Method and device for suppressing shear wave leakage noise based on multi-component pattern recognition

Abstract

The application provides a method and device for suppressing shear wave leakage noise based on multi-component pattern recognition, and belongs to the field of seismic data processing.The method comprises the following steps: obtaining P, X, Y and Z components of original seismic data; rotating the X and Y components to obtain R and T components respectively; performing pattern recognition on the P and Z components to obtain a noise model noisep; performing pattern recognition on the P and R components to obtain a noise model noiser; performing pattern recognition on the P and T components to obtain a noise model noiset; and subtracting the three models from the Z component in sequence to obtain the Z component after suppression of shear wave leakage noise. The method provided by the application can better extract other noises existing in the Z component and has a better denoising effect.

Description

Technical Field

[0001] The present invention relates to the technical field of seismic data processing, and in particular to a shear wave leakage noise suppression method based on multi-component pattern recognition, a shear wave leakage noise suppression device based on multi-component pattern recognition, an electronic device, and a computer-readable storage medium. Background Art

[0002] Ocean Bottom Node (OBN) seismic acquisition uses three-component geophones, both waterborne and landborne, buried on the ocean floor to record pressure and velocity (or acceleration) component data. The waterborne P-component geophones are pressure geophones, while the landborne three-component geophones are velocity (or acceleration) geophones, primarily collecting acceleration and velocity information from the seismic data, including the X, Y, and Z components. For a horizontal seafloor, the X and Y components are parallel to the seafloor, receiving converted shear waves, while the Z component is perpendicular to the seafloor, receiving longitudinal waves, thus enabling a complete recording of the entire wavefield. Ocean bottom node seismic acquisition has rapidly grown in popularity due to its ability to provide more information on formation media parameters than conventional streamer-based single-component data.

[0003] However, in actual construction, the rugged and variable nature of the seabed affects the coupling between the nodes and the seabed. Furthermore, it is difficult to ensure that the seabed nodes are placed completely horizontally. This means that the nodes are at a certain angle to the horizontal plane. This results in the Z component of the land detector also receiving the converted shear wave signal from the X and Y components. This is generally referred to as "shear wave leakage noise" or "Vz noise." This noise is coherent in common receiver gathers but not in common shot gathers. It mainly manifests as low-speed noise, and its intensity depends on the degree of coupling between the sensor and the seabed, and has little to do with seawater depth.

[0004] Vz noise has an important impact on subsequent processing of seismic data, such as upper and lower wavefield calibration and merging, upper and lower wavefield deconvolution multiple wave suppression, and upgoing wave migration imaging. Therefore, before subsequent processing, the Vz noise on the Z component must be removed separately.

[0005] In the prior art, conventional Vz noise suppression methods include velocity filtering, complex wavelet domain transform, pattern recognition, adaptive subtraction, etc. However, the above methods have the following disadvantages:

[0006] (1) Generally speaking, only the P component is used to construct the shear wave noise model. For example, the P component is mainly used to construct the shear wave noise model in pattern recognition methods.

[0007] (2) Although some methods take into account the advantage of OBN data itself having shear wave components and use the shear wave information of XY components to construct shear wave components, they have a natural advantage. However, the XY components have not been rotated and contain a lot of other noise, which is not conducive to the construction of the shear wave noise model.

[0008] Therefore, it is necessary to develop a new Vz noise suppression method to effectively reduce the shear wave leakage noise in the Z component. Summary of the Invention

[0009] In response to the technical problem of poor shear wave leakage noise suppression in the existing technology, the present invention provides a shear wave leakage noise suppression method and equipment based on multi-component pattern recognition. This method and equipment can effectively remove the shear wave leakage noise from the X and Y components in the Z component, and obtain a longitudinal wave signal that reflects the actual seabed stratum medium parameter information.

[0010] To achieve the above objectives, the present invention provides, in a first aspect, a method for suppressing shear wave leakage noise based on multi-component pattern recognition, the method comprising the following steps: acquiring first OBN data recorded by a first geophone and second OBN data recorded by a second geophone, the first OBN data comprising a P component of the original seismic data, and the second OBN data comprising an X component, a Y component, and a Z component of the original seismic data; rotating the X component to a radial direction to obtain an R component, and rotating the Y component to a tangential direction to obtain a T component; performing pattern recognition on the P component and the Z component to obtain a first shear wave noise prediction model in the Z component; performing pattern recognition on the P component and the R component to obtain a second shear wave noise prediction model in the Z component; performing pattern recognition on the P component and the T component to obtain a third shear wave noise prediction model in the Z component; and subtracting the first, second, and third shear wave noise prediction models from the Z component in sequence to obtain a longitudinal wave signal after the shear wave leakage noise is suppressed.

[0011] In an exemplary embodiment of the present invention, the shear wave leakage noise suppression method may further include: before performing pattern recognition, performing mathematical transformation on the P component, the R component, the T component, and the Z component respectively.

[0012] In an exemplary embodiment of the present invention, the mathematical transformation may include a curvelet domain transformation and a τ-p domain transformation.

