Method and device for passive source near-field wavelet processing imaging of multi-source air gun shooting

Abstract

The present application relates to the field of near-field wavelet processing imaging technology, and particularly discloses a kind of multi-source air gun excited passive source near-field wavelet processing imaging method and device, the method comprises: through near-field detector, near-field wavelet data is collected, and near-field wavelet data is matched with navigation positioning data, the loading of observation system is completed. Again using the difference of each channel type of active source and passive source, passive source data is extracted. Again according to the development characteristics of near-field wavelet noise, key noise such as bubble direct wave is suppressed, the signal-to-noise ratio of data is improved and seabed shallow layer image is obtained, so as to fill the blank of domestic near-field wavelet imaging field, promote the development of near-field wavelet imaging processing technology, and lay a foundation for the standardization and standardization of multi-source near-field wavelet data processing flow.

Description

Method and apparatus for near-field wavelet processing imaging of passive source excited by multi-source air gun Technical Field

[0001] This invention relates to the field of near-field wavelet processing imaging technology, and in particular to a method and apparatus for near-field wavelet processing imaging using a multi-source air gun to excite a passive source. Background Technology

[0002] In marine seismic exploration, air guns are commonly used to generate energy, and near-field geophones record the air gun wavelet signals. Specifically, the near-field geophone is typically mounted above the air gun to record the wavefield characteristics of each gun at its corresponding location. The seismic data received by the near-field geophone is primarily used to monitor the air gun's operational status and to synthesize far-field wavelets shot-by-shot. However, because the near-field geophone is very close to the air gun and records short-range data, its role is no longer limited to quality control air guns and synthesizing far-field wavelets; it can also be extended to high-resolution imaging of the shallow seabed. Therefore, how to improve the signal-to-noise ratio of shallow seabed imaging is a technical problem that needs to be solved by those skilled in the art. Summary of the Invention

[0003] In view of this, the purpose of this invention is to provide a method and apparatus for multi-source excited passive source near-field wavelet processing imaging, which can form shallow seabed imaging based on near-field wavelet data acquired by near-field detectors.

[0004] One aspect of this invention provides a method for near-field wavelet processing imaging using multi-source excited passive sources, the method comprising:

[0005] Near-field wavelet data are acquired using a near-field detector;

[0006] By leveraging the differences between each type of active and passive source data, passive source data can be extracted.

[0007] The near-field wavelet data is matched with the navigation and positioning data to complete the loading of the observation system;

[0008] Based on the development characteristics of near-field wavelet noise, bubble direct wave noise is suppressed to improve the signal-to-noise ratio of the data and obtain shallow seabed images.

[0009] Furthermore, near-field wavelet data is acquired using a near-field detector, including:

[0010] Seismic data is received using a near-field geophone.

[0011] Passive source data is extracted from the near-field data based on the differences between active and passive sources and then processed for imaging.

[0012] Furthermore, based on the development characteristics of near-field wavelet noise, bubble direct wave noise is suppressed to improve the signal-to-noise ratio and obtain shallow seabed images, including:

[0013] Based on near-field wavelet data, a bubble direct wave model is simulated using a wavelet simulation program;

[0014] The bubble direct wave data is removed from the near-field wavelet data to obtain the near-field data after bubble suppression.

[0015] Furthermore, based on the development characteristics of near-field wavelet noise, bubble direct wave noise is suppressed to improve the signal-to-noise ratio and obtain shallow seabed images, including:

[0016] Calculate the average trace or median in the near-field wavelet data;

[0017] Using the average or median channel value as a time function, bubble direct wave data with the same characteristics are calculated for each sorted channel set, and bubble direct wave model is constructed using the common signal separation principle.

[0018] By removing bubble direct wave data with the same characteristics from the near-field wavelet data, we obtain near-field data after bubble suppression.

