Multi-channel joint denoising method and processor for multi-component seismic data
By employing a multi-channel joint denoising method, the component displacement matching operator and non-causal prediction filter coefficients are used to perform autoregressive filtering on multi-component seismic data. This solves the problem of unutilized component data coherence in existing technologies and achieves high-precision denoising results.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA UNIV OF PETROLEUM (BEIJING)
- Filing Date
- 2022-08-30
- Publication Date
- 2026-04-28
AI Technical Summary
Existing technologies fail to effectively utilize the coherence between component data when processing multi-component seismic data, resulting in unsatisfactory noise reduction effects and reduced noise reduction accuracy and signal-to-noise ratio of multi-component seismic data.
A multi-channel joint denoising method is adopted. By acquiring three-dimensional multi-component seismic data, the X, Y, and Z component data are transposed and migrated respectively to determine the component displacement matching operator. Autoregressive prediction filtering is performed based on non-causal prediction filter coefficients to obtain the denoised three-dimensional multi-component seismic data.
It significantly reduces noise in three-dimensional multi-component seismic data, improves noise reduction accuracy, and provides high signal-to-noise ratio signals for subsequent multi-component signal processing.
Smart Images
Figure CN115469358B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of seismic signal processing, and specifically to a multi-channel joint denoising method, apparatus, storage medium, and processor for multi-component seismic data. Background Technology
[0002] With increasing exploration and development difficulties and rising costs of seismic data acquisition, multi-wave seismic exploration is gaining more and more attention from academia and industry. Three-component seismic detectors can simultaneously record two horizontal components and one vertical component of the incident wave field, offering advantages such as receiving multiple component data, wide bandwidth, and wide azimuth.
[0003] The multiple component data acquired by a three-component seismic detector exhibit significant differences and coherence, providing more information and constraints for data processing. Currently, single-component, single-channel methods are often used for noise reduction of multi-component seismic data, without considering the coherence between each component. This results in unsatisfactory noise suppression, significantly reducing the noise reduction accuracy of multi-component seismic data and making it difficult to provide high signal-to-noise ratio signals for subsequent multi-component signal processing. Summary of the Invention
[0004] The purpose of this application is to provide a multi-channel joint denoising method, apparatus, storage medium, and processor for multi-component seismic data.
[0005] To achieve the above objectives, the first aspect of this application provides a multi-channel joint denoising method for multi-component seismic data, comprising:
[0006] Acquire noisy three-dimensional multi-component seismic data, which includes X-component data, Y-component data and Z-component data;
[0007] Transpose and offset the X component data, Y component data, and Z component data respectively to obtain the component displacement matching operators corresponding to the X component data, Y component data, and Z component data respectively;
[0008] Determine the noncausal predictive filter coefficients for multi-component, multi-channel applications based on the component displacement matching operator;
[0009] Non-causal predictive filtering coefficients were used to perform non-causal autoregressive predictive filtering on three-dimensional multi-component seismic data to obtain denoised three-dimensional multi-component seismic data.
[0010] In the embodiments of this application, determining the non-causal prediction filter coefficients of multi-component multi-channel based on the component displacement matching operator includes: determining the actual values of the X component data, Y component data, and Z component data included in the three-dimensional multi-component seismic data when the three-dimensional multi-component seismic data includes a nonlinear phase axis; and performing prediction filtering on the three-dimensional multi-component seismic data in time and space based on the component displacement matching operator and the actual component values to determine the non-causal prediction filter coefficients.
[0011] In the embodiments of this application, the non-causal prediction filter coefficients are calculated according to formula (1):
[0012] Formula (1)
[0013] Where x, y, and t refer to the x-axis, y-axis, and time axis of space, respectively. This refers to the length of the component displacement matching operator along the x or y direction, M refers to the total number of filters, and s refers to the number of filters. This refers to the actual values of the components of three-dimensional multi-component seismic data at three-dimensional coordinates (x, y, t). This refers to the component displacement matching operator corresponding to the s-th filter at the three-dimensional coordinates (x, y, t). This refers to the non-causal predictive filter coefficient corresponding to the s-th filter at the three-dimensional coordinates (x, y, t). This refers to the positive prediction value at three-dimensional coordinates (x, y, t) obtained by multiplying and summing the non-causal predictive filter coefficients and component displacement matching operators corresponding to M filters. This refers to the inverse prediction value at the three-dimensional coordinates (x, y, t) obtained by multiplying and summing the non-causal predictive filter coefficients and component displacement matching operators corresponding to M filters. It means and Error value at three-dimensional coordinates (x, y, t).
[0014] In embodiments of this application, the method further includes: non-causal prediction filter coefficients. Determined by expression (1):
[0015] Expression (1)
[0016] in, for The regularization representation of the shaping, This refers to the non-causal prediction filter coefficients. Find the minimum norm.
[0017] In the embodiments of this application, non-causal prediction filter coefficients Through the filter coefficient matrix Represented as:
[0018]
[0019] in, This is the filter coefficient matrix of s filters corresponding to the non-causal predictive filter coefficients. It refers to the filter coefficients corresponding to the X component data of the first channel of the s-th filter. This refers to the filter coefficients corresponding to the Y component data of the first channel of the s-th filter. This refers to the filter coefficients corresponding to the Z component data of the first channel of the s-th filter. This refers to the filter coefficients corresponding to the X component data of the second channel of the s-th filter. This refers to the filter coefficients corresponding to the Y component data of the second channel of the s-th filter. This refers to the filter coefficients corresponding to the Z component data of the second channel of the s-th filter. This refers to the filter coefficients corresponding to the X component data of the 3rd channel of the s-th filter. This refers to the filter coefficients corresponding to the Y component data of the 3rd channel of the s-th filter. It refers to the filter coefficients corresponding to the Z component data of the 3rd channel of the s-th filter.
