CN109061724A - A kind of seismic data noise-reduction method based on adaptive variation mode decomposition - Google Patents

A kind of seismic data noise-reduction method based on adaptive variation mode decomposition Download PDF

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CN109061724A
CN109061724A CN201810646718.6A CN201810646718A CN109061724A CN 109061724 A CN109061724 A CN 109061724A CN 201810646718 A CN201810646718 A CN 201810646718A CN 109061724 A CN109061724 A CN 109061724A
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signal
snr
variation mode
noise
seismic data
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唐刚
徐智
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Beijing University of Chemical Technology
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Beijing University of Chemical Technology
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
    • G01V1/36—Effecting static or dynamic corrections on records, e.g. correcting spread; Correlating seismic signals; Eliminating effects of unwanted energy

Abstract

The invention discloses a kind of seismic data noise-reduction methods based on adaptive variation mode decomposition, belong to oil gas field of geophysical exploration.Seismic signal is handled using variation mode decomposition, the signal-to-noise ratio by calculating decomposition result selects optimized parameter, handles seismic signal using the variation mode decomposition of optimized parameter, reduces the noise in seismic signal.Method provided by the present invention is handled seismic data by adaptive variation mode decomposition, realizes the compacting of seismic data noise.

Description

A kind of seismic data noise-reduction method based on adaptive variation mode decomposition
Technical field
The invention belongs to oil gas field of geophysical exploration, and in particular to a kind of based on adaptive variation mode decomposition Seismic data noise-reduction method.
Background technique
Since geological conditions, the acquisition conditions such as environment and manual operation influence, seismic signal is inevitably mixed into and makes an uproar Acoustical signal.With deepening continuously for geological prospecting, influence of the noise for seismic signal is increasing, seriously affects seismic data Explanation work so that influencing the judgement to the geologic structure of objective area.Therefore, it is necessary to carry out at noise reduction to seismic signal Reason reduces influence of the noise to subsequent processing and explanation work.
Summary of the invention
It is an object of the invention to propose a kind of seismic data noise-reduction method of adaptive variation mode decomposition, by this Method makes up the deficiency of existing seismic data noise-reduction method, provides a kind of more effective seismic processes.
To achieve the above object, the technical solution adopted by the present invention is a kind of earthquake based on adaptive variation mode decomposition Data noise reduction, this method comprises:
Mode number K and penalty factor α is decomposed in S1 selection, sets control parameters K0=0, α0=0 and SNR0=0；
S2 uses the K selected and α as the parameter of variation mode decomposition (VMD), carries out variation mode point to seismic signal Solution processing；
S3 calculates the Signal to Noise Ratio (SNR) of decomposition result, with control parameters SNR0Compare, if SNR > SNR0, then SNR0=SNR, K0 =K, α0=α, if SNR≤SNR0, control parameters are constant；
S4 repeats S1, S2, S3 step, until all K and alpha parameter traverse completion, uses K0And α0Carry out variation mode It decomposes, the result decomposed is optimal de-noising signal.
Variation mode decomposition in S2 is that original signal is decomposed into specified by constructing and solving constraint variation problem Several modal components.
Signal-to-noise ratio in S3 is the logarithm of the ratio of signal energy and noise energy.
Method provided by the present invention is handled seismic data by adaptive variation mode decomposition method, avoids leading The contingency and randomness for choosing resolution parameter are seen, realizes the effect of seismic data noise compacting, treated, and signal can Meet the processing of deep seismic data and explains work.
Detailed description of the invention
Fig. 1 is a kind of process of seismic data noise-reduction method based on adaptive variation mode decomposition according to the present invention Figure.
Fig. 2 is original earthquake data figure.
Fig. 3 is seismic data figure after denoising.
Fig. 4 is the noise pattern filtered out.
Specific embodiment
For the ease of the understanding of those skilled in the art, the invention will be further described with reference to the accompanying drawing, not It can be used to limit the scope of the invention.It should be noted that in the absence of conflict, embodiment and reality in the application The various modes applied in example can be combined with each other.Drawings in the following description are only some embodiments of the invention, for this For the those of ordinary skill of field, without creative efforts, it can also be obtained according to these attached drawings others Attached drawing.
Referring to Fig. 1, the invention proposes a kind of seismic data noise-reduction method based on adaptive variation mode decomposition, packets It includes:
Step 300 will collect seismic data and decompose by road number.
Step 301 determines parameter decomposition mode number and penalty factor range, sets control parameters K0=0, α0=0 and SNR0 =0.
Mode number K and penalty factor α is decomposed in step 302 selection.
Step 303 carries out variation mode decomposition to signal using the parameter of selection.
Step 304 calculates the Signal to Noise Ratio (SNR) of signal after decomposition.
Step 305 judges signal-to-noise ratio size, if SNR > SNR0,SNR0=SNR, K0=K, α 0=α, if SNR≤SNR0, right According to parameter constant.
Step 306 repeats step 301-305 until the selection of parameter area intrinsic parameter finishes.
Step 307 uses K0And α0That is SNR0Corresponding parameter, which carries out variation mode decomposition, can be obtained earthquake number after noise reduction According to.
The model of variation mode decomposition is by signal decomposition be it is limited with bandwidth limitation intrinsic mode function and Each mode function ukAll it is centered around respective centre frequency wkAround, finally make the sum of the bandwidth of each mode minimum, constrains item Part is that the sum of each bandwidth is equal to input signal f, k=1,2 ..., K；The specific constitution step of the model of variation mode decomposition is as follows It is shown:
(1) to each mode signals ukIt carries out Hilbert transform and obtains the analytic signal of each mode signals:
δ (t) is impulse function, and j is imaginary unit, and t is the sampling time of signal.
(2) a centre frequency w is estimated to each analytic signalk, by analytic signal Spectrum Conversion to base band, demodulated Signal is as follows:
(3) two norms for calculating the gradient of above-mentioned demodulated signal, estimate the bandwidth of each component, controlled variational problem It is as follows:
Wherein, { ukIt is equal to { u1,...,uK},{wkIt is equal to { w1,...,wK},It is equal toF, which is represented, to carry out The seismic signal of decomposition.
