Controllable source surface wave suppression method, device, equipment, medium and product

Abstract

The present application relates to the technical field of geophysical exploration, in particular to a controllable source surface wave suppression method, device, equipment, medium and product, wherein the method comprises: converting original seismic data from time domain to curved wave domain to obtain curved wave transform coefficients; dividing the seismic data in curved wave domain into high-order seismic data and low-order seismic data by using the curved wave transform coefficients, and constructing a projection matrix for projecting the high-order seismic data to the low-order seismic data; constructing a target function in the curved wave domain based on the seismic data, the curved wave transform coefficients and the projection matrix; and solving the target function to obtain seismic data after surface wave suppression; and the surface wave interference of the controllable source can be suppressed.

Description

Technical Field

[0001] This invention relates to the field of geophysical exploration technology, and in particular to a method, apparatus, equipment, medium, and product for controlling surface wave suppression. Background Technology

[0002] Compared to traditional explosive seismic sources, controlled seismic sources offer numerous advantages, including safety, environmental friendliness, high efficiency, and economy. Their acquisition parameters and signal characteristics can be artificially controlled, making them a crucial direction for future seismic exploration. However, due to the surface location of the excitation device in controlled seismic sources, coupled with complex near-surface conditions, deep reservoir burial, and severe surface absorption attenuation, the signal-to-noise ratio of seismic data obtained from controlled seismic sources at the same coordinate location is lower than that from explosive sources. Among these, surface wave interference is the most significant noise in controlled seismic source data. This near-surface coherent noise exhibits characteristics of low frequency, high energy, and large range in seismic records, obscuring effective signals and posing difficulties for subsequent seismic data processing, significantly limiting the current promotion and application of controlled seismic sources. The field faces the technical challenge of severe surface wave interference in controlled seismic sources. Summary of the Invention

[0003] This invention provides a method, apparatus, equipment, medium, and product for controlling surface wave suppression of seismic sources, which solves the technical problem of severe surface wave interference from controllable seismic sources.

[0004] In a first aspect, the present invention provides a controllable source surface wave suppression method, the method comprising: converting the original seismic data from the time domain to the curvelet domain to obtain curvelet transform coefficients; using the curvelet transform coefficients to divide the seismic data in the curvelet domain into high-order seismic data and low-order seismic data, and constructing a projection matrix that projects the high-order seismic data onto the low-order seismic data; constructing an objective function in the curvelet domain based on the seismic data, the curvelet transform coefficients, and the projection matrix; and solving the objective function to obtain the surface wave suppressed seismic data.

[0005] In some embodiments, high-order seismic data includes effective signals that are unaffected by surface wave noise.

[0006] In some embodiments, in the step of constructing an objective function in the curve wave domain based on seismic data, curve wave transform coefficients, and projection matrix, the objective function includes the sum of seismic data terms and inversion regularization operator terms.

[0007] In some embodiments, a seismic data item includes: the second norm of the difference between the seismic data and the curve transform coefficient matrix and the projection matrix.

[0008] In some embodiments, the inversion regularization operator term includes: the product of the inversion regularization operator and the sparse constraint on the noise in the curve wave domain.

[0009] In some embodiments, the step of solving the objective function to obtain surface wave suppressed seismic data includes: solving the objective function to obtain the sparse terms of the effective signal in the curve wave domain; and performing an inverse curve wave transform on the sparse terms of the effective signal in the curve wave domain to obtain the surface wave suppressed seismic data.

[0010] Secondly, the present invention provides a controllable source surface wave suppression device, comprising: a transformation module for converting the original seismic data from the time domain to the curvelet domain to obtain curvelet transform coefficients; a projection module for using the curvelet transform coefficients to divide the seismic data in the curvelet domain into high-order seismic data and low-order seismic data, and constructing a projection matrix to project the high-order seismic data onto the low-order seismic data; an objective function module for constructing an objective function in the curvelet domain based on the seismic data, the curvelet transform coefficients, and the projection matrix; and a solution module for solving the objective function to obtain the surface wave suppressed seismic data.

[0011] Thirdly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any of the controllable source surface wave suppression methods described above.

[0012] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the controllable source surface wave suppression methods described above.

[0013] Fifthly, the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the steps of any of the controllable source surface wave suppression methods described above.

[0014] This invention provides a method, apparatus, device, medium, and product for suppressing surface waves from a controllable seismic source. The method includes: converting raw seismic data from the time domain to the curvelet domain to obtain curvelet transform coefficients; using the curvelet transform coefficients to divide the seismic data in the curvelet domain into high-order and low-order seismic data, and constructing a projection matrix that projects the high-order seismic data onto the low-order seismic data; constructing an objective function in the curvelet domain based on the seismic data, curvelet transform coefficients, and projection matrix; solving the objective function to obtain the surface wave suppressed seismic data; and being able to suppress surface wave interference from a controllable seismic source. Attached Figure Description

[0015] The invention will now be described in more detail with reference to embodiments and the accompanying drawings:

[0016] Figure 1 This is a schematic flowchart of a controllable source surface wave suppression method provided in an embodiment of this application;

[0017] Figure 2This is a schematic diagram of the structure of a controllable seismic source surface wave suppression device provided in an embodiment of this application;

[0018] Figure 3 This is a schematic diagram illustrating the principle of a controllable source surface wave suppression method provided in an embodiment of this application;

[0019] Figure 4 This is a schematic diagram of the curvelet domain transformation result of model data provided in an embodiment of this application;

[0020] Figure 5 This is a schematic diagram of a model data test result provided in an embodiment of this application;

[0021] Figure 6 This is a schematic diagram of actual seismic data test results provided in an embodiment of this application.

[0022] In the accompanying drawings, the same parts are referred to by the same reference numerals, and the drawings are not drawn to scale. Detailed Implementation

[0023] To enable those skilled in the art to better understand the present invention and to fully understand and implement the process of how the present invention uses technical means to solve technical problems and achieve corresponding technical effects, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. The embodiments of the present invention and the various features therein can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0025] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0026] Compared to traditional explosive seismic sources, controlled seismic sources offer numerous advantages, including safety, environmental friendliness, high efficiency, and economy. Their acquisition parameters and signal characteristics can be artificially controlled, making them a crucial direction for future seismic exploration. However, due to the surface location of the excitation device in controlled seismic sources, coupled with complex near-surface conditions, deep reservoir burial, and severe surface absorption attenuation, the signal-to-noise ratio of seismic data obtained from controlled seismic sources at the same coordinate location is lower than that from explosive sources. Among these, surface wave interference is the most significant noise in controlled seismic source data. This near-surface coherent noise exhibits characteristics of low frequency, high energy, and large range in seismic records, obscuring effective signals and posing difficulties for subsequent seismic data processing, significantly limiting the current promotion and application of controlled seismic sources. The field faces the technical challenge of severe surface wave interference in controlled seismic sources.

