Method, device and medium for noise suppression processing in vibroseis black triangle area

Abstract

The present application relates to a kind of controllable source black triangle area noise suppression processing method, device, computer equipment and storage medium, method includes the following steps: obtaining seismic reflection data;Effective signal data and black triangle area noise data are respectively constructed model;Black triangle area noise data is carried out sparse inversion, and sparse term is obtained;According to sparse term, determine black triangle area noise prediction data;Effective signal wave data is obtained by using adaptive subtraction.The above method, by sparse inversion, obtain sparse term, and then obtain the boundary of black triangle area noise data, and obtain black triangle area noise prediction data from it, subtract from seismic reflection data, obtain effective signal wave data, improve the constraint effect to black triangle noise data, improve black triangle noise and effective signal separation effect, and reduce the damage to effective signal in seismic reflection data, and then can improve subsequent imaging resolution and accuracy.

Description

Technical Field

[0001] This application relates to the field of geophysical exploration technology, and in particular to a method, apparatus, computer equipment, and storage medium for controlling noise suppression in the black triangle region of a seismic source. Background Technology

[0002] In geophysical exploration, compared to traditional explosive sources, controlled seismic sources offer numerous advantages such as wavelet control, environmental friendliness, and high efficiency. However, because controlled seismic sources can only be excited at the surface, in complex terrain and near-surface lateral inhomogeneities, a strong amplitude noise region will be generated, gradually increasing with offset and propagation time. In controlled-source seismic records, this manifests as a triangular region with the shot point as its vertex, known as "black triangle" noise. The presence of this noise complicates subsequent controlled-source data processing and significantly affects the imaging quality of deep target reservoirs, severely hindering the large-scale application of controlled seismic sources.

[0003] When a controlled seismic source is excited at the Earth's surface, most of the energy propagates along the surface as surface waves. When these waves encounter obstacles, they are scattered, forming a "black triangle" noise region defined by the direct surface waves. Considering the complexity of this "black triangle" noise, traditional noise suppression methods, such as FK filtering, Radon transform, and curvelet transform, cannot effectively constrain it. This prevents complete separation of the "black triangle" noise from the effective reflected wave signal, resulting in damage to the effective signal during suppression and ultimately affecting the imaging resolution and accuracy of the controlled seismic source data. Summary of the Invention

[0004] Therefore, it is necessary to provide a controllable source black triangle noise suppression processing method, apparatus, computer equipment, and storage medium that can improve noise confinement effect, improve the separation effect between black triangle noise and effective signal, reduce damage to effective signal, and improve subsequent imaging resolution and accuracy.

[0005] In a first aspect, this application provides a method for suppressing noise in the black triangle region of a controllable seismic source, comprising the following steps:

[0006] Acquire seismic reflection wave data;

[0007] Models are constructed for the effective signal data and the noise data in the black triangle region of the seismic reflection wave data, respectively, to achieve the separation of the effective signal data and the noise data in the black triangle region of the seismic reflection wave data in the model space.

[0008] Based on the model of the effective signal data and the model of the noise data in the black triangle region, sparse inversion is performed on the noise data in the black triangle region to obtain sparse terms.

[0009] Based on the sparse terms, determine the noise prediction data for the black triangle region;

[0010] Adaptive subtraction is used to subtract the noise prediction data of the black triangle area from the seismic reflection wave data to obtain the effective signal wave data.

[0011] In one embodiment, the step of constructing models for the effective signal data and the noise data in the black triangle region of the seismic reflection wave data to separate the effective signal data and the noise data in the black triangle region of the seismic reflection wave data within the model space includes:

[0012] Effective signal data d for seismic reflection wave data d s Constructing the model operator L s ;

[0013] The noise data d in the black triangle area of ​​the seismic reflection wave data d. bt Transform to the curve wave domain to construct the model operator L c This enables the separation of effective signal data from noise data in the black triangle region of seismic reflection wave data within the model space.

[0014] In one embodiment, the effective signal data d of the seismic reflection wave data d s Constructing the model operator L s In the steps, the model operator L s Defined using the following formula:

[0015] d s =L s x s

[0016] L s =N T S T

[0017] Where, x s N is the reflectivity. T For the dynamic correction operator, S T This is the superposition operator.

[0018] In one embodiment, the black triangle noise data d of the seismic reflection wave data d bt Transform to the curve wave domain to construct the model operator L c In the steps, the model operator L c Defined using the following formula:

[0019] d bt =L c x c

[0020] Where, x cThis represents the sparse term of the noise data in the black triangle region in the curve domain.

[0021] In one embodiment, the step of performing sparse inversion on the black triangle noise data based on the model of the effective signal data and the model of the black triangle noise data to obtain sparse terms includes:

[0022] In the curve domain, an objective function is constructed for the black triangle noise data based on the model of the effective signal data and the model of the black triangle noise data, wherein a sparse constraint is added to the black triangle noise data in the objective function.

[0023] The objective function is solved by final iterative inversion to obtain the sparse term of the noise data in the black triangle region in the curve wave domain.

[0024] In one embodiment, the objective function J for constructing noise data in the black triangle region in the curve domain is expressed by the following formula:

[0025] J=||dL s x s -L c x c || 2 +μ||x c ||1

[0026] Where μ is the inversion regularization operator, ||x c ||1 represents the sparse constraint applied to the noise data in the black triangle region.

[0027] In one embodiment, in the step of determining the noise prediction data of the black triangle region based on the sparse term, the noise prediction data of the black triangle region is determined using the following formula:

[0028] d c =L c x c

[0029] Where, d c This is noise prediction data for the black triangle area.

