Shallow water multiple suppression method, device and computing equipment

Abstract

Embodiments of the present application disclose a shallow water multiple wave suppression method and device and a computing device. The method comprises: acquiring spectral data of a plurality of seismic traces; performing smoothing processing on the spectral data of any seismic trace to generate smoothed spectrum of the seismic trace; determining, for any target seismic trace, adjacent seismic traces of the target seismic trace, performing correction processing on the smoothed spectrum of the target seismic trace by using the smoothed spectrum of the adjacent seismic traces to generate a corresponding sea bottom wavelet spectrum of the target seismic trace; and generating a water layer multiple wave model based on the sea bottom wavelet spectrum of each target seismic trace. The present application can greatly improve the suppression effect and efficiency of water layer multiple waves in a shallow water environment.

Description

Technical Field

[0001] This application relates to the field of exploration technology, specifically to a shallow water multiple wave suppression method, apparatus, computing device, computer storage medium, and computer program product. Background Technology

[0002] Water layer multiple wave suppression is a key step in marine seismic data processing. It is a data processing technique that uses targeted techniques to eliminate multiple waves formed by back-and-forth reflections within the sea layer, thereby highlighting the effective wave signal.

[0003] The most commonly used technique for suppressing water multiples is Surface-Related Multiple Elimination (SRME). SRME uses the original data to construct multiple paths of reflections from a free surface and generates a multiple prediction model through convolution operations.

[0004] However, in shallow water environments, seabed reflections cannot be completely distinguished from direct and refracted waves, and the seabed imaging effect of OBS (Ocean Bottom Seismometer) data is poor, which significantly affects the prediction accuracy of shallow water multiple wave models based on traditional SRME. Furthermore, due to the shallow depth of the seabed in shallow water environments, the period of water layer multiple waves is easily shortened, which makes it easy for multiple waves to overlap with the primary wave. This results in insufficient amplitude and data matching accuracy of shallow water multiple wave models based on traditional SRME, thereby reducing the accuracy of multiple wave suppression. Summary of the Invention

[0005] In view of the above problems, this application is made in order to provide a shallow water multiple wave suppression method, apparatus, computing device, computer storage medium and computer program product that overcomes or at least partially solves the above problems.

[0006] According to a first aspect of this application, a shallow water multiple wave suppression method is provided, comprising: Acquire spectral data from multiple seismic traces; For any given seismic trace, the spectral data of that seismic trace is smoothed to generate a smoothed spectrum for that seismic trace. For any target seismic trace, the neighboring seismic traces of the target seismic trace are determined. After the smooth spectrum of the target seismic trace is corrected by the smooth spectrum of the neighboring seismic traces, the submarine sub-wave spectrum corresponding to the target seismic trace is generated. Based on the sub-wavelength spectrum of each target seismic trace, a water layer multiple wave model is generated.

[0007] In one optional implementation, smoothing the spectral data of the seismic trace to generate a smooth spectrum of the seismic trace includes: Divide the frequency bands into multiple bands and determine the smoothing window length for each band; For any frequency band, the spectral data of that frequency band is smoothed using the corresponding smoothing window length to generate a smooth sub-spectrum; Determine the weighting coefficients for each frequency band, and generate the smooth spectrum of the seismic trace based on the weighting coefficients for each frequency band and the smooth sub-spectrum.

[0008] In one optional implementation, determining the weighting coefficients for each frequency band includes: For any given frequency band, a weighting coefficient is generated based on the sensitivity coefficient corresponding to the smooth window length of that frequency band and the signal-to-noise ratio of that frequency band.

[0009] In one optional implementation, the step of correcting the smooth spectrum of the target seismic trace using the smooth spectrum of the adjacent seismic traces to generate the seafloor sub-wave spectrum corresponding to the target seismic trace includes: Calculate the correction function for each neighboring seismic trace; The submarine sub-wave spectrum corresponding to the target seismic trace is generated based on the correction functions of each neighboring seismic trace and the smoothed spectrum.

[0010] In one optional implementation, the calculation of the correction function for each neighboring seismic trace includes: For any neighboring seismic trace, calculate the spatial distance between the neighboring seismic trace and the target seismic trace, and calculate the difference between the smoothed spectrum of the neighboring seismic trace and the smoothed spectrum of the target seismic trace; A correction function for the neighboring seismic trace is generated based on the spatial distance and difference term corresponding to the neighboring seismic trace.

