CN106610506A - Thin bed identification method of seismic prospecting - Google Patents
Thin bed identification method of seismic prospecting Download PDFInfo
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- CN106610506A CN106610506A CN201510703106.2A CN201510703106A CN106610506A CN 106610506 A CN106610506 A CN 106610506A CN 201510703106 A CN201510703106 A CN 201510703106A CN 106610506 A CN106610506 A CN 106610506A
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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
- 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 thin bed identification method of seismic prospecting, and belongs to the field of oil-gas geophysical prospecting engineering. The method comprises the following steps that (1) Hilbert transform is carried out on a seismic post-stack signal to obtain an instant phase of the seismic post-stack signal; (2) a time-phase relation diagram of the seismic post-stack signal is obtained according to the instant phase; (3) wavelet transform is carried out on the basis of the time-phase relation diagram of the seismic post-stack signal to obtain a time-scale relation diagram; (4) an abnormal point is extracted on the basis of the time-scale relation diagram; and (5) information of a reflected wave signal is obtained on the basis of the abnormal point. According to the method provided by the invention, seismic phase data can be re-explained by utilizing Hilbert transform on the basis of a phase criterion, further a position identification research can be carried out, the thin bed identification capability can be improved effectively, and interference of aftershock in a result can be eliminated to certain extent.
Description
Technical field
The present invention relates to oil gas physical prospecting engineering field, the more particularly to recognition methodss of seismic prospecting thin layer.
Background technology
Seismic data interpretation is that earthquake information is converted into into geological information, and core is exactly according to the anti-of seismic profile
Feature and earthquake information are penetrated, using PRINCIPLE OF SEISMIC PROSPECTING WITH and basic geological theory, its clear and definite geology meaning is given
Justice and conceptual model.At present, seism processing with explain in, the reservoir larger for thickness have compared with
Good display mode, typically can do composite traces according to seismic reflection signals and log, can be thickness
The larger stratum of degree clearly indicates out.
In seismic prospecting, the concept of thin layer is relative.Because thin layer is with it defined in seismic prospecting
Longitudinal resolution is foundation, i.e., for seismic wavelet, it is impossible to tells top, the stratum of bottom reflecting surface and claims
For thin layer, the TWT that seismic wave is propagated in thin layer is less than half period or half apparent cycle.For
Coating region, because amplitude and the lithology relation of thin bed reflection are a kind of complicated and changeable relations, thin layer is combined
Structural information be included in Seismic reflection character together with lithological information, and by do well shake composite traces with
And other technological means are all difficult to identify that thin layer.
As exploration targets turns to depositional trap or stratigraphic trap by structural trap is found so that the attention of people
Power is increasingly concentrated on the less oil reservoir of scale, and the huge oil and gas reserves that thin reservoir is contained is also more next
More it is subject to people's attention, therefore the emphasis of exploration and development is diverted to the research to thin reservoir.Due to routinely
The restriction of shake data resolution, thin layer identification is always seismic prospecting institute facing challenges, thus earthquake is cutd open
The requirement of the resolution in face also more and more higher.
In original seismic profile, due to the impact of the factor such as attenuation by absorption of seismic frequency and signal so that
Thin layer signal is difficult to, and traditional analysis method often has stronger office in the ability of identification thin layer
It is sex-limited.
The content of the invention
In order to solve at least one aspect of the above-mentioned problems in the prior art and defect, the present invention is provided
A kind of seismic prospecting thin layer recognition methodss.The technical scheme is as follows：
It is an object of the present invention to provide a kind of seismic prospecting thin layer recognition methodss.
According to an aspect of the invention, there is provided a kind of seismic prospecting thin layer recognition methodss, the earthquake is surveyed
Visit thin layer recognition methodss to comprise the following steps：
(1) earthquake poststack signal is carried out into Hilbert transform to obtain the instantaneous phase of the earthquake poststack signal
Position；
(2) based on the instantaneous phase obtaining the when m- phase diagram of the earthquake poststack signal；
(3) carry out wavelet transformation based on the when m- phase diagram of the earthquake poststack signal to fold to obtain earthquake
M- scaling relation figure during signal reconstruction afterwards；
(4) abnormity point extraction is carried out based on m- scaling relation figure during the earthquake poststack signal reconstruction；
(5) information of reflection wave signal is obtained based on the abnormity point.
