CN108152855A - A kind of earthquake fluid recognition methods based on EEMD-SVD - Google Patents
A kind of earthquake fluid recognition methods based on EEMD-SVD Download PDFInfo
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- 239000012530 fluid Substances 0.000 title claims abstract description 25
- 239000011159 matrix material Substances 0.000 claims description 17
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- 230000003252 repetitive effect Effects 0.000 claims description 5
- 239000002131 composite material Substances 0.000 claims description 3
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- 238000004458 analytical method Methods 0.000 description 3
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- 238000010521 absorption reaction Methods 0.000 description 1
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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. for interpretation or for event detection
- G01V1/30—Analysis
- G01V1/306—Analysis for determining physical properties of the subsurface, e.g. impedance, porosity or attenuation profiles
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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. for interpretation or for event detection
- G01V1/36—Effecting static or dynamic corrections on records, e.g. correcting spread; Correlating seismic signals; Eliminating effects of unwanted energy
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Abstract
The invention discloses a kind of earthquake fluid recognition methods based on EEMD SVD, the present invention decomposes original seismic signal using EEMD, and the Gaussian noise that remains in each layer IMF component of the seismic signal in EEMD decomposable processes is eliminated according to svd algorithm, hi-fi of amplitude is improved, eliminates invalid redundancy.The present invention utilizes the algorithm advantage of EEMD SVD, both effectively overcome modal overlap and end effect, the random noise that EEMD is generated in decomposable process can effectively be inhibited again, ensure the hi-fi of amplitude in seismic signal decomposable process, be conducive to improve earthquake fluid precision of prediction.
Description
Technical field
Present invention relates particularly to a kind of poststack earthquake fluid Forecasting Methodologies based on EEMD-SVD.
Background technology
Fluid prediction method is being carried out using seismic data, it is contemplated that the seismic wave attenuation by absorption mechanism of fluid utilizes frequency
Rate attenuation gradient method carries out fluid prediction.The frequency attenuation gradient of seismic wave refers in the base that time frequency analysis is carried out to seismic wave
On plinth, the slope value that is fitted to the amplitude envelope of seismic wave time-frequency result medium-high frequency part.When being carried out to seismic wave
Frequency analysis can obtain a variety of attributes related with frequency of seismic wave, such as the gross energy of seismic wave, seismic wave energy maximum value
Corresponding frequency values (i.e. the dominant frequency of seismic wave), instantaneous frequency, Instantaneous dominant frequency, frequency attenuation gradient etc..
2014, Xue Yajuan proposed HHT Time-Frequency Analysis Methods, was predicted for earthquake fluid.This method mainly utilizes
EMD empirical mode decompositions carry out fluid prediction using earthquake low-frequency information, and this method becomes relative to Fourier in short-term and small echo
It changes, time frequency resolution has some improvement.This method is disadvantageous in that, and for be mutated larger nonlinear properties can not gram
It takes modal overlap to imitate with endpoint, so will result in progress earthquake low-frequency information analytical error on this basis, it is pre- to influence fluid
Survey effect and precision.
Invention content
Present invention aims in view of the deficiencies of the prior art, providing, a kind of poststack earthquake fluid based on EEMD-SVD is pre-
Survey method.The present invention fully considers the non-stationary of seismic data, using the algorithm advantage of EEMD-SVD, both effectively overcomes mode
Aliasing and end effect, and can effectively inhibit the random noise that EEMD is generated in decomposable process, ensure that seismic signal decomposes
Hi-fi of amplitude in the process is conducive to improve earthquake fluid precision of prediction.
