A high-resolution radon transform method and system based on seismic data frequency band constraint

Abstract

The application discloses a high-resolution Radon transform method and system based on frequency band constraint of seismic data, and comprises the following steps: firstly, performing Radon transform on seismic data to obtain first frequency Radon domain data corresponding to multiple different frequencies; selecting multiple first frequency Radon domain data in a preset frequency band; performing iterative sparse operation on the selected first frequency Radon domain data to obtain a weighted constraint matrix; and performing constraint calculation on the first frequency Radon domain data corresponding to each frequency through the weighted constraint matrix to obtain multiple second frequency Radon domain data. The frequency band constraint method adopted in the application improves resolution, and only performs iterative operation on data in a limited frequency band to obtain a weighted matrix, so that the method has higher calculation efficiency and higher transform domain resolution than other high-resolution algorithms.

Description

Technical Field

[0001] This invention relates to the field of geophysical exploration technology, and in particular to a high-resolution Radon transform method based on frequency band constraints. Background Technology

[0002] The Radon transform is a mathematical transformation method widely used in medical CT imaging and seismic data processing. This transform has shown excellent application results in areas such as the identification and separation of phase axes in seismic data, multiple suppression, seismic data reconstruction, and velocity analysis.

[0003] In 1986, Hampson improved the τ-p transform into the parabolic Radon transform and applied it to the suppression of multiples in seismic data. This method utilizes the time difference between primary and multiple waves to suppress them. Because primary and multiple waves have different velocities, in the CMP gather after dynamic correction, primary waves are essentially corrected to a horizontal phase axis, while the hyperbolic shape of multiple waves is corrected to a parabola. At this point, the parabolic Radon transform can map the seismic data in the time-space domain to points in the Radon domain. The significant difference between primary and multiple waves in the Radon domain improves their seismicity, thus achieving the separation of primary and multiple waves and suppressing the multiple waves. However, since seismic data propagates infinitely through space, the seismic data we obtain can only be sampled within a limited space and time. Furthermore, due to the non-orthogonality of the Radon transform operators, energy leakage occurs during the calculation process in the Radon domain. This causes the energy of primary and multiple waves to not be completely focused at the theoretically predicted location, but rather distributed in a scissor-like divergence pattern around the predicted location. The energy of primary and multiple waves mixes together, severely affecting the suppression effect of multiple waves. Therefore, developing a high-resolution Radon transform method is essential.

[0004] The current theoretical basis for high-resolution Radon transform is the iterative reweighted high-resolution algorithm proposed by Sacchi. This algorithm incorporates Cauchy constraints and adds an iterative process to the calculation of each frequency, using the least-squares solution as the initial value. During the iteration, the weights are shrunk until the required sparsity is achieved. However, this method suffers from reduced resolution when processing sparsely sampled data because the finite acquisition aperture limits the Radon transform resolution, introduces spatial aliasing, and the iteration process cannot indefinitely increase resolution. Furthermore, the computational complexity is high due to the need for iteration at every frequency. Summary of the Invention

[0005] To address at least one of the aforementioned technical problems, this invention provides a high-resolution Radon transform method based on frequency band constraints, thereby solving the problem of cumbersome iterative efficiency while acquiring high-resolution data using Radon transform.

[0006] On the one hand, a high-resolution Radon transform method based on seismic data frequency band constraints is provided, including:

[0007] Radon transform was performed on the seismic data to obtain first-frequency Radon domain data corresponding to multiple different frequencies;

[0008] Select multiple Radon domain data of the first frequency within a preset frequency band;

[0009] The selected first frequency Radon domain data is subjected to iterative sparse operations to obtain a weighted constraint matrix;

[0010] Constraint calculations are performed on each of the first frequency Radon domain data using the weighted constraint matrix to obtain multiple second frequency Radon domain data.

[0011] Preferably, after obtaining multiple second-frequency Radon domain data, the method further includes:

[0012] An inverse Fourier transform is performed on multiple Radon domain data of the second frequency to obtain high-resolution Radon transform domain results.

[0013] Preferably, the step of performing iterative sparse operations on the selected first frequency Radon domain data to obtain a weighted constraint matrix includes:

[0014] Given the identity matrix as the initial weighting matrix, sparse calculation is performed on the first frequency sample point in the preset frequency band to obtain the first calculation result;

[0015] The first calculation result is used to construct a diagonal matrix. Based on the diagonal matrix construction result, iterative sparse calculation is performed on the next frequency sample to obtain the calculation result of the last frequency sample in the preset frequency band.