Specifically, in step (3), the wavelet transformation is comprised the following steps：
A1 by the little wave profile of wavelet function and the earthquake poststack signal when m- phase diagram in song
The starting point alignment of line；
A2 calculate when the starting point is alignd the earthquake poststack signal when m- phase diagram in song
The approximation ratio of line and the little wave profile of the wavelet function, to obtain the first shifted wavelet conversion coefficient；
The little wave profile is moved a unit interval by a3 along time shafts direction, then repeat step
A1-a2 obtains the second shifted wavelet conversion coefficient；
A4 again moves the little wave profile one unit interval along the time shafts direction, then repeats
Step a1-a2 obtains the 3rd wavelet conversion coefficient, by that analogy, until the little wave profile covers complete institute
State earthquake poststack signal when m- phase diagram waveform whole length till, obtain it is all of translation it is little
Wave conversion coefficient；
A5 stretches the little wave profile on yardstick, then repeat step a1-a4, obtains the flat of all yardsticks
Move wavelet conversion coefficient；
A6 is based on all of shifted wavelet conversion coefficient and the shifted wavelet transformation series of all yardsticks
M- scaling relation figure when number obtains the earthquake poststack signal reconstruction.
Further, in step a5, obtaining all of yardstick shifted wavelet conversion coefficient also includes following step
Suddenly：
B1 a flexible unit on the first yardstick, then repeat step a1-a4 acquisition by the little wave profile
First shifted wavelet conversion coefficient of all of first yardstick on a timeline；
B2 on first yardstick stretches the little wave profile two units, then repeat step a1-a4
The second shifted wavelet conversion coefficient of all of first yardstick on a timeline is obtained, by that analogy, institute is obtained
State all of shifted wavelet conversion coefficient of little wave profile flexible not commensurate on the first yardstick；
B3 on second yardstick stretches the little wave profile one unit, then repeat step a1-a4
Obtain the first shifted wavelet conversion coefficient of all of second yardstick on a timeline；
B4 on second yardstick stretches the little wave profile two units, then repeat step a1-a4
The second shifted wavelet conversion coefficient of all of second yardstick on a timeline is obtained, by that analogy, will be all
Yardstick not commensurate of stretching respectively obtain the shifted wavelet conversion coefficient of all yardsticks.
Specifically, in step b4, all yardsticks are all of value in default flexible threshold value.
Further, sentenced based on set in advance on m- scaling relation figure in the earthquake poststack signal reconstruction
Disconnected threshold value obtains the abnormity point.
Further, the information of the reflection wave signal includes the temporal information and echo letter of reflection wave signal
Number directional information.
Further, m- scaling relation figure and the earthquake poststack signal during earthquake poststack signal reconstruction
Time-energy graph of a relation data on a timeline are each other one-to-one relationship.
Further, extracted by Mathematical Method based on m- scaling relation figure during earthquake poststack signal reconstruction
The directional information of the reflection wave signal.
Specifically, in step (1), the method for obtaining the instantaneous phase comprises the following steps：
The earthquake poststack signal is carried out into the Hilbert transform, the solution that the earthquake overlaps signal is obtained
Analysis signal；
The instantaneous phase is obtained based on the analytic signal.
Specifically, the expression formula of the instantaneous phase is：
Wherein, φ (t) is the instantaneous phase；
F (t) is the earthquake poststack signal；
For the corresponding Hilbert transform of the earthquake poststack signal.
The beneficial effect of technical scheme that the present invention is provided is：
(1) the seismic prospecting thin layer recognition methodss that the present invention is provided can be solved in exploration geophysics, and thin layer is normal
The problem that Chang Wufa is differentiated；
(2) the seismic prospecting thin layer recognition methodss that the present invention is provided are changed based on phase place criterion using Hilbert,
Process is reinterpreted to seismic facies position data, layer position Study of recognition is carried out, can be improved well to thin layer
Identification ability；
(3) the seismic prospecting thin layer recognition methodss that the present invention is provided add Fourier transformation using phase information, just
A ripple space shared on a timeline can be reduced, and be isolated and come；
(4) the seismic prospecting thin layer recognition methodss that the present invention is provided can eliminate to a certain extent aftershock and result is made
Into interference.
