CN108345033A - A kind of microseism signal time-frequency domain first arrival detection method - Google Patents
A kind of microseism signal time-frequency domain first arrival detection method Download PDFInfo
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Abstract
The present invention provides a kind of microseism signal time-frequency domain first arrival detection method, when carrying out data processing using ICEEMDAN, using microseism signal as primary data, a specific white noise is added in each stage of decomposition, and a unique residual error is calculated to obtain each IMF, one sophisticated signal adaptively can be decomposed into a series of IMF components by ICEEMDAN, and IMF components meet from high frequency to low frequency Sequence distribution；Using IMF components as input, the mode of noise dominant is directly removed, DFA denoisings are carried out to other mode, residual noise is removed by being spaced hard -threshold；Result after different scale denoising is subjected to fusion reconstruct and obtains the earthquake record after denoising, as useful signal；Pass through the first break information for the useful signal that high-precision time frequency analysis detects.For the present invention compared with empirical mode decomposition result, modal overlap phenomenon has more apparent reduction, is capable of providing the Accurate Reconstruction of original signal, has better convergence.
Description
Technical field
The present invention relates to exploration geophysics field, especially relates to one kind in seism processing and be based on overall put down
Equal empirical mode decomposition ICEEMDAN and remove the united microseism signal detecting method of trend wave theory approach (DFA).
Background technology
The usual energy of micro-seismic event signal is weaker, and there are energy losses in communication process in addition, this results in ground to connect
The seismic data useful signal of receipts haves the shortcomings that energy is weak, signal-to-noise ratio is low, therefore the signal-to-noise ratio for improving microseism data is micro-
The top priority of seismic data process and explanation.With the increasing for improving positioning accuracy and the full Moment tensor inversion demand of focal mechanism
Add, the requirement to noise-removed technology also steps up.The method of tradition compacting random noise has very much, can be divided into spatial domain and transformation
The method in domain, the former includes mainly mean filter, medium filtering and anisotropic diffusion filtering etc., and the latter includes mainly Fourier
Transform domain filtering method, the Threshold Denoising Method etc. based on wavelet transformation, warp wavelet etc..Ground micro-seismic data, which has, makes an uproar by force
The characteristics of sound, weak useful signal, conventional denoising method are often difficult to obtain preferable denoising effect, thus study specifically for
The denoising method of ground micro-seismic data is highly important.For empirical mode decomposition (EMD) method it is unstable and generate mould
State aliasing (Huang, 1998), overall experience mode decomposition (EEMD) utilize the equally distributed statistics of white Gaussian noise frequency spectrum
Different white noises is added into original signal for characteristic so that signal has continuity, but this method meter on different scale
Inefficient (Wu, 2009)；Complete overall experience mode decomposition method (CEEMD) is made an uproar by the way that positive and negative pairs of auxiliary is added
Sound form, can effectively eliminate the remaining aid in noise in reconstruction signal, and computational efficiency can also be improved (Yeh,
2009) precision can be short of when, but reconstructing；Torres (2011,2014) is using improved complete overall experience mode point
Solution method (ICEEMDAN) can Accurate Reconstruction original signal, effectively reduce the noise in false mode and mode, calculate simultaneously
Cost also decreases.
But the above method can not yet effectively remove noise, achieve the purpose that accurately to detect microseism signal.
Invention content
The object of the present invention is to provide one kind to be detected based on the united microseism signal time-frequency domain first arrivals of ICEEMDAN and DFA
Method, to effectively remove noise, to make up the deficiencies in the prior art.
Contain noise in the IMF components decomposed due to ICEEMDAN, is detached by eliminating fluctuation tendency analysis method (DFA)
Go out useful signal and noise, to achieve the purpose that eliminate noise.Therefore the purpose of the present invention can by following technical measures come
It realizes：
One kind is included the following steps based on ICEEMDAN and the united microseism signal time-frequency domain first arrival detection methods of DFA：
(1) when carrying out data processing using ICEEMDAN, using microseism signal as primary data, in each of decomposition
Stage adds a specific white noise, and calculates a unique residual error to obtain each IMF, and ICEEMDAN can be adaptively by one
A sophisticated signal is decomposed into a series of intrinsic mode functions (IMF) component, and IMF components satisfaction is divided from high frequency to low frequency series
Cloth；
(2) a series of intrinsic mode functions (IMF) component for obtaining (1) processing is as input, to the mould of noise dominant
State directly removes, and DFA denoisings are carried out to other mode；
(3) data reconstruction after denoising is obtained to the result after joint denoising：After different scale denoising in step (2)
As a result it carries out fusion reconstruct and obtains the earthquake record after denoising, as useful signal；
(4) first break information of the useful signal obtained by high-precision time frequency analysis detecting step (3).
