Method and device for suppressing ghost wave and side lobe artifacts of seismic source, equipment and medium

Abstract

The embodiment of the application discloses a ghost wave and source side lobe artifact joint suppression method, device, equipment and medium, and the method comprises the following steps: acquiring observation data, decomposing the observation data into effective reflection wave, ghost wave and source side lobe artifact in the time-slope domain; constructing a unified ghost wave operator for the ghost wave in the time-slope domain, determining side lobe difference according to the source side lobe artifact, and constructing a corresponding toprizi operator for the side lobe difference; according to the ghost wave operator and the toprizi operator, jointly suppressing the ghost wave and the source side lobe artifact to obtain a joint forward formula; according to the joint forward formula, establishing an inversion target function, and then performing joint sparse inversion to obtain a target sparse model; performing time-slope domain inverse transformation on the target sparse model, and outputting the seismic data after the joint suppression of the ghost wave and the source side lobe artifact. The application realizes the simultaneous and effective suppression of double ghost waves and source side lobes by using sparse inversion technology, and improves the bandwidth and resolution of the data.

Description

Technical Field

[0001] This application relates to the field of marine seismic exploration signal processing technology, specifically to a method, apparatus, equipment, and medium for jointly suppressing ghost waves and source sidelobe artifacts. Background Technology

[0002] In marine seismic exploration, ghost waves are virtual reflections formed near the source or detector via the sea surface (seawater-air interface), delayed by the effective wave. They interfere with the effective seismic signal, causing frequency band gaps / losses and reducing resolution. To improve data quality, a series of signal processing techniques are typically employed to suppress interference and noise. Double ghost wave suppression is a commonly used method to reduce the impact of direct and multiple waves caused by water surface reflection on the effective seismic signal. After double ghost wave suppression, an abnormal rise in low-frequency components is commonly observed. Traditionally, this phenomenon is attributed to the bubble effect, specifically the amplification of low-frequency energy caused by bubble pulsation during initial excitation. Therefore, after double ghost wave suppression, a dedicated bubble suppression process is often performed to further improve data quality. Current techniques primarily suppress low-frequency distortion by using deconvolution with a larger prediction gap after double ghost wave suppression to suppress remaining bubbles. However, this step-by-step processing method has some limitations. First, this method assumes that the bubble effect is the main cause of low-frequency distortion and addresses this issue through a separate bubble suppression step. However, observations of far-field gun array signals revealed that, in addition to the bubble effect, the sidelobe effect generated by the initial excitation close to the excitation source also significantly affected the low-frequency characteristics.

[0003] Figure 1 A comparative diagram of different wavelets is shown, such as... Figure 1 As shown, graph a represents ghost waves containing gun and cable artifacts, and bubble wavelets; graph b represents ghost waves containing gun artifacts and bubble wavelets; and graph c represents wavelets without ghost waves, but with enhanced sidelobe artifacts and bubbles. Traditional methods can mostly only suppress these artifacts. Figure 1 The bubble shown in the red box in the middle is for... Figure 1The source sidelobe effect, shown in the blue box, was not specifically suppressed, leading to low-frequency distortion after double ghost wave suppression. Specifically, in the region immediately adjacent to the excitation source, the inertial effect of water pressure and confining pressure causes strong sidelobes to form, followed by bubble formation. The enhancement effect of bubble formation on low-frequency energy is far greater than that of bubble formation. Therefore, relying solely on bubble suppression may not completely eliminate the low-frequency distortion problem. Secondly, the step-by-step processing method is inefficient because each step requires individual parameter adjustment, and the result of the previous step directly affects the processing effect of subsequent steps. This serial processing method increases the time cost and complexity of the entire process. Furthermore, since the distribution of ghost waves and inertial sidelobes in the time-frequency domain may overlap, traditional step-by-step suppression methods may not be able to accurately distinguish between these two types of interference, resulting in the loss of effective signals or the retention of residual noise.