Description of the drawings
Fig. 1 is the flow chart of seismic prospecting thin layer recognition methodss according to an embodiment of the invention；
Fig. 2 is the Time-energy graph of a relation of the earthquake poststack signal shown in Fig. 1；
Fig. 3 is the when m- phase diagram of the instantaneous phase shown in Fig. 1；
Fig. 4 is the when m- scaling relation figure that the abnormity point shown in Fig. 1 is extracted；
Fig. 5 is the when m- scaling relation figure of the anomaly analysis shown in Fig. 1；
Fig. 6 is the when m- scaling relation figure of the reflection wave signal information shown in Fig. 1.
Specific embodiment
To make the object, technical solutions and advantages of the present invention clearer, below in conjunction with accompanying drawing to the present invention
Embodiment is described in further detail.
Referring to Fig. 1, the stream of seismic prospecting thin layer recognition methodss according to an embodiment of the invention is it illustrates
Journey.Seismic prospecting thin layer recognition methodss comprise the following steps：
(1) earthquake poststack signal is carried out Hilbert transform to obtain the instantaneous phase of earthquake poststack signal；
(2) based on instantaneous phase obtaining the when m- phase diagram of earthquake poststack signal；
(3) carry out wavelet transformation based on the when m- phase diagram of earthquake poststack signal to obtain earthquake poststack letter
Number reconstitution time-scaling relation figure；
(4) abnormity point extraction is carried out based on m- scaling relation figure during earthquake poststack signal reconstruction；
(5) information of reflection wave signal is obtained based on abnormity point.
The amplitude of thin bed reflection is a kind of relation complicated and changeable with the relation of lithology, and the structure of thin layer combination is believed
Breath is included in the feature of seismic reflection together with lithological information, when laminate structure is studied using amplitude information,
Frequency information is a kind of important supplement, and when dipole thickness is different, its echo spectrum of function curvilinear characteristic is not
Together, by the spectrum analyses of reflective information, can be by the difference between its echo dominant frequency and incident wavelet dominant frequency
Not, find thin layer and estimate its thickness.But in the case of ground environment complexity, using general amplitude
Spectral Analysis Method is difficult to differentiate.
In order to overcome deficiency of the prior art, the seismic prospecting thin layer recognition methodss that the present invention is provided being capable of base
Simultaneously seismic data is analyzed using the change of discrete Hilbert and other mathematical measures in phase place criterion and
Explain for exploration geophysics using realize thin layer detect.Echo is recognized and extracted based on phase place criterion
Signal is earthquake poststack signal to be analyzed and is processed by phase place and further, while (uncommon using Hilbert
That Bert) convert the processing method with the combination of wavelet transformation to process received reflection wave signal,
To carry out the identification and extraction of weak signal.
Substantially it is exactly study signal instantaneous using phase place criterion come the relevant information of perception reflex ripple signal
Phase property, its mathematical tool is Hilbert conversion.Specific practice is that f (t) is studied in multifrequency, that is, construct
One complex function with regard to f (t), while and must being fulfilled for analysis condition.Thus the earthquake that the present invention is provided is surveyed
Visit thin layer recognition methodss and can solve the problem that the resolution of seismic data does not reach the problem being identified to thin layer.
In step (1), Hilbert transform is carried out to earthquake poststack signal (as shown in Figure 2), and obtained
The analytic signal of earthquake poststack signal is obtained, the expression formula of the analytic signal is：
Wherein, Z (t) is the analytic signal of earthquake poststack signal；
F (t) is earthquake poststack signal；
For the corresponding Hilbert transform of earthquake poststack signal.
After the analytic signal for obtaining earthquake poststack signal, earthquake poststack letter can be obtained by convolution theorem
Number instantaneous phase, the expression formula of the instantaneous phase is：
Wherein, φ (t) is the instantaneous phase；
F (t) is earthquake poststack signal；
For the corresponding Hilbert transform of earthquake poststack signal.