[0016] The weighted constraint matrix is ​​obtained by constructing a diagonal matrix from the calculation results of the last frequency sample point within the preset frequency band.

[0017] Preferably, the step of performing sparse calculation on the first frequency sample point within the preset frequency band to obtain the first calculation result includes:

[0018] Multiply the initial weighting matrix, the transformation operator, and the conjugate transpose of the transformation operator, add the result of the multiplication to the μ-fold identity matrix, and use the result of the addition as the first intermediate value;

[0019] Multiply the initial weighting matrix, the conjugate transpose of the transformation operator, and the first frequency Radon domain data corresponding to the first frequency sample point in the preset frequency band, and use the result as the second intermediate value.

[0020] Divide the second intermediate value by the first intermediate value to obtain the first calculation result.

[0021] Preferably, the preset frequency band is a frequency band within a preset threshold positive or negative of the main frequency.

[0022] Preferably, the Radon transform is a parabolic Radon transform.

[0023] Secondly, a high-resolution Radon transform system based on seismic data frequency band constraints is provided, the system comprising:

[0024] The data transformation unit is used to perform Radon transformation on seismic data to obtain first-frequency Radon domain data corresponding to multiple different frequencies;

[0025] The data filtering unit is used to select multiple Radon domain data of the first frequency within a preset frequency band;

[0026] The data calculation unit is used to perform iterative sparse operations on the selected first frequency Radon domain data to obtain a weighted constraint matrix;

[0027] The data processing unit is used to perform constraint calculations on each of the first frequency Radon domain data using the weighted constraint matrix to obtain multiple second frequency Radon domain data.

[0028] Preferably, a high-resolution Radon transform system based on seismic data frequency band constraints further includes:

[0029] The data fusion unit is used to perform inverse Fourier transform on multiple second-frequency Radon domain data to obtain high-resolution Radon transform domain results.

[0030] Thirdly, an electronic device is provided, comprising: a processor, a transmitting system, an input system, an output system, and a memory, wherein the memory is used to store computer program code, the computer program code including computer instructions, and when the processor executes the computer instructions, the electronic device executes a high-resolution Radon transform method based on seismic data frequency band constraints.

[0031] Fourthly, a computer-readable storage medium is provided, wherein a computer program is stored therein, the computer program including program instructions, which, when executed by a processor of an electronic device, cause the processor to perform a high-resolution Radon transform method based on seismic data frequency band constraints.

[0032] This invention obtains multiple first-frequency Radon domain data corresponding to different frequencies by performing Radon transform on seismic data; selects multiple first-frequency Radon domain data within a preset frequency band; performs iterative sparse operations on the selected first-frequency Radon domain data to obtain a weighted constraint matrix; and performs constraint calculations on each first-frequency Radon domain data using the weighted constraint matrix to obtain multiple second-frequency Radon domain data. The frequency band constraint method used in this invention improves resolution by performing iterative operations only on data within a limited frequency band to obtain the weighted matrix, making this method more computationally efficient and having higher transform domain resolution than other high-resolution algorithms.

[0033] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention or the background art, the accompanying drawings used in the embodiments of the present invention or the background art will be described below.

[0035] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with this disclosure and, together with the specification, serve to illustrate the technical solutions disclosed in this invention.

[0036] Figure 1 A schematic diagram of a high-resolution Radon transform method based on frequency band constraints provided in an embodiment of the present invention;

[0037] Figure 2 A schematic diagram of simulation data used for multiple wave suppression according to a certain embodiment of the present invention;

[0038] Figure 3 A schematic diagram of the Radon domain result obtained by transforming a conventional least squares algorithm according to a certain embodiment of the present invention;

[0039] Figure 4 A schematic diagram of Radon domain results obtained by transforming a high-resolution algorithm based on low-frequency constraints according to a certain embodiment of the present invention;

[0040] Figure 5A schematic diagram of the Radon domain result obtained by performing a high-resolution Radon transform algorithm according to a certain embodiment of the present invention;

[0041] Figure 6 This is a schematic diagram illustrating the multiple suppression results of Radon domain data obtained by the least squares method, the low-frequency constrained high-resolution algorithm, and the algorithm of this aspect, respectively, according to a certain embodiment of the present invention. Detailed Implementation

[0042] To enable those skilled in the art to better understand the present invention, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0044] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0045] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0046] Furthermore, to better illustrate the present invention, numerous specific details are set forth in the following detailed embodiments. Those skilled in the art will understand that the present invention can be practiced without certain specific details. In some instances, methods, means, elements, and circuits well known to those skilled in the art have not been described in detail in order to highlight the spirit of the invention.