[0004] Therefore, although existing technologies can improve the quality of seismic data to some extent, they still have problems such as insufficient understanding of the impact of source sidelobes, inadequate separation and suppression of inertial sidelobes, low processing efficiency, and complex parameter optimization. Summary of the Invention

[0005] In view of the above problems, this application proposes a method, apparatus, equipment and medium for jointly suppressing ghost waves and source sidelobe artifacts, to solve the following problems: existing ghost wave suppression methods have insufficient understanding of the impact of source sidelobes, fail to fully separate and suppress inertial sidelobes, have low processing efficiency and complex parameter optimization.

[0006] According to one aspect of the embodiments of this application, a method for jointly suppressing ghost waves and source sidelobe artifacts is provided, comprising: Acquire observation data and decompose the observation data into effective reflected waves, ghost waves, and source sidelobe artifacts in the time-slope domain; In the time-slope domain, a unified ghost wave operator is constructed for the ghost wave, the sidelobe difference is determined based on the source sidelobe artifacts, and the corresponding Toplitz operator is constructed for the sidelobe difference. Based on the ghost wave operator and the Topletz operator, and by combining the ghost wave and the source sidelobe artifacts, a joint forward modeling formula is obtained; Based on the joint forward modeling formula, an inversion objective function is established; Based on the inversion objective function, a joint sparse inversion is performed to obtain the objective sparse model; The time-slope domain inverse transform is performed on the target sparse model, and the output is the seismic data after being jointly suppressed by ghost waves and source sidelobe artifacts.

[0007] Furthermore, in the time-slope domain, the observation data is decomposed into effective reflected waves, ghost waves, and source sidelobe artifacts, which further include: A time-slope domain forward transform is performed on the observation data, and the transformed observation data is decomposed into effective reflected waves, ghost waves, and source sidelobe artifacts.

[0008] Furthermore, the ghost wave operator is constructed based on the directionality of the source coding, the influence of the source ghost wave, and the received ghost wave.

[0009] Furthermore, determining sidelobe differences based on source sidelobe artifacts and constructing corresponding Toplitz operators for these differences further includes: The source sidelobes are decomposed into ideal wavelets and sidelobe differences, and the spectral differences corresponding to the sidelobe differences are determined. In the time-slope domain, the spectral differences are constructed as the corresponding Toplitz operators.

[0010] Furthermore, the inversion objective function is:

[0011] in, Represents the coefficients of the sparse model; This represents the observation data after the forward transformation; The operator representing the joint interference of ghost wave and sidelobe is determined based on the ghost wave operator and the Toplitz operator; This represents the regularization weight.

[0012] Furthermore, by performing joint sparse inversion based on the inversion objective function, the resulting objective sparse model further includes: An iterative algorithm is used to solve the inversion objective function, and the estimates of effective reflected waves, ghost waves and source sidelobe artifacts are updated step by step until the convergence criterion is met, thus obtaining the target sparse model.

[0013] According to another aspect of the embodiments of this application, a device for jointly suppressing ghost waves and source sidelobe artifacts is provided, comprising: The decomposition module is suitable for acquiring observation data and decomposing the observation data into effective reflected waves, ghost waves, and source sidelobe artifacts in the time-slope domain. The operator construction module is suitable for constructing a unified ghost wave operator for ghost waves in the time-slope domain, determining the sidelobe differences based on the source sidelobe artifacts, and constructing the corresponding Toplitz operator for the sidelobe differences. The joint forward modeling module is suitable for obtaining the joint forward modeling formula based on the ghost wave operator and the Topplitz operator, combined with the ghost wave and source sidelobe artifacts; The sparse inversion module is suitable for establishing an inversion objective function based on the joint forward modeling formula; and performing joint sparse inversion based on the inversion objective function to obtain the target sparse model. The output module is suitable for performing time-slope domain inverse transformation on the target sparse model and outputting seismic data after being jointly suppressed by ghost waves and source sidelobe artifacts.

