CN113985481B - Variable-division mode noise reduction method and device based on re-constraint - Google Patents
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Abstract
The invention provides a variation modal noise reduction method and device based on re-constraint, wherein the method comprises the following steps: acquiring original microseismic data, wherein the original microseismic data comprises a horizontal component and a vertical component; establishing a re-constraint relation between the horizontal component and the vertical component, and establishing a variation modal decomposition model based on the re-constraint relation; decomposing the horizontal component and the vertical component through a variation mode decomposition model to obtain a plurality of target horizontal variation mode components and a plurality of target vertical variation mode components; reconstructing the plurality of target horizontal variation modal components and the plurality of target vertical variation modal components to obtain noise-reduced target microseismic data. The invention restores the amplitude information of the horizontal component by establishing the re-constraint relation between the horizontal component and the vertical component, namely re-constraining the modal decomposition of the horizontal component by the vertical component with good signal to noise ratio, thereby realizing effective denoising and improving the noise reduction effect of the variation modal noise reduction method.
Description
Technical Field
The invention relates to the technical field of microseismic data processing, in particular to a variation modal noise reduction method and device based on re-constraint.
Background
When the rock breaks, microseism is induced to form microseism data, and noise pollution under the mine is serious, so that the microseism data contains a large amount of external noise, and effective microseism signals are required to be separated from the noise, so that the reliability of microseism data analysis is improved.
The conventional noise reduction methods for microseismic data comprise Empirical Mode Decomposition (EMD), integrated empirical mode decomposition (EEMD), wavelet analysis and the like, and the methods have the advantages of low operation speed, poor noise resistance, high misjudgment rate, low pickup precision and weak algorithm instantaneity. Thus, a Variational Modal Decomposition (VMD) noise reduction method is presented.
However, the existing variation mode decomposition noise reduction methods are all to directly use the variation mode decomposition method to remove noise, and the variation mode decomposition result is selectively reconstructed or the decomposition mode is selectively reconstructed after being reprocessed, and the variation mode decomposition itself is not improved, so that the final noise removal can be influenced by the decomposition effect. Namely: the existing variation mode noise reduction method has poor noise reduction effect.
Disclosure of Invention
In view of the foregoing, it is necessary to provide a method and a device for reducing noise in a variation mode based on re-constraint, so as to solve the technical problem of poor noise reduction effect of the variation mode noise reduction method in the prior art.
In order to solve the technical problems, the invention provides a variation mode noise reduction method based on re-constraint, which comprises the following steps:
acquiring original microseismic data, wherein the original microseismic data comprises a horizontal component and a vertical component;
establishing a re-constraint relation between the horizontal component and the vertical component, and establishing a variation modal decomposition model based on the re-constraint relation;
decomposing the horizontal component and the vertical component through the variation modal decomposition model to obtain a plurality of target horizontal variation modal components and a plurality of target vertical variation modal components;
reconstructing the plurality of target horizontal variation modal components and the plurality of target vertical variation modal components to obtain noise-reduced target microseismic data.
In some possible implementations, the variant modal decomposition model is:
a k ·v k (t)＝u k (t)
wherein u is k (t) is the kth target level variation modal component in the time domain; omega k Center frequencies of modal components are varied for a kth target level; k is the total number of target horizontal variation modal components; delta (t) is DiraA gram function; * Representing a convolution operation; j represents an imaginary part; t represents time; f (t) is the original microseismic data; v k (t) is the kth target vertical variation modal component in the time domain; a, a k The constraint coefficient between the kth target horizontal variation modal component in the time domain and the kth target vertical variation modal component in the time domain;representing the bias to t; i 2 Representing a binary norm.
In some possible implementations, the decomposing the horizontal component and the vertical component by the variation modal decomposition model to obtain a plurality of target horizontal variation modal components and a plurality of target vertical variation modal components includes:
performing Fourier transformation on the horizontal component and the vertical component respectively to obtain a horizontal component Fourier transformation result and a vertical component Fourier transformation result;
decomposing the horizontal component Fourier transform result through a variation modal decomposition model to obtain a plurality of target horizontal variation modal components;
and decomposing the vertical component Fourier transform result through a variation modal decomposition model to obtain a plurality of target vertical variation modal components.