[0013] In an exemplary embodiment of the present invention, the pattern recognition performed on the P component and the Z component to obtain the first shear wave noise prediction model in the Z component may include: performing dynamic correction on the P component and the Z component of the original seismic data; performing mathematical transformation on the P component and the Z component after the dynamic correction respectively; using pattern recognition to extract the difference between the P component and the Z component after the mathematical transformation to determine the first shear wave noise in the Z component; performing an inverse mathematical transformation on the first shear wave noise; and performing inverse correction on the first shear wave noise after the inverse mathematical transformation to obtain a first shear wave noise prediction model consistent with the original seismic data.

[0014] In an exemplary embodiment of the present invention, pattern recognition is performed on the P component and the R component to obtain a second shear wave noise prediction model in the Z component, including: performing dynamic correction on the P component and the R component of the original seismic data; performing mathematical transformation on the P component and the R component after the dynamic correction, respectively; using pattern recognition to extract the difference between the P component and the R component after the mathematical transformation to determine the second shear wave noise in the Z component; performing an inverse mathematical transformation on the second shear wave noise; and performing an inverse dynamic correction on the second shear wave noise after the inverse mathematical transformation to obtain a second shear wave noise prediction model consistent with the original seismic data.

[0015] In an exemplary embodiment of the present invention, the pattern recognition performed on the P component and the T component to obtain the third shear wave noise prediction model in the Z component may include: performing dynamic correction on the P component and the T component of the original seismic data; performing mathematical transformation on the P component and the T component after the dynamic correction respectively; using pattern recognition to extract the difference between the P component and the T component after the mathematical transformation to determine the third shear wave noise in the Z component; performing an inverse mathematical transformation on the third shear wave noise; and performing an inverse dynamic correction on the third shear wave noise after the inverse mathematical transformation to obtain a third shear wave noise prediction model consistent with the original seismic data.

[0016] A second aspect of the present invention provides a shear wave leakage noise suppression device based on multi-component pattern recognition, the shear wave leakage noise suppression device comprising: an acquisition unit, a shear wave component rotation unit, a PZ component pattern recognition unit, a PR component pattern recognition unit, a PT component pattern recognition unit, and an adaptive subtraction unit; the acquisition unit is used to acquire first OBN data recorded by a first geophone and second OBN data recorded by a second geophone, the first OBN data comprising a P component of original seismic data, the second OBN data comprising an X component, a Y component, and a Z component of the original seismic data; the shear wave component rotation unit is used to rotate the X component to a radial direction to obtain an R component, and the Y component to obtain an R component. The component is rotated to the tangential direction to obtain the T component; the PZ component pattern recognition unit is used to perform pattern recognition on the P component and the Z component to obtain a first shear wave noise prediction model in the Z component; the PR component pattern recognition unit is used to perform pattern recognition on the P component and the R component to obtain a second shear wave noise prediction model in the Z component; the PT component pattern recognition unit is used to perform pattern recognition on the P component and the T component to obtain a third shear wave noise prediction model in the Z component; the adaptive subtraction unit is used to subtract the first shear wave noise prediction model, the second shear wave noise prediction model and the third shear wave noise prediction model from the Z component in sequence to obtain a longitudinal wave signal after the shear wave leakage noise is suppressed.

[0017] In another exemplary embodiment of the present invention, the shear wave leakage noise suppression device may further include: a data conversion unit; the data conversion unit is used to perform mathematical conversion on the P component, R component, T component and Z component respectively before performing pattern recognition.

[0018] A third aspect of the present invention provides an electronic device, which includes a processor and a memory, wherein the memory stores at least one computer program, and the at least one computer program is loaded and executed by one or more of the above-mentioned processors. When the computer program is executed, the above-mentioned shear wave leakage noise suppression method is implemented.

[0019] A fourth aspect of the present invention provides a computer-readable storage medium, in which at least one program code is stored. The program code is loaded and executed by a processor, and when the computer program is executed, the above-mentioned shear wave leakage noise suppression method is implemented.

[0020] The technical solution provided by the present invention has at least the following technical effects:

[0021] (1) The shear wave leakage noise suppression method of the present invention fully utilizes the data information of the four components of OBN data. Through the relationship between the P component and the X, R, and T components, the shear wave leakage noise from the X and Y components in the Z component is effectively removed, thereby obtaining a longitudinal wave signal reflecting the actual submarine stratum medium parameter information.

[0022] (2) Compared with conventional shear wave leakage noise suppression methods, the shear wave leakage noise suppression method of the present invention utilizes the R variable and T variable obtained by rotating the X component and the Y component for pattern recognition, which can better extract other noises existing in the Z component and has a better denoising effect;

[0023] (3) The shear wave leakage noise suppression method of the present invention can, to a certain extent, broaden the effective frequency band range of seismic data, enrich the information of different frequency bands on the profile, and improve the resolution.