The solution procedure of the variational problem of Construction of A Model is as follows:
(1) in order to solve the variational problem, secondary penalty factor α and Lagrange multiplier operator λ (t) is introduced, will be constrained Variational problem is converted into no constraint variation problem, and expression formula is as follows:
(2) saddle point of no constraint variation problem, the as variational problem are sought using multiplier alternating direction algorithm (ADMM) Last solution, the more new algorithm of each parameter is as follows:
In formula: wkIt is equal to wk n+1,It is equal to
The step of VMD algorithm can be obtained from above are as follows:
(1) it initializesAnd n；
(2) according to formula iteration ukAnd wk；
(3) according to formula iteration λ；
(4) assigned error tol, ifThen stop iteration, otherwise returns to iteration ukAnd wkAnd λ.
The calculating of signal-to-noise ratio since the original useful signal of actual seismic signal is unknown, therefore uses the side of signal and noise The ratio between difference approximate evaluation seismic data signal-to-noise ratio.Variance calculation formula are as follows:
D (X)=E (X2)-E2(X)
The local variance for calculating seismic profile all pixels, is considered signal variance P for the maximum value of local varianceS, most Small value is noise variance PN, the calculation formula of signal-to-noise ratio are as follows:
In the present embodiment, it should be noted that above step is realized by computer programming, and then may be implemented ground The denoising humidification of seismic wave data-signal.
Processing result analysis, can be refering to Fig. 2, Fig. 3 and Fig. 4, wherein Fig. 2 is original seismic data, and Fig. 3 is into excessively originally Apply for the seismic data after the seismic processes/device provided, Fig. 4 is made an uproar by what adaptive variation mode decomposition filtered out Sound is manifested in conjunction with the information that can be seen that seismogram is masked as acoustic noise reducing and originally by noise that compares of Fig. 2 and Fig. 3 Come, denoising effect is obvious.
Although this application provides the method operating procedure as described in embodiment or flow chart, based on conventional or noninvasive The means for the property made may include more or less operating procedure.The step of enumerating in embodiment sequence is only numerous steps One of execution sequence mode, does not represent and unique executes sequence.It, can when device or client production in practice executes To execute or parallel execute (such as at parallel processor or multithreading according to embodiment or method shown in the drawings sequence The environment of reason, even distributed data processing environment).The terms "include", "comprise" or its any other variant are intended to contain Lid non-exclusive inclusion, so that process, method, product or equipment including a series of elements are not only wanted including those Element, but also including other elements that are not explicitly listed, or further include for this process, method, product or equipment Intrinsic element.In the absence of more restrictions, be not precluded include the process, method of the element, product or There is also other identical or equivalent elements in person's equipment.
Device that above-described embodiment illustrates or module etc. can specifically realize by computer chip or entity, or by having There is the product of certain function to realize.For convenience of description, it is divided into various modules when description apparatus above with function to retouch respectively It states.Certainly, the function of each module can be realized in the same or multiple software and or hardware when implementing the application, The module for realizing same function can be realized by the combination of multiple submodule etc..Installation practice described above is only Schematically, for example, the division of the module, only a kind of logical function partition, can there is other draw in actual implementation The mode of dividing, such as multiple module or components can be combined or can be integrated into another system, or some features can be ignored, Or it does not execute.
It is also known in the art that other than realizing controller in a manner of pure computer readable program code, it is complete Entirely can by by method and step carry out programming in logic come so that controller with logic gate, switch, specific integrated circuit, programmable Logic controller realizes identical function with the form for being embedded in microcontroller etc..Therefore this controller is considered one kind Hardware component, and the structure that the device for realizing various functions that its inside includes can also be considered as in hardware component.Or Person even, can will be considered as realizing the device of various functions either the software module of implementation method can be hardware again Structure in component.
The application can describe in the general context of computer-executable instructions executed by a computer, such as program Module.Generally, program module includes routines performing specific tasks or implementing specific abstract data types, programs, objects, group Part, data structure, class etc..The application can also be practiced in a distributed computing environment, in these distributed computing environments, By executing task by the connected remote processing devices of communication network.In a distributed computing environment, program module can To be located in the local and remote computer storage media including storage equipment.
As seen through the above description of the embodiments, those skilled in the art can be understood that the application can It realizes by means of software and necessary general hardware platform.Based on this understanding, the technical solution essence of the application On in other words the part that contributes to existing technology can be embodied in the form of software products, the computer software product It can store in storage medium, such as R0M/RAM, magnetic disk, CD, including some instructions are used so that a computer equipment (can be personal computer, mobile terminal, server or the network equipment etc.) executes each embodiment of the application or implementation Method described in certain parts of example.
Each embodiment in this specification is described in a progressive manner, the same or similar between each embodiment Part may refer to each other, and each embodiment focuses on the differences from other embodiments.The application can be used for In numerous general or special purpose computing system environments or configuration.Such as: personal computer, server computer, handheld device Or portable device, laptop device, multicomputer system, microprocessor-based system, set top box, programmable electronics set Standby, network PC, minicomputer, mainframe computer, distributed computing environment including any of the above system or equipment etc..
Although depicting the application by embodiment, it will be appreciated by the skilled addressee that the application there are many deformation and Variation is without departing from spirit herein, it is desirable to which the attached claims include these deformations and change without departing from the application.