[0020] In the embodiments of this application, transposing and shifting the X component data, Y component data, and Z component data respectively to obtain component displacement matching operators corresponding to the X component data, Y component data, and Z component data respectively includes:
[0021] The component displacement matching operator corresponding to the X component data is determined by formula (2):
[0022] Formula (2)
[0023] Where x, y, and t refer to the x-axis, y-axis, and time axis of space, respectively; s refers to the number of filters; and i and j refer to the indices of the spatial displacements in the x and y directions, respectively. This refers to the X-component displacement matching operator generated after the X-component data is shifted i positions along the x-axis and j positions along the y-axis under each filter, and shifted s filters. This refers to the data after shifting the X component data by i positions along the x-axis and j positions along the y-axis under each filter, and then shifting it by s filters. This refers to the length of the component displacement matching operator along the x or y direction;
[0024] The component displacement matching operator corresponding to the Y component data is determined by formula (3):
[0025] Formula (3)
[0026] Where x, y, and t refer to the x-axis, y-axis, and time axis of space, respectively; s refers to the number of filters; and i and j refer to the indices of the spatial displacements in the x and y directions, respectively. This refers to the Y-component displacement matching operator generated after shifting the Y-component data along the x-axis by i positions and the y-axis by j positions under each filter, and after shifting by s filters. This refers to the data after shifting the Y component data by i positions along the x-axis and j positions along the y-axis under each filter, and then shifting it by s filters. This refers to the length of the component displacement matching operator along the x or y direction;
[0027] The component displacement matching operator corresponding to the Z component data is determined by formula (4):
[0028] Formula (4)
[0029] Where x, y, and t refer to the x-axis, y-axis, and time axis of space, respectively; s refers to the number of filters; and i and j refer to the indices of the spatial displacements in the x and y directions, respectively. This refers to the Z-component displacement matching operator generated after shifting the Z-component data along the x-axis by i positions and the y-axis by j positions under each filter, and after shifting by s filters. This refers to the data after shifting the Z component data by i positions along the x-axis and j positions along the y-axis under each filter, and then shifting it by s filters. It refers to the length of the component displacement matching operator along the x or y direction.
[0030] In the embodiments of this application, the denoised three-dimensional multi-component seismic data is determined by formula (5):
[0031] Formula (5)
[0032] Where i refers to the index of the spatial x-direction displacement, j refers to the index of the spatial y-direction displacement, and s refers to the number of filters. This refers to the X component data after noise reduction. This refers to the noise-reduced Y component data. This refers to the Z-component data after noise reduction. It refers to the filter coefficient matrix of the s filters corresponding to the non-causal predictive filter coefficients. This refers to the X component data being shifted i positions along the x-axis and j positions along the y-axis under each filter, and the data after shifting s filters. This refers to the Y component data being shifted i positions along the x-axis and j positions along the y-axis under each filter, and the data after shifting s filters. This refers to the Z component data after being moved i positions along the x-axis and j positions along the y-axis under each filter, and after being moved s filters. M refers to the total number of filters.
[0033] A second aspect of this application provides a multi-channel joint denoising device for multi-component seismic data, comprising:
[0034] The data acquisition module is used to acquire noisy three-dimensional multi-component seismic data, which includes X-component data, Y-component data and Z-component data.
[0035] The data processing module is used to transpose and offset the X component data, Y component data and Z component data respectively, so as to obtain the component displacement matching operators corresponding to the X component data, Y component data and Z component data respectively.
[0036] The coefficient determination module is used to determine the non-causal predictive filter coefficients for multiple components and multiple channels based on the component displacement matching operator.
[0037] The prediction filtering module is used to perform non-causal autoregressive prediction filtering on three-dimensional multi-component seismic data using non-causal prediction filtering coefficients to obtain denoised three-dimensional multi-component seismic data.
[0038] A third aspect of this application provides a processor configured to perform the above-described multi-channel joint denoising method for multi-component seismic data.
[0039] A fourth aspect of this application provides a machine-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the aforementioned multi-channel joint denoising method for multi-component seismic data.
[0040] The above technical solution enables multi-channel joint denoising of multi-component seismic data, coupling the data between each component for calculation, significantly reducing the noise of noisy three-dimensional multi-component seismic data, improving the denoising accuracy of noisy three-dimensional multi-component seismic data, and providing a high signal-to-noise ratio signal for subsequent multi-component signal processing.
[0041] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description
[0042] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings:
[0043] Figure 1 The schematic diagram illustrates a multi-channel joint denoising method for multi-component seismic data according to an embodiment of this application;
[0044] Figure 2-1 An example diagram of noisy X-component data according to an embodiment of this application is illustrated schematically;
[0045] Figure 2-2 An example diagram of noisy Y component data according to an embodiment of this application is illustrated schematically;
[0046] Figure 2-3 An example diagram of noisy Z-component data according to an embodiment of this application is illustrated schematically;
[0047] Figure 3-1 An example diagram illustrating the filter coefficients corresponding to X component data in multiple channels of multiple filters according to embodiments of this application is shown.
[0048] Figure 3-2 The illustration shows example diagrams of filter coefficients corresponding to Y component data in multiple channels of multiple filters according to embodiments of the present application;
[0049] Figure 3-3 An example diagram illustrating the filter coefficients corresponding to Z component data in multiple channels of multiple filters according to embodiments of this application is shown.
[0050] Figure 4-1 An example diagram illustrating noise removal for X component data during non-causal autoregressive prediction filtering according to an embodiment of this application is shown.
[0051] Figure 4-2 An example diagram illustrating the removal of noise from Y component data during non-causal autoregressive prediction filtering according to embodiments of this application is shown.
[0052] Figure 4-3 An example diagram illustrating the removal of noise from Z-component data during non-causal autoregressive prediction filtering according to embodiments of this application is shown.
[0053] Figure 5-1 An example diagram illustrating noise-reduced X-component data according to an embodiment of this application is shown schematically.
[0054] Figure 5-2An example diagram illustrating noise-reduced Y component data according to an embodiment of this application is shown schematically.
[0055] Figure 5-3 An example diagram illustrating the noise-reduced Z-component data according to an embodiment of this application is shown schematically.