[0024] Other features and advantages of the present invention will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following detailed description, they are used to explain the embodiments of the present invention, but do not constitute a limitation of the embodiments of the present invention. In the accompanying drawings:

[0026] Figure 1 A flowchart of a method for suppressing shear wave leakage noise based on multi-component pattern recognition provided by the first embodiment of the present invention;

[0027] Figure 2 A near-offset direct-to-wave flat map of the seismic data PZXY components of the original seismic data provided by the second embodiment of the present invention;

[0028] Figure 3 The original seismic data provided for the second embodiment of the present invention is transformed into a near-offset direct-reach wave flat map of PZRT component seismic data after rotation;

[0029] Figure 4 A schematic diagram of the result of obtaining a noisep model after pattern recognition of the PZ component of the original seismic data provided by the second embodiment of the present invention;

[0030] Figure 5 A schematic diagram of the result of obtaining a noiser model after pattern recognition of the PR component of the original seismic data provided by the second embodiment of the present invention;

[0031] Figure 6 A schematic diagram of the result of obtaining a noise model after pattern recognition of the PT component of the original seismic data provided by the second embodiment of the present invention;

[0032] Figure 7 A schematic diagram of the result after adaptively subtracting the noisep model from the Z component of the original seismic data provided by the second embodiment of the present invention;

[0033] Figure 8 A schematic diagram of the results of adaptively subtracting the noisep model and the noiser model from the Z component of the original seismic data provided by the second embodiment of the present invention;

[0034] Figure 9 A schematic diagram of the results of adaptively subtracting the noisep model, noiser model, and noiset model from the Z component of the original seismic data provided by the second embodiment of the present invention;

[0035] Figure 10 Schematic diagram of the superimposed cross-section of the original Z component data and the Z component after subtracting each noise model in sequence, provided by the second embodiment of the present invention;

[0036] Figure 11 A schematic diagram of the difference between the Z component and the superimposed cross-sections obtained by sequentially subtracting each noise model provided in the second embodiment of the present invention;

[0037] Figure 12 A schematic structural diagram of a shear wave leakage noise suppression device based on multi-component pattern recognition provided by a third embodiment of the present invention;

[0038] Figure 13 This is a schematic structural diagram of an electronic device provided by a fourth embodiment of the present invention.

[0039] Description of Reference Numerals

[0040] 101 - acquisition unit, 102 - shear wave component rotation unit, 103 - PZ component pattern recognition unit, 104 - PR component pattern recognition unit, 105 - PT component pattern recognition unit, 106 - adaptive subtraction unit, 201 - processor, 202 - memory. DETAILED DESCRIPTION

[0041] The following describes the specific implementation of the embodiment of the present invention in detail with reference to the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the embodiment of the present invention and is not used to limit the embodiment of the present invention.

[0042] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0043] In the present invention, “first”, “second”, etc. are merely used for convenience of description and distinction, and cannot be understood as indicating or implying relative importance.

[0044] It should also be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "installation" and "connection" should be understood in a broad sense. For example, they may refer to fixed connection, detachable connection, or integrated connection; direct connection, indirect connection, wired connection, or wireless connection. Those skilled in the art will understand the specific meanings of the above terms in the present invention depending on the specific circumstances.

[0045] The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0046] Example 1

[0047] The first embodiment of the present invention provides a method for suppressing shear wave leakage noise based on multi-component pattern recognition, such as Figure 1 As shown, the method includes the following steps:

[0048] Step S101: Acquire first OBN data recorded by a first geophone and second OBN data recorded by a second geophone.

[0049] The first OBN data includes the P component of the original seismic data; the second OBN data includes the X component, the Y component, and the Z component of the original seismic data.

[0050] It should be noted that the first geophone is a pressure geophone at a water level detector, which is used to collect and record the pressure component (i.e., P component) data of the ocean floor. The second geophone is a velocity geophone (or acceleration geophone) at a land level detector, which is used to collect and record velocity (or acceleration) information of seismic data, including X-component data, Y-component data, and Z-component data.

[0051] OBN (Ocean Bottom Node) is the abbreviation of ocean bottom node, also known as seabed data acquisition system or seabed monitoring system. It is a technology and equipment used to collect data on the ocean bottom.

[0052] "Wet Inspection" and "Dry Inspection" refer to the two different stages of deploying and inspecting submarine node equipment. Wet Inspection refers to the deployment and installation of submarine node equipment in a marine environment. Dry Inspection refers to the process of recovering and inspecting deployed submarine node equipment from the ocean.

[0053] Step S102: rotating the X component to the radial direction to obtain the R component, and rotating the Y component to the tangential direction to obtain the T component.

[0054] Step S103: performing pattern recognition on the P component and the Z component to obtain a first shear wave noise prediction model in the Z component.

[0055] Step S104: performing pattern recognition on the P component and the R component to obtain a second shear wave noise prediction model in the Z component.

[0056] Step S105: performing pattern recognition on the P component and the T component to obtain a third shear wave noise prediction model in the Z component.

[0057] Step S106: subtracting the first shear wave noise prediction model, the second shear wave noise prediction model and the third shear wave noise prediction model from the Z component in sequence to obtain the Z component after the shear wave leakage noise is suppressed.

[0058] By rotating the shear wave components (i.e., the X and Y components), the longitudinal wave information generated in the vertical direction can be obtained. On this basis, the PRT and Z components are used for pattern recognition respectively to effectively extract the shear wave leakage noise characteristics caused by the X and Y components, and to construct the corresponding shear wave noise model. Finally, the Vz noise on the Z component can be removed by subtracting the above shear wave noise model.

