Three-dimensional signal deconvolution method and device based on near-field recording

Abstract

The application provides a three-dimensional signal deconvolution method and device based on near-field recording. The method comprises the following steps: performing inversion calculation on the amplitude-calibrated active source signal to obtain ideal wavelets corresponding to different near-field detectors; performing linear superposition calculation on the ideal wavelets according to the relative positions between the preset far-field points and the ideal wavelets to obtain three-dimensional far-field wavelets; performing bubble and virtual reflection component suppression processing on the three-dimensional far-field wavelets to obtain suppressed three-dimensional far-field wavelets; performing frequency and phase conversion on the suppressed three-dimensional far-field wavelets to obtain corresponding zero-phase expected wavelets; determining a three-dimensional signal deconvolution operator according to the three-dimensional far-field wavelets before suppression and the zero-phase expected wavelets; and performing three-dimensional deconvolution operation on original OBN seismic data according to the three-dimensional signal deconvolution operator to obtain deconvoluted OBN seismic data. The application can eliminate the influence of the source directivity on the seismic wavelet and improve the quality of seismic data processing.

Description

Technical Field

[0001] This invention relates to a method and apparatus for deconvolution of three-dimensional signals based on near-field recording. Background Technology

[0002] This section is intended to provide background or context for the embodiments of the invention set forth in the claims. The description herein is not an admission that it is prior art simply because it is included in this section.

[0003] Ocean bottom node (OBN) is a marine seismic exploration technique. Compared with traditional narrow azimuth (NAZ) towed cable acquisition, OBN seismic data acquisition has advantages such as flexible acquisition and construction, ultra-long offset reception, omnidirectional coverage, high coverage, repeatability, and broadband and multi-component data transmission. In recent years, it has gradually become a popular technique in marine seismic exploration, playing an increasingly important role in the exploration and development of complex marine oil and gas fields and reservoirs, and is gradually replacing towed cable acquisition.

[0004] Currently, the mainstream excitation method for acquiring marine OBN seismic data is the airgun array. This array consists of multiple airguns arranged in a specific spatial configuration, resulting in a limited spatial distribution. However, due to limitations imposed by exploration vessels and actual field conditions, it is difficult to design an airgun array with a perfectly symmetrical circular configuration, thus failing to meet the assumption of a point source. Furthermore, the virtual reflection effect from the sea surface causes different superposition effects of the signals from each airgun at different far-field locations, exhibiting a directional effect. Specifically, the far-field wavelet energy of the airgun array varies with the horizontal azimuth and vertical emission angle. This directional effect disrupts the lateral consistency and steady-state characteristics of the seismic wavelet, impacting subsequent OBN seismic data processing and interpretation.

[0005] In OBN seismic data processing, signal deconvolution is a crucial step, as its result significantly impacts the effectiveness of subsequent processing steps, playing a pivotal role in the entire OBN seismic data processing process. Currently, there are two main methods for implementing signal deconvolution:

[0006] One approach is based on seismic data. Using direct wave information from seismic data, far-field wavelets varying with different azimuth and emission angles are obtained through appropriate processing methods. Then, the corresponding far-field wavelet inverse operator is derived for signal deconvolution processing. However, in shallow or very shallow water conditions, it is difficult to obtain high-quality direct wave information from seismic data, making it challenging to effectively implement seismic data-based signal deconvolution processing methods.

[0007] Second, the method is based on near-field recording. Near-field records (NFH) during OBN seismic data acquisition are typically obtained by placing near-field hydrophones approximately one meter above or below each airgun or group of coherent airguns within the airgun array. These hydrophones record the airgun signals generated by different airguns. Near-field records from marine OBN seismic data acquisition include signals from active and passive sources. Near-field records are primarily used to monitor the operational status of the airguns, checking for issues such as malfunctions or leaks. With technological advancements, the application of these near-field records to synthesize far-field wavelets that vary with different azimuth and emission angles, followed by subsequent signal deconvolution processing, has been implemented in production. Summary of the Invention

[0008] The inventors discovered that in existing technologies, signal deconvolution processing based on near-field records only considers the influence of directional effects on the vertical direction of seismic wavelets, which is equivalent to one-dimensional signal deconvolution processing. This cannot effectively eliminate the influence of the source's directional effects on the seismic wavelets, resulting in unsatisfactory quality of the final processed seismic data. Therefore, a more accurate method is urgently needed to implement signal deconvolution processing to improve the quality of the final processed seismic data. Based on this, embodiments of the present invention provide a three-dimensional signal deconvolution method and apparatus based on near-field records.

[0009] As a first aspect of the present invention, the present invention provides a three-dimensional signal deconvolution method based on near-field recording, comprising:

[0010] The amplitude-calibrated active source signal is inverted to obtain the ideal wavelet corresponding to different near-field detectors;

[0011] Based on the relative positions between the preset far-field point and each ideal wavelet, the ideal wavelets are linearly superimposed to obtain the three-dimensional far-field wavelets corresponding to the preset far-field point, each azimuth angle, and each emission angle.

[0012] The three-dimensional far-field wavelet is subjected to bubble and virtual reflection component suppression processing to obtain the suppressed three-dimensional far-field wavelet.

[0013] The suppressed three-dimensional far-field wavelet is subjected to frequency and phase transformation to obtain the corresponding zero-phase desired wavelet;

[0014] Based on the three-dimensional far-field wavelet before suppression and the zero-phase expected wavelet, determine the three-dimensional signal deconvolution operator corresponding to each azimuth angle and each emission angle of the preset far-field point;

[0015] Based on the aforementioned three-dimensional signal deconvolution operator, three-dimensional deconvolution operation is performed on the original OBN seismic data to obtain OBN seismic data after three-dimensional signal deconvolution in the spatiotemporal domain.

[0016] As a second aspect of the present invention, the present invention provides a three-dimensional signal deconvolution device based on near-field recording, comprising:

[0017] The first calculation module is used to perform inversion calculations on the active source signal after amplitude calibration to obtain the ideal wavelet corresponding to different near-field detectors;

[0018] The second calculation module is used to perform linear superposition calculation on each ideal wavelet according to the relative position between the preset far-field point and each ideal wavelet, so as to obtain the three-dimensional far-field wavelet corresponding to each azimuth angle and each emission angle of the preset far-field point.