[0056] Figure 6 A schematic diagram illustrates a structural block diagram of a multi-channel joint denoising device for multi-component seismic data according to an embodiment of this application;
[0057] Figure 7 The diagram illustrates the internal structure of a computer device according to an embodiment of this application. Detailed Implementation
[0058] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.
[0059] Figure 1 A schematic flowchart illustrating a multi-channel joint denoising method for multi-component seismic data according to an embodiment of this application is shown. Figure 1 As shown in one embodiment of this application, a multi-channel joint denoising method for multi-component seismic data is provided, comprising the following steps:
[0060] Step 101: Obtain noisy three-dimensional multi-component seismic data, wherein the three-dimensional multi-component seismic data includes X-component data, Y-component data and Z-component data.
[0061] Step 102: Transpose and offset the X component data, Y component data and Z component data respectively to obtain the component displacement matching operators corresponding to the X component data, Y component data and Z component data respectively.
[0062] Step 103: Determine the non-causal prediction filter coefficients for multi-component multi-channel based on the component displacement matching operator.
[0063] Step 104: Apply non-causal predictive filtering coefficients to the three-dimensional multi-component seismic data to obtain denoised three-dimensional multi-component seismic data.
[0064] When performing multi-channel joint denoising on multi-component seismic data, the processor can first acquire the multi-component time-domain three-dimensional noisy signal and input it to a time-domain multi-component detector. The time-domain multi-component detector then converts the multi-component time-domain three-dimensional noisy signal into three-dimensional multi-component common-detector point data in (t,x,y) coordinates, i.e., noisy three-dimensional multi-component seismic data. Thus, the processor can acquire the noisy three-dimensional multi-component seismic data transmitted by the time-domain multi-component detector. The three-dimensional multi-component seismic data can include X-component data, Y-component data, and Z-component data. The X-component data, Y-component data, and Z-component data can refer to the data received from the three receiving directions of the time-domain multi-component detector, respectively.
[0065] For example, this application provides an example diagram of noisy three-dimensional multi-component seismic data. Figure 2-1 Here is an example plot of the X component data. Figure 2-2 Here is an example plot of the Y component data. Figure 2-3 This is an example diagram of Z-component data. There are significant differences and coherence among the X, Y, and Z-component data. Furthermore, even with the same source, receiver location, or velocity model, the shape and amplitude of the phase axes of the X, Y, and Z-component data can differ significantly.
[0066] When acquiring noisy 3D multi-component seismic data, the processor can transpose and migrate the X, Y, and Z component data separately to obtain component displacement matching operators corresponding to the X, Y, and Z component data, respectively. Specifically, the processor can transpose the 3D multi-component seismic data in (t,x,y) coordinates to data in (x,y,t) coordinates, and then migrate it along the x-axis and y-axis directions to obtain regularized regression displacement matching operators in (x,y,s,t) coordinates, i.e., obtain component displacement matching operators corresponding to the X, Y, and Z component data.
[0067] In one embodiment, transposing and shifting the X component data, Y component data, and Z component data respectively to obtain component displacement matching operators corresponding to the X component data, Y component data, and Z component data respectively includes:
[0068] The component displacement matching operator corresponding to the X component data is determined by formula (2):
[0069] Formula (2)
[0070] Where x, y, and t refer to the x-axis, y-axis, and time axis of space, respectively; s refers to the number of filters; and i and j refer to the indices of the spatial displacements in the x and y directions, respectively. This refers to the X-component displacement matching operator generated after the X-component data is shifted i positions along the x-axis and j positions along the y-axis under each filter, and shifted s filters. This refers to the data after shifting the X component data by i positions along the x-axis and j positions along the y-axis under each filter, and then shifting it by s filters. This refers to the length of the component displacement matching operator along the x or y direction;
[0071] The component displacement matching operator corresponding to the Y component data is determined by formula (3):
[0072] Formula (3)
[0073] Where x, y, and t refer to the x-axis, y-axis, and time axis of space, respectively; s refers to the number of filters; and i and j refer to the indices of the spatial displacements in the x and y directions, respectively. This refers to the Y-component displacement matching operator generated after shifting the Y-component data along the x-axis by i positions and the y-axis by j positions under each filter, and after shifting by s filters. This refers to the data after shifting the Y component data by i positions along the x-axis and j positions along the y-axis under each filter, and then shifting it by s filters. This refers to the length of the component displacement matching operator along the x or y direction;
[0074] The component displacement matching operator corresponding to the Z component data is determined by formula (4):
[0075] Formula (4)
[0076] Where x, y, and t refer to the x-axis, y-axis, and time axis of space, respectively; s refers to the number of filters; and i and j refer to the indices of the spatial displacements in the x and y directions, respectively. This refers to the Z-component displacement matching operator generated after shifting the Z-component data along the x-axis by i positions and the y-axis by j positions under each filter, and after shifting by s filters. This refers to the data after shifting the Z component data by i positions along the x-axis and j positions along the y-axis under each filter, and then shifting it by s filters. It refers to the length of the component displacement matching operator along the x or y direction.
[0077] Given a component displacement matching operator corresponding to the X, Y, and Z component data, the processor can determine the multi-component, multi-channel non-causal prediction filter coefficients based on the component displacement matching operator. In one embodiment, determining the multi-component, multi-channel non-causal prediction filter coefficients based on the component displacement matching operator includes: determining the actual values of the X, Y, and Z component data included in the three-dimensional multi-component seismic data when the three-dimensional multi-component seismic data includes a nonlinear phase axis; and performing temporal and spatial prediction filtering on the three-dimensional multi-component seismic data based on the component displacement matching operator and the actual component values to determine the non-causal prediction filter coefficients.
[0078] Before determining the noncausal prediction filter coefficients for multi-component, multi-channel seismic data, the processor can first determine whether the 3D multi-component seismic data includes nonlinear phase axes. If the processor determines that the noisy 3D multi-component seismic data does not include nonlinear phase axes but only linear phase axes, then the 3D multi-component seismic data can be determined to be predictable. That is, the 3D multi-component data can be predicted along different directions. In this case, the processor can perform forward and backward predictions on the 3D multi-component seismic data to determine the noncausal prediction filter coefficients.
