A method and device for suppressing multiple waves in seismic data collected by marine streamer

Abstract

This invention discloses a method and apparatus for suppressing multiples in marine towed cable seismic data. The method first acquires multi-azimuth towed cable seismic data, performs three-dimensional consistency processing to eliminate differences in excitation, reception, and sampling; constructs a pseudo-three-dimensional water layer multiple model based on the processed data, extracting models from each azimuth; suppresses shallow water multiples in each azimuth data using a subtraction method, obtaining data after multiple suppression and the removed multiples data; merges and matches multiples removed from different azimuths, extracting effective signals; finally, merges the data after multiple suppression and the effective signals to output high-quality demultiplexed seismic data. This invention solves the problems of incomplete suppression and signal loss in conventional methods through multi-azimuth collaborative modeling and effective signal re-addition, improving the signal-to-noise ratio of seismic data and the reliability of geological interpretation, and is suitable for seismic exploration in shallow marine environments.

Description

Technical Field

[0001] This invention belongs to the field of marine data processing technology, specifically relating to a method and apparatus for suppressing multiple waves in seismic data acquired by marine towed cable. Background Technology

[0002] During the acquisition of seismic data using marine towed cables, different types of multiples develop due to the influence of the seawater layer and strong subsurface reflection interfaces. Especially in shallow water environments (depths less than 100 meters), multiples associated with the seawater layer are characterized by high energy and wide bandwidth, affecting numerous layers and significantly impacting seismic data. This influence not only introduces tectonic and amplitude artifacts but also greatly affects the imaging accuracy of seismic data, posing significant challenges to offshore oil and gas exploration and development. Therefore, suppressing shallow-water multiples within the seawater layer has become a key technology in marine seismic data processing.

[0003] For shallow water multiple suppression, a commonly used method is deterministic water layer multiple suppression (DWD). This method transforms the data into the Tau-P domain and combines it with a nonlinear predictor calculated from a time-domain water layer model (time-domain multiple period). Wavefield extrapolation is then used to predict the multiple model, resulting in more accurate amplitude information for simple and micro-bow multiples. Then, adaptive subtraction or curve domain matched subtraction is used to remove the multiple model from the original data. This method is well-suited for relatively shallow water environments and requires minimal bottom undulation and a high reflectivity.

[0004] Because ocean towed cable acquisition is close to a single-azimuth observation system (narrow azimuth), current mature shallow-water multiple suppression techniques are primarily based on two-dimensional algorithms, which cannot construct accurate three-dimensional multiple models. For towed cable two-dimensional data acquired from different azimuths, conventional two-dimensional shallow-water multiple suppression techniques involve suppressing shallow-water multiples for each azimuth separately. This method cannot fully utilize the spatial information of multi-azimuth data. Although shallow-water multiple suppression techniques can remove most of the multiple energy, due to the limitations of the technical method, problems such as insufficient suppression or loss of effective signals during suppression often occur, affecting the quality of seismic data and the accuracy of subsequent exploration work. The conventional shallow-water multiple suppression process for towed cable acquired seismic data mainly includes constructing a water layer multiple model and subtracting the model through parallel or series methods, finally outputting the demultiplexed data. This process cannot effectively integrate wavefield information from different azimuths when dealing with multi-azimuth data, resulting in limitations in model accuracy and suppression effect.

[0005] In summary, existing technologies for processing multi-azimuth towed seismic data suffer from problems such as inaccurate multiple wave models, poor suppression effects, and easy loss of effective signals due to limitations in model dimensions and suppression methods. There is an urgent need for a shallow water multiple wave suppression technology that can fully utilize multi-azimuth data information, improve model accuracy, and achieve signal fidelity. Summary of the Invention

[0006] To address this, the present invention provides a method and apparatus for suppressing multiple waves in seismic data acquired by marine towed cables, which solves the problems of inaccurate multiple wave models, poor suppression effects, and easy loss of effective signals caused by limitations in model dimensions and suppression methods in traditional techniques.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for suppressing multiple waves in seismic data acquired by a marine towed cable, comprising the following steps:

[0008] Acquire multi-azimuth towed seismic data, which is obtained by using an observation system with a preset azimuth during marine towed seismic acquisition.

[0009] Three-dimensional consistency processing is performed on seismic data from all acquisition azimuths to obtain consistent seismic data for each azimuth. The three-dimensional consistency processing takes into account the amplitude differences caused by excitation and reception parameters as well as spatial sampling differences of the observation system.

[0010] Using the pseudo-three-dimensional water layer multiple models obtained from all acquisition azimuths, multiple model data for each acquisition azimuth are extracted based on the observation system of each acquisition azimuth.

[0011] Shallow water multiples suppression is performed on the seismic data in each azimuth. The subtraction method is used to obtain the seismic data after multiple suppression in that azimuth and the multiple data removed in that azimuth. The multiple wavefield information from multiple azimuths is used during the subtraction.

[0012] Multiple wave data after removal from different azimuths are merged, and the differences between the seismic data after multiple wave removal, multiple wave data, and multiple wave model data are analyzed. Multiple wave data and multiple wave models are matched, and multiple wave data removed from each azimuth are matched with data after removing shallow water multiple waves in that azimuth.

[0013] Based on the correlation matching between the multiple wave model and the noise subtraction, the effective signal is extracted from the removed multiple wave data;

[0014] The data after removing shallow water multiples are combined with the effective signals extracted from the multiple data of each azimuth to obtain the seismic data after removing shallow water multiples for each acquisition azimuth.

[0015] As a preferred method for suppressing multiple waves in seismic data acquired by marine towed cables, the three-dimensional consistency processing includes standardizing and correcting the excitation and reception parameters, and interpolating to compensate for spatial sampling differences in the observation system, so as to achieve amplitude fidelity.

[0016] The formula for standardized correction is:

[0017]

[0018] In the formula, Here are the standardized parameters, P is the original parameter, and μ is the parameter. P Let σ be the mean of the parameters. P The standard deviation of the parameter;

[0019] The interpolation compensation uses the Kriging interpolation method, and the formula is:

[0020]

[0021] In the formula, For the interpolation result of the predicted points, f(x) i ) represents known point data, λ i The weighting coefficients are determined by the semi-variogram.