In some possible implementations, the decomposing the horizontal component fourier transform result by the variational modal decomposition model to obtain a plurality of target horizontal variational modal components includes:
step S11, determining convergence parameters of the horizontal component Fourier transform result and an initial horizontal component Fourier transform result;
step S12, updating the Fourier transform result of the initial horizontal component according to a horizontal variation modal updating formula to obtain an updated horizontal variation modal component;
step S13, judging whether the updated horizontal variation modal component meets a circulation stop condition according to the convergence parameter, and if the updated horizontal variation modal component meets the circulation stop condition, the updated horizontal variation modal component is the target horizontal variation modal component; and if the updated horizontal variation modal component does not meet the cycle stop condition, taking the updated horizontal variation modal component as an initial horizontal component Fourier transform result, and repeating the step S12 and the step S13.
In some possible implementations, the decomposing the vertical component fourier transform result by the variational modal decomposition model to obtain a plurality of target vertical variational modal components includes:
step S21, determining convergence parameters of the vertical component Fourier transform result and an initial vertical component Fourier transform result;
s22, updating the Fourier transform result of the initial vertical component according to a vertical variation modal updating formula to obtain an updated vertical variation modal component;
step S23, judging whether the updated vertical variation modal component meets a circulation stopping condition according to the convergence parameter, and if the updated vertical variation modal component meets the circulation stopping condition, the updated vertical variation modal component is the target vertical variation modal component; and if the updated vertical variation modal component does not meet the cycle stop condition, taking the updated vertical variation modal component as an initial vertical component Fourier transform result, and repeating the step S22 and the step S23.
In some possible implementations, the horizontal variation modality update formula is:
in the method, in the process of the invention,the modal component is changed horizontally for the kth updated level after the (n+1) th update; />Updating the vertical variation modal component for the kth updated for the nth time; />The vertical variation modal component is updated for the (k) th updated n+1th updated; />Fourier transform results of the original microseismic data; />The modal component is horizontally changed for the ith updated level after the nth update; />The result is the Fourier transform result of Lagrangian orange; />The center frequency of the k horizontal variation modal component after the n+1st update is the center frequency of the k horizontal variation modal component; alpha is penalty factor; omega is the discrete point of the original microseismic data spectrum; the absolute value is denoted.
In some possible implementations, the vertical variation modality update formula is:
in the method, in the process of the invention,updating the vertical variation modal component for the ith updated n times; />The center frequency of the kth vertical variation modal component after the n+1th update is obtained.
In some possible implementations, before performing fourier transform on the horizontal component and the vertical component to obtain a horizontal component fourier transform result and a vertical component fourier transform result, the method further includes:
and respectively carrying out boundary processing on the horizontal component and the vertical component by a preset boundary processing method.
In some possible implementations, the reconstructing the plurality of target horizontal variation modal components and the plurality of target vertical variation modal components to obtain noise-reduced target microseismic data includes:
performing inverse Fourier transform and inverse boundary processing on the target horizontal variation modal components and the target vertical variation modal components in sequence to obtain a plurality of post-processing horizontal variation modal components and a plurality of post-processing vertical variation modal components;
reconstructing the plurality of post-processing horizontal variation modal components and the plurality of post-processing vertical variation modal components to obtain noise-reduced target microseismic data.
In another aspect, the present invention further provides a variable-mode noise reduction device based on re-constraint, including:
the data acquisition unit is used for acquiring original microseismic data, wherein the original microseismic data comprises a horizontal component and a vertical component;
the re-constraint unit is used for establishing a re-constraint relation between the horizontal component and the vertical component and establishing a variation modal decomposition model based on the re-constraint relation;
the decomposition unit is used for decomposing the horizontal component and the vertical component through the variation modal decomposition model to obtain a plurality of target horizontal variation modal components and a plurality of target vertical variation modal components;
and the reconstruction unit is used for reconstructing the plurality of target horizontal variation modal components and the plurality of target vertical variation modal components to obtain noise-reduced target microseismic data.