[0019] The suppression processing module is used to suppress the bubble and virtual reflection components of the three-dimensional far-field wavelet to obtain the suppressed three-dimensional far-field wavelet.

[0020] The conversion module is used to perform frequency and phase conversion on the three-dimensional far-field wavelet before suppression to obtain the corresponding zero-phase desired wavelet;

[0021] The deconvolution operator determination module is used to determine the three-dimensional signal deconvolution operator corresponding to each azimuth angle and each emission angle of the preset far-field point based on the suppressed three-dimensional far-field wavelet and the zero-phase expected wavelet.

[0022] The deconvolution operation module is used to perform three-dimensional deconvolution operation on the original OBN seismic data according to the three-dimensional signal deconvolution operator to obtain OBN seismic data after three-dimensional signal deconvolution in the spatiotemporal domain.

[0023] As a third aspect of the present invention, the present invention provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the three-dimensional signal deconvolution method based on near-field recording as described above.

[0024] As a fourth aspect of the present invention, the present invention provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program that performs the above-described three-dimensional signal deconvolution method based on near-field recording.

[0025] The embodiments of the present invention bring the following beneficial effects:

[0026] The three-dimensional signal deconvolution method based on near-field recordings provided in this invention performs inversion calculations on the amplitude-calibrated active source signal to obtain an ideal wavelet. Based on the ideal wavelet inversion, three-dimensional far-field wavelets corresponding to each azimuth and emission angle are obtained, and then the corresponding zero-phase expected wavelet is obtained. Three-dimensional signal deconvolution operators for each azimuth and emission angle of the corresponding preset far-field point are obtained, realizing three-dimensional deconvolution operations and obtaining OBN seismic data after three-dimensional signal deconvolution in the spatiotemporal domain. Utilizing active source signals to achieve three-dimensional signal deconvolution processing, it suppresses bubble and virtual reflection components and performs zero-phase processing on the original OBN seismic data. Compared with existing signal deconvolution processing methods based on near-field recordings, it solves the problem of one-dimensional signal deconvolution processing ignoring the wavelet variation with azimuth and emission angle, effectively eliminates the influence of source directionality on seismic wavelets, improves the signal deconvolution processing effect, and is applicable to the processing of wide-azimuth, high-density marine OBN seismic data. The quality of the processed seismic data is significantly improved, the data accuracy is higher, tectonic artifacts are effectively reduced, and effective seismic data is provided for drilling operations, which helps to improve the drilling success rate.

[0027] The three-dimensional signal deconvolution method based on near-field recording provided in this invention determines the three-dimensional signal deconvolution operator for the active source signal after amplitude calibration, and then performs optimized combination application through three-dimensional intercept slow transformation processing to achieve three-dimensional signal deconvolution processing, thereby further improving the quality of seismic data obtained by signal deconvolution processing.

[0028] The three-dimensional signal deconvolution method based on near-field records provided in this invention extracts and calibrates the amplitude of the active source signal from the near-field records. Based on the amplitude-calibrated active source signal, a three-dimensional signal deconvolution operator is determined, which facilitates three-dimensional signal deconvolution processing, improves the effect of signal deconvolution processing, and ultimately results in seismic data with less noise and better noise reduction effect.

[0029] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0030] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0031] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0032] Figure 1 A flowchart of a three-dimensional signal deconvolution method based on near-field recording provided in an embodiment of the present invention;

[0033] Figure 2a This is a schematic diagram of near-field recording of OBN seismic data acquisition provided in an embodiment of the present invention;

[0034] Figure 2b From Figure 2a A schematic diagram of the active source signal extracted from it;

[0035] Figure 2c To Figure 2b A schematic diagram of the active source signal after amplitude calibration;

[0036] Figure 3 To utilize Figure 2c The diagram shows the ideal wavelet obtained by inverting the active source signal after amplitude calibration.

[0037] Figure 4a This is a schematic diagram of far-field wavelets at different emission angles when the azimuth angle is 0°.

[0038] Figure 4b This is a schematic diagram of the far-field wavelet at different azimuth angles when the emission angle is 45°;

[0039] Figure 5a A schematic diagram of the far-field wavelet before suppression;

[0040] Figure 5b Figure 5a The diagram shows the spectrum of the far-field wavelet.

[0041] Figure 5c Figure 5a A schematic diagram of the phase spectrum of the far-field wavelet is shown.

[0042] Figure 5d To Figure 5a The diagram shows the far-field wavelet suppressing bubbles and virtual reflections, as well as the desired wavelet after zero-phase transformation.

[0043] Figure 5e Figure 5d The spectrum diagram of the desired wavelet is shown below;

[0044] Figure 5f Figure 5d The diagram shows the phase spectrum of the desired wavelet.

[0045] Figure 6 A schematic diagram illustrating the process of obtaining the deconvolution operator for a three-dimensional signal;

[0046] Figure 7a A schematic diagram of the original OBN seismic data;

[0047] Figure 7b for Figure 7a The diagram shown is a flattened version of the original OBN seismic data.

[0048] Figure 7c for Figure 7a The diagram shows a 3D Tau-P gather of the original OBN seismic data.

[0049] Figure 7d For the corresponding Figure 7c The three-dimensional Tau-P gather after regularization and interpolation processing;

[0050] Figure 8a A schematic diagram of OBN seismic data obtained by deconvolution of a three-dimensional signal in the spatiotemporal domain using the method provided in this embodiment of the invention;

[0051] Figure 8b for Figure 8a The diagram shown is a flattened version of the OBN seismic data after deconvolution of the three-dimensional signal.

[0052] Figure 8c This is a schematic diagram of OBN seismic data obtained by deconvolution of signals using a near-field recording method based on existing technology.

[0053] Figure 8d for Figure 8c The diagram shown is a flattened representation of OBN seismic data after signal deconvolution.

[0054] Figure 9 A schematic diagram of the structure of a three-dimensional signal deconvolution device based on near-field recording provided in an embodiment of the present invention;

[0055] Figure 10 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] To facilitate understanding of this embodiment, the specific implementation of the present invention will be described in detail below through several specific embodiments:

[0058] Example 1

[0059] This invention provides a three-dimensional signal deconvolution method based on near-field recordings, applied to the processing of seismic data from seafloor nodes, with reference to... Figure 1 As shown, the method includes the following steps:

[0060] S101: Perform inversion calculations on the active source signal after amplitude calibration to obtain the ideal wavelet corresponding to different near-field detectors;

[0061] S102: Based on the relative positions between the preset far-field point and each ideal wavelet, perform linear superposition calculation on each ideal wavelet to obtain three-dimensional far-field wavelets corresponding to each azimuth angle and each emission angle of the preset far-field point.

