Method and device for suppressing shear wave leakage noise of seabed seismic data

Abstract

The present disclosure provides a method and device for suppressing shear wave leakage noise of seabed seismic data, which comprises preprocessing a vertical velocity field component of seabed seismic data to obtain a first vertical velocity field component, performing a contour wave transform on the first vertical velocity field component to obtain a first coefficient set, adjusting a Bayesian shrinkage threshold of the first coefficient set to obtain a second coefficient set, calculating the second coefficient set to obtain shear wave leakage noise, and subtracting the shear wave leakage noise from the vertical velocity field component to obtain a second vertical velocity field component. The present disclosure overcomes the disadvantages of traditional denoising methods, such as insufficient near-offset suppression and easy damage to effective signals, can efficiently suppress Z-component coherent shear wave leakage noise, maximally protects effective reflection signals, improves seismic data quality and signal-to-noise ratio, stabilizes P / Z component matching relationship, and provides reliable data support for subsequent marine seismic processing.

Description

Technical Field

[0001] This disclosure relates to the field of signal processing technology, and in particular to a method and apparatus for suppressing transverse wave leakage noise in submarine seismic data. Background Technology

[0002] Offshore oil and gas exploration faces increasingly complex seismic geological conditions, making Ocean Bottom Node (OBN) data a crucial cutting-edge technology in the field. OBNs are deployed on the seabed and typically equipped with pressure detectors to record the underwater pressure field component (P-component) and velocity detectors to record the vertical velocity field component (Z-component). Existing research indicates that the P-component of many OBN datasets is largely free of shear wave interference, while the Z-component commonly exhibits shear wave-like energy with coherent characteristics. This type of energy is often described as shear wave leakage, also known as Vz noise.

[0003] The presence of Vz noise disrupts the intrinsic correspondence between P / Z components, thus affecting the stability of critical processing steps such as dual-detection merging and further limiting the processing effectiveness of subsequent stages such as multiple wave suppression, velocity analysis, and migration imaging. Therefore, how to effectively suppress Vz noise in the Z component of OBN while ensuring effective signal protection is a crucial technical challenge in current OBN data processing. Summary of the Invention

[0004] In view of this, the purpose of this disclosure is to propose a method and apparatus for suppressing shear wave leakage noise in submarine seismic data, which at least partially solves one of the technical problems in the related art.

[0005] To achieve the above objectives, an exemplary embodiment of this disclosure provides a method for suppressing shear wave leakage noise in submarine seismic data. The method includes preprocessing the vertical velocity field components of the submarine seismic data to obtain a first vertical velocity field component; performing a contour wave transform on the first vertical velocity field component to obtain a first coefficient set; adjusting the Bayesian shrinkage threshold of the first coefficient set to obtain a second coefficient set; calculating shear wave leakage noise using the second coefficient set; and subtracting the shear wave leakage noise from the vertical velocity field component to obtain a second vertical velocity field component.

[0006] Based on the same inventive concept, a second aspect of the exemplary embodiments of this disclosure provides a device for suppressing shear wave leakage noise in submarine seismic data, comprising a preprocessing module configured to preprocess the vertical velocity field component of the submarine seismic data to obtain a first vertical velocity field component; a contour wave transformation module configured to perform a contour wave transformation on the first vertical velocity field component to obtain a first set of coefficients; a threshold processing module configured to adjust the Bayesian shrinkage threshold of the first set of coefficients to obtain a second set of coefficients; an inverse processing module configured to calculate shear wave leakage noise using the second set of coefficients; and a calculation module configured to subtract the shear wave leakage noise from the vertical velocity field component to obtain a second vertical velocity field component.

[0007] Based on the same inventive concept, a third aspect of the exemplary embodiments of this disclosure provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method as described in the first aspect.

[0008] Based on the same inventive concept, a fourth aspect of the exemplary embodiments of this disclosure provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method as described in the first aspect.

[0009] Based on the same inventive concept, a fifth aspect of the exemplary embodiments of this disclosure provides a computer program product including computer program instructions that, when run on a computer, cause the computer to perform the method as described in the first aspect.

[0010] As can be seen from the above, this disclosure overcomes the shortcomings of traditional denoising methods, such as insufficient near-offset suppression and easy damage to effective signals. It can efficiently suppress Z-component coherent shear wave leakage noise, maximize the protection of effective reflection signals, improve seismic data quality and signal-to-noise ratio, stabilize the P / Z component matching relationship, and provide reliable data support for subsequent processing of marine seismic data. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in this disclosure or related technologies, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a schematic flowchart of the method for suppressing transverse wave leakage noise in submarine seismic data according to an embodiment of the present disclosure;

[0013] Figure 2This is a schematic diagram of the Bayesian threshold adjustment refinement steps in an embodiment of the present disclosure; Figure 3 This is a schematic diagram of the adaptive Bayesian shrinkage threshold calculation steps in an embodiment of this disclosure; Figure 4 This is a schematic diagram comparing noisy seismic gathers and their spectra in an embodiment of this disclosure; Figure 5 This is a thermodynamic diagram illustrating the signal-to-noise ratio of the contour wave scale and direction parameters in an embodiment of this disclosure. Figure 6 This is a schematic diagram comparing the processing effects of different noise reduction methods in the embodiments of this disclosure; Figure 7 This is a schematic diagram comparing the FK spectra of different denoising methods according to embodiments of this disclosure; Figure 8 This is a schematic diagram comparing the frequency response curves of different noise reduction methods according to embodiments of this disclosure; Figure 9 This is a structural diagram of a transverse wave leakage noise suppression device for submarine seismic data according to an embodiment of this disclosure; Figure 10 This is a schematic diagram of the hardware structure for suppressing transverse wave leakage noise in submarine seismic data according to an embodiment of this disclosure. Detailed Implementation

[0014] It is understood that before using the technical solutions disclosed in the various embodiments of this application, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this application in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0015] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the software or hardware, such as the electronic device, application, server, or storage medium performing the operations of this application's technical solution, based on the prompt message.