For this purpose, the present invention uses following technical scheme：A kind of earthquake fluid recognition methods based on EEMD-SVD, feature
It is, specific steps are as follows：
Step 1：Earthquake poststack single track CDP data are inputted, EEMD empirical mode decompositions is carried out, obtains IMF components；It will treat
It handles and equal length not constant amplitude white Gaussian noise is added in CDP data, composite signal EMD is decomposed, repetitive operation k times obtains IMF
Component cikWith remainder rik；
White noise acoustic amplitude simultaneously follows following rule：
A=ek or lne+0.5alnk=0
In formula, e is original signal standard deviation, i.e. signal and the deviation of EMD reconstruction results, and a is white noise acoustic amplitude；It is white when adding in
During noise amplitude increase, the number k of EMD repetitive assignments need to accordingly increase, to reduce influence of the noise to decomposition result.EEMD
Screening result will not generate a great difference because of some or several parameter settings difference, that is, not depend on the subjective intervention of people,
Still there is adaptivity.
Step 2：To IMF component ensemble averages,
In formula, n is the number that EMD is decomposed；
Step 3：Signal to Noise Ratio (SNR) calculating is carried out, and the IMF first for getting rid of low signal-to-noise ratio divides to the IMF components of output
Amount；
Step 4：Remaining each layer IMF is respectively adopted based on Hankle matrix singular value decomposition denoisings, by unusual
It is worth the catastrophe point selection depression of order parameter of curve；Its specific practice is as follows：If single-channel seismic CDP signals are X, road number is 1, and the time adopts
Single-channel seismic CDP signals can be then set as the matrix A of 1*n, can be decomposed by sampling point numerical digit n：
A=UWVT
A=UWV in formulaTW=diag (δ1,δ2,…,δi) it is whole non-zero singular values of matrix A, and arrange from big to small；
U and V is the left and right singular matrix of matrix A respectively；According to the characteristics of Hankle matrixes, most of energy of original signal is concentrated mainly on
In larger singular value, and the small minutia of energy then corresponds to smaller singular value in signal, and these small singular values are past
It is past to correspond to signal noise, therefore effective order of signal space singular value is determined by suitable method, then to signal space
Matrix reconstruction corresponding entry be averaged and can realize effective noise reduction process is carried out to the IMF components containing white noise information；
Step 5：To earthquake, each CDP signal cycles step 1 is to step 4, you can decomposes original earthquake 2D signal
For the IMF multicomponent seismic 2D signals after N number of noise reduction；
Step 6：Hilbert transform is done to the IMF multicomponent seismic 2D signals of low frequency, you can obtain containing independent low frequency
The earthquake fluid prediction section of information, enhances principle according to low frequency energy containing fluid, can be strong by earthquake IMF low frequency section energy
Weak identification fluid position.
The present invention can reach following advantageous effect：The present invention decomposes original seismic signal using EEMD, and is calculated according to SVD
Method eliminates the Gaussian noise that remains in each layer IMF component of the seismic signal in EEMD decomposable processes, improves hi-fi of amplitude,
Eliminate invalid redundancy.This method both effectively overcomes the EMD mistakes of HHT transformation from the non-stationary of seismic data is fully considered
The modal overlap and end effect that may be brought are decomposed in journey, and can ensure higher frequency domain resolution ratio.
Specific embodiment
The present invention is the earthquake fluid recognition methods based on EEMD-SVD, then concrete implementation step is as follows：
Step 1：Earthquake poststack single track CDP data are inputted, EEMD empirical mode decompositions is carried out, obtains IMF components.It will treat
It handles and equal length not constant amplitude white Gaussian noise is added in CDP data, composite signal EMD is decomposed, repetitive operation k times obtains IMF
Component cikWith remainder rik。
White noise acoustic amplitude should follow following rule：
A=ek or lne+0.5alnk=0
In formula, e is original signal standard deviation, i.e. signal and the deviation of EMD reconstruction results, and a is white noise acoustic amplitude.It can be seen that
When adding in white noise acoustic amplitude increase, the number k of EMD repetitive assignments need to accordingly increase, to reduce noise to decomposition result
It influences.EEMD screening results will not generate a great difference because of some or several parameter settings difference, that is, not depend on people's
Subjectivity intervention, still with adaptivity.
Step 2：To IMF component ensemble averages,
In formula, n is the number that EMD is decomposed.
Step 3：Signal to Noise Ratio (SNR) calculating is carried out, and the IMF first for getting rid of low signal-to-noise ratio divides to the IMF components of output
Amount.