[0019] Furthermore, based on the development characteristics of near-field wavelet noise, bubble direct wave noise is suppressed to improve the signal-to-noise ratio and obtain shallow seabed images, including:

[0020] The shaping factor is obtained by using the far-field wavelet data extracted from the original data and the data from the far-field wavelet data after removing the bubble oscillation part as the expected output.

[0021] Based on the shaping factor and near-field wavelet data, the bubble direct wave data is removed to obtain the near-field data after bubble compression.

[0022] Furthermore, before obtaining the near-field data after bubble suppression, the following is also included:

[0023] A Butterworth low-cutoff filter was used to perform DC noise denoising on the near-field wavelet data.

[0024] Furthermore, before obtaining the near-field data after bubble suppression, the following is also included:

[0025] A surge noise attenuation module is used to denoise the near-field wavelet data.

[0026] Furthermore, after obtaining the near-field data after bubble suppression, it also includes:

[0027] Near-field wavelet data is optimized to obtain shallow seabed images.

[0028] Furthermore, the optimization process includes at least one of the following:

[0029] Virtual reflection suppression processing;

[0030] Multiple wave suppression treatment on free surfaces;

[0031] Interpolation processing;

[0032] Random noise attenuation processing.

[0033] Another aspect of this invention provides a device for near-field wavelet processing and imaging of a passive source excited by a multi-source air gun. The device includes: an acquisition module, an extraction module, a matching module, a noise suppression processing module, and an imaging module.

[0034] The acquisition module is used to acquire near-field wavelet data through a near-field detector;

[0035] The extraction module will utilize the differences between each type of active and passive source data to extract passive source data;

[0036] The matching module is used to match the near-field wavelet data with navigation and positioning data to complete the loading of the observation system;

[0037] The noise suppression processing module and the imaging module are used to suppress bubble direct wave noise based on the development characteristics of near-field wavelet noise, improve the signal-to-noise ratio of the data, and obtain shallow seabed images.

[0038] Furthermore, the data acquisition module is used for:

[0039] Seismic data is received using a near-field geophone.

[0040] Passive source data is extracted from the near-field data based on the differences between active and passive sources, and then processed for imaging.

[0041] Furthermore, the noise suppression processing module and the imaging module are used for:

[0042] Based on near-field wavelet data, a bubble direct wave model is simulated using a wavelet simulation program;

[0043] The bubble direct wave data is removed from the near-field wavelet data to obtain the near-field data after bubble suppression.

[0044] Furthermore, the noise suppression processing module and the imaging module are used for:

[0045] Calculate the average trace or median in the near-field wavelet data;

[0046] Using the average or median channel value as a time function, bubble direct wave data with the same characteristics are calculated for each sorted channel set, and bubble direct wave model is constructed using the common signal separation principle.

[0047] By removing bubble direct wave data with the same characteristics from the near-field wavelet data, near-field data of bubble suppression is obtained.

[0048] Furthermore, the noise suppression processing module and the imaging module are used for:

[0049] The shaping factor is obtained by using the far-field wavelet data extracted from the original data and the data from the far-field wavelet data after removing the bubble oscillation part as the expected output.

[0050] Based on the shaping factor and near-field wavelet data, the bubble direct wave data is removed to obtain the near-field data of bubble suppression.

[0051] Furthermore, the noise suppression processing module and the imaging module are used for:

[0052] A Butterworth low-cutoff filter was used to perform DC noise denoising on the near-field wavelet data.

[0053] Furthermore, the noise suppression processing module and the imaging module are used for:

[0054] A surge noise attenuation module is used to denoise the near-field wavelet data.

[0055] Furthermore, the noise suppression processing module and the imaging module are used for:

[0056] Near-field wavelet data is optimized to obtain shallow seabed images.

[0057] Furthermore, the optimization process includes at least one of the following:

[0058] Virtual reflection suppression processing;

[0059] Multiple wave suppression treatment on free surfaces;

[0060] Interpolation processing;

[0061] Random noise attenuation processing.