[0016] As an optional but non-limiting implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0017] It is understood that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of this application. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this application.

[0018] It is understood that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and related provisions.

[0019] To make the objectives, technical solutions, and advantages of this disclosure clearer, the principles and spirit of this disclosure will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided merely to enable those skilled in the art to better understand and implement this disclosure, and are not intended to limit the scope of this disclosure in any way. Rather, these embodiments are provided to make this disclosure more thorough and complete, and to fully convey the scope of this disclosure to those skilled in the art.

[0020] In this article, it is important to understand that any number of elements in the accompanying figures is for illustrative purposes and not for limitation, and any naming is for distinction only and has no limiting meaning.

[0021] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this disclosure should have the ordinary meaning understood by one of ordinary skill in the art to which this disclosure pertains. The terms "first," "second," and similar words used in the embodiments of this disclosure do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly. The article "a" or "an" preceding an element does not exclude the existence of multiple such elements.

[0022] The principles and spirit of this disclosure will be explained in detail below with reference to several representative embodiments. Invention Overview As described in the background section, the presence of Vz noise disrupts the intrinsic correspondence between the P / Z components, thereby affecting the stability of key processing steps such as dual-detection merging and further restricting the processing effectiveness of subsequent stages such as multiple wave suppression, velocity analysis, and migration imaging. Therefore, how to effectively suppress shear wave leakage noise in the Z component of submarine seismic data while ensuring effective signal protection is a crucial technical problem in current submarine seismic data processing.

[0024] SWC leakage in the Z-component of submarine seismic data manifests as irregular random noise, which appears as coherent noise with low apparent velocity in the common-detector gather domain. Therefore, existing techniques generally perform directional suppression in the detector domain or common-detector gather domain. In recent years, transform-domain-based noise suppression methods have attracted attention, with typical methods including FK transform, τ-p transform, wavelet transform, and Curvelet transform. These methods utilize the separability of effective signal and noise in their respective transform domains to map spatiotemporal data to other domains to achieve noise reduction. However, existing transform-domain methods still have shortcomings in terms of direction selectivity, scale adaptability, and characterization of the travel time correlation of SWC energy. Especially in near-offset scenarios, leaked SWC energy is often difficult to suppress sufficiently, leaving room for improvement in balancing denoising effectiveness with effective signal protection.

[0025] The applicant of this disclosure has discovered that the contourlet transform, with its advantages of multi-scale and multi-directional analysis, not only inherits the multi-resolution analysis and time-frequency localization characteristics of the wavelet transform, but also has lower redundancy and higher expressive flexibility.

[0026] Although contour wave transform has been applied in the field of seismic data processing, there is still a lack of systematic solutions in the existing technology for the specific need to suppress shear wave leakage noise in the Z component of submarine seismic data, especially for a comprehensive strategy that simultaneously considers the processing of shear wave leakage travel time characteristics and the estimation of scale / direction constraint thresholds in the contour wave domain.

[0027] Based on the above reasons, the applicant hereby proposes a method and apparatus for suppressing shear wave leakage noise in submarine seismic data, aiming to achieve more sufficient noise attenuation and maximize the protection of effective signals under near offset conditions, thereby providing a more reliable data foundation for P / Z joint processing. Specifically, the method includes flattening the vertical velocity field component data of submarine seismic data to obtain a flattened vertical velocity field component gather; performing a contour wave transform on the flattened vertical velocity field component gather to obtain a set of sub-band coefficients for submarine seismic data; wherein the set of sub-band coefficients for submarine seismic data includes a predetermined number of sub-band coefficients; constraining the sub-band coefficients in the set of sub-band coefficients for submarine seismic data to obtain a set of estimated coefficients for shear wave leakage noise; performing inverse processing on the estimated coefficients set of shear wave leakage noise to obtain the original time-range domain shear wave leakage noise data; and subtracting the original time-range domain shear wave leakage noise data from the vertical velocity field component data of submarine seismic data to obtain the vertical velocity field component data after shear wave leakage noise reduction. This technical solution overcomes the shortcomings of traditional denoising methods, such as insufficient near-offset suppression and easy damage to effective signals. It can efficiently suppress Z-component coherent shear wave leakage noise, protect effective reflection signals to the maximum extent, improve seismic data quality and signal-to-noise ratio, stabilize the P / Z component matching relationship, and provide reliable data support for subsequent marine seismic processing.

[0028] After introducing the basic principles of this disclosure, various non-limiting embodiments of this disclosure will be described in detail below.

[0029] Exemplary methods specifically refer to Figure 1 , Figure 1 This application illustrates a method for suppressing shear wave leakage noise in submarine seismic data. The method is based on the following approach: utilizing the correlation characteristics of the shear wave leakage phase axis in the time-distance domain, dynamic correction is used to flatten the travel time of the shear wave leakage, thereby enhancing the statistical separability of the shear wave leakage in the subsequent transform domain; then, scale- and direction-related constraint factors are introduced into the contour wave transform domain to adaptively adjust the Bayesian contraction threshold, and a threshold function is used to estimate the shear wave leakage coefficient; finally, the shear wave leakage noise model is reconstructed through inverse contour wave transform, and the estimated noise is subtracted from the Z-component of the submarine seismic data to obtain the denoised Z-component data. Specifically, it includes: Step 101: Preprocess the vertical velocity field component of the submarine seismic data to obtain the first vertical velocity field component.