Step 4：Remaining each layer IMF is respectively adopted based on Hankle matrix singular value decomposition denoisings, by unusual
It is worth the catastrophe point selection depression of order parameter of curve.Its specific practice is as follows：If single-channel seismic CDP signals are X, road number is 1, and the time adopts
Single-channel seismic CDP signals can be then set as the matrix A of 1*n, can be decomposed by sampling point numerical digit n：
A=UWVT
A=UWV in formulaTW=diag (δ1,δ2,…,δi) it is whole non-zero singular values of matrix A, and arrange from big to small.
U and V is the left and right singular matrix of matrix A respectively.According to the characteristics of Hankle matrixes, most of energy of original signal is concentrated mainly on
In larger singular value, and the small minutia of energy then corresponds to smaller singular value in signal, and these small singular values are past
It is past to correspond to signal noise, therefore effective order of signal space singular value is determined by suitable method, then to signal space
Matrix reconstruction corresponding entry be averaged and can realize effective noise reduction process is carried out to the IMF components containing white noise information.
Step 5：To earthquake, each CDP signal cycles step 1 is to step 4, you can decomposes original earthquake 2D signal
For the IMF multicomponent seismic 2D signals after N number of noise reduction.
Step 6：Hilbert transform is done to the IMF multicomponent seismic 2D signals of low frequency, you can obtain containing independent low frequency
The earthquake fluid prediction section of information, enhances principle according to low frequency energy containing fluid, can be strong by earthquake IMF low frequency section energy
Weak identification fluid position.
Secondly, the present invention can also utilize existing HHT transform methods, using EEMD to post-stack seismic data into line frequency
After numeric field data is decomposed, then Hilbert transform is carried out, but its frequency domain data can include Gauss white noise compared with this method, influence ground
The fidelity of signal is shaken with protecting width.
The basic principles, main features and the advantages of the invention have been shown and described above.The technology of the industry
Personnel are it should be appreciated that the present invention is not limited to the above embodiments, and the above embodiments and description only describe this
The principle of invention, without departing from the spirit and scope of the present invention, various changes and improvements may be made to the invention, these changes
Change and improvement all fall within the protetion scope of the claimed invention.The claimed scope of the invention by appended claims and its
Equivalent thereof.
Claims (1)
1. a kind of earthquake fluid recognition methods based on EEMD-SVD, it is characterised in that specific steps are as follows：
Step 1：Earthquake poststack single track CDP data are inputted, EEMD empirical mode decompositions is carried out, obtains IMF components；It will be pending
Equal length not constant amplitude white Gaussian noise is added in CDP data, composite signal EMD is decomposed, repetitive operation k times obtains IMF components
cikWith remainder rik；
White noise acoustic amplitude simultaneously follows following rule：
A=ek or lne+0.5alnk=0
In formula, e is original signal standard deviation, i.e. signal and the deviation of EMD reconstruction results, and a is white noise acoustic amplitude；
Step 2：To IMF component ensemble averages,
In formula, n is the number that EMD is decomposed；
Step 3：Signal to Noise Ratio (SNR) calculating is carried out, and get rid of the first components of IMF of low signal-to-noise ratio to the IMF components of output；
Step 4：Remaining each layer IMF is respectively adopted based on Hankle matrix singular value decomposition denoisings, passes through singular value song
The catastrophe point selection depression of order parameter of line；Its specific practice is as follows：If single-channel seismic CDP signals are X, road number is 1, time sampling point
Single-channel seismic CDP signals can be then set as the matrix A of 1*n, can be decomposed by numerical digit n：
A=UWVT
A=UWV in formulaTW=diag (δ1,δ2,…,δi) it is whole non-zero singular values of matrix A, and arrange from big to small；U and V
It is the left and right singular matrix of matrix A respectively；According to the characteristics of Hankle matrixes, most of energy of original signal is concentrated mainly on larger
Singular value on, and the small minutia of energy then corresponds to smaller singular value in signal, and these small singular values are often right
Signal noise is answered, therefore effective order of signal space singular value is determined by suitable method, then the square to signal space
Battle array reconstruct corresponding entry, which is averaged, can realize to the effective noise reduction process of IMF components progress containing white noise information；
Step 5：To earthquake, each CDP signal cycles step 1 is to step 4, you can original earthquake 2D signal is decomposed into N
IMF multicomponent seismic 2D signals after a noise reduction；
Step 6：Hilbert transform is done to the IMF multicomponent seismic 2D signals of low frequency, you can obtain containing independent low-frequency information
Earthquake fluid prediction section, according to low frequency energy containing fluid enhance principle, can be known by earthquake IMF low frequency section energy power
Other fluid position.