[0062] S103: The three-dimensional far-field wavelet is subjected to bubble and virtual reflection component suppression processing to obtain the suppressed three-dimensional far-field wavelet;

[0063] S104: Perform frequency and phase conversion on the suppressed three-dimensional far-field wavelet to obtain the corresponding zero-phase desired wavelet;

[0064] S105: Based on the three-dimensional far-field wavelet before suppression and the zero-phase expected wavelet, determine the three-dimensional signal deconvolution operator corresponding to each azimuth angle and each emission angle of the preset far-field point;

[0065] S106: Based on the three-dimensional signal deconvolution operator, perform three-dimensional deconvolution operation on the original OBN seismic data to obtain OBN seismic data after three-dimensional signal deconvolution in the spatiotemporal domain.

[0066] The inventors have discovered that OBN seismic exploration processing technology is currently in its early stages, and the supporting processing technologies and methods, especially the signal deconvolution processing technology for the influence of airgun excitation factors, are not yet comprehensive and perfect. The effective application of signal deconvolution processing technology directly affects the quality of the final processed seismic data. Considering that existing signal deconvolution processing methods often only consider the changes in the vertical wavelet, equivalent to one-dimensional signal deconvolution processing, but actual source arrays have directionality, the directional effect of source excitation affects the changes in seismic wavelets in different azimuths and emission directions, the inventors propose that a directional signal deconvolution method, i.e., a three-dimensional signal deconvolution method, should be used in the signal deconvolution processing to eliminate the influence of source directionality on seismic wavelets. This invention mainly uses active source signals recorded in the near field to implement three-dimensional signal deconvolution technology, effectively improving the quality of the results and providing a reliable data foundation for subsequent seismic data processing and interpretation. This, in turn, improves the accuracy of seismic exploration data imaging, enabling a more accurate and reliable seismic exploration process based on higher-precision seismic exploration data imaging results, and reducing exploration risks.

[0067] The three-dimensional signal deconvolution method based on near-field recordings provided in this invention performs inversion calculations on the amplitude-calibrated active source signal to obtain an ideal wavelet. Based on the ideal wavelet inversion, three-dimensional far-field wavelets corresponding to each azimuth and emission angle are obtained, and then the corresponding zero-phase expected wavelet is obtained. Three-dimensional signal deconvolution operators for each azimuth and emission angle of the corresponding preset far-field point are obtained, realizing three-dimensional deconvolution operations and obtaining OBN seismic data after three-dimensional signal deconvolution in the spatiotemporal domain. Utilizing active source signals to achieve three-dimensional signal deconvolution processing, it suppresses bubble and virtual reflection components and performs zero-phase processing on the original OBN seismic data. Compared with existing signal deconvolution processing methods based on near-field recordings, it solves the problem of one-dimensional signal deconvolution processing ignoring the wavelet variation with azimuth and emission angle, effectively eliminates the influence of source directionality on seismic wavelets, improves the signal deconvolution processing effect, and is applicable to the processing of wide-azimuth, high-density marine OBN seismic data. The quality of the processed seismic data is significantly improved, the data accuracy is higher, tectonic artifacts are effectively reduced, and effective seismic data is provided for drilling operations, which helps to improve the drilling success rate.

[0068] In this embodiment of the invention, the amplitude-calibrated active source signal described in step S101 above is obtained in the following manner:

[0069] The active source signal is extracted from the near-field record received by the near-field detector, and the extracted active source signal is subjected to amplitude calibration processing to obtain the amplitude-calibrated active source signal.

[0070] Specifically, the process can be as follows: First, active source signals are extracted from the near-field records acquired by the marine OBN seismic data acquisition team. These signals are then examined and selected to extract high-quality active source signals from the near-field geophone records used for 3D signal deconvolution processing. Next, based on the amplitude variations of the active source signals recorded by different near-field geophones, amplitude calibration processing is performed on the active source signals recorded by different near-field geophones. This allows for amplitude adjustment of the extracted active source signals from the near-field records to compensate for amplitude variations that spherical diffusion compensation cannot handle.

[0071] The three-dimensional signal deconvolution method based on near-field records provided in this invention extracts and calibrates the amplitude of the active source signal from the near-field records, and then determines the three-dimensional signal deconvolution operator based on the amplitude-calibrated active source signal. This facilitates the implementation of three-dimensional signal deconvolution processing, improves the effect of signal deconvolution processing, and ultimately results in seismic data with less noise and better noise reduction.

[0072] The inventors discovered that after different air guns excite the signal, the near-field signals (active source signal and passive source signal) recorded by different near-field detectors interfere with each other and cannot reflect the characteristics of the signals excited by each air gun. Therefore, the inventors proposed that before forming the far-field wavelet, an ideal wavelet should first be generated through inversion to simulate the state of independent air gun excitation and independent near-field detector reception. In addition, the inventors found that after air gun excitation, the shock wave propagates in all directions. When the shock wave propagates to the water surface and seabed, it is reflected. The reflected wave is superimposed with the atomic wave and received by the near-field detector. That is to say, the near-field wavelet received by the near-field detector contains a virtual reflection component, which should be removed when calculating the ideal wavelet. Assuming that the measured active source signal (i.e., a near-field wavelet) is composed of multiple incoherent wavelets (i.e., ideal wavelets) below the near-field detector, by calculating these hypothetical incoherent wavelets, it is possible to synthesize a far-field wavelet using these incoherent wavelets. Based on the iterative inversion algorithm of incoherent wavelets, multiple equations can be listed, and the following set of equations (1) can be constructed. The ideal wavelet can be obtained by solving the set of equations (1). Based on this, in step S101 above, the active source signal after amplitude calibration is inverted to obtain the ideal wavelet corresponding to different near-field detectors, specifically including:

[0073] Substituting the amplitude-calibrated active source signal into the following formula (1), the ideal wavelet corresponding to different near-field detectors is calculated:

[0074]

[0075] Among them, h m (t) represents the active source signal after amplitude calibration, S mThe sensitivity of the near-field detector is represented by k, where k represents the k-th near-field detector, m represents the m-th near-field detector, and r is the sensitivity of the near-field detector. km This represents the distance from the k-th bubble to the m-th detector, (r g ) km The distance from the virtual image point of the k-th bubble to the m-th detector is represented by K, where K is the sea level reflection coefficient, α represents the seawater velocity, n represents the number of near-field detectors, and p′ is the distance from the virtual image point of the k-th bubble to the m-th detector. k Let represent the ideal wavelet corresponding to the k-th near-field detector.

