Method and device for recovering low frequency of seismic data constrained by reflection structure

Abstract

The application provides a reflection structure constrained seismic data low frequency recovery method and device, belonging to the field of seismic data processing, comprising the following steps: determining the to-be-recovered highest frequency corresponding to the to-be-recovered low frequency seismic data according to original seismic data; determining a reflection structure representation operator by using the original seismic data and an inversion algorithm; constructing a regularization constraint term by using the reflection coefficient and the reflection structure representation operator, and establishing a target functional of the reflection coefficient; inversely solving the target functional of the reflection coefficient to obtain the reflection coefficient; performing low frequency filtering on the reflection coefficient to obtain low frequency seismic data; performing high pass filtering on the original seismic data to obtain high frequency seismic data; and combining the low frequency seismic data and the high frequency seismic data to obtain seismic data after low frequency recovery. Through the method provided by the application, the reliability of the low frequency seismic data can be ensured, the seismic data resolution is enhanced, and the accuracy of full waveform velocity inversion is improved.

Description

Technical Field

[0001] This invention relates to the field of seismic data processing technology, specifically to a method and device for low-frequency recovery of seismic data constrained by reflection structures. Background Technology

[0002] Due to factors such as excitation, reception, and surface wave interference, low-frequency data in actual seismic records are suppressed, contaminated, and distorted, and in some cases, even missing certain frequency bands. For example, excitation of low-frequency data is difficult during data acquisition; or during data reception, the natural frequency of existing detectors has a certain suppressive effect on low-frequency data. Furthermore, surface wave interference contaminates low-frequency data, leading to its suppression in actual seismic records. Although various methods exist for suppressing surface wave interference, the suppression process inevitably damages low-frequency data. In addition, field environmental noise surveys and analyses show that the frequency components of environmental noise are not ideally distributed as white noise, and its low-frequency data often possesses stronger energy. The lack of low-frequency data increases seismic wavelet sidelobes, exacerbates interference effects between different strata, and reduces seismic data resolution. Low-frequency data also plays an indispensable role in wave impedance inversion.

[0003] In existing technologies, to compensate for the impact of missing low-frequency data on wave impedance inversion, a method of interpolating well logging data along the stratigraphic plane to establish a low-frequency model is often used. However, the low-frequency model constructed using this approach is limited by the accuracy of well logging calibration and stratigraphic interpretation, making it difficult to guarantee the reliability of low-frequency data. The lack of low-frequency data also significantly reduces the accuracy of full-waveform velocity inversion. Summary of the Invention

[0004] To address the technical problems of low reliability of low-frequency seismic data in existing technologies, which reduces the accuracy of full waveform velocity inversion and the resolution of seismic data, this invention provides a method and apparatus for low-frequency recovery of seismic data constrained by reflection structures. This method can ensure the reliability of low-frequency seismic data, enhance the resolution of seismic data, and improve the accuracy of full waveform velocity inversion.

[0005] To achieve the above objectives, the present invention provides a method for low-frequency recovery of seismic data constrained by reflection structures, comprising the following steps: acquiring original seismic data; determining the highest frequency to be recovered corresponding to the low-frequency seismic data to be recovered based on the original seismic data; determining a reflection structure characterization operator using the original seismic data and an inversion algorithm; constructing a regularization constraint term using the reflection coefficient and the reflection structure characterization operator, and establishing a target functional of the reflection coefficient through the regularization constraint term; solving the target functional of the reflection coefficient in reverse to obtain the reflection coefficient; performing low-frequency filtering on the reflection coefficient with a high cutoff frequency equal to the highest frequency to be recovered to obtain the low-frequency seismic data; performing high-pass filtering on the original seismic data with a low cutoff frequency equal to the highest frequency to be recovered to obtain high-frequency seismic data; and combining the low-frequency seismic data and the high-frequency seismic data to obtain seismic data after low-frequency recovery.

[0006] Further, determining the highest frequency to be recovered corresponding to the low-frequency seismic data to be recovered based on the original seismic data includes: determining data with amplitude energy less than a set energy from the original seismic data as the low-frequency seismic data; and determining the highest frequency of the low-frequency seismic data as the highest frequency to be recovered.

[0007] Furthermore, the step of determining the reflection structure characterization operator using the original seismic data and the inversion algorithm includes: establishing the objective functional of the reflection structure characterization operator using the seismic data and the reflection structure characterization operator; and using the conjugate gradient method to solve the objective functional of the reflection structure characterization operator in reverse order to determine the reflection structure characterization operator.

