A method for suppressing irregular multiple waves of marine seismic data

Abstract

The application discloses a kind of marine seismic data irregular multiple wave suppression methods, belong to marine geophysical exploration field.The method is first to the seismic data is preprocessed;Then set virtual focusing interface, calculate downlink focusing function direct wave;Based on causality constraint iterative solution uplink and downlink focusing function, and then substitute free boundary Marchenko equation group, obtain each interface uplink and downlink Green function.Coupling Green function to predict irregular multiple wave with interface by interface, and introduce Softmax adaptive weighting mechanism to the prediction result is weighted superposition, obtain full-wavefield multiple wave prediction.Last, in the least square robust framework, solve matching filter, and predicted multiple wave and reflection data are adaptively subtracted once, and complete suppression.The application breaks through regular multiple wave hypothesis, adapts complex geology, and improves multiple wave suppression precision through adaptive weighting and robust matching.

Description

Technical Field

[0001] This invention belongs to the field of marine geophysical exploration and relates to a method for suppressing irregular multiples in marine seismic data. Specifically, it is a method for suppressing irregular multiples based on the Marchenko equations of free boundary, which is used to address the complex situation where surface multiples and interlayer multiples develop simultaneously and interfere with each other to form irregular multiples. Background Technology

[0002] In conventional seismic exploration, the development of surface multiples can severely affect the accuracy of seismic data processing and interpretation, adversely impacting the inference of subsurface rock properties, structural characteristics, and potential mineral resource distribution. Therefore, the suppression of multiples has always been an essential and indispensable part of seismic data processing.

[0003] Most current methods for suppressing multiples are based on the assumption that multiples are regular disturbances. These mainly include filtering-based methods such as predictive deconvolution and Radon transform, as well as wave theory-based predictive subtraction methods. However, under complex geological conditions, surface multiples and interlayer multiples often coexist, interfering and overlapping to form irregular multiples. These wavefields do not satisfy the regular disturbance assumption, leading to a decrease in the suppression effectiveness of existing methods. The problem of irregular multiples is particularly prominent in marine oil and gas seismic exploration. Filtering is a feasible strategy for suppressing irregular multiples, but the suppression accuracy is often insufficient. Combining multiple methods can also suppress irregular multiples, but this method is computationally expensive and requires specific prediction and suppression order and accuracy. When the prediction and suppression accuracy of surface and interlayer multiples is insufficient, or when the prediction and suppression order is inappropriate, the combined method may produce poor suppression results or even introduce new noise.

[0004] In recent years, multiple suppression methods based on Marchenko theory have been formally proposed, and their role in processing multiples has attracted widespread attention. However, current methods for processing surface and interlayer multiples are still mainly applied in the imaging field, and research on irregular multiple suppression still lacks comprehensive methods and theories. Summary of the Invention

[0005] To address the aforementioned problems in existing technologies, this invention provides a method for suppressing irregular multiples in marine seismic data. This method can effectively suppress irregular multiples in marine seismic data under complex geological conditions.

[0006] To achieve the above objectives, the present invention provides a method for suppressing irregular multiples in marine seismic data, comprising the following steps:

[0007] S1. Preprocess the acquired marine seismic data, including data regularization and reconstruction, ghost wave removal and wavelet deconvolution, to obtain the reflection response data volume after removing the influence of the wavelet, and obtain the time-domain velocity field and its depth-domain transformation through velocity analysis.

[0008] S2, based on the reference surface construction strategy, selects several virtual focusing interfaces at the abrupt changes in near-sea surface and depth velocity, uses each grid point on the interface as a virtual seismic source, and calculates the Green's function direct wave at the sea surface receiving point through forward modeling based on the depth domain velocity field, and then reverses the time of the direct wave to construct the downfocusing function direct wave.

[0009] S3, taking the direct wave and reflection response data of the downlink focusing function as input, and based on causal constraints, calculate the uplink and downlink focusing functions through iterative derivation of formulas;

[0010] S4. Substitute the up and down focusing functions into the Marchenko equations for the free boundary to obtain the up and down Green's functions at the focal points of each virtual interface on the near-sea surface and underground.