[0030] Specifically, in step 101, the submarine seismic data can be expressed as formula (1). (1) in, This represents observed submarine earthquake data. This represents an ideal noise-free signal. This indicates the leakage of transverse wave noise. Indicates earthquake path, These are the sampling points.

[0031] The applicant's research revealed that shear wave leakage noise exhibits kinematic characteristics similar to those of converted waves in terms of travel time. Under the assumption that the travel time of shear wave leakage noise is equivalent to that of a converted wave, the converted wave travel time equation can be applied to approximate the description of shear wave leakage noise, effectively flattening the shear wave leakage phase axis. This preprocessing, termed Vz Noise Equivalent Normal Moveout Correction (Vz NMO), enhances the spatial separability of shear wave leakage noise from the effective signal.

[0032] In step 101, the method for obtaining the vertical velocity field component data of the seafloor seismic data may include obtaining it through direct hardware measurement (such as a three-component velocity detector, a suspended vertical detector, the pressure gradient method, etc.).

[0033] Step 102: Perform contour wave transformation on the first vertical velocity field component to obtain the first set of coefficients.

[0034] Step 102 of this disclosure is based on the following principle: the contour wave transform can generate a series of coefficients with different scales and orientation indices, which together characterize the energy and geometric features of the signal in the contour wave domain. Let the input two-dimensional data be... Contour wave transformation Two-dimensional data go through Layer decomposition, whose coefficient set in the contour wave domain can be expressed as formula (2): (2) in, The representative scale, whose assignment can be... ; For the finest scale; This represents the direction index, and its assignment can be... ; It is a preset direction index at each scale; These are the contour wave coefficients at different scales and in different directions. It is the coarsest scale contour wave coefficient.

[0035] Based on the above formula, it can be seen that the seismic data after shear wave leakage noise equivalent dynamic correction... The sub-band coefficients in the contour wave domain can be expressed as formula (3): (3) in, The representative scale, whose assignment can be... ; For the finest scale; This represents the direction index, and its assignment can be... ; It is a preset direction index at each scale; These are the contour wave coefficients of the corrected seismic data at different scales and in different directions, which can be determined by the forward transformation of the contour waves. These are the contour wave coefficients of the low-frequency basis coefficients at the coarsest scale, which can be determined through multi-layer tower decomposition.

[0036] In one specific embodiment, synthetic recording is used to analyze the characteristic differences between shear wave leakage noise and effective reflected signal in the contour wave domain, and to evaluate the impact of shear wave leakage noise equivalent dynamic correction preprocessing on contour wave transform.

[0037] In one specific embodiment, contour wave transformation is performed on the Z-component common receiver point gathers of the seafloor seismic data. A 97-pin tower decomposition filter can be used to perform a 3-level scale decomposition on the Z-component common receiver point gathers of the seafloor seismic data. The time corresponds to the coarsest scale, scale The time corresponds to the finest scale; directional filter banks are used separately within each scale. , and The directional decomposition yields 4, 8, and 8 directional subbands, respectively.

[0038] The applicant's research revealed that before the equivalent dynamic correction for shear wave leakage noise, the energy of the vertical subband in the contour wave domain is mainly dominated by the first arrival of the P-wave (i.e., the moment / sampling point when the P-wave first arrives at the detector) and significant shear wave leakage noise energy, while the response of the reflected P-wave in this subband is relatively and significantly suppressed. However, the equivalent dynamic correction for shear wave leakage noise, by correcting the apparent tilt angle of the shear wave leakage noise to near zero, causes its contour wave coefficients to be highly concentrated in the near-horizontal subband set in the transform domain. Meanwhile, the contour wave coefficients of the vertical subband mainly reflect the distribution characteristics of the reflected P-wave.

[0039] In summary, the applicant has identified the following characteristics of effective reflected waves and transverse wave leakage noise in the contour wave domain: Considering the difference in travel time characteristics between shear wave leakage noise and effective signal, after the equivalent dynamic correction of shear wave leakage noise described above, the contour wave coefficient of shear wave leakage noise is mainly concentrated at the coarse scale, while the reflected P-wave tends to appear at the fine scale, which represents event edges and local undulations.

[0040] The coarse-scale represents the shallow low-frequency subband of the profile wave decomposition, characterizing a large range of low-frequency coherent energy and representing the main accumulation region of shear wave leakage noise. The fine-scale represents the deep high-frequency subband of the profile wave decomposition, depicting the details of formation reflection and local amplitude variations, with effective reflection signals as the main focus.

[0041] Furthermore, the equivalent dynamic correction of shear wave leakage noise brings the apparent tilt angle of the shear wave leakage noise close to zero, and its energy is significantly concentrated in the near-horizontal sub-band set. Meanwhile, the directional characteristics of the reflected P-wave still maintain a certain tilt, and its energy is more prominent in the near-vertical sub-band set.

[0042] Furthermore, under the joint constraints of scale and direction, shear wave leakage noise and reflected P-waves exhibit significant spatial separability in the contour wave domain. Shear wave leakage noise is mainly concentrated in and dominates the coarse-scale subband, while reflected P-waves are more distributed in the finer-scale near-vertical subband set. This spatial separation characteristic provides a key basis for constructing a shear wave leakage noise model and designing an adaptive threshold function.