[0008] Furthermore, the objective functional of the reflection structure characterization operator is established in the following manner:

[0009] ;

[0010] Where e is the target functional of the reflection structure characterization operator. The original seismic data, The operator characterizing the reflection structure. The number of seismic traces. The number of time samples in the earthquake record. The spatial length of the operator is characterized by the reflection structure. The time length of the operator characterizes the reflection structure.

[0011] Further, the step of constructing regularization constraint terms using the reflection coefficients and the reflection structure characterization operator, and establishing the target functional of the reflection coefficients through the regularization constraint terms, includes: establishing the inversion target functional using a convolution model; establishing a sparse structure function of the reflection coefficients using the reflection coefficients as the first regularization constraint term of the inversion target functional; establishing a function of the reflection structure using the reflection structure characterization operator as the second regularization constraint term of the inversion target functional; and establishing the target functional of the reflection coefficients using the inversion target functional, the first regularization constraint term, and the second regularization constraint term.

[0012] Furthermore, the inversion objective functional is established in the following manner:

[0013] ;

[0014] in, Let the inversion objective functional be... For seismic wavelets, The number of seismic traces. The number of time samples in the earthquake record. The reflection coefficient is denoted as .

[0015] Furthermore, the seismic wavelet is obtained by: fitting the seismic wavelet amplitude spectrum from the amplitude spectrum of the original seismic data using spectral simulation; and performing a Fourier transform on the seismic wavelet amplitude spectrum to obtain the seismic wavelet.

[0016] Furthermore, the sparse structure function of the reflection coefficient is established in the following way:

[0017] ;

[0018] in, is the sparse structure function of the reflection coefficient.

[0019] Furthermore, the function for the reflection structure is established in the following way:

[0020] ;

[0021] in, A function of the reflection structure, Let be a matrix composed of operators characterizing the reflection structure. For high cutoff frequency The matrix formed by the low-pass filter operators, It is a matrix composed of the reflection coefficients.

[0022] Furthermore, the target functional of the reflection coefficient is established in the following manner:

[0023] ;

[0024] in, Let the target functional be the reflection coefficient. Let be the regularization factor of the first regularization constraint term. is the regularization factor for the second regularization constraint term.

[0025] Further, the step of inversely solving the objective functional of the reflection coefficient to obtain the reflection coefficient includes: using an iterative weighted least squares method to inversely solve the objective functional of the reflection coefficient to obtain the reflection coefficient.

[0026] Furthermore, the step of combining the low-frequency seismic data and the high-frequency seismic data to obtain the low-frequency recovered seismic data includes: performing a weighted combination of the low-frequency seismic data and the high-frequency seismic data in the frequency domain to obtain a combination result; and transforming the combination result to the time domain to obtain the low-frequency recovered seismic data.

[0027] A second aspect of the present invention provides a low-frequency recovery device for seismic data constrained by reflection structure. The device comprises: a module for determining the highest frequency to be recovered, used to acquire original seismic data and determine the highest frequency to be recovered corresponding to the low-frequency seismic data to be recovered based on the original seismic data; a module for determining a reflection structure characterization operator, used to determine a reflection structure characterization operator using the original seismic data and an inversion algorithm; a module for determining the target functional of reflection coefficients, used to construct a regularization constraint term using the reflection coefficients and the reflection structure characterization operator, and to establish a target functional of the reflection coefficients through the regularization constraint term; a module for determining reflection coefficients, used to solve the target functional of the reflection coefficients in reverse to obtain the reflection coefficients; a low-frequency seismic data determination module, used to perform low-frequency filtering on the reflection coefficients with a high cutoff frequency equal to the highest frequency to be recovered, to obtain the low-frequency seismic data; a high-frequency seismic data determination module, used to perform high-pass filtering on the original seismic data with a low cutoff frequency equal to the highest frequency to be recovered, to obtain high-frequency seismic data; and a combination module, used to combine the low-frequency seismic data and the high-frequency seismic data to obtain seismic data after low-frequency recovery.