[0011] In one optional implementation, generating a water layer multiple wave model based on the seafloor sub-wave spectra of each target seismic trace includes: The time-domain wavelet is generated by performing an inverse Fourier transform on the aforementioned seabed wavelet spectrum. Reconstruct the seabed reflected wave based on the time-domain wavelet and the seabed reflection coefficient; A water layer multiple wave model is generated based on the seabed reflected waves.

[0012] According to a second aspect of this application, a shallow water multiple wave suppression device is provided, comprising: The acquisition module is used to acquire spectral data from multiple seismic traces; The smoothing module is used to smooth the spectral data of any given seismic trace to generate a smooth spectrum for that trace. The correction module is used to determine the neighboring seismic traces of any target seismic trace, and after correcting the smooth spectrum of the target seismic trace using the smooth spectrum of the neighboring seismic traces, generate the submarine sub-wave spectrum corresponding to the target seismic trace. The suppression module is used to generate water layer multiple wave models based on the seafloor sub-wave spectra of each target seismic trace.

[0013] In one alternative implementation, the smoothing module is used to: divide multiple frequency bands and determine the smoothing window length for each frequency band; For any frequency band, the spectral data of that frequency band is smoothed using the corresponding smoothing window length to generate a smooth sub-spectrum; Determine the weighting coefficients for each frequency band, and generate the smooth spectrum of the seismic trace based on the weighting coefficients for each frequency band and the smooth sub-spectrum.

[0014] In one alternative implementation, the smoothing module is used to: for any frequency band, generate a weighting coefficient for that frequency band based on the sensitivity coefficient corresponding to the smoothing window length of that frequency band and the signal-to-noise ratio of that frequency band.

[0015] In one optional implementation, the correction module is used to: calculate the correction function for each adjacent seismic trace; The submarine sub-wave spectrum corresponding to the target seismic trace is generated based on the correction functions of each neighboring seismic trace and the smoothed spectrum.

[0016] In one optional implementation, the correction module is used to: calculate the spatial distance between the neighboring seismic trace and the target seismic trace for any neighboring seismic trace, and calculate the difference between the smoothed spectrum of the neighboring seismic trace and the smoothed spectrum of the target seismic trace. A correction function for the neighboring seismic trace is generated based on the spatial distance and difference term corresponding to the neighboring seismic trace.

[0017] In one optional implementation, the suppression module is used to: generate a time-domain wavelet by performing an inverse Fourier transform on the seabed wavelet spectrum; Reconstruct the seabed reflected wave based on the time-domain wavelet and the seabed reflection coefficient; A water layer multiple wave model is generated based on the seabed reflected waves.

[0018] According to a third aspect of this application, a computing device is provided, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus; The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the shallow water multiple wave suppression method described above.

[0019] According to a fourth aspect of this application, a computer storage medium is provided, wherein the storage medium stores at least one executable instruction that causes a processor to perform the operation corresponding to the shallow water multiple wave suppression method described above.

[0020] According to a fifth aspect of this application, a computer program product is provided, comprising at least one executable instruction that causes a processor to perform operations corresponding to the shallow water multiple wave suppression method described above.

[0021] According to the shallow water multiples suppression method, apparatus, computing device, computer storage medium, and computer program product provided in the embodiments of this application, spectral data of multiple seismic traces are acquired; for any seismic trace, the spectral data of that seismic trace is smoothed to generate a smoothed spectrum for that seismic trace; for any target seismic trace, neighboring seismic traces are determined, and the smoothed spectrum of the target seismic trace is corrected using the smoothed spectra of the neighboring seismic traces to generate the seafloor wavelet spectrum corresponding to the target seismic trace; based on the seafloor wavelet spectra of each target seismic trace, a water layer multiples model is generated. Using this scheme, the suppression effect and efficiency of water layer multiples in shallow water environments can be significantly improved.