[0030] Secondly, this application provides a controllable source black triangle noise suppression processing device, the device comprising:

[0031] The raw data acquisition module is used to acquire seismic reflection wave data;

[0032] The model building module is used to build models for the effective signal data and the noise data in the black triangle region of the seismic reflection wave data, thereby achieving the separation of the effective signal data and the noise data in the black triangle region of the seismic reflection wave data in the model space.

[0033] The sparse inversion operation module is used to perform sparse inversion on the noise data in the black triangle region based on the model of the effective signal data and the model of the noise data in the black triangle region, to obtain sparse terms.

[0034] The noise prediction module is used to determine the noise prediction data for the black triangle region based on the sparse term.

[0035] The effective signal wave data generation module is used to subtract the noise prediction data of the black triangle area from the seismic reflection wave data using adaptive subtraction to obtain the effective signal wave data.

[0036] Thirdly, this application provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method as described in any of the above embodiments.

[0037] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method as described in any of the above embodiments.

[0038] The aforementioned controllable seismic source black triangle noise suppression method addresses the problem that traditional transform domain filtering methods cannot solve the "black triangle" noise issue. It provides a controllable seismic source "black triangle" noise suppression method based on sparse inversion. This method constructs models for the effective signal data and black triangle noise data of the original seismic reflection wave data, achieving separation of these two data within the model space. Through sparse inversion, sparse terms are obtained, leading to the boundary of the black triangle noise data. Based on this boundary, predicted black triangle noise data is derived. Adaptive subtraction is then used to subtract the predicted black triangle noise data from the seismic reflection wave data, yielding the effective signal wave data. This sparse inversion-based method can predict and calculate the black triangle noise data relatively accurately, effectively separating the black triangle noise data from the effective signal data in the original seismic reflection wave data. Solving for the sparse terms through sparse inversion improves the constraint effect on the black triangle noise data, enhances the separation of black triangle noise from the effective signal, and reduces damage to the effective signal in the seismic reflection wave data, thereby improving subsequent imaging resolution and accuracy.

[0039] In other embodiments, a controlled-source "black triangle" noise suppression method based on curve wave domain sparse inversion is presented. This method models the effective signal and "black triangle" noise in seismic data in different model spatial domains and constructs an objective function. Then, it solves the objective function through sparse inversion, transforming the "black triangle" noise suppression problem into a least-squares problem of solving the objective function. The multi-dimensional inversion process replaces the traditional transform domain filtering process. By adding sparse constraints and a linear search step size to constrain the inversion results, and using the output of each iteration as feedback for the next iteration, the internal algorithm maximizes the protection of the effective signal. Processing results of actual data show that this method can effectively suppress "black triangle" noise in controlled-source seismic data. Compared with traditional transform domain filtering suppression methods, this method can maximize the protection of the effective signal and further improve the resolution of controlled-source seismic data. Attached Figure Description

[0040] Figure 1 This is a flowchart of a method for suppressing noise in the black triangle region of a controllable seismic source in one embodiment.

[0041] Figure 2 This is a schematic diagram of the sparse inversion process in a controllable source black triangle noise suppression method in one embodiment.

[0042] Figures 3a-3f This is the frequency division processing result of a single shot data from a controllable source seismic source in a desert area, as shown in one embodiment.

[0043] Figures 4a-4c The diagram shows the effect of applying a controllable source black triangle noise suppression method to single-shot data in one embodiment.

[0044] Figure 5a This is a superimposed profile of the effective signal obtained by the traditional transform domain filtering method;

[0045] Figure 5b This is a superimposed profile of the effective signal obtained by using the controllable source black triangle noise suppression processing method of this application.

[0046] Figure 6 This is a schematic diagram of the controllable source black triangle noise suppression processing device in one embodiment.

[0047] Figure 7 This is a schematic diagram of the structure of a computer device in one embodiment. Detailed Implementation

[0048] To facilitate understanding of this application and to make the aforementioned objects, features, and advantages of this application more apparent, a detailed description of specific embodiments of this application is provided below in conjunction with the accompanying drawings. Numerous specific details are set forth in the following description to provide a thorough understanding of this application, and preferred embodiments of this application are shown in the accompanying drawings. However, this application can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the disclosure of this application. This application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of this application; therefore, this application is not limited to the specific embodiments disclosed below. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. When an element is considered to be "connected" to another element, it may be directly connected to the other element or may have an intervening element present. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this application. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0049] Example 1

[0050] In one embodiment, such as Figure 1 As shown, the method for suppressing noise in the black triangle region of a controllable seismic source specifically includes the following steps:

[0051] S210: Acquire seismic reflection wave data;

[0052] In this embodiment, the seismic reflection wave data is controlled-source data containing black triangle noise, which refers to the actual earthquake data collected. Specifically, when a controlled source is excited at the Earth's surface, under complex terrain and near-surface lateral inhomogeneity, a strong amplitude noise region will be generated, which gradually increases with the offset distance and propagation time. In the controlled-source seismic record, this appears as a triangular region with the shot point as the vertex, called "black triangle" noise. When a controlled source is excited at the Earth's surface, most of the energy generated propagates along the Earth's surface in the form of surface waves. When it encounters surface obstacles, it generates scattered surface waves. These scattered surface waves interfere with each other, forming a controlled-source "black triangle" noise region with the direct surface waves as the boundary. In this embodiment, the seismic reflection wave data is the reflected wave signal data received by the instrument.

