Method for processing seismic data

Abstract

The application discloses a processing method of seismic data, and relates to the technical field of geophysical seismic exploration. The method comprises the following steps: acquiring three-dimensional seismic data; performing segmentation processing on the three-dimensional seismic data based on a space window and a time window to obtain a plurality of local seismic data bodies; performing horizontal projection processing in a first direction and horizontal projection processing in a second direction on the plurality of local seismic data bodies respectively to obtain a plurality of projection data bodies; performing anti-projection on the plurality of projection data bodies respectively to obtain a plurality of local seismic data bodies after acquisition footprint suppression; performing space window overlap weighting processing in the first direction and the second direction on the plurality of local seismic data bodies after acquisition footprint suppression based on a plurality of time windows to obtain a plurality of overlap data bodies corresponding to the plurality of time windows respectively; and performing time window overlap weighting processing on the plurality of overlap data bodies to obtain seismic data after acquisition footprint suppression. The acquisition footprint can be effectively suppressed.

Description

Technical Field

[0001] This application relates to the field of geophysical seismic exploration technology, and in particular to a method for processing seismic data. Background Technology

[0002] Seismic data acquisition is crucial for understanding the Earth's internal structure, predicting earthquake hazards, and exploring oil and gas resources. However, seismic data may contain acquisition footprint noise, which refers to artificial traces left during the acquisition and processing of seismic data. This noise manifests as regular amplitude variations in seismic profile depth or time slices. Acquisition footprint noise interferes with the accurate interpretation of true underground geological information, thus affecting the accuracy and reliability of subsequent geological analysis, seismic imaging, and resource exploration. Therefore, it is necessary to suppress acquisition footprints.

[0003] In related technologies, post-stack suppression methods are commonly used to suppress acquisition footprint noise, such as spatial smoothing filtering. The principle of spatial smoothing filtering is based on the assumption that acquisition footprint noise changes slowly in space, while the effective seismic signal changes relatively quickly. This method reduces spatially slow-changing noise components by performing a spatially weighted average of the seismic data, calculating the average or weighted average of the data within a sliding window, and replacing the data value at the center of the window.

[0004] However, the methods described above are unsuitable for suppressing acquisition footprints in seismic data from complex underground structures. Seismic reflection signals from complex structures exhibit complex spatial variations, similar to the spatial characteristics of acquisition footprint noise. Spatial smoothing filtering treats the effective signals generated by complex structures as noise, leading to loss and distortion of the effective signal, making it impossible to accurately distinguish between acquisition footprint noise and genuine geological structural information. Therefore, how to effectively suppress acquisition footprints in seismic data from complex underground structures is an urgent problem to be solved. Summary of the Invention

[0005] This application provides a method for processing seismic data, capable of effectively acquiring and suppressing seismic data from complex underground structures. The technical solution is as follows:

[0006] On the one hand, a method for processing seismic data is provided, the method comprising:

[0007] Acquire three-dimensional seismic data, which is used to characterize the geological characteristics of a geological region. The three-dimensional seismic data includes acquisition footprint noise, which is noise left during the acquisition of the three-dimensional seismic data.

[0008] The three-dimensional seismic data is segmented based on spatial windows and time windows to obtain multiple local seismic data volumes. The spatial window is used to segment the three-dimensional seismic data in the spatial dimension, and the time window is used to define the three-dimensional seismic data in the time dimension.

[0009] The plurality of local seismic data volumes are subjected to horizontal projection processing in a first direction and horizontal projection processing in a second direction, respectively, to obtain a plurality of projected data volumes; the first direction is perpendicular to the second direction;

[0010] Back-projection is performed on the multiple projection data volumes to obtain multiple local seismic data volumes after the footprints are collected;

[0011] Based on the multiple time windows, the multiple local seismic data volumes after the acquisition footprints are pressed are sequentially subjected to spatial window overlap weighting processing in the first direction to obtain multiple first spatial window data; and the multiple first spatial window data are sequentially subjected to spatial window overlap weighting processing in the second direction to obtain multiple overlapping data volumes corresponding to the multiple time windows respectively.

[0012] The multiple overlapping data volumes are subjected to time window overlap weighting processing to obtain the seismic data after the acquisition footprints are suppressed.

[0013] On the other hand, a seismic data processing apparatus is provided, the apparatus comprising:

[0014] The acquisition module is used to acquire three-dimensional seismic data, which is used to characterize the geological characteristics of a geological region. The three-dimensional seismic data includes acquisition footprint noise, which is noise left during the acquisition of the three-dimensional seismic data.

[0015] The segmentation module is used to segment the three-dimensional seismic data based on spatial windows and time windows to obtain multiple local seismic data volumes. The spatial window is used to segment the three-dimensional seismic data in the spatial dimension, and the time window is used to define the three-dimensional seismic data in the time dimension.

[0016] The projection module is used to perform horizontal projection processing in a first direction and horizontal projection processing in a second direction on the multiple local seismic data volumes to obtain multiple projected data volumes; the first direction is perpendicular to the second direction;

[0017] The back projection module is used to back project the multiple projection data volumes respectively to obtain multiple local seismic data volumes after the footprints are collected;

[0018] The overlap module is used to perform spatial window overlap weighting processing on the multiple local seismic data volumes after the acquisition footprints are pressed, respectively, based on the multiple time windows, to obtain multiple first spatial window data; and to perform spatial window overlap weighting processing on the multiple first spatial window data in the second direction, respectively, to obtain multiple overlap data volumes corresponding to the multiple time windows.

[0019] The overlapping module is also used to perform time window overlapping weighting processing on the multiple overlapping data volumes to obtain the seismic data after the acquisition footprints are suppressed.

[0020] In an optional embodiment, the projection module is further configured to perform horizontal projection processing on the plurality of local seismic data volumes in the first direction to obtain a plurality of first projection data volumes; and to perform horizontal projection processing on the plurality of first projection data volumes in the second direction to obtain the plurality of projection data volumes.

[0021] In an optional embodiment, the projection module is further configured to: superimpose two adjacent and centered data lines of the i-th local seismic data volume in the first direction to obtain a first model data line; i is a positive integer; perform frequency-wavenumber transformation on the first model data line to obtain first frequency-wavenumber domain data; perform dip angle scanning on the first frequency-wavenumber domain data based on a preset first energy dip angle range to determine a first target dip angle, wherein the first target dip angle refers to the dip angle with the strongest energy within the first energy dip angle range; wherein the seismic wave reflection energy is strongest in the direction of the first target dip angle; calculate the horizontal projection time difference of the i-th local seismic data volume in the first direction based on the first target dip angle to obtain a first horizontal projection time difference; and perform horizontal projection processing on the i-th local seismic data volume in the first direction based on the first horizontal projection time difference to obtain a first projection data volume corresponding to the i-th local seismic data volume.

[0022] In an optional embodiment, the projection module is further configured to: superimpose two adjacent and centered data lines of the first projected data volume corresponding to the i-th local seismic data volume in the second direction to obtain a second model data line; perform frequency-wavenumber transformation on the second model data line to obtain second frequency-wavenumber domain data; perform dip angle scanning on the second frequency-wavenumber domain data based on a preset second energy dip angle range to determine a second target dip angle, wherein the second target dip angle refers to the dip angle with the strongest energy within the second energy dip angle range; calculate the horizontal projection time difference in the second direction for the first projected data volume corresponding to the i-th local seismic data volume based on the second target dip angle to obtain a second horizontal projection time difference; and perform horizontal projection processing in the second direction on the first projected data volume corresponding to the i-th local seismic data volume based on the second horizontal projection time difference to obtain a projection data volume corresponding to the i-th local seismic data volume.