[0027] The technical solution of this application will be described below with reference to specific embodiments.

[0028] Example 1

[0029] Figure 1 This is a flowchart illustrating a controllable source surface wave suppression method provided in an embodiment of this application, as shown below. Figure 1 As shown in the technical solution of this embodiment, a controllable source surface wave suppression method is provided. The method includes: converting the original seismic data from the time domain to the curvelet domain to obtain curvelet transform coefficients; using the curvelet transform coefficients to divide the seismic data in the curvelet domain into high-order seismic data and low-order seismic data, and constructing a projection matrix that projects the high-order seismic data onto the low-order seismic data; constructing an objective function in the curvelet domain based on the seismic data, the curvelet transform coefficients, and the projection matrix; and solving the objective function to obtain the seismic data after surface wave suppression.

[0030] The technical problem to be solved in this embodiment is how to suppress surface wave interference from controlled seismic sources. In the field of geophysical exploration, although controlled seismic sources have many advantages, due to the complex environmental factors such as the location of the excitation device on or near the surface, the signal-to-noise ratio in its seismic data is low. Surface wave interference, a near-surface coherent noise, exhibits characteristics of low frequency, high energy, and large range in seismic records, causing the effective signal to be obliterated and bringing difficulties to subsequent seismic data processing. There is a technical problem in this field that surface wave interference from controlled seismic sources is severe and difficult to suppress effectively.

[0031] In this embodiment, the original seismic data can first be considered as the sum of effective signals, surface wave noise, and random noise. This original seismic data refers to the basic data containing various information actually acquired in this embodiment. Then, the original seismic data is transformed from the time domain to the curvelet domain using the curvelet transform, thereby obtaining curvelet transform coefficients. The curvelet transform can transform data according to certain rules, facilitating subsequent processing. Next, the curvelet transform coefficients are used to divide the seismic data in the curvelet domain into high-order and low-order seismic data. The high-order seismic data consists of the effective signal portion unaffected by surface wave noise. A projection matrix is ​​then constructed to project the high-order seismic data onto the low-order seismic data. This projection matrix is ​​a diagonal matrix with elements of 1 or 0, and it determines which data should be projected and how based on preset conditions, thereby achieving the reconstruction of the low-order seismic signal. Finally, based on the previously obtained seismic data, curvelet transform coefficients, and projection matrix, an objective function is constructed in the curvelet domain, transforming the surface wave noise suppression problem into a problem of solving the objective function.

[0032] The technical solution in this embodiment first uses curvelet transform to convert the data, allowing it to be presented in the curvelet domain, which is easier to process. Then, by dividing the high-order and low-order seismic data, projection reconstruction is performed using a projection matrix, allowing the effective signals of the low-order seismic waves to be better integrated. The process of constructing and solving the objective function further quantifies and makes the surface wave suppression problem operable. Actual model data and actual seismic data tests show that the method is highly effective in suppressing surface wave interference. For example, in model data testing, model data that originally had significant surface wave interference showed significant suppression of surface waves after processing with this method, while the effective signals were protected to the greatest extent and remained undamaged, greatly improving the quality of the seismic data and providing a good foundation for subsequent seismic data analysis. In actual seismic data testing, strong amplitude surface wave noise in the actual single-shot records of the selected controllable source was also effectively suppressed, and the effective signals could be well recovered and reconstructed.

[0033] Example 2

[0034] Based on the above embodiments, high-order seismic data includes effective signals that are unaffected by surface wave noise.

[0035] The technical problem this embodiment aims to solve is how to distinguish between high-order and low-order seismic data. In the process of controlled-source surface wave suppression, to accurately perform subsequent processing, such as recovering effective low-frequency signals through projection, it is necessary to reasonably classify the seismic data in the curve wave domain and distinguish between high-order and low-order seismic data. There is a technical problem in this field that makes it difficult to accurately distinguish between high-order and low-order seismic data.

[0036] In this embodiment, the original seismic data contains multiple components. After converting it to the curvelet domain using the curvelet transform to obtain curvelet transform coefficients, the seismic data in the curvelet domain is divided based on these coefficients. These curvelet transform coefficients actually carry various characteristic information of the seismic data in the curvelet domain and are an important basis for classification. Higher-order seismic data are defined as effective signals that are not affected by surface wave noise. Through analysis of the curvelet transform coefficients, the seismic data in the curvelet domain can be clearly divided into higher-order and lower-order seismic data.

[0037] The technical solution in this embodiment distinguishes between high-order and low-order seismic data by defining high-order seismic data as including effective signals unaffected by surface wave noise, making the entire surface wave suppression process more scientific and orderly. In practical applications, such as when processing specific model data, after converting the model data to the curve domain, this solution can accurately distinguish seismic data corresponding to different orders and angles. The effective signal portions unaffected by surface wave noise in the high-order domain are accurately identified, providing a reliable prerequisite for subsequently projecting high-order seismic data onto low-order data to reconstruct low-order seismic signals. Similarly, in actual seismic data processing, accurate distinction ensures that subsequent operations revolve around accurately classified seismic data, avoiding processing errors caused by unclear data differentiation. Ultimately, this makes the surface wave suppression process more efficient and accurate, effectively protecting the effective signals while suppressing surface waves, improving the overall effect of surface wave suppression, and greatly enhancing the quality and usability of seismic data.

[0038] Example 3

[0039] Based on the above embodiments, in the step of constructing an objective function in the curve wave domain based on seismic data, curve wave transform coefficients, and projection matrix, the objective function includes the sum of seismic data terms and inversion regularization operator terms.

[0040] The technical problem to be solved in this embodiment is how to construct the objective function. In the controlled-source surface wave suppression method, in order to accurately solve the surface wave suppression problem through a reasonable model and to express this complex physical process in terms of an expression, constructing a suitable objective function is the key. There is a technical problem in the field that it is difficult to construct a scientific and reasonable objective function to effectively solve the surface wave suppression problem.