[0011] S5 uses the principle of super-interference to perform data convolution between the downlink Green's function and the uplink Green's function at the virtual interface where the direct wave component is removed, and predicts the corresponding irregular multiple waves interface by interface.

[0012] S6 introduces a Softmax adaptive weighting mechanism driven by cross-correlation coefficients to weight and superimpose the multiple components predicted by each interface to obtain the prediction results of irregular multiples in the whole wavefield.

[0013] S7 takes the obtained full-wavefield irregular multiple prediction results and reflection response data as input, solves the matched filter under the least squares robust framework, performs a one-time subtraction, and finally completes the suppression processing of irregular multiples.

[0014] Preferably, the irregular multiples refer to multiples that no longer satisfy the regular interference assumption under complex geological conditions, including surface multiples and interlayer multiples, which are represented as the sum of all surface multiples and interlayer multiples:

[0015] (1)

[0016] in, , , Representing the surface points Excitation at the ground point The received irregular multiples, surface multiples, and interlayer multiples are observed.

[0017] Preferably, the virtual focus interface in S2 is set according to the following rules:

[0018] (1) For surface multiple waves, a horizontal virtual interface of the near-sea surface is set as the prediction reference surface;

[0019] (2) For interlayer multiple waves, the layer depth h=v * t0 / 2 is estimated based on the two-way travel time t0 of each strong reflection axis in the zero offset channel and the velocity field in the depth domain. The velocity field change trend near the layer is referenced, and several horizontal virtual interfaces are set as prediction reference surfaces in the area with significant velocity changes.

[0020] Preferably, the formula for iteratively obtaining the focusing function based on causal constraints in S3 is expressed as follows:

[0021] (2a)

[0022] (2b)

[0023] in, Indicates the number of iterations. It is a time window cutoff function used to maintain the causality of the wave field during the iteration process. Represents the reflection response data. and These represent the downlink and uplink focus functions, respectively. Represented as the direct wave of the downlink focusing function and the scattered wake wave. The iterative formula is:

[0024] (2c)

[0025] Preferably, the Marchenko equations for the free boundary in S4 take into account the reflection conditions of the free surface of seawater, as expressed below:

[0026] (3a)

[0027] (3b)

[0028] in, This indicates that under free boundary conditions, the surface... Stimulate, underground focal point Received uplink and downlink Green's functions.

[0029] Preferably, the prediction of irregular multiple waves at each virtual interface in S5 is represented as follows:

[0030] (4)

[0031] in, This represents the irregular multiple waves associated with the nth virtual interface predicted by the Marchenko equations for free boundaries. ), including related surface multiples and interlayer multiple waves , This represents the downlink Green's function after removing the direct wave portion.

[0032] Preferably, the irregular multiples predicted in S6 are represented by a Softmax adaptive weighted superposition driven by the cross-correlation coefficient of the irregular multiples predicted by each virtual interface, as follows:

[0033] , (5)

[0034] Where N is the number of virtual interfaces selected. Predict multiple waves for the nth virtual interface The normalized weighted coefficients, This represents the cross-correlation coefficient between the predicted multiple wave and the reflection response data.

[0035] Preferably, the one-time adaptive matching subtraction process in S7 introduces robust constraints, and its optimization objective function is as follows:

[0036] (6)

[0037] Where a is the filtering operator, λ is the regularization coefficient with a value between 0.001 and 0.1, and the L1 norm is used to enhance robustness and suppress the influence of outliers on filter estimation.

[0038] This invention addresses the complex situation in marine seismic exploration where surface multiples and interlayer multiples develop simultaneously and interferentially, forming irregular multiples. It proposes a suppression method based on the Marchenko equations for free boundaries. Compared with existing technologies, this method offers the following advantages:

[0039] 1) This method breaks through the traditional assumption of regular multiples and can adapt to complex conditions. By constructing virtual focusing interfaces at the near-sea surface and velocity abrupt change layers, and combining this with the Marchenko equations for free boundaries, it can simultaneously predict and suppress surface and interlayer multiples, overcoming the limitations of the traditional "regular interference" assumption and significantly improving adaptability under complex geological conditions. Furthermore, this method does not require consideration of the processing order of base-level related multiples, and different prediction orders and accuracies have no significant impact on the suppression effect.