[0062] The present invention has at least the following beneficial technical effects:

[0063] Near-field wavelet data is acquired using a near-field detector. By utilizing the differences between each channel of active and passive sources, passive source data is extracted. The passive source near-field wavelet data is then matched with navigation and positioning data to complete the loading of the observation system. Based on the development characteristics of near-field wavelet noise, noise suppression such as bubble direct waves is performed to improve the signal-to-noise ratio of the data and obtain shallow seabed images. This fills the gap in the field of near-field wavelet imaging in China, promotes the development of near-field wavelet imaging processing technology, and lays the foundation for the standardization and normalization of multi-source near-field wavelet data processing procedures.

[0064] By extracting passive source data from seismic data and using it as near-field wavelet data, it is possible to image the shallow seabed using passive near-field wavelets.

[0065] By targeting the characteristics of direct waves from bubbles, various attenuation theories are proposed and applied to suppress them, thereby effectively improving the imaging quality of shallow seabed layers and helping to quickly understand the stratigraphic structure of the exploration area, especially shallow geological information.

[0066] Through one or more optimization processes, such as DC noise suppression, surge noise suppression, virtual reflection suppression, free surface multiple wave suppression, interpolation, and random noise attenuation, the imaging quality of shallow seabed can be effectively improved, which helps to quickly understand the stratigraphic structure of the exploration area, especially shallow geological information. Attached Figure Description

[0067] To more clearly illustrate the technical solutions in the embodiments of the present 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 some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained based on these drawings without creative effort.

[0068] Figure 1-1 is a schematic diagram of an embodiment of a passive source near-field wavelet processing imaging method provided by the present invention;

[0069] Figures 1-2 are schematic diagrams of an embodiment of a passive source near-field wavelet processing imaging method provided by the present invention;

[0070] Figure 2 is a schematic diagram of the near-field wavelet active and passive source data of the three-source excitation provided by the present invention;

[0071] Figure 3 is a schematic diagram of the wavelet simulation program provided by the present invention simulating the direct wave of a bubble at any near-field position;

[0072] Figure 4 is a comparison diagram of the near-field wavelet suppression of bubble direct waves by the passive source in deep water provided by the present invention.

[0073] Figure 5 is a comparison diagram of the near-field wavelet suppression of bubble direct waves in shallow water provided by the present invention.

[0074] Figure 6 is a comparison diagram before and after the passive source near-field wavelet provided by the present invention uses a shaping factor to suppress the bubble direct wave.

[0075] Figure 7 is a comparison diagram of the passive source near-field wavelet DC noise suppression before and after the present invention.

[0076] Figure 8 is a comparison diagram of passive source near-field wavelet surge noise suppression before and after the present invention.

[0077] Figure 9 is a diagram showing the effect of virtual reflection at the detector point and the shot point of the passive source near-field wavelet provided by the present invention.

[0078] Figure 10 shows the spectrum analysis before and after virtual reflection provided by the present invention;

[0079] Figure 11 is a comparison diagram of the suppression of multiples of the passive source near-field wavelet provided by the present invention before and after;

[0080] Figure 12 is a schematic diagram of an embodiment of the near-field wavelet processing imaging device provided by the present invention. Detailed Implementation

[0081] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to specific examples and the accompanying drawings.

[0082] It should be noted that all uses of "first" and "second" in the embodiments of the present invention are for the purpose of distinguishing two entities or parameters with the same name but different names. It is clear that "first" and "second" are only for the convenience of expression and should not be construed as limiting the embodiments of the present invention. Subsequent embodiments will not explain this in detail.

[0083] Based on the above objectives, a first aspect of the present invention provides an embodiment of a method for near-field wavelet processing imaging using a multi-source air gun to excite a passive source. Figures 1-1 and 1-2 show schematic diagrams of an embodiment of the near-field wavelet processing imaging method provided by the present invention. As shown in Figures 1-1 and 1-2, the method may include steps S101-S104 (some of which are optional). Specifically, steps S101-S104 may be:

[0084] S101. Acquire near-field wavelet data using a near-field detector.