[0023] In an optional embodiment, the back-projection module is further configured to replace the sample amplitude value of each seismic trace sample point in the plurality of projection data volumes to obtain the suppressed plurality of projection data volumes; and to perform back-projection on the suppressed plurality of projection data volumes to obtain the plurality of local seismic data volumes after the acquisition footprints are suppressed.

[0024] In an optional embodiment, the back-projection module is further configured to: obtain a rectangular slice for the j-th projection data volume, wherein the length and width of the rectangular slice are preset values, and the edges of the rectangular slice are parallel to the first direction or the second direction; j is a positive integer; for the k-th seismic trace sample point in the j-th projection data volume, based on the rectangular slice, obtain multiple surrounding seismic trace sample points corresponding to the k-th seismic trace sample point, with the k-th seismic trace sample point as the center; k is a positive integer; sort the sample point amplitude values ​​corresponding to the k-th seismic trace sample point and the multiple surrounding seismic trace sample points respectively to obtain a first sorting result; determine the sample point amplitude value located in the middle position in the first sorting result as the k-th replacement amplitude value; and replace the sample point amplitude value of the k-th seismic trace sample point with the k-th replacement amplitude value.

[0025] In an optional embodiment, the multiple local seismic data volumes after the acquisition footprints are pressed are divided into multiple spatial windows, and each spatial window contains at least two local seismic data volumes after the acquisition footprints are pressed.

[0026] The overlapping module is further configured to, for the m-th spatial window within the q-th time window, overlap the local seismic data volume after the n-th acquisition footprint is suppressed with the local seismic data volume after the (n+1)-th acquisition footprint is suppressed, to obtain the n-th merged data volume; q and m are positive integers, and n is a non-negative integer; based on the overlap of the n-th merged data volume and the local seismic data volume after the (n+2)-th acquisition footprint is suppressed, the (n+1)-th merged data volume is obtained, until all the local seismic data volumes after the acquisition footprint are suppressed within the m-th spatial window are overlapped, to obtain the data of the m-th first spatial window within the q-th time window.

[0027] In an optional embodiment, the overlapping module is further configured to sequentially perform spatial window overlap weighting processing on multiple first spatial window data within the q-th time window in the second direction to obtain an overlapping data volume corresponding to the q-th time window; wherein, the time window overlap weighting processing in the second direction is the same as the time window overlap weighting processing in the first direction.

[0028] In an optional embodiment, the segmentation module is further configured to perform empty channel interpolation on the three-dimensional seismic data to obtain first seismic data; the constituent unit of the three-dimensional seismic data is a seismic trace, and the empty channel interpolation refers to supplementing the seismic traces with missing data values ​​in the three-dimensional seismic data, adding seismic trace data to the three-dimensional seismic data; the first seismic data is processed to supplement line numbers in the first direction and the second direction to obtain post-stack preprocessed three-dimensional seismic data; the post-stack preprocessed three-dimensional seismic data is segmented based on the spatial window and the temporal window to obtain the plurality of local seismic data volumes.

[0029] In an optional embodiment, the apparatus further includes:

[0030] An iterative module is used to obtain at least one preset energy tilt angle, which is used to reflect the energy intensity of the seismic wave in at least one direction; based on the at least one energy tilt angle, iterative suppression processing is performed on the seismic data after the acquisition footprint is suppressed, and the iterative suppression processing refers to repeatedly executing the acquisition footprint suppression process.

[0031] On the other hand, a computer device is provided, the computer device including a processor and a memory, the memory storing at least one instruction, at least one program, code set or instruction set, the at least one instruction, the at least one program, the code set or instruction set being loaded and executed by the processor to implement the earthquake data processing method as described in any of the above embodiments of this application.

[0032] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction, at least one program, code set, or instruction set is stored therein, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the seismic data processing method as described in any of the embodiments of this application above.

[0033] On the other hand, a computer program product or computer program is provided, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform any of the seismic data processing methods described in the above embodiments.

[0034] The beneficial effects of the technical solutions provided in this application include at least the following:

[0035] Segmenting 3D seismic data into multiple local seismic data volumes can adapt to complex subsurface structures and simplify data processing. Projecting these local seismic data volumes horizontally in two perpendicular directions transforms complex geological structures into simpler horizontal structures, facilitating the suppression of acquisition footprint noise within the local seismic data volumes. Back-projecting the horizontally projected local seismic data volumes yields acquisition footprint-suppressed local seismic data volumes. Overlapping and weighting processing of these acquisition footprint-suppressed local seismic data volumes based on both spatial and temporal dimensions improves the signal-to-noise ratio and resolution of the seismic data, exhibits good amplitude preservation capabilities, and enhances the effectiveness of acquisition footprint suppression for 3D seismic data. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a schematic diagram of a seismic data processing system provided in an exemplary embodiment of this application;

[0038] Figure 2 This is a flowchart of a seismic data processing method provided in an exemplary embodiment of this application;

[0039] Figure 3 This is a schematic diagram showing the effect of footprint suppression in earthquake data acquisition, provided by an exemplary embodiment of this application.

[0040] Figure 4 This is a schematic diagram showing the comparison of 1000ms time slices after footprint compression using the method of this scheme and the time slice spatial smoothing method, provided in an exemplary embodiment of this application.

[0041] Figure 5 This is a comparison diagram of the effects of collecting and pressing footprints on post-stack preprocessed seismic data, provided by an exemplary embodiment of this application.

[0042] Figure 6 This is a schematic diagram comparing time slices before and after footprint compression, provided in an exemplary embodiment of this application.

[0043] Figure 7 This is a structural block diagram of a seismic data processing apparatus provided in an exemplary embodiment of this application;

[0044] Figure 8 This is a structural block diagram of a seismic data processing apparatus provided in another exemplary embodiment of this application;

[0045] Figure 9 This is a structural block diagram of a computer device provided in an exemplary embodiment of this application. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0047] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0048] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0049] It should be noted that all information and data involved in this application are authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0050] It should be understood that although the terms first, second, etc., may be used in this application to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, a first parameter may also be referred to as a second parameter, and similarly, a second parameter may also be referred to as a first parameter. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0051] First, a brief introduction to the terms used in the embodiments of this application:

[0052] Seismic trace: A seismic trace is a basic unit in the seismic record, representing the seismic wave signal recorded by a geophone (an instrument used to receive seismic waves) over a period of time. Each seismic trace specifically records the changes in seismic waves received by a geophone from a specific location over time.

[0053] For example, at a seismic exploration site, multiple geophones are deployed, and the recording channel corresponding to each geophone is a seismic channel.

[0054] Seismic traces are the basic building blocks of 3D seismic data. 3D seismic exploration involves deploying numerous geophones over a large area to receive seismic waves; the signal recorded by each geophone forms a seismic trace. These numerous seismic traces, combined together, spatially constitute a 3D seismic data volume.

[0055] Line direction: In the field of seismic exploration, "line" usually refers to a seismic survey line. The line direction is the direction along these seismic survey lines. Seismic survey lines are lines laid out on the ground (or sea surface) to collect seismic data. Instruments place detectors along these lines to receive seismic wave signals.

[0056] CMP (Common Mid-Point): In seismic exploration, when seismic data is acquired, seismic waves are generated and reflected waves are received at different locations. Many seismic traces correspond to underground reflection points that share a common midpoint. This midpoint is called the Common Mid-Point (CMP). The CMP direction can be understood as the direction perpendicular to the line connecting these Common Mid-Points.