[0079] Specifically, the forward prediction process of three-dimensional multi-component seismic data can be achieved through... This is represented. Among them, This refers to the actual values of the components of three-dimensional multi-component seismic data at three-dimensional coordinates (x, y, t). This refers to the three-dimensional multi-component seismic data being moved i positions along the x-axis and j positions along the y-axis, and then moved s filters, in the three-dimensional coordinate system (x, s). i, ys The data at position j, t), where M refers to the total number of filters. This refers to the stationary predictive filter coefficients corresponding to the s-th filter. The processor can substitute the component shift matching operators corresponding to the determined X, Y, and Z component data into the above equation to obtain: .in, This refers to the component displacement matching operator corresponding to the s-th filter at the three-dimensional coordinates (x, y, t). The forward and backward prediction processes for this three-dimensional multi-component seismic data can be expressed as: .in, This refers to the positive prediction value at three-dimensional coordinates (x, y, t) obtained by multiplying and summing the non-causal predictive filter coefficients and component displacement matching operators corresponding to M filters respectively. It refers to the inverse prediction value at the three-dimensional coordinates (x, y, t) obtained by multiplying and summing the non-causal prediction filter coefficients and component displacement matching operators corresponding to M filters respectively.
[0080] If the processor determines that the noisy 3D multi-component seismic data includes nonlinear phase axes, it can determine the actual values of the X, Y, and Z components of the 3D multi-component seismic data. The processor can then further perform temporal and spatial predictive filtering on the 3D multi-component seismic data based on the component displacement matching operator and the actual component values to determine the non-causal predictive filtering coefficients. This temporal and spatial predictive filtering of the 3D multi-component seismic data can address continuous variations in dip angle.
[0081] Specifically, the processor can determine the non-causal prediction filter coefficients according to formula (1). In one embodiment, the non-causal prediction filter coefficients are calculated according to formula (1):
[0082] Formula (1)
[0083] Where x, y, and t refer to the x-axis, y-axis, and time axis of space, respectively. This refers to the length of the component displacement matching operator along the x or y direction, M refers to the total number of filters, and s refers to the number of filters. This refers to the actual values of the components of three-dimensional multi-component seismic data at three-dimensional coordinates (x, y, t). This refers to the component displacement matching operator corresponding to the s-th filter at the three-dimensional coordinates (x, y, t). This refers to the non-causal predictive filter coefficient corresponding to the s-th filter at the three-dimensional coordinates (x, y, t). This refers to the positive prediction value at three-dimensional coordinates (x, y, t) obtained by multiplying and summing the non-causal predictive filter coefficients and component displacement matching operators corresponding to M filters respectively. This refers to the inverse prediction value at the three-dimensional coordinates (x, y, t) obtained by multiplying and summing the non-causal predictive filter coefficients and component displacement matching operators corresponding to M filters. It means and Error value at three-dimensional coordinates (x, y, t).
[0084] In one embodiment, the method further includes: non-causal prediction filter coefficients It can be determined by expression (1):
[0085] Expression (1)
[0086] in, for The regularization representation of the shaping, This refers to the non-causal prediction filter coefficients. Find the minimum norm.
[0087] If the surface phase axis in three-dimensional multi-component seismic data is a local plane, then The x-axis and y-axis of the space can be smooth. In this case, the non-causal predictive filter coefficients... The noncausal prediction filter coefficients can be determined using expression (1). Specifically, the processor can determine the noncausal prediction filter coefficients using the least squares method by simultaneously minimizing the forward and backward prediction errors. .
[0088] In one embodiment, non-causal predictive filter coefficients It can be done through the filter coefficient matrix Represented as:
[0089]
[0090] in, This is the filter coefficient matrix of s filters corresponding to the non-causal predictive filter coefficients. It refers to the filter coefficients corresponding to the X component data of the first channel of the s-th filter. This refers to the filter coefficients corresponding to the Y component data of the first channel of the s-th filter. This refers to the filter coefficients corresponding to the Z component data of the first channel of the s-th filter. This refers to the filter coefficients corresponding to the X component data of the second channel of the s-th filter. This refers to the filter coefficients corresponding to the Y component data of the second channel of the s-th filter. This refers to the filter coefficients corresponding to the Z component data of the second channel of the s-th filter. This refers to the filter coefficients corresponding to the X component data of the 3rd channel of the s-th filter. This refers to the filter coefficients corresponding to the Y component data of the 3rd channel of the s-th filter. It refers to the filter coefficients corresponding to the Z component data of the 3rd channel of the s-th filter.
[0091] For example, if the number of filters is 2, then the filter coefficient matrix can be... Expanding into the equation of a second-order autoregressive model:
[0092]
[0093] in, It refers to the three-component data at time n. This refers to the X component. This refers to the Y component. This refers to the Z component. This refers to the filter coefficients corresponding to the X component data of the first channel of the first filter. This refers to the filter coefficients corresponding to the Y component data of the first channel of the first filter. This refers to the filter coefficients corresponding to the Z component data of the first channel of the first filter. This refers to the filter coefficients corresponding to the X component data of the second channel of the first filter. This refers to the filter coefficients corresponding to the Y component data of the second channel of the first filter. This refers to the filter coefficients corresponding to the Z component data of the second channel of the first filter. This refers to the filter coefficients corresponding to the X component data of the third channel of the first filter. This refers to the filter coefficients corresponding to the Y component data of the third channel of the first filter. This refers to the filter coefficients corresponding to the Z component data of the third channel of the first filter. This refers to all values of the X-component displacement matching operator. This refers to all values of the Y-component displacement matching operator. This refers to all values of the Z-component displacement matching operator. This refers to the filter coefficients corresponding to the X component data of the first channel of the second filter. This refers to the filter coefficients corresponding to the Y component data of the first channel of the second filter. This refers to the filter coefficients corresponding to the Z component data of the first channel of the second filter. This refers to the filter coefficients corresponding to the X component data of the second channel of the second filter. This refers to the filter coefficients corresponding to the Y component data of the second channel of the second filter. This refers to the filter coefficients corresponding to the Z component data of the second channel of the second filter. This refers to the filter coefficients corresponding to the X component data of the third channel of the second filter. This refers to the filter coefficients corresponding to the Y component data of the third channel of the second filter. It refers to the filter coefficients corresponding to the Z component data of the third channel of the second filter.