After instantaneous phase is obtained, seismic wave (i.e. earthquake poststack signal) can be obtained based on the instantaneous phase
Time and phase field graph of a relation (as shown in Figure 3).In the when m- phase relation for obtaining earthquake poststack signal
After figure, wavelet transformation is carried out based on the figure, the wavelet transformation is comprised the following steps：
A1 by the little wave profile of wavelet function and earthquake poststack signal when m- phase diagram in curve
Starting point is alignd；
A2 calculate when starting point is alignd earthquake poststack signal when m- phase diagram in curve and small echo
The approximation ratio of the little wave profile of function, to obtain the first shifted wavelet conversion coefficient；
Little wave profile is moved a unit interval by a3 along time shafts direction, then repeat step a1-a2
Obtain the second shifted wavelet conversion coefficient；
A4 again moves little wave profile one unit interval along time shafts direction, then repeat step a1-a2
The 3rd wavelet conversion coefficient is obtained, by that analogy, until little wave profile covers complete individual earthquake poststack signal
When m- phase diagram waveform whole length till, thus just obtain all of shifted wavelet transformation series
Number；
A5 stretches little wave profile on yardstick, then repeat step a1-a4, to obtain the translation of all yardsticks
Wavelet conversion coefficient；
Shifted wavelet conversion coefficients of the a6 based on all of shifted wavelet conversion coefficient and all yardsticks obtains ground
M- scaling relation figure during shake poststack signal reconstruction.
In step a5, all of yardstick shifted wavelet conversion coefficient is obtained further comprising the steps of：
B1 by little wave profile on the first yardstick stretch a unit, then repeat step a1-a4 obtain when
First shifted wavelet conversion coefficient of all of first yardstick on countershaft；
B2 by little wave profile on the first yardstick stretch two units, then repeat step a1-a4 obtain when
Second shifted wavelet conversion coefficient of all of first yardstick on countershaft, by that analogy, obtains little wave profile and exists
The all of shifted wavelet conversion coefficient of flexible not commensurate on first yardstick；
B3 by little wave profile on the second yardstick stretch a unit, then repeat step a1-a4 obtain when
First shifted wavelet conversion coefficient of all of second yardstick on countershaft；
B4 by little wave profile on the second yardstick stretch two units, then repeat step a1-a4 obtain when
Second shifted wavelet conversion coefficient of all of second yardstick on countershaft, by that analogy, by all of yardstick point
Not Shen Suo not commensurate obtain the shifted wavelet conversion coefficient of all yardsticks.
In an example of the present invention, in step b4, all yardsticks are the institute in default flexible threshold value
Some values.I.e. little wave profile is stretched on yardstick, and the yardstick is that have a range of, and is stretched
The range scale of contracting is range scale set in advance before wavelet transformation is carried out.And stretched
Unit be also a scope, this needs is adjusted according to practical situation, and different small echos are in same chi
It is different that degree carries out flexible unit number, and those skilled in the art can be selected accordingly as needed
Select and adjust.In another example of the present invention, little wave profile is extended or shortened, needed basis
The when m- phase diagram of the earthquake poststack signal of acquisition select during wavelet transformation elongation also to determine
It is to shorten, those skilled in the art should accordingly be selected according to practical situation.
Wavelet transformation is a kind of energy while carrying out the new signal point of localization analysis in time domain or frequency domain
Analysis method.A kind of special small echo is generally adopted in use --- zero logical small echo, it is a smooth function
Second dervative.Therefore the dead-center position of the wavelet transformation under each yardstick, corresponding is in the yardstick
The violent point (being corner position) of signal intensity after lower smoothing.And under normal circumstances, change violent position
Put and usually contain abundant information.Considered based on this point, can be with little in seismic prospecting data process
Wave conversion is filtered and denoising, and zero logical wavelet transformation is applied in the identification of the thin layer of seismic prospecting.
It is inferred to the increase with phase place number to differentiate the standard that thin layer is present, but there is larger mistake in this method
Difference.