[0022] The above description is merely an overview of the technical solutions of the embodiments of this application. In order to better understand the technical means of the embodiments of this application and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of this application more obvious and understandable, specific implementation methods of the embodiments of this application are described below. Attached Figure Description

[0023] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the embodiments of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A schematic flowchart of a shallow water multiple wave suppression method provided in an embodiment of this application is shown; Figure 2 A flowchart illustrating a spectrum data smoothing processing method provided in an embodiment of this application is shown. Figure 3 A schematic flowchart of a method for generating a sub-wavelength spectrum according to an embodiment of this application is shown; Figure 4 A flowchart illustrating a method for generating a water layer multiple wave model based on the seabed sub-wave spectrum provided in an embodiment of this application is shown. Figure 5 This illustration shows a schematic diagram of a raw seismic data gather provided in an embodiment of this application; Figure 6 This illustration shows a schematic diagram of a seabed reflection channel set provided in an embodiment of this application; Figure 7 This paper presents a comparison diagram of the effects of multiple wave suppression provided in an embodiment of this application; Figure 8 This paper shows a schematic diagram of a shallow water multiple wave suppression device provided in an embodiment of this application; Figure 9 A schematic diagram of the structure of a computing device provided in an embodiment of this application is shown. Detailed Implementation

[0024] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0025] Figure 1 A schematic flowchart of a shallow water multiple suppression method provided in an embodiment of this application is shown. The shallow water multiple suppression method provided in this embodiment is used to suppress water multiples in OBS shallow water environments.

[0026] like Figure 1 As shown, the method specifically includes the following steps: Step S110: Obtain spectral data from multiple seismic traces.

[0027] Obtain OBS acquisition data and preprocess the OBS acquisition data to generate spectral data for multiple seismic traces. The preprocessing in this embodiment includes, but is not limited to, the following processes: separation of uplink and downlink waves, superposition of common detector points, random noise suppression, and conversion of time-domain data to frequency-domain data, etc.

[0028] Step S120: For any seismic trace, smooth the spectral data of the seismic trace to generate a smooth spectrum of the seismic trace.

[0029] Specifically, since the amplitude spectrum of a seismic wavelet should be a smooth amplitude spectrum, this step performs smoothing processing on the spectral data of the seismic trace. The spectrum obtained after smoothing is called the smooth spectrum of the seismic trace.

[0030] In one alternative implementation, to enhance the suppression effect of subsequent water layer multiple waves, the following can be employed: Figure 2 Steps S121-S124, as shown, smooth the spectral data to generate the corresponding smooth spectrum: S121, divide into multiple frequency bands and determine the smooth window length for each frequency band.

[0031] Existing technologies use a single-length smoothing window for spectral smoothing. This implementation differs from existing technologies by dividing the spectrum into multiple frequency bands and generating a matching smoothing window length for each band. The smoothing window length varies across different frequency bands. For example, the smoothing window length can be negatively correlated with the signal-to-noise ratio (SNR) of the corresponding frequency band; that is, the higher the SNR, the smaller the smoothing window length, thus preserving more signal details; conversely, the lower the SNR, the longer the smoothing window length, thus reducing noise interference.

[0032] S122, For any frequency band, the spectral data of that frequency band is smoothed using the corresponding smoothing window length to generate a smooth sub-spectrum.

[0033] For each frequency band, the spectral data of that band is smoothed using the smoothing window length matched to that band, resulting in the corresponding smoothed sub-spectrum.

[0034] This implementation does not limit the specific smoothing algorithm. For example, an average smoothing algorithm as shown in Formula 1 can be used, or a smoothing algorithm such as polynomial fitting can be used. (Formula 1) in, Represents the smooth subspectral of the i-th frequency band; This represents half the length of the smooth window for the i-th frequency band; Indicates the frequency sampling interval; Represents spectrum data; Indicates frequency.

[0035] S123, determine the weighting coefficients for each frequency band.

[0036] Each frequency band has a matching weighting coefficient. Specifically, for any given frequency band, a weighting coefficient is generated based on the sensitivity coefficient corresponding to the smoothing window length of that band and the signal-to-noise ratio (SNR) of that band. The weighting coefficient is positively correlated with the SNR of the corresponding frequency band and negatively correlated with the sensitivity coefficient.

[0037] For example, the weighting coefficients can be calculated using the following formula 2.

[0038] (Formula 2) in, This represents the weighting coefficient for the i-th frequency band; This represents the sensitivity coefficient corresponding to the smooth window length of the i-th frequency band; represents the signal-to-noise ratio of the i-th frequency band; N represents the number of frequency bands.