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109884697A (en) * | 2019-03-20 | 2019-06-14 | 中国石油化工股份有限公司 | Glutenite sedimentary facies earthquake prediction method based on complete overall experience mode decomposition |
| CN109991657A (en) * | 2018-11-15 | 2019-07-09 | 成都理工大学 | High resolution seismic data processing method based on inverse two points of recursion singular value decompositions |
| CN110780343A (en) * | 2020-01-02 | 2020-02-11 | 四川大学 | Automatic microseismic signal identification method based on waveform frequency band characteristics |
| CN111290024A (en) * | 2020-03-05 | 2020-06-16 | 吉林大学 | SVD self-adaptive seismic data noise suppression method |
| CN111709279A (en) * | 2020-04-30 | 2020-09-25 | 天津城建大学 | Algorithm for separating microseism noise mixed signals by utilizing SVD-EMD (singular value decomposition-empirical mode decomposition) algorithm |
| CN112101089A (en) * | 2020-07-27 | 2020-12-18 | 北京建筑大学 | Signal noise reduction method and device, electronic equipment and storage medium |
| CN112162314A (en) * | 2020-09-25 | 2021-01-01 | 武汉市工程科学技术研究院 | Two-dimensional interpolation method for artificial seismic signal profile |
| CN112379450A (en) * | 2020-10-30 | 2021-02-19 | 中国石油天然气集团有限公司 | Signal-to-noise ratio obtaining method and device for time-frequency electromagnetic square wave signal |
| CN113255482A (en) * | 2021-05-11 | 2021-08-13 | 福建工程学院 | HHT pulse parameter identification-based far-field harmonic wave earthquake motion synthesis method |
| CN116636423A (en) * | 2023-07-26 | 2023-08-25 | 云南农业大学 | Efficient cultivation method of poria cocos strain |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6529794B1 (en) * | 1997-08-22 | 2003-03-04 | Siemens Aktiengesellschaft | Method and device for measuring distance and speed |
| CN107024718A (en) * | 2017-05-31 | 2017-08-08 | 西南石油大学 | Poststack earthquake fluid Forecasting Methodology based on CEEMD SPWVD Time-frequency Spectrum Analysis |
| CN107192553A (en) * | 2017-06-28 | 2017-09-22 | 石家庄铁道大学 | Gear-box combined failure diagnostic method based on blind source separating |
| KR20170127939A (en) * | 2016-05-13 | 2017-11-22 | 국방과학연구소 | Apparatus for estimating direction of arrival based on a circularly arraying antenna compensating intermutual interference and method therefor |
| CN107422381A (en) * | 2017-09-18 | 2017-12-01 | 西南石油大学 | A kind of earthquake low-frequency information fluid prediction method based on EEMD ICA |
-
2017
- 2017-12-14 CN CN201711333698.9A patent/CN108152855A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6529794B1 (en) * | 1997-08-22 | 2003-03-04 | Siemens Aktiengesellschaft | Method and device for measuring distance and speed |
| KR20170127939A (en) * | 2016-05-13 | 2017-11-22 | 국방과학연구소 | Apparatus for estimating direction of arrival based on a circularly arraying antenna compensating intermutual interference and method therefor |
| CN107024718A (en) * | 2017-05-31 | 2017-08-08 | 西南石油大学 | Poststack earthquake fluid Forecasting Methodology based on CEEMD SPWVD Time-frequency Spectrum Analysis |
| CN107192553A (en) * | 2017-06-28 | 2017-09-22 | 石家庄铁道大学 | Gear-box combined failure diagnostic method based on blind source separating |