Further, in the step (1), the step of ICEEDAN, is described below：
101：The calculating x of signal after different noises are added is realized by EMDi=x+ ε0E1(ωi), it is residual that level-one is obtained later
Difference
102：Calculate first IMF components IMF1=x-r1；
103：EMD realizes r1+ε1E2(ωi), it is residual to calculate two level
104：The K to k=3 ... calculates k rank residual errors
105：Calculate k-th of IMF components IMFk=rk-1-rk；
106：Step 104,105 are repeated until residual error cannot be decomposed；
Wherein：Define operator Ej() is to acquire j-th of mode by EMD to Setting signal；ωiZero for unit variance is equal
It is worth white Gaussian noise, i=1,2 ... .I, xi=x+ ωiFor the signal after different noises is added；εkAllow to select to believe in each stage
It makes an uproar ratio；M () indicates local mean value operator, E in EMD1=x-M (x).
Further, in the step (2), DFA denoising methods are specially：
For i-th of IMF component signals IMF of a given microseism signali(t), its accumulated deviation y is calculatedi(t),
The average value of the sequence has been filtered off first, sequence reconstruct is then carried out, to yi(t) isometric segmentation is carried out respectively, will be grown with length k
Degree is n sequences segmentations into m nonoverlapping sections, wherein m=[n/k] (round numbers)；Due to sequence length not always increment k
Integral multiple, therefore, sequence tail end is it sometimes appear that the data information of fraction fails to be utilized；In order to make full use of data,
The reverse sequence of sequence is similarly operated, the section of an equal length is obtained；Then to each section, least square is used
The k number that method is included respectively to each section is according to carrying out first-order linear fitting；It calculates square after each section elimination trend
Difference, by sequence, formula calculates respectively with backward herein；It averages and evolution to all equal length sections, DFA is calculated
Wave function.
Further, in the step (2), the DFA wave functions are white to reduce by threshold range removal IMF components
Noise, threshold range are defined as α=φ ± 0.5.
The advantages of the present invention：
Population mean empirical mode decomposition in the present invention and the united microseism signal time-frequency domain first arrival detection sides DFA
Method, population mean empirical mode decomposition, which is utilized, has the characteristics that good multiple dimensioned and DFA validity, can suppress micro-
Stronger random noise in seismic signal.Population mean empirical mode decomposition in the present invention and the united microseism signals of DFA
It is combined with improved DFA methods, further increases on the basis of population mean empirical mode decomposition by detection method
The detection result of microseism signal.The present invention utilizes population mean empirical mode decomposition and DFA, has obtained the micro- of high s/n ratio
For seismic processing as a result, being conducive to subsequent microseism data positioning and mechanism analysis etc., this method is that the inverting of microseism positions
Etc. laying a good foundation.
For the present invention compared with empirical mode decomposition result, modal overlap phenomenon has more apparent reduction, is capable of providing
The Accurate Reconstruction of original signal has better convergence.Go trend wave theory approach (DFA) that can detect noise-containing micro-
It is noise which accumulates mode in seismic signal in, and it is purified signal which accumulates mode in, and then is retaining microseism signal physics
Under the premise of meaning, the noise contained in microseism is removed, the signal-to-noise ratio of seismic signal is improved.
Description of the drawings
Fig. 1 is the particular flow sheet of the present invention；
Fig. 2 is the waveform recording figure of microseism signal in embodiment；
Fig. 3 is the result figure that microseism signal passes through ICEEMADAN high-precision time frequency analysis in Fig. 2；
Fig. 4 is that microseism signal passes through the intrinsic modal graph that ICEEMADAN is decomposited in Fig. 2；
Fig. 5 is the microseismograms in embodiment after denoising and the result figure by ICEEMADAN high-precision time frequency analysis.
Specific implementation mode
For enable the present invention above and other objects, features and advantages be clearer and more comprehensible, it is cited below particularly go out preferable implementation
Example, and coordinate institute's accompanying drawings, it is described in detail below.
Embodiment 1：Select the microseism data in somewhere.