The beneficial effects of adopting the embodiment are as follows: according to the variation modal noise reduction method based on the re-constraint, a variation modal decomposition model is established based on the re-constraint relation between the horizontal component and the vertical component, the horizontal component and the vertical component are decomposed through the variation modal decomposition model, and the decomposed multiple target horizontal variation modal components and multiple target vertical variation modal components are reconstructed to obtain noise-reduced target microseismic data. Namely: in the decomposition process, the modal decomposition of the horizontal component is re-constrained through the vertical component with good signal-to-noise ratio, so that the amplitude information of the horizontal component is recovered, effective denoising is realized, and the noise reduction effect of the variation modal noise reduction method is improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of an embodiment of a method for reducing noise in a variation mode based on re-constraint according to the present invention;
FIG. 2 is a flow chart of the embodiment of S103 in FIG. 1 according to the present invention;
FIG. 3 is a flow chart of the embodiment of S202 in FIG. 2 according to the present invention;
FIG. 4 is a flowchart illustrating the process of S203 in FIG. 2 according to an embodiment of the present invention;
FIG. 5 is a flow chart of the embodiment of S104 in FIG. 1 according to the present invention;
FIG. 6 is a graph comparing original microseismic data with target microseismic data;
FIG. 7 is a schematic structural diagram of an embodiment of a variable-mode noise reduction device based on re-constraint according to the present invention;
fig. 8 is a schematic structural diagram of an embodiment of an electronic device according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the embodiments of the present application, unless otherwise indicated, the meaning of "a plurality" is two or more.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
The invention provides a variation mode noise reduction method and device based on re-constraint, which are respectively described below.
Fig. 1 is a schematic flow chart of an embodiment of a variation mode denoising method based on re-constraint, where, as shown in fig. 1, the variation mode denoising method based on re-constraint includes:
s101, acquiring original microseismic data, wherein the original microseismic data comprises a horizontal component and a vertical component;
s102, establishing a re-constraint relation between a horizontal component and a vertical component, and establishing a variation modal decomposition model based on the re-constraint relation;
s103, decomposing the horizontal component and the vertical component through a variation modal decomposition model to obtain a plurality of target horizontal variation modal components and a plurality of target vertical variation modal components;
s104, reconstructing the plurality of target horizontal variation modal components and the plurality of target vertical variation modal components to obtain noise-reduced target microseismic data.
Compared with the prior art, the variation modal noise reduction method based on the reconstraction provided by the embodiment of the invention has the advantages that the variation modal decomposition model is built based on the reconstraction relation between the horizontal component and the vertical component and the reconstraction relation, the horizontal component and the vertical component are decomposed through the variation modal decomposition model, and the decomposed multiple target horizontal variation modal components and multiple target vertical variation modal components are reconstructed to obtain noise-reduced target microseismic data. Namely: in the decomposition process, the modal decomposition of the horizontal component is re-constrained through the vertical component with good signal-to-noise ratio, so that the amplitude information of the horizontal component is recovered, effective denoising is realized, and the noise reduction effect of the variation modal noise reduction method is improved.
It should be noted that: in some embodiments of the present invention, the raw microseismic data comprises three components, namely: two horizontal components and one vertical component, in practical application, step S102 specifically includes: and respectively establishing a re-constraint relation between the two horizontal components and the vertical component, namely: each horizontal component corresponds to a re-constraint relation, and a variation modal decomposition model of the two horizontal components is established through the re-constraint relation. For simplicity of explanation, in the following embodiments of the present invention, a horizontal component is taken as an example.
In some embodiments of the invention, the variant modal decomposition model is:
a k ·v k (t)＝u k (t)
wherein u is k (t) is the kth target level variation modal component in the time domain; omega k Center frequencies of modal components are varied for a kth target level; k is the total number of target horizontal variation modal components; delta (t) is a dirac function; * Representing a convolution operation; j represents an imaginary part; t represents time; f (t) is original microseismic data; v k (t) is the kth target vertical variation modal component in the time domain; a, a k The constraint coefficient between the kth target horizontal variation modal component in the time domain and the kth target vertical variation modal component in the time domain;representing the bias to t; i 2 Representing a binary norm.