[0047] Currently, the theoretical basis of high-resolution Radon transform is the iterative reweighted high-resolution algorithm proposed by Sacchi. This algorithm incorporates Cauchy constraints and an iterative process for each frequency calculation, using the least-squares solution as the initial value and shrinking the weights during iteration until the required sparsity is achieved. This method suffers from reduced resolution when processing sparsely sampled data because the limited acquisition aperture restricts the Radon transform resolution, introduces spatial aliasing, and the iteration cannot indefinitely increase resolution. Furthermore, the computational workload is significant due to the need for iteration at each frequency. Therefore, this application aims to provide a high-resolution Radon transform method based on seismic data frequency band constraints. This method includes: performing Radon transform on seismic data to obtain multiple first-frequency Radon domain data corresponding to different frequencies; selecting multiple first-frequency Radon domain data within a preset frequency band; performing iterative sparse operations on the selected first-frequency Radon domain data to obtain a weighted constraint matrix; and performing constraint calculations on each first-frequency Radon domain data using the weighted constraint matrix to obtain multiple second-frequency Radon domain data. This avoids the inefficiency caused by cumbersome iterations while acquiring high-resolution data through Radon transform.

[0048] Example 1

[0049] A high-resolution Radon transform method based on seismic data frequency band constraints, reference Figure 1 ,include:

[0050] S100 performs Radon transform on the seismic data to obtain first-frequency Radon domain data corresponding to multiple different frequencies;

[0051] S200, select multiple first-frequency Radon domain data within a preset frequency band;

[0052] S300, the selected first frequency Radon domain data is subjected to iterative sparse operation to obtain the weighted constraint matrix;

[0053] S400 performs constraint calculations on each first-frequency Radon domain data using a weighted constraint matrix to obtain multiple second-frequency Radon domain data.

[0054] It should be noted that the current theoretical basis of high-resolution Radon transform is the iterative reweighted high-resolution algorithm. This algorithm incorporates Cauchy constraints and adds an iterative process to the calculation of each frequency, using the least-squares solution as the initial value. During the iteration, the weights are shrunk until the required sparsity is achieved. This method suffers from reduced resolution when processing sparsely sampled data because the finite acquisition aperture limits the Radon transform resolution, introduces spatial aliasing, and the iteration cannot increase resolution indefinitely. Furthermore, the computational load is substantial because it requires iteration for each frequency.

[0055] In this embodiment, since the weighting matrix is ​​obtained by iterative calculation on data within a limited frequency band, the algorithm is more efficient than other high-resolution algorithms. When the scale of seismic data is very large, it can take into account both the requirements of high resolution and high computational efficiency.

[0056] Preferably, after obtaining multiple second-frequency Radon domain data, the method further includes:

[0057] Inverse Fourier transforms were performed on multiple second-frequency Radon domain data to obtain high-resolution Radon transform domain results.

[0058] Preferably, the selected first-frequency Radon domain data is subjected to iterative sparse operations to obtain a weighted constraint matrix, including:

[0059] Given the identity matrix as the initial weighting matrix, sparse computation is performed on the first frequency sample point in the preset frequency band to obtain the first calculation result;

[0060] A diagonal matrix is ​​constructed from the first calculation result. Based on the diagonal matrix construction result, iterative sparse calculation is performed on the next frequency sample to obtain the calculation result of the last frequency sample in the preset frequency band.

[0061] The calculation results of the last frequency sample within the preset frequency band are used to construct a diagonal matrix to obtain the weighted constraint matrix.

[0062] In this embodiment, let the spatiotemporal seismic data be d(t,x), with time t and offset x (there are nx offsets in total, namely x1, x2, ..., x). nx When performing the Radon transform, the Radon domain data is m(τ,q), with a corresponding time τ and a curvature parameter q (there are nq curvatures in total, namely q1, q2, ..., q). nq If ), then the Radon transform of the parabola can be written as:

[0063] d(t,x)=∫m(τ=t-qx 2 ,q)dq

[0064] Its corresponding frequency domain discrete form is:

[0065]

[0066] In the formula, ω is the angular frequency, and there are N angular frequencies in total, namely ω1, ω2, ω3, ..., ω N M ω This is Radon data in the frequency domain.