[0028] The present invention has at least the following technical effects through the technical solution provided by the present invention:

[0029] The low-frequency recovery method for seismic data constrained by reflection structure of the present invention first acquires the original seismic data, determines the highest frequency to be recovered corresponding to the low-frequency seismic data based on the original seismic data, determines the reflection structure characterization operator using the original seismic data and the inversion algorithm, constructs a regularization constraint term using the reflection coefficient and the reflection structure characterization operator, establishes the objective functional of the reflection coefficient through the regularization constraint term, and solves the objective functional of the reflection coefficient in reverse to obtain the reflection coefficient. The reflection coefficient is then subjected to low-frequency filtering with a high cutoff frequency equal to the highest frequency to be recovered to obtain low-frequency seismic data. The original seismic data is then subjected to high-pass filtering with a low cutoff frequency equal to the highest frequency to be recovered to obtain high-frequency seismic data. Finally, the low-frequency and high-frequency seismic data are combined to obtain the seismic data after low-frequency recovery. The low-frequency seismic data recovery method for seismic data constrained by reflection structure of the present invention can ensure the reliability of low-frequency seismic data, enhance seismic data resolution, and improve the accuracy of full waveform velocity inversion.

[0030] Other features and advantages of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0031] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0032] Figure 1 A flowchart of a low-frequency recovery method for seismic data constrained by reflection structures provided in an embodiment of the present invention;

[0033] Figure 2 A schematic diagram of seismic model data synthesized using a complex model in the low-frequency recovery method of seismic data constrained by reflection structure provided in an embodiment of the present invention;

[0034] Figure 3 This is a schematic diagram of the low-frequency model data after high-cut-frequency low-pass filtering of seismic model data in the seismic data low-frequency recovery method for seismic data constrained by reflection structure provided in an embodiment of the present invention.

[0035] Figure 4 This is a schematic diagram of high-frequency model data after low-frequency cutoff high-pass filtering of seismic model data in the low-frequency recovery method of seismic data constrained by reflection structure provided in an embodiment of the present invention.

[0036] Figure 5 This is a schematic diagram of the low-frequency seismic data recovered from the seismic model data in the low-frequency seismic data recovery method with reflection structure constraint provided in an embodiment of the present invention.

[0037] Figure 6This is a schematic diagram of the seismic data after low-frequency recovery of the seismic model data in the seismic data low-frequency recovery method with reflection structure constraint provided in the embodiments of the present invention;

[0038] Figure 7 A schematic diagram of actual raw seismic data from an oilfield in the low-frequency recovery method of seismic data constrained by reflection structure provided in an embodiment of the present invention.

[0039] Figure 8 The low-frequency seismic data to be recovered in the reflection structure-constrained seismic data low-frequency seismic data below 10Hz in the actual original seismic data provided in the embodiments of the present invention;

[0040] Figure 9 This is the low-frequency seismic data recovered from the actual original seismic data in the low-frequency seismic data recovery method for seismic data constrained by reflection structure provided in the embodiments of the present invention;

[0041] Figure 10 The seismic data obtained by low-frequency recovery of the actual original seismic data in the seismic data low-frequency recovery method of the reflection structure constraint provided in the embodiments of the present invention;

[0042] Figure 11 This is a schematic diagram of a low-frequency seismic data recovery device constrained by a reflection structure, provided in an embodiment of the present invention. Detailed Implementation

[0043] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0044] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0045] In this invention, unless otherwise stated, directional terms such as "upper," "lower," "top," and "bottom" are generally used to describe the relative positions of components in relation to the directions shown in the accompanying drawings or in relation to the vertical, perpendicular, or gravitational directions.

[0046] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0047] Please refer to Figure 1This invention provides a method for low-frequency recovery of seismic data constrained by reflection structures. The method includes the following steps: S101: acquiring original seismic data and determining the highest frequency to be recovered corresponding to the low-frequency seismic data to be recovered based on the original seismic data; S102: determining the reflection structure characterization operator using the original seismic data and an inversion algorithm; S103: constructing a regularization constraint term using the reflection coefficient and the reflection structure characterization operator, and establishing a target functional of the reflection coefficient through the regularization constraint term; S104: solving the target functional of the reflection coefficient in reverse to obtain the reflection coefficient; S105: performing low-frequency filtering on the reflection coefficient with a high cutoff frequency equal to the highest frequency to be recovered to obtain the low-frequency seismic data; S106: performing high-pass filtering on the original seismic data with a low cutoff frequency equal to the highest frequency to be recovered to obtain high-frequency seismic data; S107: combining the low-frequency seismic data and the high-frequency seismic data to obtain seismic data after low-frequency recovery.