[0014] According to another aspect of the embodiments of this application, a computing device is provided, including: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus; The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the above-mentioned method of jointly suppressing ghost waves and source sidelobe artifacts.

[0015] According to another aspect of the embodiments of this application, a computer storage medium is provided, wherein at least one executable instruction is stored in the storage medium, the executable instruction causing a processor to perform operations corresponding to the above-described method for jointly suppressing ghost waves and source sidelobe artifacts.

[0016] According to another aspect of the embodiments of this application, a computer program product is provided, including at least one executable instruction, which causes a processor to perform operations corresponding to the above-described method for jointly suppressing ghost waves and source sidelobe artifacts.

[0017] According to the technical solution provided in the embodiments of this application, a sparse inversion framework for simultaneous suppression of dual ghost waves and source sidelobes is proposed. By establishing a unified ghost wave operator in the time-slope domain to describe all ghost waves, and combining it with source sidelobe modeling, the sparse inversion technique is used to simultaneously process both ghost waves and source sidelobes. In a single sparse inversion process, broadband seismic data without ghost waves and sidelobes can be obtained, realizing the simultaneous and effective suppression of dual ghost waves and source sidelobes. This effectively improves data processing efficiency and effect, significantly increases data bandwidth and resolution, avoids the cumulative error and parameter trade-off problems caused by traditional step-by-step processing, conveniently improves the quality of observation data and extracts more accurate seismic data, more thoroughly solves the low-frequency distortion problem, and expands the true bandwidth of the data.

[0018] The above description is merely an overview of the technical solutions of the embodiments of this application. In order to better understand the technical means of the embodiments of this application and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of this application more obvious and understandable, specific implementation methods of the embodiments of this application are described below. Attached Figure Description

[0019] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the embodiments of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A comparative schematic diagram of different wavelets is shown; Figure 2A flowchart illustrating a method for jointly suppressing ghost waves and source sidelobe artifacts according to an embodiment of this application is shown. Figure 3 A schematic diagram of a gun array wavelet with and without side lobes is shown. Figure 4 It shows Figure 3 Spectral diagrams of the corresponding different wavelets; Figure 5 This diagram illustrates the effect of combining ghost waves and source sidelobe artifacts on actual data. Figure 6 A structural block diagram of a device for jointly suppressing ghost waves and source sidelobe artifacts according to an embodiment of this application is shown; Figure 7 A schematic diagram of the structure of a computing device according to an embodiment of this application is shown. Detailed Implementation

[0020] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0021] Figure 2 A flowchart illustrating a method for jointly suppressing ghost waves and source sidelobe artifacts according to an embodiment of this application is shown, as follows: Figure 2 As shown, the method includes the following steps: Step S201: Acquire observation data and decompose the observation data into effective reflected waves, ghost waves, and source sidelobe artifacts in the time-slope domain.

[0022] Seismic data acquired by marine towed cables is simultaneously affected by two types of interference: source-cable combined ghost waves (i.e., double ghost waves) and source sidelobes (or source wavelet sidelobes). The former produces notch distortion between the uplink and downlink paths of the cable, while the latter introduces artifacts near the reflection phase axis due to non-ideal pulses from the source. The superposition of these two types of interference severely reduces the data bandwidth and resolution. Traditional methods involve suppressing residual bubbles after suppressing double ghost waves to reduce low-frequency distortion. However, traditional methods do not consider the influence of source sidelobes, and the step-by-step processing is prone to accumulating errors and makes it difficult to balance parameters.

[0023] To address the aforementioned issues, this application provides an efficient seismic data processing method that can simultaneously suppress source and cable ghost waves and source sidelobes in marine seismic exploration, achieving joint suppression of ghost wave and source sidelobe artifacts. This method is particularly effective against the anomalous low-frequency distortion caused by the coupling of the initial pulse energy with the hydrodynamic field during marine gun array excitation. Based on multiphysics coupling analysis, this method proposes a sparse inversion framework that simultaneously suppresses dual ghost waves and source sidelobes. Utilizing this integrated sparse inversion framework, broadband seismic data free of ghost waves and sidelobes can be obtained in a single solution, effectively removing dual noise and significantly improving data bandwidth and resolution. This avoids the cumulative errors and parameter trade-offs inherent in traditional step-by-step processing.