[0022] As a preferred method for suppressing multiples in seismic data acquired by marine towed cables, the establishment of the pseudo-three-dimensional water layer multiple model includes:

[0023] Multiple wavefield predictions are performed on the seismic data from each acquisition azimuth. The predicted multiple wavefields from each azimuth are then merged in the spatial domain to generate pseudo-three-dimensional multiple wavefield model data containing multi-azimuth information.

[0024] Multiple wave field prediction is based on the extension of the wave equation, and the formula is:

[0025]

[0026] In the formula, ψ is the wave field value, x, z are spatial coordinates, t is time, Δx, Δz, Δt are sampling intervals, and a m,n is the extension coefficient; m and n are the summation index variables, where m corresponds to the horizontal sampling point offset and n corresponds to the time sampling point offset; M and N are the upper limits of the summation range, which determine the number of adjacent sampling points involved in the calculation.

[0027] As a preferred method for suppressing multiples in seismic data acquired by marine towed cables, the method utilizes the pseudo-three-dimensional water layer multiple model obtained from all acquisition azimuths. Based on the observation system of each acquisition azimuth, multiple model data for each acquisition azimuth is extracted. Specifically, this is achieved by constructing a Green's function and predicting multiples. The formula for constructing the Green's function is as follows:

[0028] G0(s,r;ω)=∫ τ(x) G(s,k;ω)R(s,x,r)G(k,r;ω)dk

[0029] The formula for predicting multiple waves is:

[0030]

[0031] In the formula, s represents the source location, r represents the receiver location, ω represents the angular frequency, k represents the wavenumber, x represents the location of the subsurface interface, G(s,k;ω) and G(k,r;ω) are the Green's functions from the source to the interface and from the interface to the receiver, respectively, R(s,x,r) represents the reflection coefficient, and D(x k ,x s ;w) represents earthquake data, x r Let x be the coordinates of the receiving point. s Let x be the coordinates of the earthquake source. k These are the coordinates of the underground scattering point.

[0032] As a preferred method for suppressing multiples of seismic data acquired by marine towed cables, when merging multiples data after removing data from different azimuths, the authenticity of the merged data is ensured by constraining the spatial sampling interval, amplitude consistency, and phase continuity of the data from each azimuth.

[0033] Amplitude consistency constraints are achieved through normalization, as shown in the formula:

[0034]

[0035] In the formula, A norm The normalized amplitude is A, where A is the original amplitude. max A min The amplitude extrema are given; the phase continuity constraint is achieved through least-squares phase correction, as shown in the formula. φ is the original phase. To fit the phase function.

[0036] As a preferred method for suppressing multiples in seismic data acquired by marine towed cables, the matching of multiple data with multiple models includes matching based on amplitude spectrum, frequency response, and phase characteristics.

[0037] Amplitude spectrum matching is performed by calculating the correlation coefficient:

[0038]

[0039] In the formula, A 1i A 2i For amplitude spectrum data, The mean;

[0040] Frequency response matching uses a transfer function:

[0041]

[0042] In the formula, X(f) and Y(f) are the spectra of the input and output signals;

[0043] Phase feature matching is performed using a cross-correlation function:

[0044]

[0045] In the formula, x(t) and y(t) are phase signals, and τ is the time offset.

[0046] As a preferred method for suppressing multiple waves in seismic data acquisition using marine towed cables, the azimuth angle θ of the observation system ranges from 0° to 360°, and the azimuth interval Δθ ≤ 45°, satisfying the Nyquist sampling theorem.

[0047] As a preferred method for suppressing multiples in seismic data acquired by marine towed cables, when performing shallow water multiple suppression on seismic data in each azimuth, the multiple model is removed from the original data by means of adaptive subtraction or curve domain matching subtraction.

[0048] Adaptive subtraction updates the weight coefficients using the minimum mean square error criterion, as shown in the formula:

[0049] w(n+1)=w(n)+2μe(n)x(n)

[0050] In the formula, w is the weight coefficient vector, μ is the step size factor, e(n) is the error signal, and x(n) is the input signal;

[0051] Curved wave domain matched subtraction via curved wave transform:

[0052]

[0053] In the formula, Let j, l, k be the curve wave basis functions, and j, l, k be the scale, orientation, and position parameters, respectively.

[0054] The present invention also provides a device for suppressing multiple waves in marine towed cable seismic data acquisition, comprising:

[0055] The data acquisition module is used to acquire multi-azimuth towed cable seismic data, which is obtained by using an observation system with a preset azimuth in marine towed cable seismic acquisition.

[0056] The three-dimensional consistency processing module is used to perform three-dimensional consistency processing on seismic data from all acquired azimuths to obtain consistent seismic data for each azimuth. The three-dimensional consistency processing takes into account the amplitude differences caused by excitation and reception parameters as well as spatial sampling differences of the observation system.

[0057] The multiple wave model extraction module is used to extract multiple wave model data for each acquisition azimuth based on the observation system of each acquisition azimuth, using the pseudo-three-dimensional water layer multiple wave model obtained from all acquisition azimuths.

[0058] The shallow water multiple suppression module is used to suppress the seismic data in each azimuth. It uses a subtraction method to obtain the seismic data after multiple suppression in that azimuth and the multiple data removed in that azimuth. The multiple wavefield information from multiple azimuths is used during the subtraction.

[0059] The data merging and matching module is used to merge multiple wave data after removal from different azimuths, analyze the differences between the seismic data after multiple wave removal and the multiple wave data and multiple wave model data, match the multiple wave data and the multiple wave model, and match the multiple wave data removed from each azimuth with the data after removing shallow water multiple waves in that azimuth.

[0060] The effective signal extraction module is used to extract the effective signal from the removed multiple wave data based on the correlation matching of the multiple wave model and noise subtraction.

[0061] The effective signal merging module is used to merge the data after removing shallow water multiples with the effective signals extracted from the multiples data of each azimuth to obtain the seismic data after removing shallow water multiples for each acquisition azimuth.

[0062] As a preferred solution for a multiple wave suppression device for seismic data acquisition by marine towed cables, the three-dimensional consistency processing module performs standardized correction on the excitation and reception parameters and interpolates to compensate for spatial sampling differences in the observation system in order to achieve amplitude fidelity.

