CN111324598A - Denoising method and device for microseismic record - Google Patents
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Abstract
The invention discloses a denoising method and a denoising device for microseismic recording. Wherein, the method comprises the following steps: acquiring microseismic records of seismic channels, selecting a reference block from microseismic data in the microseismic records, and searching in a preset range of the reference block to obtain a data block, wherein the reference block is microseismic data selected from the microseismic data according to a preset specification; integrating the data block and the reference block to obtain a three-dimensional matrix; determining a two-dimensional matrix corresponding to the three-dimensional matrix, and performing singular value decomposition on the two-dimensional matrix to obtain singular values and a decomposition matrix, wherein the singular values are multiple; reconstructing the decomposition matrix, and obtaining a filtering factor according to the reconstructed decomposition matrix; and denoising the microseismic data in the microseismic record according to the filtering factor. The invention solves the technical problems that the traditional denoising method is not ideal for denoising the microseismic data and cannot reach the purpose of highlighting effective signals in the related technology.
Description
Technical Field
The invention relates to the field of microseism monitoring, in particular to a denoising method and device for microseism records.
Background
In the process of processing the microseismic data, denoising the microseismic data is an intuitive and important step in the whole process flow of processing the microseismic data. However, for the microseismic data received on the ground, the energy of the effective signal is weak, the effective signal is completely submerged in the noise, and the signal-to-noise ratio of the microseismic profile is extremely low. The traditional denoising method usually requires data with a high signal-to-noise ratio, so that an ideal denoising effect is difficult to obtain by the traditional denoising method.
Aiming at the problems that the traditional denoising method is not ideal for denoising the microseismic data in the related technology and cannot reach the purpose of highlighting effective signals, an effective solution is not provided at present.
Disclosure of Invention
The embodiment of the invention provides a denoising method and a denoising device for microseismic recording, which at least solve the technical problems that the denoising of microseismic data by using a traditional denoising method in the related art is not ideal and effective signals cannot be highlighted.
According to an aspect of the embodiments of the present invention, there is provided a method for denoising a microseismic record, including: acquiring microseismic records of seismic channels, selecting a reference block from microseismic data in the microseismic records, and searching in a preset range of the reference block to obtain a data block, wherein the reference block is the microseismic data selected from the microseismic data according to a preset specification; integrating the data block and the reference block to obtain a three-dimensional matrix; determining a two-dimensional matrix corresponding to the three-dimensional matrix, and performing singular value decomposition on the two-dimensional matrix to obtain singular values and a decomposition matrix, wherein the singular values are multiple; reconstructing the decomposition matrix, and obtaining a filtering factor according to the reconstructed decomposition matrix; and denoising the microseismic data in the microseismic record according to the filtering factor.
Optionally, integrating the data block and the reference block to obtain a three-dimensional matrix includes: determining a selection range of the data block based on the reference block; searching in the preset range of the reference block in the selection range to obtain a data block with the difference degree smaller than a preset threshold value; and integrating the data blocks, and simultaneously integrating the reference blocks to obtain the three-dimensional matrix.
Optionally, performing singular value decomposition on the two-dimensional matrix to obtain singular values includes: performing singular value decomposition on the two-dimensional matrix through a first formula, wherein the first formula is as follows:
a represents the two-dimensional matrix, U represents a first orthogonal matrix, and the two-dimensional matrix A and a transpose matrix A of the two-dimensional matrixTForming a feature vector obtained by the multiplication; v represents a second orthogonal matrix formed by the transpose A of the two-dimensional matrixT∑ is the characteristic vector obtained by the product of the two-dimensional matrix A and the transpose matrix A of the two-dimensional matrix ATOr the transposed matrix A of said two-dimensional matrixTDetermining a singular value sigma by multiplying the two-dimensional matrix AiDiagonal matrices, σ, formed in descending orderiDenotes the ith singular value, i being a positive integer.
Optionally, the diagonal matrix ∑ is Σ diag (σ)1,σ2,…σr) Wherein r represents a rank of the two-dimensional matrix A,ρ represents a singular value.