[0048] Specifically, in this embodiment of the invention, raw seismic data is first acquired. Based on the raw seismic data, the highest frequency to be recovered corresponding to the low-frequency seismic data to be recovered is determined. A reflection structure characterization operator is determined using the raw seismic data and an inversion algorithm. Then, a regularization constraint term is constructed using the reflection coefficient and the reflection structure characterization operator. An objective functional of the reflection coefficient is established using the regularization constraint term, and the objective functional of the reflection coefficient is solved in reverse to obtain the reflection coefficient. The reflection coefficient is then subjected to a low-frequency filter with a high cutoff frequency equal to the highest frequency to be recovered to obtain low-frequency seismic data. The raw seismic data is then subjected to a high-pass filter with a low cutoff frequency equal to the highest frequency to be recovered to obtain high-frequency seismic data. Finally, the low-frequency and high-frequency seismic data are combined to obtain the seismic data after low-frequency recovery.

[0049] The low-frequency recovery method for seismic data constrained by reflection structures provided by the present invention can ensure the reliability of low-frequency seismic data, enhance seismic data resolution, and improve the accuracy of full-waveform velocity inversion.

[0050] First, execute step S101: acquire raw seismic data, and determine the highest frequency to be recovered corresponding to the low-frequency seismic data to be recovered based on the raw seismic data.

[0051] Further, determining the highest frequency to be recovered corresponding to the low-frequency seismic data to be recovered based on the original seismic data includes: determining data with amplitude energy less than a set energy from the original seismic data as the low-frequency seismic data; and determining the highest frequency of the low-frequency seismic data as the highest frequency to be recovered.

[0052] Specifically, in this embodiment of the invention, raw seismic data is first acquired. ,in, It is the reflection time. This refers to the surface location, i.e., different seismic traces. Raw seismic data. This is a post-stack 2D profile of the overall data, which is missing low-frequency seismic data. (Based on the original seismic data...) Spectral analysis was performed to identify the data segment with amplitude energy (amplitude value) lower than a set energy level as low-frequency seismic data to be recovered. The highest frequency of the low-frequency seismic data was determined as the highest frequency to be recovered. .

[0053] Next, step S102 is executed: the reflection structure characterization operator is determined using the original seismic data and the inversion algorithm.

[0054] Furthermore, the step of determining the reflection structure characterization operator using the original seismic data and the inversion algorithm includes: establishing the objective functional of the reflection structure characterization operator using the seismic data and the reflection structure characterization operator; and using the conjugate gradient method to solve the objective functional of the reflection structure characterization operator in reverse order to determine the reflection structure characterization operator.

[0055] Specifically, in this embodiment of the invention, the effective data in actual seismic data exhibits spatial correlation, meaning that seismic data profiles have a certain predictive relationship in both the temporal and spatial directions. The reflection structure represents the geometric and energy variation characteristics of the seismic reflection axis in space, signifying the spatial continuity and predictability of the seismic data. The reflection structure characterization operator... This refers to the spatiotemporal operator that describes the spatial and energy relationships of reflection structures. It can be assumed that any point in a seismic profile can be obtained by convolving its surrounding points with the reflection structure operator; that is, the relationships between points on a seismic data profile can be linearly described by the operator. Therefore, based on the spatial coherence of seismic data, using the original seismic data... It is possible to compute the reflection structure characterization operator. :

[0056] First, the objective functional of the reflection structure characterization operator is established using the seismic data and the reflection structure characterization operator:

[0057] ;

[0058] Where e is the objective functional of the reflection structure characterization operator. The original earthquake data, To characterize the reflection structure operator, The number of seismic traces. The number of time samples in the earthquake record. The spatial length of the operator is characterized by the reflection structure. Let the time length of the reflection structure characterization operator be denoted by this objective functional. The meaning of this objective functional is that the optimal characterization operator obtained by solving the algorithm ensures that any data point on the seismic data profile is within the range of its neighbors. * The error energy is between the data points and the predicted data values ​​after convolution with the characterization operator. Minimum.

[0059] Then, the conjugate gradient method is used to solve the objective functional of the reflection structure characterization operator in reverse order to determine the reflection structure characterization operator. .

[0060] Next, step S103 is executed: a regularization constraint term is constructed using the reflection coefficient and the reflection structure characterization operator, and the objective functional of the reflection coefficient is established through the regularization constraint term.