[0040] 2) A Softmax adaptive weighting mechanism driven by cross-correlation coefficients is proposed to improve the robustness of the multiple suppression algorithm. Unlike traditional fixed weights or manual experience selection, this mechanism can dynamically allocate weights according to the similarity between the signal and the observed data, automatically highlighting high-precision prediction results and suppressing low-quality components, effectively improving the reliability and robustness of irregular multiple suppression.

[0041] 3) A robust matching objective function was constructed, and the adaptive subtraction was more accurate. L1 regularization robust constraints were introduced into the least squares framework, and "one-time adaptive matching subtraction" was adopted, which reduced outlier interference and energy leakage, and avoided the accumulation of errors from multiple iterations, thus ensuring the suppression accuracy while reducing computational overhead. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a flowchart of a method for suppressing irregular multiples in marine seismic data according to an embodiment of the present invention;

[0044] Figure 2 The velocity field of a complex model containing synchro and curved interfaces in an embodiment of the present invention;

[0045] Figure 3 This is a single-shot record of a complex model in an embodiment of the present invention;

[0046] Figure 4 These are the irregular multiple prediction results corresponding to each virtual focusing interface in the embodiments of the present invention, wherein (a)-(f) are the multiple prediction results corresponding to the first to sixth virtual focusing interfaces, respectively.

[0047] Figure 5 This embodiment of the invention provides a predicted multiple wavelet based on cross-correlation coefficient-driven adaptive weighted superposition.

[0048] Figure 6 This is the result of a single wave after removing multiple waves, obtained by robust one-time matching subtraction in an embodiment of the present invention. Detailed Implementation

[0049] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. It should also be noted that, to make the embodiments more comprehensive, the following embodiments are preferred embodiments, and those skilled in the art can use other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0050] It should be noted that the use of terms such as "an embodiment," "an embodiment," "an exemplary embodiment," and "some embodiments" in the specification indicates that the described embodiment may include a specific feature, structure, or characteristic, but not every embodiment necessarily includes that specific feature, structure, or characteristic. Furthermore, when a specific feature, structure, or characteristic is described in connection with an embodiment, implementing such a feature, structure, or characteristic in conjunction with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the art.

[0051] Generally, terms can be understood at least partly from their use in context. For example, depending at least partly on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in a singular sense, or a combination of features, structures, or characteristics in a plural sense. Additionally, the term "based on" can be understood not necessarily to convey an exclusive set of factors, but rather, alternatively, depending at least partly on the context, to allow for the presence of other factors that are not necessarily explicitly described.

[0052] This invention addresses the complex situation in marine seismic exploration where surface multiples and interlayer multiples develop simultaneously and interfere with each other to form irregular multiples. It proposes a method for suppressing irregular multiples in marine seismic data to address the mutual interference and influence between surface and interlayer multiples. Given the limitations of traditional methods, this method proposes a Softmax adaptive weighting mechanism driven by cross-correlation coefficients for full-wavefield irregular multiple prediction by appropriately setting a virtual focusing interface. Furthermore, it introduces a one-time matching subtraction within a least-squares robust framework, ultimately achieving efficient and high-precision suppression of irregular multiples. This method overcomes the traditional assumption of regular interference in multiples, enabling the simultaneous suppression of both surface and interlayer multiples under complex conditions without considering the processing order of reference-level related multiples. Different prediction orders and accuracies do not significantly affect the suppression effect. The following section will provide a detailed description of this method for suppressing irregular multiples in marine seismic data, with reference to the accompanying figures.

[0053] See Figure 1 This invention provides a method for suppressing irregular multiples in marine seismic data, the specific steps of which are as follows:

[0054] S1. Preprocess the acquired marine seismic data, including data regularization and reconstruction, ghost wave removal and wavelet deconvolution, to obtain the reflection response data volume after removing the influence of the wavelet, and obtain the time-domain velocity field and its depth-domain transformation through velocity analysis.