Optionally, the two-dimensional matrix a and the transpose of the two-dimensional matrix aTSingular values determined by the product of (a) or a transposed matrix A of said two-dimensional matrixTDetermining a singular value sigma by multiplying the two-dimensional matrix AiAnd a characteristic value lambdaiThe corresponding relation is as follows:
optionally, reconstructing the decomposition matrix comprises: selecting partial singular values positioned in the middle position in singular values obtained by decomposition; and reconstructing the decomposition matrix by using the partial singular values.
Optionally, obtaining the filter factor according to the reconstructed decomposition matrix includes: and determining the filtering factor according to the two-dimensional matrix and the two-dimensional matrix obtained by reconstruction.
According to another aspect of the embodiments of the present invention, there is provided a denoising device for microseismic recording, including: the selection unit is used for acquiring microseismic records of seismic channels, selecting a reference block from the microseismic data in the microseismic records, and searching in a preset range of the reference block to obtain a data block, wherein the reference block is the microseismic data selected from the microseismic data according to a preset specification; the integration unit is used for integrating the data block and the reference block to obtain a three-dimensional matrix; the decomposition unit is used for determining a two-dimensional matrix corresponding to the three-dimensional matrix and performing singular value decomposition on the two-dimensional matrix to obtain singular values and a decomposition matrix, wherein the singular values are multiple; the reconstruction unit is used for reconstructing the decomposition matrix and obtaining a filtering factor according to the reconstructed decomposition matrix; and the denoising unit is used for denoising the microseismic data in the microseismic record according to the filtering factor.
Optionally, the integration unit comprises: a first determining module, configured to determine a selection range of the data block based on the reference block; the searching module is used for searching in the preset range of the reference block in the selection range to obtain a data block with the difference degree smaller than a preset threshold value; and the integration module is used for integrating the data block and integrating the reference block to obtain the three-dimensional matrix.
Optionally, the decomposition unit comprises: a decomposition module, configured to perform singular value decomposition on the two-dimensional matrix through a first formula, where the first formula is:
a represents the two-dimensional matrix, U represents a first orthogonal matrix, and the two-dimensional matrix A and a transpose matrix A of the two-dimensional matrixTForming a feature vector obtained by the multiplication; v represents a second orthogonal matrix formed by the transpose A of the two-dimensional matrixT∑ is the characteristic vector obtained by the product of the two-dimensional matrix A and the transpose matrix A of the two-dimensional matrix ATOr the transposed matrix A of said two-dimensional matrixTDetermining a singular value sigma by multiplying the two-dimensional matrix AiDiagonal matrices, σ, formed in descending orderiDenotes the ith singular value, i being a positive integer.
Optionally, the diagonal matrix ∑ is Σ diag (σ)1,σ2,…σr) Where r represents the rank of the two-dimensional matrix a and ρ represents a singular value.
Optionally, the two-dimensional matrix a and the transpose of the two-dimensional matrix aTSingular values determined by the product of (a) or a transposed matrix A of said two-dimensional matrixTDetermining a singular value sigma by multiplying the two-dimensional matrix AiAnd a characteristic value lambdaiThe corresponding relation is as follows:
optionally, the reconstruction unit comprises: the selecting module is used for selecting partial singular values positioned in the middle position in the singular values obtained by decomposition; and the reconstruction module is used for reconstructing the decomposition matrix by using the partial singular values.
Optionally, the reconstruction unit comprises: and the second determining module is used for determining the filtering factor according to the two-dimensional matrix and the two-dimensional matrix obtained by reconstruction.
According to another aspect of the embodiments of the present invention, there is also provided a storage medium including a stored program, wherein the program executes the denoising method for microseismic recording described in any one of the above.
According to another aspect of the embodiment of the present invention, there is provided a processor, configured to run a program, where the program is executed to perform the method for denoising a microseismic recording described in any one of the above.