[0039] Furthermore, the sensitivity coefficient can be obtained based on expert experience; it can also be determined based on the residual between the smoothed sub-spectrum and the original spectrum data within the frequency band. For example, the sensitivity coefficient can be positively correlated with this residual, meaning that the larger the residual, the greater the difference between the smoothed sub-spectrum and the original spectrum, and the more signal information is lost during smoothing. Therefore, the larger the corresponding sensitivity coefficient, the smaller the final weighting coefficient.

[0040] S124 generates a smooth spectrum of the seismic trace based on the weighting coefficients of each frequency band and the smooth sub-spectrum.

[0041] The final smooth spectrum is obtained by weighted summation of the smooth sub-spectrums of each frequency band. For example, the smooth spectrum can be obtained using the following formula 3.

[0042] (Formula 3) in, This represents the smooth spectrum corresponding to the seismic trace; This represents the weighting coefficient for the i-th frequency band; Let N represent the smooth subspectrum of the i-th frequency band; N represents the number of frequency bands.

[0043] This step allows for the smoothing of the spectral data of each seismic trace, resulting in a smoothed spectrum for each trace.

[0044] Step S130: For any target seismic trace, determine the neighboring seismic traces of the target seismic trace, and use the smoothed spectrum of the neighboring seismic traces to correct the smoothed spectrum of the target seismic trace to generate the submarine sub-wave spectrum corresponding to the target seismic trace.

[0045] By selecting any seismic trace from multiple seismic traces as the target seismic trace and generating the corresponding seabed sub-wave spectrum, the target seismic trace can be continuously transformed to obtain the corresponding seabed sub-wave spectrum for each seismic trace.

[0046] Specifically, since seismic data are correlated laterally, this step further uses nearby seismic trace data to correct the target seismic trace data, and the corrected data yields the corresponding submarine sub-wave spectrum.

[0047] First, other seismic traces whose spatial distance from the target seismic trace is less than a preset distance threshold are identified, and these other seismic traces that meet the distance condition are designated as neighboring seismic traces of the target seismic trace. There can be one or more neighboring seismic traces. Further, the target seismic trace is corrected using the smoothed spectrum of each neighboring seismic trace.

[0048] In one alternative implementation, the following can be used: Figure 3Steps S131-S132, as shown, perform correction processing to generate the seabed sub-wave spectrum, thereby improving the accuracy of the seabed sub-wave spectrum determination: S131, calculate the correction function for each neighboring seismic trace.

[0049] The correction function is used to quantitatively assess the degree of constraint exerted by neighboring seismic traces on the target seismic trace. Specifically, the correction function is a function of the change in correction coefficients with frequency values; that is, it describes the amount of constraint exerted by neighboring seismic traces on the target seismic trace at a given frequency. Different neighboring seismic traces correspond to different correction functions.

[0050] Alternatively, to improve the accuracy of the correction function determination, the correction function for adjacent seismic traces can be calculated as follows: For any neighboring seismic trace, the spatial distance between the neighboring trace and the target seismic trace is calculated, as well as the difference term between the smoothed spectra of the neighboring trace and the target seismic trace. A correction function for the neighboring seismic trace is generated based on the spatial distance and the difference term. Specifically, the difference term for the neighboring seismic trace is a function of the frequency variation of the difference between the smoothed spectra of the neighboring trace and the target seismic trace. At a specified frequency, this difference term can be used to determine the amplitude difference between the smoothed spectra of the neighboring trace and the target seismic trace at that specified frequency.

[0051] Furthermore, at the same frequency value, the correction function value is negatively correlated with both the spatial distance and the difference term value. That is, the closer the spectral data of a neighboring seismic trace is to the smoothed spectrum, the smaller the corresponding difference term value, and thus the larger the correction function value; correspondingly, the closer the neighboring seismic traces are to the seismic trace, the smaller the corresponding spatial distance, and thus the larger the correction function value.

[0052] For example, the spatial distance between a neighboring seismic trace and the target seismic trace can be calculated using the following formula 4: (Formula 4) in, This represents the spatial distance between the m-th neighboring seismic trace and the target seismic trace; , , () represents the spatial coordinates corresponding to the m-th nearest seismic trace; , , () represents the spatial coordinates corresponding to the target seismic trace.