[0085] In one specific implementation, as shown in Figures 1-2, S101 can specifically be: S1011, receiving seismic data through a near-field detector, which can be referred to as near-field data. This seismic data can be in SEG-Y format; for example, as shown in Figure 2, the seismic data can include active source data and passive source data. In multi-source excitation, the wavelet received by the excitation source near-field detector is called the active source near-field wavelet, and the wavelet received by the unexcited source near-field detector is called the passive source near-field wavelet.

[0086] S102. Extract passive source data by utilizing the differences between each type of active and passive source. Specifically, extract the passive source data closest to the active source from the seismic data.

[0087] In this embodiment of the application, passive source data is extracted from seismic data, thereby achieving the purpose of imaging the shallow seabed using passive source near-field wavelets.

[0088] S103. Match the near-field wavelet data with the navigation and positioning data to complete the loading of the observation system.

[0089] In one feasible implementation, near-field wavelet data is loaded into an observation system. This observation system can then match the near-field wavelet data with navigation and positioning data to obtain the coordinates of the shot point and the receiver point.

[0090] S104. Based on the development characteristics of near-field wavelet noise, suppress noise such as bubble direct waves to improve the signal-to-noise ratio of the data and obtain shallow seabed images.

[0091] In this embodiment of the invention, near-field wavelet data is acquired by a near-field detector, and the passive source near-field wavelet data is processed to obtain a shallow seabed image, thereby filling the gap in the field of near-field wavelet imaging in China, promoting the development of near-field wavelet imaging processing technology, and laying the foundation for the standardization and normalization of the processing flow of passive source near-field wavelet data excited by multi-source air guns.

[0092] Since noise interference in near-field wavelet data is primarily due to bubble direct wave data, it is necessary to remove this data to obtain higher-quality shallow seabed imaging. Methods for removing bubble direct wave data can include the following:

[0093] Method 1: Establish a bubble direct wave model (simulated using a wavelet simulation program). Details are as follows:

[0094] In one specific implementation method, due to the significant undulations in the formation of deep-water areas, the direct wave data of bubbles can be removed using the following method. Specifically, as shown in Figures 1-2, S104 can be: S1041, based on the near-field wavelet data, simulate the direct wave of bubbles at the near-field location using a wavelet simulation program. For example, Figure 3 shows a schematic diagram of simulating the direct wave of bubbles at the near-field location using the near-field wavelet data through a wavelet simulation program. S1042, remove the direct wave data of bubbles from the near-field wavelet data to obtain reflected wave data. For example, Figure 4 shows a comparison before and after passive source near-field wavelet suppression of direct wave of bubbles in deep-water areas. The reflected wave data obtained in S1042 can be presented as the right part of Figure 4.

[0095] Method 2: Establish a bubble direct wave model (based on the common signal separation principle). Details are as follows:

[0096] In one specific implementation method, for shallow water areas, as shown in Figure 1-2, S104 can specifically be: S1043, calculate the average trace value or median in the near-field wavelet data. S1044, using the average trace value or median as a time function, calculate the bubble direct wave data with the same characteristics for each sorted trace set. S1045, remove the bubble direct wave data with the same characteristics from the near-field wavelet data to obtain the reflected wave data. That is to say, the characteristics of the air gun wavelet signal are relatively stable during the acquisition process, while the reflected wave varies relatively greatly due to the influence of seafloor structures. Mainly based on the spatial coherence of the near-field signal, by sorting the near-field detectors, calculating the average trace value or calculating the median as a time function to estimate the common signal of each sorted trace set, and removing the direct wave signals with the same characteristics from the near-field wavelet recorded data, the reflected wave is obtained. Figure 5 shows a comparison before and after passive source near-field wavelet suppression of bubble direct waves in shallow water areas. The reflected wave data obtained in S1045 can be presented as the right part of Figure 5.