[0041] In this embodiment, an objective function is constructed based on the obtained seismic data, curvelet transform coefficients, and the constructed projection matrix. This objective function comprises the sum of a seismic data term and an inversion regularization operator term. The seismic data term is determined by calculating the second norm of the difference between the seismic data, the curvelet transform coefficient matrix, and the projection matrix. The second norm is a measure of vector size, reflecting the difference between the current data and the desired ideal data (i.e., the effective data after removing surface wave noise). The inversion regularization operator term is the product of an inversion regularization operator and a sparse constraint on the noise in the curvelet domain. The inversion regularization operator is a parameter used to control the stability and rationality of the solution during the inversion process, while the sparse constraint is a restriction on the noise in the curvelet domain, ensuring that the noise is processed as desired in this embodiment. By adding these two carefully constructed parts, the objective function is formed, transforming the practical physical problem of surface wave suppression into a solvable objective function optimization problem.

[0042] The technical solution of this embodiment, by constructing an objective function, concretizes the originally abstract and complex surface wave suppression problem into a solvable form. When testing model data, based on the constructed objective function, the surface wave noise in the model data can be effectively suppressed through the subsequent solution process. Because the terms in the objective function accurately reflect the actual situation of the data and the processing effect expected in this embodiment, the solution process guides the data towards suppressing surface waves while protecting the effective signal. The same applies to actual seismic data processing. Through this objective function, the surface wave suppression problem in actual single-shot records and other data is quantified, and then processed using corresponding solution algorithms. The final result is that strong amplitude surface wave noise in single-shot records is significantly suppressed, while the effective signal is well recovered and reconstructed. This demonstrates that the constructed objective function is feasible, providing solid support for the entire controlled-source surface wave suppression method, greatly improving the effectiveness and fidelity of surface wave suppression, and allowing seismic data to better serve subsequent exploration and other work.

[0043] Example 4

[0044] Based on the above embodiments, the seismic data item includes: the second norm of the difference between the seismic data and the curve transform coefficient matrix and the projection matrix.

[0045] The technical problem to be solved in this embodiment is how to construct the seismic data term in the objective function. When constructing an objective function to solve the controllable source surface wave suppression problem, the accuracy and rationality of the construction of the seismic data term directly affects whether the entire objective function can accurately reflect the actual situation and effectively guide subsequent solution operations. There is a technical problem in the art of accurately constructing the seismic data term in the objective function.

[0046] In the technical solution of this embodiment, the seismic data item in the objective function is constructed by calculating the second norm of the difference between the seismic data and the curve transform coefficient matrix and the projection matrix.

[0047] ||dc T Pc|| 2

[0048] Among them, c T It is the adjoint matrix of the curve coefficient matrix.

[0049] The seismic data here refers to the raw data actually acquired in this embodiment, containing various components such as effective signals, surface wave noise, and random noise. It is the foundation of the entire processing. The curvelet transform coefficient matrix is ​​obtained by transforming the raw seismic data into the curvelet domain through the positive curvelet transform. It carries the characteristic information of the data in the curvelet domain and is a key basis for subsequent processing. The projection matrix is ​​a tool used to project high-order seismic data onto low-order seismic data, with a clearly defined element composition and function. By calculating the difference between these three, and then taking their second norm, the second norm can comprehensively measure the magnitude of this difference. To a certain extent, it reflects the deviation between the current actual data and the data expected in this embodiment after processing (such as removing the influence of surface wave noise), thereby constructing accurate seismic data terms so that the objective function can better fit the actual surface wave suppression requirements.

[0050] The technical solution in this embodiment, through this meticulous construction of seismic data terms, makes the objective function more accurate in reflecting the actual surface wave suppression situation. When testing model data, because the seismic data terms accurately consider the relationship between the actual data and the relevant processing matrix, the subsequent application of the objective function to suppress surface waves allows the surface wave noise in the model data to be suppressed in a way that better matches actual expectations. For example, the effective signal portion that was originally masked by surface wave noise gradually becomes clearer, and the damage to the effective signal during suppression is minimal. This is because the seismic data terms reasonably guide the solution direction, leading the entire processing towards removing surface wave noise and preserving the effective signal. This is also true in actual seismic data processing. For actual single-shot records and other data, this well-constructed seismic data terms allow the objective function to better target the characteristics of the actual data during application, resulting in significant suppression of surface wave noise in the final single-shot records, and excellent recovery and reconstruction of the effective signal. This greatly improves the quality and effect of surface wave suppression, providing a higher quality data foundation for further analysis and utilization of seismic data.

[0051] Example 5

[0052] Based on the above embodiments, the inversion regularization operator term includes: the product of the inversion regularization operator and the sparse constraint on the noise in the curve wave domain.

[0053] The technical problem to be solved in this embodiment is how to construct the inversion regularization operator term in the objective function. When constructing the objective function for controlled-source surface wave suppression, the inversion regularization operator term plays a key role in controlling the stability of the solution process and ensuring the rationality of the final result. Whether its construction is appropriate directly affects the overall surface wave suppression effect. There is a technical problem in the art of properly constructing the inversion regularization operator term in the objective function.

[0054] In the technical solution of this embodiment, the inversion regularization operator term in the objective function is composed of the product of the inversion regularization operator and the sparse constraint on the noise in the curve wave domain.

[0055] μ||c||2

[0056] Where μ is the inversion regularization operator, and ||c||2 represents the sparse constraint on the noise in the curve wave domain.

[0057] The inversion regularization operator is a crucial parameter in the inversion solution process. It regulates the solution process, preventing overfitting or unreasonable solutions, and ensuring that the final result is consistent with actual physical meaning and is stable and reliable. The sparsity constraint on noise in the curvelet domain is based on the unique distribution characteristics of noise in this domain. By setting such a constraint, noise can be processed in the manner desired in this embodiment, such as making it as sparse as possible to reduce its interference with the effective signal. Multiplying these two factors to construct the inversion regularization operator term allows for reasonable control over the solution process and noise processing within the objective function. This ensures that the entire objective function can balance accuracy and stability in subsequent solutions, better serving the goal of surface wave suppression.

[0058] The technical solution in this embodiment provides strong support for the effective application of the objective function by rationally constructing an inversion regularization operator. When processing model data, the inversion regularization operator accurately controls the solution process and noise handling, preventing damage to the effective signal due to instability or improper noise processing during the objective function's surface wave suppression process. For example, surface wave noise in the model data can be steadily suppressed while the effective signal is well preserved, making the entire processing more scientific and reasonable. In processing actual seismic data, such as single-shot records, this well-constructed inversion regularization operator allows the objective function to better handle complex real-world situations, ensuring the reliability of the final surface wave suppression result. Specifically, strong amplitude surface wave noise in single-shot records is significantly suppressed, and the effective signal is well recovered and reconstructed, effectively improving the overall quality and fidelity of surface wave suppression and providing high-quality data support for subsequent seismic exploration work.