[0094] Given the non-causal predictive filter coefficients for multi-component, multi-channel data, the processor can apply these coefficients to perform causal autoregressive predictive filtering on the 3D multi-component seismic data to obtain denoised 3D multi-component seismic data. For example, this application provides an example diagram of non-causal predictive filter coefficients. Figure 3-1 This is an example diagram showing the filter coefficients corresponding to the X component data across multiple channels of multiple filters. Figure 3-2 This is an example diagram showing the filter coefficients corresponding to the Y component data across multiple channels of multiple filters. Figure 3-3 This is an example diagram showing the filter coefficients corresponding to Z-component data across multiple channels of multiple filters.
[0095] In one embodiment, the denoised three-dimensional multi-component seismic data is determined by formula (5):
[0096] Formula (5)
[0097] Where i refers to the index of the spatial x-direction displacement, j refers to the index of the spatial y-direction displacement, and s refers to the number of filters. This refers to the X component data after noise reduction. This refers to the noise-reduced Y component data. This refers to the Z-component data after noise reduction. It refers to the filter coefficient matrix of the s filters corresponding to the non-causal predictive filter coefficients. This refers to the X component data being shifted i positions along the x-axis and j positions along the y-axis under each filter, and the data after shifting s filters. This refers to the Y component data being shifted i positions along the x-axis and j positions along the y-axis under each filter, and the data after shifting s filters. This refers to the Z component data after being moved i positions along the x-axis and j positions along the y-axis under each filter, and after being moved s filters. M refers to the total number of filters.
[0098] For example, this application provides an example diagram of noise removal during non-causal autoregressive prediction filtering. Wherein, Figure 4-1 This is an example of noise removal when performing non-causal autoregressive prediction filtering on X component data. Figure 4-2 This is an example of noise removal when performing non-causal autoregressive prediction filtering on Y component data. Figure 4-3 This is an example of noise removal when performing non-causal autoregressive prediction filtering on Z-component data.
[0099] For example, this application provides an example image of denoised three-dimensional multi-component seismic data. Figure 5-1Here is an example image of the denoised X component data. Figure 5-2 Here is an example image of the denoised Y component data. Figure 5-3 This is an example image of the denoised Z-component data.
[0100] The above technical solution enables multi-channel joint denoising of multi-component seismic data, coupling the data between each component for calculation, significantly reducing the noise of noisy three-dimensional multi-component seismic data, improving the denoising accuracy of noisy three-dimensional multi-component seismic data, and providing a high signal-to-noise ratio signal for subsequent multi-component signal processing.
[0101] Figure 1 This is a flowchart illustrating a multi-channel joint denoising method for multi-component seismic data in one embodiment. It should be understood that, although... Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.
[0102] In one embodiment, such as Figure 6 As shown, a multi-channel joint denoising device 600 for multi-component seismic data is provided, including a data acquisition module 601, a data processing module 602, a coefficient determination module 603, and a prediction filtering module 604, wherein:
[0103] The data acquisition module 601 is used to acquire noisy three-dimensional multi-component seismic data, which includes X-component data, Y-component data and Z-component data.
[0104] The data processing module 602 is used to transpose and offset the X component data, Y component data and Z component data respectively, so as to obtain the component displacement matching operators corresponding to the X component data, Y component data and Z component data respectively.
[0105] The coefficient determination module 603 is used to determine the non-causal prediction filter coefficients of multi-component multi-channel based on the component displacement matching operator.
[0106] The prediction filtering module 604 is used to perform non-causal autoregressive prediction filtering on three-dimensional multi-component seismic data using non-causal prediction filtering coefficients to obtain denoised three-dimensional multi-component seismic data.
[0107] When performing multi-channel joint denoising on multi-component seismic data, the data acquisition module 601 can first acquire the multi-component time-domain three-dimensional noisy signal, and then input the multi-component time-domain three-dimensional noisy signal to a time-domain multi-component detector. The time-domain multi-component detector converts the multi-component time-domain three-dimensional noisy signal into three-dimensional multi-component common-detector point data in (t,x,y) coordinates, i.e., noisy three-dimensional multi-component seismic data. Thus, the data acquisition module 601 can acquire the noisy three-dimensional multi-component seismic data transmitted by the time-domain multi-component detector. The three-dimensional multi-component seismic data can include X-component data, Y-component data, and Z-component data. The X-component data, Y-component data, and Z-component data can refer to the data received from the three receiving directions of the time-domain multi-component detector, respectively.
[0108] When noisy 3D multi-component seismic data is acquired, the data processing module 602 can transpose and offset the X-component data, Y-component data, and Z-component data respectively to obtain component displacement matching operators corresponding to the X-component data, Y-component data, and Z-component data. Specifically, the data processing module 602 can transpose the 3D multi-component seismic data in (t,x,y) coordinates into data in (x,y,t) coordinates, and can offset it along the spatial axes x-axis and y-axis respectively to obtain the regularized regression displacement matching operator in (x,y,s,t) coordinates, that is, to obtain the component displacement matching operator corresponding to the X-component data, Y-component data, and Z-component data.
[0109] Given the component displacement matching operators corresponding to the X, Y, and Z component data, the coefficient determination module 603 can determine the multi-component, multi-channel non-causal prediction filter coefficients based on the component displacement matching operators. With the multi-component, multi-channel non-causal prediction filter coefficients determined, the prediction filtering module 604 can apply these coefficients to perform non-causal autoregressive prediction filtering on the three-dimensional multi-component seismic data to obtain denoised three-dimensional multi-component seismic data.