[0063] The formula for standardized correction is:

[0064]

[0065] In the formula, Here are the standardized parameters, P is the original parameter, and μ is the parameter. P Let σ be the mean of the parameters. P The standard deviation of the parameter;

[0066] The interpolation compensation uses the Kriging interpolation method, and the formula is:

[0067]

[0068] In the formula, For the interpolation result of the predicted points, f(x) i ) represents known point data, λ i The weighting coefficients are determined by the semi-variogram.

[0069] As a preferred option for a multiple wave suppression device for seismic data acquisition using marine towed cables, the multiple wave model extraction module includes:

[0070] Multiple wavefield predictions are performed on the seismic data from each acquisition azimuth. The predicted multiple wavefields from each azimuth are then merged in the spatial domain to generate pseudo-three-dimensional multiple wavefield model data containing multi-azimuth information.

[0071] Multiple wave field prediction is based on the extension of the wave equation, and the formula is:

[0072]

[0073] In the formula, ψ is the wave field value, x, z are spatial coordinates, t is time, Δx, Δz, Δt are sampling intervals, and a m,n is the extension coefficient; m and n are the summation index variables, where m corresponds to the horizontal sampling point offset and n corresponds to the time sampling point offset; M and N are the upper limits of the summation range, which determine the number of adjacent sampling points involved in the calculation.

[0074] As a preferred solution for a seismic multiple suppression device for marine towed cable acquisition, the shallow water multiple suppression module extracts multiple model data for each acquisition azimuth, specifically by constructing a Green's function and predicting multiples. The formula for constructing the Green's function is as follows:

[0075] G0(s,r;ω)=∫ τ(x) G(s,k;ω)R(s,x,r)G(k,r;ω)dk

[0076] The formula for predicting multiple waves is:

[0077]

[0078] In the formula, s represents the source location, r represents the receiver location, ω represents the angular frequency, k represents the wavenumber, x represents the location of the subsurface interface, G(s,k;ω) and G(k,r;ω) are the Green's functions from the source to the interface and from the interface to the receiver, respectively, R(s,x,r) represents the reflection coefficient, and D(x k ,x s ;w) represents earthquake data, x r Let x be the coordinates of the receiving point. s Let x be the coordinates of the earthquake source. k These are the coordinates of the underground scattering point.

[0079] As a preferred solution for a multiple wave suppression device for seismic data acquisition by marine towed cables, the data merging and matching module ensures the authenticity of the merged data by constraining the spatial sampling interval, amplitude consistency, and phase continuity of data from all directions.

[0080] Amplitude consistency constraints are achieved through normalization, as shown in the formula:

[0081]

[0082] In the formula, A norm The normalized amplitude is A, where A is the original amplitude. max A min The amplitude extrema are given; the phase continuity constraint is achieved through least-squares phase correction, as shown in the formula. φ is the original phase. To fit the phase function.

[0083] As a preferred embodiment of the ocean towed cable seismic data multiple suppression device, the data merging and matching module performs matching of multiple data and multiple models, including matching based on amplitude spectrum, frequency response and phase characteristics.

[0084] Amplitude spectrum matching is performed by calculating the correlation coefficient:

[0085]

[0086] In the formula, A 1i A 2i For amplitude spectrum data, The mean;

[0087] Frequency response matching uses a transfer function:

[0088]

[0089] In the formula, X(f) and Y(f) are the spectra of the input and output signals;

[0090] Phase feature matching is performed using a cross-correlation function:

[0091]

[0092] In the formula, x(t) and y(t) are phase signals, and τ is the time offset.

[0093] As a preferred option for a multiple wave suppression device for seismic data acquisition via marine towed cable, the azimuth angle θ of the observation system ranges from 0° to 360°, and the azimuth interval Δθ ≤ 45°, satisfying the Nyquist sampling theorem.

[0094] As a preferred solution for a multiple suppression device for seismic data acquired by marine towed cables, the shallow water multiple suppression module uses adaptive subtraction or curve domain matching subtraction to remove the multiple model from the original data.

[0095] Adaptive subtraction updates the weight coefficients using the minimum mean square error criterion, as shown in the formula:

[0096] w(n+1)=w(n)+2μe(n)x(n)

[0097] In the formula, w is the weight coefficient vector, μ is the step size factor, e(n) is the error signal, and x(n) is the input signal;

[0098] Curved wave domain matched subtraction via curved wave transform:

[0099]

[0100] In the formula, Let j, l, k be the curve wave basis functions, and j, l, k be the scale, orientation, and position parameters, respectively.

[0101] The present invention has the following advantages:

[0102] First, by acquiring multi-azimuth towed seismic data and performing three-dimensional consistency processing, wavefield information from different azimuths is integrated, overcoming the limitations of traditional two-dimensional algorithms. Compared to the narrow azimuth acquisition based on a single-azimuth observation system in existing technologies, this method enables the multiple wave model to cover more complete spatial wavefield characteristics, reduces model prediction errors, and significantly improves the accuracy of the quasi-three-dimensional multiple wave model.

[0103] Secondly, a multi-directional multiple wave model is used for shallow water multiple wave suppression. By extracting and fusing multi-directional wave field information through a pseudo-3D model, the problem that the two-dimensional algorithm cannot construct a three-dimensional model in the existing technology is solved. By extracting the effective signal from the removed multiple wave data and adding it back, based on the multi-dimensional matching of the model and the data, the accurate restoration of the lost signal during the suppression process is achieved, especially the fidelity of the effective signal in the mid-to-high frequency range is significantly improved. Attached Figure Description

[0104] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0105] The structures, proportions, sizes, etc. illustrated in this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed herein, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0106] Figure 1This is a schematic diagram of the process for suppressing multiple waves of seismic data acquired by a marine towed cable, provided in an embodiment of the present invention.

[0107] Figure 2 This is a schematic diagram comparing the suppression effect provided in the embodiments of the present invention;

[0108] Figure 3 This is a schematic diagram of the architecture of the marine towed cable seismic data acquisition multiple wave suppression device provided in an embodiment of the present invention. Detailed Implementation

[0109] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0110] Example 1

[0111] See Figure 1 This invention provides a method for suppressing multiple waves in seismic data acquired by a marine towed cable, comprising the following steps:

[0112] Step S1: Acquire multi-azimuth towed cable seismic data, which is obtained by using an observation system with a preset azimuth in marine towed cable seismic acquisition.