[0053] For example, the square of the difference between the smoothed spectrum of the neighboring seismic trace and the smoothed spectrum of the target seismic trace can be used as the difference term between the smoothed spectrum of the neighboring seismic trace and the smoothed spectrum of the target seismic trace. The correction function can then be expressed as shown in Formula 5: (Formula 5) in, Let represent the correction function for the m-th neighboring seismic trace. This represents a frequency variable, where m is an integer greater than 1; This represents the spatial distance between the m-th neighboring seismic trace and the target seismic trace; The smooth spectrum of the m-th neighboring seismic trace; The smoothed spectrum representing the target seismic trace; This represents a coefficient related to the length of space, typically ranging from 1 / 3 to 1 / 2 of the horizontal length of the space. This represents the difference between the smoothed spectrum of the m-th neighboring seismic trace and the smoothed spectrum of the target seismic trace; this difference is the frequency. The function.

[0054] S132, generate the submarine sub-wave spectrum corresponding to the target seismic trace based on the correction functions of each neighboring seismic trace and the smoothed spectrum.

[0055] The submarine sub-spectrum corresponding to the target seismic trace is generated by summing the products of the correction functions of each neighboring seismic trace and their corresponding smooth spectra.

[0056] For example, the following formula 6 can be used to obtain the sub-wave spectrum of the seabed: (Formula 6) in, This represents the submarine sub-wave spectrum corresponding to the target seismic trace; This represents the correction function for the m-th neighboring seismic trace; The smooth spectrum of the m-th neighboring seismic trace; M represents the number of neighboring seismic traces.

[0057] To further improve the accuracy of the seafloor wavelet spectrum construction, the sum of the products of the correction functions and corresponding smoothed spectra of each neighboring seismic trace can be calculated and normalized to generate the seafloor wavelet spectrum corresponding to the target seismic trace. The seafloor wavelet spectrum can then be obtained using the following formula 7: (Formula 7) in, The submarine sub-wave spectrum corresponding to the target seismic trace; This represents the correction function for the m-th neighboring seismic trace; This represents the correction function for the k-th neighboring seismic trace; The smooth spectrum of the m-th neighboring seismic trace; M represents the number of neighboring seismic traces; The normalization factor represents the sum of the correction function values ​​of each neighboring seismic trace at a specified frequency.

[0058] Furthermore, the seafloor wavelet spectrum corresponding to the target seismic trace is generated based on the correction functions and smoothed spectra of each neighboring seismic trace, combined with the smoothed spectrum of the target seismic trace. A fixed correction coefficient can be assigned to the target seismic trace, and this fixed correction coefficient is greater than the correction function values ​​of its neighboring seismic traces. The seafloor wavelet spectrum can then be obtained using the following formula 8: (Formula 8) in, The seafloor sub-wave spectrum corresponding to the target seismic trace; when m > 0. Let represent the correction function for the m-th neighboring seismic trace. Let represent the correction function for the m-th neighboring seismic trace. The smooth spectrum of the m-th neighboring seismic trace; when m=0 This represents the correction factor for the target seismic trace. M represents the smooth spectrum of the target seismic trace; M represents the number of neighboring seismic traces.

[0059] Step S140: Generate a water layer multiple wave model based on the seafloor sub-wave spectrum of each target seismic trace.

[0060] By implementing the above steps S110-S130, the seabed sub-wave spectrum of each target seismic trace can be obtained. Then, the seabed reflection wave field is reconstructed based on the seabed sub-wave spectrum of each target seismic trace, and the water layer multiple wave model is constructed using the reconstructed seabed reflection wave field.

[0061] In one alternative implementation, specifically, it can be adopted Figure 4 The steps shown generate the water layer multiple wave model: S141 generates a time-domain wavelet by performing an inverse Fourier transform on the spectrum of the seabed wavelet.

[0062] The ocean wavelet spectrum obtained in step S130 is a frequency domain spectrum. The frequency domain spectrum is further converted into a time domain spectrum by inverse Fourier transform, thereby obtaining the corresponding time domain wavelet.

[0063] S142, reconstructing seabed reflected waves based on time-domain wavelet and seabed reflection coefficient.