[0053] S220: Construct models for the effective signal data and the noise data in the black triangle region of the seismic reflection wave data; realize the separation of the effective signal data and the noise data in the black triangle region of the seismic reflection wave data in the model space;

[0054] In this embodiment, models are constructed separately for the effective signal data and the noise data in the "black triangle" region of the seismic reflection wave data, thereby achieving the separation of the effective signal data and the noise data in the "black triangle" region within the model space. Specifically, the seismic reflection wave data is the received raw data, which is the sum of the effective signal data and the noise data in the "black triangle" region. In other words, the raw seismic data can be represented as the sum of the effective reflected wave signal and the noise data in the "black triangle" region. By constructing a model based on the raw seismic reflection wave data, the separation of the effective signal data and the noise data in the "black triangle" region of the seismic reflection wave data is achieved within the model space domain.

[0055] In practical applications, the models and related methods include, but are not limited to, filter models, statistical models, neural network models, wavelet transform models, Fourier transforms, etc. In actual applications, by constructing a signal data model, the original seismic data is divided into data within the "black triangle" region and data outside the "black triangle" region. The data within the "black triangle" region is the noise data, and the data inside the "black triangle" region is the effective signal data. However, there is actually a mixed region containing both data within and outside the "black triangle" region. Therefore, this application, after separating the effective signal data and the noise data within the "black triangle" region of the spatial seismic reflection wave data through a model, further uses optimization and iterative calculation methods to find the boundary between the noise data and the effective signal data within the "black triangle" region. This allows for better suppression of the noise within the "black triangle" region, reducing its impact on the effective signal, and thus better separating the noise from the effective signal within the "black triangle" region. In one specific embodiment, effective signal data and black triangle noise data can be modeled separately to separate the effective signal data and black triangle noise data of seismic reflection wave data within the model space. For example, effective signal data and black triangle noise data can be modeled in different model space domains and objective functions can be constructed. Then, the objective function can be solved through sparse inversion, thereby transforming the black triangle noise suppression problem into a least-squares problem of solving the objective function. The multi-dimensional inversion process replaces the traditional transform domain filtering process, thus ensuring the suppression of black triangle noise data. It should be noted that the black triangle noise data in this application can also be understood as black triangle noise data. In one specific embodiment, since the effective signal and "black triangle" noise have different dispersion and distribution characteristics, converting the data to the curve domain can achieve the separation of effective signal and "black triangle" noise.

[0056] S230: Based on the model of the effective signal data and the model of the black triangle noise data, perform sparse inversion on the black triangle noise data to obtain sparse terms;

[0057] In this application, after separating the effective signal data and the noise data in the black triangle region within the model space, the sparse solution is obtained by performing sparse inversion on the noise data in the black triangle region to obtain sparse terms. This transforms the problem of suppressing "black triangle" noise into a mathematical problem. The multidimensional inversion process replaces the traditional transform domain filtering process, thereby effectively suppressing "black triangle" noise in controlled-source seismic data. Compared with the traditional transform domain filtering suppression method, this method can protect the effective signal to the greatest extent and improve the resolution of controlled-source seismic data.

[0058] S240: Determine the noise prediction data for the black triangle region based on the sparse term;

[0059] In this application, sparse inversion is used to perform sparse solution, and the noise prediction data of the black triangle region is determined thereby. That is, based on the sparse term, the noise prediction data of the black triangle region is determined by prediction calculation.

[0060] S250: Adaptive subtraction is used to subtract the noise prediction data of the black triangle area from the seismic reflection wave data to obtain the effective signal wave data.

[0061] In this application, the effective signal wave data is the original seismic reflection wave data after removing the noise prediction data from the black triangle region. This application uses sparse inversion to determine the sparse solution, thereby defining the boundary of the black triangle region noise data and subsequently determining the black triangle region noise prediction data, thus achieving the separation of the black triangle region noise prediction data. After determining the black triangle region noise prediction data, adaptive subtraction is used to subtract the black triangle region noise prediction data from the seismic reflection wave data to obtain the effective signal wave data. It should be noted that, after determining the black triangle region noise prediction data, how to remove the black triangle region noise prediction data based on adaptive subtraction to obtain the effective signal wave data is described in existing technology and will not be elaborated here.

[0062] The aforementioned controllable seismic source black triangle noise suppression method addresses the problem that traditional transform domain filtering methods cannot solve the "black triangle" noise issue. It provides a controllable seismic source "black triangle" noise suppression method based on sparse inversion. This method constructs models for the effective signal data and black triangle noise data of the original seismic reflection wave data, achieving separation of these two data within the model space. Through sparse inversion, sparse terms are obtained, leading to the boundary of the black triangle noise data. Based on this boundary, predicted black triangle noise data is derived. Adaptive subtraction is then used to subtract the predicted black triangle noise data from the seismic reflection wave data, yielding the effective signal wave data. This sparse inversion-based method can predict and calculate the black triangle noise data relatively accurately, effectively separating the black triangle noise data from the effective signal data in the original seismic reflection wave data. Solving for the sparse terms through sparse inversion improves the constraint effect on the black triangle noise data, enhances the separation of black triangle noise from the effective signal, and reduces damage to the effective signal in the seismic reflection wave data, thereby improving subsequent imaging resolution and accuracy.

[0063] In one embodiment, step S220, which involves constructing models for the effective signal data and the noise data in the black triangle region of the seismic reflection wave data, and separating the effective signal data and the noise data in the black triangle region of the seismic reflection wave data within the model space, includes:

[0064] Effective signal data d for seismic reflection wave data d s Constructing the model operator L s ;

[0065] The noise data d in the black triangle area of ​​the seismic reflection wave data d. bt Transform to the curve wave domain to construct the model operator L c This enables the separation of effective signal data from noise data in the black triangle region of seismic reflection wave data within the model space.