[0061] Further, the step of constructing regularization constraint terms using the reflection coefficients and the reflection structure characterization operator, and establishing the target functional of the reflection coefficients through the regularization constraint terms, includes: establishing the inversion target functional using a convolution model; establishing a sparse structure function of the reflection coefficients using the reflection coefficients as the first regularization constraint term of the inversion target functional; establishing a function of the reflection structure using the reflection structure characterization operator as the second regularization constraint term of the inversion target functional; and establishing the target functional of the reflection coefficients using the inversion target functional, the first regularization constraint term, and the second regularization constraint term.

[0062] Specifically, in the embodiments of the present invention, a convolution model is used. Establish the inversion objective functional.

[0063] ;

[0064] in, To invert the objective functional, For seismic wavelets, The number of seismic traces. The number of time samples in the earthquake record. This is the reflection coefficient.

[0065] Furthermore, the seismic wavelet is obtained as follows: the seismic wavelet amplitude spectrum is obtained by fitting it to the amplitude spectrum of the original seismic data using spectral simulation; the seismic wavelet is then obtained by performing a Fourier transform on the seismic wavelet amplitude spectrum. .

[0066] Furthermore, based on the probability distribution of the reflection coefficient and the assumption that the reflection coefficient is sparse, a sparse structure function for the reflection coefficient is established:

[0067] ;

[0068] in, It is a sparse structure function for the reflection coefficient.

[0069] Furthermore, based on the fact that reflection coefficients have the same predictability as seismic records, a function for reflection structure is established:

[0070] ;

[0071] in, For functions of the reflection structure.

[0072] The matrix is ​​composed of operators characterizing the reflection structure:

[0073] ;

[0074] in, This is the reflection structure characterization operator obtained for the nth seismic trace.

[0075] For high cutoff frequency The matrix formed by the low-pass filtering operators represents the low-pass filtering process of the reflection coefficient. This method uses the sinc function to construct the low-pass filter, then:

[0076] ;

[0077] Where sinc(x) is the value of the filter operator used.

[0078] The matrix consists of reflection coefficients:

[0079] ;

[0080] in, Let be the reflection coefficient to be solved for the nth seismic trace.

[0081] Furthermore, the target functional of the reflection coefficient is established in the following manner:

[0082] ;

[0083] in, Let the objective functional be the reflection coefficient. The regularization factor is the first regularization constraint term. This is the regularization factor for the second regularization constraint. The regularization factor can be determined based on the actual situation and is used to adjust the impact of the regularization constraint on the result. The larger the reflection coefficient, the more pronounced the temporal sparsity. The larger the value, the stronger the spatial continuity of the reflective structure.

[0084] Next, step S104 is executed: the target functional of the reflection coefficient is solved in reverse to obtain the reflection coefficient.

[0085] Furthermore, the objective functional of the reflection coefficient is solved in reverse to obtain the reflection coefficient, including: solving the objective functional of the reflection coefficient in reverse using the Iterative Reweighted Least Squares (IRLS) method. The reflection coefficient is obtained. .

[0086] Next, proceed to step S105: adjust the reflection coefficient. The high cutoff frequency is the highest frequency to be recovered. Low-frequency filtering yields low-frequency seismic data. .

[0087] Next, proceed to step S106: process the raw seismic data. The lowest cutoff frequency is the highest frequency to be recovered. High-pass filtering is used to obtain high-frequency seismic data.

[0088] Finally, step S107 is executed: the low-frequency seismic data and the high-frequency seismic data are combined to obtain the seismic data after low-frequency recovery.

[0089] Furthermore, the step of combining the low-frequency seismic data and the high-frequency seismic data to obtain the low-frequency recovered seismic data includes: performing a weighted combination of the low-frequency seismic data and the high-frequency seismic data in the frequency domain to obtain a combination result; and transforming the combination result to the time domain to obtain the low-frequency recovered seismic data.

[0090] Specifically, in this embodiment of the invention, low-frequency seismic data... The high-frequency seismic data and the low-frequency seismic data are both transformed to the frequency domain, and their spectra are weighted and combined within the transition band to obtain the combined result. This combined result is then transformed to the time domain to form the seismic record reconstructed from the low-frequency seismic data.