[0057] The Line direction and the CMP direction are usually perpendicular to each other. The Line direction runs along the seismic survey line and is mainly used to define the horizontal distribution path of seismic data on the ground (or sea surface), while the CMP direction is perpendicular to the line connecting common midpoints. Geometrically, the distribution of common midpoints in space can be viewed as a plane (or surface) formed by a series of points, and the Line direction can be seen as the horizontal direction along this plane (or surface), while the CMP direction is perpendicular to this plane (or surface).

[0058] Frequency-Wavenumber Transform (FK Transform): Also known as the FK transform, this is a mathematical transformation method widely used in fields such as seismic data processing. Physically speaking, frequency represents the number of wave vibrations per unit time, describing the wave's characteristics in the time domain; wavenumber, on the other hand, describes the wave's characteristics in the spatial domain, representing the number of waves per unit length (or more precisely, the number of phase changes). The FK transform converts seismic data (usually in the time-space domain) from a time-space coordinate system to a frequency-wavenumber coordinate system.

[0059] Seismic data acquisition is beneficial for gaining a deeper understanding of the Earth's internal structure, predicting seismic hazards, and exploring oil and gas resources. Improving the quality of seismic data imaging helps solve problems related to complex and detailed geological structures and the precise description of reservoir parameters, enabling the discovery of oil and gas reservoirs with great depth and complexity. When interpreting seismic data for oil and gas prediction, a type of interference energy unrelated to oil and gas prediction is often found in the time slices or depth slices of the imaging data: acquisition footprint noise. This noise needs to be suppressed or attenuated before oil and gas reservoir prediction to improve the accuracy and efficiency of oil and gas prediction, laying the foundation for better solving the problems of precise description of oil and gas reservoir parameters.

[0060] Acquisition footprint noise, also known as acquisition trace noise, is a type of seismic noise generated by human factors. It is a human-made trace left during the acquisition and processing of seismic data. It manifests as a regular amplitude change artifact on the depth or time slice of the seismic profile. High-energy acquisition footprint noise can seriously interfere with seismic interpretation, attribute analysis, and reservoir prediction, creating artificial artifacts, reducing the accuracy of reservoir parameter description, and seriously affecting the prediction accuracy and efficiency of oil and gas reservoirs.

[0061] For this type of acquisition footprint noise, post-stack suppression methods are typically used, such as spatial smoothing filtering, which suppresses the acquisition footprint on time or depth slices. The principle of spatial smoothing filtering is based on the assumption that acquisition footprint noise exhibits a slow spatial variation, while the effective seismic signal changes more rapidly. Specifically, it involves performing a weighted average calculation within the spatial scope of the seismic data; that is, calculating the average or weighted average of the data within a sliding window, and replacing the data value at the center of the window with this average, thereby reducing the slowly changing spatial noise components.

[0062] However, the above methods have limitations when suppressing acquisition footprints in seismic data from complex underground structures. This is because the spatial variations of seismic reflection signals within complex structures are extremely complex, similar to the spatial characteristics of acquisition footprint noise. Using spatial smoothing filtering methods can easily misclassify the effective signals generated by complex structures as noise and process them accordingly, resulting in loss and distortion of the effective signals, making it impossible to accurately distinguish between acquisition footprint noise and true geological structure information.

[0063] Therefore, how to effectively collect and suppress seismic data from complex underground structures is an urgent problem to be solved.

[0064] This application provides a method for processing seismic data, which can effectively suppress footprint noise in seismic data with complex underground structures, thereby improving the quality of seismic data.

[0065] Secondly, the seismic data processing system involved in the embodiments of this application will be described, for illustrative purposes only. Please refer to [the relevant documentation]. Figure 1 The system involves terminal 100.

[0066] First, 3D seismic data 101 is acquired. Post-stack preprocessing is performed on the 3D seismic data 101 to supplement the seismic traces that have missing data. That is, seismic trace data is added to the 3D seismic data 101. The post-stack preprocessing methods include missing trace difference processing and line trace number supplementation and alignment processing in the Line and CMP directions, so that the preprocessed 3D seismic data 101 is regularized.

[0067] Following the post-stack preprocessing step, a spatial window of preset size and a time window of preset duration are obtained. Based on the spatial window, the 3D seismic data 101 is divided into multiple local seismic data volumes 102, and each local seismic data volume 102 is defined in the time dimension based on the time window, resulting in local seismic data volumes 102 corresponding to different time windows. This simplifies the complexity of the footprint imprinting process.

[0068] Each local seismic data volume 102 corresponds to multiple identical time windows. For example, the local seismic data volume 102 is divided based on spatial windows, resulting in 10 spatial windows, each containing at least one local seismic data volume 102. Each local seismic data volume 102 is then divided into 10 time periods, resulting in 10 time windows, each focusing on the data changes of the local seismic data volume 102 within a specific time period.

[0069] For each local seismic data volume 102, two horizontal projections are performed: taking one local seismic data volume 102 as an example, a horizontal projection along the Line direction is first performed to obtain the first projected data volume, and then a horizontal projection along the CMP direction is performed based on the first projected data volume to obtain the second projected data volume. This horizontal projection process can transform complex underground structures into simple horizontal structures, improving the noise suppression effect.

[0070] The second projection data volume obtained after two horizontal projections is back-projected to obtain a local seismic data volume 102 with preliminary noise suppression.

[0071] After repeating the above steps for each local seismic data volume 102, multiple local seismic data volumes 102 are subjected to overlapping weighting processing in different dimensions based on the divided spatial and temporal windows.

[0072] For example, the size of the time window is 80ms. The first time window corresponds to the data of the local seismic data volume 102 within all spatial windows in the first 80 milliseconds. The number of spatial windows is 10.

[0073] For each spatial window within the first time window, the local seismic data volume 102 within each spatial window is subjected to spatial window overlap weighting along the Line direction to obtain the first spatial window data after merging multiple local seismic data volumes 102 within that spatial window.

[0074] At this point, the data of the 10 spatial windows within the first time window are obtained after processing in the Line direction. Based on these first spatial window data, the above spatial window overlap weighting process is repeated in the CMP direction until all spatial window data within the first time window has completed spatial window overlap weighting process in both the Line and CMP directions, resulting in the overlapping data volume corresponding to the first time window.

[0075] The above steps are repeated for each time window to obtain multiple overlapping data volumes corresponding to the multiple time windows. Time window overlap weighting processing is performed on the multiple overlapping data volumes to complete the noise suppression of the three-dimensional seismic data 101 and remove the seismic trace data added in the post-stack preprocessing step to obtain the seismic data after acquisition footprint suppression.

[0076] The aforementioned terminal 100 can be a variety of terminal devices such as mobile phones, tablets, desktop computers, portable laptops, smart TVs, vehicle terminals, and smart home devices, and this application embodiment does not limit it to any particular type.

[0077] In some embodiments, the above process may be implemented by terminal 100 alone, by server alone, or by both server and terminal 100.

[0078] It is worth noting that the aforementioned servers can be independent physical servers, server clusters or distributed systems composed of multiple physical servers, or cloud servers that provide basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0079] Cloud technology refers to a hosting technology that unifies hardware, software, and network resources within a wide area network (WAN) or local area network (LAN) to achieve data computation, storage, processing, and sharing. Based on the cloud computing business model, cloud technology encompasses network technology, information technology, integration technology, management platform technology, and application technology. It can form resource pools, providing flexible and convenient on-demand access. Cloud computing technology will become a crucial support. Backend services of technical network systems require substantial computing and storage resources, such as video websites, image websites, and many portal websites. With the rapid development and application of the internet industry, every item may have its own identification mark in the future, requiring transmission to backend systems for logical processing. Data at different levels will be processed separately, and various industry data will require robust system support, which can only be achieved through cloud computing.