[0067] Will Let L be the transformation operator. Then the frequency domain discrete form can be written in operator form:

[0068] D ω =LM ω

[0069] The specific expression for matrix L is as follows:

[0070]

[0071] Let the main frequency of the seismic data be ωp, and the bandwidth d of the weighting matrix be calculated. The sample locations ωp1 and ωp2 corresponding to the frequency domain ωp-d~ωp+d are calculated. Given an initial weighting matrix W0=I, where I is the identity matrix, the first calculation result is obtained for the first frequency sample ωp1 based on the following formula:

[0072] M ωp1 =W0L H (LW0L H +μI) -1 D ωp1

[0073] In the formula, the superscript H represents the conjugate transpose; μ is the damping factor added to make the matrix inversion stable; and D... ωp1 This is the frequency domain seismic data for the first frequency sample point.

[0074] The weighting matrix of the second frequency samples is obtained by constructing a diagonal matrix from the first calculation result:

[0075] W = diag(|M ωp1 |)

[0076] Substituting the weighting matrix into the following formula, we can calculate the result of the frequency sample ωp1+1:

[0077] M ωp1+1 =WL H (LWL H +μI) -1 D ωp1+1

[0078] Substitute the result of ωp1+1 into W=diag(|Mωp1 |), update the weighting matrix, and further calculate the result of the next frequency sample point, until the frequency sample point ωp2 is calculated (i.e., all frequencies within the preset frequency band are iterated), and based on the calculation result of the last frequency sample point and the formula W=diag(|M ωp1 |), thus obtaining a weighted constraint matrix applicable to all frequencies;

[0079] Applying the weighted constraint matrix to the first frequency Radon domain data for each frequency ωi, the second frequency Radon domain data for each sample is calculated:

[0080] M ωi =WL T (LWL T +μI) -1 D ωi

[0081] Get M ω1 M ω2 M ω3 M ωN ;

[0082] The calculation results M for each frequency ω1 M ω2 M ω3 M ωN Perform an inverse Fourier transform to obtain high-resolution Radon transform domain results.

[0083] Preferably, sparse calculation is performed on the first frequency sample within the preset frequency band to obtain a first calculation result, including:

[0084] Multiply the initial weighting matrix, the transformation operator, and the conjugate transpose of the transformation operator, add the result of the multiplication to the μ-fold identity matrix, and use the result of the addition as the first intermediate value;

[0085] Multiply the initial weighting matrix, the conjugate transpose of the transformation operator, and the first frequency Radon domain data corresponding to the first frequency sample point in the preset frequency band, and use the result as the second intermediate value.

[0086] Divide the second intermediate value by the first intermediate value to obtain the first calculation result.

[0087] In this embodiment, the following formula is referenced:

[0088] M ωp1+1 =WL H (LWL H +μI) -1 D ωp1+1

[0089] In the formula, L is the transformation operator, L HLet W be the conjugate transpose of the transformation operator, W be the initial weighting matrix, I be the identity matrix, and D be the... ωp1+1 This is the first frequency Radon domain data for the current frequency sample, where the first intermediate value is LWL. H +μI, the second intermediate value is WL H D ωp1+1 .

[0090] Preferably, the preset frequency band is a frequency band within a preset threshold positive and negative of the main frequency.

[0091] In this embodiment, since the seismic wave energy is strong and the waveform is stable in the main frequency band, it can exert the same constraint effect on all frequencies at the same time when performing sparse operations. The calculation results of the frequency band data near the main frequency of the seismic data are used as the constraint operator, and this constraint operator is applied to the calculation of all frequencies, which improves the resolution of the transform domain and better distinguishes between primary and secondary waves. The frequency band data near the main frequency is the frequency band within the positive and negative preset threshold of the main frequency.

[0092] Preferably, the Radon transform is a parabolic Radon transform.

[0093] In this embodiment, the Radon transform of the seismic data is performed using the parabolic Radon transform.