[0059] Example 6

[0060] Based on the above embodiments, the steps of solving the objective function to obtain the surface wave suppressed seismic data include: solving the objective function to obtain the sparse terms of the effective signal in the curve wave domain; and performing an inverse curve wave transform on the sparse terms of the effective signal in the curve wave domain to obtain the surface wave suppressed seismic data.

[0061] The technical problem to be solved in this embodiment is how to obtain surface wave suppressed seismic data based on the objective function. In the controlled-source surface wave suppression method, constructing the objective function is only the first step. How to effectively solve the objective function and convert the solution back to the time domain to obtain the finally usable surface wave suppressed seismic data is the key link in realizing the entire surface wave suppression. There is a technical problem in the art that it is difficult to accurately obtain surface wave suppressed seismic data through the objective function.

[0062] In this embodiment, the objective function must first be solved. This solution is not straightforward but requires the use of algorithms, such as iterative inversion to perform least-squares calculations on the objective function, ultimately yielding the sparse terms of the effective signal in the curvelet domain. Iterative inversion is a method that gradually approximates the optimal solution. By continuously adjusting parameters and calculations, the result becomes increasingly closer to the sparse terms that accurately reflect the effective signal in the curvelet domain, as desired in this embodiment. After obtaining the sparse terms of the effective signal in the curvelet domain, an inverse curvelet transform is performed. This inverse curvelet transform corresponds to the previous forward curvelet transform and converts the data in the curvelet domain back to the time domain, thus obtaining surface wave suppressed seismic data. This is the seismic data ultimately desired in this embodiment, which removes surface wave noise interference and preserves the effective signal well, achieving the conversion from curvelet domain processing results to usable time-domain seismic data.

[0063] The technical solution in this embodiment, by first scientifically and rationally solving the objective function and then using inverse curvelet transform for domain transformation, successfully obtained surface wave suppressed seismic data. When testing the model data, following this process, after solving the objective function and performing the inverse transform, the surface wave suppressed model data showed significant effectiveness in removing surface wave noise. The obvious surface wave interference in the original model data disappeared, replaced by clear and effective signals. Moreover, the integrity of the effective signals was well guaranteed during this process, with no loss or damage due to processing operations. This greatly improved the quality of the model data, making it more conducive to subsequent analysis and research based on the model data. In actual seismic data processing, this solution can also accurately obtain surface wave suppressed seismic data from actual single-shot records. Strong amplitude surface wave noise in single-shot records is effectively suppressed, and effective signals can be well recovered and reconstructed, providing a high-quality data foundation for subsequent seismic exploration, geological structure analysis, and other practical work. This effectively solves the key problem of transforming the objective function into usable surface wave suppressed seismic data.

[0064] Example 7

[0065] Figure 2 This is a schematic diagram of the structure of a controllable seismic source surface wave suppression device provided in an embodiment of this application, as shown below. Figure 2As shown in the technical solution of this embodiment, a controllable source surface wave suppression device is provided. The device includes: a transformation module for converting the original seismic data from the time domain to the curve wave domain to obtain curve wave transformation coefficients; a projection module for using the curve wave transformation coefficients to divide the seismic data in the curve wave domain into high-order seismic data and low-order seismic data, and constructing a projection matrix to project the high-order seismic data onto the low-order seismic data; an objective function module for constructing an objective function in the curve wave domain based on the seismic data, curve wave transformation coefficients, and projection matrix; and a solution module for solving the objective function to obtain the seismic data after surface wave suppression.

[0066] The technical problem to be solved in this embodiment is how to suppress surface wave interference from controlled seismic sources. In the field of geophysical exploration, although controlled seismic sources have many advantages, due to the complex environmental factors such as the location of the excitation device on or near the surface, the signal-to-noise ratio in its seismic data is low. Surface wave interference, a near-surface coherent noise, exhibits characteristics of low frequency, high energy, and large range in seismic records, causing the effective signal to be obliterated and bringing difficulties to subsequent seismic data processing. There is a technical problem in this field that surface wave interference from controlled seismic sources is severe and difficult to suppress effectively.

[0067] In this embodiment, the original seismic data can first be considered as the sum of effective signals, surface wave noise, and random noise. This original seismic data refers to the basic data containing various information actually acquired in this embodiment. Then, the original seismic data is transformed from the time domain to the curvelet domain using the curvelet transform, thereby obtaining curvelet transform coefficients. The curvelet transform can transform data according to certain rules, facilitating subsequent processing. Next, the curvelet transform coefficients are used to divide the seismic data in the curvelet domain into high-order and low-order seismic data. The high-order seismic data consists of the effective signal portion unaffected by surface wave noise. A projection matrix is ​​then constructed to project the high-order seismic data onto the low-order seismic data. This projection matrix is ​​a diagonal matrix with elements of 1 or 0, and it determines which data should be projected and how based on preset conditions, thereby achieving the reconstruction of the low-order seismic signal. Finally, based on the previously obtained seismic data, curvelet transform coefficients, and projection matrix, an objective function is constructed in the curvelet domain, transforming the surface wave noise suppression problem into a problem of solving the objective function.

[0068] The technical solution in this embodiment first uses curvelet transform to convert the data, allowing it to be presented in the curvelet domain, which is easier to process. Then, by dividing the high-order and low-order seismic data, projection reconstruction is performed using a projection matrix, allowing the effective signals of the low order to be better integrated. The process of constructing and solving the objective function further quantifies and makes the surface wave suppression problem operable. Actual model data and actual seismic data tests show that it is highly effective in suppressing surface wave interference. For example, in model data testing, model data that originally had significant surface wave interference showed significant suppression of surface waves after processing by this device, while the effective signals were protected to the greatest extent and remained undamaged, greatly improving the quality of the seismic data and providing a good foundation for subsequent seismic data analysis. In actual seismic data testing, strong amplitude surface wave noise in the actual single-shot records of the selected controllable source was also effectively suppressed, and the effective signals could be well recovered and reconstructed.

[0069] Based on the above embodiments, high-order seismic data includes effective signals that are unaffected by surface wave noise.

[0070] The technical problem this embodiment aims to solve is how to distinguish between high-order and low-order seismic data. In the process of controlled-source surface wave suppression, to accurately perform subsequent processing, such as recovering effective low-frequency signals through projection, it is necessary to reasonably classify the seismic data in the curve wave domain and distinguish between high-order and low-order seismic data. There is a technical problem in this field that makes it difficult to accurately distinguish between high-order and low-order seismic data.