[0080] In some embodiments, the server described above can also be implemented as a node in a blockchain system.

[0081] Based on the above-described terminology and application scenarios, the seismic data processing method provided in this application will be explained. This method can be executed by a server or a terminal, or by both a server and a terminal. In this embodiment, the method is illustrated by being executed by a terminal. Figure 2 As shown, Figure 2 This is a flowchart of a seismic data processing method provided in an exemplary embodiment of this application. The method includes the following steps.

[0082] Step 210: Obtain 3D seismic data.

[0083] Among them, 3D seismic data is used to characterize the geological characteristics of a geological region. 3D seismic data contains acquisition footprint noise, which is noise left during the acquisition of 3D seismic data.

[0084] After acquiring 3D seismic data, post-stack preprocessing is required to remove some noise and enhance the effective signal characteristics.

[0085] For example, three-dimensional seismic data is subjected to air channel interpolation to obtain the first seismic data.

[0086] The unit of 3D seismic data is the seismic trace. Missing trace interpolation refers to supplementing the seismic traces in 3D seismic data that are missing data values, thus adding seismic trace data to the 3D seismic data.

[0087] In 3D seismic data, some seismic traces are missing data; these traces are called empty traces. After identifying empty traces, interpolation methods are used to calculate and fill in the missing data. For example, if a seismic trace has all its data as zero or outside a reasonable range, and is marked as invalid data, it is determined to be an empty trace.

[0088] Common interpolation methods include linear interpolation and spline interpolation. Linear interpolation assumes that the data variation between adjacent seismic traces is linear, calculating the data values ​​of the empty traces based on the straight-line pattern between seismic traces (considered as a point). Spline interpolation fits a smooth curve through known data points and then calculates the data of the empty traces based on this curve.

[0089] For example, this embodiment uses zero-padding for empty trace interpolation, adding zero values ​​to the empty traces to regularize the seismic data. In digital seismic data processing, seismic trace data is usually stored in the form of arrays, with zero elements inserted at the corresponding array positions. Zero-padding can quickly make the data complete and does not introduce too many human interference factors. That is, the added seismic trace data is zeros added to the empty traces.

[0090] The first seismic data is processed by adding trace numbers in the first and second directions to obtain post-stack preprocessed three-dimensional seismic data.

[0091] In this embodiment, the first direction is the Line direction and the second direction is the CMP direction, which will be used as an example for explanation.

[0092] In 3D seismic data, each seismic trace is identified by a corresponding trace number along both the Line and CMP directions. The sequence of trace numbers is checked for completeness and continuity. For example, in the Line direction, the trace numbers should normally increase sequentially (e.g., 1, 2, 3…). If any trace numbers are missing or discontinuous (e.g., skipping 2 in the sequence 1, 3, 4…), then these trace numbers need to be supplemented. When supplementing trace numbers, the trace numbers corresponding to the empty traces in the above regularization process are recorded. For example, if the 5th seismic trace is empty, seismic trace data is supplemented within it. When supplementing trace numbers, the 5th seismic trace is also supplemented with trace numbers in both the Line and CMP directions, resulting in 2 and 5 respectively. Therefore, the trace number is recorded as: Line direction 2, CMP direction 5.

[0093] Recording the trace number corresponding to the empty trace facilitates the removal of supplementary seismic trace data after the acquisition footprint suppression is completed, thus achieving both noise suppression and restoration of 3D seismic data.

[0094] Step 220: The three-dimensional seismic data is segmented based on spatial and temporal windows to obtain multiple local seismic data volumes.

[0095] Optionally, the post-stack preprocessed 3D seismic data can be segmented based on spatial and temporal windows to obtain multiple local seismic data volumes.

[0096] The spatial window is used to segment the 3D seismic data in the spatial dimension, and the temporal window is used to define the 3D seismic data in the temporal dimension. Each local seismic data volume corresponds to multiple identical time windows.

[0097] For example, the post-stack preprocessed 3D seismic data contains 300×300 data lines, 300 in the Line direction and 300 in the CMP direction, and these lines are represented as regular squares.

[0098] The 3D seismic data is segmented using a spatial window and a time window with unit sizes of 20×20×80, where the spatial window length is 10 and the time window duration is 80 milliseconds.

[0099] The smaller the time window and spatial window, the better it can adapt to changes in complex underground structures, resulting in more refined data segmentation and better suppression of footprint noise.

[0100] Each local seismic data volume has the same data format as the 3D seismic data, but is represented as a smaller 3D seismic data volume.

[0101] Step 230: Perform horizontal projection processing in the first direction and horizontal projection processing in the second direction on multiple local seismic data volumes to obtain multiple projected data volumes.

[0102] The first direction is the Line direction, and the second direction is the CMP direction, with the first direction perpendicular to the second direction. The two horizontal projection processes are performed sequentially.

[0103] Optionally, multiple local seismic data volumes are subjected to horizontal projection processing in a first direction to obtain multiple first projection data volumes.

[0104] For the i-th local seismic data volume, the two adjacent and centered data lines of the i-th local seismic data volume are superimposed in the first direction to obtain the first model data line. i is a positive integer.

[0105] For example, in the first local seismic data volume, the Line numbers are 1 to 20 and the CMP numbers are 1 to 20. The adjacent and centrally located Line 10 and Line 11 are selected in the Line direction and superimposed to form a first model data line composed of 20 different CMP lines in the Line direction.

[0106] The first model data line is subjected to frequency wavenumber transformation to obtain the first frequency wavenumber domain data.

[0107] Based on a preset first energy dip angle range, dip angle scanning is performed on the first frequency wavenumber domain data to determine the first target dip angle. The first target dip angle refers to the dip angle with the strongest energy within the first energy dip angle range. Specifically, the seismic wave reflection energy is strongest in the direction of the first target dip angle.

[0108] The first energy tilt angle range can be a user-defined range. In this embodiment, the first energy tilt angle range is 1 to 80 degrees as an example for explanation.

[0109] The first model data line is subjected to frequency wavenumber transformation processing. In the frequency wavenumber domain (FK domain), the tilt angle is scanned through the first energy tilt angle range. The energy in the frequency wavenumber domain is scanned from 1 to 80 degrees to obtain the strongest energy tilt angle, which is determined as the first target tilt angle.

[0110] For the i-th local seismic data volume, calculate the horizontal projection time difference in the first direction based on the first target dip angle to obtain the first horizontal projection time difference. Based on the first horizontal projection time difference, perform horizontal projection processing in the first direction on the i-th local seismic data volume to obtain the first projected data volume corresponding to the i-th local seismic data volume.

[0111] The first horizontal projection time difference in the Line direction is saved for subsequent back projection.

[0112] Optionally, the multiple first projection data volumes are subjected to horizontal projection processing in the second direction to obtain multiple projection data volumes.

[0113] For the first projected data volume corresponding to the i-th local seismic data volume, the two adjacent and centered data lines of the first projected data volume corresponding to the i-th local seismic data volume in the second direction are superimposed to obtain the second model data line.

[0114] For example, in the first projection data volume, adjacent and centered CMP10 and CMP11 are selected in the CMP direction and superimposed to form a second model data line composed of 20 different Lines in the CMP direction.

[0115] The second energy tilt angle range can be a user-defined range. In this embodiment, the second energy tilt angle range is 1 to 80 degrees as an example for explanation.