[0043] Based on the patterns discovered by the applicants above, this method also includes: Step 103: Adjust the Bayesian shrinkage threshold of the first coefficient set to obtain the second coefficient set. This step, based on the amplitude distribution of each sub-band coefficient in the contour wave domain of the first coefficient set, introduces scaling and orientation adjustment factors to adaptively adjust the Bayesian shrinkage threshold; and combines this with a preset threshold function to complete the thresholding of the sub-band coefficients, thereby obtaining the second coefficient set. The preset threshold function may include an exponential attenuation term for smooth attenuation, used to improve the thresholding process's ability to suppress noise coefficients and reduce the erroneous deletion of valid signal coefficients. The specific implementation method of step 103 is detailed below.

[0044] Step 104: Calculate the shear wave leakage noise using the second coefficient set. Perform an inverse contour wave transform on the second coefficient set obtained in step 103 to reconstruct the shear wave leakage noise in the flattened domain; then perform the inverse correction corresponding to step 101 to map the noise model back to the original time-distance domain. The specific implementation method of step 104 is detailed below.

[0045] And in step 105, the shear wave leakage noise is subtracted from the vertical velocity field component to obtain the second vertical velocity field component. The vertical velocity field component data after shear wave leakage noise reduction is obtained by subtracting the original Z-component data of the original submarine seismic data from the original time-range domain shear wave leakage noise data obtained in step 104.

[0046] The threshold estimation strategy proposed in this disclosure, based on coarser horizontal subband smooth exponential decay (CHS-SED), employs dynamic correction preprocessing to enhance noise separability and achieves adaptive Bayesian shrinkage threshold adjustment and threshold function processing in the contour wave domain through scale and direction constraints. This effectively suppresses shear wave leakage noise and improves the signal-to-noise ratio and effective signal protection capability of the denoising results.

[0047] After profile wave transformation, the first data vertical field component is decomposed into a series of components with different scales. and direction The sub-bands. The contour wave coefficient in each sub-band can be denoted as... ,in The representative coefficient represents the spatial location within the subband. The technical solution disclosed herein can select subband sets at a coarser scale and near the horizontal direction. The coefficients of the coarserhorizontal subband (CHS) are used as the contour wave coefficients of the initially extracted shear wave leakage noise. Then, the coefficient estimation of the initial Vz noise is... This can be expressed as formula (4): (4) in, , , It is the size of the sub-band coefficient matrix. The cutoff scale is typically chosen to be coarser to include the main energy components of transverse wave leakage noise.

[0048] Estimate the coefficients of the extracted shear wave leakage noise. An inverse contour wave transform is performed to reconstruct the initial model of the shear wave leakage noise in the spatiotemporal domain, thus reconstructing the shear wave leakage noise. This process can be expressed as formula (5): (5) The above method utilizes the distribution pattern of shear wave leakage noise in the contour wave domain, but relying solely on extracting the horizontal sub-band coefficients is insufficient to achieve complete separation of shear wave leakage noise from reflected P-waves. This is because directional filters inevitably experience band overlap and energy leakage at the cutoff frequency. This means that even sub-bands with theoretically high separation may contain energy from other frequencies or directions. On the other hand, shallow, low-velocity reflected P-waves may have extremely small apparent tilt angles under large offset conditions.

[0049] These factors inevitably result in some effective P-wave energy still being mixed into the horizontal sub-band coefficients extracted using the above method. If this initial model is directly used to subtract the original Z component to suppress shear wave leakage noise, the reflected signal will inevitably be damaged during the subtraction.

[0050] Therefore, in order to achieve accurate modeling and separation of shear wave leakage noise, while protecting the reflected P-wave to the greatest extent, it is necessary to introduce adjustment factors in the contour wave domain to constrain and correct these preliminary extracted coefficients.

[0051] specifically refer to Figure 2 In one possible implementation, adjusting the Bayesian shrinkage threshold of the first coefficient set to obtain the second coefficient set includes: Step 201 involves filtering sub-band coefficients from the first coefficient set by scale and direction to obtain an intermediate coefficient set. This precisely identifies the coarse-scale, near-horizontal sub-band range specific to shear wave leakage noise, eliminating sub-bands dominated by effective signals, reducing the false negative impact of subsequent thresholding on effective reflected waves, and improving the targeting of noise separation. A detailed implementation of step 201 is provided below.

[0052] Step 202: Calculate the Bayesian contraction threshold for shear wave leakage noise. An adaptive threshold is set based on the statistical distribution characteristics of the noise, unlike a fixed global threshold. This adapts to the differences in noise intensity across different scales and directional sub-bands, enhancing the suppression capability in weak noise regions. Detailed implementation of step 202 is provided below.

[0053] Step 203 involves processing the intermediate coefficient set using a Bayesian shrinkage threshold and a smooth exponential decay threshold function to obtain a second coefficient set. By replacing hard truncation with a continuous, smooth exponential decay transition, artifacts caused by abrupt threshold changes are avoided, ensuring the continuity of the coefficient shrinkage process and preserving local texture and weak effective signals in the seismic data. The specific implementation of step 203 is detailed below.

[0054] In one possible implementation, filtering subband coefficients from a first set of coefficients by scale and direction to obtain an intermediate set of coefficients includes selecting subband coefficients from the first set of coefficients that meet preset criteria to obtain a filtered subband set; wherein the preset criteria are defined by the scale and direction of the subband coefficients in the first set of coefficients; extracting set coefficients from the filtered subband set; and performing an inverse contour wave transform on the noise-dominant coefficients to obtain the intermediate set of coefficients.