[0059] Furthermore, in this embodiment, the shear wave leakage noise suppression method further includes: prior to pattern recognition, mathematically transforming the P component, R component, T component, and Z component. By performing mathematical transformations on each component of the raw seismic data, local features in the seismic data signal can be effectively extracted and analyzed, thereby achieving better results in pattern recognition.

[0060] Furthermore, in this embodiment, the mathematical transformation may include a curvelet domain transformation and / or a τ-p domain transformation. In other words, the same mathematical transformation method may be used to process data for the P component, R component, T component, and Z component; or different mathematical transformation methods may be used to process data for the P component, R component, T component, and Z component. For example, a curvelet domain transformation (or a τ-p domain transformation) may be performed on the P component, R component, T component, and Z component; or a curvelet domain transformation may be performed on the P component and Z component, while a τ-p domain transformation may be performed on the P component, R component, and T component.

[0061] Exemplarily, in step S103 , pattern recognition is performed on the P component and the Z component to obtain the first shear wave noise prediction model in the Z component, including but not limited to the following sub-steps S1031 to S1035 .

[0062] Sub-step S1031: Perform dynamic correction on the P component and Z component of the original seismic data.

[0063] Sub-step S1032: performing curvelet domain transformation or τ-p domain transformation on the P component and Z component after dynamic correction.

[0064] Sub-step S1033: using pattern recognition to extract the difference between the P component and the Z component after the curvelet domain transformation or the τ-p domain transformation, and determining the first shear wave noise in the Z component.

[0065] Sub-step S1034: performing an inverse curvelet domain transform or an inverse τ-p domain transform on the first shear wave noise.

[0066] Sub-step S1035: performing a back-correction on the first shear wave noise after the inverse curve domain transformation or the inverse τ-p domain transformation to obtain a first shear wave noise prediction model consistent with the original seismic data.

[0067] Exemplarily, in step S104 , pattern recognition is performed on the P component and the R component to obtain the second shear wave noise prediction model in the Z component, including but not limited to the following sub-steps S1041 to S1045 .

[0068] Sub-step S1041: Perform dynamic correction on the P component and R component of the original seismic data.

[0069] Sub-step S1042: performing curvelet domain transformation or τ-p domain transformation on the P component and R component after dynamic correction.

[0070] Sub-step S1043: using pattern recognition to extract the difference between the P component and the R component after the curvelet domain transformation or the τ-p domain transformation, and determining the second shear wave noise in the Z component.

[0071] Sub-step S1044: performing an inverse curvelet domain transform or an inverse τ-p domain transform on the second shear wave noise.

[0072] Sub-step S1045: performing a back-correction on the second shear wave noise after the inverse curvelet domain transformation or the inverse τ-p domain transformation to obtain a second shear wave noise prediction model consistent with the original seismic data.

[0073] Exemplarily, in step S105 , pattern recognition is performed on the P component and the T component to obtain the third shear wave noise prediction model in the Z component, including but not limited to the following sub-steps S1051 to S1055 .

[0074] Sub-step S1051: Perform dynamic correction on the P component and T component of the original seismic data.

[0075] Sub-step S1052: performing curvelet domain transformation or τ-p domain transformation on the P component and T component after dynamic correction respectively.

[0076] Sub-step S1053: using pattern recognition to extract the difference between the P component and the T component after the curvelet domain transformation or the τ-p domain transformation, and determining the third shear wave noise in the Z component.

[0077] Sub-step S1054: performing an inverse curvelet domain transform or an inverse τ-p domain transform on the third shear wave noise.

[0078] Sub-step S1055: performing a reaction correction on the third shear wave noise after the inverse curve domain transformation or the inverse τ-p domain transformation to obtain a third shear wave noise prediction model consistent with the original seismic data.

[0079] It should be noted that the "pattern recognition" in this embodiment refers to a pattern recognition method, which is used to suppress Vz noise (noise in the vertical direction) and improve the quality of seismic data. The following is a general process of using the pattern recognition method to suppress Vz noise:

[0080] 1) Data preprocessing: The collected seismic data are first preprocessed, including steps such as removing DC offset, time correction and frequency domain filtering, to reduce the impact of noise and enhance signal clarity.

[0081] 2) Establish a training data set: Select a portion of sample data from the preprocessed seismic data as a training set, and manually annotate these data or use known accurate seismic reflection markers for annotation.

[0082] 3) Feature extraction: Extract features from the training set. Common features include amplitude, frequency, phase, etc. These features can be used to describe the characteristics of seismic signals.

[0083] 4) Classifier Construction: A classifier is constructed using a pattern recognition algorithm (e.g., neural network, support vector machine, etc.). The classifier learns and builds a model based on the features and labels of the training set to distinguish between Vz noise and seismic signals.

[0084] 5) Classification and noise suppression: Use a trained classifier to classify unknown data and determine which parts are Vz noise. By suppressing or filtering the parts classified as noise, the impact of noise can be effectively reduced, improving the clarity and accuracy of the seismic signal.