[0116] The second model data line is subjected to frequency wavenumber transformation to obtain the second frequency wavenumber domain data.

[0117] Based on a preset second energy tilt angle range, tilt angle scanning is performed on the second frequency wavenumber domain data to determine the second target tilt angle, which refers to the tilt angle with the strongest energy within the second energy tilt angle range.

[0118] The horizontal projection time difference in the second direction is calculated based on the second target dip angle for the first projection data volume corresponding to the i-th local seismic data volume, thus obtaining the second horizontal projection time difference.

[0119] Based on the second horizontal projection time difference, the first projection data volume corresponding to the i-th local seismic data volume is subjected to horizontal projection processing in the second direction to obtain the projection data volume corresponding to the i-th local seismic data volume.

[0120] The first horizontal projection time difference in the CMP direction is saved for subsequent back projection.

[0121] It is worth noting that the process of horizontal projection in the first direction is the same as the process of horizontal projection in the second direction.

[0122] Step 240: Back-project the multiple projection data volumes to obtain multiple local seismic data volumes after the acquisition footprints are pressed.

[0123] The sample amplitude values ​​of each seismic trace point in multiple projected data volumes are replaced to obtain multiple suppressed projected data volumes. Backprojection is then performed on each of these suppressed projected data volumes to obtain multiple local seismic data volumes suppressed with the acquisition footprint.

[0124] Optionally, for the j-th projected data volume, a rectangular slice is obtained. The length and width of the rectangular slice are preset values, and the sides of the rectangular slice are parallel to either the first or the second direction. j is a positive integer.

[0125] For example, if two filter operators of length 5×5 are selected in the Line direction and the CMP direction respectively as the length and width of the rectangular slice, the rectangular slice will be a square.

[0126] For the k-th seismic trace sample point in the j-th projection data volume, multiple surrounding seismic trace sample points corresponding to the k-th seismic trace sample point are obtained by dividing the range based on rectangular slices, with the k-th seismic trace sample point as the center. k is a positive integer.

[0127] For example, the rectangular slice is 7×7 in size, centered on the k-th seismic trace sample point, and contains 49 sample points (including the k-th seismic trace sample point) within this rectangular slice. Each sample point has a corresponding sample point amplitude value.

[0128] The amplitude values ​​of the sample points corresponding to the k-th seismic trace sample point and multiple surrounding seismic trace sample points are sorted to obtain the first sorting result.

[0129] The amplitude value of the sample point located in the middle position in the first sorting result is determined as the kth replacement amplitude value.

[0130] For example, the amplitude values ​​of 49 sample points are sorted according to their magnitude to obtain the first sorting result, and the amplitude value of the sample point with the sequence number 25 is determined as the kth replacement amplitude value.

[0131] Replace the sample amplitude value of the kth seismic trace with the kth replacement amplitude value.

[0132] For example, if the original amplitude value of the kth seismic trace sample point is 50, and the replacement amplitude value of the kth sample point is 100, then the amplitude value of the kth seismic trace sample point will be replaced from 50 to 100.

[0133] Performing the above process on each seismic trace point in all projected data volumes enables preliminary suppression of seismic data.

[0134] Using the first horizontal projection time difference and the second horizontal projection time difference calculated in step 230 above, back projection is performed on the multiple suppressed projection data volumes to obtain multiple local seismic data volumes after the acquisition footprints are suppressed.

[0135] For example, the principle of dip scanning is as follows: the data is transformed to the FK domain, and an angle scan is performed within a dip range in the FK domain. The principal dip energy is identified by the energy magnitude at the angle. Assume that d(t, x) and D(f, k) are the expressions of seismic data in the TX domain (time-space domain) and FK domain, respectively.

[0136] Angle scanning is performed within the dip angle range to identify the main energy dip angle. The starting point of the angle ray is located at (f, k) = (0, 0). Due to the symmetry of the frequency axis in the FK domain, only the positive frequency part is calculated. Additionally, to simplify the theory, the concepts of normalized frequency and wavenumber are used. The normalized frequency axis and wavenumber axis are calculated according to Δt = 1 and Δx = 1 respectively, which makes the range of the normalized frequency axis 0 < f < 0.5 (symmetry of the frequency axis), and the range of the normalized wavenumber axis -0.5 < k < 0.5. The mapping of the main dip angle can be obtained by summing along the angle ray, and its expression is as follows:

[0137] where M(p) is a function of the parameter p, representing a certain cumulative result at a specific dip angle. p is the slope of the summation path in the FK domain, and is a sampling point of the normalized frequency, including any frequency interval, is the largest integer less than ·.

[0138] N represents the upper limit of the summation, usually the number of samples in the dataset. Here, the summation is performed from n = 1 to N, indicating an operation on the entire dataset.

[0139] ω n represents the normalized frequency. In seismic data processing, frequencies are usually normalized for ease of analyzing and comparing seismic waves at different frequencies.

[0140] D(ω n , k) is a data function in the FK domain. D may represent a certain representation of seismic data in the FK domain, ω is the normalized frequency, and k is the wavenumber. This expression is used to calculate the wavenumber k, where, is the floor function, and this expression reflects the relationship between the wavenumber and frequency at a specific dip angle p.

[0141] The range of p values is determined by the dip angle selected by the user, and this range can be evenly divided into several parts. Due to the complexity of the seismic data's in-phase axis and the interference of noise, the dip angle interval must be made fine enough to better identify the interference outside the signal. Therefore, in practical applications, the scanned angle ray should connect each positive frequency sampling point and count the total energy of this ray to avoid missing any main energy dip angle. The scanned dip angle range is determined by the user according to the approximate dip angle of the in-phase axis in the Line direction. The smaller the scanned dip angle range, the higher the efficiency of the acquisition footprint suppression method of this application.

[0142] Step 250: Based on multiple time windows, perform spatial window overlap weighting processing on multiple local seismic data volumes after the acquisition of footprints in the first direction to obtain multiple first spatial window data; and perform spatial window overlap weighting processing on multiple first spatial window data in the second direction to obtain multiple overlapping data volumes corresponding to the multiple time windows respectively.

[0143] Multiple local seismic data volumes after acquisition footprint compression are divided into multiple spatial windows, each containing at least two local seismic data volumes after acquisition footprint compression.

[0144] For the m-th spatial window within the q-th time window, the local seismic data volume after the n-th acquisition footprint compression and the local seismic data volume after the (n+1)-th acquisition footprint compression within the m-th spatial window are overlapped to obtain the n-th merged data volume. q and m are positive integers, and n is a non-negative integer.

[0145] It is worth noting that since the process of acquiring footprints and pressing them for each local seismic data volume is the same as described above, to improve the convenience of spatial window overlay processing, the 3D seismic data can be segmented by partial overlap. For example, the Line numbers in the first local seismic data volume are 1 to 20, and the CMP lines are 1 to 20; the Line numbers in the second local seismic data volume are 1 to 20, and the CMP lines are 10 to 30, resulting in an overlap of 10 CMP lines between the first and second local seismic data volumes. And so on.

[0146] The nth merged data volume and the (n+2)th local seismic data volume after the acquisition footprints are pressed are overlapped to obtain the (n+1)th merged data volume. This process continues until all the local seismic data volumes after the acquisition footprints are pressed within the mth spatial window are overlapped, resulting in the data for the mth first spatial window within the qth time window.