[0055] In one possible implementation, the technical solution included in this disclosure obtains a more accurate set of estimated coefficients for the shear wave leakage noise by using the Bayesian shrinkage threshold of the shear wave leakage noise. Specifically, refer to... Figure 3 Calculating the Bayesian contraction threshold of the transverse wave leakage noise includes: Step 301: Based on the coefficients of the filtered sub-band set at the coarsest scale, calculate the variance estimate of the shear wave leakage noise. Specifically, the Bayesian shrinkage threshold incorporates the prior statistical characteristics of the transform domain coefficients into the threshold setting, making the threshold selection more adaptive, and thus usually achieving a balance between suppressing noise and preserving effective signal characteristics. The Bayesian shrinkage threshold for shear wave leakage noise can be expressed as formula (6): (6) in, The contour wave coefficient is in the first position. Layer Bayesian shrinkage threshold in each direction, This is a variance estimate of the transverse wave leakage noise. It is an estimate of the standard deviation of the effective signal profile wave coefficients. For zero-mean Gaussian noise, its variance is usually robustly estimated using the Median Absolute Deviation (MAD) method.

[0056] Considering that shear wave leakage noise is mainly distributed at a coarser scale, the method disclosed herein uses the low-frequency subband coefficient of the contour wave at the coarsest scale to extract noise features and estimate its variance. The variance estimate of shear wave leakage noise can be expressed as formula (7): (7) in, This represents the set of profile wave constants at the coarsest scale, with the denominator 0.6745 being the statistical calibration constant for the standard normal distribution. The coefficients at the coarsest scale are primarily contributed by noise, containing only a very small amount of effective signal. The MAD method effectively suppresses this bias, thus obtaining a robust and efficient estimate of the noise variance.

[0057] Step 302: Calculate the scale adjustment factor for the filtered sub-band set. Specifically, the contour wave coefficient energy of the effective signal is mainly distributed at finer scales, while the contour wave coefficients of shear wave leakage noise are more concentrated at coarser scales. If the low-frequency dominant noise variance estimate is directly used for fine-scale sub-bands, the high-frequency noise variance will be overestimated, resulting in an overly large threshold and excessive shrinkage of details. Therefore, in order to achieve cross-scale adaptive calculation of shear wave leakage noise variance in the contour wave domain, the technical solution of this disclosure introduces a scale adjustment factor in the estimation of shear wave leakage variance.

[0058] This disclosure introduces a scale adjustment factor. To balance the noise variance bias at different decomposition scales, the scale adjustment factor is calculated based on the number of layers in the profile wave decomposition of the first vertical velocity field component. Scale adjustment factor This can be expressed as formula (8): (8) in, This is the scale basis correction factor, which can be assigned a value of 0.001. Control the decay rate, The larger the scale, the faster the decay. This scale adjustment factor satisfies the condition that the scale... At that time, scale adjustment factor When the scale At that time, scale adjustment factor This allows the noise variance to decrease continuously from low frequency to high frequency as the scale increases.

[0059] Step 303: Calculate the direction adjustment factor of the filtered sub-band set. Specifically, to express the directional non-uniformity of shear wave leakage noise within the same scale in the contour wave domain, this disclosure introduces a direction adjustment factor in the shear wave leakage variance estimation. Directional filters at each scale Within each directional sub-band, the index is divided into near-vertical sets. and near-horizontal sets Define the center position of the horizontal sub-band index interval. For example, when At that time, the center position of the horizontal sub-band Then, the circular index distance is introduced into the direction weight calculation. , where the circular index distance It can be expressed as formula (9): (9) The directional adjustment factor is calculated based on the middle position of the horizontal sub-band index interval of the shear wave leakage noise. According to formula (9), the directional adjustment factor... This can be expressed as formula (10): (10) in, It is the directional concentration control coefficient, which controls the width of the horizontal interval. The smaller the value, the more concentrated the width of the horizontal interval; It is the directional attenuation coefficient, used to control the attenuation rate of non-preferred directional coefficients. The larger the value, the faster the attenuation of the sub-band deviating from the horizontal direction. Adding the direction adjustment factor provides stronger constraint to the near-horizontal sub-band. hour, The direction adjustment factor of the sub-band far from the horizontal Approaching 0 reduces damage to the effective signal in the vertical subband.

[0060] The applicant discovered through research that when setting parameters , At that time, the adaptive adjustment factor varies with scale and direction at different scales. The increase gradually decreases, among which The corresponding decay rate is the most rapid, eventually converging to the baseline. When setting parameters , hour, In It reaches a maximum value of 1 in a specific direction centered on it. And as... The increase, from It rapidly decreased to near zero in both directions. With As the value increases, the moderating factor becomes more selective for the preferred direction and has a more significant inhibitory effect on the non-preferred direction.

[0061] Step 304: Calculate the standard deviation of the effective signal profile wave coefficient based on the variance estimate of the shear wave leakage noise and each sub-band in the filtered sub-band set. Traditional methods assume that the effective signal sub-band coefficients at different scales and in different directions have similar statistical distributions. Therefore, the standard deviation estimate of the effective signal profile wave coefficient can be expressed as formula (11). (11) This method also includes step 305, obtaining the Bayesian contraction threshold of the transverse wave leakage noise based on the variance estimate, the standard deviation, the scale adjustment factor, and the direction adjustment factor. Specifically, because the contour wave coefficient of the reflected wave is not uniformly distributed throughout the entire subband, but is concentrated in several local clustering regions consistent with the trajectory of the in-phase axis, applying the global average based on the second moment of the entire subband using formula (11) would lead to a significant downward bias in the signal standard deviation estimate.

[0062] Therefore, in the technical solution disclosed herein, for each sub-band position At point Take a fixed window around The standard deviation of the effective signal is spatially adaptively estimated using the local second moments within a sliding window of fixed size. This adaptive estimate can be expressed as formula (12): (12) By combining the derivations of noise variance estimation, scale and orientation adjustment factors, and local signal standard deviation, an adaptive Bayesian threshold can be obtained. Formula (13): (13) In signal processing and image denoising, thresholding functions are commonly used to shrink contour domain coefficients to suppress noise. Classical methods include hard thresholding, soft thresholding, and Garrote thresholding. These traditional thresholding functions typically suppress or zero out small-amplitude coefficients in the transform domain that are below a threshold, thereby suppressing random noise. Their core assumption is that the transform domain coefficients of an effective signal usually have large amplitudes and are sparsity, while noise coefficients exhibit small amplitudes and are typically uniformly distributed.

