Diffracted wave separation method, apparatus, device, storage medium and program product

Abstract

The embodiment of the application provides a diffraction wave separation method, device, equipment, storage medium and program product. The method comprises the following steps: performing conversion processing on seismic data to obtain a seismic trace set; performing multi-scale and multi-angle wave field decomposition on the seismic trace set to obtain a wave field component set; the wave field component set comprises mixed wave components containing diffraction waves and reflection waves; performing modal decomposition on the mixed wave components in the wave field component set to obtain a plurality of modal components; and determining separated diffraction wave data based on the plurality of modal components.

Description

Technical Field

[0001] This application relates to the field of signal processing technology, and in particular to a diffraction wave separation method, apparatus, device, storage medium, and program product. Background Technology

[0002] In seismic exploration, diffracted waves are weak energy fluctuations caused by discontinuous structures (such as fractures and pores) in geological bodies, and their imaging capabilities are of great significance for characterizing small-scale reservoir features. However, because the energy of diffracted waves is much lower than that of reflected waves, and the two have similar morphologies in common-field data, traditional methods are unable to effectively separate diffracted waves, limiting the accuracy of identifying complex geological structures. Summary of the Invention

[0003] This application provides a diffraction wave separation method, apparatus, device, storage medium, and program product.

[0004] The technical solution of this application embodiment is implemented as follows: This application provides a method for diffraction wave separation, the method comprising: Seismic data is transformed and processed to obtain seismic gathers; The seismic gather is subjected to multi-scale, multi-angle wavefield decomposition to obtain a wavefield component set; the wavefield component set includes a mixed wave component containing diffracted and reflected waves. Modal decomposition is performed on the mixed wave components in the wave field component set to obtain multiple modal components; The separated diffraction wave data is determined based on the multiple modal components.

[0005] This application provides a diffraction wave separation device, the device comprising: The conversion module is used to convert and process seismic data to obtain seismic gathers; The wavefield decomposition module is used to perform multi-scale and multi-angle wavefield decomposition on the seismic gather to obtain a wavefield component set; the wavefield component set includes a mixed wave component containing diffracted waves and reflected waves. The mode decomposition module is used to perform mode decomposition on the mixed wave components in the wave field component set to obtain multiple mode components; The determination module is used to determine the separated diffraction wave data based on the multiple modal components.

[0006] This application provides a diffraction wave separation device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the diffraction wave separation method provided by one or more of the aforementioned technical solutions.

[0007] This application provides a computer storage medium storing a computer program; when the computer program is executed, it can implement the diffraction wave separation method provided by one or more of the aforementioned technical solutions.

[0008] This application provides a computer program product, including a computer program that, when executed by a processor, implements the diffraction wave separation method provided by one or more of the aforementioned technical solutions.

[0009] The diffraction wave separation method provided in this application transforms and processes seismic data to obtain seismic gathers, providing a basic data structure for subsequent wavefield separation. By performing multi-scale and multi-directional wavefield decomposition on the seismic gathers, the seismic signal can be finely characterized in both scale and angle dimensions, enhancing the ability to distinguish between diffracted and reflected waves. Furthermore, for mixed wave components in the wavefield component set, mode decomposition facilitates the removal of mode components containing reflected wave components, improving the accuracy of diffraction wave separation. Since diffracted waves usually carry information about some fine structures in the subsurface medium, accurate separation of diffracted waves helps improve the identification accuracy of complex geological structures. Attached Figure Description

[0010] Figure 1 A schematic flowchart of a diffraction wave separation method provided in an embodiment of this application; Figure 2 A schematic diagram illustrating the distribution of point seismic sources and plane wave sources provided in an embodiment of this application; Figure 3 A schematic diagram of the ray paths of a reflected wave and a diffracted wave provided for an embodiment of this application; Figure 4 A schematic flowchart of another diffraction wave separation method provided in an embodiment of this application; Figure 5 This is a schematic diagram of the composition structure of a diffraction wave separation device according to an embodiment of this application; Figure 6 This is a schematic diagram of the structure of the diffraction wave separation device provided in the embodiments of this application. Detailed Implementation

[0011] The technical solutions in this application will now be clearly and completely described with reference to the accompanying drawings.

[0012] The present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the embodiments provided herein are merely illustrative of the present application and are not intended to limit the present application. Furthermore, the embodiments provided below are some embodiments for implementing the present application, and not all embodiments for implementing the present application. Unless otherwise specified, the technical solutions described in the present application can be implemented in any combination. It should be noted that, in this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a method or system that includes a list of elements includes not only the elements expressly described, but also other elements not expressly listed, or elements inherent to implementing the method or system. Without further limitations, an element defined by the phrase "comprising a..." does not exclude the presence of other related elements (e.g., steps in the method or units in the system, such as a portion of a processor, a portion of a program, or software, etc.) in the method or system that includes that element.