Claims (8)

1. a kind of seismic data noise-reduction method based on adaptive variation mode decomposition, it is characterised in that: this method includes,
Mode number K and penalty factor α is decomposed in S1 selection, sets control parameters K0=0, α0=0 and SNR0=0；
S2 uses the K selected and α as the parameter of variation mode decomposition (VMD), carries out at variation mode decomposition to seismic signal Reason；
S3 calculates the Signal to Noise Ratio (SNR) of decomposition result, with control parameters SNR0Compare, if SNR > SNR0, then SNR0=SNR, K0=K, α0=α, if SNR≤SNR0, control parameters are constant；
S4 repeats S1, S2, S3 step, until all K and alpha parameter traverse completion, uses K0And α0Carry out variation mode point Solution, the result decomposed is optimal de-noising signal.
2. a kind of seismic data noise-reduction method based on adaptive variation mode decomposition according to claim 1, feature It is that adaptive optimal parameter selection can be carried out to signal, avoids the randomness and contingency of subjective selection resolution parameter, leads to Crossing variation mode decomposition improves seismic data noise reduction effect.
3. a kind of seismic data noise-reduction method based on adaptive variation mode decomposition according to claim 1, feature Be: the variation mode decomposition in S2 is that original signal is decomposed into specified number by constructing and solving constraint variation problem Modal components.
4. a kind of seismic data noise-reduction method based on adaptive variation mode decomposition according to claim 1, feature Be: the signal-to-noise ratio in S3 is the logarithm of the ratio of signal energy and noise energy.
5. a kind of seismic data noise-reduction method based on adaptive variation mode decomposition according to claim 1, feature It is:
Step 300 will collect seismic data and decompose by road number；
Step 301 determines parameter decomposition mode number and penalty factor range, sets control parameters K0=0, α0=0 and SNR0=0；
Mode number K and penalty factor α is decomposed in step 302 selection；
Step 303 carries out variation mode decomposition to signal using the parameter of selection；
Step 304 calculates the Signal to Noise Ratio (SNR) of signal after decomposition；
Step 305 judges signal-to-noise ratio size, if SNR > SNR0,SNR0=SNR, K0=K, α0=α, if SNR≤SNR0, control parameters It is constant；
Step 306 repeats step 301-305 until the selection of parameter area intrinsic parameter finishes；
Step 307 uses K0And α0That is SNR0Corresponding parameter, which carries out variation mode decomposition, can be obtained seismic data after noise reduction.
6. a kind of seismic data noise-reduction method based on adaptive variation mode decomposition according to claim 1, feature It is:
It is limited intrinsic mode function with bandwidth limitation and each that the model of variation mode decomposition, which is by signal decomposition, Mode function ukAll it is centered around respective centre frequency wkAround, finally make the sum of the bandwidth of each mode minimum, constraint condition is The sum of each bandwidth is equal to input signal f, k=1,2 ..., K；The following institute of the specific constitution step of the model of variation mode decomposition Show:
(1) to each mode signals ukIt carries out Hilbert transform and obtains the analytic signal of each mode signals:
δ (t) is impulse function, and j is imaginary unit, and t is the sampling time of signal；
(2) a centre frequency w is estimated to each analytic signalk, by analytic signal Spectrum Conversion to base band, obtain demodulated signal It is as follows:
(3) two norms for calculating the gradient of above-mentioned demodulated signal, estimate the bandwidth of each component, controlled variational problem is such as Under:
