Transverse wave leakage noise suppression method and device based on multi-component pattern recognition

Abstract

The invention provides a transverse wave leakage noise suppression method and device based on multi-component pattern recognition, and belongs to the field of seismic data processing, and the method comprises the steps: obtaining a P component, an X component, a Y component and a Z component of original seismic data; respectively rotating the X and Y components to obtain an R component and a T component; performing pattern recognition on the P component and the Z component to obtain a noisep noise model; performing pattern recognition on the P component and the R component to obtain a noisser noise model; performing pattern recognition on the P component and the T component to obtain a noisset noise model; and sequentially subtracting the three models from the Z component to obtain the Z component after transverse wave leakage noise suppression. Through the method provided by the invention, other noises in the Z component can be better extracted, and a better denoising effect is achieved.

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 buries three-component geophones, both water and land, at the bottom of the ocean to record pressure and velocity (or acceleration) component data. The P-component geophone of the water is a pressure geophone; the three-component geophone of the land is a velocity (or acceleration) geophone, which mainly collects acceleration and velocity information of seismic data, including X-component, Y-component and Z-component. For the horizontal seabed, the X-component and Y-component are parallel to the seabed and receive the converted shear wave signals, while the Z-component is perpendicular to the seabed and receives the longitudinal wave signals, so that the entire wave field can be fully recorded. Since seabed node seismic acquisition can provide more formation medium parameter information than conventional towed single-component data, it has developed rapidly.

[0003] However, in the actual construction process, due to the rugged and changeable characteristics of the seabed, on the one hand, it affects the coupling between the node and the seabed, and on the other hand, it is difficult to ensure that the seabed node is completely horizontal, that is, there is a certain angle between the node and the horizontal plane, which leads to the Z component of the land inspection also receiving the converted shear wave signal from the X and Y components, which is generally called "shear wave leakage noise" or "Vz noise". This noise is coherent noise in the common detection point gather, but not in the common shot point gather. It is mainly manifested as low speed, and its intensity depends on the coupling degree between the sensor and the seabed, and has little to do with the depth of the seawater.

[0004] Vz noise has an important impact on the subsequent processing of seismic acquisition data, such as upper and lower wave field calibration and merging, upper and lower wave field 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 method, complex wavelet domain transformation method, pattern recognition method, adaptive subtraction method, 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 in terms of shear wave components and use the shear wave information of the XY components to construct the 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 view of the technical problem that the shear wave leakage noise suppression effect is not good in the prior art, the present invention provides a shear wave leakage noise suppression method and equipment based on multi-component pattern recognition. The method and equipment can effectively remove the shear wave leakage noise from the X component and the Y component in the Z component, and obtain the longitudinal wave signal reflecting the real seabed stratum medium parameter information.

[0010] To achieve the above-mentioned purpose, the first aspect of the present invention provides a method for suppressing shear wave leakage noise based on multi-component pattern recognition, the method comprising the following steps: obtaining first OBN data recorded by a first detector and second OBN data recorded by a second detector, 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; 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; 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 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 pattern recognition, mathematically transforming 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 the 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 inverse 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 inverse 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 the 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 the 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 the 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 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 transformation on the P component, R component, T component and Z component respectively before pattern recognition.

[0018] A third aspect of the present invention provides an electronic device, comprising 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, and the computer program implements the above-mentioned shear wave leakage noise suppression method when executed.

[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] Through the technical solution provided by the present invention, 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, and effectively removes the shear wave leakage noise from the X component and the Y component in the Z component through the relationship between the P component and the X, R, and T components, thereby obtaining a longitudinal wave signal reflecting the real seabed stratum medium parameter information;

[0022] (2) Compared with the conventional shear wave leakage noise suppression method, the shear wave leakage noise suppression method of the present invention uses 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 specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0026] Figure 1 A flow chart 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 seismic data near-offset direct wave flat map of the original seismic data PZXY component 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 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 original seismic data PZ component provided in 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 original seismic data PR component provided in 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 in the second embodiment of the present invention;