| CN107422381A (en) * | 2017-09-18 | 2017-12-01 | 西南石油大学 | A kind of earthquake low-frequency information fluid prediction method based on EEMD ICA |
Non-Patent Citations (4)
| Title |
|---|
| 丁建明 等: "高速列车万向轴动不平衡检测的EEMD-Hankel-SVD方法", 《机械工程学报》 * |
| 杨萍 等: "亚洲季风区过去700年来夏季极端干/湿事件多尺度变化特征分析", 《灾害学》 * |
| 王红军: "《基于知识的机电***故障诊断与预测技术》", 31 January 2014, 中国财富出版社 * |
| 郑旭 等: "基于EEMD与广义S变换的内燃机噪声源识别研究", 《内燃机工程》 * |
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109991657A (en) * | 2018-11-15 | 2019-07-09 | 成都理工大学 | High resolution seismic data processing method based on inverse two points of recursion singular value decompositions |
| CN109991657B (en) * | 2018-11-15 | 2021-10-15 | 成都理工大学 | Seismic data high-resolution processing method based on inverse binary recursive singular value decomposition |
| CN109884697B (en) * | 2019-03-20 | 2021-06-22 | 中国石油化工股份有限公司 | Glutenite sedimentary facies earthquake prediction method based on complete ensemble empirical mode decomposition |
| CN109884697A (en) * | 2019-03-20 | 2019-06-14 | 中国石油化工股份有限公司 | Glutenite sedimentary facies earthquake prediction method based on complete overall experience mode decomposition |
| CN110780343A (en) * | 2020-01-02 | 2020-02-11 | 四川大学 | Automatic microseismic signal identification method based on waveform frequency band characteristics |
| CN111290024A (en) * | 2020-03-05 | 2020-06-16 | 吉林大学 | SVD self-adaptive seismic data noise suppression method |
| CN111709279A (en) * | 2020-04-30 | 2020-09-25 | 天津城建大学 | Algorithm for separating microseism noise mixed signals by utilizing SVD-EMD (singular value decomposition-empirical mode decomposition) algorithm |
| CN111709279B (en) * | 2020-04-30 | 2023-07-21 | 天津城建大学 | Algorithm for separating microseism noise mixed signal by SVD-EMD algorithm |
| CN112101089A (en) * | 2020-07-27 | 2020-12-18 | 北京建筑大学 | Signal noise reduction method and device, electronic equipment and storage medium |
| CN112101089B (en) * | 2020-07-27 | 2023-10-10 | 北京建筑大学 | Signal noise reduction method and device, electronic equipment and storage medium |
| CN112162314A (en) * | 2020-09-25 | 2021-01-01 | 武汉市工程科学技术研究院 | Two-dimensional interpolation method for artificial seismic signal profile |
| CN112162314B (en) * | 2020-09-25 | 2024-01-02 | 武汉市工程科学技术研究院 | Two-dimensional interpolation method of artificial seismic signal section |
| CN112379450A (en) * | 2020-10-30 | 2021-02-19 | 中国石油天然气集团有限公司 | Signal-to-noise ratio obtaining method and device for time-frequency electromagnetic square wave signal |
| CN113255482A (en) * | 2021-05-11 | 2021-08-13 | 福建工程学院 | HHT pulse parameter identification-based far-field harmonic wave earthquake motion synthesis method |
| CN113255482B (en) * | 2021-05-11 | 2023-07-18 | 福建工程学院 | Far-field harmonic like and earthquake motion synthesis method based on HHT pulse parameter identification |
| CN116636423A (en) * | 2023-07-26 | 2023-08-25 | 云南农业大学 | Efficient cultivation method of poria cocos strain |
| CN116636423B (en) * | 2023-07-26 | 2023-09-26 | 云南农业大学 | Efficient cultivation method of poria cocos strain |
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