[0091] Example 1

[0092] To verify the effectiveness of this embodiment, a complex model was synthesized. Figure 2 The earthquake model data shown is for Figure 2 High cutoff frequency of earthquake model data A low-pass filter with a frequency of 10Hz is obtained. Figure 3 The low-frequency model data shown. For Figure 2 Low cutoff frequency of earthquake model data A high-pass filter with a frequency of 10Hz is obtained. Figure 4The high-frequency model data shown. Figure 4 The high-frequency model data below 10Hz is missing. Figure 5 It is using the method of this embodiment from Figure 4 The recovered low-frequency seismic data, low-frequency seismic data and Figure 3 Compared to the low-frequency model data, it has a high degree of consistency. Figure 6 It is Figure 5 Low-frequency seismic data recovered in the middle and Figure 4 The seismic data, after being overlaid with high-frequency model data, effectively reconstructed the seismic data. Figure 2 The earthquake model data shown.

[0093] Example 2

[0094] Get Figure 7 The actual raw seismic data of a certain oil field shown is as follows. For raw seismic data Perform spectral analysis, such as Figure 8 As shown, the data indicates that the energy is weak below 10Hz, necessitating the recovery of low-frequency seismic data below 10Hz, i.e., the highest frequency to be recovered from the low-frequency seismic data. The frequency is 10 Hz. Using spectral simulation, the wavelet amplitude spectrum is obtained by fitting it to the amplitude spectrum of the original seismic data. The wavelet is then obtained by performing a Fourier transform on the wavelet amplitude spectrum. .

[0095] By solving a least-squares problem, the reflection structure characterization operator is determined using raw seismic data and an inversion algorithm. Reflection structure characterization operator This represents the spatial continuity and predictability of seismic data, minimizing the error energy between the original data points and the predicted data obtained by multiplying the original data points with multiple neighboring data points and the characterization operator. A target functional for the reflection coefficients is established using a convolution model; that is, seismic data can be viewed as a convolution of the reflection coefficient sequence and the seismic wavelet, and solving for the reflection coefficient sequence from the seismic data is its corresponding inverse problem, achieved by solving the target functional for the reflection coefficients. Inversion problems often have multiple solutions. To obtain more accurate results, the target functional is solved using regularization theory, which adds a constraint term to the inversion objective function to reduce the singularity of the solution. Regularization constraints are then constructed as follows: based on the probability distribution of the reflection coefficients and the assumption that the reflection coefficients are sparse, a sparse structure function of the reflection coefficients is established as the first regularization constraint, constraining the position of the reflection phase axis in the time direction; based on the fact that the reflection coefficients and seismic records have the same predictability, a function of the reflection structure is established as the second regularization constraint, constraining the continuity of the reflection phase axis in the spatial direction. The two constraint terms are multiplied by their corresponding coefficients. These coefficients can be adjusted by the user; the larger the coefficient, the more significant the impact of the constraint term on the result. In this embodiment, and Both are set to 0.25, ensuring both the sparsity of the reflection coefficients and the lateral continuity of the reflection structure. Then, the IRLS algorithm is used to solve the objective functional of the reflection coefficients with added regularization constraints. To obtain the reflection coefficient The low-frequency seismic data with reflection coefficients has the same low-frequency components as the seismic data. Using a low-frequency filter with a high cutoff frequency of 10Hz and a filter transition band of 7Hz to 13Hz, the low-frequency seismic data with reflection coefficients below 10Hz are separated, resulting in... Figure 9 The low-frequency seismic data shown is then analyzed using a high-pass filter with a low cutoff frequency of 10Hz and a transition band of 7Hz to 13Hz. Figure 6 Low-frequency data below 10Hz was filtered out from the original seismic data to obtain high-frequency seismic data. Then, in the frequency domain, the low-frequency seismic data with reflection coefficients and the high-frequency seismic data (after filtering out the low-frequency data) were spectrally combined. The spectra of the two data sets were weighted within the transition band, i.e., the spectral amplitude values ​​were weighted and added together. For the low-frequency seismic data with reflection coefficients, the weight was 1 at 7Hz and 0 at 13Hz, with the weight decreasing linearly within the transition band. For the high-frequency seismic data from the original seismic data, the weight was 0 at 7Hz and 1 at 13Hz, with the weight increasing linearly within the transition band. The combined spectrum was then transformed to the time domain to obtain the final result as shown below. Figure 10 The low-frequency recovered seismic data shown represents the completion of the low-frequency recovery process constrained by the reflection structure.

