compensated; S320, calculate the adaptive regularization parameter using the following formula. :

[0033] in, and These represent the minimum and maximum values ​​of the preset adaptive regularization parameters.

[0034] By establishing a mapping relationship between the time-frequency signal-to-noise ratio of seismic signals and regularization parameters, the compensation parameters can be automatically adjusted according to the signal-to-noise ratio characteristics of seismic signals. This effectively enhances the signal while suppressing noise amplification, achieving an ideal balance between signal compensation and noise suppression, thereby improving the accuracy and fidelity of amplitude recovery.

[0035] S400, perform inverse Q filtering on the seismic data to be compensated according to the adaptive regularization parameters to obtain adaptive compensated seismic data, thus completing the adaptive inverse Q filtering method.

[0036] As one embodiment, performing inverse Q-filtering on the seismic data to be compensated based on the adaptive regularization parameter includes: S410, Solve The objective function of the decay compensation inverse problem of norm regularization constraints is:

[0037] in, The column vector is formed by rearranging the time-spectral coefficients of the synchronous compression transform domain of the seismic data to be compensated. The column vector is formed by rearranging the time-spectral coefficients of the synchronous compression transform domain of the compensated seismic data column by column. The column vector formed by rearranging the adaptive regularization parameters column by column, It is a diagonal matrix, and the elements on the diagonal are matrices. Generated by rearranging columns, where, , , , These represent the number of time indices and the number of frequency indices, respectively. Q For quality factor; Considering of norm The derivative is discontinuous at zero, making it difficult to solve using gradient-based algorithms. Therefore, [the following is a more detailed explanation:] norm By approximation, the objective function can be rewritten as:

[0038] in, For a given non-negative small quantity; Then, using the iterative reweighted least squares method, the iterative reweighted format is obtained as follows:

[0039] in, For a weighted diagonal matrix, the elements on the diagonal are: Generated by rearranging columns; S420, when the iteration stopping condition is met, the synchronous compression transform domain time-spectral coefficients of the compensated seismic data are... Rearranged into a two-dimensional time-frequency matrix representation The adaptively compensated seismic data are obtained using the inverse formula of the synchronous compression transform, which is as follows:

[0040] in, It is related to the mother wavelet of wavelet transform. Relevant constants.

[0041] Figure 1 The image shows the noisy attenuated synthetic seismic data used as the seismic data to be compensated, where the red waveform represents the noise-free, attenuated (Q=Inf) reference seismic record used to compare the attenuation compensation effect.

[0042] Figure 2 The time-frequency signal-to-noise ratio of the noisy attenuated synthetic seismic data is shown when the quality factor Q=60. It can be observed that as time (depth) increases, the effective frequency bandwidth gradually narrows and the signal-to-noise ratio decreases accordingly. This indicates that the time-frequency signal-to-noise ratio effectively characterizes the changes in effective seismic signal and noise with time and frequency.

[0043] Figure 3 The adaptive regularization parameter values ​​for the noisy attenuation synthetic seismic data are shown. It can be seen that regions with low time-frequency signal-to-noise ratio are assigned larger regularization parameters, which can better suppress noise amplification; while regions with high time-frequency signal-to-noise ratio are assigned smaller regularization parameters, which can better compensate for useful seismic signals.

[0044] Figure 4 The results show the adaptive attenuation compensation of the synthesized seismic data with noise attenuation. As can be seen, the adaptive inverse Q-filtering algorithm based on time-frequency signal-to-noise ratio provided in this embodiment effectively and fully compensates for the seismic signal while suppressing noise amplification, thus verifying the effectiveness and accuracy of the adaptive inverse Q-filtering algorithm based on time-frequency signal-to-noise ratio of the present invention.

[0045] Figure 5 The actual seismic data to be compensated is shown to verify the application effect of the adaptive inverse Q filtering algorithm based on time-frequency signal-to-noise ratio in this embodiment on actual seismic data.

[0046] Figure 6 The compensation results for actual seismic data show that the energy of deep seismic data is effectively restored, the seismic reflection phase axis is more continuous, and the resolution of seismic profiles is significantly improved. This further verifies the effectiveness and accuracy of the adaptive inverse Q-filtering algorithm based on time-frequency signal-to-noise ratio in this embodiment.

[0047] In one embodiment of this application, such as Figure 9 As shown, an adaptive inverse Q filtering system based on time-frequency signal-to-noise ratio is provided, including: The synchronous compression transform domain time spectrum calculation module is used to acquire the seismic data to be compensated and calculate the synchronous compression transform domain time spectrum of the seismic data to be compensated. The time-frequency signal-to-noise ratio calculation module is used to calculate the time-frequency signal-to-noise ratio of the seismic data to be compensated based on the time spectrum of the synchronous compression transform domain. The adaptive regularization parameter calculation module is used to calculate the statistical characteristics of the time-frequency signal-to-noise ratio of the seismic data to be compensated, and map it to a preset regularization parameter range to obtain the adaptive regularization parameter. The adaptive inverse Q filtering module is used to perform inverse Q filtering on the seismic data to be compensated according to the adaptive regularization parameters to obtain adaptive compensated seismic data, thus completing the adaptive inverse Q filtering method.