[0097] Method 3: Using a data-driven approach to suppress direct-flow bubbles. Details are as follows:

[0098] In one specific implementation method, as shown in Figure 1-2, S104 can be: S1046, obtaining a shaping factor, which is determined based on the far-field wavelet data and the data from which bubble oscillations have been removed. S1047, based on the shaping factor and the near-field wavelet data, removing the bubble direct wave data to obtain the reflected wave data. That is, the far-field wavelet is extracted from the original data and used as input, the bubble oscillation portion is removed as the desired output, the shaping factor is calculated, and then the factor is applied to the seismic data to suppress the bubble direct wave. Figure 6 shows a comparison before and after the passive source near-field wavelet uses the shaping factor to suppress the bubble direct wave. The reflected wave data obtained in S1047 can be presented as the right part of Figure 6.

[0099] In this embodiment of the invention, various attenuation theories are proposed and applied to suppress the characteristics of bubble direct waves, thereby effectively improving the imaging quality of shallow seabed and helping to quickly understand the stratigraphic structure of the exploration area, especially shallow geological information.

[0100] In some embodiments, in order to present higher quality shallow seabed imaging, if DC noise exists in the near-field wavelet data, it is necessary to suppress the DC noise before performing bubble direct wave suppression. Therefore, before obtaining the reflected wave data, the method further includes: S1048, using a Butterworth low-cut filter to perform DC noise denoising processing on the near-field wavelet data.

[0101] For example, if the Butterworth filter cutoff frequency is chosen to be 3Hz, the Butterworth low-cutoff filter can be expressed as the square of the amplitude versus the frequency using the following formula:

[0102]

[0103] Figure 7 shows a comparison of passive source near-field wavelet DC noise suppression before and after.

[0104] In some embodiments, in order to present higher quality shallow seabed imaging, if surge noise exists in the near-field wavelet data, it is necessary to suppress the surge noise before performing bubble direct wave suppression. Therefore, before obtaining the reflected wave data, the method further includes: S1049, using a surge noise attenuation module to perform surge noise denoising processing on the near-field wavelet data.

[0105] Surge noise caused by wind and waves is characterized by low frequencies below 20Hz, large amplitude, and random occurrence. A surge noise attenuation module was used to suppress surge noise, as shown in Figure 8, which compares the passive source near-field wavelet surge noise before and after suppression.

[0106] In some embodiments, to present higher-quality shallow seabed imaging, if other noise exists in the near-field wavelet data, it is necessary to suppress this noise. Therefore, after obtaining the reflected wave data, the method further includes: S1050, optimizing the near-field wavelet data to obtain a shallow seabed image. The optimization process includes at least one of the following: virtual reflection suppression processing; free surface multiple wave suppression processing; interpolation processing; and random noise attenuation processing.

[0107] In one example, the optimization process may include virtual reflection suppression. Specifically, the TX domain is transformed to the FK domain to suppress horizontal cable virtual reflections. Virtual reflections are removed based on a calculated virtual reflection suppression operator, which can suppress both receiver virtual reflections and shot virtual reflections. A seismic record containing only receiver virtual reflections can be represented in the FK domain as follows:

[0108]

[0109] in: D(ω,k x ) is the earthquake record, P(ω,k x ) is the effective wave, r is the sea surface reflection coefficient, and z is the depth at which the towed cable is laid.

[0110] make

[0111] Then P(ω,k) x ) = G -1 (ω,k x )D(ω,k x )

[0112] Find the virtual reflection suppression operator G -1 (ω,k x The data after suppressing virtual reflections can be obtained using the above formula, as shown in Figure 9. The spectrum after suppressing virtual reflections is shown in Figure 10. It can be seen that low frequencies and notch points are compensated, thus widening the effective frequency band.