[0096] As can be seen, this embodiment effectively recovers the low-frequency seismic data from the original seismic data. The recovered seismic data can more accurately reflect the seismic response of the actual underground structure and oil and gas reservoir.

[0097] Please refer to Figure 11 The second aspect of the present invention provides a low-frequency recovery device for seismic data constrained by reflection structure. The device comprises: a module for determining the highest frequency to be recovered, used to acquire original seismic data and determine the highest frequency to be recovered corresponding to the low-frequency seismic data to be recovered based on the original seismic data; a module for determining a reflection structure characterization operator, used to determine a reflection structure characterization operator using the original seismic data and an inversion algorithm; a module for determining the target functional of reflection coefficients, used to construct a regularization constraint term using the reflection coefficients and the reflection structure characterization operator, and to establish a target functional of the reflection coefficients through the regularization constraint term; a module for determining reflection coefficients, used to solve the target functional of the reflection coefficients in reverse to obtain the reflection coefficients; a low-frequency seismic data determination module, used to perform low-frequency filtering on the reflection coefficients with a high cutoff frequency equal to the highest frequency to be recovered, to obtain the low-frequency seismic data; a high-frequency seismic data determination module, used to perform high-pass filtering on the original seismic data with a low cutoff frequency equal to the highest frequency to be recovered, to obtain high-frequency seismic data; and a combination module, used to combine the low-frequency seismic data and the high-frequency seismic data to obtain seismic data after low-frequency recovery.

[0098] The preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the specific details of the above embodiments. Within the scope of the technical concept of the present invention, various simple modifications can be made to the technical solution of the present invention, and these simple modifications all fall within the protection scope of the present invention.

[0099] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not describe the various possible combinations separately.

[0100] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the present invention, they should also be regarded as the content disclosed by the present invention.

Claims

1. A method for low-frequency recovery of seismic data constrained by reflection structures, characterized in that, The low-frequency recovery method for seismic data constrained by the reflection structure includes: Acquire raw seismic data, and determine the highest frequency of the low-frequency seismic data to be recovered based on the raw seismic data; The original seismic data and inversion algorithm are used to determine the reflection structure characterization operator; A regularization constraint term is constructed using the reflection coefficient and the reflection structure characterization operator, and a target functional of the reflection coefficient is established through the regularization constraint term. Specifically, this includes: establishing an inversion target functional using a convolution model; establishing a sparse structure function of the reflection coefficient using the reflection coefficient as the first regularization constraint term of the inversion target functional; establishing a function of the reflection structure using the reflection structure characterization operator as the second regularization constraint term of the inversion target functional; establishing the target functional of the reflection coefficient using the inversion target functional, the first regularization constraint term, and the second regularization constraint term; and establishing the function of the reflection structure in the following manner: ;in, A function of the reflection structure, Let be a matrix composed of operators characterizing the reflection structure. For high cutoff frequency The matrix formed by the low-pass filter operators, The matrix is ​​composed of the reflection coefficients; the objective functional of the reflection coefficients is established in the following manner: ;in, Let the target functional be the reflection coefficient. Let be the regularization factor of the first regularization constraint term. The regularization factor is the regularization factor for the second regularization constraint term; The inversion objective functional; The sparse structure function of the reflection coefficient; The reflection coefficient is obtained by solving the objective functional of the reflection coefficient in reverse. The reflection coefficient is subjected to low-frequency filtering with a high cutoff frequency equal to the highest frequency to be recovered, to obtain the low-frequency seismic data; The original seismic data is subjected to a high-pass filter with a low cutoff frequency equal to the highest frequency to be recovered, to obtain high-frequency seismic data; The low-frequency seismic data and the high-frequency seismic data are combined to obtain the low-frequency recovered seismic data.

2. The method for low-frequency recovery of seismic data constrained by reflection structures according to claim 1, characterized in that, The step of determining the highest frequency to be recovered corresponding to the low-frequency seismic data to be recovered based on the original seismic data includes: The low-frequency seismic data are determined from the original seismic data based on the amplitude energy being less than a set energy. The highest frequency of the low-frequency seismic data is determined as the highest frequency to be recovered.

3. The method for low-frequency recovery of seismic data constrained by reflection structures according to claim 1, characterized in that, The step of determining the reflection structure characterization operator using the original seismic data and inversion algorithm includes: The objective functional of the reflection structure characterization operator is established using the earthquake data and the reflection structure characterization operator. The objective functional of the reflection structure characterization operator is solved in reverse using the conjugate gradient method to determine the reflection structure characterization operator.