[0048] Specific limitations regarding the adaptive inverse Q-filtering system based on time-frequency signal-to-noise ratio (TSNR) can be found in the limitations described above for the adaptive inverse Q-filtering method based on TNR; the corresponding technical effects are equivalent and will not be repeated here. Each module in the aforementioned adaptive inverse Q-filtering system based on TNR can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware or independently of the processor in a computer device, or stored in software in the memory of the computer device, so that the processor can call and execute the corresponding operations of each module.

[0049] Figure 10 An internal structural diagram of a computer device is shown in one embodiment. This computer device may specifically be a terminal or a server. Figure 10As shown, the computer device includes a processor, memory, network interface, display, camera, and input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements an adaptive inverse Q filtering method based on the time-frequency signal-to-noise ratio. The display screen can be an LCD screen or an e-ink display screen. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0050] As will be understood by those skilled in the art, computer equipment Figure 10 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device to which the present invention is applied. A specific computing device may include more or fewer components than those shown in the figure, or combine certain components, or have the same component arrangement.

[0051] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described above.

[0052] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0053] In summary, the adaptive inverse Q-filtering method, system, computer device, and storage medium based on time-frequency signal-to-noise ratio (TSNR) provided in this application first calculates the synchronous compression transform time-frequency spectrum of the seismic data to be compensated; then, it calculates the TNR of the seismic data to be compensated based on the synchronous compression transform time-frequency spectrum; further, it calculates the value of the adaptive regularization parameter based on the TNR of the seismic data to be compensated; finally, it performs inverse Q-filtering in the synchronous compression transform domain using the calculated adaptive regularization parameter value, and obtains the adaptively compensated seismic data through the inverse synchronous compression transform. This method uses the TNR of the seismic data as prior information to adaptively adjust the value of the regularization parameter, assigning a larger regularization parameter to regions with a lower TNR and a smaller regularization parameter to regions with a higher TNR, thereby achieving an ideal balance between signal compensation and noise suppression and improving the accuracy and fidelity of amplitude recovery.

[0054] Since the amplitude compensation operator is an exponential function of travel time and frequency, traditional inverse Q-filtering algorithms based on wavefield extension face numerical instability problems, especially when the signal-to-noise ratio of seismic data is low. Inverse Q-filtering algorithms based on stability factors address this instability to some extent by introducing a stability factor, but their fixed compensation method does not reflect the varying attenuation levels of the subsurface medium at different times (depths). Inverse Q-filtering algorithms based on regularization constraints can achieve stable compensation through regularization constraints. However, in these methods, the choice of regularization parameter is crucial to the quality of the compensation result. A smaller regularization parameter can adequately compensate for the effective signal but easily amplifies noise; while an excessively large regularization parameter, although it can suppress noise amplification, leads to insufficient compensation of the effective signal. To address these issues, this embodiment proposes an adaptive inverse Q-filtering algorithm based on the time-frequency signal-to-noise ratio (SNR), using the seismic signal's SNR as prior information to achieve adaptive adjustment of the regularization parameter. In regions with low time-frequency signal-to-noise ratio (SNR), a larger regularization parameter is assigned to suppress noise amplification; while in regions with high SNR, a smaller regularization parameter is assigned to fully compensate for the effective signal. This differentiated compensation strategy better suits the actual compensation needs of seismic data, enabling amplitude compensation to achieve higher accuracy and fidelity.

[0055] The various embodiments in this specification are described in a progressive manner. For directly identical or similar parts of the embodiments, refer to each other. Each embodiment focuses on its differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. It should be noted that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0056] The above-described embodiments are merely preferred embodiments of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various improvements and substitutions without departing from the principles of the present invention, and these improvements and substitutions should also be considered within the scope of protection of the present invention. Therefore, the scope of protection of this invention should be determined by the scope of the claims.

Claims

1. An adaptive inverse Q filtering method based on time-frequency signal-to-noise ratio, characterized in that, Includes the following steps: Acquire the seismic data to be compensated, and calculate the synchronous compression transform domain time spectrum of the seismic data to be compensated; Calculate the time-frequency signal-to-noise ratio of the seismic data to be compensated based on the synchronous compression transform domain time spectrum; The statistical characteristics of the time-frequency signal-to-noise ratio of the earthquake data to be compensated are calculated and mapped to a preset regularization parameter range to obtain adaptive regularization parameters; The adaptive inverse Q-filter is applied to the seismic data to be compensated based on the adaptive regularization parameters to obtain adaptively compensated seismic data, thus completing the adaptive inverse Q-filtering method.