In the embodiment of the invention, microseismic records of an acquired seismic channel are adopted, a reference block is selected from microseismic data in the microseismic records, and searching is carried out in a preset range of the reference block to obtain a data block, wherein the reference block is the microseismic data selected from the microseismic data according to a preset specification; integrating the data block and the reference block to obtain a three-dimensional matrix; determining a two-dimensional matrix corresponding to the three-dimensional matrix, and performing singular value decomposition on the two-dimensional matrix to obtain singular values and a decomposition matrix, wherein the singular values are multiple; reconstructing the decomposition matrix, and obtaining a filtering factor according to the reconstructed decomposition matrix; and denoising the microseismic data in a mode of denoising the microseismic data in the microseismic record according to the filter factor. Compared with the method for denoising the microseismic record in the related technology, the method for denoising the microseismic record has higher signal-to-noise ratio and causes the defect that the traditional denoising method is difficult to obtain the ideal denoising effect.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a flow chart of a method of denoising microseismic recordings according to an embodiment of the invention;
FIG. 2 is an original cross-sectional view of a microseismic recording according to an embodiment of the present invention;
FIG. 3 is a cross-sectional view of a reconstructed image after denoising in BM3D-SVD according to an embodiment of the present invention;
FIG. 4 is a flow chart of a method of denoising microseismic recordings in accordance with an embodiment of the present invention; and
FIG. 5 is a schematic diagram of a denoising apparatus for microseismic recording according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
For convenience of description, some nouns or terms appearing in the embodiments of the present invention will be described in detail below.
Signal-to-noise ratio: also known as signal to noise ratio, refers to the ratio of signal to noise in an electronic device or system.
Seismic channel: the seismic waves are recorded at each observation point and must pass through three basic links of a geophone, an amplifying system and a recording system, and the three basic links are connected together and are generally called as a seismic trace. The device mainly means that in order to record the seismic reflection waves or the refraction waves propagated to the seismic measuring points, the reflection waves or the refraction waves propagated to each measuring point need to pass through a set of independent devices with the same performance, and the device mainly comprises a wave detector, an amplifier, a galvanometer and the like.
Three-dimensional block matching: the image denoising algorithm has good effect. And matching with adjacent image blocks, integrating a plurality of similar blocks into a three-dimensional matrix, filtering in a three-dimensional space, and performing inverse transformation and fusion on a result to two dimensions to form a denoised image.
Singular value decomposition: the method is an important matrix decomposition in linear algebra and is the popularization of regular-matrix unitary diagonalization in matrix analysis.
Wiener filtering: the method is an optimal estimator for the stationary process based on the minimum mean square error criterion. The mean square error between the output of such a filter and the desired output is minimal and therefore it is an optimal filtering system for extracting signals contaminated by stationary noise.
Example 1
In accordance with an embodiment of the present invention, there is provided a method embodiment of a method for denoising microseismic recordings, it is noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions, and that while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than here.
Fig. 1 is a flowchart of a method for denoising a microseismic recording according to an embodiment of the present invention, and as shown in fig. 1, the method for denoising a microseismic recording includes the following steps:
step S102, collecting microseismic records of seismic channels, selecting a reference block from the microseismic data in the microseismic records, and searching in a preset range of the reference block to obtain a data block, wherein the reference block is the microseismic data selected from the microseismic data according to a preset specification.
Where microseismic recording is the recording of microseismic signals emitted by microseismic events. In addition, the recording of seismic waves generated by microseismic events at each observation point must be performed through three basic links of a geophone, an amplification system and a recording system, which are connected together to form a seismic trace, wherein the seismic trace is composed of energy in a certain frequency range, and the seismic trace data is composed of a series of seismic waveform traces, and each seismic trace is a one-dimensional signal trace.
In step S102, a three-dimensional block matching BM 3D-singular value decomposition SVD microseismic record (microseismic data) denoising method is adopted, and the original data (i.e., microseismic record) is a single-channel microseismic record x (n) with n sampling points. Fig. 2 is an original cross-sectional view of microseismic recording according to an embodiment of the present invention, and it can be seen from fig. 2 that the noise energy of microseismic recording is strong, and the noise types are various, including periodic noise with strong regularity, random noise without clutter, and the signal-to-noise ratio of data is low.
And step S104, integrating the data block and the reference block to obtain a three-dimensional matrix.