[0013] For example, the diffraction wave separation method provided in the embodiments of this application includes a series of steps, but the diffraction wave separation method provided in the embodiments of this application is not limited to the steps described. Similarly, the diffraction wave separation device provided in the embodiments of this application includes a series of modules, but the diffraction wave separation device provided in the embodiments of this application is not limited to the modules explicitly described, but may also include modules that need to be set up for obtaining relevant information or processing based on information.

[0014] In the following description, the terms first / second are used merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that first / second may be interchanged in a particular order or sequence where permissible, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0015] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0016] The following are various embodiments.

[0017] In some embodiments of this application, the diffraction wave separation method can be implemented by a processor in the diffraction wave separation device. The processor can be at least one of the following: Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), Central Processing Unit (CPU), controller, microcontroller, and microprocessor.

[0018] Figure 1 This is a flowchart illustrating a diffraction wave separation method provided in an embodiment of this application, as shown below. Figure 1 As shown, the process may include: Step 100: Transform the seismic data to obtain seismic gathers.

[0019] In this embodiment of the application, seismic data can be acquired first, and then the seismic data can be converted and processed to obtain seismic gathers.

[0020] For example, seismic data refers to seismic data that requires diffraction wave separation. It can be two-dimensional seismic data or three-dimensional seismic data. No specific limitation is made here. The following explanation will use two-dimensional seismic data as an example.

[0021] Here, no specific restrictions are placed on the method of acquiring earthquake data. For example, it can be obtained through seismic exploration or directly from earthquake data.

[0022] For example, the purpose of transforming seismic data is to convert it into a specific data structure to facilitate subsequent wavefield analysis and separation; seismic gathers refer to the data sets formed after transforming seismic data (such as plane wave encoding, common offset conversion, etc.). Seismic gathers can include plane wave gathers or common offset gathers, without specific limitations here.

[0023] Among them, plane wave gathers organize seismic data according to the concept of plane waves, while common offset gathers group seismic data with the same offset (horizontal distance from the source to the receiver) into one group.

[0024] For example, when the seismic data is two-dimensional seismic data, it can be converted using formula (1) to obtain a two-dimensional plane wavelet set. : (1) in, Indicates the position of the detector; Indicates frequency; Indicates the angle of departure; This indicates a common receiver point gather, corresponding to two-dimensional seismic data; Represents ray parameters, Indicates speed; The x-coordinate of the point source is represented by the formula (1). According to the formula (1), by performing integral calculations on the common receiver point gather and considering factors such as frequency and emission angle, the conversion from two-dimensional seismic data to two-dimensional plane wave gather can be realized.

[0025] For example, to facilitate understanding of the concepts of point source, plane wave source, reflected wave, and diffracted wave, the following will be combined with... Figure 2 and Figure 3 Provided as an example; Figure 2 The distribution of point sources and plane wave sources is shown, with solid circles representing point sources and hollow circles representing plane wave sources; Figure 3 The diagram shows the ray paths of reflected and diffracted waves when a plane wave encounters an obstacle. After a plane wave source is excited, the wave propagation will generate reflected and diffracted waves when it encounters a reflection interface and a diffraction point, respectively. The solid lines with arrows correspond to the ray paths of reflected waves, and the dashed lines with arrows correspond to the ray paths of diffracted waves.

[0026] In practice, by transforming and processing seismic data, the kinematic characteristics of seismic waves can be effectively extracted. This transformation process is a step in seismic data preprocessing and helps to conduct more in-depth analysis and processing of seismic data in the future.

[0027] Step 101: Perform multi-scale, multi-angle wavefield decomposition on the seismic gathers to obtain wavefield component sets.

[0028] For example, mathematical transformation methods can be used to decompose the complex wavefield in seismic traces into multi-scale and multi-angle wavefields to obtain wavefield component sets.

[0029] Here, the type of mathematical transformation method is not specifically limited. For example, it can be the Generalized Curvelet Transform (GCT) method or other curvelet transformation methods; the GCT method will be used as an example for explanation later.

[0030] Among them, the GCT method is a multi-scale and multi-angle analysis method; it can decompose seismic gathers into wavefield components of different scales and different angles; different scales correspond to different frequency ranges, and different angles reflect the propagation angle information of the wavefield; by performing wavefield decomposition, various characteristics of seismic waves propagating in complex media can be captured, providing a data foundation for subsequent frequency analysis and mode decomposition.

[0031] It should be noted that the wave field component set may include only a mixed wave component containing diffracted and reflected waves, or it may include only a diffracted wave component, a mixed wave component containing diffracted and reflected waves, or it may include only a diffracted wave component, a diffracted wave component containing only reflected waves, or a mixed wave component containing diffracted and reflected waves; the third case will be used as an example for explanation later.