[0064] The reconstructed seabed reflected wave can be obtained by convolving the time-domain wavelet with the seabed reflection coefficient. This seabed reflected wave field accurately simulates the reflection response of seismic waves at the seabed interface, achieving precise reconstruction of the three-dimensional seabed. This enables subsequent prediction and suppression of water layer multiples based on high-quality seabed reflection data.

[0065] The seabed reflected waves can be obtained as shown in Formula 9: (Formula 9) Where P represents the reconstructed seabed reflected wave; R represents the seabed reflection coefficient, which can be -1; The obtained sub-wavelength spectra are represented by IFFT; IFFT represents the inverse Fourier transform. This indicates the calculation of convolution.

[0066] like Figure 5 and Figure 6 As shown, this applies to the same shallow water OBC data collection area (water depth 25 meters). Figure 5 This is a schematic diagram of the gather obtained based on the superimposed data of the up-wave common receiver points in this acquisition area. Figure 5 This diagram shows a gather diagram of the original seismic data; the scheme provided in this application can reconstruct the seafloor reflected wavefield, thereby obtaining... Figure 6 The diagram shows the reconstructed seabed reflected wave field gather. (Comparison) Figure 5 and Figure 6 As can be seen, the seabed reflected wave field reconstructed through the embodiments of this application is clearer than the original seismic data, which facilitates the improvement of the subsequent multiple wave suppression effect.

[0067] S143, a water layer multiple wave model is generated based on the reflected waves from the seabed.

[0068] In existing technologies, SRME directly approximates the primary wave using raw data when constructing a water layer multiple wave model. However, in this embodiment, the reconstructed seabed reflected wave is used to approximate the primary wave, thereby constructing the water layer multiple wave model. This significantly improves the prediction accuracy of water layer multiple waves and reduces the number of iterations, thus enhancing the prediction and suppression efficiency of water layer multiple waves.

[0069] Specifically, the water layer multiple wave model in this application embodiment can be as shown in Equation 10: (Formula 10) Where M represents water layer multiples; D represents the original seismic data; P represents the reconstructed seafloor reflection; and S represents the source wavelet.

[0070] In practical implementation, water layer multiples can be obtained by iteratively solving Formula 10. This application embodiment does not limit the specific iterative solution algorithm. In this embodiment, the order of the water layer multiples is lower than a preset order threshold, meaning that this application does not require prediction and suppression of higher-order water layer multiples. Finally, water layer multiples are removed from the original seismic data to achieve suppression.

[0071] like Figure 7As shown, the black solid arrows indicate water layer multiples. It can be seen that after using this method to suppress water layer multiples, the multiples are significantly reduced compared to before suppression. Furthermore, the suppression effect obtained by this method is significantly better than that of existing water layer multiple suppression methods.

[0072] Therefore, the shallow water multiple suppression method provided in this application first smooths the spectral data to obtain a smooth spectrum, then corrects the smooth spectrum of the target seismic trace using the smooth spectrum of neighboring seismic traces, thereby reconstructing the seafloor wavelet spectrum of the target seismic trace, and finally suppressing water layer multiples based on the seafloor wavelet spectrum of the target seismic trace. Using this method, the suppression effect and efficiency of water layer multiples in shallow water environments can be significantly improved.

[0073] Figure 8 A schematic diagram of a shallow water multiple wave suppression device provided in an embodiment of this application is shown. Figure 8 As shown, the device 800 includes: an acquisition module 810, a smoothing module 820, a correction module 830, and a pressing module 840.

[0074] The acquisition module 810 is used to acquire spectral data from multiple seismic traces; The smoothing module 820 is used to smooth the spectral data of any seismic trace to generate a smooth spectrum of the seismic trace. The correction module 830 is used to determine the neighboring seismic channels of any target seismic channel, and after correcting the smooth spectrum of the target seismic channel using the smooth spectrum of the neighboring seismic channels, generate the submarine sub-wave spectrum corresponding to the target seismic channel. The suppression module 840 is used to generate water layer multiple wave models based on the seafloor sub-wave spectra of each target seismic trace.