As shown in Figure 1, a kind of being based on ICEEMDAN and the united microseism signal time-frequency domain first arrival detection methods of DFA, packet
Include following steps：
(1) when carrying out data processing using ICEEMDAN, using microseism signal as primary data, in each of decomposition
Stage adds a specific white noise, and calculates a unique residual error to obtain each IMF, and ICEEMDAN can be adaptively by one
A sophisticated signal is decomposed into a series of intrinsic mode functions (IMF) component, and IMF components satisfaction is divided from high frequency to low frequency series
Cloth；
(2) a series of intrinsic mode functions (IMF) component for obtaining (1) processing is as input, to the mould of noise dominant
State directly removes, and DFA denoisings are carried out to other mode；
(3) by after denoising data reconstruction obtain joint denoising after as a result, by after different scale denoising in step (2)
As a result it carries out fusion reconstruct and obtains the earthquake record after denoising, as useful signal；
(4) first break information of the useful signal obtained by high-precision time frequency analysis detecting step (3).
The above method is specially：
(1) the microseism waveform recording for needing to carry out noise compacting is selected, as shown in Figure 2.It is carried out using ICEEMDAN
When data processing, regard microseism signal as primary data, ICEEMDAN decomposition is carried out to original signal, the IMF decomposited divides
Amount meets Sequence distribution from high frequency to low frequency, and Fig. 3 gives important time-frequency distributions before denoising.Define operator Ej(.)
To acquire j-th of mode by EMD to Setting signal；ωiFor the zero mean Gaussian white noise of unit variance, i=1,2 ... .I, xi
=x+ ωiFor the signal after different noises is added；εkAllow to select signal-to-noise ratio in each stage；M () indicates local mean value operator,
E in EMD1The step of=x-M (x), ICEEDAN, is described below：
101：The calculating x of signal after different noises are added is realized by EMDi=x+ ε0E1(ωi), it is residual that level-one is obtained later
Difference
102：Calculate first IMF components IMF1=x-r1；
103：EMD realizes r1+ε1E2(ωi), calculate two level residual error
104：The K to k=3 ... calculates k rank residual errors
105：Calculate k-th of IMF components IMFk=rk-1-rk；
106：Step 104,105 are repeated until residual error cannot be decomposed；
Flow enters step (2)；
(2) microseismograms after the decomposition for obtaining step (1) processing, as shown in figure 4, as input, to noise master
The mode led can be removed directly, and DFA denoisings are carried out to other mode, and it is that identification contains noise to eliminate fluctuation tendency analysis method
A kind of new method of IMF components：
For i-th of IMF component signal IMFi (t) of a given microseism signal, its accumulated deviation yi is calculated
(t), the average value of the sequence has been filtered off first, is then carried out sequence reconstruct, isometric segmentation is carried out respectively to yi (t), with length k
It is n sequences segmentations into the nonoverlapping sections m, wherein m=[n/k] (round numbers) by length；Not always due to sequence length
The integral multiple of increment k, therefore, sequence tail end is it sometimes appear that the data information of fraction fails to be utilized.In order to make full use of
Data similarly operate the reverse sequence of sequence, and the section of an equal length is obtained.Then to each section, with most
The k number that small square law is included respectively to each section is according to carrying out first-order linear fitting.After calculating each section elimination trend
Mean square deviation (herein by sequence formula calculates respectively with backward).It averages and evolution, calculates to all equal length sections
Obtain DFA wave functions；
Change window size, minimum 5 sampling periods, maximum is no more than a quarter of timed sample sequence quantity.
Draw the log-log graph of the root mean square fluctuation of corresponding time window length, straight slopeFor scaling exponent；Scaling exponent can be used as
The index of roughness；Value is bigger, and signal is more steady；In brief,It is worth the more rapid fluctuation of smaller expression signal.Utilize DFA
Slope realizes the Denoising Algorithm based on ICEEMD, threshold valueThe IMF components containing noise can be distinguished；Generally speaking threshold valueModel
It encloses for α=φ ± 0.25；The purpose of this method first stage ICCEMDAN is white to reduce by threshold range removal IMF components
Noise, therefore, threshold range are defined as α=φ ± 0.5；After processing if the IMF components there is also residual noises, may be used
Interval hard thresholding method carries out post-processing removal residual noise；Flow enters step (3) later；
(3) result after different scale denoising in step (2) is subjected to fusion reconstruct and obtains the earthquake record after denoising, i.e.,
For joint denoising method obtain as a result, as shown in Figure 5；
(4) microseismograms after denoising will be subjected to the high-resolution time frequency analysis based on ICEEMDAN in step (3),
The time-frequency distributions of signal Analysis, to detect the first break information of useful signal.