[0024] In step S201, the observation data collected by the marine towed cable during the marine seismic exploration process is acquired, and the observation data is decomposed into effective reflected waves, ghost waves and source sidelobe artifacts in the time-slope domain.

[0025] A data model is established, specifically assuming that the observed data contains effective reflected wave components, ghost wave components, and source sidelobe components. These components can be expressed as: d(x, t) = s(x, t) + g(x, t) + b(x, t) Wherein, d(x, t) represents the observed data; s(x, t) represents the effective reflected wave; g(x, t) represents the ghost wave interference, specifically the source and cable combined ghost wave (double ghost wave) interference; and b(x, t) represents the source sidelobe artifact.

[0026] To effectively separate and suppress these components, a suitable sparse basis is chosen, and the coefficient space is divided into a ghost wave subspace and a source sidelobe subspace. This partitioning in the time-slope domain (i.e., the τ-p domain) helps capture different types of noise features. A time-slope domain forward transform is performed on the observation data, decomposing the transformed data into three parts: effective reflected waves, ghost waves, and source sidelobe artifacts.

[0027] in, This represents the observation data after the forward transformation; This represents the effective reflected wave, i.e., the target wave. This refers to a ghost wave, specifically a combined ghost wave from the source and the cable. This indicates artifacts on the side lobes of the seismic source.

[0028] Step S202: Construct a unified ghost wave operator for the ghost wave in the time-slope domain, determine the sidelobe differences based on the source sidelobe artifacts, and construct the corresponding Toplitz operator for the sidelobe differences.

[0029] For ghost waves, a unified ghost wave operator is introduced in the sparse time-slope domain. This is used to describe all ghost waves. The ghost wave operator is constructed comprehensively based on the influence of source coding directionality, source ghost waves, and received ghost waves. Then, the ghost waves in the time-slope domain... It can be represented as:

[0030] in, This represents the coefficients of the sparse model, specifically the seismic data to be solved, which is devoid of ghost waves and sidelobes.

[0031] In this embodiment, a Toeplitz operator corresponding to the sidelobe difference is also constructed. Specifically, the source sidelobe is decomposed into an ideal wavelet and the sidelobe difference, and the spectral difference corresponding to the sidelobe difference is determined; the spectral difference is then constructed into the corresponding Toeplitz operator in the time-slope domain.

[0032] Specifically, for the source sidelobes, the actual wavelet can be decomposed into the sum of the differences between the ideal wavelet and the sidelobes:

[0033] in, Indicates the sidelobe of the earthquake source; Represents an ideal wavelet; This indicates differences in side lobes.

[0034] Spectral differences corresponding to sidelobe differences for:

[0035] in, The spectrum representing the side lobes of the seismic source; This represents the spectrum of an ideal wavelet.

[0036] The source sidelobe artifact can be considered as an effect applied to the "ghost wave data". Result:

[0037] in, The spectrum representing the source sidelobe artifact; The spectrum representing the ghost wave; Represents the spectrum of the ghost wave operator; This represents the spectrum of seismic data without ghost waves or side lobes.

[0038] Spectral differences in the time-slope domain Construct the corresponding Toplitz operator ,but

[0039] in, Indicates artifacts on the side lobes of the epicenter; The Topplitz operator represents the spectral difference; Indicates the ghost wave operator; This represents the coefficients of the sparse model.

[0040] Step S203: Based on the ghost wave operator and the Toplitz operator, the ghost wave and source sidelobe artifacts are combined to obtain the joint forward modeling formula.