And S106, determining a two-dimensional matrix corresponding to the three-dimensional matrix, and performing singular value decomposition on the two-dimensional matrix to obtain singular values and a decomposition matrix, wherein the singular values are multiple.
And S108, reconstructing the decomposition matrix, and obtaining a filter factor according to the reconstructed decomposition matrix.
And step S110, denoising the microseismic data in the microseismic record according to the filtering factor.
Through the steps, microseismic records of seismic channels can be collected, a reference block is selected from the microseismic data in the microseismic records, and searching is carried out in a preset range of the reference block to obtain a data block, wherein the reference block is the microseismic data selected from the microseismic data according to a preset specification; integrating the data block and the reference block to obtain a three-dimensional matrix; determining a two-dimensional matrix corresponding to the three-dimensional matrix, and performing singular value decomposition on the two-dimensional matrix to obtain singular values and a decomposition matrix, wherein the singular values are multiple; reconstructing the decomposition matrix, and obtaining a filtering factor according to the reconstructed decomposition matrix; and denoising the microseismic data in the microseismic record according to the filtering factor. Compared with the method for denoising the microseismic record in the related technology, the method for denoising the microseismic record has higher signal-to-noise ratio and causes the defect that the traditional denoising method is difficult to obtain the ideal denoising effect.
In step S104, integrating the data block and the reference block to obtain a three-dimensional matrix may include: determining a selection range of the data block based on the reference block; searching in a preset range of the reference block in the selection range to obtain a data block with the difference degree smaller than a preset threshold value; and integrating the data blocks, and integrating the reference blocks to obtain a three-dimensional matrix.
For example, some reference blocks with k × k size may be selected from x (n), a search is performed in a region with an appropriate size n × n around the reference block, several data blocks with the minimum difference are found, and the databases are integrated into a three-dimensional matrix, and the result is not affected by the integration order. The reference block itself is also integrated into a three-dimensional matrix, and the degree of difference is 0, so that matrix data a is obtained.
In step S106, performing singular value decomposition on the two-dimensional matrix, and obtaining singular values may include: singular value decomposition is performed on the two-dimensional matrix by a first formula, wherein,the first formula is:
a represents a two-dimensional matrix, U represents a first orthogonal matrix, and is composed of the two-dimensional matrix A and a transposed matrix A of the two-dimensional matrixTForming a feature vector obtained by the multiplication; v denotes a second orthogonal matrix formed by the transpose A of the two-dimensional matrixT∑ is composed of two-dimensional matrix A and transposed matrix A of the two-dimensional matrixTSingular values determined by multiplication of (A), or transpose matrix A of two-dimensional matrixTDetermination of singular values sigma by multiplication with a two-dimensional matrix AiDiagonal matrices, σ, formed in descending orderiDenotes the ith singular value, i being a positive integer.
For example, after obtaining the three-dimensional matrix according to step S104, the three-dimensional matrix may be expanded to obtain a two-dimensional matrix (a plurality of sections are formed, the arrangement does not affect the final processing result, and the two-dimensional matrix is still represented by a), and then the singular value decomposition SVD may be performed on the two-dimensional matrix a.
The diagonal matrix ∑ is Σ ═ diag (σ ═ σ { (σ })1,σ2,…σr) Where r represents the rank of the two-dimensional matrix a and ρ represents a singular value.
In addition, the two-dimensional matrix a and the transposed matrix a of the two-dimensional matrixTA transposed matrix A of singular values or two-dimensional matrices determined by the product ofTDetermination of singular values sigma by multiplication with a two-dimensional matrix AiAnd a characteristic valueλ iThe corresponding relation is as follows:
and, satisfy σ1≥σ2≥…≥σr≥0。
In the step S108, reconstructing the decomposition matrix may include: selecting partial singular values positioned in the middle position in singular values obtained by decomposition; and reconstructing the decomposition matrix by using partial singular values.
For example, the decomposition matrix is reconstructed by selecting appropriate singular values, the main purpose here beingPeriodic noise with strong correlation and random noise with weak correlation are removed, so that singular values of the middle part are selected to reconstruct the decomposition matrix. That is to say that the first and second electrodes,
at this time, each row corresponding to a' is the reconstructed block denoising data.