[0094] It should be noted that the parabolic Radon transform represents seismic data in linear parameter form through line integrals. It can map data on a two-dimensional plane to a coordinate system of linear parameters, and more accurately represent and analyze the characteristics and properties of the data. Compared with the traditional linear Radon transform, the parabolic Radon transform can better suppress interference such as multiple waves in seismic data, thereby extracting purer seismic signal features and information.

[0095] To compare the effectiveness and computational efficiency of this method, refer to Figure 2 , Figure 2 This is a schematic diagram of simulation data used for multi-wave suppression provided in an embodiment. (Reference) Figure 3 , Figure 3 This diagram illustrates the Radon domain result obtained by transforming the conventional least squares algorithm. It shows a clear divergence artifact in the result. (Reference) Figure 4 , Figure 4 This represents the Radon domain result obtained by transforming a high-resolution algorithm based on low-frequency constraints. This result shows a significant improvement in resolution compared to the least squares algorithm. (Reference) Figure 5 , Figure 5This represents the Radon domain result obtained by performing a high-resolution Radon transform algorithm based on this embodiment. The resolution of this result is improved compared to the low-frequency constrained high-resolution algorithm, with fewer artifacts around the focal point and significantly improved resolution in both time and space. (Reference) Figure 6 , Figure 6 Multiple wave suppression was performed on Radon domain data obtained by the least squares method, the low-frequency constrained high-resolution algorithm, and the algorithm presented in this paper. The results of the multiple wave suppression clearly show that the results obtained by the algorithm of this paper and the low-frequency constrained algorithm are very good due to the advantage of high resolution. However, since this algorithm only iterates on a small number of frequency samples to obtain the weighting matrix, the computational efficiency of this algorithm is higher. The computation time of the low-frequency constrained algorithm is 0.19 seconds, while the computation time of this algorithm is 0.10 seconds, which is 47.38% higher.

[0096] Example 2 provides a high-resolution Radon transform system based on seismic data frequency band constraints. The system includes:

[0097] The data transformation unit is used to perform Radon transformation on seismic data to obtain first-frequency Radon domain data corresponding to multiple different frequencies;

[0098] The data filtering unit is used to select multiple first-frequency Radon domain data within a preset frequency band;

[0099] The data computation unit is used to perform iterative sparse operations on the selected first frequency Radon domain data to obtain a weighted constraint matrix.

[0100] The data processing unit is used to perform constraint calculations on each first-frequency Radon domain data using a weighted constraint matrix to obtain multiple second-frequency Radon domain data.

[0101] Preferably, a high-resolution Radon transform system based on seismic data frequency band constraints further includes:

[0102] The data fusion unit is used to perform inverse Fourier transform on multiple second-frequency Radon domain data to obtain high-resolution Radon transform domain results.

[0103] Example 3 provides an electronic device, including: a processor, a transmitting system, an input system, an output system, and a memory. The memory is used to store computer program code, which includes computer instructions. When the processor executes the computer instructions, the electronic device executes a high-resolution Radon transform method based on seismic data frequency band constraints.

[0104] Example 4 provides a computer-readable storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor of an electronic device, cause the processor to execute a high-resolution Radon transform method based on seismic data frequency band constraints.

[0105] This invention provides a high-resolution Radon transform method based on frequency band constraints of seismic data, comprising: performing Radon transform on seismic data to obtain multiple first-frequency Radon domain data corresponding to different frequencies; selecting multiple first-frequency Radon domain data within a preset frequency band; performing iterative sparse operations on the selected first-frequency Radon domain data to obtain a weighted constraint matrix; and performing constraint calculations on each first-frequency Radon domain data using the weighted constraint matrix to obtain multiple second-frequency Radon domain data. This invention achieves higher computational efficiency than other high-resolution algorithms by performing iterative operations only on data within a limited frequency band to obtain the weighted matrix.