[0063] However, the applicant's research revealed that shear wave leakage noise exhibits large amplitude and concentrated spatial distribution in specific scale and direction sub-bands of the contour wave domain. This characteristic contradicts the assumption of small noise amplitude underlying traditional threshold functions, making it difficult to effectively separate shear wave leakage noise while avoiding damage to the reflected signal when directly applying traditional methods.

[0064] Therefore, a differentiated processing strategy is needed, moving beyond simply suppressing small-amplitude coefficients as noise. Instead, to effectively separate and characterize shear wave leakage, coefficients that conform to the characteristics of shear wave noise should be selectively retained.

[0065] Therefore, a smooth exponential decay thresholding function (SED-Thresholding) is introduced into the existing variance estimation of shear wave leakage noise to achieve more accurate extraction and modeling of the shear wave leakage component, denoted as . The variance estimate of the final transverse wave leakage noise can be expressed as formula (14): (14) Here, α>0 is an adjustable parameter. By adjusting the parameter α, the approximation speed of the function can be flexibly controlled.

[0066] The variance estimate of the final transverse wave leakage noise is a continuous function. Its left and right limits are calculated respectively, and these left and right limits can be expressed as formula (15): (15) From formula (15), we can see that, The function at the threshold point Continuous.

[0067] Therefore, the smooth exponential decay threshold function proposed in this disclosure at the threshold point It exhibits good continuity and a relatively smooth transition; with the parameters As the threshold increases, the decay rate outside the threshold increases, and the coefficient approaches zero more quickly.

[0068] In one possible implementation, calculating the shear wave leakage noise using the second set of coefficients includes: An inverse contour wave transform is performed on the estimated coefficient set of the shear wave leakage noise to obtain a modified shear wave leakage model. Specifically, an exponential decay threshold function is applied to estimate the coefficients of the final shear wave leakage. An inverse contour wave transformation is performed to achieve precise separation of transverse wave leakage. The formula for the inverse contour wave transformation can be expressed as formula (16): (16) Furthermore, the vertical velocity field component data of the modified shear wave leakage model are subjected to reverse flattening to obtain the original time-distance domain shear wave leakage.

[0069] To verify the effectiveness of the method disclosed herein, the applicant applied the shear wave leakage attenuation method based on contour wave variation disclosed herein to the generated Z-component common detector point gather to evaluate the performance of the technique.

[0070] specifically refer to Figure 4 The sampling interval for this record is 4ms, and the total recording time is 3s. Shear wave leakage extracted from actual seismic data is superimposed onto the forward modeling record. After amplitude correction, a noisy common-detector gather and its corresponding spectral curve are obtained. In the common-detector domain, shear wave leakage manifests as low-frequency coherent noise, submerging reflected P-waves and causing a significant decrease in the signal-to-noise ratio. Simultaneously, it is accompanied by high-energy low-frequency components, causing the dominant frequency of the seismic record to drop to approximately 26Hz.

[0071] To quantitatively analyze the suppression effect of the methods included in this disclosure, the signal-to-noise ratio (SNR) is defined as Equation (17) and the root mean square error (MSE) is defined as Equation (18) during the verification: (17) (18) in, Data representing purity This represents the denoised signal, and n represents the amount of data.

[0072] In the validation process, the impact of scale and orientation parameters on the denoising effect was verified. A scale set was then set. , direction index set ,produce Group parameter settings. Scale decomposition uses a 97 filter, and multi-directional decomposition is completed using a PKVA filter at each scale.

[0073] The signal-to-noise ratio (SNR) calculated according to formula (17) is used to evaluate the denoising results of different parameters, where the SNR of the noisy data is... .

[0074] specifically refer to Figure 5 , Figure 5 The signal-to-noise ratio (SNR) heatmaps for 16 combinations of scale and direction are shown, with warmer colors indicating higher SNR. The maximum SNR of 18.5389 dB is obtained when l=6 and k=32; therefore, this parameter combination is selected as the default profiler decomposition setting for subsequent experiments.

[0075] In techniques for removing transverse wave leakage, common denoising methods include FK filtering, wavelet transform, and curvelet transform. To verify the effectiveness of the method proposed in this disclosure, the applicant compared the above three methods with the method proposed herein. Experimental parameters were set. , , , , Using noise-free data as a reference, the signal-to-noise ratio (SNR) and root mean square error (RMSE) of the denoising results for the four methods were calculated respectively. As shown in Table 1, the method included in this disclosure has an SNR of 25.6087 dB and an RMS of 4.6957, which are superior overall performance under the current evaluation criteria.

[0076] Table 1. Quantitative comparison of denoising results of different methods applied to synthetic data

[0077] specifically refer to Figure 6 When comparing the results of the four methods, it can be found that ringing artifacts appeared in the FK filtering denoising result, and the in-phase axis of the reflected P-wave was observed on the removed noise. This indicates that the method damages some effective signal while filtering out noise. Due to the inherent directional limitation of wavelet transform, shear wave leakage was not completely suppressed, and there was a lot of residual shear wave leakage near the offset of the gather.

[0078] Near offsets of the curvelet transform also retain some transverse wave leakage.