[0113] In marine seismic exploration, seismic waves are generated by the seismic source, reflected from the subsurface interface, and received by detectors on the towed cable. Traditional single-azimuth observation systems (narrow azimuth, small azimuth coverage) can only capture wavefield information in a single direction, resulting in insufficient spatial descriptive ability of multiple wave models. This step utilizes a multi-azimuth observation system with a preset azimuth angle θ (range 0°~360°, azimuth interval Δθ≤45°) to allow the towed cable to collect data at different headings (e.g., 0°, 45°, 90°, etc.). The core principle of multi-azimuth data is to utilize wavefield incident information from different angles to cover a more complete three-dimensional spatial wavefield feature, providing multi-angle data support for the subsequent construction of a pseudo-three-dimensional multiple wave model. For example, when there is a tilted interface on the seabed, observations from different azimuths can capture the reflection characteristics of the interface in different directions, avoiding model deviations caused by single-direction observations. The Nyquist sampling theorem requires an azimuth interval Δθ≤45° to ensure sufficient spatial sampling density and avoid wavefield information aliasing.

[0114] Step S2: Perform three-dimensional consistency processing on the seismic data from all acquisition azimuths to obtain consistent seismic data for each azimuth. The three-dimensional consistency processing takes into account the amplitude differences caused by excitation and reception parameters and spatial sampling differences of the observation system.

[0115] Multi-azimuth observation systems can lead to inconsistencies in amplitude scales across different azimuths due to variations in excitation and reception conditions (such as source type, detector sensitivity, and sampling rate). Furthermore, differences in spatial sampling intervals (such as cable spacing and source density) can cause inconsistencies in data spatial resolution.

[0116] The formula for standardized correction is:

[0117]

[0118] In the formula, Here are the standardized parameters, P is the original parameter, and μ is the parameter. P Let σ be the mean of the parameters. P The standard deviation is the parameter value. This formula converts the original parameter P into a standard normal distribution with a mean of 0 and a standard deviation of 1, eliminating the differences in dimensions and distribution of parameters in different azimuths. For example, if the mean intensity of the seismic source in one azimuth is 1000 and the standard deviation is 200, and the mean intensity in another azimuth is 800 and the standard deviation is 150, after standardization, they can be unified into dimensionless data.

[0119] The interpolation compensation uses the Kriging interpolation method, and the formula is as follows:

[0120]

[0121] In the formula, For the interpolation result of the predicted points, f(x) i ) represents known point data, λ i The weighting coefficients are determined by the semi-variogram. This method calculates the known point data f(x) using the semi-variogram. i The weighting coefficient λ i Interpolation is performed on the predicted point x0. The semi-variogram describes the correlation of spatial points, with points that are closer together having higher weights, thereby compensating for spatial discontinuities caused by different sampling intervals and ensuring the amplitude consistency of multi-directional data in three-dimensional space.

[0122] Step S3: Using the pseudo-three-dimensional water layer multiple wave models obtained from all acquisition azimuths, extract the multiple wave model data for each acquisition azimuth based on the observation system of each acquisition azimuth.

[0123] In this process, multiple wavefield predictions are performed on the seismic data from each acquisition azimuth, and the predicted multiple wavefields from each azimuth are merged in the spatial domain to generate pseudo-three-dimensional multiple wavefield model data containing multi-azimuth information.

[0124] Multiple wave field prediction is based on the extension of the wave equation, and the formula is:

[0125]

[0126] In the formula, ψ is the wave field value, x, z are spatial coordinates, t is time, Δx, Δz, Δt are sampling intervals, and a m,n denoted as ... m,n It is obtained by discretizing the wave equation and reflects the contribution weight of adjacent spatiotemporal points to the current point. For example, when M=2 and N=2, the wave field value of the current point (x,z,t) is obtained by weighted summation of the wave field values ​​of its 2×2 neighboring points, reflecting the spatiotemporal continuity of the wave field.

[0127] This process involves superimposing multiple predicted wavefields (such as wavefields at 0° and 45° azimuths) in three-dimensional space, and generating a pseudo-3D model containing multi-azimuth information through coordinate transformation and weight allocation. For example, for the same underground scattering point, the arrival time and amplitude of multiple waves observed from different azimuths are different. After merging, a more comprehensive wavefield description can be formed, improving the model's adaptability to complex geological structures.

[0128] Step S4: Perform shallow water multiple suppression on the seismic data in each azimuth, and use the subtraction method to obtain the seismic data after multiple suppression in that azimuth and the multiple data removed in that azimuth. The multiple wavefield information from multiple azimuths is used during the subtraction.

[0129] Specifically, this step achieves precise suppression by constructing a Green's function and predicting multiple wave models:

[0130] The formula for constructing the Green's function is:

[0131] G0(s,r;ω)=∫ τ(x) G(s,k;ω)R(s,x,r)G(k,r;ω)dk

[0132] The formula for predicting multiple waves is:

[0133]

[0134] In the formula, s represents the source location, r represents the receiver location, ω represents the angular frequency, k represents the wavenumber, x represents the location of the subsurface interface, G(s,k;ω) and G(k,r;ω) are the Green's functions from the source to the interface and from the interface to the receiver, respectively, R(s,x,r) represents the reflection coefficient, and D(x k ,x s ;w) represents earthquake data, xr Let x be the coordinates of the receiving point. s Let x be the coordinates of the earthquake source. k These are the coordinates of the underground scattering point.

[0135] The algorithm updates the weight coefficients using the minimum mean square error (LMS) criterion: w(n+1) = w(n) + 2μe(n)x(n), where e(n) is the current error signal, x(n) is the input signal, and μ is the step size factor. This algorithm dynamically adjusts the weight coefficients to achieve an optimal match between the predicted multiple wave model and the multiple waves in the actual data, thus enabling precise subtraction. It utilizes curvelet transform. Transform the data to the curvelet domain. Curvelet basis functions. Multiple waves (such as linear in-phase axes) can be sparsely represented, while the effective signal exhibits different coefficient distributions in the curve domain. By setting a threshold, multiple waves and the effective signal can be separated to achieve matched subtraction.

[0136] Step S5: Merge the multiple wave data after removing multiple waves from different azimuths, analyze the differences between the seismic data after removing multiple waves and the multiple wave data and multiple wave model data, match the multiple wave data and the multiple wave model, and match the multiple wave data removed from each azimuth with the data after removing shallow water multiple waves from that azimuth.