[0106] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A high-resolution Radon transform method based on seismic data frequency band constraints, characterized in that, include: Radon transform was performed on the seismic data to obtain first-frequency Radon domain data corresponding to multiple different frequencies; Select multiple Radon domain data of the first frequency within a preset frequency band; The selected first frequency Radon domain data is subjected to iterative sparse operations to obtain a weighted constraint matrix; The weighted constraint matrix is ​​used to perform constraint calculations on each first frequency Radon domain data to obtain multiple second frequency Radon domain data. The step of performing iterative sparse operations on the selected first frequency Radon domain data to obtain a weighted constraint matrix includes: Given the identity matrix as the initial weighting matrix, sparse calculation is performed on the first frequency sample point in the preset frequency band to obtain the first calculation result; The first calculation result is used to construct a diagonal matrix. Based on the diagonal matrix construction result, iterative sparse calculation is performed on the next frequency sample to obtain the calculation result of the last frequency sample in the preset frequency band. The weighted constraint matrix is ​​obtained by constructing a diagonal matrix from the calculation results of the last frequency sample point within the preset frequency band; The step of performing sparse calculation on the first frequency sample point within the preset frequency band to obtain the first calculation result includes: Multiply the initial weighting matrix, the transformation operator, and the conjugate transpose of the transformation operator together, and then multiply the result by... The identity matrices are added together, and the result is used as the first intermediate value. μ is the damping factor added to make the matrix inversion stable. Multiply the initial weighting matrix, the conjugate transpose of the transformation operator, and the first frequency Radon domain data corresponding to the first frequency sample point in the preset frequency band, and use the result of the multiplication as the second intermediate value; Divide the second intermediate value by the first intermediate value to obtain the first calculation result.

2. The high-resolution Radon transform method based on seismic data frequency band constraints according to claim 1, characterized in that, After obtaining multiple second-frequency Radon domain data, the process further includes: An inverse Fourier transform is performed on multiple Radon domain data of the second frequency to obtain high-resolution Radon transform domain results.

3. The high-resolution Radon transform method based on seismic data frequency band constraints according to claim 1, characterized in that, The preset frequency band is the frequency band within the positive and negative preset threshold of the main frequency.

4. The high-resolution Radon transform method based on seismic data frequency band constraints according to claim 1, characterized in that, The Radon transform is a parabolic Radon transform.

5. A high-resolution Radon transform system based on seismic data frequency band constraints, characterized in that, include: The data transformation unit is used to perform Radon transformation on seismic data to obtain first-frequency Radon domain data corresponding to multiple different frequencies; The data filtering unit is used to select multiple Radon domain data of the first frequency within a preset frequency band; The data calculation unit is used to perform iterative sparse operations on the selected first frequency Radon domain data to obtain a weighted constraint matrix; The data processing unit is used to perform constraint calculations on each first frequency Radon domain data using the weighted constraint matrix to obtain multiple second frequency Radon domain data. The step of performing iterative sparse operations on the selected first frequency Radon domain data to obtain a weighted constraint matrix includes: Given the identity matrix as the initial weighting matrix, sparse calculation is performed on the first frequency sample point in the preset frequency band to obtain the first calculation result; The first calculation result is used to construct a diagonal matrix. Based on the diagonal matrix construction result, iterative sparse calculation is performed on the next frequency sample to obtain the calculation result of the last frequency sample in the preset frequency band. The weighted constraint matrix is ​​obtained by constructing a diagonal matrix from the calculation results of the last frequency sample point within the preset frequency band; The step of performing sparse calculation on the first frequency sample point within the preset frequency band to obtain the first calculation result includes: Multiply the initial weighting matrix, the transformation operator, and the conjugate transpose of the transformation operator together, and then multiply the result by... The identity matrices are added together, and the result is used as the first intermediate value. μ is the damping factor added to make the matrix inversion stable. Multiply the initial weighting matrix, the conjugate transpose of the transformation operator, and the first frequency Radon domain data corresponding to the first frequency sample point in the preset frequency band, and use the result of the multiplication as the second intermediate value; Divide the second intermediate value by the first intermediate value to obtain the first calculation result.

6. A high-resolution Radon transform system based on seismic data frequency band constraints according to claim 5, characterized in that, Also includes: The data fusion unit is used to perform inverse Fourier transform on multiple second-frequency Radon domain data to obtain high-resolution Radon transform domain results.

7. An electronic device, characterized in that, include: The electronic device comprises a processor, a transmitting system, an input system, an output system, and a memory, the memory being used to store computer program code, the computer program code including computer instructions, wherein when the processor executes the computer instructions, the electronic device performs the high-resolution Radon transform method based on seismic data frequency band constraints as described in any one of claims 1 to 4.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which includes program instructions that, when executed by a processor of an electronic device, cause the processor to perform the high-resolution Radon transform method based on seismic data frequency band constraints as described in any one of claims 1 to 4.

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