[0113] In one example, the optimization process includes free surface multiple suppression. The surface multiple suppression process is completed in two steps: free surface multiple prediction and adaptive subtraction. The surface multiple prediction is based on the common-track and common-detector convolution method proposed by Delft University, commonly known as the SRME (Surface-Related Multiple Elimination) method. This method can predict all surface-related multiples (i.e., multiples with at least one downward reflection at the surface) in one step, simulating multiples entirely through the data itself. Specifically, in this embodiment, a least-squares adaptive subtraction method is used, with single-track and multi-track iterations to overcome the limitations of a single adaptive subtraction method, thus better suppressing multiples, as shown in Figure 11.

[0114] In one example, the optimization process includes interpolation. Specifically, the seismic traces are interpolated based on the principle of two-dimensional wavelet inverse transform. This method does not generate spatial aliasing or background noise, and can better maintain the reliability of discontinuities and faults, as well as the continuity of phase axes. It features high accuracy and high speed.

[0115] The decomposition formula for wavelet transform is as follows:

[0116]

[0117]

[0118] In the formula, h(n-2k)=<φ jk ,φ j-1n >;g(n-2k=<φ jk ,ψ j-1n >; <φ,ψ> represents the inner product of φ and ψ; h(n) is equivalent to a low-pass filter; g(n) is equivalent to a high-pass filter. They are a type of orthogonal mirror filter.

[0119] The inverse wavelet transform is:

[0120]

[0121] therefore,

[0122]

[0123] In the formula, It is known. This is unknown. This is the interpolation formula. Here, high-frequency details are ignored. get:

[0124]

[0125] This formula can be used to perform data interpolation.

[0126] In one example, the optimization process includes random noise attenuation. Specifically, it employs a three-dimensional frequency-space F-XYO domain prediction denoising technique. It is assumed that the effective waves in the seismic data are predictable in the F-XYO domain, while random noise does not. A three-dimensional prediction operator is obtained using the multichannel complex least squares principle, and this operator is then used to perform predictive filtering on the four-dimensional seismic data volume containing this frequency component, thereby attenuating random noise.

[0127] In this embodiment of the invention, one or more optimization processes, such as DC noise suppression, surge noise suppression, virtual reflection suppression, free surface multiple wave suppression, interpolation, and random noise attenuation, are used to effectively improve the imaging quality of shallow seabed, which helps to quickly understand the stratigraphic structure of the exploration area, especially shallow geological information.

[0128] Based on the above objectives, a second aspect of the present invention provides a near-field wavelet processing imaging apparatus. Figure 12 shows a schematic diagram of an embodiment of the near-field wavelet processing imaging apparatus provided by the present invention. As shown in Figure 12, the near-field wavelet processing imaging apparatus 1200 may include: an acquisition module 1210, an extraction module 1220, a matching module 1230, a noise suppression processing module, and an imaging module 1240. The acquisition module 1210 is used to acquire near-field wavelet data using a near-field geophone. The extraction module 1220 is used to extract passive source data by utilizing the differences in each channel type between active and passive sources. The matching module 1230 is used to match the near-field wavelet data with navigation and positioning data to complete the loading of the observation system; that is, it matches the near-field wavelet data with the navigation and positioning data to obtain the coordinate information of the shot point and the geophone point. The noise suppression processing module and the imaging module 1240 are used to suppress noise such as bubble direct waves according to the development characteristics of near-field wavelet noise, improve the signal-to-noise ratio of the data, and obtain shallow seabed images.

[0129] In this embodiment of the invention, near-field wavelet data is acquired by a near-field detector, and the near-field wavelet data is processed to obtain shallow seabed images, thereby filling the gap in the field of near-field wavelet imaging in China, promoting the development of near-field wavelet imaging processing technology, and laying the foundation for the standardization and normalization of the processing flow of multi-source near-field wavelet data.