[0066] In this application, the seismic reflection wave data d can be represented as the effective signal data d of the reflected wave. s Noise data d in the "black triangle" region bt The sum, which can be expressed by the following formula:

[0067] d = d s +d bt

[0068] This embodiment analyzes the valid signal data d separately. s and noise data in the black triangle region d btSeparate models are established, meaning that the effective signals and "black triangle" noise in different pairs of seismic data are modeled in different spatial domains, and the black triangle noise data d of the seismic reflection wave data d is used as the model. bt The model operator is constructed in the curve wave domain, and then the model is optimized and solved through sparse inversion. This transforms the problem of suppressing "black triangle" noise into a least-squares problem of solving the objective function. The multidimensional inversion process replaces the traditional transform domain filtering process, thereby effectively suppressing "black triangle" noise in controlled-source seismic data and further improving the resolution of controlled-source seismic data.

[0069] In one specific embodiment, the effective signal data d of the seismic reflection wave data d s Constructing the model operator L s In the steps, the model operator L s Defined using the following formula:

[0070] d s =L s x s

[0071] L s =N T S T

[0072] Where, x s N is the reflectivity. T For the dynamic correction operator, S T This is the superposition operator.

[0073] Using the above model operator, and based on the hyperbolic motion correction assumption, the effective signal data d of the reflected wave is... s It can be represented as the model operator L s With reflectivity x S The product of the model operators, where the model operator is composed of the dynamic correction operator N. T and superposition operator S T Composition, thus we can obtain:

[0074] d s =L s x s =N T S T x s

[0075] Thus, by constructing a model operator for effective signal data, it is easier to provide a boundary optimization basis for suppressing noise data in the black triangle region, thereby ensuring an improved suppression effect on noise data in the black triangle region and reducing noise interference to the effective signal.

[0076] In one embodiment, the black triangle noise data d of the seismic reflection wave data dbt Transform to the curve wave domain to construct the model operator L c In the steps, the model operator L c Defined using the following formula:

[0077] d bt =L c x c

[0078] Where, x c This represents the sparse term of the noise data in the black triangle region in the curve domain.

[0079] In this embodiment, the black triangle noise data d contained in the original seismic data d is... bt Transform to the curve wave domain to construct the model operator L c Thus, the noise data d in the black triangle region is reduced. bt It can obtain sparse solutions based on sparse inversion and transform the problem of suppressing "black triangle" noise into the least squares problem of solving the objective function. The multidimensional inversion process replaces the traditional transform domain filtering process, thereby effectively suppressing "black triangle" noise in controlled source seismic data and further improving the resolution of controlled source seismic data.

[0080] In one embodiment, the step of performing sparse inversion on the black triangle noise data based on the model of the effective signal data and the model of the black triangle noise data to obtain sparse terms includes:

[0081] Based on the model of the effective signal data and the model of the black triangle noise data, an objective function is constructed for the black triangle noise data in the curve domain, wherein a sparse constraint is added to the black triangle noise data in the objective function.

[0082] The objective function is solved by final iterative inversion to obtain the sparse term of the noise data in the black triangle region in the curve wave domain.

[0083] In this embodiment, an objective function is constructed, and sparse constraints are added to the noise data in the black triangle region within the objective function. The objective function is then solved using a final iterative inversion to obtain the sparse solution of the black triangle noise in the curvelet domain, i.e., the sparse term. In this embodiment, the inversion result is constrained by adding sparse constraints and a linear search step size in the final iterative inversion. The output of each iteration is used as feedback for the next iteration, maximizing the protection of valid signal data within the internal algorithm and ensuring that the objective function obtains the optimal solution and the sparse term. It should be noted that the specific process of the final iterative inversion is detailed in existing technologies and will not be repeated here. Specifically, for example, the final iterative inversion is based on an iterative inversion solved using the least squares method. Please refer to [link to relevant documentation]. Figure 2As shown, this is the final iterative inversion process of this application. After inputting the initial corresponding model operator data, the least squares method is used to solve the objective function based on the constructed objective function, and the final iterative inversion is performed through sparse inversion to finally obtain the sparse solution.

[0084] In one embodiment, the objective function J for constructing noise data in the black triangle region in the curve domain is expressed by the following formula:

[0085] J=||dL s x s -L c x c || 2 +μ||x c ||1

[0086] Where μ is the inversion regularization operator, ||x c ||1 represents the sparse constraint applied to the noise data in the black triangle region.

[0087] Thus, by employing the aforementioned objective function, and modeling the effective signal and "black triangle" noise in seismic data in different model spatial domains, the objective function is constructed. Then, sparse inversion is used to solve the objective function, transforming the "black triangle" noise suppression problem into a least-squares problem of solving the objective function. The multi-dimensional inversion process replaces the traditional transform-domain filtering process. By adding sparse constraints and a linear search step size to constrain the inversion results, and using the output of each iteration as feedback for the next iteration, the internal algorithm maximizes the protection of the effective signal. Processing results of actual data show that this method can effectively suppress "black triangle" noise in controlled-source seismic data. Compared to traditional transform-domain filtering suppression methods, this method can maximize the protection of the effective signal and further improve the resolution of controlled-source seismic data.

[0088] In one embodiment, in the step of determining the noise prediction data of the black triangle region based on the sparse term, the noise prediction data of the black triangle region is determined using the following formula:

[0089] d c =L c x c

[0090] Where, d c This is noise prediction data for the black triangle area.

[0091] In this embodiment, based on the sparse term x obtained from the sparse solution... c Thus, through the model operator L c With sparse term x cMultiplying these data yields the predicted noise data for the black triangle region. This predicted noise data is then used to suppress noise in the black triangle region. After removing the predicted noise data from the original seismic reflection wave data, the effective signal data is obtained. This method can maximize the protection of the effective signal, minimize the impact of black triangle noise removal on the effective signal, and further improve the resolution of controllable source seismic data.