As can be seen from the above formula, the constraint relationship between the horizontal component and the vertical component is a linear constraint, and by this arrangement, the noise reduction effect of the variation mode noise reduction method can be further improved, because: the wireless relation between random noises can further suppress random noises and retain effective microseismic data by setting the constraint relation between horizontal components and vertical components as a linear relation, so that the noise reduction effect of the variation modal noise reduction method is further improved.
Since the original microseismic data is usually time domain data, and the variation mode noise reduction method needs to be performed in the frequency domain, in some embodiments of the present invention, as shown in fig. 2, step S103 includes:
s201, carrying out Fourier transform on a horizontal component and a vertical component respectively to obtain a horizontal component Fourier transform result and a vertical component Fourier transform result;
s202, decomposing a horizontal component Fourier transform result through a variation modal decomposition model to obtain a plurality of target horizontal variation modal components;
s203, decomposing the vertical component Fourier transform result through a variation modal decomposition model to obtain a plurality of target vertical variation modal components.
The embodiment of the invention can transform the horizontal component and the vertical component from the time domain to the frequency domain by carrying out Fourier transform on the horizontal component and the vertical component.
In some embodiments of the present invention, as shown in fig. 3, step S202 includes:
s301, determining convergence parameters of a horizontal component Fourier transform result and an initial horizontal component Fourier transform result;
s302, updating an initial horizontal component Fourier transform result according to a horizontal variation modal updating formula to obtain an updated horizontal variation modal component;
s303, judging whether the updated horizontal variation modal component meets the circulation stop condition according to the convergence parameter, and if the updated horizontal variation modal component meets the circulation stop condition, the updated horizontal variation modal component is the target horizontal variation modal component; if the updated horizontal-variation modal component does not satisfy the loop stop condition, the updated horizontal-variation modal component is taken as the initial horizontal component fourier transform result, and S302 and S303 are repeated.
Similarly, in some embodiments of the present invention, as shown in fig. 4, step S203 includes:
s401, determining convergence parameters of a vertical component Fourier transform result and an initial vertical component Fourier transform result;
s402, updating an initial vertical component Fourier transform result according to a vertical variation modal updating formula to obtain an updated vertical variation modal component;
s403, judging whether the updated vertical variation modal component meets the circulation stop condition according to the convergence parameter, and if the updated vertical variation modal component meets the circulation stop condition, updating the vertical variation modal component into a target vertical variation modal component; if the updated vertical-variant modal component does not satisfy the loop stop condition, the updated vertical-variant modal component is taken as the initial vertical component fourier transform result, and S402 and S403 are repeated.
In a specific embodiment of the present invention, the convergence parameter is 1×e -6 E is a natural constant. And the cycle stop conditions in step S303 and step S403 are: first, theIf the difference between the components updated for n times and the (n+1) th time is smaller than the convergence parameter, the circulation stopping condition is satisfied, and if not, the circulation stopping condition is not satisfied.
In one embodiment of the present invention, the horizontal variation modality update formula is:
in the method, in the process of the invention,the modal component is changed horizontally for the kth updated level after the (n+1) th update; />Updating the vertical variation modal component for the kth updated for the nth time; />The vertical variation modal component is updated for the (k) th updated n+1th updated; />Fourier transform results of the original microseismic data; />The modal component is horizontally changed for the ith updated level after the nth update; />Fourier transform of lagrangian orangeResults; />The center frequency of the k horizontal variation modal component after the n+1st update is the center frequency of the k horizontal variation modal component; alpha is penalty factor; omega is the discrete point of the original microseismic data spectrum; the absolute value is denoted.
In a specific embodiment of the present invention, the penalty factor is 2000.
In one embodiment of the present invention, the vertical variation modality update formula is:
in the method, in the process of the invention,updating the vertical variation modal component for the ith updated n times; />The center frequency of the kth vertical variation modal component after the n+1th update is obtained.