Fig. 3 is a reconstructed cross-sectional view after denoising by adopting the BM3D-SVD method according to the embodiment of the present invention, and it can be seen from the reconstruction effect that various noises are suppressed to different degrees compared with the original cross-section, although the local noise interference is still serious, the signal-to-noise ratio of the data is improved to a greater extent, which also indicates that BM3D-SVD denoising is a denoising method suitable for microseismic data.
In the step S110, obtaining the filter factor according to the reconstructed decomposition matrix may include: and determining a filtering factor according to the two-dimensional matrix and the two-dimensional matrix obtained by reconstruction. For example, taking a two-dimensional matrix a as input data and a 'as output data, involving a wiener filter, an optimal filter factor in the least-squares sense is obtained that can smooth the two-dimensional matrix a to a', a 'and a' that are similar but not equal. The two-dimensional matrix corresponding to the A' data is an estimation of the denoised data, and the denoised data is obtained by fusing the two-dimensional matrix to the original position. The amplitude value of each point of the de-noised data during the fusion is determined by the correlation coefficient of the current data block and the reference block, the maximum is 1, and the minimum is 0.
The following describes in detail a denoising method for microseismic recording in an embodiment of the present invention with reference to the accompanying drawings. FIG. 4 is a flowchart of a method for denoising microseismic records according to an embodiment of the present invention, as shown in FIG. 4, inputting cannon data (i.e., cannon microseismic data), wherein the BM3D-SVD denoising method is based on single-shot microseismic records; selecting a reference block, wherein the position of the reference block is artificially determined, the selected reference block is the data position with typical noise in the microseismic data, the size of the reference block is usually 5 x 5, and the appropriate increase can improve the denoising capability but increase the calculation amount; determining data blocks according to the selected reference blocks, specifically, calculating the correlation by matching the data with the same size of the reference blocks, and determining the database which finally participates in calculation, wherein the automatically selected data blocks are all data with good correlation; performing SVD, wherein a conventional SVD method is used, or other improved SVD methods can be adopted, and the purpose of decomposition is to obtain a diagonal matrix (singular value) capable of representing the signal correlation distribution rule; at the same time, the intermediate singular values are retained. The selection range of the singular values can be designed according to the characteristics of the noise to be removed. Relevant noise and random noise are removed, so that a matrix is reconstructed by selecting singular values of the middle part, and the purposes of removing signals with extremely strong coherence and signals with extremely weak coherence are achieved; reconstructing the decomposition matrix by using partial singular values; designing a wiener filter, calculating a wiener filter factor for data before and after SVD denoising, and optimizing the data in the least square sense by the wiener filter in order to ensure the stability of the data; the amplitude of the denoised data is determined according to the correlation direction of the current data block and the reference block data, the larger the correlation is, the larger the fusion amplitude is, and the worse the correlation is, the smaller the fusion amplitude is. The maximum amplitude coefficient is 1 and the minimum amplitude coefficient is 0.
The denoising method for the microseismic record provided by the embodiment of the invention can suppress the interference in the microseismic data by utilizing BM3D (three-dimensional block matching filtering). Conventional three-dimensional block matched filtering can be divided into two steps: the first step is the basic estimation and the second step is the final estimation. Each step can be divided into three small steps which are respectively as follows: grouping similar blocks, collaborative filtering and aggregation. As the core of the BM3D method, the synergistic filtering effect directly influences the final denoising result. In the embodiment of the invention, the SVD method is applied to the collaborative filtering step, and the block data is subjected to SVD conversion, so that the information with high correlation is reserved, and the denoising of the block data is realized. Practical data experiments prove that the denoising effect of the partitioned data by using SVD (singular value decomposition) transformation is superior to that of the conventional threshold denoising method.