[0085] 6) Post-processing and optimization: After noise suppression of the data, some post-processing and optimization steps are required to further improve the data quality and meet specific application requirements.

[0086] The "Curvelet Transform" in this embodiment refers to a mathematical transformation method used for multi-scale analysis and image processing. The Curvelet Transform can convert a signal or image from the time domain or spatial domain to the Curvelet domain to better describe and analyze the local characteristics of the signal. The main idea of ​​the Curvelet Transform is to capture the geometric structure of the signal through local smoothing and local differentiation. It takes advantage of the advantages of two-dimensional wavelet transform and Fourier transform and introduces the concept of curves, making it possible to better process non-stationary signal features such as curves, edges, and textures.

[0087] The "τ-p domain transform" in this embodiment refers to a transformation method commonly used in marine seismic data processing and analysis, specifically for processing seismic data with time and offset (or spatial) dimensions. The basic concept of the τ-p domain transform is to convert seismic data from the conventional time-trace domain representation to the τ-p domain representation, where τ represents the time offset (or time delay) and p represents the offset (or spatial) coordinate. This transformation allows for a better description and analysis of the offset and time characteristics of seismic data.

[0088] In this embodiment, "dynamic correction" and "reverse dynamite correction" are two methods used in seismic data processing to address the time offset caused by the propagation of the first arrival wave generated by the earthquake source. Dynamic correction is a method of adjusting the time of seismic data based on the velocity changes of the underground medium to align the arrival time of the seismic wave with the source excitation time.

[0089] In addition, the implementation environment of this embodiment includes at least one terminal and a server, and the method is executed on the terminal or the server respectively. The terminal and the server can be connected in communication to realize the interactive transmission of information.

[0090] Among them, the terminal can be any electronic product that can interact with the user through one or more methods such as keyboard, touchpad, touch screen, voice interaction, etc., such as PC (Personal Computer), PPC (Pocket Personal Computer), tablet computer, etc.

[0091] A server can be a single server or a server cluster consisting of multiple servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), as well as big data and artificial intelligence platforms.

[0092] Example 2

[0093] A second embodiment of the present invention provides a method for suppressing shear wave leakage noise based on multi-component pattern recognition, comprising the following steps:

[0094] Step S201: Acquire first OBN data recorded by a first geophone and second OBN data recorded by a second geophone.

[0095] Step S202: rotating the horizontal component of the second OBN data acquisition according to the energy minimization method.

[0096] Specifically, the X component of the second OBN data acquisition is rotated to the radial direction to obtain the R component; the Y component of the second OBN data acquisition is rotated to the tangential direction to obtain the T component. Figure 2 The figure shows the near-offset direct wave flat map of the original seismic data P, Z, X, and Y components; Figure 3 The figure shows the near-offset direct-wave flat map of the original seismic data after rotation into P, Z, R, and T components.

[0097] Step S203: Perform curvelet domain (or τ-p domain) transformation on the P component and the Z component respectively, and perform pattern recognition on the P and Z components in the curvelet domain (or τ-p domain), and construct the first shear wave noise prediction model (referred to as noisep model) in the Z component obtained based on the P component pattern recognition.

[0098] For example, Figure 4 The figure is a schematic diagram of the noisep model obtained after pattern recognition of the PZ component of the original seismic data. Figure 4 (a) in the figure represents the shot gather data of the P component of the original seismic data. Figure 4 (b) in the figure represents the shot gather data of the Z component of the original seismic data. Figure 4 (c) in the figure represents the noisep model obtained after pattern recognition of the shot data of the PZ component of the original seismic data. It can be seen from the original seismic data that the Z component of the seismic data (such as Figure 4 (b) in the figure) and the P component of the seismic data (as shown in Figure 4 Compared with (a) in Figure 1, there are many shear wave noises that appear at low speeds, different inclination angles and frequencies. After pattern recognition in the curvelet domain, the first shear wave noise prediction model in the Z component can be obtained (as shown in Figure 1). Figure 4 (as shown in (c) in the figure).

[0099] Step S204: Perform curvelet domain transformation (or τ-p domain transformation) on the P component and the R component respectively, and perform pattern recognition using the P component and the R component in the curvelet domain (or τ-p domain) to construct a second shear wave noise prediction model (referred to as noiser model) in the Z component obtained based on the PR component pattern recognition.

[0100] For example, Figure 5 The figure is a schematic diagram of the noise model obtained after pattern recognition of the PR component of the original seismic data. Figure 5 (a) in the figure represents the shot gather data of the P component of the original seismic data; Figure 5 (b) in the figure represents the shot gather data of the R component of the original seismic data; Figure 5 (c) shows the schematic diagram of the second shear wave noise prediction model noiser obtained after pattern recognition of the shot data of the original seismic data PR component. It can be seen from the original seismic data that the seismic data R component (such as Figure 5 (b) in the figure) and the P component of the seismic data (as shown in Figure 5 Compared with (a) in Figure 2, all the information is shear wave information, and the shear wave noise in the R component is different from the noisep model at deep and near offset distances.