[0055] Specifically, the irregular multiples refer to multiples that no longer satisfy the regular disturbance assumption under complex geological conditions, including surface multiples and interlayer multiples, which are represented as the sum of all surface multiples and interlayer multiples:

[0056] (1)

[0057] in, , , Representing the surface points Excitation at the ground point The received irregular multiples, surface multiples, and interlayer multiples are observed.

[0058] S2, based on the reference surface construction strategy, selects several virtual focusing interfaces at the abrupt changes in near-sea surface and depth velocity, uses each grid point on the interface as a virtual seismic source, and calculates the Green's function direct wave at the sea surface receiving point through forward modeling based on the depth domain velocity field, and then reverses the time of the direct wave to construct the downfocusing function direct wave.

[0059] Specifically, the virtual focus interface is set up according to the following rules:

[0060] (1) For surface multiple waves, a horizontal virtual interface of the near-sea surface is set as the prediction reference surface;

[0061] (2) For interlayer multiple waves, the layer depth h=v * t0 / 2 is estimated based on the two-way travel time t0 of each strong reflection axis in the zero offset channel and the velocity field in the depth domain. The velocity field change trend near the layer is referenced, and several horizontal virtual interfaces are set as prediction reference surfaces in the area with significant velocity changes.

[0062] S3, taking the direct wave and reflection response data of the downlink focusing function as input, and based on causal constraints, calculate the uplink and downlink focusing functions through iterative derivation of formulas;

[0063] Specifically, based on the inverse relationship between the direct wave of the focusing function and the direct wave of the Green's function, and considering the time intervals in which the Green's function and the focusing function exist, iterative formulas for the uplink and downlink focusing functions considering the free surface case can be obtained:

[0064] (2a)

[0065] (2b)

[0066] in, Indicates the number of iterations. It is a time window cutoff function used to maintain the causality of the wave field during the iteration process. Represents the reflection response data. and These represent the downlink and uplink focus functions, respectively. Represented as the direct wave of the downlink focusing function and the scattered wake wave. The iterative formula is:

[0067] (2c)

[0068] S4. Substitute the up and down focusing functions into the Marchenko equations for the free boundary to obtain the up and down Green's functions at the focal points of each virtual interface on the near-sea surface and underground.

[0069] Specifically, after obtaining the convergent solutions of the scattered wake wave and the uplink and downlink focusing functions through iterative solutions, these solutions can be substituted into the Marchenko equations for the free boundary derived for the free surface case to construct the uplink and downlink Green's functions at the subsurface focusing point. .

[0070] (3a)

[0071] (3b)

[0072] In the formula, This indicates that under free boundary conditions, the surface... Stimulate, underground focal point Received uplink and downlink Green's functions, Let be the reflection coefficient of the free surface of seawater. Therefore, the above formula can be further derived as follows:

[0073] (3c)

[0074] (3d)

[0075] S5 uses the principle of super-interference to perform data convolution between the downlink Green's function and the uplink Green's function at the virtual interface where the direct wave component is removed, and predicts the corresponding irregular multiple waves interface by interface.

[0076] Specifically, the prediction of irregular multiple waves at each virtual interface is represented as follows:

[0077] (4)

[0078] in, This represents the irregular multiple waves associated with the nth virtual interface predicted by the Marchenko equations for free boundaries. ), including related surface multiples and interlayer multiple waves , This represents the descending Green's function after removing the direct wave component. It should be noted that, since the inputs are all raw reflection response data, this differs from the layer-stripping method used in traditional interlayer multiple suppression techniques; the prediction order of multiples at each subsurface interface does not affect the final result.

[0079] S6 introduces a Softmax adaptive weighting mechanism driven by cross-correlation coefficients to weight and superimpose the multiple components predicted by each interface to obtain the prediction results of irregular multiples in the whole wavefield.