[0110] In one embodiment, the coefficient determination module is further configured to: determine the actual values of the X-component data, Y-component data, and Z-component data included in the three-dimensional multi-component seismic data when the three-dimensional multi-component seismic data includes a nonlinear phase axis; and perform predictive filtering on the three-dimensional multi-component seismic data in time and space based on the component displacement matching operator and the actual values of the components to determine the non-causal predictive filtering coefficients.
[0111] If the coefficient determination module 603 determines that the noisy 3D multi-component seismic data includes nonlinear phase axes, then the actual values of the X, Y, and Z components included in the 3D multi-component seismic data can be determined. The coefficient determination module 603 can further perform predictive filtering on the 3D multi-component seismic data in time and space based on the component displacement matching operator and the actual component values to determine the non-causal predictive filtering coefficients. Predictive filtering of the 3D multi-component seismic data in time and space can address continuous changes in dip angle.
[0112] The above technical solution enables multi-channel joint denoising of multi-component seismic data, coupling the data between each component for calculation, significantly reducing the noise of noisy three-dimensional multi-component seismic data, improving the denoising accuracy of noisy three-dimensional multi-component seismic data, and providing a high signal-to-noise ratio signal for subsequent multi-component signal processing.
[0113] The multi-channel joint denoising device for multi-component seismic data includes a processor and a memory. The aforementioned data acquisition module, data processing module, coefficient determination module, and prediction filtering module are all stored as program units in the memory. The processor executes the aforementioned program modules stored in the memory to implement the corresponding functions.
[0114] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and adjusting kernel parameters allows for multi-channel joint denoising methods used for multi-component seismic data.
[0115] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.
[0116] This application provides a storage medium storing a program that, when executed by a processor, implements the above-described multi-channel joint denoising method for multi-component seismic data.
[0117] This application provides a processor for running a program, wherein the program executes the above-described multi-channel joint denoising method for multi-component seismic data.
[0118] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 7As shown, the computer device includes a processor A01, a network interface A02, a memory (not shown), and a database (not shown) connected via a system bus. The processor A01 provides computational and control capabilities. The memory includes internal memory A03 and a non-volatile storage medium A04. The non-volatile storage medium A04 stores an operating system B01, a computer program B02, and a database (not shown). The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 stored in the non-volatile storage medium A04. The database stores data such as denoised three-dimensional multi-component seismic data. The network interface A02 communicates with external terminals via a network connection. When the processor A01 executes the computer program B02, it implements a multi-channel joint denoising method for multi-component seismic data.
[0119] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.
[0120] This application provides an apparatus including a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps: acquiring noisy three-dimensional multi-component seismic data, wherein the three-dimensional multi-component seismic data includes X-component data, Y-component data, and Z-component data; transposing and shifting the X-component data, Y-component data, and Z-component data respectively to obtain component displacement matching operators corresponding to the X-component data, Y-component data, and Z-component data respectively; determining multi-component multi-channel non-causal prediction filter coefficients based on the component displacement matching operators; and applying the non-causal prediction filter coefficients to perform non-causal autoregressive prediction filtering on the three-dimensional multi-component seismic data to obtain denoised three-dimensional multi-component seismic data.
[0121] In one embodiment, determining the non-causal prediction filter coefficients of multi-component multi-channel based on the component displacement matching operator includes: determining the actual values of the X, Y, and Z components of the three-dimensional multi-component seismic data when the three-dimensional multi-component seismic data includes a nonlinear phase axis; and performing prediction filtering on the three-dimensional multi-component seismic data in time and space based on the component displacement matching operator and the actual component values to determine the non-causal prediction filter coefficients.
[0122] In one embodiment, the non-causal prediction filter coefficients are calculated according to formula (1):
[0123] Formula (1)
[0124] Where x, y, and t refer to the x-axis, y-axis, and time axis of space, respectively. This refers to the length of the component displacement matching operator along the x or y direction, M refers to the total number of filters, and s refers to the number of filters. This refers to the actual values of the components of three-dimensional multi-component seismic data at three-dimensional coordinates (x, y, t). This refers to the component displacement matching operator corresponding to the s-th filter at the three-dimensional coordinates (x, y, t). This refers to the non-causal predictive filter coefficient corresponding to the s-th filter at the three-dimensional coordinates (x, y, t). This refers to the positive prediction value at three-dimensional coordinates (x, y, t) obtained by multiplying and summing the non-causal predictive filter coefficients and component displacement matching operators corresponding to M filters respectively. This refers to the inverse prediction value at the three-dimensional coordinates (x, y, t) obtained by multiplying and summing the non-causal predictive filter coefficients and component displacement matching operators corresponding to M filters. It means and Error value at three-dimensional coordinates (x, y, t).
[0125] In one embodiment, the method further includes: non-causal prediction filter coefficients Determined by expression (1):
[0126] Expression (1)
[0127] in, for The regularization representation of the shaping, This refers to the non-causal prediction filter coefficients. Find the minimum norm.
[0128] In one embodiment, non-causal predictive filter coefficients Through the filter coefficient matrix Represented as:
[0129]
[0130] in, This is the filter coefficient matrix of s filters corresponding to the non-causal predictive filter coefficients. It refers to the filter coefficients corresponding to the X component data of the first channel of the s-th filter. This refers to the filter coefficients corresponding to the Y component data of the first channel of the s-th filter. It refers to the filter coefficient corresponding to the Z - component data of the first channel of the s - th filter. It refers to the filter coefficient corresponding to the X - component data of the second channel of the s - th filter. It refers to the filter coefficient corresponding to the Y - component data of the second channel of the s - th filter. It refers to the filter coefficient corresponding to the Z - component data of the second channel of the s - th filter. It refers to the filter coefficient corresponding to the X - component data of the third channel of the s - th filter. It refers to the filter coefficient corresponding to the Y - component data of the third channel of the s - th filter. It refers to the filter coefficient corresponding to the Z - component data of the third channel of the s - th filter.