[0032] Figure 7 A schematic diagram of the result after the noisep model is adaptively subtracted 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 result after 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] Fig. 9 A schematic diagram of the result after adaptively subtracting the noisep model, the noiser model and the noiset model from the Z component of the original seismic data provided by the second embodiment of the present invention;

[0035] Fig.10 A schematic diagram of the superimposed cross-section of the original Z component data and the Z component after each noise model is subtracted in sequence, provided in the second embodiment of the present invention;

[0036] Fig.11 A schematic diagram of the difference of the superimposed cross-sections after the Z component provided by the second embodiment of the present invention is subtracted from each noise model in sequence;

[0037] Fig.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] Fig.13 A schematic structural diagram of an electronic device provided in 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 specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here 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] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "installation", "connection", etc. should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integrated connection; it can be a direct connection, or an indirect connection; it can be a wired connection, or a wireless connection. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to 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] Embodiment 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 comprises 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 detector refers to the pressure detector of the water detector, which is used to collect and record the pressure component (i.e., P component) data of the bottom of the ocean. The second detector refers to the velocity detector (or acceleration detector) of the land detector, which is used to collect and record the velocity (or acceleration) information of the 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 at the bottom of the ocean.

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

[0053] Step S102: 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.

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

[0055] Step S104: Perform 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: Perform pattern recognition on the P component and the T component to obtain the 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 component and the Y component), the longitudinal wave information generated in the vertical direction can be obtained. On this basis, the PRT and the Z component are used for pattern recognition respectively, which can effectively extract the shear wave leakage noise characteristics caused by the X component and the Y component, and 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: before pattern recognition, mathematically transforming the P component, the R component, the T component and the Z component respectively. By mathematically transforming each component of the original seismic data, local features in the seismic data signal can be effectively extracted and analyzed, so as to show a better effect in pattern recognition.

[0060] Further, 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 for data processing for the P component, the R component, the T component, and the Z component; or different types of mathematical transformation methods may be used for data processing for the P component, the R component, the T component, and the Z component. For example, a curvelet domain transformation (or a τ-p domain transformation) may be performed on the P component, the R component, the T component, and the Z component; or a curvelet domain transformation may be performed on the P component and the Z component, and a τ-p domain transformation may be performed on the P component, the R component, and the T component.

[0061] Exemplarily, in step S103, pattern recognition is performed on the P component and the Z component, and the process of obtaining the first shear wave noise prediction model in the Z component includes but is 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: Perform curvelet domain transformation or τ-p domain transformation on the P component and Z component after dynamic correction respectively.

[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: Perform inverse 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 the dynamic correction respectively.

[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: Perform inverse correction on the second shear wave noise after the inverse curve 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: Perform curvelet domain transformation or τ-p domain transformation on the P component and T component after the dynamic correction.

[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: Perform a back-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 the 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 the general process of using the pattern recognition method to suppress Vz noise:

[0080] 1) Data preprocessing: First, the collected seismic data is 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) Establishing a training data set: Select a portion of sample data from the preprocessed seismic data as a training set, and annotate these data manually or with known accurate seismic reflection markers.

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

[0083] 4) Constructing a classifier: Use a pattern recognition algorithm (such as neural network, support vector machine, etc.) to construct a classifier. 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 the 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 and the clarity and accuracy of seismic signals can be improved.

[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 for multi-scale analysis and image processing. The curvelet transform can convert a signal or image from the time domain or space domain to the curvelet domain in order 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, so that non-stationary signal features such as curves, edges and textures can be better processed.

[0087] The "τ-p domain transformation" in this embodiment refers to a transformation method commonly used in marine seismic data processing and analysis, which is used to process seismic data with time and offset (or space) dimensions. The basic idea of ​​the τ-p domain transformation is to convert seismic data from conventional time-channel domain representation to τ-p domain representation, where τ represents time offset (or time delay) and p represents offset (or space) coordinates. Through this transformation, the offset and time characteristics of seismic data can be better described and analyzed.