[0076] In this embodiment of the invention, the specific implementation of the iterative inversion method used when performing inversion calculation on the amplitude-calibrated active source signal can be found in the detailed description in the relevant technology, and will not be repeated here.

[0077] In step S102 above, based on the relative positions between the preset far-field point and each ideal wavelet, the ideal wavelets are linearly superimposed to obtain three-dimensional far-field wavelets corresponding to each azimuth angle and each emission angle of the preset far-field point. Specifically, this includes:

[0078] Based on the relative positions between the preset far-field point and each ideal wavelet, the amplitude ratio and distance of each ideal wavelet are scaled to obtain the scaled ideal wavelets.

[0079] Based on the obtained shot point depth and sea level reflection coefficient of each near-field detector, the virtual reflection components corresponding to each ideal wavelet are simulated using a preset forward modeling algorithm.

[0080] The three-dimensional far-field wavelet is obtained by linearly superimposing each scaled ideal wavelet and the corresponding virtual reflection component of each ideal wavelet.

[0081] In this embodiment of the invention, based on the different distances of each ideal wavelet from the preset far-field point to the far-field point, the amplitude ratio and distance of each ideal wavelet are scaled, simulating the distances corresponding to each ideal wavelet as equal distances, and the amplitude ratio of each ideal wavelet is scaled to obtain scaled ideal wavelets. Considering that ideal wavelets do not contain virtual reflection components, the inventors first calculate the virtual reflection components corresponding to each ideal wavelet before synthesizing the three-dimensional far-field wavelets through linear superposition. Thus, during the linear superposition process, each ideal wavelet and the virtual reflection components are linearly superimposed to obtain three-dimensional far-field wavelets corresponding to each azimuth and emission angle of the preset far-field point. These far-field wavelets are related to the azimuth and emission angle excited by the air gun source.

[0082] In this embodiment of the invention, the virtual reflection component corresponding to the ideal wavelet can be calculated based on the shot point depth and sea level reflection coefficient using a forward modeling algorithm. The specific implementation process can be referred to in the detailed description of the relevant technology. The forward modeling algorithm can be, for example, the finite difference forward modeling algorithm, or other forward modeling algorithms recorded in the prior art. Here, no specific limitation is made.

[0083] In step S103 above, the three-dimensional far-field wavelet is subjected to bubble and virtual reflection component suppression processing to obtain the suppressed three-dimensional far-field wavelet, specifically including:

[0084] According to a preset step size, a deconvolution operation is performed on the three-dimensional far-field wavelet to suppress the bubble and virtual reflection components of the three-dimensional far-field wavelet, resulting in a suppressed three-dimensional far-field wavelet; the preset step size is determined based on the distance of the bubble or virtual reflection component from each primary wavelet in the three-dimensional far-field wavelet.

[0085] In this embodiment of the invention, the step size is determined based on the distance between the bubble or virtual reflection component and each primary wave in the three-dimensional far-field wavelet. Through deconvolution, the bubble and virtual reflection components of the three-dimensional far-field wavelet are suppressed to improve the signal-to-noise ratio of the three-dimensional far-field wavelet. Alternatively, in this embodiment, the method for suppressing the bubble and virtual reflection components of the three-dimensional far-field wavelet can also be implemented by waveform clipping. The waveform of the bubble or virtual reflection component is determined, and the bubble and virtual reflection components of the three-dimensional far-field wavelet are clipped. Specific implementation methods can be found in existing technical records and will not be elaborated upon here.

[0086] In step S104 above, by performing frequency and phase conversion on the suppressed three-dimensional far-field wavelet, the corresponding zero-phase desired wavelet is obtained, which can eliminate the interference artifacts caused by the non-zero phase conversion of the three-dimensional far-field wavelet and improve the resolution of the seismic wavelet.

[0087] In step S105 above, the determination of the three-dimensional signal deconvolution operator corresponding to each azimuth angle and each emission angle of the preset far-field point based on the three-dimensional far-field wavelet before suppression and the zero-phase expected wavelet is achieved by matching the three-dimensional far-field wavelet before suppression and the zero-phase expected wavelet. Specifically, since the three-dimensional signal deconvolution operator is the inverse signal of the three-dimensional far-field wavelet, based on this, the following set of equations (2) is constructed using the three-dimensional signal deconvolution operator as the coefficients to be solved. Substituting the three-dimensional far-field wavelet before suppression and the zero-phase expected wavelet into equation (2), the three-dimensional signal deconvolution operator corresponding to each azimuth angle and each emission angle of the preset far-field point is calculated in the spatiotemporal domain:

[0088] b(t)*a(t)=δ(t), formula (2);

[0089] Where a(t) represents the three-dimensional signal deconvolution operator, b(t) represents the three-dimensional far-field wavelet before suppression, and δ(t) represents the zero-phase expected wavelet.

[0090] In step S106 above, the original OBN seismic data is subjected to three-dimensional deconvolution operation according to the three-dimensional signal deconvolution operator to obtain the OBN seismic data after three-dimensional signal deconvolution in the spatiotemporal domain, specifically including:

[0091] The original OBN seismic data is regularized and interpolated to obtain the first OBN seismic data;

[0092] Based on the three-dimensional signal deconvolution operator, the first OBN seismic data is subjected to three-dimensional deconvolution operation to obtain the OBN seismic data after three-dimensional signal deconvolution in the spatiotemporal domain.