[0075] In one alternative implementation, the smoothing module 820 is used to: divide multiple frequency bands and determine the smoothing window length for each frequency band; For any frequency band, the spectral data of that frequency band is smoothed using the corresponding smoothing window length to generate a smooth sub-spectrum; Determine the weighting coefficients for each frequency band, and generate the smooth spectrum of the seismic trace based on the weighting coefficients for each frequency band and the smooth sub-spectrum.

[0076] In one alternative implementation, the smoothing module 820 is configured to: for any frequency band, generate a weighting coefficient for that frequency band based on the sensitivity coefficient corresponding to the smoothing window length of that frequency band and the signal-to-noise ratio of that frequency band.

[0077] In one alternative implementation, the correction module 830 is used to: calculate the correction function for each adjacent seismic trace; The submarine sub-wave spectrum corresponding to the target seismic trace is generated based on the correction functions of each neighboring seismic trace and the smoothed spectrum.

[0078] In one optional implementation, the correction module 830 is used to: calculate the spatial distance between the neighboring seismic trace and the target seismic trace for any neighboring seismic trace, and calculate the difference between the smoothed spectrum of the neighboring seismic trace and the smoothed spectrum of the target seismic trace. A correction function for the neighboring seismic trace is generated based on the spatial distance and difference term corresponding to the neighboring seismic trace.

[0079] In one optional implementation, the suppression module 840 is used to: generate a time-domain wavelet by performing an inverse Fourier transform on the seabed wavelet spectrum; Reconstruct the seabed reflected wave based on the time-domain wavelet and the seabed reflection coefficient; A water layer multiple wave model is generated based on the seabed reflected waves.

[0080] Therefore, the shallow water multiple suppression device provided in this application first smooths the spectral data to obtain a smooth spectrum, then corrects the smooth spectrum of the target seismic trace using the smooth spectrum of neighboring seismic traces, thereby reconstructing the seafloor wavelet spectrum of the target seismic trace, and then suppressing water layer multiples based on the seafloor wavelet spectrum of the target seismic trace. Using this method, the suppression effect and efficiency of water layer multiples in shallow water environments can be significantly improved.

[0081] This application provides a non-volatile computer storage medium storing at least one executable instruction or computer program that enables a processor to perform the operation corresponding to the shallow water multiple wave suppression method in any of the above method embodiments.

[0082] This application provides a computer program product, which includes at least one executable instruction or computer program that enables a processor to perform the operation corresponding to the shallow water multiple wave suppression method in any of the above method embodiments.

[0083] Figure 9 The diagram shows a structural schematic of a computing device provided in an embodiment of this application. The specific embodiments of this application do not limit the specific implementation of the computing device.

[0084] like Figure 9As shown, the computing device may include: a processor 902, a communications interface 904, a memory 906, and a communications bus 908.

[0085] The processor 902, communication interface 904, and memory 906 communicate with each other via communication bus 908. Communication interface 904 is used to communicate with other network elements such as clients or other servers. The processor 902 executes program 910, specifically performing the relevant steps of the shallow water multiple wave suppression method described in the embodiment for the computing device.

[0086] Specifically, program 910 may include program code that includes computer operation instructions.

[0087] The processor 902 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application. The computing device includes one or more processors, which may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0088] Memory 906 is used to store program 910. Memory 906 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0089] Specifically, program 910 can be used to cause processor 902 to execute the shallow water multiple wave suppression method in any of the above method embodiments. The specific implementation of each step in program 410 can be found in the corresponding descriptions of the steps and units in the above shallow water multiple wave suppression method embodiments, and will not be repeated here. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the devices and modules described above can be referred to the corresponding process descriptions in the foregoing method embodiments, and will not be repeated here.

[0090] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, the embodiments of this application are not directed to any particular programming language. It should be understood that the contents of the embodiments of this application described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best implementation of the embodiments of this application.

[0091] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0092] Similarly, it should be understood that, in order to streamline this disclosure and aid in understanding one or more of the various inventive aspects, features of the embodiments of this application are sometimes grouped together in a single embodiment, figure, or description thereof in the foregoing description of exemplary embodiments of the present application. However, this approach to disclosure should not be construed as reflecting an intention that the claimed embodiments of the present application require more features than expressly recited in each claim. Rather, as reflected in the claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of the present application.