[0032] Step 102: Perform mode decomposition on the mixed wave components in the wave field component set to obtain multiple mode components.

[0033] In this embodiment of the application, after obtaining the wave field component set according to the above steps, the mixed wave component in the wave field component set can be decomposed using the mode decomposition method to obtain multiple mode components; wherein, the mode component may be the mode component corresponding to the diffracted wave or the mode component corresponding to the reflected wave.

[0034] Here, the type of mode decomposition method is not specifically limited. For example, it can be variational mode decomposition (VMD) or other mode decomposition methods. The VMD method will be used as an example for explanation later.

[0035] For example, mode decomposition is a generalized sparse representation. The VMD method, as an adaptive and completely non-recursive mode variational and signal processing method, can decompose a signal into a set of intrinsic mode functions (IMFs) with different center frequencies and finite bandwidths. By analyzing the different kinematic and dynamic characteristics of diffracted and reflected waves, linear reflected waves can be effectively estimated, thereby achieving the separation of diffracted waves.

[0036] Step 103: Determine the separated diffraction wave data based on multiple modal components.

[0037] In this embodiment of the application, after completing the above-mentioned multi-scale and multi-angle wavefield decomposition and mode decomposition, multiple modal components can be obtained. Based on the multiple modal components, the separated diffraction wave data can be determined, that is, the diffraction wave separated from the seismic data.

[0038] For example, multiple modal components can be filtered to obtain target modal components related to the diffraction wave. The filtered target modal components can be reconstructed to obtain reconstructed diffraction wave components. Then, all reconstructed diffraction wave components can be superimposed to obtain separated diffraction wave data.

[0039] Here, the target mode component associated with the diffraction wave refers to the mode component that contains only diffraction wave components, which can be directly used to reconstruct the diffraction wave field.

[0040] For example, if the above wave field component set also includes a diffracted wave component containing only reflected waves, the diffracted wave component containing only reflected waves can be superimposed with the separated diffracted wave data to obtain the separated diffracted wave data.

[0041] Understandably, by filtering multiple modal components, modal components other than the target modal component, i.e., modal components containing only reflected wave components, can be eliminated to ensure the accuracy of the separated diffraction wave data. The separated diffraction wave data can be used for subsequent imaging processing to reveal small-scale geological structures underground.

[0042] As can be seen, the diffraction wave separation method provided in this application, by comprehensively utilizing GCT and VMD technologies, makes full use of the differences between diffracted waves and reflected waves in multiple dimensions such as scale and angle, to achieve high-precision and high-amplitude-preserving diffraction wave separation, thereby effectively improving the seismic imaging quality under complex geological conditions.

[0043] In some embodiments, performing multi-scale, multi-angle wavefield decomposition on seismic gathers to obtain wavefield component sets may include: performing sliding segmentation on the seismic gathers through a preset window to obtain multiple data blocks; performing multi-scale, multi-angle wavefield decomposition on each data block to obtain wavefield component subsets; the wavefield component sets include wavefield component subsets of multiple data blocks.

[0044] For example, sliding partitioning is a technique that divides a continuous data sequence into multiple data blocks according to a sliding step size and a preset window; wherein, the preset window defines the amount of continuous data contained in each data block; the sliding step size refers to the number of new data added to the data sequence each time the preset window moves; if the sliding step size is less than the preset window, there is partial overlap between adjacent data blocks.

[0045] Understandably, to avoid edge effects, the multiple data blocks obtained after sliding segmentation can be multiple partially overlapping data blocks; that is, by performing sliding segmentation on the seismic gather through a preset window, the seismic gather can be divided into multiple partially overlapping data blocks, at which point each data block contains local seismic signal information. Here, the size of the preset window and the sliding step can be set according to the actual situation, and there is no specific limitation. For example, the preset window can be 400 sampling points in both the vertical and horizontal directions. Taking the horizontal sliding as an example, if the sliding step is 100 sampling points, then there will be 300 repeated sampling points in the horizontal direction of adjacent data blocks.

[0046] Understandably, sliding partitioning can improve the parallelism and computational efficiency of data processing. By dividing the data into multiple overlapping blocks, it can also reduce edge effects caused by data segmentation and ensure the subsequent wavefield separation effect.

[0047] In this embodiment of the application, after obtaining multiple partially overlapping data blocks, wavefield decomposition of each data block can be performed at multiple scales and angles to obtain wavefield component subsets; each data block corresponds to a wavefield component subset.