[0137] When merging multiple wave data after removing data from different azimuths, the authenticity of the merged data is ensured by constraining the spatial sampling interval, amplitude consistency, and phase continuity of the data from each azimuth.

[0138] Amplitude consistency constraints are achieved through normalization, as shown in the formula:

[0139]

[0140] In the formula, A norm The normalized amplitude is A, where A is the original amplitude. max A min The amplitude extrema are given; the phase continuity constraint is achieved through least-squares phase correction, as shown in the formula. φ is the original phase. To fit the phase function.

[0141] Specifically, the sampling intervals of data from different azimuths are standardized (e.g., the horizontal sampling interval Δx). Spatial grid consistency is ensured through resampling or interpolation to avoid jagged distortion in the merged data. Normalization is used to unify the amplitudes of different azimuths to the [0,1] interval, eliminating amplitude scale differences caused by variations in observation systems. For example, if the amplitude range for one azimuth is [-10,10] and for another is [-5,5], normalization maps both to [0,1].

[0142] Least squares phase correction in To eliminate phase differences between different azimuth data by using the phase function obtained through polynomial fitting, ensure the phase continuity of the merged wave field, and avoid artifacts caused by phase jumps.

[0143] The matching of multiple wave data with multiple wave models includes matching based on amplitude spectrum, frequency response and phase characteristics;

[0144] Amplitude spectrum matching is performed by calculating the correlation coefficient:

[0145]

[0146] In the formula, A 1i A 2i For amplitude spectrum data, ρ is the mean; the correlation coefficient measures the degree of linear correlation between the two amplitude spectra. A The closer the value is to 1, the higher the consistency between the model and the frequency components of the data, and the more accurate the multiple wave model is.

[0147] Frequency response matching uses a transfer function:

[0148]

[0149] In the formula, X(f) and Y(f) are the input and output signal spectra; the transfer function describes the frequency transformation characteristics from the input signal X(f) to the output signal Y(f). In multiple wave suppression, if the model is accurate, H(f) should reflect the frequency characteristics of the multiple waves (such as high-frequency attenuation), and can be used to verify whether the model's response to different frequency components conforms to reality.

[0150] Phase feature matching is performed using a cross-correlation function:

[0151]

[0152] In the formula, x(t) and y(t) are phase signals, and τ is the time offset. The cross-correlation function calculates the phase correlation at different time offsets τ. When τ = 0, the cross-correlation value is the largest, indicating that the phase consistency between the model and the data is optimal, which can avoid the loss of effective signal due to phase differences during the suppression process.

[0153] Step S6: Based on the correlation matching of the multiple wave model and noise subtraction, extract the effective signal from the removed multiple wave data.

[0154] Specifically, some valid signals may be mistakenly removed due to their similarity to multiple wave characteristics. This step establishes screening criteria based on the multi-dimensional matching results from step S5 (amplitude spectrum correlation coefficient, frequency response transfer function, phase cross-correlation value):

[0155] If the correlation coefficient ρ between the amplitude spectrum of a certain component in the removed noise data and the model amplitude spectrum is... A If the value is below a threshold (e.g., 0.3), then the component is considered a valid signal (because the multiple waves and the model's ρ...). A It should be close to 1).

[0156] The frequency response transfer function H(f) of the effective signal should be significantly different from that of the multiple wave model (e.g., stronger energy in the high-frequency band). The effective signal is separated by setting a threshold range for H(f).

[0157] Phase cross-correlation function φ between effective signal and raw data cc The peak value of (τ) at τ=0 should be higher than that of the multiple wave model, indicating that its phase is more consistent with the original data. Through the above multi-dimensional constraints, the effective signal can be accurately identified and extracted from the removed noise, providing a guarantee for subsequent re-addition.

[0158] Step S7: Combine the data after removing shallow water multiples with the effective signals extracted from the multiple data of each azimuth to obtain the seismic data after removing shallow water multiples for each acquisition azimuth.

[0159] Specifically, the amplitude scale of the extracted effective signal is adjusted to match the amplitude level of the compressed data, avoiding amplitude jumps after re-addition. Using the phase correction result from step S5, the effective signal is phase-adjusted to ensure phase consistency with the compressed data, preventing waveform distortion caused by phase differences. The adjusted effective signal and the compressed data are then directly superimposed in the spatial domain, using the formula D. final (x,z,t)=D denoised (x,z,t)+S extracted (x,z,t), where D denoised For the compressed data, S extracted The extracted effective signals are then superimposed. The superimposed data retains the integrity of the effective signals while achieving efficient suppression of multiple waves, ultimately outputting high signal-to-noise ratio seismic data and improving the accuracy of subsequent exploration imaging.

[0160] See Figure 2 This paper compares three seismic profiles (before multiple suppression, after conventional methods, and after this technique) from three dimensions: the effect of multiple suppression, the fidelity of the effective signal, and the clarity of geological information.

[0161] I. Comparison of Suppression Effects of Multiple Waves

[0162] 1. Before multiple wave suppression

[0163] Characteristics: In the profile, multiple waves appear as continuous, high-energy in-phase axis interference (such as the messy wavy signal in the yellow / red bands), especially in the shallow to medium layers (the upper region of the figure), where multiple waves superimpose with effective reflected waves to form a "rough texture" noise band.

[0164] In shallow water environments, the seawater layer reflects multiple times at strong underground reflective interfaces (such as the seabed and high-velocity layers), generating high-energy, wide-bandwidth multiple waves that mask effective signals.

[0165] 2. After conventional shallow water multiple wave suppression method

[0166] Characteristics: The energy of multiple waves is partially weakened (the chaotic ripples in the middle and shallow layers are reduced), but there are still residual interferences (such as the blurred ripples at the edges of the red stripes), and some effective in-phase axes show "breakage" or "weakening" (loss of effective signal).

[0167] Conventional methods (such as two-dimensional DWD) rely on single-azimuth models and cannot accurately match three-dimensional wave fields, resulting in incomplete suppression of multiple waves; at the same time, due to overfitting of the subtraction algorithm, some effective signals are mistakenly deleted.

[0168] 3. After the application of the technology of this invention

[0169] Features: Significant elimination of multiple wave interference (clear texture in the middle and shallow layers, with most of the messy ripples disappearing), and effective phase axis continuity and sharpness (clear boundaries of red / black stripes, with no blurring residue).