4. The method for low-frequency recovery of seismic data constrained by reflection structures according to claim 3, characterized in that, The objective functional of the reflection structure characterization operator is established in the following manner: ； Where e is the target functional of the reflection structure characterization operator. The original seismic data, The operator characterizing the reflection structure. The number of seismic traces. The number of time samples in the earthquake record. The spatial length of the operator is characterized by the reflection structure. The time length of the operator characterizing the reflection structure.

5. The method for low-frequency recovery of seismic data constrained by reflection structures according to claim 1, characterized in that, The inversion objective functional is established using the following method: ； in, Let the inversion objective functional be... For seismic wavelets, The number of seismic traces. The number of time samples in the earthquake record. The reflection coefficient is denoted as .

6. The method for low-frequency recovery of seismic data constrained by reflection structures according to claim 5, characterized in that, The seismic wavelet was obtained in the following way: The seismic wavelet amplitude spectrum was obtained by fitting the amplitude spectrum of the original seismic data using spectral simulation. The seismic wavelet is obtained by performing a Fourier transform on the amplitude spectrum of the seismic wavelet.

7. The method for low-frequency recovery of seismic data constrained by reflection structures according to claim 6, characterized in that, The sparse structure function of the reflection coefficient is established in the following way: ； in, is the sparse structure function of the reflection coefficient.

8. The method for low-frequency recovery of seismic data constrained by reflection structures according to claim 1, characterized in that, The reverse solution of the objective functional of the reflection coefficient to obtain the reflection coefficient includes: The objective functional of the reflection coefficient is solved in reverse using the iterative weighted least squares method to obtain the reflection coefficient.

9. The method for low-frequency recovery of seismic data constrained by reflection structures according to claim 1, characterized in that, The process of combining the low-frequency seismic data and the high-frequency seismic data to obtain the low-frequency recovered seismic data includes: The low-frequency seismic data and the high-frequency seismic data are weighted and combined in the frequency domain to obtain the combined result. The combined results are transformed to the time domain to obtain the seismic data after low-frequency recovery.

10. A seismic data low-frequency recovery device constrained by a reflection structure, characterized in that, The low-frequency seismic data recovery device constrained by the reflection structure includes: The module for determining the highest frequency to be recovered is used to acquire raw seismic data and determine the highest frequency to be recovered corresponding to the low-frequency seismic data to be recovered based on the raw seismic data. The reflection structure characterization operator determination module is used to determine the reflection structure characterization operator using the original seismic data and the inversion algorithm; The target functional determination module for reflection coefficients is used to construct regularization constraint terms using the reflection coefficients and the reflection structure characterization operator, and to establish the target functional of the reflection coefficients through the regularization constraint terms. Specifically, this includes: establishing the inversion target functional using a convolution model; establishing a sparse structure function of the reflection coefficients using the reflection coefficients as the first regularization constraint term of the inversion target functional; establishing a function of the reflection structure using the reflection structure characterization operator as the second regularization constraint term of the inversion target functional; establishing the target functional of the reflection coefficients using the inversion target functional, the first regularization constraint term, and the second regularization constraint term; and establishing the function of the reflection structure in the following manner: ;in, A function of the reflection structure, Let be a matrix composed of operators characterizing the reflection structure. For high cutoff frequency The matrix formed by the low-pass filter operators, The matrix is ​​composed of the reflection coefficients; the objective functional of the reflection coefficients is established in the following manner: ;in, Let the target functional be the reflection coefficient. Let be the regularization factor of the first regularization constraint term. The regularization factor is the regularization factor for the second regularization constraint term; The inversion objective functional; The sparse structure function of the reflection coefficient; The reflection coefficient determination module is used to solve the target functional of the reflection coefficient in reverse order to obtain the reflection coefficient. The low-frequency seismic data determination module is used to perform low-frequency filtering on the reflection coefficient, with the high cutoff frequency being the highest frequency to be recovered, to obtain the low-frequency seismic data; The high-frequency seismic data determination module is used to perform high-pass filtering on the original seismic data with a low cutoff frequency of the highest frequency to be recovered, to obtain high-frequency seismic data. The combination module is used to combine the low-frequency seismic data and the high-frequency seismic data to obtain the low-frequency recovered seismic data.

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