[0092] The aforementioned controllable seismic source black triangle noise suppression method addresses the problem that traditional transform domain filtering methods cannot solve the "black triangle" noise issue. It provides a controllable seismic source "black triangle" noise suppression method based on sparse inversion. This method constructs models for the effective signal data and black triangle noise data of the original seismic reflection wave data, achieving separation of these two data within the model space. Through sparse inversion, sparse terms are obtained, leading to the boundary of the black triangle noise data. Based on this boundary, predicted black triangle noise data is derived. Adaptive subtraction is then used to subtract the predicted black triangle noise data from the seismic reflection wave data, yielding the effective signal wave data. This sparse inversion-based method can predict and calculate the black triangle noise data relatively accurately, effectively separating the black triangle noise data from the effective signal data in the original seismic reflection wave data. Solving for the sparse terms through sparse inversion improves the constraint effect on the black triangle noise data, enhances the separation of black triangle noise from the effective signal, and reduces damage to the effective signal in the seismic reflection wave data, thereby improving subsequent imaging resolution and accuracy.

[0093] In other embodiments, a controlled-source "black triangle" noise suppression method based on curve wave domain sparse inversion is presented. This method models the effective signal and "black triangle" noise in seismic data in different model spatial domains and constructs an objective function. Then, it solves the objective function through sparse inversion, transforming the "black triangle" noise suppression problem into a least-squares problem of solving the objective function. The multi-dimensional inversion process replaces the traditional transform domain filtering process. By adding sparse constraints and a linear search step size to constrain the inversion results, and using the output of each iteration as feedback for the next iteration, the internal algorithm maximizes the protection of the effective signal. Processing results of actual data show that this method can effectively suppress "black triangle" noise in controlled-source seismic data. Compared with traditional transform domain filtering suppression methods, this method can maximize the protection of the effective signal and further improve the resolution of controlled-source seismic data.

[0094] Example 2

[0095] In another embodiment, a method for suppressing noise in the black triangle region of a controllable seismic source includes the following steps:

[0096] Step 1: Obtain raw seismic reflection wave data.

[0097] Step 2: Extract the effective signal data d from the original seismic reflection wave data d. s Constructing the model operator L s Among them, the original seismic reflection wave data d can be represented as the effective signal data d of the reflected wave. s Noise data d in the "black triangle" region bt The sum of is:

[0098] d = d s +d bt

[0099] Based on the hyperbolic motion correction assumption, the effective signal data of the reflected wave can be further expressed as the model operator L. s With reflectivity x S The product of the model operators L, where L is the model operator ... s Dynamic correction operator N T and superposition operator S T The composition can be specifically represented as:

[0100] d s =L s x s =N T S T x s

[0101] Step 3: Extract the noise data d from the "black triangle" area in the original seismic reflection wave data d. bt Transform to the curve wave domain to construct the model operator L c This achieves the separation of signal and noise in the model space domain; specifically, it separates the noise data d from the "black triangle" region contained in the original seismic reflection wave data d. bt Transform to the curve wave domain to construct the model operator L c Specifically, it is expressed as:

[0102] d bt =L c x c

[0103] Among them, L c The curvelet domain model operator for noise data in the "black triangle" region is composed of curvelet domain coefficients, x c The sparse term is the sparse representation of the noise data in the curve domain of the "black triangle" region.

[0104] Step 4: Construct the objective function in the curvelet domain and add a sparse constraint Δx to the "black triangle" noise data. cThe problem of suppressing "black triangle" noise is transformed into solving an objective function. Specifically, by constructing the objective function in the curve domain, the problem of suppressing "black triangle" noise data can be transformed into solving an objective function, J, which can be expressed as:

[0105] J=||dL s x s -L c x c || 2 +μ||x c ||1

[0106] Where μ is the inversion regularization operator, ||x c ||1 indicates that the "black triangle" noise is subject to sparse constraint.

[0107] Step 5: Solve for the objective function and predict the sparse term x of the "black triangle" noise through sparse inversion. c x obtained by the final iterative inversion c The noise data of the "black triangle" area is obtained directly, specifically:

[0108] d c =L c x c

[0109] Where, d c This is noise prediction data for the black triangle area.

[0110] Step Six: Subtract the predicted noise data d from the "black triangle" region using adaptive subtraction. c Subtracting the original seismic reflection wave data d from the original seismic reflection wave data d yields the effective signal wave data d. s1 Specifically:

[0111] d s1 =dd c

[0112] In this way, the goal of suppressing "black triangle" noise is achieved; then, the suppression of "black triangle" noise in seismic data is achieved through adaptive subtraction.