[0041] By combining the ghost wave and the source sidelobe artifacts, we obtain the joint forward modeling formula:

[0042] in, This represents the observation data after the forward transformation; Represents the identity matrix; The Topplitz operator represents the spectral difference; Indicates the ghost wave operator; Represents the coefficients of the sparse model; The operator representing the joint interference of the ghost wave and sidelobe is determined based on the ghost wave operator and the Topletz operator, where... .

[0043] By constructing a combined ghost wave and sidelobe interference operator in the τ-p domain, which integrates the effects of ghost waves and source sidelobes, it is possible to process both noise components simultaneously in a single model.

[0044] Step S204: Establish the inversion objective function based on the joint forward modeling formula.

[0045] Utilizing the sparsity of the subsurface reflection coefficient in the τ-p domain, an inversion objective function is established. This objective function includes a data fitting term, weighted L1 regularization of the effective reflected wave, sparsity constraints on the ghost wave, and energy constraints on the inertial sidelobes. The specific inversion objective function is as follows:

[0046] in, Represents the coefficients of the sparse model; This represents the observation data after the forward transformation; The operator representing the joint interference of ghost wave and sidelobe is determined based on the ghost wave operator and the Toplitz operator; This represents the regularization weight, used to balance data fitting and sparsity constraints.

[0047] The first term in the inversion objective function is used to simultaneously suppress ghost waves and source sidelobes, while the second term is used to constrain model sparsity. The goal of this inversion objective function is to recover the effective reflected wave signal as faithfully as possible while simultaneously suppressing ghost waves and source sidelobes. By establishing an inversion objective function that models both effects collaboratively, ghost waves and the dominant distortion effect of source sidelobes can be suppressed simultaneously, thus achieving the joint suppression of ghost wave and source sidelobe artifacts.

[0048] Step S205: Perform joint sparse inversion based on the inversion objective function to obtain the target sparse model.

[0049] Among them, the sidelobe suppression operator can be obtained by comparing the results of the gun array wavelet with and without sidelobes. Figure 3 A schematic diagram of a gun array wavelet with and without sidelobes is shown, as follows: Figure 3 As shown, wavelet a is a wavelet containing gun and cable ghost waves, wavelet b is a wavelet without ghost waves but containing source sidelobe artifacts, and wavelet c is a wavelet without ghost waves and without source sidelobe artifacts. Figure 4 It shows Figure 3 Spectral diagrams of the corresponding different wavelets, such as Figure 4 As shown, the blue curve represents the spectrum of the wavelet containing the ghost wave and cable, i.e., the wavelet spectrum before ghost wave suppression; the red curve represents the spectrum of the wavelet without ghost wave but containing source sidelobe artifacts, i.e., the wavelet spectrum after ghost wave suppression; and the green curve represents the spectrum of the wavelet without ghost wave and without source sidelobe artifacts, i.e., the wavelet spectrum after combined suppression of ghost wave and source sidelobe artifacts.

[0050] An iterative algorithm is used to solve the inversion objective function, and the estimated values ​​of effective reflected waves, ghost waves and source sidelobe artifacts are updated step by step until the convergence criterion is met, thus obtaining the target sparse model. The target sparse model refers to the optimal sparse model obtained by inversion optimization.

[0051] Step S206: Perform time-slope domain inverse transform on the target sparse model and output the seismic data after joint suppression by ghost wave and source sidelobe artifacts.

[0052] After obtaining the target sparse model through inversion, an inverse τ-p domain transformation is performed:

[0053] in, The output data refers to the seismic data (i.e., seismic reflection signal) after being jointly suppressed by ghost waves and source sidelobe artifacts. The jointly suppressed seismic data is broadband data without double ghost waves or source sidelobes, realizing high-quality processing of broadband seismic data, which can be directly used for high-resolution imaging and AVO analysis.

[0054] Optionally, embodiments of this application also support degradation verification. Specifically, when When the ghost wave is 0, pure ghost wave suppression is achieved; when the ghost wave is 0, pure source sidelobe suppression is achieved.