[0080] Specifically, selecting the first virtual interface near the sea surface below the waterline allows for the prediction of all surface multiples. Then, selecting multiple subsurface virtual interfaces below the seabed enables the prediction of other irregular multiples. Finally, by performing a Softmax adaptive weighted superposition based on cross-correlation coefficients on the irregular multiples predicted from each virtual interface, the predicted full-wavefield irregular multiples can be obtained, expressed as:

[0081] , (5)

[0082] Where N is the number of virtual interfaces selected. Predict multiple waves for the nth virtual interface The normalized weighted coefficients, This represents the cross-correlation coefficient between the predicted multiple wave and the reflection response data.

[0083] S7 takes the obtained full-wavefield irregular multiple prediction results and reflection response data as input, solves the matched filter under the least squares robust framework, performs a one-time subtraction, and finally completes the suppression processing of irregular multiples.

[0084] Specifically, after completing the weighted superposition of the multiple prediction results to achieve full-field irregular multiple prediction, an L1 robust constraint is introduced under the least squares framework, and the following optimization objective function is solved:

[0085] (6)

[0086] Where 'a' is the filtering operator, λ is the regularization coefficient with a value between 0.001 and 0.1, and the L1 norm is used to enhance robustness and suppress the influence of outliers on filter estimation. The above one-time adaptive matched subtraction process avoids the high computational cost of multiple adaptive matched subtractions in traditional methods, and also avoids the cumulative error introduced by each adaptive subtraction.

[0087] By completing the above steps, the suppression of irregular multiples in marine seismic data can be achieved.

[0088] To more clearly illustrate the above-described method of the present invention, a specific embodiment will be described below.

[0089] Example:

[0090] This invention is applied to complex model data containing synchro and curved interfaces, whose velocity field is as follows: Figure 2 As shown. The model has a lateral distance of 2000 m, a longitudinal depth of 2000 m, and a grid spacing of 2.5 m. A single-shot record from the seismic record obtained through finite-difference forward modeling using the 20 Hz Ricker wavelet as the dominant frequency is shown. Figure 3 As shown. Due to the presence of strong reflection interfaces at the sea surface and underground, there are a considerable number of irregular multiples in seismic records. Failure to remove these multiples will seriously affect the accuracy of seismic data processing and interpretation.

[0091] To address the aforementioned issues, this invention preprocesses the simulated data to obtain a deconvolutioned reflection response data volume, and constructs six subsurface virtual interfaces between different layers based on the velocity field. Then, using each grid point on the interface as a virtual seismic source, the Green's function direct wave at the sea surface receiver is calculated using forward modeling based on the depth-domain velocity field, and time-reversed to obtain the downlink focusing function direct wave. Next, the downlink focusing function direct wave and the reflection response data are used as input to calculate the uplink and downlink focusing functions, which are then substituted into the Marchenko equations for the free boundary to obtain the uplink and downlink Green's functions. After processing the obtained Green's functions, the irregular multiple wave prediction results related to the six subsurface virtual interfaces are obtained, such as... Figure 4 As shown, Figure 4 (a)-(f) in the figure represent the multiple wave prediction results related to the first to sixth underground virtual interfaces, respectively. It can be seen that the multiple waves related to each virtual interface were predicted very well.

[0092] After obtaining the irregular multiple prediction results related to each interface, the prediction results are weighted and superimposed using a Softmax adaptive weighting method driven by the cross-correlation coefficient to obtain the full-wavefield irregular multiple prediction results, such as... Figure 5As shown. Finally, it is subjected to a one-time adaptive subtraction with the reflection response data under least-squares robust constraints to obtain the seismic data after suppressing the irregular multiples, as shown. Figure 6 As shown in the figure, irregular multiples in the recording were significantly suppressed, and each reflection phase axis was clearly presented, thus effectively verifying the feasibility and effectiveness of the invention.