[0113] This embodiment addresses the problem of "black triangle" noise, which traditional transform domain filtering methods cannot solve, by providing a controlled-source "black triangle" noise suppression method based on curvewave domain sparse inversion. This method models the effective signal and "black triangle" noise in seismic data in different model spatial domains and constructs an objective function. Then, it solves the objective function through sparse inversion, transforming the "black triangle" noise suppression problem into a least-squares problem of solving the objective function. The multi-dimensional inversion process replaces the traditional transform domain filtering process. By adding sparse constraints and a linear search step size to constrain the inversion results, and using the output of each iteration as feedback for the next iteration, the internal algorithm maximizes the protection of the effective signal. Processing results of actual data show that this method can effectively suppress "black triangle" noise in controlled-source seismic data. Compared to traditional transform domain filtering methods, this method can maximize the protection of the effective signal and improve the resolution of controlled-source seismic data. This invention addresses the problem of "black triangle" noise, which traditional transform domain filtering methods cannot solve, by providing a controlled-source "black triangle" noise suppression method based on curvewave domain sparse inversion. This method models the effective signal and "black triangle" noise in seismic data in different model spatial domains and constructs an objective function. Then, it solves the objective function through sparse inversion, transforming the "black triangle" noise suppression problem into a least-squares problem of solving the objective function. The multi-dimensional inversion process replaces the traditional transform-domain filtering process. By adding sparse constraints and a linear search step size to constrain the inversion results, and using the output of each iteration as feedback for the next iteration, the internal algorithm maximizes the protection of the effective signal. Processing results on actual data show that this method can effectively suppress "black triangle" noise in controlled-source seismic data. Compared to traditional transform-domain filtering methods, this method can maximize the protection of the effective signal and improve the resolution of controlled-source seismic data.

[0114] Example 3

[0115] The following describes the application of the controllable source black triangle noise suppression method provided in this application, and verifies the method using controllable source seismic data from an actual desert area.

[0116] The method was tested using actual controllable source data from a work area in western China. Frequency band analysis of the actual data revealed (…). Figures 3a to 3f The effective signal and the "black triangle" noise have different dispersion and distribution characteristics, so converting the data to the curve domain can achieve separation of the effective signal and the "black triangle" noise. Figures 3a to 3f The result is the frequency division processing of single-shot data from a controllable seismic source in a desert area. Figure 3a This data is for a single gun across the entire frequency band, 1.5-90Hz. Figures 3b-3f This provides single-shot data from a controllable source at different frequency bands.

[0117] Select the gun set data of a specific gun, such as Figure 4a As shown, using this as input data to suppress "black triangle" noise, the suppression effect and noise removal are as follows: Figure 4b (valid signal) and Figure 4c (Noise in the black triangle area) is shown. Figure 4a For input single-shot data with "black triangle noise", Figure 4b This is the single-shot data after suppressing the "black triangle" noise using this method. Figure 4c The suppressed "black triangle" noise. As can be seen, the strong amplitude noise in the "black triangle" region is significantly suppressed, and the effective signal is well recovered and reconstructed.

[0118] Further analysis of the cross-section after zero offset superposition shows that... (Please refer to...) Figure 5a and Figure 5b , Figure 5a This is an effective signal diagram of suppressing "black triangle" noise using traditional transform domain filtering methods. Figure 5b This is an effective signal diagram of "black triangle" noise suppressed based on the controllable source black triangle noise suppression processing method provided in this application. Figure 5a This represents the superposition profile obtained by the traditional transform domain filtering method. Figure 5b This represents the stacked profile obtained by the controllable source black triangle noise suppression method provided in this application. (Comparison) Figure 5a and 5b It can be seen that the "black triangle" noise suppression method based on curve domain sparse inversion in this application suppresses "black triangle" noise more thoroughly. Compared with the zero-offset profile generated by the input data, the "black triangle" noise suppression method based on curve domain sparse inversion can better recover the effective signal while suppressing strong energy noise, improve the contribution of the "black triangle" region, and improve the imaging quality.

[0119] This method is based on pure data and does not require prior information such as the structure and velocity model of the underground medium. Compared with traditional noise suppression methods based on transform domain filtering, this method can obtain the denoised signal data through iterative inversion, effectively protecting the primary wave signal data and improving the fidelity of denoising.

[0120] Example 4

[0121] In one embodiment, this application provides a controllable source black triangle noise suppression processing device. Please refer to [link to relevant documentation]. Figure 6The device includes: a raw data acquisition module, a model building module, a sparse inversion operation module, a noise prediction module, and an effective signal wave data generation module. The raw data acquisition module, the model building module, the sparse inversion operation module, the noise prediction module, and the effective signal wave data generation module are connected sequentially.

[0122] The raw data acquisition module is used to acquire seismic reflection wave data;

[0123] The model building module is used to build models for the effective signal data and the noise data in the black triangle region of seismic reflection wave data, thereby achieving the separation of the effective signal data and the noise data in the black triangle region of seismic reflection wave data in the model space.

[0124] The sparse inversion operation module is used to perform sparse inversion on the noise data in the black triangle region based on the model of the effective signal data and the model of the noise data in the black triangle region, to obtain sparse terms.

[0125] The noise prediction module is used to determine the noise prediction data for the black triangle region based on the sparse terms;

[0126] The effective signal wave data generation module is used to subtract the noise prediction data of the black triangle area from the seismic reflection wave data using adaptive subtraction to obtain the effective signal wave data.

[0127] The aforementioned controllable seismic source black triangle noise suppression processing device addresses the problem that traditional transform domain filtering methods cannot solve the "black triangle" noise issue. It provides a controllable seismic source "black triangle" noise suppression method based on sparse inversion. This method constructs models for the effective signal data and black triangle noise data of the original seismic reflection wave data, achieving separation of these two data within the model space. Through sparse inversion, sparse terms are obtained, leading to the boundary of the black triangle noise data. Based on this boundary, predicted black triangle noise data is derived. Adaptive subtraction is then used to subtract the predicted black triangle noise data from the seismic reflection wave data, yielding the effective signal wave data. This sparse inversion method enables relatively accurate prediction of the black triangle noise data, effectively separating the noise from the effective signal data in the original seismic reflection wave data. Solving for the sparse terms through sparse inversion improves the constraint effect on the black triangle noise data, enhances the separation of black triangle noise from the effective signal, and reduces damage to the effective signal in the seismic reflection wave data, thereby improving subsequent imaging resolution and accuracy.