[0093] In one specific embodiment, the above-mentioned three-dimensional deconvolution operation on the first OBN seismic data according to the three-dimensional signal deconvolution operator to obtain the spatiotemporal domain three-dimensional signal deconvolutioned OBN seismic data includes:

[0094] The first OBN seismic data is subjected to a three-dimensional intercept slow transform to obtain the second OBN seismic data;

[0095] According to the three-dimensional signal deconvolution operator, the second OBN seismic data is subjected to three-dimensional deconvolution operation in the three-dimensional intercept slow domain to obtain the third OBN seismic data;

[0096] The third OBN seismic data is subjected to a three-dimensional intercept slow inverse transform to obtain the OBN seismic data after three-dimensional signal deconvolution in the spatiotemporal domain.

[0097] In this embodiment of the invention, before performing the three-dimensional signal deconvolution operation, the original OBN seismic data is processed accordingly, and the seismic data is regularized and interpolated to avoid spurious frequencies after Tau-P transformation.

[0098] In one specific embodiment, the above-mentioned three-dimensional intercept slow transformation (Tau-P transformation) is performed on the first OBN seismic data to obtain the second OBN seismic data. The transformation of the regularized and interpolated first OBN seismic data in the spatiotemporal domain to the three-dimensional Tau-P domain can be achieved by the following formula (3):

[0099]

[0100] Where u(t, x, y) represents the first OBN seismic data, U(τ, p x ,p y ) indicates the second OBN seismic data, p x p represents the slow speed in the x-direction.y Indicates slow speed in the y-direction;

[0101] Next, a three-dimensional signal deconvolution operator is applied in the Tau-P domain to perform a three-dimensional deconvolution operation on the second OBN seismic data to obtain the third OBN seismic data in the Tau-P domain;

[0102] Finally, in the Tau-P domain, the third OBN seismic data in the Tau-P domain is transformed into the spatiotemporal domain through the inverse Tau-P transform to obtain the OBN seismic data after the three-dimensional signal deconvolution in the spatiotemporal domain. The specific implementation method can refer to the implementation method of the above formula (3). Substitute the third OBN seismic data into the above formula (3) to obtain the OBN seismic data after the three-dimensional signal deconvolution in the spatiotemporal domain.

[0103] The following detailed explanation of the implementation process of the three-dimensional signal deconvolution technique in this application embodiment will be provided using specific application examples:

[0104] In one example, refer to Figure 2a As shown, during OBN seismic data acquisition, the near-field record received from the near-field geophone is referenced. Figure 2b As shown, it is from Figure 2a The active source signal extracted from the near-field recordings is shown. Then, by analyzing the amplitude variations of the active source signals recorded by different near-field detectors, amplitude calibration processing is performed on the active source signals recorded by different near-field detectors. This achieves amplitude adjustment of the extracted active source signal in the near-field recordings to compensate for amplitude variations that spherical diffusion compensation cannot achieve. (Refer to...) Figure 2c As shown, the active source signal after amplitude calibration is obtained.

[0105] Reference Figure 3 As shown, Figure 2c Substituting the amplitude-calibrated active source signal shown below into the following formula, the ideal wavelet corresponding to different near-field detectors can be calculated:

[0106]

[0107] Among them, h m (t) represents the active source signal after amplitude calibration, S m The sensitivity of the near-field detector is represented by k, where k represents the k-th near-field detector, m represents the m-th near-field detector, and r is the sensitivity of the near-field detector. km This represents the distance from the k-th bubble to the m-th detector, (r g ) km The distance from the virtual image point of the k-th bubble to the m-th detector is represented by K, where K is the sea level reflection coefficient, α represents the seawater velocity, n represents the number of near-field detectors, and p′ is the distance from the virtual image point of the k-th bubble to the m-th detector. k Let represent the ideal wavelet corresponding to the k-th near-field detector.

[0108] Based on the relative positions between the preset far-field point and each ideal wavelet, the amplitude ratio and distance of each ideal wavelet are scaled to obtain the scaled ideal wavelets.

[0109] Based on the obtained shot point depth and sea level reflection coefficient of each near-field detector, the virtual reflection components corresponding to each ideal wavelet are simulated using a preset forward modeling algorithm.

[0110] The three-dimensional far-field wavelet is obtained by linearly superimposing each scaled ideal wavelet and the corresponding virtual reflection component of each ideal wavelet.

[0111] Reference Figure 4a and Figure 4b The figures shown are examples of far-field wavelets with different azimuth and emission angles corresponding to the same far-field point. Figure 4a The figure shows far-field wavelets with different exit angles in the range of 0-70° when the azimuth angle is 0°. Figure 4b The diagram shows the far-field wavelet at different azimuth angles in the range of 0-90° when the emission angle is 45°.

[0112] The step size is determined based on the distance of the bubble or virtual reflection component from each primary wave in the three-dimensional far-field wavelet. Through deconvolution, the bubble and virtual reflection components of the three-dimensional far-field wavelet are suppressed to improve the signal-to-noise ratio. Then, by performing frequency and phase transformation on the suppressed three-dimensional far-field wavelet, the corresponding zero-phase desired wavelet is obtained. This eliminates interference artifacts caused by the non-zero phase transformation of the three-dimensional far-field wavelet and improves the resolution of the seismic wavelet.

[0113] In one specific embodiment, refer to Figures 5a-5c As shown, based on the waveform, spectrum, and phase spectrum of the far-field wavelet before suppression, an interactive editing design is performed to correct the spectrum and phase spectrum of the far-field wavelet, making the corrected phase spectrum tend towards zero phase, ultimately obtaining the corresponding... Figure 5e and Figure 5f The zero-phase expected wavelet of the spectrum and phase spectrum, referenced Figure 5d As shown, zero-phase generation of the three-dimensional far-field wavelet is achieved. In this embodiment of the invention, since the wavelet characteristics differ at different azimuth and emission angles, a unified desired output should be designed to make the overall data wavelets more consistent. Combining the characteristics of the far-field wavelet and actual data, a zero-phase desired wavelet without bubbles or virtual reflection components is designed through interactive editing.

[0114] Reference Figure 6As shown, based on the three-dimensional far-field wavelet before suppression and the zero-phase expected wavelet, the three-dimensional far-field wavelet before suppression and the zero-phase expected wavelet are substituted into formula (2) to calculate the three-dimensional signal deconvolution operator of each azimuth angle and each emission angle of the corresponding preset far-field point in the spatiotemporal domain:

[0115] b(t)*a(t)=δ(t), formula (2);

[0116] Where a(t) represents the three-dimensional signal deconvolution operator, b(t) represents the three-dimensional far-field wavelet before suppression, and δ(t) represents the zero-phase expected wavelet.