[0048] In some embodiments, performing multi-scale, multi-angle wavefield decomposition on each data block to obtain a subset of wavefield components may include: performing a curvelet transform on each data block to obtain a set of curvelet coefficients; grouping the curvelet coefficients in the set of curvelet coefficients according to multiple preset angle ranges to obtain a first subset of curvelet coefficients, a second subset of curvelet coefficients, and a third subset of curvelet coefficients; performing an inverse curvelet transform on each curvelet coefficient in the second subset of curvelet coefficients to obtain the corresponding mixed wave components; the subset of wavefield components includes the mixed wave components corresponding to each curvelet coefficient in the second subset of curvelet coefficients.

[0049] For example, a curvelet transform can be performed on each data block to obtain a curvelet coefficient set; here, the curvelet coefficient set may include multiple curvelet coefficients of different scales and different angles.

[0050] For example, curvelet transform can decompose the original signal into multiple sub-bands, each corresponding to a specific scale and angle, thereby enabling multi-dimensional analysis of the signal; that is, curvelet transform is equivalent to multi-scale, multi-angle wavefield decomposition. Performing curvelet transform on each data block obtained in the above steps helps to distinguish different types of wavefield components, such as reflected waves and diffracted waves.

[0051] In some embodiments, performing a curvelet transform on each data block may include: determining wavefield decomposition parameters; and performing a curvelet transform on each data block using a curvelet transform method that includes the wavefield decomposition parameters.

[0052] Here, the curvelet transform method can be the GCT method or other curvelet transform methods; the GCT method will be used as an example for explanation in the following sections.

[0053] Among them, the wave field decomposition parameters are also called anisotropic parameters, which may include scale scaling parameters and angle scaling parameters. In the embodiments of this application, after determining the scale scaling parameters and angle scaling parameters, the GCT method containing these two parameters can be used to perform curvelet transform on each data block.

[0054] For example, the scale scaling parameter in the GCT method u and angle scaling parameters v These are related to the division of scale and angle, respectively, and their value ranges can be limited to... In practical applications, the scaling parameter u and angle scaling parameters v The selection rule is as follows: if a high scale resolution of the signal is required, then the scale scaling parameter... u The value of should be close to As scale division becomes more refined, angular resolution decreases; if high angular resolution of the signal is required, the angle scaling parameter v should approach a certain value. When the angular resolution changes, the scale resolution remains unchanged; the scaling matrices corresponding to the radial scaling parameter u and the angular scaling parameter v are shown in formula (2): (2) in, It is the distance from the center of a curved wave to the origin in the frequency domain.

[0055] In practice, the scaling parameters can be determined first according to the selection rules mentioned above. u and angle scaling parameters v Then, the GCT method, which includes wave field decomposition parameters, is used to perform curvelet transform on each data block to obtain a set of curvelet coefficients.

[0056] For example, the curvature coefficient of one of the data blocks can be calculated using formula (3): (3) in, For simplicity, we will ignore the variables of one of the data blocks. It is a curve wave function; Represents the set of curvature coefficients of a time-domain data block. These represent the indexes for the scale dimension, angle dimension, and position dimension, respectively. In other words, each curve coefficient in the curve coefficient set contains scale information, angle information, and position information.

[0057] In this embodiment of the application, after obtaining the set of curved coefficients, the curved coefficients in the set of curved coefficients can be grouped according to multiple preset angle ranges to obtain a first subset of curved coefficients, a second subset of curved coefficients, and a third subset of curved coefficients. It should be noted that each curve coefficient in the curve coefficient set contains angle information. Therefore, the curve coefficients in the curve coefficient set can be grouped according to multiple preset angle ranges.

[0058] In this embodiment of the application, multiple preset angle ranges are used to distinguish different types of wave field components. These ranges may include a small angle range representing the reflected wave component, a large angle range representing the diffracted wave component, and an intermediate angle range between the two angle ranges representing the mixed wave component. The number of large angle range, small angle range, and intermediate angle range may be one or more.

[0059] Here, the values ​​of each preset angle range can be set according to the actual situation, and no specific limitation is made here.

[0060] For example, after obtaining the set of curved coefficients, the angle information contained in each curved coefficient in the set can be matched with multiple preset angle ranges (e.g., large angle range, small angle range, and intermediate angle range) respectively, and the curved coefficients belonging to the same preset angle range can be grouped together to obtain the first curved coefficient subset, the second curved coefficient subset, and the third curved coefficient subset.

[0061] The angle of the second subset of curvature coefficients is greater than that of the third subset of curvature coefficients and less than that of the first subset of curvature coefficients; that is, the first subset of curvature coefficients includes all curvature coefficients in the large angle range of the curvature coefficient set, the second subset of curvature coefficients includes all curvature coefficients in the middle angle range of the curvature coefficient set, and the third subset of curvature coefficients includes all curvature coefficients in the small angle range of the curvature coefficient set.