[0128] In one embodiment, the model building module includes an effective signal model building unit and a noise model building unit;

[0129] The effective signal model construction unit is used to construct the effective signal data d from the seismic reflection wave data d. s Constructing the model operator L s ;

[0130] The noise model construction unit is used to construct the noise data d in the black triangle area of ​​the seismic reflection wave data d. bt Transform to the curve wave domain to construct the model operator L c This enables the separation of effective signal data from noise data in the black triangle region of seismic reflection wave data within the model space.

[0131] In one embodiment, the effective signal model construction unit is used to construct the effective signal data d from the seismic reflection wave data d. s Constructing the model operator L s Among them, the model operator L s Defined using the following formula:

[0132] d s =L s x s

[0133] L s =N T S T

[0134] Where, x s N is the reflectivity. T For the dynamic correction operator, S T This is the superposition operator.

[0135] Using the above model operator, and based on the hyperbolic motion correction assumption, the effective signal data d of the reflected wave is... s It can be represented as the model operator L s With reflectivity x S The product of the model operators, where the model operator is composed of the dynamic correction operator N. T and superposition operator S T Composition, thus we can obtain:

[0136] d s =L s x s =N T S T x s

[0137] Thus, by constructing a model operator for effective signal data, it is easier to provide a boundary optimization basis for suppressing noise data in the black triangle region, thereby ensuring an improved suppression effect on noise data in the black triangle region and reducing noise interference to the effective signal.

[0138] In one embodiment, the noise model construction unit is used to convert the black triangle noise data d of the seismic reflection wave data d into a single unit.bt Transform to the curve wave domain to construct the model operator L c In the model operator L c Defined using the following formula:

[0139] d bt =L c x c

[0140] Where, x c This represents the sparse term of the noise data in the black triangle region in the curve domain.

[0141] In this embodiment, the black triangle noise data d contained in the original seismic data d is... bt Transform to the curve wave domain to construct the model operator L c Thus, the noise data d in the black triangle region is reduced. bt It can obtain sparse solutions based on sparse inversion and transform the problem of suppressing "black triangle" noise into the least squares problem of solving the objective function. The multidimensional inversion process replaces the traditional transform domain filtering process, thereby effectively suppressing "black triangle" noise in controlled source seismic data and further improving the resolution of controlled source seismic data.

[0142] In one embodiment, the sparse inversion operation module includes an objective function construction unit and an iterative inversion solution unit;

[0143] The objective function construction unit is used to construct an objective function for the black triangle noise data in the curve domain based on the model of the effective signal data and the model of the black triangle noise data, wherein sparse constraints are added to the black triangle noise data in the objective function.

[0144] The iterative inversion solution unit is used to solve the objective function using the final iterative inversion to obtain the sparse term of the black triangle noise data in the curve domain.

[0145] In this embodiment, an objective function is constructed, and sparse constraints are added to the noise data in the black triangle region within the objective function. The objective function is then solved using a final iterative inversion to obtain the sparse solution of the black triangle noise in the curvelet domain, i.e., the sparse term. In this embodiment, the inversion result is constrained by adding sparse constraints and a linear search step size in the final iterative inversion. The output of each iteration is used as feedback for the next iteration, maximizing the protection of valid signal data within the internal algorithm and ensuring that the objective function obtains the optimal solution and the sparse term. It should be noted that the specific process of the final iterative inversion is detailed in existing technologies and will not be elaborated upon here. Specifically, for example, the final iterative inversion is based on an iterative inversion solved using the least squares method.

[0146] In one embodiment, the objective function construction unit is used to construct an objective function for the noise data in the black triangle region in the curve domain. The objective function J is expressed by the following formula:

[0147] J=||dL s x s -L c x c || 2 +μ||x c ||1

[0148] Where μ is the inversion regularization operator, ||x c ||1 represents the sparse constraint applied to the noise data in the black triangle region.

[0149] Thus, by employing the aforementioned objective function, and modeling the effective signal and "black triangle" noise in seismic data in different model spatial domains, the objective function is constructed. Then, sparse inversion is used to solve the objective function, transforming the "black triangle" noise suppression problem into a least-squares problem of solving the objective function. The multi-dimensional inversion process replaces the traditional transform-domain filtering process. By adding sparse constraints and a linear search step size to constrain the inversion results, and using the output of each iteration as feedback for the next iteration, the internal algorithm maximizes the protection of the effective signal. Processing results of actual data show that this method can effectively suppress "black triangle" noise in controlled-source seismic data. Compared to traditional transform-domain filtering suppression methods, this application can maximize the protection of the effective signal and further improve the resolution of controlled-source seismic data.

[0150] In one embodiment, the noise prediction module is used to determine noise prediction data for the black triangle region based on the sparse term, wherein the noise prediction data for the black triangle region is determined using the following formula:

[0151] d c =L c x c

[0152] Where, d c This is noise prediction data for the black triangle area.

[0153] In this embodiment, based on the sparse term x obtained from the sparse solution... c Thus, through the model operator L c With sparse term x cMultiplying these data yields the predicted noise data for the black triangle region. This predicted noise data is then used to suppress noise in the black triangle region. After removing the predicted noise data from the original seismic reflection wave data, the effective signal data is obtained. This method can maximize the protection of the effective signal, minimize the impact of black triangle noise removal on the effective signal, and further improve the resolution of controllable source seismic data.