2. The adaptive inverse Q filtering method based on time-frequency signal-to-noise ratio according to claim 1, characterized in that, The calculation of the synchronous compression transform domain time spectrum of the seismic data to be compensated includes: Read the time variable and trace number of the seismic data to be compensated; Continuous wavelet transform is performed on each channel of the seismic data to be compensated to obtain the wavelet transform time spectrum of a single channel of the seismic data to be compensated. The phase change rate of the wavelet transform time spectrum is calculated along the direction of the time variable to obtain the instantaneous frequency estimate. Based on the time variables and number of traces of the seismic data to be compensated, and according to the wavelet transform time spectrum and instantaneous frequency estimate, the synchronous compression transform domain time spectrum is calculated trace by trace.

3. The adaptive inverse Q filtering method based on time-frequency signal-to-noise ratio according to claim 1, characterized in that, The calculation of the time-frequency signal-to-noise ratio of the seismic data to be compensated includes: Based on the synchronous compression transform domain time spectrum, calculate the average instantaneous power spectrum of the seismic data to be compensated and the average instantaneous power spectrum of the effective signal; The time-frequency signal-to-noise ratio of the seismic data to be compensated is calculated based on the average instantaneous power spectrum of the seismic data to be compensated and the average instantaneous power spectrum of the effective signal.

4. The adaptive inverse Q filtering method based on time-frequency signal-to-noise ratio according to claim 1, characterized in that, The statistical characteristics of the time-frequency signal-to-noise ratio of the seismic data to be compensated are calculated and mapped to a preset regularization parameter range to obtain adaptive regularization parameters, including: The empirical cumulative distribution function for calculating the time-frequency signal-to-noise ratio of the seismic data to be compensated is used. Based on the empirical cumulative distribution function and according to the preset regularization parameter range, the adaptive regularization parameter is calculated.

5. The adaptive inverse Q filtering method based on time-frequency signal-to-noise ratio according to claim 1, characterized in that, The step of performing inverse Q-filtering on the seismic data to be compensated based on the adaptive regularization parameter includes: Construct and solve The objective function of the inverse problem of attenuation compensation with norm regularization constraints yields the column vector of time-spectrum coefficients in the synchronous compression transform domain of the compensated seismic data. When the objective function reaches the preset iteration stopping condition, the column vector of the synchronous compression transform domain time-frequency coefficients of the compensated seismic data is rearranged into a two-dimensional time-frequency matrix, and an inverse synchronous compression transform is performed to obtain adaptively compensated seismic data.

6. The adaptive inverse Q filtering method based on time-frequency signal-to-noise ratio according to claim 5, characterized in that, The construction and solution The objective function of the decay compensation inverse problem of norm regularization constraints includes: The The objective function of the decay compensation inverse problem of norm regularization constraint The norm term is approximately differentiable, and the solution is obtained using the iterative reweighted least squares method. In each iteration of the iterative reweighted least squares method, the weighted diagonal matrix is ​​updated based on the synchronous compression transform domain time-spectrum coefficient column vector of the compensated seismic data from the previous iteration, and the synchronous compression transform domain time-spectrum coefficient column vector of the compensated seismic data for the current iteration is calculated based on the updated weighted diagonal matrix, until the preset iteration stop condition is met.

7. An adaptive inverse Q filtering system based on time-frequency signal-to-noise ratio, characterized in that, include: The synchronous compression transform domain time spectrum calculation module is used to acquire the seismic data to be compensated and calculate the synchronous compression transform domain time spectrum of the seismic data to be compensated. The time-frequency signal-to-noise ratio calculation module is used to calculate the time-frequency signal-to-noise ratio of the seismic data to be compensated based on the time spectrum of the synchronous compression transform domain. The adaptive regularization parameter calculation module is used to calculate the statistical characteristics of the time-frequency signal-to-noise ratio of the seismic data to be compensated, and map it to a preset regularization parameter range to obtain the adaptive regularization parameter. The adaptive inverse Q filtering module is used to perform inverse Q filtering on the seismic data to be compensated according to the adaptive regularization parameters to obtain adaptive compensated seismic data, thus completing the adaptive inverse Q filtering method.

8. A computer device, characterized in that, include: Processor and computer-readable storage media; A processor, adapted to execute computer programs; A computer-readable storage medium storing a computer program, which, when executed by the processor, implements the adaptive inverse Q filtering method based on time-frequency signal-to-noise ratio as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program adapted to be loaded by a processor and executed as described in any one of claims 1 to 7, the adaptive inverse Q filtering method based on time-frequency signal-to-noise ratio.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the adaptive inverse Q filtering method based on time-frequency signal-to-noise ratio as described in any one of claims 1 to 7.

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