Wherein, { ukIt is equal to { u1,...,uK},{wkIt is equal to { w1,...,wK},It is equal toF representative is decomposed Seismic signal.
7. a kind of seismic data noise-reduction method based on adaptive variation mode decomposition according to claim 6, feature It is:
The solution procedure of the variational problem of Construction of A Model is as follows:
(1) in order to solve the variational problem, secondary penalty factor α and Lagrange multiplier operator λ (t) is introduced, by constraint variation Problem is converted into no constraint variation problem, and expression formula is as follows:
(2) saddle point of no constraint variation problem, the as variational problem are sought using multiplier alternating direction algorithm (ADMM) most Solution, the more new algorithm of each parameter are as follows eventually:
In formula: wkIt is equal to wk n+1,It is equal to
The step of VMD algorithm are as follows:
(1) it initializesAnd n；
(2) according to formula iteration ukAnd wk；
(3) according to formula iteration λ；
(4) assigned error tol, ifThen stop iteration, otherwise returns to iteration ukAnd wkAnd λ.
8. a kind of seismic data noise-reduction method based on adaptive variation mode decomposition according to claim 4, feature It is:
The calculating of signal-to-noise ratio, since the original useful signal of actual seismic signal is unknown, therefore use signal and noise variance it Than approximate evaluation seismic data signal-to-noise ratio；Variance calculation formula are as follows:
D (X)=E (X2)-E2(X)
The local variance for calculating seismic profile all pixels, is considered signal variance P for the maximum value of local varianceS, minimum value is Noise variance PN, the calculation formula of signal-to-noise ratio are as follows:
CN201810646718.6A 2018-06-21 2018-06-21 A kind of seismic data noise-reduction method based on adaptive variation mode decomposition Pending CN109061724A (en)

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CN109765624A (en) * 2019-02-20 2019-05-17 吉林大学 A kind of frequency domain aviation electromagnetic data de-noising method based on variation mode decomposition
CN109813230A (en) * 2019-01-09 2019-05-28 上海电力学院 A kind of optical grating Moire fringe denoising method based on VMD
CN110764147A (en) * 2019-11-06 2020-02-07 吉林大学 Desert exploration weak signal recovery method based on VMD local F-X spectrum decomposition
CN113640660A (en) * 2021-08-05 2021-11-12 国网江苏省电力有限公司电力科学研究院 Method and device for reducing noise of vibration signal of on-load tap-changer
CN113985481A (en) * 2021-10-26 2022-01-28 长江大学 Variational modal noise reduction method and device based on re-constraint

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Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109813230A (en) * 2019-01-09 2019-05-28 上海电力学院 A kind of optical grating Moire fringe denoising method based on VMD
CN109765624A (en) * 2019-02-20 2019-05-17 吉林大学 A kind of frequency domain aviation electromagnetic data de-noising method based on variation mode decomposition
CN110764147A (en) * 2019-11-06 2020-02-07 吉林大学 Desert exploration weak signal recovery method based on VMD local F-X spectrum decomposition
CN110764147B (en) * 2019-11-06 2020-11-03 吉林大学 Desert exploration weak signal recovery method based on VMD local F-X spectrum decomposition
CN113640660A (en) * 2021-08-05 2021-11-12 国网江苏省电力有限公司电力科学研究院 Method and device for reducing noise of vibration signal of on-load tap-changer
CN113985481A (en) * 2021-10-26 2022-01-28 长江大学 Variational modal noise reduction method and device based on re-constraint
CN113985481B (en) * 2021-10-26 2023-07-18 长江大学 Variable-division mode noise reduction method and device based on re-constraint

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Application publication date: 20181221