[0055] To illustrate the effectiveness of the proposed method for jointly suppressing ghost waves and source sidelobe artifacts in the embodiments of this application, the method is applied to a deep-water engineering area. Figure 5 This diagram illustrates the effect of combining ghost waves and source sidelobe artifacts on actual data suppression. Figure 5 As shown, Figure a represents the actual input data, and Figure b represents the superposition of data after suppression using the traditional double ghost wave method. Comparing Figure b with Figure a reveals that after double ghost wave suppression, the ghost wave in the stratum between the arrows is well suppressed, but low frequencies are excessive due to seismic sidelobe artifacts. Figure c shows the superposition of ghost wave and source sidelobe artifacts after combined suppression. This method suppresses both ghost waves and source sidelobe artifacts, corrects the distorted low frequencies, and expands the true bandwidth. Figures d, e, and f show the autocorrelation of Figures a, b, and c, respectively. The autocorrelation data in Figures d, e, and f shows that the low-frequency artifacts caused by source sidelobe artifacts are well suppressed.

[0056] Based on the ghost wave and source sidelobe artifact suppression method provided in the embodiments of this application, a sparse inversion framework for simultaneous suppression of dual ghost waves and source sidelobes is proposed. A unified ghost wave operator is established in the time-slope domain to describe all ghost waves. Combined with source sidelobe modeling, sparse inversion technology is used to simultaneously process both ghost waves and source sidelobes. Broadband seismic data without ghost waves or sidelobes can be obtained in a single sparse inversion process, achieving simultaneous and effective suppression of dual ghost waves and source sidelobes. This effectively improves data processing efficiency and quality, significantly increases data bandwidth and resolution, avoids the cumulative errors and parameter trade-offs caused by traditional step-by-step processing, conveniently improves the quality of observational data and extracts more accurate seismic data, more thoroughly solves the low-frequency distortion problem, and expands the true bandwidth of the data.

[0057] Figure 6 A structural block diagram of a device for jointly suppressing ghost waves and source sidelobe artifacts according to an embodiment of this application is shown, as follows: Figure 6 As shown, the device includes: a decomposition module 610, an operator construction module 620, a joint forward modeling module 630, a sparse inversion module 640, and an output module 650.

[0058] The decomposition module 610 is suitable for: acquiring observation data and decomposing the observation data into effective reflected waves, ghost waves, and source sidelobe artifacts in the time-slope domain.

[0059] The operator construction module 620 is suitable for: constructing a unified ghost wave operator for ghost waves in the time-slope domain, determining sidelobe differences based on source sidelobe artifacts, and constructing corresponding Toplitz operators for sidelobe differences.

[0060] The joint forward modeling module 630 is suitable for obtaining the joint forward modeling formula by combining the ghost wave operator and the Topletz operator, along with the ghost wave and source sidelobe artifacts.

[0061] The sparse inversion module 640 is suitable for: establishing an inversion objective function based on the joint forward modeling formula; and performing joint sparse inversion based on the inversion objective function to obtain the target sparse model.

[0062] The output module 650 is suitable for performing time-slope domain inverse transform on the target sparse model and outputting seismic data after joint suppression by ghost waves and source sidelobe artifacts.

[0063] Optionally, the decomposition module 610 is further adapted to: perform a time-slope domain forward transform on the observation data, and decompose the forward-transformed observation data into effective reflected waves, ghost waves, and source sidelobe artifacts.

[0064] Optionally, the ghost wave operator is constructed based on the directionality of the source coding, the influence of the source ghost wave, and the received ghost wave.

[0065] Optionally, the operator building module 620 is further adapted to: decompose the source sidelobes into ideal wavelets and sidelobe differences, determine the spectral differences corresponding to the sidelobe differences, and construct the spectral differences into corresponding Toplitz operators in the time-slope domain.

[0066] Optionally, the inversion objective function is:

[0067] in, Represents the coefficients of the sparse model; This represents the observation data after the forward transformation; The operator representing the joint interference of ghost wave and sidelobe is determined based on the ghost wave operator and the Toplitz operator; This represents the regularization weight.