[0147] For example, taking m and n as 1, the local seismic data volume after the first acquisition footprint is suppressed and the local seismic data volume after the second acquisition footprint are subjected to 10 CMP spatial window overlap weighting processing in the Line direction to obtain the first merged data volume. Based on the first merged data volume and the local seismic data volume after the third acquisition footprint are suppressed, the spatial window overlap weighting processing is performed in the same way to obtain the second merged data volume, and so on, until the data of the first spatial window in the Line direction is processed.

[0148] Repeat the above steps for each spatial window to obtain multiple first spatial window data.

[0149] The data from multiple first spatial windows within the q-th time window are sequentially subjected to spatial window overlap weighting in the second direction to obtain the overlapping data volume corresponding to the q-th time window.

[0150] The time window overlap weighting process in the second direction is the same as that in the first direction.

[0151] The above steps are repeated for each time window to obtain multiple overlapping data volumes. These overlapping data volumes can reflect the effect of collecting footprints and suppressing local seismic data volumes in different time periods.

[0152] Step 260: Perform time window overlap weighting on multiple overlapping data volumes to obtain the seismic data after the acquisition footprints are suppressed.

[0153] The overlap between adjacent time windows is a preset ratio; for example, the overlap ratio is 50% for each time window.

[0154] For example, the first time window is 0ms to 80ms, the second time window is 40ms to 120ms, and so on. Overlapping and weighting processing is performed sequentially on each time window to obtain the seismic data after the acquisition footprints are suppressed.

[0155] In some embodiments, after obtaining the seismic data with the footprints pressed, the added seismic trace data is removed from the seismic data based on the trace number corresponding to the empty trace stored in the post-stack preprocessing step.

[0156] In some embodiments, after the three-dimensional seismic data is collected and imprinted using the above steps, the seismic data after imprinting can be iteratively processed using different dip angles according to the actual situation.

[0157] Optionally, at least one preset energy tilt angle is obtained, which is used to reflect the energy intensity of the seismic wave in at least one direction.

[0158] Iterative suppression processing is performed on the seismic data after acquisition footprint suppression based on at least one energy tilt angle. Iterative suppression processing refers to the process of repeatedly performing acquisition footprint suppression.

[0159] Each iteration follows the same process as steps 230 to 260 above, except that the first target tilt angle and the second target tilt angle are replaced with at least one preset energy tilt angle, and the number of iterations is the same as the number of energy tilt angles.

[0160] Indicative, such as Figure 3 As shown, Figure 3This is a comparative diagram showing the effect of footprint suppression on seismic data acquisition, presented as a seismic data profile. Comparing seismic data 301 before footprint suppression, the noise is significantly reduced in seismic data 302 after footprint suppression using this method. Comparing seismic data 303 acquired using the time-slice spatial smoothing method after footprint suppression, the effective signal in seismic data 302 after footprint suppression using this method is significantly enhanced (the lines are darker).

[0161] Indicative, such as Figure 4 As shown, Figure 4 This is a schematic diagram comparing the 1000ms time slices after footprint compression using the proposed method and the time-slice spatial smoothing method.

[0162] Comparing the original seismic data time slice 401 with the data slice 402 after the time slice spatial smoothing method, the proposed method achieves both noise removal and effective signal enhancement in the footprint-suppressed time slice 403. Here, noise refers to blurred black dots, and effective signal refers to the black lines at the edges.

[0163] Indicative, such as Figure 5 As shown, Figure 5 This is a comparison of the effects of acquisition footprint suppression on post-stack preprocessed seismic data before and after the suppression. Comparing post-stack seismic data 502 after acquisition footprint suppression with post-stack seismic data 501 before, the signal-to-noise ratio is improved, and there is no significant loss of energy along the tilted phase axis (the black lines in the figure retain their original width and shape). This demonstrates that the acquisition footprint suppression method described in this scheme can indeed better reduce the loss of effective signal energy in complex structures, and that acquisition footprint suppression has a high amplitude preservation capability.

[0164] Indicative, such as Figure 6 As shown, Figure 6 This is a schematic diagram comparing time slices before and after the footprints were collected and pressed.

[0165] Compared to time slice 601 before footprint compression, time slice 602 after footprint compression shows a significant reduction in footprint noise. Discontinuities (representing noise) between different regions are reduced, resulting in a more harmonious transition.

[0166] In summary, the seismic data processing method provided in this application segments 3D seismic data into multiple local seismic data volumes, adapting to complex underground structures and simplifying data processing complexity. Projecting these local seismic data volumes horizontally in two perpendicular directions transforms complex geological structures into simpler horizontal structures, facilitating the suppression of acquisition footprint noise within the local seismic data volumes. Back-projecting the horizontally projected local seismic data volumes yields acquisition footprint-suppressed local seismic data volumes. Overlapping and weighting processing of these acquisition footprint-suppressed local seismic data volumes based on both spatial and temporal dimensions improves the signal-to-noise ratio and resolution of the seismic data, exhibits good amplitude preservation capabilities, and enhances the effectiveness of acquisition footprint suppression for 3D seismic data.

[0167] Figure 7 This is a structural block diagram of a seismic data processing apparatus provided in an exemplary embodiment of this application, as shown below. Figure 7 As shown, the device includes the following parts.

[0168] The acquisition module 710 is used to acquire three-dimensional seismic data, which is used to characterize the geological characteristics of a geological region. The three-dimensional seismic data includes acquisition footprint noise, which is noise left during the acquisition of the three-dimensional seismic data.

[0169] The segmentation module 720 is used to segment the three-dimensional seismic data based on a spatial window and a temporal window to obtain multiple local seismic data volumes. The spatial window is used to segment the three-dimensional seismic data in a spatial dimension, and the temporal window is used to define the three-dimensional seismic data in a temporal dimension.

[0170] Projection module 730 is used to perform horizontal projection processing in a first direction and horizontal projection processing in a second direction on the plurality of local seismic data volumes respectively to obtain a plurality of projected data volumes; the first direction is perpendicular to the second direction;

[0171] The back projection module 740 is used to back project the multiple projection data volumes respectively to obtain multiple local seismic data volumes after the footprints are collected and pressed.

[0172] The overlap module 750 is used to perform spatial window overlap weighting processing on the multiple local seismic data volumes after the acquisition footprints are pressed, respectively, based on the multiple time windows, to obtain multiple first spatial window data; and to perform spatial window overlap weighting processing on the multiple first spatial window data in the second direction, respectively, to obtain multiple overlap data volumes corresponding to the multiple time windows.

[0173] The overlapping module 750 is also used to perform time window overlapping weighting processing on the multiple overlapping data volumes to obtain the seismic data after the acquisition footprints are suppressed.

[0174] In an optional embodiment, the projection module 730 is further configured to perform horizontal projection processing on the plurality of local seismic data volumes in the first direction to obtain a plurality of first projection data volumes; and to perform horizontal projection processing on the plurality of first projection data volumes in the second direction to obtain the plurality of projection data volumes.

[0175] In an optional embodiment, the projection module 730 is further configured to: superimpose two adjacent and centered data lines of the i-th local seismic data volume in the first direction to obtain a first model data line; i is a positive integer; perform frequency-wavenumber transformation on the first model data line to obtain first frequency-wavenumber domain data; perform dip angle scanning on the first frequency-wavenumber domain data based on a preset first energy dip angle range to determine a first target dip angle, wherein the first target dip angle refers to the dip angle with the strongest energy within the first energy dip angle range; wherein the seismic wave reflection energy is strongest in the direction of the first target dip angle; calculate the horizontal projection time difference of the i-th local seismic data volume in the first direction based on the first target dip angle to obtain a first horizontal projection time difference; and perform horizontal projection processing on the i-th local seismic data volume in the first direction based on the first horizontal projection time difference to obtain a first projection data volume corresponding to the i-th local seismic data volume.