[0079] After applying the CSH-SED method included in this disclosure, most of the shear wave leakage was effectively suppressed, with even less residual shear wave leakage near the offset. No significant effective reflected signal energy was also observed in the corresponding noise removal process.

[0080] specifically refer to Figure 7 and Figure 8 , Figure 7 The comparison of four denoising methods based on the spectrum is shown. Figure 7 The frequency response curves of the four denoising methods are compared, and the FK spectrum of the data containing shear wave leakage is shown. The area indicated by the red arrow corresponds to the shear wave leakage-related region. Comparing the FK spectrum obtained by the CSH-SED method with the FK spectra processed by each denoising method, it can be clearly observed that the FK spectrum of the CSH-SED method included in this disclosure is closer to the noise-free FK spectrum, and the attenuation of noise-related energy is more obvious.

[0081] like Figure 8As shown, the frequency curve of the FK filter exhibits an abnormal peak near 0Hz, indicating reconstruction distortion in the reconstructed signal. In contrast, the method disclosed herein more closely approximates the noise-free original spectrum in terms of frequency distribution and maintains its spectral characteristics well. These results collectively demonstrate the superior performance of the proposed method in suppressing shear wave leakage.

[0082] It should be noted that the method of this disclosure embodiment can be executed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method of this disclosure embodiment, and the multiple devices will interact with each other to complete the method described.

[0083] It should be noted that the above description describes some embodiments of this disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0084] Exemplary device Based on the same inventive concept, corresponding to any of the above-described embodiments, this disclosure also provides a device for suppressing transverse wave leakage noise in submarine seismic data.

[0085] refer to Figure 9 The aforementioned device for suppressing transverse wave leakage noise in submarine seismic data includes: The preprocessing module is configured to preprocess the vertical velocity field components of the submarine seismic data to obtain the first vertical velocity field component.

[0086] The contour wave transformation module is configured to perform a contour wave transformation on the first vertical velocity field component to obtain a first set of coefficients.

[0087] The threshold processing module is configured to adjust the Bayesian shrinkage threshold of the first set of coefficients to obtain the second set of coefficients.

[0088] The inverse processing module is configured to calculate the transverse wave leakage noise using a second set of coefficients.

[0089] The calculation module is configured to subtract transverse wave leakage noise from the vertical velocity field component to obtain the second vertical velocity field component.

[0090] For ease of description, the above apparatus is described in terms of its functions, divided into various modules. Of course, in implementing this disclosure, the functions of each module can be implemented in one or more software and / or hardware.

[0091] The apparatus described above is used to implement a corresponding method for suppressing transverse wave leakage noise in submarine seismic data in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0092] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements a method for suppressing transverse wave leakage noise of submarine seismic data as described in any of the above embodiments.

[0093] Figure 10 This embodiment illustrates a more specific hardware structure of an electronic device, which may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0094] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0095] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0096] The input / output interface 1030 is used to connect input / output modules to realize information input and output. The input / output modules can be configured as components in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touch screens, microphones, various sensors, etc., and output devices may include displays, speakers, vibrators, indicator lights, etc.

[0097] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0098] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0099] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0100] The electronic devices described above are used to implement the corresponding shear wave leakage noise suppression method for submarine seismic data in any of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0101] The memory 1620 stores machine-readable instructions executable by the processor 1610. When the electronic device is running, the processor 1610 communicates with the memory 1620 via the bus 1630, causing the processor 1610 to execute the following instructions during operation: The vertical velocity field components of the submarine seismic data are preprocessed to obtain the first vertical velocity field component. A profile wave transform is performed on the first vertical velocity field component to obtain the first set of coefficients; Adjust the Bayesian shrinkage threshold of the first coefficient set to obtain the second coefficient set; The transverse wave leakage noise is calculated using the second set of coefficients; and The second vertical velocity field component is obtained by subtracting the transverse wave leakage noise from the vertical velocity field component.

[0102] By employing the above methods, this disclosure overcomes the shortcomings of traditional denoising methods, such as insufficient near-offset suppression and easy damage to effective signals. It can efficiently suppress Z-component coherent shear wave leakage noise, maximize the protection of effective reflection signals, improve seismic data quality and signal-to-noise ratio, stabilize the P / Z component matching relationship, and provide reliable data support for subsequent processing of marine seismic data.

[0103] Exemplary program product Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this disclosure also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the method for suppressing transverse wave leakage noise of submarine seismic data as described in any of the above embodiments.

[0104] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0105] The aforementioned non-transitory computer-readable storage media can be any available medium or data storage device that a computer can access, including but not limited to magnetic storage (e.g., floppy disks, hard disks, magnetic tapes, magneto-optical disks (MOs), etc.), optical storage (e.g., CDs, DVDs, BDs, HVDs, etc.), and semiconductor storage (e.g., ROMs, EPROMs, EEPROMs, non-volatile memory (NAND flash), solid-state drives (SSDs)).

[0106] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute the method for suppressing transverse wave leakage noise of submarine seismic data as described in any of the embodiments in the exemplary method section above, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0107] Based on the same inventive concept, corresponding to the method for suppressing shear wave leakage noise in submarine seismic data described in any of the above embodiments, this disclosure also provides a computer program product, which includes computer program instructions. In some embodiments, the computer program instructions can be executed by one or more processors of a computer to cause the computer and / or the processor to perform the method for suppressing shear wave leakage noise in submarine seismic data. Corresponding to the execution entity for each step in each embodiment of the method for suppressing shear wave leakage noise in submarine seismic data, the processor executing the corresponding step can belong to the corresponding execution entity.