[0068] Optionally, the sparse inversion module 640 is further adapted to: solve the inversion objective function using an iterative algorithm, and gradually update the estimates of the effective reflected wave, ghost wave and source sidelobe artifacts until the convergence criterion is met, thereby obtaining the target sparse model.

[0069] The descriptions of the above modules refer to the corresponding descriptions in the method embodiments, and will not be repeated here.

[0070] Based on the ghost wave and source sidelobe artifact suppression device provided in the embodiments of this application, a sparse inversion framework for simultaneous suppression of dual ghost waves and source sidelobes is proposed. A unified ghost wave operator is established in the time-slope domain to describe all ghost waves. Combined with source sidelobe modeling, sparse inversion technology is used to simultaneously process both ghost waves and source sidelobes. Broadband seismic data without ghost waves or sidelobes can be obtained in a single sparse inversion process, achieving simultaneous and effective suppression of dual ghost waves and source sidelobes. This effectively improves data processing efficiency and quality, significantly increases data bandwidth and resolution, avoids the cumulative errors and parameter trade-offs caused by traditional step-by-step processing, conveniently improves the quality of observational data and extracts more accurate seismic data, more thoroughly solves the low-frequency distortion problem, and expands the true bandwidth of the data.

[0071] This application provides a non-volatile computer storage medium storing at least one executable instruction or computer program that enables a processor to perform the operation corresponding to the ghost wave and source sidelobe artifact joint suppression method in any of the above method embodiments.

[0072] This application provides a computer program product, which includes at least one executable instruction or computer program that enables a processor to perform the operation corresponding to the ghost wave and source sidelobe artifact joint suppression method in any of the above method embodiments.

[0073] Figure 7 The diagram shows a structural schematic of a computing device according to one embodiment of the present application. The specific embodiments of the present application do not limit the specific implementation of the computing device.

[0074] like Figure 7 As shown, the computing device may include: a processor 702, a communications interface 704, a memory 706, and a communications bus 708.

[0075] The processor 702, communication interface 704, and memory 706 communicate with each other via communication bus 708. Communication interface 704 is used to communicate with other network elements such as clients or other servers. Processor 702 executes program 710, specifically performing the relevant steps in the embodiment of the method for jointly suppressing ghost waves and source sidelobe artifacts for computing devices.

[0076] Specifically, program 710 may include program code that includes computer operation instructions.

[0077] The processor 702 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application. The computing device includes one or more processors, which may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0078] Memory 706 is used to store program 710. Memory 706 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0079] Specifically, program 710 can be used to cause processor 702 to execute the ghost wave and source sidelobe artifact joint suppression method in any of the above method embodiments. The specific implementation of each step in program 710 can be found in the corresponding descriptions of the steps and units in the above embodiments of ghost wave and source sidelobe artifact joint suppression, and will not be repeated here. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the devices and modules described above can be referred to the corresponding process descriptions in the foregoing method embodiments, and will not be repeated here.

[0080] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, the embodiments of this application are not directed to any particular programming language. It should be understood that the contents of the embodiments of this application described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best implementation of the embodiments of this application.

[0081] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0082] Similarly, it should be understood that, in order to simplify this disclosure and aid in understanding one or more of the various inventive aspects, in the foregoing description of exemplary embodiments of the present application, various features of the present application embodiments are sometimes grouped together into a single embodiment, figure, or description thereof. However, this approach to disclosure should not be construed as reflecting an intention that the claimed embodiments of the present application require more features than expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of the present application.

[0083] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0084] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are meant to be within the scope of the embodiments of this application and form different embodiments. For example, in the following claims, any one of the claimed embodiments can be used in any combination.