[0117] Based on the aforementioned three-dimensional signal deconvolution operator, three-dimensional deconvolution operations can be performed on the original OBN seismic data to obtain OBN seismic data with three-dimensional signal deconvolution in the spatiotemporal domain, which can then be used for... Figure 7a and Figure 7b Taking the original OBN seismic data as an example, the specific implementation process of performing three-dimensional deconvolution operation is explained below:

[0118] right Figure 7a and Figure 7b The original OBN seismic data shown is regularized and interpolated to obtain the first OBN seismic data; (Refer to...) Figure 7c and Figure 7d As shown, where Figure 7c As shown Figure 7a The diagram shown is a 3D Tau-P gather diagram of the original OBN seismic data. Figure 7d The image shows the 3D Tau-P gather after regularization and interpolation. It can be seen that spurious frequencies are eliminated in the regularized and interpolated Tau-P gather. Subsequently, 3D intercept slow transform processing is used for optimized combination applications to achieve 3D signal deconvolution processing, further improving the quality of the seismic data obtained from signal deconvolution processing.

[0119] The transformation of the first OBN seismic data after regularization and interpolation in the above spatiotemporal domain to the three-dimensional Tau-P domain can be achieved by the following formula (3):

[0120]

[0121] Where u(t, x, y) represents the first OBN seismic data, U(τ, p x ,p y ) indicates the second OBN seismic data, p x p represents the slow speed in the x-direction. y Indicates slow speed in the y-direction;

[0122] Next, a three-dimensional signal deconvolution operator is applied in the Tau-P domain to perform a three-dimensional deconvolution operation on the second OBN seismic data to obtain the third OBN seismic data in the Tau-P domain;

[0123] Finally, in the Tau-P domain, the third OBN seismic data in the Tau-P domain is transformed to the spatiotemporal domain through the inverse Tau-P transform, resulting in the OBN seismic data after deconvolution of the three-dimensional signal in the spatiotemporal domain. The specific implementation method can refer to the implementation method of the above formula (3). Substitute the third OBN seismic data into the above formula (3) to obtain the OBN seismic data after deconvolution of the three-dimensional signal in the spatiotemporal domain. The final OBN seismic data after deconvolution of the three-dimensional signal in the spatiotemporal domain is obtained by referring to... Figure 8a and Figure 8b As shown.

[0124] In this embodiment of the invention, to better demonstrate the accuracy of the OBN seismic data obtained by the three-dimensional signal deconvolution method of this scheme, conventional signal deconvolution processing methods based on near-field records in the prior art are used to process the data. Figure 7a and Figure 7b The OBN seismic data shown is processed to obtain... Figure 8c and Figure 8d The image shows OBN seismic data after signal deconvolution. By comparison... Figure 8a and Figure 8c and comparison Figure 8b and Figure 8d As can be seen, the three-dimensional signal deconvolution method provided by this invention can better eliminate bubbles and shot point ghost waves, resulting in higher quality and accuracy of the final processed seismic data. It effectively reduces structural artifacts, lays a good foundation for improving subsequent seismic data processing and interpretation, provides effective seismic data for drilling operations, and helps improve drilling success rate.

[0125] Example 2

[0126] Based on the same inventive concept, this invention also provides a three-dimensional signal deconvolution device based on near-field recording, as described in Embodiment 2 below. Since the principle by which this device solves the problem is similar to the aforementioned three-dimensional signal deconvolution method based on near-field recording, the specific implementation methods of these devices can be found in the detailed description of the relevant methods described above; repeated details will not be repeated.

[0127] This invention also provides a three-dimensional signal deconvolution device based on near-field recording, see [link to documentation]. Figure 9 As shown, the device includes:

[0128] The first calculation module 101 is used to perform inversion calculation on the active source signal after amplitude calibration to obtain the ideal wavelet corresponding to different near-field detectors;

[0129] The second calculation module 102 is used to perform linear superposition calculation on each ideal wavelet according to the relative position between the preset far-field point and each ideal wavelet, so as to obtain the three-dimensional far-field wavelet corresponding to each azimuth angle and each emission angle of the preset far-field point.

[0130] The suppression processing module 103 is used to suppress the bubble and virtual reflection components of the three-dimensional far-field wavelet to obtain the suppressed three-dimensional far-field wavelet.

[0131] The conversion module 104 is used to perform frequency and phase conversion on the three-dimensional far-field wavelet before suppression to obtain the corresponding zero-phase desired wavelet;

[0132] The deconvolution operator determination module 105 is used to determine the three-dimensional signal deconvolution operator corresponding to each azimuth angle and each emission angle of the preset far-field point based on the suppressed three-dimensional far-field wavelet and the zero-phase expected wavelet.

[0133] The deconvolution operation module 106 is used to perform three-dimensional deconvolution operation on the original OBN seismic data according to the three-dimensional signal deconvolution operator to obtain OBN seismic data after three-dimensional signal deconvolution in the spatiotemporal domain.

[0134] Based on the same inventive concept, this invention also provides a computer device embodiment for implementing all or part of the above-described three-dimensional signal deconvolution method based on near-field recording. This computer device specifically includes the following:

[0135] The computer device comprises a processor, memory, a communications interface, and a bus; wherein the processor, memory, and communications interface communicate with each other via the bus; the communications interface is used to realize information transmission between related devices; the computer device can be a desktop computer, tablet computer, or mobile terminal, etc., and this embodiment is not limited to these. In this embodiment, the computer device can be implemented with reference to the embodiments for implementing the above-described three-dimensional signal deconvolution method based on near-field recording and the embodiments for implementing the above-described three-dimensional signal deconvolution device based on near-field recording, the contents of which are incorporated herein, and repeated parts will not be described again.

[0136] Figure 10 This is a schematic diagram of the system composition structure of a computer device provided in an embodiment of the present invention. Figure 10 As shown, the computer device 100 may include a processor 1001 and a memory 1002; the memory 1002 is coupled to the processor 1001. It is worth noting that... Figure 10This is an example; other types of structures can also be used to supplement or replace this structure to achieve telecommunications functions or other functions.