[0108] The computer program product of the above embodiments is used to cause the computer and / or the processor to execute a method for suppressing transverse wave leakage noise in submarine seismic data as described in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0109] Those skilled in the art will recognize that embodiments of this disclosure can be implemented as a system, method, or computer program product. Therefore, this disclosure can be implemented as entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, this disclosure can also be implemented as a computer program product contained in one or more computer-readable media, which includes computer-readable program code.

[0110] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example,, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (not exhaustive) of a computer-readable storage medium may include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0111] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0112] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0113] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0114] It should be understood that each block of a flowchart and / or block diagram, as well as combinations of blocks in a flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine that, when executed by a computer or other programmable data processing device, creates means for implementing the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0115] These computer program instructions may also be stored in a computer-readable medium that enables a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable medium produce a product comprising an instruction apparatus that implements the functions / operations specified in the boxes of a flowchart and / or block diagram.

[0116] Computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, such that the instructions that execute on the computer or other programmable apparatus can provide a process for implementing the functions / operations specified in the boxes of a flowchart and / or block diagram.

[0117] Furthermore, although the operations of the methods of this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all of the operations shown must be performed to achieve the desired result. Rather, the steps depicted in the flowcharts may be executed in a different order. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0118] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0119] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0120] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this application (including the claims) is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this application as described above, which are not provided in the details for the sake of brevity.

[0121] Additionally, to simplify the description and discussion, and to avoid obscuring the embodiments of this application, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring the embodiments of this application, and this also takes into account the fact that the details of the implementation of these block diagram apparatuses are highly dependent on the platform on which the embodiments of this application will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuits) have been set forth to describe exemplary embodiments of this application, it will be apparent to those skilled in the art that the embodiments of this application can be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0122] Although this application has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0123] The embodiments of this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this application should be included within the protection scope of this application.

[0124] While the spirit and principles of this disclosure have been described with reference to several specific embodiments, it should be understood that this disclosure is not limited to the disclosed specific embodiments, and the division of aspects does not imply that features in these aspects cannot be combined for benefit; such division is merely for convenience of expression. This disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims. The scope of the appended claims is to be interpreted in the broadest sense, thereby encompassing all such modifications and equivalent structures and functions.

Claims

1. A method for suppressing shear wave leakage noise in submarine seismic data, comprising: The vertical velocity field component of the submarine seismic data is preprocessed to obtain the first vertical velocity field component. The first vertical velocity field component is subjected to a contour wave transformation to obtain a first set of coefficients; Adjust the Bayesian shrinkage threshold of the first coefficient set to obtain the second coefficient set; The transverse wave leakage noise is calculated using the second set of coefficients. as well as The second vertical velocity field component is obtained by subtracting the transverse wave leakage noise from the vertical velocity field component.

2. The method according to claim 1, wherein, Adjusting the Bayesian shrinkage threshold of the first coefficient set to obtain the second coefficient set includes: Sub-band coefficients are selected from the first coefficient set according to scale and direction to obtain an intermediate coefficient set; Calculate the Bayesian contraction threshold of the shear wave leakage noise; and The intermediate coefficient set is processed using the Bayesian shrinkage threshold and the smoothing exponential decay threshold functions to obtain the second coefficient set.

3. The method according to claim 2, wherein, Sub-band coefficients are filtered from the first coefficient set according to scale and direction to obtain an intermediate coefficient set, which includes: Subband coefficients that meet preset criteria are selected from the first set of coefficients to obtain a filtered subband set; wherein the preset criteria are defined by the scale and direction of the subband coefficients in the first set of coefficients. Extract set coefficients from the filtered subband set; and The noise-dominant coefficients are subjected to inverse contour wave transformation to obtain the intermediate coefficient set.

4. The method according to claim 3, wherein, Calculating the Bayesian contraction threshold of the shear wave leakage noise includes: Based on the coefficients of the filtered subband set at the coarsest scale, the variance estimate of the shear wave leakage noise is calculated; Calculate the scaling factor of the filtered subband set; Calculate the orientation adjustment factor of the filtered subband set; The standard deviation of the effective signal profile wave coefficients is calculated based on the variance estimate of the shear wave leakage noise and each subband in the filtered subband set; and The Bayesian shrinkage threshold of the transverse wave leakage noise is obtained based on the variance estimate, the standard deviation, the scale adjustment factor, and the direction adjustment factor.

5. The method according to claim 4, wherein, The scale adjustment factor is calculated based on the number of layers of the contour wave decomposition of the first vertical velocity field component; the direction adjustment factor is calculated based on the middle position of the horizontal sub-band index interval of the shear wave leakage noise.

6. The method according to claim 1, wherein, The transverse wave leakage noise, calculated using the second set of coefficients, includes: An inverse profile wave transform is performed on the estimated coefficient set of the shear wave leakage noise to obtain a modified shear wave leakage model; and The vertical velocity field component data of the modified shear wave leakage model are subjected to reverse flattening to obtain the original time-distance domain shear wave leakage noise.

7. A device for suppressing shear wave leakage noise in submarine seismic data, comprising: The preprocessing module is configured to preprocess the vertical velocity field components of the submarine seismic data to obtain a first vertical velocity field component. The contour wave transformation module is configured to perform a contour wave transformation on the first vertical velocity field component to obtain a first set of coefficients; The threshold processing module is configured to adjust the Bayesian shrinkage threshold of the first coefficient set to obtain the second coefficient set; The inverse processing module is configured to calculate the transverse wave leakage noise using the second set of coefficients; The calculation module is configured to subtract the transverse wave leakage noise from the vertical velocity field component to obtain a second vertical velocity field component.

8. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions for causing the computer to perform the method of any one of claims 1 to 6.

10. A computer program product, characterized in that, It includes computer program instructions that, when run on a computer, cause the computer to perform the method as described in any one of claims 1 to 6.

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