[0170] This invention achieves precise suppression of multiple waves while preserving the effective signal by multi-directional pseudo-3D modeling (integrating wave field information from different directions) and effective signal re-addition (based on multi-dimensional matching to compensate for erroneous deletion components).

[0171] II. Comparison of Effective Signal Fidelity

[0172] 1. Before multiple wave suppression

[0173] Effective signals (such as continuous red / black phase axes) are "submerged" by multiple waves, and the continuity of waveforms and the consistency of amplitude are disturbed, making it impossible to accurately identify geological strata.

[0174] 2. After conventional methods

[0175] Although some effective signals are visible, the identification error of geological interfaces (such as stratigraphic pinch-out and faults) is increased due to phase / amplitude distortion (fracture of the same phase axis and uneven intensity); and the effective signals in the middle and deep layers (lower part of the figure) are significantly attenuated due to excessive suppression.

[0176] 3. After the application of the technology of this invention

[0177] The effective signals are fully preserved (continuous phase axis and stable amplitude), and the geological strata of the shallow to deep layers (such as the lateral extension of the red band and the undulation of the black band) are clearly distinguishable, providing a reliable basis for tectonic interpretation (such as faults and stratigraphic sedimentation).

[0178] III. Comparison of Geological Information Clarity

[0179] 1. Before multiple wave suppression

[0180] Geological information is unclear: multiple wave interference makes it difficult to identify stratigraphic interfaces (such as the boundaries of red / black stripes), and structural details (such as small faults and stratigraphic pinch-outs) are completely obscured.

[0181] 2. After conventional methods

[0182] Geological information was partially restored: after the weakening of multiple waves in the shallow and middle layers, the stratigraphic interfaces began to appear, but due to the loss of effective signals, structural details (such as the subtle undulations of bands) still showed "sawtooth" distortion; in the middle and deep layers, the information remained blurred due to energy attenuation.

[0183] 3. After the application of the technology of this invention

[0184] The geological information is highly clear: multiple waves completely suppress the signal and preserve the effective signal, making the stratigraphic interface continuously traceable (the red / black stripes extend laterally and are stable), and the structural details (such as the slight undulations of the stripes and the interlayer contact relationship) are clearly presented, which can accurately identify geological features such as faults and stratigraphic pinch-outs.

[0185] It should be noted that the method of this embodiment can be executed by a single device, such as a computer or server. The method of this embodiment can also be applied in 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 embodiment, and these multiple devices will interact with each other to complete the described method for suppressing multiple waves of seismic data acquired by a marine towed cable.

[0186] 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.

[0187] Example 2

[0188] See Figure 3Embodiment 2 of the present invention also provides a device for suppressing multiple waves of seismic data acquired by a marine towed cable, comprising:

[0189] The data acquisition module 100 is used to acquire multi-azimuth towed cable seismic data, which is obtained by using an observation system with a preset azimuth in marine towed cable seismic acquisition.

[0190] The three-dimensional consistency processing module 200 is used to perform three-dimensional consistency processing on seismic data from all acquired azimuths to obtain consistent seismic data for each azimuth. The three-dimensional consistency processing takes into account the amplitude differences caused by excitation and reception parameters and spatial sampling differences of the observation system.

[0191] The multiple wave model extraction module 300 is used to extract multiple wave model data for each acquisition azimuth based on the observation system of each acquisition azimuth, using the pseudo-three-dimensional water layer multiple wave model obtained from all acquisition azimuths.

[0192] The shallow water multiple suppression module 400 is used to suppress the seismic data in each azimuth using a subtraction method to obtain the seismic data after multiple suppression in that azimuth and the multiple data removed in that azimuth. The multiple wavefield information from multiple azimuths is utilized during the subtraction.

[0193] The data merging and matching module 500 is used to merge multiple wave data after removal from different azimuths, analyze the differences between the seismic data after removal of multiple waves and the multiple wave data and multiple wave model data, match the multiple wave data and the multiple wave model, and match the multiple wave data removed from each azimuth with the data after removing shallow water multiple waves in that azimuth.

[0194] The effective signal extraction module 600 is used to extract the effective signal from the removed multiple wave data based on the correlation matching of the multiple wave model and noise subtraction.

[0195] The effective signal merging module 700 is used to merge the data after removing shallow water multiples with the effective signals extracted from the multiples data of each azimuth to obtain the seismic data after removing shallow water multiples for each acquisition azimuth.

[0196] In this embodiment, the three-dimensional consistency processing module 200 performs standardized correction on the excitation parameters and the receiving parameters, and performs interpolation compensation on the spatial sampling differences of the observation system to achieve amplitude fidelity.

[0197] The formula for standardized correction is:

[0198]

[0199] In the formula, Here are the standardized parameters, P is the original parameter, and μ is the parameter.P Let σ be the mean of the parameters. P The standard deviation of the parameter;

[0200] The interpolation compensation uses the Kriging interpolation method, and the formula is:

[0201]

[0202] In the formula, For the interpolation result of the predicted points, f(x) i ) represents known point data, λ i The weighting coefficients are determined by the semi-variogram.

[0203] In this embodiment, the multiple wave model extraction module 300 includes:

[0204] Multiple wavefield predictions are performed on the seismic data from each acquisition azimuth. The predicted multiple wavefields from each azimuth are then merged in the spatial domain to generate pseudo-three-dimensional multiple wavefield model data containing multi-azimuth information.

[0205] Multiple wave field prediction is based on the extension of the wave equation, and the formula is:

[0206]

[0207] In the formula, ψ is the wave field value, x, z are spatial coordinates, t is time, Δx, Δz, Δt are sampling intervals, and a m,n is the extension coefficient; m and n are the summation index variables, where m corresponds to the horizontal sampling point offset and n corresponds to the time sampling point offset; M and N are the upper limits of the summation range, which determine the number of adjacent sampling points involved in the calculation.