[0101] Step S205: Perform curvelet domain transformation (or τ-p domain transformation) on the P component and the T component respectively, and perform pattern recognition using the P component and the T component in the curvelet domain (or τ-p domain) to construct a third shear wave noise prediction model (referred to as noiset model) in the Z component obtained based on PT component pattern recognition.

[0102] For example, Figure 6 The figure is a schematic diagram of the noise model obtained after pattern recognition of the PT component of the original seismic data. Figure 6 (a) in the figure represents the shot gather data of the P component of the original seismic data; Figure 6 (b) in the figure represents the shot gather data of the T component of the original seismic data; Figure 6 (c) in the figure represents the noise model obtained after pattern recognition of the shot gather data of the PT component of the original seismic data. It can be seen from the original seismic data that the T component of the seismic data (such as Figure 6 (b) in the figure) and the P component of the seismic data (as shown in Figure 6 Compared with (a) in Figure 2, all the information is shear wave information, and the shear wave noise in the T component is different from that of the noisep model and the noiser model at deep and near offset distances.

[0103] Step S206: Finally, the three noise models are sequentially subtracted from the Z component (ie, Z component - noisep model - noiser model - noiset model), thereby achieving the effect of shear wave noise suppression.

[0104] For example, Figure 7 This is a schematic diagram of the result after adaptively subtracting the noisep model from the Z component of the original seismic data. Figure 7 (a) in the figure represents the shot gather data of the P component of the original seismic data; Figure 7 (b) in the figure represents the shot gather data of the Z component of the original seismic data; Figure 7 (c) in the figure shows the result after subtracting the noisep model data from the original Z component data; Figure 7 (d) in the figure represents the first difference between the original Z component data and the Z component data after removing noisep (i.e. Figure 7 The first difference between (b) and (c) in the figure is the noisep model.

[0105] Figure 8 The diagram below shows the result after adaptively subtracting the noisep model and noiser model from the Z component of the original seismic data. Figure 8 (a) shows the result after subtracting the noisep model data from the Z component data; Figure 8 (b) shows the result after subtracting the noisep model data and the noiser model data from the Z component data; Figure 8 (c) in the figure represents the second difference between the Z component data after removing noisep (ie Z-noisep) and the Z component data after removing noisep and noiseiser (ie Z-noisep-noiser). Figure 8 The second difference between (a) and (b) in the figure), where the second difference is the noiser model.

[0106] Figure 9 The figure is a schematic diagram of the results after adaptively subtracting the noisep model, noiser model and noiset model from the Z component of the original seismic data. Figure 9 (a) represents the result after subtracting the noisep model data and the noiser model data from the Z component data; Figure 9 (b) represents the result after subtracting noisep model data, noiser model data and noiset model data from Z component data; Figure 9 (c) in the figure represents the third difference between the Z component data after removing noisep and noiseiser (i.e. Z-noisep-noiser) and the Z component data after removing noisep, noiseiser and noiset (i.e. Z-noisep-noiser-noiset). Figure 9 The third difference between (a) and (b) in the figure), where the third difference is the noiset model.

[0107] Step S207: performing quality control on the original shot gather data; and superimposing the original data for quality control.

[0108] For example, Figure 10 It is a schematic diagram of the superimposed cross-section of the original Z component data and the Z component after subtracting each noise model in turn. Figure 10 (a) in the figure shows the stacked section of the original Z component data; Figure 10 (b) shows the superimposed section after subtracting the noisep model data from the original Z component data; Figure 10 (c) in the figure shows the superimposed section after subtracting the noisep model data and the noiser model data from the original Z component data; Figure 10 (d) in the figure represents the superimposed section after subtracting the noisep model data, noiser model data and noiset model data from the original Z component data.

[0109] Figure 11 This is a schematic diagram of the difference between the Z component and the superimposed profile after subtracting each noise model in turn. Figure 11 (a) shows the difference between the original Z-component data stacking section and the Z-component data minus the noisep model data stacking section; Figure 11 (b) in the figure shows the difference between the stacked section after subtracting the noisep model data from the original Z component data and the stacked section after subtracting the noisep model data and the noiser model data from the original Z component data; Figure 11 (c) in the figure represents the difference between the superimposed section after subtracting the noisep model data and the noiser model data from the original Z component data and the superimposed section after subtracting the noisep model data, the noiser model data and the noiset model data in sequence from the original Z component data.

[0110] Figure 10 as well as Figure 11 The suppressed shear wave noise in the stacked sections is shown, and it can be seen that the method of this embodiment is effective in suppressing shear wave noise.

[0111] Example 3

[0112] The third embodiment of the present invention provides a shear wave leakage noise suppression device based on multi-component pattern recognition, such as Figure 12 As shown, the shear wave leakage noise suppression device includes: an acquisition unit 101, a shear wave component rotation unit 102, a PZ component pattern recognition unit 103, a PR component pattern recognition unit 104, a PT component pattern recognition unit 105 and an adaptive subtraction unit 106.