[0154] The aforementioned controllable seismic source black triangle noise suppression processing device addresses the problem that traditional transform domain filtering methods cannot solve the "black triangle" noise issue. It provides a controllable seismic source "black triangle" noise suppression method based on sparse inversion. This method constructs models for the effective signal data and black triangle noise data of the original seismic reflection wave data, achieving separation of these two data within the model space. Through sparse inversion, sparse terms are obtained, leading to the boundary of the black triangle noise data. Based on this boundary, predicted black triangle noise data is derived. Adaptive subtraction is then used to subtract the predicted black triangle noise data from the seismic reflection wave data, yielding the effective signal wave data. This sparse inversion method enables relatively accurate prediction of the black triangle noise data, effectively separating the noise from the effective signal data in the original seismic reflection wave data. Solving for the sparse terms through sparse inversion improves the constraint effect on the black triangle noise data, enhances the separation of black triangle noise from the effective signal, and reduces damage to the effective signal in the seismic reflection wave data, thereby improving subsequent imaging resolution and accuracy.

[0155] Specific limitations regarding the controlled-source black triangle noise suppression processing device can be found in the limitations of the controlled-source black triangle noise suppression processing method described above, and will not be repeated here. Each module in the aforementioned controlled-source black triangle noise suppression processing device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0156] Example 5

[0157] Thirdly, this application provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method as described in any of the above embodiments.

[0158] In one embodiment, the internal structure diagram of the computer device can be as follows: Figure 7As shown. The computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used for communication via a network connection. When executed by the processor, the computer program implements the steps of a controllable seismic source black triangle noise suppression processing method as described in any of the above embodiments. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0159] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0160] Example 6

[0161] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method as described in any of the above embodiments.

[0162] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0163] This invention addresses the problem of "black triangle" noise, which traditional transform domain filtering methods cannot solve, by providing a controlled-source "black triangle" noise suppression method based on curve domain sparse inversion. This method models the effective signal and "black triangle" noise in seismic data in different model spatial domains and constructs an objective function. Then, it solves the objective function through sparse inversion, transforming the "black triangle" noise suppression problem into a least-squares problem of solving the objective function. The multi-dimensional inversion process replaces the traditional transform domain filtering process. By adding sparse constraints and a linear search step size to constrain the inversion results, and using the output of each iteration as feedback for the next iteration, the internal algorithm maximizes the protection of the effective signal. Processing results of actual data show that this method can effectively suppress "black triangle" noise in controlled-source seismic data. Compared with traditional transform domain filtering methods, this method can maximize the protection of the effective signal and improve the resolution of controlled-source seismic data.

[0164] The technical features of the embodiments described above can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification. It should be noted that the terms "in one embodiment," "for example," and "again," etc., in this application are intended to illustrate the application and not to limit it. The embodiments described above only illustrate several implementation methods of this application, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the patent application. It should be pointed out that for those skilled in the art, several modifications and improvements can be made without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for suppressing noise in the black triangle region of a controllable seismic source, characterized in that, Includes the following steps: Acquire seismic reflection wave data; Models were constructed for the effective signal data and the noise data in the black triangle region of the seismic reflection wave data, respectively. Based on the model of the effective signal data and the model of the noise data in the black triangle region, sparse inversion is performed on the noise data in the black triangle region to obtain sparse terms. Based on the sparse terms, determine the noise prediction data for the black triangle region; Adaptive subtraction is used to subtract the noise prediction data of the black triangle area from the seismic reflection wave data to obtain the effective signal wave data; The steps for constructing models from the effective signal data of the seismic reflection wave data and the noise data of the black triangle region respectively include: Seismic reflection wave data Valid signal data Model operator construction ; Seismic reflection wave data Noise data in the black triangle area Transform to the curve wave domain to construct model operators ; The seismic reflection wave data Valid signal data Model operator construction In the steps, the model operator Defined using the following formula: in, For reflectivity, For dynamic correction operators, For superposition operators; The earthquake reflection wave data Noise data in the black triangle area Transform to the curve wave domain to construct model operators In the steps, the model operator Defined using the following formula: in, This represents the sparse term of the noise data in the black triangle region in the curve domain.

2. The method according to claim 1, characterized in that, The step of performing sparse inversion on the noise data in the black triangle region based on the model of the effective signal data and the model of the black triangle region noise data to obtain the sparse term includes: In the curve domain, an objective function is constructed for the black triangle noise data based on the model of the effective signal data and the model of the black triangle noise data, wherein a sparse constraint is added to the black triangle noise data in the objective function. The objective function is solved by final iterative inversion to obtain the sparse term of the noise data in the black triangle region in the curve wave domain.

3. The method according to claim 2, characterized in that, The objective function is constructed for the noise data in the black triangle region in the curve wave domain. It is expressed by the following formula: in, For the inversion regularization operator, This is a sparse constraint applied to the noise data in the black triangle region.

4. The method according to claim 3, characterized in that, In the step of determining the noise prediction data for the black triangle region based on the sparse term, the noise prediction data for the black triangle region is determined using the following formula: in, This is noise prediction data for the black triangle area.

5. A controllable seismic source black triangle noise suppression processing device for implementing the controllable seismic source black triangle noise suppression processing method according to any one of claims 1-4, characterized in that, The device includes: The raw data acquisition module is used to acquire seismic reflection wave data; The model building module is used to build models for the effective signal data and the noise data of the black triangle region of the seismic reflection wave data respectively. The sparse inversion operation module is used to perform sparse inversion on the noise data in the black triangle region based on the model of the effective signal data and the model of the noise data in the black triangle region, to obtain sparse terms. The noise prediction module is used to determine the noise prediction data for the black triangle region based on the sparse term. The effective signal wave data generation module is used to subtract the noise prediction data of the black triangle area from the seismic reflection wave data using adaptive subtraction to obtain the effective signal wave data.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-4.

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