[0137] In one embodiment, the functionality implemented by the three-dimensional signal deconvolution method based on near-field recording can be integrated into the processor 1001. The processor 1001 can be configured to perform the following control:

[0138] The amplitude-calibrated active source signal is inverted to obtain the ideal wavelet corresponding to different near-field detectors;

[0139] Based on the relative positions between the preset far-field point and each ideal wavelet, the ideal wavelets are linearly superimposed to obtain the three-dimensional far-field wavelets corresponding to the preset far-field point, each azimuth angle, and each emission angle.

[0140] The three-dimensional far-field wavelet is subjected to bubble and virtual reflection component suppression processing to obtain the suppressed three-dimensional far-field wavelet.

[0141] The suppressed three-dimensional far-field wavelet is subjected to frequency and phase transformation to obtain the corresponding zero-phase desired wavelet;

[0142] Based on the three-dimensional far-field wavelet before suppression and the zero-phase expected wavelet, determine the three-dimensional signal deconvolution operator corresponding to each azimuth angle and each emission angle of the preset far-field point;

[0143] Based on the aforementioned three-dimensional signal deconvolution operator, three-dimensional deconvolution operation is performed on the original OBN seismic data to obtain OBN seismic data after three-dimensional signal deconvolution in the spatiotemporal domain.

[0144] In another embodiment, the device can be configured separately from the processor 1001. For example, the three-dimensional signal deconvolution device based on near-field recording can be configured as a chip connected to the processor 1001, and the function of suppressing the shot point multiple waves of the up-going wave can be realized through the control of the processor.

[0145] like Figure 10 As shown, the computer device 100 may further include: a communication module 1003, an input unit 1004, an audio processing unit 1005, a display 1006, and a power supply 1007. It is worth noting that the computer device 100 does not necessarily need to include... Figure 10 All components shown; in addition, computer device 100 may also include Figure 10 For components not shown, please refer to existing technologies.

[0146] like Figure 10As shown, processor 1001, sometimes also referred to as controller or operation control, may include a microprocessor or other processor device and / or logic device, which receives input and controls the operation of various components of computer device 100.

[0147] The memory 1002 may be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It may store the aforementioned failure-related information, and also store a program for executing that information. The processor 1001 may execute the program stored in the memory 1002 to perform information storage or processing, etc.

[0148] Input unit 1004 provides input to processor 1001. Input unit 1004 may be, for example, a keypad or touch input device. Power supply 1007 provides power to computer device 100. Display 1006 displays images and text. Display may be, for example, an LCD display, but is not limited thereto.

[0149] The memory 1002 can be a solid-state memory, such as a read-only memory (ROM), random access memory (RAM), a SIM card, etc. It can also be a memory that retains information even when power is off, can be selectively erased, and contains more data; examples of this type of memory are sometimes referred to as EPROMs. The memory 1002 can also be some other type of device. The memory 1002 includes a buffer memory 10021 (sometimes referred to as a buffer). The memory 1002 may include an application / function storage unit 10022 for storing application programs and function programs or processes for executing operations of the computer device 100 via the processor 1001.

[0150] The memory 1002 may also include a data storage unit 10023 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the computer device. The driver storage unit 10024 of the memory 1002 may include various drivers for the computer device for communication functions and / or for performing other functions of the computer device (such as messaging applications, address book applications, etc.).

[0151] The communication module 1003 is a transmitter / receiver that transmits and receives signals via the antenna 1008. The communication module (transmitter / receiver) 1003 is coupled to the processor 1001 to provide input signals and receive output signals, which is the same as in a conventional mobile communication terminal.

[0152] Based on different communication technologies, multiple communication modules 1003 can be configured in the same computer device, such as cellular network modules, Bluetooth modules, and / or wireless LAN modules. The communication module (transmitter / receiver) 1003 is also coupled to a speaker 1009 and a microphone 1010 via an audio processing unit 1005 to provide audio output via the speaker 1009 and receive audio input from the microphone 1010, thereby realizing typical telecommunications functions. The audio processing unit 1005 may include any suitable buffer, decoder, amplifier, etc. Furthermore, the audio processing unit 1005 is also coupled to a processor 1001, enabling on-device recording via the microphone 1010 and on-device playback of stored audio via the speaker 1009.

[0153] Based on the same inventive concept, embodiments of the present invention also provide a computer-readable storage medium for implementing all steps of the above-described three-dimensional signal deconvolution method based on near-field recording. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements all steps of the three-dimensional signal deconvolution method based on near-field recording in the above embodiments. For example, when the processor executes the computer program, it implements the following steps:

[0154] The amplitude-calibrated active source signal is inverted to obtain the ideal wavelet corresponding to different near-field detectors;

[0155] Based on the relative positions between the preset far-field point and each ideal wavelet, the ideal wavelets are linearly superimposed to obtain the three-dimensional far-field wavelets corresponding to the preset far-field point, each azimuth angle, and each emission angle.

[0156] The three-dimensional far-field wavelet is subjected to bubble and virtual reflection component suppression processing to obtain the suppressed three-dimensional far-field wavelet.

[0157] The suppressed three-dimensional far-field wavelet is subjected to frequency and phase transformation to obtain the corresponding zero-phase desired wavelet;

[0158] Based on the three-dimensional far-field wavelet before suppression and the zero-phase expected wavelet, determine the three-dimensional signal deconvolution operator corresponding to each azimuth angle and each emission angle of the preset far-field point;

[0159] Based on the aforementioned three-dimensional signal deconvolution operator, three-dimensional deconvolution operation is performed on the original OBN seismic data to obtain OBN seismic data after three-dimensional signal deconvolution in the spatiotemporal domain.

[0160] While this invention provides the method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive labor. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only possible execution order. In actual device or client product execution, the methods shown in the embodiments or drawings can be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment).

[0161] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0162] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0163] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0164] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0165] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0166] In this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, without necessarily requiring or implying any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. The terms "upper," "lower," etc., indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as limiting the invention.

[0167] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0168] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention is not limited to any single aspect, nor to any single embodiment, nor to any combination and / or substitution of these aspects and / or embodiments. Each aspect and / or embodiment of the present invention can be used alone, or in combination with one or more other aspects and / or other embodiments.