[0113] The acquisition unit 101 is configured to acquire first OBN data recorded by a first geophone and second OBN data recorded by a second geophone. The first OBN data includes the P component of the original seismic data, and the second OBN data includes the X, Y, and Z components of the original seismic data. Specifically, the acquisition unit may include a first acquisition module and a second acquisition module. The first acquisition module is configured to acquire the first OBN data recorded by the first geophone, and the second acquisition module is configured to acquire the second OBN data recorded by the second geophone.

[0114] The shear wave component rotation unit 102 is connected to the acquisition unit 101 and is configured to rotate the X component to a radial direction to obtain an R component, and to rotate the Y component to a tangential direction to obtain a T component.

[0115] The PZ component pattern recognition unit 103 is connected to the acquisition unit 101 and is used to perform pattern recognition on the P component and the Z component to obtain the first shear wave noise prediction model in the Z component.

[0116] The PR component pattern recognition unit 104 is connected to the acquisition unit 101 and the shear wave component rotation unit 102 respectively, and is used to perform pattern recognition on the P component and the R component to obtain the second shear wave noise prediction model in the Z component.

[0117] The PT component pattern recognition unit 105 is connected to the acquisition unit 101 and the shear wave component rotation unit 102 respectively, and is used to perform pattern recognition on the P component and the T component to obtain the third shear wave noise prediction model in the Z component.

[0118] The adaptive subtraction unit 106 is respectively connected to the acquisition unit 101, the PZ component pattern recognition unit 103, the PR component pattern recognition unit 104 and the PT component pattern recognition unit 105, and is used to subtract the first shear wave noise prediction model, the second shear wave noise prediction model and the third shear wave noise prediction model from the Z component in sequence to obtain the Z component after the shear wave leakage noise is suppressed.

[0119] Furthermore, in this embodiment, the shear wave leakage noise suppression device may further include a data conversion unit. The data conversion unit is provided before each recognition unit and is used to perform mathematical conversion on the P component, R component, T component and Z component respectively before pattern recognition.

[0120] Specifically, the data transformation unit may include a dynamic correction module, a counter-dynamic correction module, a mathematical transformation module, and an inverse mathematical transformation module. The dynamic correction module is used to perform dynamic correction on each component (e.g., P component, R component, T component, and Z component) in the original seismic data. The mathematical transformation module is used to perform mathematical transformation on each component (e.g., P component, R component, T component, and Z component) after dynamic correction. The inverse mathematical transformation module is used to perform inverse mathematical transformation on each shear wave noise (e.g., first shear wave noise, second shear wave noise, and third shear wave noise) after pattern recognition. The counter-dynamic correction module is used to perform counter-dynamic correction on each shear wave noise (e.g., first shear wave noise, second shear wave noise, and third shear wave noise) after inverse mathematical transformation.

[0121] Furthermore, in this embodiment, the shear wave leakage noise suppression device may further include: a data superposition unit and a quality control unit. The data superposition unit is used to superimpose the results of the above-mentioned shear wave leakage noise suppression method. The quality control unit is used to perform quality control measures such as spectrum analysis.

[0122] It should be noted that the above-mentioned device only uses the division of the above-mentioned functional modules as an example to illustrate its functions. In actual applications, the above-mentioned 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 device provided in the above embodiment and the method provided in the first and second embodiments are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0123] Example 4

[0124] The fourth embodiment of the present invention further provides an electronic device, see Figure 13 The electronic device includes a processor 201 and a memory 202, in which at least one computer program is stored. The at least one computer program is loaded and executed by one or more of the above-mentioned processors, and when the computer program is executed, the shear wave leakage noise suppression method as described above is implemented.

[0125] Of course, the electronic device may also have components such as a wired or wireless network interface, a keyboard, and an input / output interface for input and output. The electronic device may also include other components for realizing various functions of the device, which will not be described in detail here.

[0126] The third embodiment of the present invention further provides a computer-readable storage medium, in which at least one program code is stored. The program code is loaded and executed by a processor, and when the computer program is executed, the shear wave leakage noise suppression method as described above is implemented.

[0127] Optionally, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, and an optical disc data storage device. Those skilled in the art will appreciate that all or part of the steps in the above-mentioned embodiment method can be accomplished by instructing the relevant hardware through a program, and the program is stored in a storage medium, including several instructions for causing a single-chip microcomputer, a chip, or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc.

[0128] The preferred embodiments of the present invention are described in detail above in conjunction with the accompanying drawings. However, the present invention is not limited to the specific details in the above embodiments. Within the technical concept of the present invention, various simple modifications can be made to the technical solution of the present invention, and these simple modifications all fall within the scope of protection of the present invention.

[0129] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any appropriate manner without contradiction. In order to avoid unnecessary repetition, the present invention will not further describe various possible combinations.

[0130] In addition, the various embodiments of the present invention may be arbitrarily combined, and as long as they do not violate the concept of the present invention, they should also be regarded as the contents disclosed by the present invention.