[0093] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0094] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for suppressing irregular multiple waves in marine seismic data, characterized in that, Includes the following steps: S1. Preprocess the acquired marine seismic data, including data regularization and reconstruction, ghost wave removal and wavelet deconvolution, to obtain the reflection response data volume after removing the influence of the wavelet, and obtain the time-domain velocity field and its depth-domain transformation through velocity analysis. S2, based on the reference surface construction strategy, selects several virtual focusing interfaces at the abrupt changes in near-sea surface and depth velocity, uses each grid point on the interface as a virtual seismic source, and calculates the Green's function direct wave at the sea surface receiving point through forward modeling based on the depth domain velocity field, and then reverses the time to obtain the downlink focusing function direct wave. S3, taking the direct wave and reflection response data of the downlink focusing function as input, and based on causal constraints, calculate the uplink and downlink focusing functions through iterative derivation of formulas; S4. Substitute the up and down focusing functions into the Marchenko equations for the free boundary to obtain the up and down Green's functions at the focal points of the virtual interfaces at the near-sea surface and underground. S5 uses the principle of super-interference to perform data convolution between the downlink Green's function and the uplink Green's function at the virtual interface where the direct wave component is removed, and predicts the corresponding irregular multiple waves interface by interface. S6 introduces a Softmax adaptive weighting mechanism driven by cross-correlation coefficients to weight and superimpose the multiple components predicted by each interface to obtain the prediction results of irregular multiples in the whole wavefield. S7 takes the obtained full-wavefield irregular multiple prediction results and reflection response data as input, solves the matched filter under the least squares robust framework, performs a one-time subtraction, and finally completes the suppression processing of irregular multiples.

2. The method according to claim 1, characterized in that, The irregular multiples include surface multiples and interlayer multiples, which are represented as the sum of all surface multiples and interlayer multiples: (1) in, , , Representing the surface points Excitation at the ground, surface point The received irregular multiples, surface multiples, and interlayer multiples are observed.

3. The method according to claim 1, characterized in that, The virtual focus interface settings in S2 follow these rules: (1) For surface multiple waves, a horizontal virtual interface of the near-sea surface is set as the prediction reference surface; (2) For interlayer multiple waves, the layer depth h=v * t0 / 2 is estimated based on the two-way travel time t0 of each strong reflection axis in the zero offset channel and the velocity field in the depth domain. The velocity field change trend near the layer is referenced, and several horizontal virtual interfaces are set as prediction reference surfaces in the area with significant velocity changes.

4. The method according to claim 1, characterized in that, The formula for iteratively obtaining the focusing function based on causal constraints in S3 is expressed as follows: (2a) (2b) in, Indicates the number of iterations. It is a time window cutoff function used to maintain the causality of the wave field during the iteration process. Represents the reflection response data. and These represent the downlink and uplink focus functions, respectively. Represented as the direct wave of the downlink focusing function and the scattered wake wave. The iterative formula is: (2c)。 5. The method according to claim 1, characterized in that, The Marchenko equations for the free boundary in S4 take into account the reflection conditions of the free surface of seawater, and are expressed as follows: (3a) (3b) in, This indicates that under free boundary conditions, the surface... Stimulate, underground focal point Received uplink and downlink Green's functions.

6. The method according to claim 1, characterized in that, The prediction of irregular multiple waves at each virtual interface in S5 is represented as follows: (4) in, This represents the irregular multiple waves associated with the nth virtual interface predicted by the Marchenko equations for free boundaries. ), including related surface multiples and interlayer multiple waves , This represents the downlink Green's function after removing the direct wave portion.

7. The method according to claim 1, characterized in that, The irregular multiples predicted in S6 across the entire wavefield are represented by a Softmax adaptive weighted superposition driven by the cross-correlation coefficient of the irregular multiples predicted by each virtual interface, as follows: ， (5) Where N is the number of virtual interfaces selected. Predict multiple waves for the nth virtual interface The normalized weighted coefficients, This represents the cross-correlation coefficient between the predicted multiple wave and the reflection response data.

8. The method according to claim 1, characterized in that, The one-time adaptive matching subtraction process in S7 introduces robust constraints, and its optimization objective function is as follows: (6) Where a is the filtering operator, λ is the regularization coefficient with a value between 0.001 and 0.1, and the L1 norm is used to enhance robustness and suppress the influence of outliers on filter estimation.

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