The determination of each variant mode component is realized by a plurality of screening processes, and each screening process must find an upper envelope formed by a local maximum value and a lower envelope formed by a local minimum value and calculate the interpolation and average value of the upper envelope and the lower envelope respectively by 3 times of spline interpolation. Since the end points of the microseismic data are not local extreme points at the same time, the microseismic data must be extended to ensure that the envelope can reach short. Envelope epitaxy inaccuracy can cause errors, and any errors due to such boundary effects can render the final component inaccurate and meaningless, and therefore, in some embodiments of the invention, prior to step S201, further comprises:
and respectively carrying out boundary processing on the horizontal component and the vertical component by a preset boundary processing method.
By performing boundary processing on the horizontal component and the vertical component by adopting a preset boundary processing method, errors caused by boundary effects can be reduced.
In a specific embodiment of the present invention, the preset boundary processing method is mirroring, that is: the horizontal component and the vertical component are mirrored separately.
Since the boundary processing and fourier transformation are performed on the horizontal component and the vertical component before the plurality of target horizontal variation modal components and the plurality of target vertical variation modal components are obtained, in order to restore microseismic data, in some embodiments of the present invention, as shown in fig. 5, step S104 includes:
s501, sequentially performing inverse Fourier transform and inverse boundary processing on a plurality of target horizontal variation modal components and a plurality of target vertical variation modal components to obtain a plurality of post-processing horizontal variation modal components and a plurality of post-processing vertical variation modal components;
s502, reconstructing the plurality of post-processing horizontal variation modal components and the plurality of post-processing vertical variation modal components to obtain noise-reduced target microseismic data.
It should be understood that: when the boundary processing method is mirroring, the inverse boundary processing is inverse mirroring.
It should be noted that: the reconstruction of the plurality of post-processing horizontal-variant modal components and the plurality of post-processing vertical-variant modal components in step S502 may include two ways, one being the direct reconstruction of the plurality of post-processing horizontal-variant modal components and the plurality of post-processing vertical-variant modal components. The other is to reprocess the plurality of post-processing horizontal variation modal components and the plurality of post-processing vertical variation modal components and then reconstruct. The reprocessing may be wavelet threshold denoising or any other denoising method.
As shown in fig. 6, the abscissa in fig. 6 is time, the ordinate is amplitude, H1 represents a first horizontal component, H2 represents a second horizontal component, and Vertical is a Vertical component; by comparing the original microseismic data with the target microseismic data, the denoising method based on the variation mode based on the reconstraction provided by the embodiment of the invention can be used for denoising the original signal more completely, so that the waveform characteristics of the original signal are reserved, the peak and the abrupt change part of the original signal are reserved better, the noise is effectively denoised, and the better denoising effect is obtained.
In order to better implement the re-constraint-based variation mode noise reduction method in the embodiment of the present invention, correspondingly, as shown in fig. 7, the embodiment of the present invention further provides a re-constraint-based variation mode noise reduction device 700, which includes:
a data acquisition unit 701, configured to acquire original microseismic data, where the original microseismic data includes a horizontal component and a vertical component;
a re-constraint unit 702, configured to establish a re-constraint relationship between the horizontal component and the vertical component, and establish a variational modal decomposition model based on the re-constraint relationship;
a decomposition unit 703 for decomposing the horizontal component and the vertical component by a variation mode decomposition model to obtain a plurality of target horizontal variation mode components and a plurality of target vertical variation mode components;
and a reconstruction unit 704, configured to reconstruct the plurality of target horizontal variation modal components and the plurality of target vertical variation modal components to obtain noise-reduced target microseismic data.
The variable-mode noise reduction device 700 based on the re-constraint provided in the foregoing embodiment may implement the technical solution described in the foregoing variable-mode noise reduction method embodiment based on the re-constraint, and the specific implementation principle of each module or unit may refer to the corresponding content in the foregoing variable-mode noise reduction method embodiment based on the re-constraint, which is not repeated herein.
As shown in fig. 8, the present invention further provides an electronic device 800 accordingly. The electronic device 800 includes a processor 801, a memory 802, and a display 803. Fig. 8 shows only some of the components of the electronic device 800, but it should be understood that not all of the illustrated components are required to be implemented and that more or fewer components may be implemented instead.