It should be noted that the core of the denoising method for microseismic recording provided by the embodiment of the present invention includes: three-dimensional block matched filtering (BM3D) and Singular Value Decomposition (SVD). The three-dimensional block matching filtering (BM3D) is a new method for processing nonlinear non-stationary signals developed in recent years, combines the advantages of non-local mean and transform domain denoising, and has good adaptivity to data. Singular Value Decomposition (SVD) is essentially an orthogonal decomposition method that can exploit signal correlation in two-dimensional space to enhance the effective signal and suppress uncorrelated noise, thereby improving the signal-to-noise ratio of seismic data. In consideration of the limitation of incomplete denoising of BM3D, the method of combining SVD and BM3D to denoise the microseismic data can more effectively remove the noise of the microseismic data and highlight effective signals. The method for denoising the microseismic data based on BM3D and SVD better solves the problem of denoising the microseismic data by the traditional denoising method, and meets the requirement of industrial production.
Example 2
The embodiment of the invention also provides a denoising device for the microseismic record, and it should be noted that the denoising device for the microseismic record of the embodiment of the invention can be used for executing the denoising method for the microseismic record provided by the embodiment of the invention. The following introduces a denoising device for microseismic recording provided by the embodiment of the invention.
Fig. 5 is a schematic diagram of a denoising apparatus for microseismic recording according to an embodiment of the present invention, as shown in fig. 5, the denoising apparatus for microseismic recording includes: the device comprises a selection unit 51, an integration unit 53, a decomposition unit 55, a reconstruction unit 57 and a denoising unit 59. The denoising apparatus for the microseismic recording will be described in detail below.
The selection unit 51 is configured to acquire microseismic records of a seismic channel, select a reference block from microseismic data in the microseismic records, and search in a predetermined range of the reference block to obtain a data block, where the reference block is microseismic data selected from the microseismic data according to a predetermined specification.
And an integration unit 53, connected to the selection unit 51, for integrating the data block and the reference block to obtain a three-dimensional matrix.
And the decomposition unit 55 is connected to the integration unit 53, and is configured to determine a two-dimensional matrix corresponding to the three-dimensional matrix, and perform singular value decomposition on the two-dimensional matrix to obtain singular values and a decomposition matrix, where the singular values are multiple.
And the reconstruction unit 57 is connected with the decomposition unit 55 and is configured to reconstruct the decomposition matrix and obtain a filter factor according to the reconstructed decomposition matrix.
And the denoising unit 59 is connected with the reconstruction unit 57 and is used for denoising the microseismic data in the microseismic record according to the filter factor.
It should be noted that the selecting unit 51 in this embodiment may be configured to execute the step S102 in this embodiment, the integrating unit 53 in this embodiment may be configured to execute the step S104 in this embodiment, the decomposing unit 55 in this embodiment may be configured to execute the step S106 in this embodiment, the reconstructing unit 57 in this embodiment may be configured to execute the step S108 in this embodiment, and the denoising unit 59 in this embodiment may be configured to execute the step S110 in this embodiment. The modules are the same as the corresponding steps in the realized examples and application scenarios, but are not limited to the disclosure of the above embodiments.
In the embodiment of the present invention, the selection unit 51 may be used to collect microseismic records of seismic traces, select a reference block from the microseismic data in the microseismic records, and search in a predetermined range of the reference block to obtain a data block, wherein the reference block is selected from the microseismic data according to a predetermined specification; then, integrating the data block and the reference block by using an integration unit 53 to obtain a three-dimensional matrix; determining a two-dimensional matrix corresponding to the three-dimensional matrix by using a decomposition unit 55, and performing singular value decomposition on the two-dimensional matrix to obtain singular values and decomposition matrices, wherein the singular values are multiple; meanwhile, the reconstruction unit 57 is used for reconstructing the decomposition matrix and obtaining a filter factor according to the reconstructed decomposition matrix; and de-noising the microseismic data in the microseismic record by using a de-noising unit 59 according to the filtering factor. Compared with the method for denoising the microseismic record in the related technology, the method for denoising the microseismic record has higher signal-to-noise ratio and causes the defect that the traditional denoising method is difficult to obtain the ideal denoising effect.
As an alternative embodiment, the integration unit may include: the first determining module is used for determining the selection range of the data block based on the reference block; the searching module is used for searching in the preset range of the reference block in the selected range to obtain a data block with the difference degree smaller than a preset threshold value; and the integration module is used for integrating the data blocks and integrating the reference blocks to obtain the three-dimensional matrix.