[0131] In one embodiment, transposing and offsetting the X - component data, Y - component data, and Z - component data respectively to obtain the component displacement matching operators corresponding to the X - component data, Y - component data, and Z - component data respectively includes:
[0132] Determine the component displacement matching operator corresponding to the X - component data through formula (2):
[0133] Formula (2)
[0134] Where x, y, and t respectively refer to the x - axis, y - axis, and time - axis of the spatial axis, s refers to the number of filters, and i and j respectively refer to the indices of displacements in the spatial x and y directions. refers to the X - component displacement matching operator generated by moving the X - component data i positions and j positions along the x - axis and y - axis respectively under each filter and moving s filters. refers to the data obtained by moving the X - component data i positions and j positions along the x - axis and y - axis respectively under each filter and moving s filters. refers to the length of the component displacement matching operator along the x or y direction;
[0135] Determine the component displacement matching operator corresponding to the Y - component data through formula (3):
[0136] Formula (3)
[0137] Where x, y, and t respectively refer to the x - axis, y - axis, and time - axis of the spatial axis, s refers to the number of filters, and i and j respectively refer to the indices of displacements in the spatial x and y directions. refers to the Y - component displacement matching operator generated by moving the Y - component data i positions and j positions along the x - axis and y - axis respectively under each filter and moving s filters. This refers to the data after shifting the Y component data by i positions along the x-axis and j positions along the y-axis under each filter, and then shifting it by s filters. This refers to the length of the component displacement matching operator along the x or y direction;
[0138] The component displacement matching operator corresponding to the Z component data is determined by formula (4):
[0139] Formula (4)
[0140] Where x, y, and t refer to the x-axis, y-axis, and time axis of space, respectively; s refers to the number of filters; and i and j refer to the indices of the spatial displacements in the x and y directions, respectively. This refers to the Z-component displacement matching operator generated after shifting the Z-component data along the x-axis by i positions and the y-axis by j positions under each filter, and after shifting by s filters. This refers to the data after shifting the Z component data by i positions along the x-axis and j positions along the y-axis under each filter, and then shifting it by s filters. It refers to the length of the component displacement matching operator along the x or y direction.
[0141] In one embodiment, the denoised three-dimensional multi-component seismic data is determined by formula (5):
[0142] Formula (5)
[0143] Where i refers to the index of the spatial x-direction displacement, j refers to the index of the spatial y-direction displacement, and s refers to the number of filters. This refers to the X component data after noise reduction. This refers to the noise-reduced Y component data. This refers to the Z-component data after noise reduction. It refers to the filter coefficient matrix of the s filters corresponding to the non-causal predictive filter coefficients. This refers to the X component data being shifted i positions along the x-axis and j positions along the y-axis under each filter, and the data after shifting s filters. This refers to the Y component data being shifted i positions along the x-axis and j positions along the y-axis under each filter, and the data after shifting s filters. This refers to the Z component data after being moved i positions along the x-axis and j positions along the y-axis under each filter, and after being moved s filters. M refers to the total number of filters.
[0144] This application also provides a computer program product that, when executed on a data processing device, is adapted to perform a program that initializes a multi-channel joint denoising method for multi-component seismic data.
[0145] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0146] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0147] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0148] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0149] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.
[0150] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.
[0151] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.
[0152] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.
[0153] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.
Claims
1. A multi-channel joint denoising method for multi-component seismic data, characterized in that, The multi-channel joint denoising method includes: Acquire noisy three-dimensional multi-component seismic data, wherein the three-dimensional multi-component seismic data includes X-component data, Y-component data and Z-component data; The X component data, Y component data, and Z component data are transposed and offset respectively to obtain component displacement matching operators corresponding to the X component data, Y component data, and Z component data respectively; The multi-component, multi-channel noncausal predictive filter coefficients are determined based on the component displacement matching operator. The non-causal prediction filter coefficients are used to perform non-causal autoregressive prediction filtering on the three-dimensional multi-component seismic data to obtain denoised three-dimensional multi-component seismic data. The step of determining the multi-component, multi-channel noncausal prediction filter coefficients based on the component displacement matching operator includes: In the case that the three-dimensional multi-component seismic data includes a nonlinear phase axis, determine the actual values of the X-component data, Y-component data, and Z-component data included in the three-dimensional multi-component seismic data; Based on the component displacement matching operator and the actual values of the components, the three-dimensional multi-component seismic data are subjected to predictive filtering in time and space to determine the non-causal predictive filtering coefficients, wherein the non-causal predictive filtering coefficients are calculated according to formula (1): Official (1) Where x, y, and t refer to the x-axis, y-axis, and time axis of space, respectively. This refers to the length of the component displacement matching operator along the x or y direction, M refers to the total number of filters, and s refers to the number of filters. This refers to the actual values of the components of the aforementioned three-dimensional multi-component seismic data at the three-dimensional coordinates (x, y, t). This refers to the component displacement matching operator corresponding to the s-th filter at the three-dimensional coordinates (x, y, t). This refers to the non-causal predictive filter coefficient corresponding to the s-th filter at the three-dimensional coordinates (x, y, t). This refers to the positive prediction value at three-dimensional coordinates (x, y, t) obtained by multiplying and summing the non-causal predictive filter coefficients and component displacement matching operators corresponding to M filters. This refers to the inverse prediction value at the three-dimensional coordinates (x, y, t) obtained by multiplying and summing the non-causal predictive filter coefficients and component displacement matching operators corresponding to M filters. It means and Error value at three-dimensional coordinates (x, y, t).
2. The method according to claim 1, characterized in that, The method further includes: The non-causal predictive filter coefficients Determined by expression (1): Expression (1) in, for The regularization representation of the shaping, This refers to the non-causal prediction filter coefficients. Find the minimum norm.
3. The method according to claim 2, characterized in that, The non-causal predictive filter coefficients Through the filter coefficient matrix Represented as: in, This is the filter coefficient matrix of the s filters corresponding to the non-causal predictive filter coefficients. It refers to the filter coefficients corresponding to the X component data of the first channel of the s-th filter. This refers to the filter coefficients corresponding to the Y component data of the first channel of the s-th filter. This refers to the filter coefficients corresponding to the Z component data of the first channel of the s-th filter. This refers to the filter coefficients corresponding to the X component data of the second channel of the s-th filter. This refers to the filter coefficients corresponding to the Y component data of the second channel of the s-th filter. This refers to the filter coefficients corresponding to the Z component data of the second channel of the s-th filter. This refers to the filter coefficients corresponding to the X component data of the 3rd channel of the s-th filter. This refers to the filter coefficients corresponding to the Y component data of the 3rd channel of the s-th filter. It refers to the filter coefficients corresponding to the Z component data of the 3rd channel of the s-th filter.