[0071] In this embodiment, the original seismic data contains multiple components. After converting it to the curvelet domain using the curvelet transform to obtain curvelet transform coefficients, the seismic data in the curvelet domain is divided based on these coefficients. These curvelet transform coefficients actually carry various characteristic information of the seismic data in the curvelet domain and are an important basis for classification. Higher-order seismic data are defined as effective signals that are not affected by surface wave noise. Through analysis of the curvelet transform coefficients, the seismic data in the curvelet domain can be clearly divided into higher-order and lower-order seismic data.

[0072] The technical solution in this embodiment distinguishes between high-order and low-order seismic data by defining high-order seismic data as including effective signals unaffected by surface wave noise, making the entire surface wave suppression process more scientific and orderly. In practical applications, such as when processing specific model data, after converting the model data to the curve domain, this solution can accurately distinguish seismic data corresponding to different orders and angles. The effective signal portions unaffected by surface wave noise in the high-order domain are accurately identified, providing a reliable prerequisite for subsequently projecting high-order seismic data onto low-order data to reconstruct low-order seismic signals. Similarly, in actual seismic data processing, accurate distinction ensures that subsequent operations revolve around accurately classified seismic data, avoiding processing errors caused by unclear data differentiation. Ultimately, this makes the surface wave suppression process more efficient and accurate, effectively protecting the effective signals while suppressing surface waves, improving the overall effect of surface wave suppression, and greatly enhancing the quality and usability of seismic data.

[0073] Based on the above embodiments, in the step of constructing an objective function in the curve wave domain based on seismic data, curve wave transform coefficients, and projection matrix, the objective function includes the sum of seismic data terms and inversion regularization operator terms.

[0074] The technical problem to be solved in this embodiment is how to construct the objective function. In a controllable source surface wave suppression device, in order to accurately solve the surface wave suppression problem through a reasonable model and to present this complex physical process with an expression, constructing a suitable objective function is the key. There is a technical problem in the art that it is difficult to construct a scientific and reasonable objective function to effectively solve the surface wave suppression problem.

[0075] In this embodiment, an objective function is constructed based on the obtained seismic data, curvelet transform coefficients, and the constructed projection matrix. This objective function comprises the sum of a seismic data term and an inversion regularization operator term. The seismic data term is determined by calculating the second norm of the difference between the seismic data, the curvelet transform coefficient matrix, and the projection matrix. The second norm is a measure of vector size, reflecting the difference between the current data and the desired ideal data (i.e., the effective data after removing surface wave noise). The inversion regularization operator term is the product of an inversion regularization operator and a sparse constraint on the noise in the curvelet domain. The inversion regularization operator is a parameter used to control the stability and rationality of the solution during the inversion process, while the sparse constraint is a restriction on the noise in the curvelet domain, ensuring that the noise is processed as desired in this embodiment. By adding these two carefully constructed parts, the objective function is formed, transforming the practical physical problem of surface wave suppression into a solvable objective function optimization problem.

[0076] The technical solution of this embodiment, by constructing an objective function, concretizes the originally abstract and complex surface wave suppression problem into a solvable form. When testing model data, based on the constructed objective function, the surface wave noise in the model data can be effectively suppressed through the subsequent solution process. Because the terms in the objective function accurately reflect the actual situation of the data and the processing effect expected in this embodiment, the solution process guides the data towards suppressing surface waves while protecting the effective signal. The same applies to actual seismic data processing. Through this objective function, the surface wave suppression problem in actual single-shot records and other data is quantified, and then processed using corresponding solution algorithms. The final result is that strong amplitude surface wave noise in single-shot records is significantly suppressed, while the effective signal is well recovered and reconstructed. This demonstrates that the constructed objective function is feasible, providing solid support for the entire controlled-source surface wave suppression device, greatly improving the effectiveness and fidelity of surface wave suppression, and allowing seismic data to better serve subsequent exploration and other work.

[0077] Based on the above embodiments, the seismic data item includes: the second norm of the difference between the seismic data and the curve transform coefficient matrix and the projection matrix.

[0078] The technical problem to be solved in this embodiment is how to construct the seismic data term in the objective function. When constructing an objective function to solve the controllable source surface wave suppression problem, the accuracy and rationality of the construction of the seismic data term directly affects whether the entire objective function can accurately reflect the actual situation and effectively guide subsequent solution operations. There is a technical problem in the art of accurately constructing the seismic data term in the objective function.

[0079] In the technical solution of this embodiment, the seismic data item in the objective function is constructed by calculating the second norm of the difference between the seismic data and the curve transform coefficient matrix and the projection matrix.

[0080] ||dc T Pc|| 2

[0081] Among them, c T It is the adjoint matrix of the curve coefficient matrix.

[0082] The seismic data here refers to the raw data actually acquired in this embodiment, containing various components such as effective signals, surface wave noise, and random noise. It is the foundation of the entire processing. The curvelet transform coefficient matrix is ​​obtained by transforming the raw seismic data into the curvelet domain through the positive curvelet transform. It carries the characteristic information of the data in the curvelet domain and is a key basis for subsequent processing. The projection matrix is ​​a tool used to project high-order seismic data onto low-order seismic data, with a clearly defined element composition and function. By calculating the difference between these three, and then taking their second norm, the second norm can comprehensively measure the magnitude of this difference. To a certain extent, it reflects the deviation between the current actual data and the data expected in this embodiment after processing (such as removing the influence of surface wave noise), thereby constructing accurate seismic data terms so that the objective function can better fit the actual surface wave suppression requirements.

[0083] The technical solution in this embodiment, through this meticulous construction of seismic data terms, makes the objective function more accurate in reflecting the actual surface wave suppression situation. When testing model data, because the seismic data terms accurately consider the relationship between the actual data and the relevant processing matrix, the subsequent application of the objective function to suppress surface waves allows the surface wave noise in the model data to be suppressed in a way that better matches actual expectations. For example, the effective signal portion that was originally masked by surface wave noise gradually becomes clearer, and the damage to the effective signal during suppression is minimal. This is because the seismic data terms reasonably guide the solution direction, leading the entire processing towards removing surface wave noise and preserving the effective signal. This is also true in actual seismic data processing. For actual single-shot records and other data, this well-constructed seismic data terms allow the objective function to better target the characteristics of the actual data during application, resulting in significant suppression of surface wave noise in the final single-shot records, and excellent recovery and reconstruction of the effective signal. This greatly improves the quality and effect of surface wave suppression, providing a higher quality data foundation for further analysis and utilization of seismic data.