As an alternative embodiment, the decomposition unit comprises: the decomposition module is used for performing singular value decomposition on the two-dimensional matrix through a first formula, wherein the first formula is as follows:
a represents a two-dimensional matrix, U represents a first orthogonal matrix, and is composed of the two-dimensional matrix A and a transposed matrix A of the two-dimensional matrixTForming a feature vector obtained by the multiplication; v denotes a second orthogonal matrix formed by the transpose A of the two-dimensional matrixT∑ is composed of two-dimensional matrix A and transposed matrix A of the two-dimensional matrixTSingular values determined by multiplication of (A), or transpose matrix A of two-dimensional matrixTDetermination of singular values sigma by multiplication with a two-dimensional matrix AiDiagonal matrices, σ, formed in descending orderiDenotes the ith singular value, i being a positive integer.
As an alternative embodiment, the diagonal matrix ∑ is Σ ═ diag (σ)1,σ2,…σr) Where r represents the rank of the two-dimensional matrix a and ρ represents a singular value.
As an alternative embodiment, the two-dimensional matrix a and the transpose matrix a of the two-dimensional matrix are described aboveTA transposed matrix A of singular values or two-dimensional matrices determined by the product ofTDetermination of singular values sigma by multiplication with a two-dimensional matrix AiAnd a characteristic value lambdaiThe corresponding relation is as follows:
as an alternative embodiment, the above reconstruction unit may include: the selecting module is used for selecting partial singular values positioned in the middle position in the singular values obtained by decomposition; and the reconstruction module is used for reconstructing the decomposition matrix by using part of singular values.
As an alternative embodiment, the above reconstruction unit may include: and the second determining module is used for determining a filtering factor according to the two-dimensional matrix and the two-dimensional matrix obtained by reconstruction.
The information prompting device comprises a selection unit 51, an integration unit 53, a decomposition unit 55, a reconstruction unit 57, a denoising unit 59 and the like which are used as program units to jointly execute the denoising function.
The embodiment of the present invention further provides an apparatus, which includes a processor, a memory, and a program stored in the memory and executable on the processor, and when the processor executes the program, the following steps are implemented: acquiring microseismic records of seismic channels, selecting a reference block from microseismic data in the microseismic records, and searching in a preset range of the reference block to obtain a data block, wherein the reference block is microseismic data selected from the microseismic data according to a preset specification; integrating the data block and the reference block to obtain a three-dimensional matrix; determining a two-dimensional matrix corresponding to the three-dimensional matrix, and performing singular value decomposition on the two-dimensional matrix to obtain singular values and a decomposition matrix, wherein the singular values are multiple; reconstructing the decomposition matrix, and obtaining a filtering factor according to the reconstructed decomposition matrix; and denoising the microseismic data in the microseismic record according to the filtering factor.
There is also provided in an embodiment of the invention a computer program product adapted to perform a program for initializing the following method steps when executed on a data processing device: acquiring microseismic records of seismic channels, selecting a reference block from microseismic data in the microseismic records, and searching in a preset range of the reference block to obtain a data block, wherein the reference block is microseismic data selected from the microseismic data according to a preset specification; integrating the data block and the reference block to obtain a three-dimensional matrix; determining a two-dimensional matrix corresponding to the three-dimensional matrix, and performing singular value decomposition on the two-dimensional matrix to obtain singular values and a decomposition matrix, wherein the singular values are multiple; reconstructing the decomposition matrix, and obtaining a filtering factor according to the reconstructed decomposition matrix; and denoising the microseismic data in the microseismic record according to the filtering factor.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A method for denoising a microseismic recording, comprising:
acquiring microseismic records of seismic channels, selecting a reference block from microseismic data in the microseismic records, and searching in a preset range of the reference block to obtain a data block, wherein the reference block is the microseismic data selected from the microseismic data according to a preset specification;
integrating the data block and the reference block to obtain a three-dimensional matrix;
determining a two-dimensional matrix corresponding to the three-dimensional matrix, and performing singular value decomposition on the two-dimensional matrix to obtain singular values and a decomposition matrix, wherein the singular values are multiple;
reconstructing the decomposition matrix, and obtaining a filtering factor according to the reconstructed decomposition matrix;
and denoising the microseismic data in the microseismic record according to the filtering factor.