[0062] For example, based on the wave field component types characterized by the different angle ranges mentioned above, it can be seen that the curve wave coefficients in the first curve wave coefficient subset contain only diffraction wave components, the curve wave coefficients in the second curve wave coefficient subset contain both diffraction wave components and reflection wave components, and the curve wave coefficients in the third curve wave coefficient subset contain only reflection wave components.

[0063] Understandably, since both the first and second subsets of curvilinear coefficients contain diffraction components, in order to achieve accurate separation of diffraction waves, an inverse curvilinear transform can be performed on each curvilinear coefficient in both subsets. It is unnecessary to perform an inverse curvilinear transform on the curvilinear coefficients of the third subset because the curvilinear coefficients of the third subset only contain reflected wave components and do not involve diffraction components. Here, the inverse curvilinear transform is also called the inverse generalized curvilinear transform.

[0064] For example, since the curvelet transform is a tight frame that allows the original signal to be reconstructed from the curvelet coefficients, its inverse curvelet transform process can be expressed as Equation (4): (4) For example, the decomposition of each data block by the above curvelet transform can be described by formula (5): (5) in, Represents data blocks in matrix form; and These represent the curve transform and the inverse curve transform, respectively.

[0065] Understandably, since the curve coefficients in the first subset of curve coefficients only contain diffraction wave components, the corresponding diffraction wave components can be obtained by performing an inverse curve transformation on each curve coefficient in the first subset of curve coefficients. Since the curve coefficients in the second subset of curve coefficients contain both diffraction wave components and reflection wave components, the corresponding mixed wave components can be obtained by performing an inverse curve transformation on each curve coefficient in the second subset of curve coefficients. The mixed wave components refer to the mixed wave components that contain both diffraction waves and reflection waves.

[0066] In this embodiment, the wavefield component subset may include the diffracted wave components corresponding to each curvature coefficient in the first curvature coefficient subset and the mixed wave components corresponding to each curvature coefficient in the second curvature coefficient subset; the wavefield component subset includes wavefield component subsets of multiple data blocks; that is, the wavefield component subset includes mixed wave components containing both diffracted and reflected waves, and diffracted wave components containing only diffracted waves. Since the mixed wave components in the wavefield component subset contain both diffracted and reflected waves, further decomposition is required.

[0067] In this embodiment of the application, the VMD method can be used to perform mode decomposition on the mixed wave components in the wave field component set to obtain multiple mode components; for example, the mode decomposition process can be as follows: first, construct the diffraction wave separation constraint variational problem through formula (6): (6) in, The decomposed reflected wave components are represented by s; the seismic data is represented by s; and the number of decomposed modes is represented by N. This represents the center frequency of the k-th mode. This represents the partial derivative with respect to time t. It is the L2 norm, and j is the imaginary unit.

[0068] For example, to transform the above constrained variational problem into an unconstrained problem for optimization, the Lagrange multiplier method can be used to incorporate the constraints into the objective function, thus obtaining the Lagrange function. Due to the existence of the diffracted wave field, the sum of the decomposed reflected wave components is less than the seismic data; therefore, the corresponding Lagrange function can be expressed by formula (7): (7) in, This is a quadratic penalty term factor that balances variational energy and the penalty term. Essentially, this parameter is related to the complexity of the seismic data and has relatively weak sensitivity to prediction results; therefore, a larger value is recommended. value; It is the Lagrange operator, which is set to zero due to the presence of noise; N represents the number of modes decomposed. Theoretically, the larger N is, the more reflected wave data will be predicted. In order to effectively suppress reflected waves, it is recommended to select a larger value of N to predict linear reflected waves. This indicates that the earthquake data is about spatial location. and time The function is the original data foundation for the entire decomposition and optimization process. Represents the sum of all decomposed modes , The method is used to measure the fit between the decomposition results and the seismic data. Then, the alternating direction multiplier method is used for iterative calculation to output the modal components corresponding to the reflected waves.

[0069] For example, after obtaining multiple modal components according to the above process, the separated diffraction wave data can be determined based on the multiple modal components.

[0070] In some embodiments, determining the separated diffracted wave data based on multiple modal components may include: selecting target modal components related to the diffracted wave from the multiple modal components, reconstructing the target modal components to obtain reconstructed diffracted wave components; and determining the separated diffracted wave data based on the reconstructed diffracted wave components.

[0071] Here, the target mode component associated with the diffracted wave can be a target mode component that matches the propagation characteristics or other characteristics of the diffracted wave; the propagation characteristics of the diffracted wave will be used as an example for explanation later.

[0072] For example, the propagation characteristics of diffraction waves refer to the kinematic and dynamic features of scattered waves generated by small-scale geological bodies (such as cracks, cavities, etc.) in seismic signals. For example, the propagation characteristics of diffraction waves are manifested as a hyperbolic time-distance relationship, weak amplitude, and multi-directional propagation paths.