The memory 802 may be an internal storage unit of the electronic device 800, such as a hard disk or memory of the electronic device 800, in some embodiments. The memory 802 may also be an external storage device of the electronic device 800 in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device 800.
Further, the memory 802 may also include both internal storage units and external storage devices of the electronic device 800. The memory 802 is used to store application software and various types of data for installing the electronic device 800.
The processor 801 may be a central processing unit (Central Processing Unit, CPU), microprocessor or other data processing chip in some embodiments for executing program code or processing data stored in the memory 802, such as the re-constrained based variation mode noise reduction method of the present invention.
The display 803 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like in some embodiments. The display 803 is for displaying information at the electronic device 800 and for displaying a visual user interface. The components 801-803 of the electronic device 800 communicate with each other over a system bus.
In one embodiment, when the processor 801 executes the re-constraint based variant mode noise reduction program in the memory 802, the following steps may be implemented:
acquiring original microseismic data, wherein the original microseismic data comprises a horizontal component and a vertical component;
establishing a re-constraint relation between the horizontal component and the vertical component, and establishing a variation modal decomposition model based on the re-constraint relation;
decomposing the horizontal component and the vertical component through a variation mode decomposition model to obtain a plurality of target horizontal variation mode components and a plurality of target vertical variation mode components;
reconstructing the plurality of target horizontal variation modal components and the plurality of target vertical variation modal components to obtain noise-reduced target microseismic data.
It should be understood that: the processor 801, when executing the re-constraint based variation modality noise reduction program in the memory 802, may implement other functions in addition to the above functions, particularly as described above with respect to the corresponding method embodiments.
Further, the type of the electronic device 800 is not particularly limited, and the electronic device 800 may be a mobile phone, a tablet computer, a personal digital assistant (personal digital assistant, PDA), a wearable device, a laptop (laptop), or other portable electronic devices. Exemplary embodiments of portable electronic devices include, but are not limited to, portable electronic devices that carry IOS, android, microsoft or other operating systems. The portable electronic device described above may also be other portable electronic devices, such as a laptop computer (laptop) or the like having a touch-sensitive surface, e.g. a touch panel. It should also be appreciated that in other embodiments of the invention, the electronic device 800 may not be a portable electronic device, but rather a desktop computer having a touch-sensitive surface (e.g., a touch panel).
Correspondingly, the embodiment of the application also provides a computer readable storage medium, which is used for storing a computer readable program or instruction, and when the program or instruction is executed by a processor, the steps or functions of the variation mode noise reduction method based on the re-constraint provided by the embodiment of the method can be realized.
Those skilled in the art will appreciate that all or part of the flow of the methods of the embodiments described above may be accomplished by way of a computer program stored in a computer readable storage medium, instructing the relevant hardware. The computer readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory.
The above describes the method and apparatus for reducing noise in a variation mode based on re-constraint, and specific examples are applied to illustrate the principle and implementation of the present invention, and the above examples are only used to help understand the method and core idea of the present invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in light of the ideas of the present invention, the present description should not be construed as limiting the present invention.