[0088] In this embodiment, "Dynamite correction" and "Reverse dynamite correction" are two methods in seismic data processing, which are used to process the time offset caused by the first arrival wave generated by the earthquake source during the propagation process. Among them, dynamic correction is a method of adjusting the time of seismic data according to the velocity change of the underground medium, so as to correct the arrival time of the seismic wave to be consistent with the excitation time of the earthquake source.

[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] The server can be a single server or a server cluster composed 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] Embodiment 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: Rotate 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 the P, Z, R, and T component seismic data.

[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 a 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 Fig. 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 prediction model of the first shear wave noise in the Z component can be obtained (as shown in Fig. Figure 4 ).

[0099] Step S204: Perform curvelet domain transform (or τ-p domain transform) on the P component and the R component respectively, and perform pattern recognition in the curvelet domain (or τ-p domain) using the P component and the R component 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 This 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) in the figure shows the schematic diagram of the second shear wave noise prediction model noiser obtained after pattern recognition of the shot data of the PR component of the original seismic data. It can be seen from the original seismic data that the R component of the seismic data (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 the figure, it is all 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 in the curvelet domain (or τ-p domain) using the P component and the T component 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 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 the figure, all the information is shear wave, 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 above three noise models are subtracted from the Z component in sequence (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 represents 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), where the first difference is the noisep model.

[0105] Figure 8 This is a schematic diagram of the result after adaptively subtracting the noisep model and the 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 (i.e., Z-noisep) between the Z component data after removing noisep and noiser (i.e., Z-noisep-noiser) and the Z component data after removing noisep and noiser (i.e., Z-noisep-noiser). Figure 8 The second difference between (a) and (b) in the figure), where the second difference is the noiser model.

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

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

[0108] For example, Fig.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. Fig.10 (a) in the figure shows the stacked section of the original Z component data; Fig.10 (b) shows the stacked section after subtracting the noisep model data from the original Z component data; Fig.10 (c) in the figure represents the stacked section after subtracting the noisep model data and the noiser model data from the original Z component data; Fig.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] Fig.11 It is a schematic diagram of the difference of the superimposed profile after subtracting each noise model from the Z component. Fig.11 (a) in the figure shows the difference between the stacked section of the original Z component data and the stacked section after the Z component data is subtracted from the noisep model data; Fig.11 (b) in the figure represents 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; Fig.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] Fig.10 as well as Fig.11 The suppressed shear wave noise in the stacked sections is shown, and it can be seen that the method implemented in this embodiment is effective in suppressing shear wave noise.

[0111] Embodiment 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 Fig.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 used to acquire first OBN data recorded by the first geophone and second OBN data recorded by the second geophone. The first OBN data includes the P component of the original seismic data, and the second OBN data includes the X component, Y component and Z component 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 used to acquire the first OBN data recorded by the first geophone, and the second acquisition module is used 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 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.

[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 arranged 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 also 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 means 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 when implementing 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 is 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 belong to the same concept. The specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0123] Embodiment 4

[0124] A fourth embodiment of the present invention further provides an electronic device, see Fig.13 The electronic device includes a processor 201 and a memory 202, wherein at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by one or more of the above 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. 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 may understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, and the program is stored in a storage medium, including several instructions to enable 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, a variety of simple modifications can be made to the technical solution of the present invention, and these simple modifications all belong to the protection scope 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 suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not further describe various possible combinations.

[0130] In addition, 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 component and the R component to obtain the second shear wave noise prediction model in the Z component; Perform pattern recognition on the P component and the T component 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 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 is 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 is characterized in that: The method of 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; The difference between the mathematically transformed P component and the Z component is extracted by pattern recognition to determine the first shear wave noise in the Z component; 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 is 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; The difference between the mathematically transformed P component and the R component is extracted using pattern recognition 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 inverse correction to obtain a second shear wave noise prediction model consistent with the original seismic data.

6. The shear wave leakage noise suppression method based on multi-component pattern recognition according to claim 2 is characterized in that: The method of performing pattern recognition on the P component and the T component to obtain the 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; The difference between the mathematically transformed P component and the T component is extracted using pattern recognition to 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 inverse correction to obtain a third shear wave noise prediction model 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 comprises: 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 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 described in 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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