[0073] For example, the target modal component can be a component that matches the diffraction wave propagation characteristics selected from all modal components; the above-mentioned selection of the target modal component can be achieved by setting a specific threshold or using an adaptive algorithm to select modal components with diffraction wave propagation characteristics.

[0074] It should be noted that the reconstructed diffraction wave component is a local signal with diffraction wave characteristics recovered from the selected target mode components through inverse transformation or other reconstruction methods; the generation process of the reconstructed diffraction wave component can remove the influence of the reflected wave component, thereby improving the fidelity and integrity of the diffraction wave component.

[0075] For example, the reconstructed diffracted wave components can be determined using formula (8): (8) in, This indicates noise in the seismic data; Indicates from earthquake data Extracted reflected wave components; This represents the reconstructed diffraction wave component.

[0076] In this embodiment, after obtaining the reconstructed diffraction wave components, the separated diffraction wave data can be determined based on these components. It can be understood that the separated diffraction wave data refers to the set of diffraction wave signals, independent of the reflected waves, obtained after screening and reconstruction. These data contain information from small-scale discontinuous geological bodies in the seismic response, and are of great significance for characterizing underground structures.

[0077] As can be seen, in this embodiment of the application, by selecting the target modal component related to the diffraction wave from multiple modal components and reconstructing the target modal component, the diffraction wave information can be extracted more accurately, avoiding the energy loss and signal distortion caused by reflected wave interference in traditional methods, thereby improving the accuracy and reliability of seismic data processing.

[0078] For example, as can be seen from the above, the wave field component set includes not only the mixed wave component containing diffracted waves and reflected waves, but also the diffracted wave component containing only diffracted waves; therefore, after obtaining the reconstructed diffracted wave component, the diffracted wave component containing only diffracted waves in the wave field component set and the reconstructed diffracted wave component can be superimposed to obtain the separated diffracted wave data.

[0079] Understandably, the diffracted wave component refers to the diffracted wave component extracted from seismic data during the generalized curve transform (GCT) process, which contains only diffracted wave components. By superimposing the diffracted wave component containing only diffracted wave components with the reconstructed diffracted wave component, the amplitude preservation and integrity of the final diffracted wave data can be ensured. Thus, the purpose of separating diffracted waves from the original complex seismic data is achieved. Since diffracted waves usually carry information about some fine structures in the subsurface medium, their accurate separation helps to improve the resolution of seismic data and provides a reliable data foundation for subsequent seismic imaging and geological interpretation.

[0080] To facilitate understanding, the diffraction wave separation method provided in the embodiments of this application will be further described below.

[0081] Figure 4 A schematic flowchart of another diffraction wave separation method provided in this application embodiment is shown below. Figure 4 As shown, the process may include: Step 400: Seismic data acquisition.

[0082] For example, seismic data can be obtained directly from raw seismic data; the raw seismic data contains information on the response of the subsurface medium to seismic waves, and the wave field composition is complex, requiring diffraction wave separation processing. Step 401: Seismic data preprocessing.

[0083] For example, the acquired seismic data can be preprocessed, such as by transformation, to obtain plane wave gathers or common offset gathers, which helps in the subsequent analysis and processing of the wavefield. Step 402: Generalized curve wave transform.

[0084] For example, after obtaining the plane wave channel set or the common offset channel set, the GCT method can be used to perform generalized curve transform to obtain the wave field component set. The corresponding process has been described in the above embodiments, and will not be repeated here to avoid repetition.

[0085] Step 403: Modal decomposition.

[0086] For example, after obtaining the wave field component set, the VMD method can be used to perform mode decomposition on the mixed wave component in the wave field component set that contains both diffracted and reflected waves to obtain multiple mode components.

[0087] Step 404: Filter the target modal components and reconstruct them.

[0088] For example, after obtaining multiple modal components, the target modal components related to the diffraction wave can be screened out according to the propagation characteristics of the diffraction wave; then, the screened target modal components can be reconstructed into diffraction wave components through operations such as inverse transformation. Step 405: Superposition of diffracted wave components.

[0089] For example, as described above, the wavefield component set includes not only mixed wave components containing diffracted and reflected waves, but also diffracted wave components containing only diffracted waves. Therefore, after obtaining the reconstructed diffracted wave components, the diffracted wave components containing only diffracted waves in the wavefield component set and the reconstructed diffracted wave components can be superimposed to obtain the separated diffracted wave data. In this way, the goal of separating diffracted waves from the original complex seismic data is achieved. Understandably, diffracted waves usually carry information about the subtle structures of the subsurface medium; accurate separation of diffracted waves helps improve the resolution of seismic data and facilitates better geological interpretation.