[0208] In this embodiment, the shallow water multiple suppression module 400 extracts multiple model data for each acquisition azimuth, specifically by constructing a Green's function and predicting multiples. The formula for constructing the Green's function is as follows:

[0209] G0(s,r;ω)=∫ τ(x) G(s,k;ω)R(s,x,r)G(k,r;ω)dk

[0210] The formula for predicting multiple waves is:

[0211]

[0212] In the formula, s represents the source location, r represents the receiver location, ω represents the angular frequency, k represents the wavenumber, x represents the location of the subsurface interface, G(s,k;ω) and G(k,r;ω) are the Green's functions from the source to the interface and from the interface to the receiver, respectively, R(s,x,r) represents the reflection coefficient, and D(x k ,x s;w) represents earthquake data, x r Let x be the coordinates of the receiving point. s Let x be the coordinates of the earthquake source. k These are the coordinates of the underground scattering point.

[0213] In this embodiment, the data merging and matching module 500 ensures the authenticity of the merged data by constraining the spatial sampling interval, amplitude consistency and phase continuity of the data from each direction.

[0214] Amplitude consistency constraints are achieved through normalization, as shown in the formula:

[0215]

[0216] In the formula, A norm The normalized amplitude is A, where A is the original amplitude. max A min The amplitude extrema are given; the phase continuity constraint is achieved through least-squares phase correction, as shown in the formula. φ is the original phase. To fit the phase function.

[0217] In this embodiment, the data merging and matching module 500 performs matching between multiple wave data and multiple wave models, including matching based on amplitude spectrum, frequency response and phase characteristics.

[0218] Amplitude spectrum matching is performed by calculating the correlation coefficient:

[0219]

[0220] In the formula, A 1i A 2i For amplitude spectrum data, The mean;

[0221] Frequency response matching uses a transfer function:

[0222]

[0223] In the formula, X(f) and Y(f) are the spectra of the input and output signals;

[0224] Phase feature matching is performed using a cross-correlation function:

[0225]

[0226] In the formula, x(t) and y(t) are phase signals, and τ is the time offset.

[0227] In this embodiment, the azimuth angle θ of the observation system ranges from 0° to 360°, and the azimuth interval Δθ ≤ 45°, satisfying the Nyquist sampling theorem.

[0228] In this embodiment, the shallow water multiple suppression module 400 uses adaptive subtraction or curve domain matching subtraction to remove the multiple model from the original data.

[0229] Adaptive subtraction updates the weight coefficients using the minimum mean square error criterion, as shown in the formula:

[0230] w(n+1)=w(n)+2μe(n)x(n)

[0231] In the formula, w is the weight coefficient vector, μ is the step size factor, e(n) is the error signal, and x(n) is the input signal;

[0232] Curved wave domain matched subtraction via curved wave transform:

[0233]

[0234] In the formula, Let j, l, k be the curve wave basis functions, and j, l, k be the scale, orientation, and position parameters, respectively.

[0235] It should be noted that the information interaction and execution process between the modules of the above-mentioned device are based on the same concept as the method embodiment in Embodiment 1 of this application, and the resulting technical effects are the same as those in the method embodiment of this application. For details, please refer to the description in the method embodiment shown above in this application, and it will not be repeated here.

[0236] Example 3

[0237] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium storing program code for a method of suppressing multiple waves of seismic data acquired by a marine towed cable. The program code includes instructions for executing the method of suppressing multiple waves of seismic data acquired by a marine towed cable as described in Embodiment 1 or any possible implementation thereof.

[0238] Computer-readable storage media can be any available medium that a computer can access, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives, SSDs).

[0239] Example 4

[0240] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0241] The processor and the memory communicate with each other via a bus; the memory stores program instructions that can be executed by the processor, and the processor can call the program instructions to execute the method for suppressing multiple waves of seismic data acquired by marine towed cable in Embodiment 1 or any possible implementation thereof.

[0242] Specifically, a processor can be implemented in hardware or software. When implemented in hardware, the processor can be a logic circuit, an integrated circuit, etc. When implemented in software, the processor can be a general-purpose processor that reads software code stored in memory. This memory can be integrated into the processor or located outside the processor and exist independently.

[0243] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0244] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0245] Although the present invention has been described in detail above with general descriptions and specific embodiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

Claims

1. A method for suppressing multiple waves in seismic data acquired by a marine towed cable, characterized in that, Includes the following steps: Acquire multi-azimuth towed seismic data, which is obtained by using an observation system with a preset azimuth during marine towed seismic acquisition. Three-dimensional consistency processing is performed on seismic data from all acquisition azimuths to obtain consistent seismic data for each azimuth. The three-dimensional consistency processing takes into account the amplitude differences caused by excitation and reception parameters as well as spatial sampling differences of the observation system. Using the pseudo-three-dimensional water layer multiple models obtained from all acquisition azimuths, multiple model data for each acquisition azimuth are extracted based on the observation system of each acquisition azimuth. Shallow water multiples suppression is performed on the seismic data in each azimuth. The subtraction method is used to obtain the seismic data after multiple suppression in that azimuth and the multiple data removed in that azimuth. The multiple wavefield information from multiple azimuths is used during the subtraction. Multiple wave data after removal from different azimuths are merged, and the differences between the seismic data after multiple wave removal, multiple wave data, and multiple wave model data are analyzed. Multiple wave data and multiple wave models are matched, and multiple wave data removed from each azimuth are matched with data after removing shallow water multiple waves in that azimuth. Based on the correlation matching between the multiple wave model and the noise subtraction, the effective signal is extracted from the removed multiple wave data; The data after removing shallow water multiples are combined with the effective signals extracted from the multiple data of each azimuth to obtain the seismic data after removing shallow water multiples for each acquisition azimuth.

2. The method for suppressing multiple waves in seismic data acquired by marine towed cable according to claim 1, characterized in that, The three-dimensional consistency processing includes standardizing and correcting the excitation and reception parameters, and interpolating to compensate for spatial sampling differences in the observation system, in order to achieve amplitude fidelity. The formula for standardized correction is: In the formula, The parameters are standardized, P is the original parameter, and μ is the parameter. P Let σ be the mean of the parameters. P The standard deviation of the parameter; The interpolation compensation uses the Kriging interpolation method, and the formula is: In the formula, For the interpolation result of the predicted points, f(x) i ) represents known point data, λ i The weighting coefficients are determined by the semi-variogram.