[0130] Furthermore, the acquisition module 1210 is used to: receive seismic data through a near-field detector; extract passive source data from the near-field data based on the differences between active and passive sources, and process and image it.

[0131] In this embodiment of the application, passive source data is extracted from seismic data and used as near-field wavelet data, thereby enabling the imaging of shallow seabed using passive near-field wavelets.

[0132] Furthermore, the noise suppression processing module and the imaging module 1240 are used to: simulate the bubble direct wave model using a wavelet simulation program based on the near-field wavelet data; and remove the bubble direct wave data from the near-field wavelet data to obtain the near-field data after bubble suppression.

[0133] Furthermore, the noise suppression processing module and the imaging module 1240 are used to: calculate the average trace value or median in the near-field wavelet data; use the average trace value or median as a time function to calculate the bubble direct wave data with the same characteristics for each sorted trace set, and construct the bubble direct wave model using the common signal separation principle; remove the bubble direct wave data with the same characteristics from the near-field wavelet data to obtain the near-field data after bubble suppression.

[0134] Furthermore, the noise suppression processing module and the imaging module 1240 are used to: obtain a shaping factor, which is obtained based on the far-field wavelet data extracted from the original data and the data from the far-field wavelet data after removing the bubble oscillation part as the desired output; and remove the bubble direct wave data according to the shaping factor and the near-field wavelet data to obtain the near-field data after bubble suppression.

[0135] In this embodiment of the invention, various attenuation theories are proposed and applied to suppress the characteristics of bubble direct waves, thereby effectively improving the imaging quality of shallow seabed and helping to quickly understand the stratigraphic structure of the exploration area, especially shallow geological information.

[0136] Furthermore, the noise suppression processing module and the imaging module 1240 are used to: perform DC noise denoising processing on the near-field wavelet data using a Butterworth low-cut filter.

[0137] Furthermore, the noise suppression processing module and the imaging module 1240 are used to: perform surge noise denoising processing on the near-field wavelet data using the surge noise attenuation module.

[0138] Furthermore, the noise suppression processing module and the imaging module 1240 are used to optimize the near-field wavelet data to obtain a shallow seabed image with a high signal-to-noise ratio.

[0139] Furthermore, the optimization process includes at least one of the following: virtual reflection suppression processing; free surface multiple wave suppression processing; interpolation processing; and random noise attenuation processing.

[0140] In this embodiment of the invention, one or more optimization processes, such as DC noise reduction, surge noise reduction, virtual reflection suppression, free surface multiple wave suppression, interpolation, and random noise attenuation, are used to effectively improve the imaging quality of shallow seabed, which helps to quickly understand the stratigraphic structure of the exploration area, especially shallow geological information.

[0141] Those skilled in the art will also understand that the various exemplary logic blocks, modules, circuits, and algorithm steps described in conjunction with the disclosure herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability between hardware and software, the functionality of various illustrative components, blocks, modules, circuits, and steps has been generally described. Whether this functionality is implemented as software or as hardware depends on the specific application and the design constraints imposed on the system as a whole. Those skilled in the art can implement the functionality in various ways for each specific application, but such implementation decisions should not be construed as departing from the scope of the embodiments disclosed herein.

[0142] The above are exemplary embodiments disclosed in this invention. However, it should be noted that various changes and modifications can be made without departing from the scope of the embodiments of this invention as defined by the claims. The functions, steps, and / or actions of the methods according to the disclosed embodiments described herein do not need to be performed in any particular order. Furthermore, although the elements disclosed in the embodiments of this invention may be described or claimed individually, they may be understood as multiple unless explicitly limited to a singular number.

[0143] It should be understood that, as used herein, the singular form “a” is intended to include the plural form as well, unless the context clearly supports an exception. It should also be understood that, as used herein, “and / or” refers to any and all possible combinations of one or more of the associated listed items.