[0084] Based on the above embodiments, the inversion regularization operator term includes: the product of the inversion regularization operator and the sparse constraint on the noise in the curve wave domain.

[0085] The technical problem to be solved in this embodiment is how to construct the inversion regularization operator term in the objective function. When constructing the objective function for controlled-source surface wave suppression, the inversion regularization operator term plays a key role in controlling the stability of the solution process and ensuring the rationality of the final result. Whether its construction is appropriate directly affects the overall surface wave suppression effect. There is a technical problem in the art of properly constructing the inversion regularization operator term in the objective function.

[0086] In the technical solution of this embodiment, the inversion regularization operator term in the objective function is composed of the product of the inversion regularization operator and the sparse constraint on the noise in the curve wave domain.

[0087] μ||c||2

[0088] Where μ is the inversion regularization operator, and ||c||2 represents the sparse constraint on the noise in the curve wave domain.

[0089] The inversion regularization operator is a crucial parameter in the inversion solution process. It regulates the solution process, preventing overfitting or unreasonable solutions, and ensuring that the final result is consistent with actual physical meaning and is stable and reliable. The sparsity constraint on noise in the curvelet domain is based on the unique distribution characteristics of noise in this domain. By setting such a constraint, noise can be processed in the manner desired in this embodiment, such as making it as sparse as possible to reduce its interference with the effective signal. Multiplying these two factors to construct the inversion regularization operator term allows for reasonable control over the solution process and noise processing within the objective function. This ensures that the entire objective function can balance accuracy and stability in subsequent solutions, better serving the goal of surface wave suppression.

[0090] The technical solution in this embodiment provides strong support for the effective application of the objective function by rationally constructing an inversion regularization operator. When processing model data, the inversion regularization operator accurately controls the solution process and noise handling, preventing damage to the effective signal due to instability or improper noise processing during the objective function's surface wave suppression process. For example, surface wave noise in the model data can be steadily suppressed while the effective signal is well preserved, making the entire processing more scientific and reasonable. In processing actual seismic data, such as single-shot records, this well-constructed inversion regularization operator allows the objective function to better handle complex real-world situations, ensuring the reliability of the final surface wave suppression result. Specifically, strong amplitude surface wave noise in single-shot records is significantly suppressed, and the effective signal is well recovered and reconstructed, effectively improving the overall quality and fidelity of surface wave suppression and providing high-quality data support for subsequent seismic exploration work.

[0091] Based on the above embodiments, the steps of solving the objective function to obtain the surface wave suppressed seismic data include: solving the objective function to obtain the sparse terms of the effective signal in the curve wave domain; and performing an inverse curve wave transform on the sparse terms of the effective signal in the curve wave domain to obtain the surface wave suppressed seismic data.

[0092] The technical problem to be solved in this embodiment is how to obtain surface wave suppressed seismic data based on the objective function. In a controllable source surface wave suppression device, constructing the objective function is only the first step. How to effectively solve the objective function and convert the solution back to the time domain to obtain the finally usable surface wave suppressed seismic data is the key link in realizing the entire surface wave suppression. There is a technical problem in the art that it is difficult to accurately obtain surface wave suppressed seismic data through the objective function.

[0093] In this embodiment, the objective function must first be solved. This solution is not straightforward but requires the use of algorithms, such as iterative inversion to perform least-squares calculations on the objective function, ultimately yielding the sparse terms of the effective signal in the curvelet domain. Iterative inversion here is a device that gradually approximates the optimal solution. By continuously adjusting parameters and performing calculations, the result becomes increasingly closer to the sparse terms that accurately reflect the effective signal in the curvelet domain, as desired in this embodiment. After obtaining the sparse terms of the effective signal in the curvelet domain, an inverse curvelet transform is performed. This inverse curvelet transform corresponds to the previous forward curvelet transform and converts the data in the curvelet domain back to the time domain, thus obtaining surface wave suppressed seismic data. This is the seismic data ultimately desired in this embodiment, which removes surface wave noise interference and preserves the effective signal well, achieving the conversion from curvelet domain processing results to usable time-domain seismic data.

[0094] The technical solution in this embodiment, by first scientifically and rationally solving the objective function and then using inverse curvelet transform for domain transformation, successfully obtained surface wave suppressed seismic data. When testing the model data, following this process, after solving the objective function and performing the inverse transform, the surface wave suppressed model data showed significant effectiveness in removing surface wave noise. The obvious surface wave interference in the original model data disappeared, replaced by clear and effective signals. Moreover, the integrity of the effective signals was well guaranteed during this process, with no loss or damage due to processing operations. This greatly improved the quality of the model data, making it more conducive to subsequent analysis and research based on the model data. In actual seismic data processing, this solution can also accurately obtain surface wave suppressed seismic data from actual single-shot records. Strong amplitude surface wave noise in single-shot records is effectively suppressed, and effective signals can be well recovered and reconstructed, providing a high-quality data foundation for subsequent seismic exploration, geological structure analysis, and other practical work. This effectively solves the key problem of transforming the objective function into usable surface wave suppressed seismic data.

[0095] Example 8

[0096] In the technical solution of this embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the steps of any of the controllable source surface wave suppression methods described in the above embodiments.

[0097] In the technical solution of this embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of any of the controllable source surface wave suppression methods described in the above embodiments.

[0098] In the technical solution of this embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of any of the controllable source surface wave suppression methods described above.

[0099] The processor may include, but is not limited to, one or more processors or microprocessors. Each processor may be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic component, for performing the methods in the above embodiments. The computer-readable storage medium may be implemented by any type of volatile or non-volatile storage device or a combination thereof, and may include, but is not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, computer storage media (e.g., hard disk, floppy disk, solid-state drive, removable disk, CD-ROM, DVD-ROM, Blu-ray disc, etc.).

[0100] Computer-readable storage media may also store at least one computer-executable program / instruction, such as computer-readable instructions. Computer-readable storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Computer-readable storage media may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.

[0101] In addition, the computer device may also include (but is not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (e.g., a keyboard, mouse, speakers, etc.). The processor can communicate with external devices via the I / O bus through a wired or wireless network. In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product / computer program product, wherein one or more computer-executable instructions, when executed by the processor, perform the steps of the various functions and / or methods in the embodiments described herein.

[0102] Example 9

[0103] Based on the above embodiments, this embodiment provides an application example.

[0104] This application example provides a controlled-source surface wave suppression method. This invention belongs to the field of geophysical exploration, and specifically relates to a controlled-source surface wave suppression method.