Claims

1. A method for suppressing shear wave leakage noise based on multi-component pattern recognition, characterized in that: The shear wave leakage noise suppression method comprises: Acquire first OBN data recorded by the first geophone and second OBN data recorded by the second geophone, wherein the first OBN data includes a P component of the original seismic data, and the second OBN data includes an X component, a Y component, and a Z component of the original seismic data; Rotate the X component to the radial direction to obtain the R component, and rotate the Y component to the tangential direction to obtain the T component; Perform pattern recognition on the P component and the Z component to obtain the first shear wave noise prediction model in the Z component; Perform pattern recognition on the P and R components to obtain the second shear wave noise prediction model in the Z component; Perform pattern recognition on the P and T components to obtain the third shear wave noise prediction model in the Z component; The first shear wave noise prediction model, the second shear wave noise prediction model and the third shear wave noise prediction model are sequentially subtracted from the Z component to obtain the Z component after the shear wave leakage noise is suppressed.

2. The shear wave leakage noise suppression method based on multi-component pattern recognition according to claim 1 is characterized in that: The shear wave leakage noise suppression method further includes: before performing pattern recognition, mathematically transforming the P component, the R component, the T component, and the Z component respectively.

3. The shear wave leakage noise suppression method based on multi-component pattern recognition according to claim 2, characterized in that: The mathematical transformation includes curvelet domain transformation and τ-p domain transformation.

4. The shear wave leakage noise suppression method based on multi-component pattern recognition according to claim 2, characterized in that: The performing pattern recognition on the P component and the Z component to obtain the first shear wave noise prediction model in the Z component includes: Perform dynamic correction on the P and Z components of the original seismic data; Perform mathematical transformation on the P component and Z component after dynamic correction respectively; Using pattern recognition to extract the difference between the mathematically transformed P component and the Z component, the first shear wave noise in the Z component is determined; Performing an inverse mathematical transformation on the first shear wave noise; The first shear wave noise after the inverse mathematical transformation is subjected to inverse correction to obtain a first shear wave noise prediction model consistent with the original seismic data.

5. The shear wave leakage noise suppression method based on multi-component pattern recognition according to claim 2, characterized in that: The method of performing pattern recognition on the P component and the R component to obtain a second shear wave noise prediction model in the Z component includes: Perform dynamic correction on the P and R components of the original seismic data; Perform mathematical transformation on the P component and R component after dynamic correction respectively; Pattern recognition is used to extract the difference between the mathematically transformed P and R components to determine the second shear wave noise in the Z component. Performing an inverse mathematical transformation on the second shear wave noise; The second shear wave noise after the inverse mathematical transformation is subjected to counter-correction to obtain a second shear wave noise prediction model that is consistent with the original seismic data.

6. The shear wave leakage noise suppression method based on multi-component pattern recognition according to claim 2, characterized in that: The method of performing pattern recognition on the P component and the T component to obtain a third shear wave noise prediction model in the Z component includes: Perform dynamic correction on the P and T components of the original seismic data; Perform mathematical transformation on the P component and T component after dynamic correction respectively; Pattern recognition is used to extract the difference between the mathematically transformed P and T components and determine the third shear wave noise in the Z component. Performing an inverse mathematical transformation on the third shear wave noise; The third shear wave noise after the inverse mathematical transformation is subjected to counter-correction to obtain a third shear wave noise prediction model that is consistent with the original seismic data.

7. A shear wave leakage noise suppression device based on multi-component pattern recognition, characterized in that: The shear wave leakage noise suppression device includes: an acquisition unit, a shear wave component rotation unit, a PZ component pattern recognition unit, a PR component pattern recognition unit, a PT component pattern recognition unit and an adaptive subtraction unit; The acquisition unit is configured to acquire first OBN data recorded by the first geophone and second OBN data recorded by the second geophone, wherein the first OBN data includes a P component of the original seismic data, and the second OBN data includes an X component, a Y component, and a Z component of the original seismic data; The shear wave component rotation unit is used to rotate the X component to the radial direction to obtain the R component, and to rotate the Y component to the tangential direction to obtain the T component; The PZ component pattern recognition unit is used to perform pattern recognition on the P component and the Z component to obtain a first shear wave noise prediction model in the Z component; The PR component pattern recognition unit is used to perform pattern recognition on the P component and the R component to obtain a second shear wave noise prediction model in the Z component; The PT component pattern recognition unit is used to perform pattern recognition on the P component and the T component to obtain a third shear wave noise prediction model in the Z component; The adaptive subtraction unit is used to subtract the first shear wave noise prediction model, the second shear wave noise prediction model and the third shear wave noise prediction model from the Z component in sequence to obtain the Z component after the shear wave leakage noise is suppressed.

8. The shear wave leakage noise suppression device based on multi-component pattern recognition according to claim 7, characterized in that: The shear wave leakage noise suppression device further includes: a data conversion unit; The data conversion unit is used to perform mathematical conversion on the P component, the R component, the T component and the Z component respectively before pattern recognition.

9. An electronic device, characterized in that: The electronic device includes a processor and a memory, wherein the memory stores at least one computer program, and the at least one computer program is loaded and executed by one or more of the above processors. When the computer program is executed, the shear wave leakage noise suppression method according to any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium, characterized in that At least one program code is stored in the computer-readable storage medium. The program code is loaded and executed by a processor. When the computer program is executed, the shear wave leakage noise suppression method according to any one of claims 1 to 6 is implemented.

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