Claims (4)
1. A method of reducing noise in a variation mode based on re-constraint, comprising:
acquiring original microseismic data, wherein the original microseismic data comprises a horizontal component and a vertical component;
establishing a re-constraint relation between the horizontal component and the vertical component, and establishing a variation modal decomposition model based on the re-constraint relation;
decomposing the horizontal component and the vertical component through the variation modal decomposition model to obtain a plurality of target horizontal variation modal components and a plurality of target vertical variation modal components;
reconstructing the plurality of target horizontal variation modal components and the plurality of target vertical variation modal components to obtain noise-reduced target microseismic data;
the variational modal decomposition model is as follows:
wherein u is k (t) is the kth target level variation modal component in the time domain; omega k Center frequencies of modal components are varied for a kth target level; k is the total number of target horizontal variation modal components; delta (t) is a dirac function; * Representing a convolution operation; j represents an imaginary part; t represents time; f (t) is the originalInitiating microseismic data; v k (t) is the kth target vertical variation modal component in the time domain;the constraint coefficient between the kth target horizontal variation modal component in the time domain and the kth target vertical variation modal component in the time domain; />Representing the bias to t; />Representing a binary norm;
decomposing the horizontal component and the vertical component through the variation modal decomposition model to obtain a plurality of target horizontal variation modal components and a plurality of target vertical variation modal components, including:
performing Fourier transformation on the horizontal component and the vertical component respectively to obtain a horizontal component Fourier transformation result and a vertical component Fourier transformation result;
decomposing the horizontal component Fourier transform result through a variation modal decomposition model to obtain a plurality of target horizontal variation modal components;
decomposing the vertical component Fourier transform result through a variation modal decomposition model to obtain a plurality of target vertical variation modal components;
decomposing the horizontal component Fourier transform result through a variation modal decomposition model to obtain a plurality of target horizontal variation modal components, wherein the method comprises the following steps:
step S11, determining convergence parameters of the horizontal component Fourier transform result and an initial horizontal component Fourier transform result;
step S12, updating the Fourier transform result of the initial horizontal component according to a horizontal variation modal updating formula to obtain an updated horizontal variation modal component;
step S13, judging whether the updated horizontal variation modal component meets a circulation stop condition according to the convergence parameter, and if the updated horizontal variation modal component meets the circulation stop condition, the updated horizontal variation modal component is the target horizontal variation modal component; if the updated horizontal variation modal component does not meet the cycle stop condition, taking the updated horizontal variation modal component as an initial horizontal component Fourier transform result, and repeating the step S12 and the step S13;
decomposing the vertical component Fourier transform result through a variation modal decomposition model to obtain a plurality of target vertical variation modal components, wherein the method comprises the following steps:
step S21, determining convergence parameters of the vertical component Fourier transform result and an initial vertical component Fourier transform result;
s22, updating the Fourier transform result of the initial vertical component according to a vertical variation modal updating formula to obtain an updated vertical variation modal component;
step S23, judging whether the updated vertical variation modal component meets a circulation stopping condition according to the convergence parameter, and if the updated vertical variation modal component meets the circulation stopping condition, the updated vertical variation modal component is the target vertical variation modal component; if the updated vertical variation modal component does not meet the cycle stop condition, taking the updated vertical variation modal component as an initial vertical component Fourier transform result, and repeating the step S22 and the step S23;
the horizontal variation modal updating formula is as follows:
in the method, in the process of the invention,the modal component is changed horizontally for the kth updated level after the (n+1) th update; />Updating the vertical variation modal component for the kth updated for the nth time; />The vertical variation modal component is updated for the (k) th updated n+1th updated; />Fourier transform results of the original microseismic data; />The modal component is horizontally changed for the ith updated level after the nth update; />Fourier transform results, which are lagrangian multipliers; />The center frequency of the k horizontal variation modal component after the n+1st update is the center frequency of the k horizontal variation modal component; />Is a penalty factor; />Discrete points of the original microseismic data spectrum; />Representing an absolute value;
the vertical variation modal updating formula is as follows:
in the method, in the process of the invention,updating the vertical variation modal component for the ith updated n times; />The center frequency of the kth vertical variation modal component after the n+1th update is obtained.
2. The re-constraint based variation modality noise reduction method of claim 1, further comprising, before the fourier transforming the horizontal component and the vertical component, respectively, obtaining a horizontal component fourier transform result and a vertical component fourier transform result:
and respectively carrying out boundary processing on the horizontal component and the vertical component by a preset boundary processing method.
3. The method of denoising the variance modes based on the re-constraint according to claim 2, wherein reconstructing the plurality of target horizontal variance mode components and the plurality of target vertical variance mode components to obtain denoised target microseismic data comprises:
performing inverse Fourier transform and inverse boundary processing on the target horizontal variation modal components and the target vertical variation modal components in sequence to obtain a plurality of post-processing horizontal variation modal components and a plurality of post-processing vertical variation modal components;
reconstructing the plurality of post-processing horizontal variation modal components and the plurality of post-processing vertical variation modal components to obtain noise-reduced target microseismic data.