[0105] Compared to traditional explosive seismic sources, controlled seismic sources offer numerous advantages, including safety, environmental friendliness, high efficiency, and economy. Their acquisition parameters and signal characteristics can be artificially controlled, making them a crucial direction for future seismic exploration. However, due to the surface location of the excitation device in controlled seismic sources, coupled with complex near-surface conditions, deep reservoir burial, and severe surface absorption attenuation, the signal-to-noise ratio of seismic data obtained from controlled seismic sources at the same coordinate location is lower than that from explosive sources. Among these, surface wave interference is the most significant noise in controlled seismic source data. This near-surface coherent noise exhibits characteristics of low frequency, high energy, and large range in seismic records, obscuring effective signals and posing difficulties for subsequent seismic data processing, significantly limiting the current promotion and application of controlled seismic sources. The field faces the technical challenge of severe surface wave interference in controlled seismic sources.

[0106] As a form of coherent noise, controlled-source surface waves exhibit a high degree of overlap with the effective reflected signal in seismic records. Conventional transform-domain filtering methods, such as fk dip filtering and Radon transform, cannot completely separate surface wave noise from the effective signal in the transform domain. Therefore, surface wave noise suppression inevitably damages the effective signal, reducing the signal-to-noise ratio of seismic data. Curves transform can constrain the effective signal in seismic records through local dip and frequency decomposition, but it can only extract the effective signal at the high-frequency end of the seismic record, failing to recover the effective signal at the low-frequency end that is obscured by surface wave noise. Therefore, there is an urgent need to develop a new curves domain processing method that can recover the effective signal in the low-frequency range while suppressing surface waves, thereby improving the fidelity of controlled-source surface wave suppression.

[0107] This invention addresses the challenge of traditional transform domain filtering methods in suppressing surface wave noise from controlled sources by proposing a controlled-source surface wave suppression method based on higher-order projection in the curve wave domain. This method transforms the data into the curve wave domain using curve wave transform. Utilizing the higher-order distribution characteristics of the effective signal in the curve wave domain, a projection matrix is ​​constructed to project the higher-order effective signal sequentially to lower-order signals, thereby recovering the effective signal in the low-frequency band of the curve wave domain and eliminating surface wave noise with low-order dispersion characteristics. Furthermore, to further improve signal fidelity during surface wave suppression, a higher-order projection operator in the curve wave domain is constructed, and an objective function for curve wave denoising is established. The solution to the objective function is obtained using optimization inversion, thus achieving the reconstruction of seismic data after surface wave suppression. Test results using models and actual data demonstrate that this method can effectively suppress surface wave noise in controlled-source seismic data. Compared to traditional surface wave suppression methods, this method can protect the effective signal from damage to the greatest extent while suppressing surface waves, thereby improving the fidelity of surface wave suppression.

[0108] A controlled-source surface wave suppression method includes the following steps: Step 1: Convert the time-domain controlled-source seismic data d to the curvelet domain using the forward curvelet transform to obtain the curvelet transform coefficients c; Step 2: Establish a projection matrix P, and successively project the higher-order parts of the curvelet domain data to lower-order parts to reconstruct the lower-order seismic signal; Step 3: Construct an objective function and add a sparsity constraint Δc to the curvelet transform coefficients, transforming the controlled-source surface wave noise suppression problem into an optimization problem of the objective function; Step 4: Solve the objective function and obtain the sparse term c of the effective signal in the curvelet domain through sparse inversion prediction; Step 5: Convert the solved curvelet domain seismic signal data to the time domain using the inverse curvelet transform to obtain the surface wave suppressed seismic data d. ′ .

[0109] Step 1: Use the curvelet transform to convert the time-domain controllable source seismic data d into the curvelet domain, and obtain the curvelet transform coefficients c.

[0110] Furthermore, the raw seismic data d in step one can be expressed as the sum of the effective signal d1, surface wave noise d2, and random noise d3, i.e.,

[0111] d=d1+d2+d3 (1)

[0112] The original seismic data d can be transformed from the time domain to the curve wave domain by using the curve wave forward transform, thereby obtaining the curve wave transform coefficients c.

[0113] Step 2: Establish a projection matrix P, and successively project the higher-order parts of the curve wave domain data to the lower-order parts to realize the reconstruction of the lower-order seismic signal.

[0114] In step two, the curvelet transform coefficients *c* are used to divide the curvelet domain seismic data into high-order and low-order components, where the high-order seismic data are effective signals unaffected by surface wave noise. Therefore, a projection matrix *P* can be constructed to successively project the high-order seismic data onto the low-order components, thus reconstructing the low-order curvelet domain seismic data. The projection matrix *P* is a diagonal matrix with elements of 1 or 0, and its elements can be represented as:

[0115]

[0116] Here, s and a represent the order and angle of the curvelet transform, respectively, and λ is the threshold for sparsity in the curvelet transform. Curvelet transform data that reaches this threshold is defined as high-order data. By using the curvelet transform to classify the order and angle of seismic data, high-order seismic data can be successively projected onto lower-order data through interpolation, thereby reconstructing low-order seismic data.

[0117] Step 3: Construct the objective function and add a sparse constraint Δc to the curve transform coefficients to transform the controllable source surface wave noise suppression problem into an optimization problem of the objective function.

[0118] In step three, ignoring the influence of random noise in the seismic data, the surface wave noise suppression problem is transformed into a problem of solving the objective function by constructing an objective function in the curve wave domain. The specific form of the objective function J can be expressed as:

[0119] J = ||dc T Pc|| 2 +μ||c||2 (3)

[0120] Where μ is the inversion regularization operator, c T Let ||c||2 be the adjoint matrix of the curve wave coefficient matrix, and ||c||2 denotes the sparse constraint on the noise in the curve wave domain.

[0121] Step 4: Solve for the objective function and obtain the sparse term c of the effective signal in the curve domain through sparse inversion prediction;

[0122] Step 5: Convert the solved curve wave domain seismic signal data into the time domain through inverse curve wave transform to obtain the surface wave suppressed seismic data d′.

[0123] In step four, the objective function in equation (3) can be solved by least squares through iterative inversion, and finally the sparse term representation c of the effective signal in the curve wave domain is obtained. Then, the data is converted to the time domain through the inverse curve wave transformation in step five, so as to suppress the surface wave noise of the controllable source.

[0124] Figure 3 This is a schematic diagram illustrating the principle of a controllable seismic source surface wave suppression method according to the present invention.