[0090] Figure 5 This is a schematic diagram of the composition structure of a diffraction wave separation device according to an embodiment of this application, as shown below. Figure 5 As shown, the diffraction wave separation device 50 may include: a conversion module 500, a wave field decomposition module 501, a mode decomposition module 502, and a determination module 503, wherein: The conversion module 500 is used to convert and process seismic data to obtain seismic gathers; The wavefield decomposition module 501 is used to perform multi-scale and multi-angle wavefield decomposition on the seismic gather to obtain a wavefield component set; the wavefield component set includes a mixed wave component containing diffracted waves and reflected waves. The mode decomposition module 502 is used to perform mode decomposition on the mixed wave components in the wave field component set to obtain multiple mode components; The determination module 503 is used to determine the separated diffraction wave data based on the multiple modal components.

[0091] In some embodiments, the determining module 503 is further configured to: Target mode components related to the diffraction wave are selected from the multiple mode components, and the target mode components are reconstructed to obtain the reconstructed diffraction wave components. Based on the reconstructed diffraction wave components, the separated diffraction wave data is determined.

[0092] In some embodiments, the wave field component set further includes a diffracted wave component containing only the diffracted wave, and the determining module 503 is further configured to: The diffracted wave component containing only diffracted waves in the wave field component set is superimposed with the reconstructed diffracted wave component to obtain the separated diffracted wave data.

[0093] In some embodiments, the wave field decomposition module 501 is further configured to: The seismic gather is divided into multiple data blocks by sliding segmentation through a preset window; Each data block is subjected to multi-scale, multi-angle wavefield decomposition to obtain a wavefield component subset; the wavefield component subset includes the wavefield component subsets of the multiple data blocks.

[0094] In some embodiments, the wave field decomposition module 501 is further configured to: Perform curvelet transform on each data block to obtain a curvelet coefficient set; the curvelet coefficient set includes multiple curvelet coefficients of different scales and angles; The curvature coefficients in the curvature coefficient set are grouped according to multiple preset angle ranges to obtain a first curvature coefficient subset, a second curvature coefficient subset, and a third curvature coefficient subset; wherein, the angle of the second curvature coefficient subset is greater than the angle of the third curvature coefficient subset and less than the angle of the first curvature coefficient subset. Perform inverse curvelet transform on each curvelet coefficient in the second subset of curvelet coefficients to obtain the corresponding hybrid wave component; the wave field component subset includes the hybrid wave component corresponding to each curvelet coefficient in the second subset of curvelet coefficients.

[0095] In some embodiments, the wave field decomposition module 501 is further configured to: Determine the wavefield decomposition parameters; the wavefield decomposition parameters include scale scaling parameters and angle scaling parameters; Each data block is subjected to a curvelet transform using a generalized curvelet transform method that incorporates the wavefield decomposition parameters.

[0096] In some embodiments, the mode decomposition module 502 is further configured to: The mixed wave components in the wave field component set are decomposed using the mode decomposition method to obtain multiple mode components.

[0097] In practical applications, the aforementioned conversion module 500, wave field decomposition module 501, mode decomposition module 502, and determination module 503 can all be implemented by a processor located in the diffraction wave separation device. The processor can be at least one of ASIC, DSP, DSPD, PLD, FPGA, CPU, controller, microcontroller, and microprocessor.

[0098] Furthermore, in this embodiment, the functional modules can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional module.

[0099] If the integrated unit is implemented as a software functional module and is not sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this embodiment, in essence, or the part that contributes to related technologies, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the method of this embodiment. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0100] Specifically, the computer program instructions corresponding to a diffraction wave separation method in this embodiment can be stored on storage media such as optical discs, hard disks, and USB flash drives. When the computer program instructions corresponding to a diffraction wave separation method in the storage media are read or executed by a diffraction wave separation device, any one of the diffraction wave separation methods in the aforementioned embodiments is implemented.

[0101] Based on the same technical concept as the foregoing embodiments, see Figure 6 It illustrates a diffraction wave separation device 600 provided in an embodiment of this application, which may include: a memory 601 and a processor 602; wherein, Memory 601 is used to store computer programs and data; The processor 602 is configured to execute a computer program stored in a memory to implement any of the diffraction wave separation methods described in the foregoing embodiments.

[0102] In practical applications, the aforementioned memory 601 can be volatile memory, such as RAM; or non-volatile memory, such as ROM, flash memory, hard disk drive (HDD), or solid-state drive (SSD); or a combination of the above types of memory, and provide instructions and data to the processor 602.

[0103] The processor 602 described above can be at least one of ASIC, DSP, DSPD, PLD, FPGA, CPU, controller, microcontroller, and microprocessor. Understandably, for different diffraction wave separation devices, the electronic device used to implement the above processor function can also be other types; this application embodiment does not specifically limit the specific types.