4. A re-constrained based variational modal noise reduction device, comprising:
the data acquisition unit is used for acquiring original microseismic data, wherein the original microseismic data comprises a horizontal component and a vertical component;
the re-constraint unit is used for establishing a re-constraint relation between the horizontal component and the vertical component and establishing a variation modal decomposition model based on the re-constraint relation;
the decomposition unit is used for decomposing the horizontal component and the vertical component through the variation modal decomposition model to obtain a plurality of target horizontal variation modal components and a plurality of target vertical variation modal components;
the reconstruction unit is used for reconstructing the plurality of target horizontal variation modal components and the plurality of target vertical variation modal components to obtain noise-reduced target microseismic data;
the variational modal decomposition model is as follows:
wherein u is k (t) is the kth target level variation modal component in the time domain; omega k Center frequencies of modal components are varied for a kth target level; k is the total number of target horizontal variation modal components; delta (t) is a dirac function; * Representing a convolution operation; j represents an imaginary part; t represents time; f (t) is the original microseismic data; v k (t) is the kth target vertical variation modal component in the time domain;the constraint coefficient between the kth target horizontal variation modal component in the time domain and the kth target vertical variation modal component in the time domain; />Representing the bias to t; />Representing a binary norm;
decomposing the horizontal component and the vertical component through the variation modal decomposition model to obtain a plurality of target horizontal variation modal components and a plurality of target vertical variation modal components, including:
performing Fourier transformation on the horizontal component and the vertical component respectively to obtain a horizontal component Fourier transformation result and a vertical component Fourier transformation result;
decomposing the horizontal component Fourier transform result through a variation modal decomposition model to obtain a plurality of target horizontal variation modal components;
decomposing the vertical component Fourier transform result through a variation modal decomposition model to obtain a plurality of target vertical variation modal components;
decomposing the horizontal component Fourier transform result through a variation modal decomposition model to obtain a plurality of target horizontal variation modal components, wherein the method comprises the following steps:
step S11, determining convergence parameters of the horizontal component Fourier transform result and an initial horizontal component Fourier transform result;
step S12, updating the Fourier transform result of the initial horizontal component according to a horizontal variation modal updating formula to obtain an updated horizontal variation modal component;
step S13, judging whether the updated horizontal variation modal component meets a circulation stop condition according to the convergence parameter, and if the updated horizontal variation modal component meets the circulation stop condition, the updated horizontal variation modal component is the target horizontal variation modal component; if the updated horizontal variation modal component does not meet the cycle stop condition, taking the updated horizontal variation modal component as an initial horizontal component Fourier transform result, and repeating the step S12 and the step S13;
decomposing the vertical component Fourier transform result through a variation modal decomposition model to obtain a plurality of target vertical variation modal components, wherein the method comprises the following steps:
step S21, determining convergence parameters of the vertical component Fourier transform result and an initial vertical component Fourier transform result;
s22, updating the Fourier transform result of the initial vertical component according to a vertical variation modal updating formula to obtain an updated vertical variation modal component;
step S23, judging whether the updated vertical variation modal component meets a circulation stopping condition according to the convergence parameter, and if the updated vertical variation modal component meets the circulation stopping condition, the updated vertical variation modal component is the target vertical variation modal component; if the updated vertical variation modal component does not meet the cycle stop condition, taking the updated vertical variation modal component as an initial vertical component Fourier transform result, and repeating the step S22 and the step S23;
the horizontal variation modal updating formula is as follows:
in the method, in the process of the invention,is n+1th time moreThe k new updated horizontal variation modal component; />Updating the vertical variation modal component for the kth updated for the nth time; />The vertical variation modal component is updated for the (k) th updated n+1th updated; />Fourier transform results of the original microseismic data; />The modal component is horizontally changed for the ith updated level after the nth update; />Fourier transform results, which are lagrangian multipliers; />The center frequency of the k horizontal variation modal component after the n+1st update is the center frequency of the k horizontal variation modal component; />Is a penalty factor; />Discrete points of the original microseismic data spectrum; />Representing an absolute value;
the vertical variation modal updating formula is as follows:
in the method, in the process of the invention,updating the vertical variation modal component for the ith updated n times; />The center frequency of the kth vertical variation modal component after the n+1th update is obtained.
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