[0125] Figure 4 This is the result of the curve wave domain transformation of the model data. Figure 4 (a) is the model data. Figure 4 (b) shows the result of converting the model data to the curve domain.

[0126] Figure 5 These are the test results for the model data. Figure 5 (a) is the input data. Figure 5 (b) shows the result after noise suppression. Figure 5 (c) shows the suppressed surface wave noise data.

[0127] Figure 6 These are the results of actual earthquake data testing. Figure 6 (a) represents the actual seismic data before surface wave suppression. Figure 6 (b) represents the actual seismic data after surface wave suppression. Figure 6 (c) represents the suppressed surface wave noise.

[0128] The controllable source surface wave noise suppression method involved in this invention is verified through model and actual seismic data tests. This method is based on the fundamental idea of ​​data-driven computation, requiring no prior information such as the subsurface medium structure during the calculation process, thus exhibiting greater applicability compared to model-driven methods. Compared to traditional surface wave suppression methods based on transform domain filtering, this method can transform time-domain seismic data to the curve wave domain, successively project high-order curve wave domain seismic data to lower-order domains by constructing a projection matrix, and transform the surface wave noise suppression problem into a least-squares solution problem by constructing an objective function. Figure 3 The effective signal in the curve wave domain is extracted through an iterative inversion process, thereby protecting the effective signal from damage to the greatest extent and improving the fidelity of surface wave denoising.

[0129] Ignoring the influence of random noise, the established data of the controllable source model containing surface wave noise are as follows: Figure 4 As shown in (a), the result obtained by converting the model data to the curve wave domain is as follows: Figure 4As shown in (b), after conversion to the curve wave domain, the seismic data is divided into multiple data blocks according to frequency and angle. The horizontal direction represents the angle size, ranging from -90° to 90°, while the vertical direction is divided into orders 1 to 6 according to frequency from high to low, with higher orders corresponding to the high-frequency end and lower orders corresponding to the low-frequency band. It can be seen that the seismic data of orders 5 and 6 in the figure are not affected by surface wave noise.

[0130] The controllable source surface wave suppression method described in this invention can suppress surface wave noise in model data, ultimately yielding surface wave suppressed seismic data. Figure 5 b) and suppressed surface wave noise ( Figure 5 c). It can be seen that the surface waves in the model data are significantly suppressed, while the effective signal is protected from damage to the greatest extent during the suppression process.

[0131] The method was further tested using actual data from a controlled seismic source in a land-based work area. Actual single-shot data from a controlled seismic source was selected as the input data. Figure 6 a) Surface wave noise suppression is performed using the method described in this invention, and the result of the surface wave suppression is as follows: Figure 6 As shown in (b), it can be seen that the strong amplitude surface wave noise in the single-shot record is significantly suppressed, and the effective signal is also well recovered and reconstructed.

[0132] This invention addresses the challenge of traditional transform domain filtering methods in suppressing surface wave noise from controlled sources by proposing a controlled-source surface wave suppression method based on higher-order projection in the curve wave domain. This method transforms the data into the curve wave domain using curve wave transform. Utilizing the higher-order distribution characteristics of the effective signal in the curve wave domain, a projection matrix is ​​constructed to project the higher-order effective signal sequentially to lower-order signals, thereby recovering the effective signal in the low-frequency band of the curve wave domain and eliminating surface wave noise with low-order dispersion characteristics. Furthermore, to further improve signal fidelity during surface wave suppression, a higher-order projection operator in the curve wave domain is constructed, and an objective function for curve wave denoising is established. The solution to the objective function is obtained using optimization inversion, thus achieving the reconstruction of seismic data after surface wave suppression. Test results using models and actual data demonstrate that this method can effectively suppress surface wave noise in controlled-source seismic data. Compared to traditional surface wave suppression methods, this method can protect the effective signal from damage to the greatest extent while suppressing surface waves, thereby improving the fidelity of surface wave suppression.

[0133] In the embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0134] It should be noted that, in this invention, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element limited by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0135] While the embodiments disclosed in this invention are as described above, the above content is merely for the purpose of facilitating understanding of this invention and is not intended to limit the invention. Any person skilled in the art to which this invention pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope disclosed in this invention; however, the scope of patent protection of this invention shall still be determined by the scope defined in the appended claims.

Claims

1. A controllable source surface wave suppression method, characterized in that, The method includes: The original seismic data is transformed from the time domain to the curve wave domain to obtain the curve wave transform coefficients; Using the curve wave transform coefficients, the curve wave domain seismic data is divided into high-order and low-order seismic data, and a projection matrix is ​​constructed to project the high-order seismic data onto the low-order seismic data. Based on seismic data, curvelet transform coefficients, and projection matrix, an objective function is constructed in the curvelet domain. Solve the objective function to obtain the seismic data after surface wave suppression.

2. The controllable source surface wave suppression method according to claim 1, characterized in that, The high-order seismic data includes effective signals that are unaffected by surface wave noise.

3. The controllable source surface wave suppression method according to claim 1, characterized in that, In the step of constructing an objective function in the curve wave domain based on seismic data, curve wave transform coefficients, and projection matrix, the objective function includes the sum of seismic data terms and inversion regularization operator terms.

4. The controllable source surface wave suppression method according to claim 3, characterized in that, The earthquake data items include: The second norm of the difference between the seismic data and the curve transform coefficient matrix and the projection matrix.

5. The controllable source surface wave suppression method according to claim 3, characterized in that, The inversion regularization operator includes: The product of the inversion regularization operator and the sparse constraint on the noise in the curve wave domain.

6. The controllable source surface wave suppression method according to claim 1, characterized in that, The steps of solving the objective function to obtain the seismic data after surface wave suppression include: Solving the objective function yields the sparse terms of the effective signal in the curve domain; The effective signal is subjected to inverse curve wave transform in the sparse term in the curve wave domain to obtain the surface wave suppressed seismic data.

7. A controllable seismic source surface wave suppression device, characterized in that, The device includes: The transformation module is used to convert the raw seismic data from the time domain to the curve wave domain to obtain the curve wave transform coefficients; The projection module is used to divide the seismic data in the curve wave domain into high-order and low-order seismic data using curve wave transform coefficients, and to construct a projection matrix that projects the high-order seismic data onto the low-order seismic data. The objective function module is used to construct an objective function in the curve wave domain based on seismic data, curve wave transform coefficients, and projection matrices. The solver module is used to solve the objective function to obtain the seismic data after surface wave suppression.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the controllable source surface wave suppression method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the controllable source surface wave suppression method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the controllable source surface wave suppression method according to any one of claims 1 to 6.

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