2. The method of claim 1, wherein integrating the data block and the reference block to obtain a three-dimensional matrix comprises:
determining a selection range of the data block based on the reference block;
searching in the preset range of the reference block in the selection range to obtain a data block with the difference degree smaller than a preset threshold value;
and integrating the data blocks, and simultaneously integrating the reference blocks to obtain the three-dimensional matrix.
3. The method of claim 1, wherein performing a singular value decomposition on the two-dimensional matrix to obtain singular values comprises:
performing singular value decomposition on the two-dimensional matrix through a first formula, wherein the first formula is as follows:
a represents the two-dimensional matrix, U represents a first orthogonal matrix, and the two-dimensional matrix A and a transpose matrix A of the two-dimensional matrixTForming a feature vector obtained by the multiplication; v represents a second orthogonal matrix formed by the transpose A of the two-dimensional matrixT∑ is the characteristic vector obtained by the product of the two-dimensional matrix A and the transpose matrix A of the two-dimensional matrix ATOr the transposed matrix A of said two-dimensional matrixTDetermining a singular value sigma by multiplying the two-dimensional matrix AiDiagonal matrices, σ, formed in descending orderiDenotes the ith singular value, i being a positive integer.
4. The method of claim 3, wherein the diagonal matrix ∑ is Σ diag (σ)1,σ2,…σr) Where r represents the rank of the two-dimensional matrix a and ρ represents a singular value.
5. The method of claim 4, wherein the two-dimensional matrix A and the transpose of the two-dimensional matrix A areTSingular values determined by the product of (a) or a transposed matrix A of said two-dimensional matrixTDetermining a singular value sigma by multiplying the two-dimensional matrix AiAnd a characteristic value lambdaiThe corresponding relation is as follows:
6. the method of claim 5, wherein reconstructing the decomposition matrix comprises:
selecting partial singular values positioned in the middle position in singular values obtained by decomposition;
and reconstructing the decomposition matrix by using the partial singular values.
7. The method of claim 6, wherein deriving the filter factor from the reconstructed decomposition matrix comprises: and determining the filtering factor according to the two-dimensional matrix and the two-dimensional matrix obtained by reconstruction.
8. A denoising apparatus for microseismic recording, comprising:
the selection unit is used for acquiring microseismic records of seismic channels, selecting a reference block from the microseismic data in the microseismic records, and searching in a preset range of the reference block to obtain a data block, wherein the reference block is the microseismic data selected from the microseismic data according to a preset specification;
the integration unit is used for integrating the data block and the reference block to obtain a three-dimensional matrix;
the decomposition unit is used for determining a two-dimensional matrix corresponding to the three-dimensional matrix and performing singular value decomposition on the two-dimensional matrix to obtain singular values and a decomposition matrix, wherein the singular values are multiple;
the reconstruction unit is used for reconstructing the decomposition matrix and obtaining a filtering factor according to the reconstructed decomposition matrix;
and the denoising unit is used for denoising the microseismic data in the microseismic record according to the filtering factor.
9. A storage medium comprising a stored program, wherein the program performs the method of denoising a microseismic recording of any of claims 1-7.
10. A processor, wherein the processor is configured to run a program, wherein the program is configured to perform the method for denoising a microseismic recording of any of claims 1-7 when running.
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| GB201301190D0 (en) * | 2012-01-31 | 2013-03-06 | Cggveritas Services Sa | Method and apparatus for processing seismic data |
| CN105607125A (en) * | 2016-01-15 | 2016-05-25 | 吉林大学 | Seismic data noise suppression method based on block matching algorithm and singular value decompression |
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| GB201301190D0 (en) * | 2012-01-31 | 2013-03-06 | Cggveritas Services Sa | Method and apparatus for processing seismic data |
| CN105607125A (en) * | 2016-01-15 | 2016-05-25 | 吉林大学 | Seismic data noise suppression method based on block matching algorithm and singular value decompression |
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