[0085] The various component embodiments of this application can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components according to the embodiments of this application. The embodiments of this application can also be implemented as device or apparatus programs (e.g., computer programs and computer program products) for performing part or all of the methods described herein. Such programs implementing the embodiments of this application can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0086] It should be noted that the above embodiments are illustrative of the embodiments of this application and not limiting of the embodiments of this application, and those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. Embodiments of this application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

Claims

1. A method for jointly suppressing ghost waves and source sidelobe artifacts, characterized in that, include: Acquire observation data and decompose the observation data into effective reflected waves, ghost waves, and source sidelobe artifacts in the time-slope domain; In the time-slope domain, a unified ghost wave operator is constructed for the ghost wave, the sidelobe difference is determined based on the source sidelobe artifact, and a corresponding Toplitz operator is constructed for the sidelobe difference. Based on the ghost wave operator and the Toplitz operator, and by combining the ghost wave and the source sidelobe artifacts, a joint forward modeling formula is obtained; Based on the joint forward modeling formula, an inversion objective function is established; Based on the inversion objective function, a joint sparse inversion is performed to obtain the target sparse model; The target sparse model is subjected to time-slope domain inverse transform, and the output is seismic data after being jointly suppressed by ghost waves and source sidelobe artifacts.

2. The method according to claim 1, characterized in that, The decomposition of the observation data into effective reflected waves, ghost waves, and source sidelobe artifacts in the time-slope domain further includes: The observation data is subjected to a time-slope domain forward transform, and the transformed observation data is decomposed into effective reflected waves, ghost waves, and source sidelobe artifacts.

3. The method according to claim 1, characterized in that, The ghost wave operator is constructed based on the directionality of the source coding, the influence of the source ghost wave and the received ghost wave.

4. The method according to claim 1, characterized in that, The step of determining sidelobe differences based on the source sidelobe artifacts and constructing corresponding Toplitz operators for the sidelobe differences further includes: The source sidelobes are decomposed into ideal wavelets and sidelobe differences, and the spectral differences corresponding to the sidelobe differences are determined. The spectral differences are constructed into the corresponding Toplitz operator in the time-slope domain.

5. The method according to claim 1, characterized in that, The inversion objective function is: in, Represents the coefficients of the sparse model; This represents the observation data after the forward transformation; The ghost wave and sidelobe joint interference operator is determined based on the ghost wave operator and the Toplitz operator; This represents the regularization weight.

6. The method according to any one of claims 1-5, characterized in that, The step of performing joint sparse inversion based on the inversion objective function to obtain the target sparse model further includes: An iterative algorithm is used to solve the inversion objective function, and the estimated values ​​of the effective reflected wave, the ghost wave, and the source sidelobe artifacts are updated step by step until the convergence criterion is met, thus obtaining the target sparse model.

7. A device for jointly suppressing ghost waves and source sidelobe artifacts, characterized in that, include: The decomposition module is suitable for acquiring observation data and decomposing the observation data into effective reflected waves, ghost waves, and source sidelobe artifacts in the time-slope domain. The operator construction module is adapted to construct a unified ghost wave operator for the ghost wave in the time-slope domain, determine the sidelobe difference based on the source sidelobe artifact, and construct the corresponding Toplitz operator for the sidelobe difference. The joint forward modeling module is adapted to obtain a joint forward modeling formula based on the ghost wave operator and the Toplitz operator, combined with the ghost wave and the source sidelobe artifacts; The sparse inversion module is adapted to establish an inversion objective function based on the joint forward modeling formula; and to perform joint sparse inversion based on the inversion objective function to obtain the target sparse model. The output module is adapted to perform time-slope domain inverse transform on the target sparse model and output seismic data after joint suppression by ghost waves and source sidelobe artifacts.

8. A computing device, comprising: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction that causes the processor to perform the operation corresponding to the ghost wave and source sidelobe artifact joint suppression method as described in any one of claims 1-6.

9. A computer storage medium storing at least one executable instruction that causes a processor to perform an operation corresponding to the ghost wave and source sidelobe artifact joint suppression method as described in any one of claims 1-6.

10. A computer program product comprising at least one executable instruction that causes a processor to perform an operation corresponding to the ghost wave and source sidelobe artifact joint suppression method as described in any one of claims 1-6.

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