[0144] The embodiment numbers disclosed in the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0145] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0146] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the invention (including the claims) is limited to these examples. Within the framework of the invention, technical features of the above embodiments or different embodiments can be combined, and many other variations of different aspects of the invention exist, which are not provided in the details for the sake of brevity. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the invention should be included within the protection scope of the invention.

Claims

1. A method for near-field wavelet processing imaging using a multi-source air gun to excite a passive source, characterized in that, The method includes: acquiring near-field wavelet data through a near-field geophone, including: receiving seismic data through the near-field geophone; wherein, in multi-source excitation, the wavelet received by the excitation source near-field geophone is called the active source near-field wavelet, and the wavelet received by the non-excitation source near-field geophone is called the passive source near-field wavelet; extracting passive source data by utilizing the differences between each type of active and passive source, including: extracting passive source data from the near-field wavelet data according to the differences between active and passive sources, and processing and imaging it; wherein, the passive source data closest to the active source is extracted from the seismic data; matching the passive source near-field wavelet data with navigation and positioning data to complete the loading of the observation system; suppressing bubble direct wave noise according to the development characteristics of near-field wavelet noise to improve the signal-to-noise ratio and obtain shallow seabed images, including: simulating a bubble direct wave model using a wavelet simulation program based on the near-field wavelet data; removing bubble direct wave data from the near-field wavelet data to obtain bubble-suppressed near-field data.

2. The method according to claim 1, characterized in that, The method of suppressing bubble direct wave noise based on the development characteristics of near-field wavelet noise to improve the signal-to-noise ratio and obtain shallow seabed images includes: calculating the average trace value or median in the near-field wavelet data; using the average trace value or median as a time function to calculate bubble direct wave data with the same characteristics for each sorted trace set, and constructing a bubble direct wave model using the common signal separation principle; and removing bubble direct wave data with the same characteristics from the near-field wavelet data to obtain near-field data after bubble suppression.

3. The method according to any one of claims 1-2, characterized in that, Before obtaining the near-field data after bubble compression, the process also includes: performing DC noise denoising on the near-field wavelet data using a Butterworth low-cut filter.

4. The method according to any one of claims 1-2, characterized in that, Before obtaining the near-field data after bubble suppression, the process also includes: using a surge noise attenuation module to perform surge noise denoising on the near-field wavelet data.

5. The method according to any one of claims 1-2, characterized in that, After obtaining the near-field data after bubble suppression, the process further includes: optimizing the near-field wavelet data to obtain a shallow seabed image with a high signal-to-noise ratio.

6. The method according to claim 5, characterized in that, The optimization process includes at least one of the following: virtual reflection suppression processing; free surface multiple wave suppression processing; interpolation processing; random noise attenuation processing.

7. A device for near-field wavelet processing imaging using a multi-source air gun to excite a passive source, characterized in that, The device includes: an acquisition module, an extraction module, a matching module, a noise suppression processing module, and an imaging module. The acquisition module is used to acquire near-field wavelet data using a near-field geophone, and further used to receive seismic data using the near-field geophone. In multi-source excitation, the wavelet received by the excitation source near-field geophone is called the active source near-field wavelet, and the wavelet received by the non-excitation source near-field geophone is called the passive source near-field wavelet. The extraction module utilizes the differences in each channel type between the active and passive sources to extract passive source data, and further used to extract passive source data from the near-field wavelet data based on the differences between the active and passive sources. The system collects and processes source data for imaging. Specifically, it extracts the closest passive source data from the seismic data. The matching module matches the passive source near-field wavelet data with navigation and positioning data to load the observation system. The noise suppression and imaging modules suppress bubble direct wave noise based on the development characteristics of near-field wavelet noise, improving the signal-to-noise ratio and obtaining shallow seabed images. Furthermore, they simulate the bubble direct wave model using a wavelet simulation program based on the near-field wavelet data. Finally, they remove the bubble direct wave data from the near-field wavelet data to obtain bubble-suppressed near-field data.

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