3. The method for suppressing multiple waves in seismic data acquired by marine towed cable according to claim 1, characterized in that, The establishment of the pseudo-three-dimensional water layer multiple wave model includes: Multiple wavefield predictions are performed on the seismic data from each acquisition azimuth. The predicted multiple wavefields from each azimuth are then merged in the spatial domain to generate pseudo-three-dimensional multiple wavefield model data containing multi-azimuth information. Multiple wave field prediction is based on the extension of the wave equation, and the formula is: In the formula, ψ is the wave field value, x, z are spatial coordinates, t is time, Δx, Δz, Δt are sampling intervals, and a m,n is the extension coefficient; m and n are the summation index variables, where m corresponds to the horizontal sampling point offset and n corresponds to the time sampling point offset; M and N are the upper limits of the summation range, which determine the number of adjacent sampling points involved in the calculation.

4. The method for suppressing multiple waves in seismic data acquired by marine towed cable according to claim 1, characterized in that, The pseudo-three-dimensional water layer multiple model obtained from all acquisition azimuths extracts multiple model data for each acquisition azimuth based on the observation system at each acquisition azimuth. This is specifically achieved by constructing a Green's function and predicting multiples, where the formula for constructing the Green's function is: G0(s,r;ω)=∫ τ(x) G(s,k;ω)R(s,x,r)G(k,r;ω)dk The formula for predicting multiple waves is: In the formula, s represents the source location, r represents the receiver location, ω represents the angular frequency, k represents the wavenumber, x represents the location of the subsurface interface, G(s,k;ω) and G(k,r;ω) are the Green's functions from the source to the interface and from the interface to the receiver, respectively, R(s,x,r) represents the reflection coefficient, and D(x k ,x s ;w) represents earthquake data, x r Let x be the coordinates of the receiving point. s Let x be the coordinates of the earthquake source. k These are the coordinates of the underground scattering point.

5. The method for suppressing multiple waves in seismic data acquired by marine towed cable according to claim 1, characterized in that, When merging multiple wave data after removing data from different orientations, the authenticity of the merged data is ensured by constraining the spatial sampling interval, amplitude consistency, and phase continuity of the data from each orientation. Amplitude consistency constraints are achieved through normalization, as shown in the formula: In the formula, A norm The normalized amplitude is A, where A is the original amplitude. max A min This represents the extreme value of the amplitude. Phase continuity constraints are achieved through least-squares phase correction, as shown in the formula: φ is the original phase. To fit the phase function.

6. The method for suppressing multiple waves in seismic data acquired by marine towed cable according to claim 1, characterized in that, The matching of multiple wave data with multiple wave models includes matching based on amplitude spectrum, frequency response, and phase characteristics; Amplitude spectrum matching is performed by calculating the correlation coefficient: In the formula, A 1i A 2i For amplitude spectrum data, The mean; Frequency response matching uses a transfer function: In the formula, X(f) and Y(f) are the spectra of the input and output signals; Phase feature matching is performed using a cross-correlation function: In the formula, x(t) and y(t) are phase signals, and τ is the time offset.

7. The method for suppressing multiple waves in seismic data acquired by marine towed cable according to claim 1, characterized in that, The azimuth angle θ of the observation system ranges from 0° to 360°, and the azimuth interval Δθ ≤ 45°, satisfying the Nyquist sampling theorem.

8. The method for suppressing multiple waves in seismic data acquired by marine towed cable according to claim 1, characterized in that, When performing shallow water multiple suppression on seismic data in each azimuth, the multiple model is removed from the original data using adaptive subtraction or curve domain matched subtraction. Adaptive subtraction updates the weight coefficients using the minimum mean square error criterion, as shown in the formula: w(n+1)=w(n)+2μe(n)x(n) In the formula, w is the weight coefficient vector, μ is the step size factor, e(n) is the error signal, and x(n) is the input signal; Curved wave domain matched subtraction via curved wave transform: In the formula, Let j, l, k be the curve wave basis functions, and j, l, k be the scale, orientation, and position parameters, respectively.

9. A device for suppressing multiple waves in marine towed cable seismic data acquisition, characterized in that, include: The data acquisition module is used to acquire multi-azimuth towed cable seismic data, which is obtained by using an observation system with a preset azimuth in marine towed cable seismic acquisition. The three-dimensional consistency processing module is used to perform three-dimensional consistency processing on seismic data from all acquired azimuths to obtain consistent seismic data for each azimuth. The three-dimensional consistency processing takes into account the amplitude differences caused by excitation and reception parameters as well as spatial sampling differences of the observation system. The multiple wave model extraction module is used to extract multiple wave model data for each acquisition azimuth based on the observation system of each acquisition azimuth, using the pseudo-three-dimensional water layer multiple wave model obtained from all acquisition azimuths. The shallow water multiple suppression module is used to suppress the seismic data in each azimuth. It uses a subtraction method to obtain the seismic data after multiple suppression in that azimuth and the multiple data removed in that azimuth. The multiple wavefield information from multiple azimuths is used during the subtraction. The data merging and matching module is used to merge multiple wave data after removal from different azimuths, analyze the differences between the seismic data after multiple wave removal and the multiple wave data and multiple wave model data, match the multiple wave data and the multiple wave model, and match the multiple wave data removed from each azimuth with the data after removing shallow water multiple waves in that azimuth. The effective signal extraction module is used to extract the effective signal from the removed multiple wave data based on the correlation matching of the multiple wave model and noise subtraction. The effective signal merging module is used to merge the data after removing shallow water multiples with the effective signals extracted from the multiples data of each azimuth to obtain the seismic data after removing shallow water multiples for each acquisition azimuth.

10. A device for suppressing multiple waves in marine towed cable seismic data acquisition according to claim 9, characterized in that, The multiple wave model extraction module is implemented by constructing a Green's function and predicting multiple waves, wherein the formula for constructing the Green's function is: G0(s,r;ω)=∫ τ(x) G(s,k;ω)R(s,x,r)G(k,r;ω)dk The formula for predicting multiple waves is: In the formula, s represents the source location, r represents the receiver location, ω represents the angular frequency, k represents the wavenumber, x represents the location of the subsurface interface, G(s,k;ω) and G(k,r;ω) are the Green's functions from the source to the interface and from the interface to the receiver, respectively, R(s,x,r) represents the reflection coefficient, and D(x k ,x s ;w) represents earthquake data, x r Let x be the coordinates of the receiving point. s Let x be the coordinates of the earthquake source. k These are the coordinates of the underground scattering point.

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