[0104] In some embodiments, this application also provides a computer program product, including a computer program that, when executed by a processor, implements any of the diffraction wave separation methods described in the foregoing embodiments.

[0105] In some embodiments, the functions or modules of the apparatus provided in this application can be used to perform the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0106] The descriptions of the various embodiments above tend to emphasize the differences between them. Similarities or commonalities can be referred to interchangeably, and for the sake of brevity, they will not be repeated here. The methods disclosed in the various method embodiments provided in this application can be arbitrarily combined to obtain new method embodiments without conflict. The features disclosed in the various product embodiments provided in this application can be arbitrarily combined to obtain new product embodiments without conflict. The features disclosed in the various method or device embodiments provided in this application can be arbitrarily combined to obtain new method or device embodiments without conflict.

[0107] Those skilled in the art will understand that embodiments of this application can be provided as methods, apparatus, or computer program products. Therefore, this application can take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage) containing computer-usable program code.

[0108] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatuses, devices (systems), and computer program products according to embodiments of this application. 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... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0109] 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.

[0110] The above are merely preferred embodiments of this application and are not intended to limit the scope of protection of this application.

Claims

1. A method for separating diffracted waves, characterized in that, The method includes: Seismic data is transformed and processed to obtain seismic gathers; The seismic gather is subjected to multi-scale, multi-angle wavefield decomposition to obtain a wavefield component set; the wavefield component set includes a mixed wave component containing diffracted and reflected waves. Modal decomposition is performed on the mixed wave components in the wave field component set to obtain multiple modal components; The separated diffraction wave data is determined based on the multiple modal components.

2. The method according to claim 1, characterized in that, The determination of the separated diffracted wave data based on the multiple modal components includes: Target mode components related to the diffraction wave are selected from the multiple mode components, and the target mode components are reconstructed to obtain the reconstructed diffraction wave components. Based on the reconstructed diffraction wave components, the separated diffraction wave data is determined.

3. The method according to claim 2, characterized in that, The wave field component set also includes diffracted wave components containing only diffracted waves. The step of determining the separated diffracted wave data based on the reconstructed diffracted wave components includes: The diffracted wave component containing only diffracted waves in the wave field component set is superimposed with the reconstructed diffracted wave component to obtain the separated diffracted wave data.

4. The method according to claim 1, characterized in that, The multi-scale, multi-angle wavefield decomposition of the seismic gather to obtain wavefield component sets includes: The seismic gather is divided into multiple data blocks by sliding segmentation through a preset window; Each data block is subjected to multi-scale, multi-angle wavefield decomposition to obtain a wavefield component subset; the wavefield component subset includes the wavefield component subsets of the multiple data blocks.

5. The method according to claim 4, characterized in that, The process of performing multi-scale, multi-angle wavefield decomposition on each data block to obtain a subset of wavefield components includes: Perform curvelet transform on each data block to obtain a curvelet coefficient set; the curvelet coefficient set includes multiple curvelet coefficients of different scales and angles; The curvature coefficients in the curvature coefficient set are grouped according to multiple preset angle ranges to obtain a first curvature coefficient subset, a second curvature coefficient subset, and a third curvature coefficient subset; wherein, the angle of the second curvature coefficient subset is greater than the angle of the third curvature coefficient subset and less than the angle of the first curvature coefficient subset. Perform inverse curvelet transform on each curvelet coefficient in the second subset of curvelet coefficients to obtain the corresponding hybrid wave component; the wave field component subset includes the hybrid wave component corresponding to each curvelet coefficient in the second subset of curvelet coefficients.

6. The method according to claim 5, characterized in that, The process of performing curvelet transform on each data block includes: Determine the wavefield decomposition parameters; the wavefield decomposition parameters include scale scaling parameters and angle scaling parameters; Each data block is subjected to a curvelet transform using a curvelet transform method that includes the wavefield decomposition parameters.

7. The method according to any one of claims 1 to 6, characterized in that, The modal decomposition of the mixed wave components in the wave field component set yields multiple modal components, including: The mixed wave components in the wave field component set are decomposed using the mode decomposition method to obtain multiple mode components.

8. A diffraction wave separation device, characterized in that, The device includes: The conversion module is used to convert and process seismic data to obtain seismic gathers; The wavefield decomposition module is used to perform multi-scale and multi-angle wavefield decomposition on the seismic gather to obtain a wavefield component set; the wavefield component set includes a mixed wave component containing diffracted waves and reflected waves. The mode decomposition module is used to perform mode decomposition on the mixed wave components in the wave field component set to obtain multiple mode components; The determination module is used to determine the separated diffraction wave data based on the multiple modal components.

9. A diffraction wave separation device, characterized in that, The diffraction wave separation device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method according to any one of claims 1 to 7.

10. A computer storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method described in any one of claims 1 to 7.

11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1 to 7.

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