[0176] In an optional embodiment, the projection module 730 is further configured to: superimpose two adjacent and centered data lines of the first projected data volume corresponding to the i-th local seismic data volume in the second direction to obtain a second model data line; perform frequency-wavenumber transformation on the second model data line to obtain second frequency-wavenumber domain data; perform dip angle scanning on the second frequency-wavenumber domain data based on a preset second energy dip angle range to determine a second target dip angle, wherein the second target dip angle refers to the dip angle with the strongest energy within the second energy dip angle range; calculate the horizontal projection time difference of the first projected data volume corresponding to the i-th local seismic data volume in the second direction based on the second target dip angle to obtain a second horizontal projection time difference; and perform horizontal projection processing on the first projected data volume corresponding to the i-th local seismic data volume in the second direction based on the second horizontal projection time difference to obtain a projection data volume corresponding to the i-th local seismic data volume.

[0177] In an optional embodiment, the back-projection module 740 is further configured to replace the sample amplitude value of each seismic trace sample point in the plurality of projection data volumes to obtain the suppressed plurality of projection data volumes; and to perform back-projection on the suppressed plurality of projection data volumes to obtain the plurality of local seismic data volumes after the acquisition footprints are suppressed.

[0178] In an optional embodiment, the back-projection module 740 is further configured to: acquire a rectangular slice for the j-th projection data volume, wherein the length and width of the rectangular slice are preset values, and the edges of the rectangular slice are parallel to the first direction or the second direction; j is a positive integer; for the k-th seismic trace sample point in the j-th projection data volume, obtain multiple surrounding seismic trace sample points corresponding to the k-th seismic trace sample point based on the rectangular slice, with the k-th seismic trace sample point as the center; k is a positive integer; sort the sample point amplitude values ​​corresponding to the k-th seismic trace sample point and the multiple surrounding seismic trace sample points respectively to obtain a first sorting result; determine the sample point amplitude value located in the middle position in the first sorting result as the k-th replacement amplitude value; and replace the sample point amplitude value of the k-th seismic trace sample point with the k-th replacement amplitude value.

[0179] In an optional embodiment, the multiple local seismic data volumes after the acquisition footprints are pressed are divided into multiple spatial windows, and each spatial window contains at least two local seismic data volumes after the acquisition footprints are pressed.

[0180] The overlapping module 750 is further configured to, for the m-th spatial window within the q-th time window, overlap the local seismic data volume after the n-th acquisition footprint is suppressed with the local seismic data volume after the (n+1)-th acquisition footprint is suppressed to obtain the n-th merged data volume; q and m are positive integers, and n is a non-negative integer; based on the overlap of the n-th merged data volume and the local seismic data volume after the (n+2)-th acquisition footprint is suppressed to obtain the (n+1)-th merged data volume, until all local seismic data volumes after the acquisition footprint is suppressed within the m-th spatial window are overlapped to obtain the data of the m-th first spatial window within the q-th time window.

[0181] In an optional embodiment, the overlap module 750 is further configured to sequentially perform spatial window overlap weighting processing on multiple first spatial window data within the q-th time window in the second direction to obtain an overlap data volume corresponding to the q-th time window; wherein, the time window overlap weighting processing in the second direction is the same as the time window overlap weighting processing in the first direction.

[0182] In an optional embodiment, the segmentation module 720 is further configured to perform empty channel interpolation on the three-dimensional seismic data to obtain first seismic data; the constituent unit of the three-dimensional seismic data is a seismic trace, and the empty channel interpolation refers to supplementing the seismic traces with missing data values ​​in the three-dimensional seismic data, adding seismic trace data to the three-dimensional seismic data; the first seismic data is processed to supplement line numbers in the first direction and the second direction to obtain post-stack preprocessed three-dimensional seismic data; the post-stack preprocessed three-dimensional seismic data is segmented based on the spatial window and the temporal window to obtain the plurality of local seismic data volumes.

[0183] In an optional embodiment, such as Figure 8 As shown, the device further includes:

[0184] The iteration module 760 is used to obtain at least one preset energy tilt angle, which is used to reflect the energy strength of the seismic wave in at least one direction; based on the at least one energy tilt angle, the seismic data after the acquisition footprint is suppressed is subjected to iterative suppression processing, which means repeatedly executing the acquisition footprint suppression process.

[0185] In summary, the seismic data processing apparatus provided in this application segments 3D seismic data into multiple local seismic data volumes, adapting to complex underground structures and simplifying data processing complexity. By sequentially projecting these local seismic data volumes horizontally in two vertical directions, complex geological structures can be transformed into simpler horizontal structures, facilitating the suppression of acquisition footprint noise within the local seismic data volumes. Back-projecting the horizontally projected local seismic data volumes yields acquisition footprint-suppressed local seismic data volumes. Overlapping and weighting processing of these acquisition footprint-suppressed local seismic data volumes based on both spatial and temporal dimensions improves the signal-to-noise ratio and resolution of the seismic data, exhibits good amplitude preservation capabilities, and enhances the effectiveness of acquisition footprint suppression for 3D seismic data.

[0186] It should be noted that the seismic data processing apparatus provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the seismic data processing apparatus and the seismic data processing method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.

[0187] Figure 9This illustration shows a structural block diagram of a computer device 900 provided in an exemplary embodiment of this application. The computer device 900 may be a smartphone, tablet computer, MP3 player (Moving Picture Experts Group Audio Layer III), MP4 player (Moving Picture Experts Group Audio Layer IV), laptop computer, or desktop computer. The computer device 900 may also be referred to as user equipment, portable terminal, laptop terminal, desktop terminal, or other names.

[0188] Typically, computer device 900 includes a processor 901 and a memory 902.

[0189] Processor 901 may include one or more processing cores, such as a quad-core processor or an octa-core processor. Processor 901 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). Processor 901 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 901 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, processor 901 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0190] The memory 902 may include one or more computer-readable storage media, which may be non-transitory. The memory 902 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory 902 are used to store at least one instruction, which is executed by the processor 901 to implement the seismic data processing method provided in the method embodiments of this application.

[0191] In some embodiments, the computer device 900 also includes other components 903, the type and number of which can be selected based on the functional needs of the computer device 900. Those skilled in the art will understand that... Figure 9 The structure shown does not constitute a limitation on the computer device 900, and may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0192] Optionally, the computer-readable storage medium may include: read-only memory (ROM), random access memory (RAM), solid-state drives (SSDs), or optical discs, etc. The random access memory may include resistive random access memory (ReRAM) and dynamic random access memory (DRAM). The sequence numbers of the embodiments in this application are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0193] This application also provides a computer device, which includes a processor and a memory. The memory stores at least one instruction, at least one program, a code set, or an instruction set. The at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the earthquake data processing method as described in any of the above embodiments of this application.

[0194] This application also provides a computer-readable storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the seismic data processing method as described in any of the above embodiments of this application.

[0195] This application also provides a computer program product or computer program that includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform any of the earthquake data processing methods described in the above embodiments.