4. The method according to claim 1, characterized in that, The step of transposing and offsetting the X component data, the Y component data, and the Z component data respectively to obtain component displacement matching operators corresponding to the X component data, the Y component data, and the Z component data respectively includes: The component displacement matching operator corresponding to the X component data is determined by formula (2): Official (2) Where x, y, and t refer to the x-axis, y-axis, and time axis of space, respectively; s refers to the number of filters; and i and j refer to the indices of the spatial displacements in the x and y directions, respectively. This refers to the X-component displacement matching operator generated after the X-component data is shifted i positions along the x-axis and j positions along the y-axis under each filter, and shifted s filters. This refers to the data after shifting the X component data by i positions along the x-axis and j positions along the y-axis under each filter, and then shifting it by s filters. This refers to the length of the component displacement matching operator along the x or y direction; The component displacement matching operator corresponding to the Y component data is determined by formula (3): Official (3) Where x, y, and t refer to the x-axis, y-axis, and time axis of space, respectively; s refers to the number of filters; and i and j refer to the indices of the spatial displacements in the x and y directions, respectively. This refers to the Y-component displacement matching operator generated after shifting the Y-component data along the x-axis by i positions and the y-axis by j positions under each filter, and after shifting by s filters. This refers to the data after shifting the Y component data by i positions along the x-axis and j positions along the y-axis under each filter, and then shifting it by s filters. This refers to the length of the component displacement matching operator along the x or y direction; The component displacement matching operator corresponding to the Z component data is determined by formula (4): Official (4) Where x, y, and t refer to the x-axis, y-axis, and time axis of space, respectively; s refers to the number of filters; and i and j refer to the indices of the spatial displacements in the x and y directions, respectively. This refers to the Z-component displacement matching operator generated after shifting the Z-component data along the x-axis by i positions and the y-axis by j positions under each filter, and after shifting by s filters. This refers to the data after shifting the Z component data by i positions along the x-axis and j positions along the y-axis under each filter, and then shifting it by s filters. It refers to the length of the component displacement matching operator along the x or y direction.
5. The method according to claim 1, characterized in that, The denoised 3D multi-component seismic data is determined by formula (5): Official (5) Where i refers to the index of the spatial x-direction displacement, j refers to the index of the spatial y-direction displacement, and s refers to the number of filters. This refers to the X component data after noise reduction. This refers to the noise-reduced Y component data. This refers to the Z-component data after noise reduction. It refers to the filter coefficient matrix of the s filters corresponding to the non-causal predictive filter coefficients. This refers to the X component data being shifted i positions along the x-axis and j positions along the y-axis under each filter, and the data after shifting s filters. This refers to the Y component data being shifted i positions along the x-axis and j positions along the y-axis under each filter, and the data after shifting s filters. This refers to the Z component data after being moved i positions along the x-axis and j positions along the y-axis under each filter, and after being moved s filters. M refers to the total number of filters.
6. A multi-channel joint denoising device for multi-component seismic data, characterized in that, The multi-channel joint noise reduction device includes: The data acquisition module is used to acquire noisy three-dimensional multi-component seismic data, wherein the three-dimensional multi-component seismic data includes X-component data, Y-component data and Z-component data; The data processing module is used to transpose and offset the X component data, the Y component data and the Z component data respectively, so as to obtain the component displacement matching operators corresponding to the X component data, the Y component data and the Z component data respectively; The coefficient determination module is used to determine the multi-component, multi-channel noncausal prediction filter coefficients based on the component displacement matching operator. The prediction filtering module is used to perform non-causal autoregressive prediction filtering on the three-dimensional multi-component seismic data using the non-causal prediction filtering coefficients to obtain denoised three-dimensional multi-component seismic data. The step of determining the multi-component, multi-channel noncausal prediction filter coefficients based on the component displacement matching operator includes: In the case that the three-dimensional multi-component seismic data includes a nonlinear phase axis, determine the actual values of the X-component data, Y-component data, and Z-component data included in the three-dimensional multi-component seismic data; Based on the component displacement matching operator and the actual values of the components, the three-dimensional multi-component seismic data are subjected to predictive filtering in time and space to determine the non-causal predictive filtering coefficients, wherein the non-causal predictive filtering coefficients are calculated according to formula (1): Official (1) Where x, y, and t refer to the x-axis, y-axis, and time axis of space, respectively. This refers to the length of the component displacement matching operator along the x or y direction, M refers to the total number of filters, and s refers to the number of filters. This refers to the actual values of the components of the aforementioned three-dimensional multi-component seismic data at the three-dimensional coordinates (x, y, t). This refers to the component displacement matching operator corresponding to the s-th filter at the three-dimensional coordinates (x, y, t). This refers to the non-causal predictive filter coefficient corresponding to the s-th filter at the three-dimensional coordinates (x, y, t). This refers to the positive prediction value at three-dimensional coordinates (x, y, t) obtained by multiplying and summing the non-causal predictive filter coefficients and component displacement matching operators corresponding to M filters. This refers to the inverse prediction value at the three-dimensional coordinates (x, y, t) obtained by multiplying and summing the non-causal predictive filter coefficients and component displacement matching operators corresponding to M filters. It means and Error value at three-dimensional coordinates (x, y, t).
7. A processor, characterized in that, It is configured to perform the multi-channel joint denoising method for multi-component seismic data as described in any one of claims 1 to 5.
8. A machine-readable storage medium storing instructions thereon, characterized in that, When executed by a processor, this instruction causes the processor to be configured to perform a multi-channel joint denoising method for multi-component seismic data according to any one of claims 1 to 5.