[0093] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0094] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features included in other embodiments but not others, combinations of features from different embodiments are meant to be within the scope of the embodiments of this application and form different embodiments. For example, in the claims, any one of the claimed embodiments can be used in any combination.

[0095] The various component embodiments of this application can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components according to the embodiments of this application. The embodiments of this application can also be implemented as device or apparatus programs (e.g., computer programs and computer program products) for performing part or all of the methods described herein. Such programs implementing the embodiments of this application can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0096] It should be noted that the above embodiments are illustrative of the embodiments of this application and not limiting of the embodiments of this application, and those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. Embodiments of this application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

Claims

1. A method for suppressing multiple waves in shallow water, characterized in that, include: Acquire spectral data from multiple seismic traces; For any given seismic trace, the spectral data of that seismic trace is smoothed to generate a smoothed spectrum for that seismic trace. For any target seismic trace, neighboring seismic traces are determined. The smoothed spectrum of the target seismic trace is then corrected using the smoothed spectrum of the neighboring seismic traces to generate the corresponding seafloor sub-wave spectrum. Specifically, for any neighboring seismic trace, the spatial distance between the neighboring trace and the target seismic trace is calculated, as well as the difference between the smoothed spectra of the neighboring trace and the target seismic trace. A correction function for the neighboring seismic trace is generated based on the spatial distance and the difference. Finally, the seafloor sub-wave spectrum corresponding to the target seismic trace is generated based on the correction functions and smoothed spectra of each neighboring seismic trace. Based on the sub-wavelength spectrum of each target seismic trace, a water layer multiple wave model is generated.

2. The method according to claim 1, characterized in that, The smoothing process for the spectral data of the seismic trace to generate a smooth spectrum includes: Divide the frequency bands into multiple bands and determine the smoothing window length for each band; For any frequency band, the spectral data of that frequency band is smoothed using the corresponding smoothing window length to generate a smooth sub-spectrum; Determine the weighting coefficients for each frequency band, and generate the smooth spectrum of the seismic trace based on the weighting coefficients for each frequency band and the smooth sub-spectrum.

3. The method according to claim 2, characterized in that, The determination of the weighting coefficients for each frequency band includes: For any given frequency band, a weighting coefficient is generated based on the sensitivity coefficient corresponding to the smooth window length of that frequency band and the signal-to-noise ratio of that frequency band.

4. The method according to any one of claims 1-3, characterized in that, The generation of water layer multiple wave models based on the seafloor sub-wave spectra of each target seismic trace includes: The time-domain wavelet is generated by performing an inverse Fourier transform on the aforementioned seabed wavelet spectrum. Reconstruct the seabed reflected wave based on the time-domain wavelet and the seabed reflection coefficient; A water layer multiple wave model is generated based on the seabed reflected waves.

5. A shallow water multiple wave suppression device, characterized in that, include: The acquisition module is used to acquire spectral data from multiple seismic traces; The smoothing module is used to smooth the spectral data of any given seismic trace to generate a smooth spectrum for that trace. The correction module is used to determine the neighboring seismic traces of any target seismic trace, correct the smooth spectrum of the target seismic trace using the smooth spectrum of the neighboring seismic traces, and generate the seafloor sub-wave spectrum corresponding to the target seismic trace. Specifically, for any neighboring seismic trace, the module calculates the spatial distance between the neighboring seismic trace and the target seismic trace, and calculates the difference term between the smooth spectrum of the neighboring seismic trace and the smooth spectrum of the target seismic trace. A correction function for the neighboring seismic trace is generated based on the spatial distance and the difference term. Finally, the seafloor sub-wave spectrum corresponding to the target seismic trace is generated based on the correction functions and smooth spectra of each neighboring seismic trace. The suppression module is used to generate water layer multiple wave models based on the seafloor sub-wave spectra of each target seismic trace.

6. A computing device, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction that causes the processor to perform the operation corresponding to the shallow water multiple wave suppression method as described in any one of claims 1-4.

7. A computer storage medium, characterized in that, The storage medium stores at least one executable instruction that causes the processor to perform the operation corresponding to the shallow water multiple wave suppression method as described in any one of claims 1-4.

8. A computer program product, characterized in that, It includes at least one executable instruction that causes the processor to perform the operation corresponding to the shallow water multiple wave suppression method as described in any one of claims 1-4.

Images