[0169] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for 3D signal deconvolution based on near-field recording, characterized in that, include: The amplitude-calibrated active source signal is inverted to obtain the ideal wavelet corresponding to different near-field detectors; Based on the relative positions between the preset far-field point and each ideal wavelet, the ideal wavelets are linearly superimposed to obtain the three-dimensional far-field wavelets corresponding to the preset far-field point, each azimuth angle, and each emission angle. The three-dimensional far-field wavelet is subjected to bubble and virtual reflection component suppression processing to obtain the suppressed three-dimensional far-field wavelet. The suppressed three-dimensional far-field wavelet is subjected to frequency and phase transformation to obtain the corresponding zero-phase desired wavelet; Based on the three-dimensional far-field wavelet before suppression and the zero-phase expected wavelet, determine the three-dimensional signal deconvolution operator corresponding to each azimuth angle and each emission angle of the preset far-field point; Based on the aforementioned three-dimensional signal deconvolution operator, three-dimensional deconvolution operation is performed on the original OBN seismic data to obtain OBN seismic data after three-dimensional signal deconvolution in the spatiotemporal domain.

2. The method of claim 1, wherein, Based on the aforementioned three-dimensional signal deconvolution operator, a three-dimensional deconvolution operation is performed on the original OBN seismic data to obtain OBN seismic data with three-dimensional signal deconvolution in the spatiotemporal domain, including: The original OBN seismic data is regularized and interpolated to obtain the first OBN seismic data; Based on the three-dimensional signal deconvolution operator, the first OBN seismic data is subjected to three-dimensional deconvolution operation to obtain the OBN seismic data after three-dimensional signal deconvolution in the spatiotemporal domain.

3. The method of claim 2, wherein, The step of performing a three-dimensional deconvolution operation on the first OBN seismic data according to the three-dimensional signal deconvolution operator to obtain the spatiotemporal domain three-dimensional signal deconvolutioned OBN seismic data includes: The first OBN seismic data is subjected to a three-dimensional intercept slow transform to obtain the second OBN seismic data; According to the three-dimensional signal deconvolution operator, the second OBN seismic data is subjected to three-dimensional deconvolution operation in the three-dimensional intercept slow domain to obtain the third OBN seismic data; The third OBN seismic data is subjected to a three-dimensional intercept slow inverse transform to obtain the OBN seismic data after three-dimensional signal deconvolution in the spatiotemporal domain.

4. The method according to any one of claims 1 to 3, characterized in that, The inversion calculation of the amplitude-calibrated active source signal to obtain the ideal wavelet corresponding to different near-field detectors includes: Substituting the amplitude-calibrated active source signal into the following formula, the ideal wavelet corresponding to different near-field detectors is calculated: Among them, h m (t) represents the active source signal after amplitude calibration, S m The sensitivity of the near-field detector is represented by k, where k represents the k-th near-field detector, m represents the m-th near-field detector, and r is the sensitivity of the near-field detector. km This represents the distance from the k-th bubble to the m-th detector, (r g ) km The distance from the virtual image point of the k-th bubble to the m-th detector is represented by K, where K is the sea level reflection coefficient, α represents the seawater velocity, n represents the number of near-field detectors, and p' is the distance from the virtual image point of the k-th bubble to the m-th detector. k Let represent the ideal wavelet corresponding to the k-th near-field detector.

5. The method of claim 4, wherein, The step of linearly superimposing the ideal wavelets according to the relative positions between the preset far-field point and each ideal wavelet to obtain the three-dimensional far-field wavelets corresponding to the preset far-field point in terms of azimuth and emission angles includes: Based on the relative positions between the preset far-field point and each ideal wavelet, the amplitude ratio and distance of each ideal wavelet are scaled to obtain the scaled ideal wavelets. Based on the obtained shot point depth and sea level reflection coefficient of each near-field detector, the virtual reflection components corresponding to each ideal wavelet are simulated using a preset forward modeling algorithm. The three-dimensional far-field wavelet is obtained by linearly superimposing each scaled ideal wavelet and the corresponding virtual reflection component of each ideal wavelet.

6. The method of claim 5, wherein, The process of suppressing the bubble and virtual reflection components of the three-dimensional far-field wavelet to obtain the suppressed three-dimensional far-field wavelet includes: According to a preset step size, a deconvolution operation is performed on the three-dimensional far-field wavelet to suppress the bubble and virtual reflection components of the three-dimensional far-field wavelet, resulting in a suppressed three-dimensional far-field wavelet; the preset step size is determined based on the distance of the bubble or virtual reflection component from each primary wavelet in the three-dimensional far-field wavelet.

7. The method of claim 1, wherein, Before performing inversion calculations on the amplitude-calibrated active source signal to obtain the ideal wavelet corresponding to different near-field detectors, the following steps are also included: The amplitude-calibrated active source signal is obtained by extracting the active source signal from the near-field record received by the near-field detector and performing amplitude calibration processing on the extracted active source signal.

8. A device for 3D signal deconvolution based on near-field recording, characterized in that, include: The first calculation module is used to perform inversion calculations on the active source signal after amplitude calibration to obtain the ideal wavelet corresponding to different near-field detectors; The second calculation module is used to perform linear superposition calculation on each ideal wavelet according to the relative position between the preset far-field point and each ideal wavelet, so as to obtain the three-dimensional far-field wavelet corresponding to each azimuth angle and each emission angle of the preset far-field point. The suppression processing module is used to suppress the bubble and virtual reflection components of the three-dimensional far-field wavelet to obtain the suppressed three-dimensional far-field wavelet. The conversion module is used to perform frequency and phase conversion on the suppressed three-dimensional far-field wavelet to obtain the corresponding zero-phase desired wavelet; The deconvolution operator determination module is used to determine the three-dimensional signal deconvolution operator corresponding to each azimuth and emission angle of the preset far-field point based on the three-dimensional far-field wavelet before suppression and the zero-phase expected wavelet. The deconvolution operation module is used to perform three-dimensional deconvolution operation on the original OBN seismic data according to the three-dimensional signal deconvolution operator to obtain OBN seismic data after three-dimensional signal deconvolution in the spatiotemporal domain.

9. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the three-dimensional signal deconvolution method based on near-field recording as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that performs the three-dimensional signal deconvolution method based on near-field recording as described in any one of claims 1-7.

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