Wherein, Fig. 3 is the result that microseism signal passes through ICEEMADAN high-precision time frequency analysis；Due to microseism data
In include a large amount of noise, effective first break information is difficult to effectively pick up；Fig. 4 is that microseism signal passes through ICEEMADAN in Fig. 2
The intrinsic mode decomposited；Fig. 5 be microseismograms after denoising and by ICEEMADAN high-precision time frequency analysis as a result,
The first arrival of microseism data can be easily picked up from Fig. 5, as shown in phantom in Figure 5；It can be seen that and be based on from Fig. 2 to Fig. 5
ICEEMADAN decomposes the good application effect for theoretical model, noise with the joint denoising method of DFA and has obtained effective pressure
System.Joint denoising method provided by the invention can effectively suppress the random noise in section, highlight effective microseism letter
Number, it is good to practical microseismograms progress noise pressing result with the joint denoising method of DFA by being decomposed based on CEEMADAN,
Signal-to-noise ratio is significantly improved.This method is that inverting positioning of microseism etc. is laid a good foundation.
Claims (4)
1. one kind is based on ICEEMDAN and the united microseism signal time-frequency domain first arrival detection methods of DFA, which is characterized in that including
Following steps：
(1) when carrying out data processing using ICEEMDAN, using microseism signal as primary data, in each stage of decomposition
A specific white noise is added, and calculates a unique residual error to obtain each IMF, ICEEMDAN can be adaptively multiple by one
Miscellaneous signal decomposition is a series of IMF components, and IMF components meet from high frequency to low frequency Sequence distribution；
(2) a series of IMF components for obtaining (1) processing directly remove the mode of noise dominant, as input to others
Mode carries out DFA denoisings；
(3) data reconstruction after denoising is obtained to the result after joint denoising：By the result after different scale denoising in step (2)
It carries out fusion reconstruct and obtains the earthquake record after denoising, as useful signal；
(4) first break information of the useful signal obtained by high-precision time frequency analysis detecting step (3).
2. detection method as described in claim 1, which is characterized in that in the step (1), the step of ICEEDAN is as follows：
101：The calculating x of signal after different noises are added is realized by EMDi=x+ ε0E1(ωi), level-one residual error is obtained later
102：Calculate first IMF components IMF1=x-r1；
103：EMD realizes r1+ε1E2(ωi), it is residual to calculate two level
104：The K to k=3 ... calculates k rank residual errors
105：Calculate k-th of IMF components IMFk=rk-1-rk；
106：Step 104,105 are repeated until residual error cannot be decomposed；
Wherein：Define operator Ej() is to acquire j-th of mode by EMD to Setting signal；ωiIt is high for the zero-mean of unit variance
This white noise, i=1,2 ... .I, xi=x+ ωiFor the signal after different noises is added；εkAllow to select noise in each stage
Than；M () indicates local mean value operator, E in EMD1=x-M (x).
3. detection method as described in claim 1, which is characterized in that in the step (2), DFA denoising methods are specially：
For i-th of IMF component signals IMF of a given microseism signali(t), its accumulated deviation y is calculatedi(t), first
The average value of the sequence has been filtered off, sequence reconstruct has then been carried out, to yi(t) isometric segmentation is carried out respectively, is by length with length k
N sequences segmentations are at m nonoverlapping sections, wherein m=[n/k] (round numbers)；It is whole due to sequence length not always increment k
Several times, therefore, sequence tail end is it sometimes appear that the data information of fraction fails to be utilized；In order to make full use of data, to sequence
The reverse sequence of row is similarly operated, and the section of an equal length is obtained；Then to each section, with least square method point
The other k number for being included to each section is according to progress first-order linear fitting；The mean square deviation after each section elimination trend is calculated, this
By sequence, formula calculates respectively with backward at place；It averages and evolution to all equal length sections, DFA fluctuation letters is calculated
Number.
4. detection method as claimed in claim 3, which is characterized in that in the step (2), the DFA wave functions pass through
Threshold range removes IMF components to reduce white noise, and threshold range is defined as α=φ ± 0.5.
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| CN113608259B (en) * | 2021-07-16 | 2024-03-26 | 长江大学 | Seismic thin layer detection method based on ICEEMDAN constraint generalized S transformation |
| CN114112013A (en) * | 2021-11-04 | 2022-03-01 | 北京建筑大学 | Method and device for measuring safety of ancient architecture, electronic equipment and storage medium |
| CN114564996A (en) * | 2022-03-03 | 2022-05-31 | 南京航空航天大学 | Method and device for online detection of surge precursors of aero-engine |
| CN116973977A (en) * | 2022-04-24 | 2023-10-31 | 中国人民解放军海军工程大学 | Self-adaptive denoising method for high-speed mobile platform low-frequency electric field target detection |
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