[0196] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0197] The above description is merely an optional embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method of processing seismic data, characterized by, The method includes: Acquire three-dimensional seismic data, which is used to characterize the geological characteristics of a geological region. The three-dimensional seismic data includes acquisition footprint noise, which is noise left during the acquisition of the three-dimensional seismic data. The three-dimensional seismic data is segmented based on spatial windows and time windows to obtain multiple local seismic data volumes. The spatial window is used to segment the three-dimensional seismic data in the spatial dimension, and the time window is used to define the three-dimensional seismic data in the time dimension. The plurality of local seismic data volumes are subjected to horizontal projection processing in a first direction and horizontal projection processing in a second direction, respectively, to obtain a plurality of projected data volumes; the first direction is perpendicular to the second direction; Back-projection is performed on the multiple projection data volumes to obtain multiple local seismic data volumes after the footprints are collected; Based on the multiple time windows, the multiple local seismic data volumes after the acquisition footprints are pressed are sequentially subjected to spatial window overlap weighting processing in the first direction to obtain multiple first spatial window data; and the multiple first spatial window data are sequentially subjected to spatial window overlap weighting processing in the second direction to obtain multiple overlapping data volumes corresponding to the multiple time windows respectively. The multiple overlapping data volumes are subjected to time window overlap weighting processing to obtain the seismic data after the acquisition footprints are suppressed.

2. The method of claim 1, wherein, The process of performing horizontal projection processing in a first direction and horizontal projection processing in a second direction on the plurality of local seismic data volumes respectively yields a plurality of projected data volumes, including: The plurality of local seismic data volumes are subjected to horizontal projection processing in the first direction to obtain a plurality of first projection data volumes; The plurality of first projection data volumes are subjected to horizontal projection processing in the second direction to obtain the plurality of projection data volumes.

3. The method of claim 2, wherein, The process of performing horizontal projection processing in the first direction on the plurality of local seismic data volumes respectively to obtain a plurality of first projection data volumes includes: For the i-th local seismic data volume, the two adjacent and centered data lines of the i-th local seismic data volume in the first direction are superimposed to obtain the first model data line; i is a positive integer; Perform frequency wavenumber transformation on the first model data line to obtain the first frequency wavenumber domain data; Based on a preset first energy tilt angle range, the first frequency wavenumber domain data is scanned to determine a first target tilt angle, which is the tilt angle with the strongest energy within the first energy tilt angle range; wherein, the seismic wave reflection energy is strongest in the direction of the first target tilt angle. The horizontal projection time difference in the first direction is calculated for the i-th local seismic data volume based on the first target dip angle to obtain the first horizontal projection time difference; Based on the first horizontal projection time difference, the i-th local seismic data volume is subjected to horizontal projection processing in the first direction to obtain a first projection data volume corresponding to the i-th local seismic data volume.

4. The method of claim 3, wherein, The step of performing horizontal projection processing in the second direction on the plurality of first projection data volumes to obtain the plurality of projection data volumes includes: For the first projected data volume corresponding to the i-th local seismic data volume, two adjacent and centered data lines of the first projected data volume corresponding to the i-th local seismic data volume in the second direction are superimposed to obtain the second model data line; Perform frequency wavenumber transformation on the second model data line to obtain the second frequency wavenumber domain data; Based on a preset second energy tilt angle range, the second frequency wavenumber domain data is tilted to determine the second target tilt angle, which refers to the tilt angle with the strongest energy within the second energy tilt angle range. The horizontal projection time difference in the second direction is calculated based on the second target dip angle for the first projection data volume corresponding to the i-th local seismic data volume, to obtain the second horizontal projection time difference; Based on the second horizontal projection time difference, the first projection data volume corresponding to the i-th local seismic data volume is subjected to horizontal projection processing in the second direction to obtain the projection data volume corresponding to the i-th local seismic data volume.

5. The method according to any one of claims 1 to 4, characterized in that, Before performing back projection on the multiple projection data volumes to obtain multiple local seismic data volumes after the acquisition footprints are pressed, the method further includes: The sample amplitude value of each seismic trace sample point in the plurality of projection data volumes is replaced to obtain the suppressed plurality of projection data volumes; The back-projection of the multiple projection data volumes yields multiple local seismic data volumes after the acquisition of footprint compression, including: The multiple projected data volumes after compression are back-projected to obtain multiple local seismic data volumes after the collected footprints are compressed.

6. The method of claim 5, wherein, The step of replacing the sample amplitude values ​​of each seismic trace sample point in the plurality of projection data volumes to obtain the suppressed plurality of projection data volumes includes: For the j-th projection data volume, a rectangular slice is obtained, the length and width of the rectangular slice are preset values, and the sides of the rectangular slice are parallel to the first direction or the second direction; j is a positive integer; For the k-th seismic trace sample point in the j-th projection data volume, multiple surrounding seismic trace sample points corresponding to the k-th seismic trace sample point are obtained based on the rectangular slice division range centered on the k-th seismic trace sample point; k is a positive integer; The amplitude values ​​of the sample points corresponding to the kth seismic trace sample point and the multiple surrounding seismic trace sample points are sorted to obtain the first sorting result; The amplitude value of the sample point located in the middle position in the first sorting result is determined as the kth replacement amplitude value; Replace the sample amplitude value of the kth seismic trace with the kth replacement amplitude value.

7. The method according to any one of claims 1 to 4, characterized in that, The multiple local seismic data volumes after the acquisition footprints are pressed are divided into multiple spatial windows, and each spatial window contains at least two local seismic data volumes after the acquisition footprints are pressed. Based on the multiple time windows, the multiple local seismic data volumes after the acquisition footprints are pressed are sequentially subjected to spatial window overlap weighting processing in the first direction to obtain multiple first spatial window data, including: For the m-th spatial window within the q-th time window, the local seismic data volume after the n-th acquisition footprint is pressed and the local seismic data volume after the (n+1)-th acquisition footprint is overlapped to obtain the n-th merged data volume; q and m are positive integers, and n is a non-negative integer; The nth merged data volume and the (n+2)th local seismic data volume after the acquisition footprints are pressed are overlapped to obtain the (n+1)th merged data volume. This process continues until all the local seismic data volumes after the acquisition footprints are pressed within the mth spatial window are overlapped, resulting in the mth first spatial window data within the qth time window.

8. The method of claim 7, wherein, The step of sequentially performing spatial window overlap weighting processing on the multiple first spatial window data in the second direction to obtain multiple overlapping data volumes corresponding to the multiple time windows includes: The data of multiple first spatial windows within the q-th time window are sequentially subjected to spatial window overlap weighting processing in the second direction to obtain the overlapping data volume corresponding to the q-th time window; The time window overlap weighting process in the second direction is the same as the time window overlap weighting process in the first direction.

9. The method according to any one of claims 1 to 4, characterized in that, Before segmenting the three-dimensional seismic data based on spatial and temporal windows to obtain multiple local seismic data volumes, the process further includes: The three-dimensional seismic data is subjected to interpolation to obtain the first seismic data. The unit of the three-dimensional seismic data is a seismic trace. The interpolation process refers to supplementing the seismic traces with missing data values ​​in the three-dimensional seismic data and adding seismic trace data to the three-dimensional seismic data. The first seismic data is supplemented with trace numbers in the first and second directions to obtain post-stack preprocessed three-dimensional seismic data. The three-dimensional seismic data is segmented based on spatial and temporal windows to obtain multiple local seismic data volumes, including: The post-stack preprocessed 3D seismic data is segmented based on the spatial window and the temporal window to obtain the multiple local seismic data volumes.

10. The method according to any one of claims 1 to 4, characterized in that, After performing time-window overlap weighting processing on the multiple overlapping data volumes to obtain the seismic data after footprint compression, the process further includes: Obtain at least one preset energy tilt angle, which is used to reflect the energy intensity of the seismic wave in at least one direction; Based on the at least one energy tilt angle, the seismic data after the acquisition footprint is suppressed is subjected to iterative suppression processing, which means repeatedly performing the acquisition footprint suppression process.

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