And generalized S-transform combines short time discrete Fourier transform with wavelet transformation as a kind of Time-Frequency Analysis Method
Advantage, when being obtained in that preferable-frequency analysis effect, while taking into account temporal resolution and frequency resolution.
Can choose suitable wavelet basis function and calculated according to actual seismic data.Extract with generalized S-transform
Single-frequency section, the top bottom interface of thin layer can be clearly depicted, with higher resolution capability, with carrying
The raising of the single-frequency section frequency for taking, the ability that can recognize thin layer is greatly improved, but while vacation also occurs
Geology thin layer.
And the seismic prospecting thin layer recognition methodss that the present invention is provided be complicated mathematical calculation and software processes each other
A kind of method for organically combining, can preferably recognize the reflection wave signal of thin layer generation and extract echo
The significant information such as time, the direction of signal.
Wavelet transformation can simply be described as a kind of function, and this function changes in the range of finite time,
And meansigma methodss are 0.This qualitatively description means that small echo has two kinds of properties：A, hold with limited
Continuous time and the frequency and amplitude of mutation；B, in the range of finite time meansigma methodss be 0.In finite energy
In signal space, wavelet transformation meets " allowing " condition, i.e.,：
And meet condition：
Wherein, CxFor x-th wavelet conversion coefficient of earthquake poststack signal；
X (ω) is the corresponding Hilbert transform of earthquake poststack signal；
X (τ) is earthquake poststack signal.
Because m- scaling relation figure can be entirely free of during earthquake poststack signal reconstruction after wavelet transformation
Direct current trend components, therefore for the signal of non-stationary can be analyzed effectively, and letter can be extracted
Number local feature.
It is m- in earthquake poststack signal reconstruction when abnormity point extraction is carried out in an also example of the present invention
Abnormity point is obtained based on judgment threshold set in advance on scaling relation figure.It is m- in earthquake poststack signal reconstruction
It is normal point when represented data are in the range of judgment threshold in scaling relation figure, when in earthquake poststack signal
It is anti-for abnormity point, i.e. earthquake when represented data are outside judgment threshold scope in reconstitution time-scaling relation figure
Firing area face.
For seismic signal, because the instantaneous phase of Aftershocks signal is identical.When original earthquake
When having new reflected signal (each reflecting interface there can be a new reflected signal) to arrive in signal, lead to
The instantaneous phase crossed after wavelet transformation is changed, and by the change of instantaneous phase earthquake information is may determine that
In abnormity point, afterwards seismic reflector is found out by the abnormity point, though signal to noise ratio be less than 1 when,
The changing features of instantaneous phase are also fairly obvious.In oil exploration at this stage, due to sand body it is thin, scattered
The features such as cause resolution low, thus seismic data is shown it is difficult to carry out finely to sand body by conventional amplitude
Description, and the present invention effectively can be described by the data obtained with the extraction to phase place to thin sand.
During the earthquake poststack signal reconstruction for obtaining after wavelet transform in m- scaling relation figure, lateral coordinates value is (i.e.
Data on time shafts), with the Time-energy graph of a relation of earthquake poststack signal in time shafts on data be one
One corresponding relation, thus can by the extraction to abnormity point obtain reflection wave signal arrival temporal information or
Information when person says away, can also extract the sense of reflection wave signal by the method for mathematical analyses afterwards
Information.
In traditional analysis method, Fourier transformation is the important means in signal digital processing, and it is in letter
Number process in obtained commonly used.When by Fourier transformation process signal, no matter in time domain or frequency
In rate domain, its sampling interval is constant.So cause in some process of signal, if necessary to improve
Resolution, then be accomplished by reducing the sampling interval.And always do not avoided using such method and thus being brought
Average effect problem.And signal can only be transformed from the time domain to frequency domain by Fourier transform, therefore can not be effective
Ground detects non-stationary signal frequency information over time, and then is difficult to the local characteristicses of signal Analysis.
And short time discrete Fourier transform is the improvement of conventional Fourier Transform, its can from time and frequency zone to signal analysis,
Yet with short time discrete Fourier transform the use of window function is fixed window function, thus short time discrete Fourier transform
Resolution as time frequency analysis is fixed, so will result in the time of short time discrete Fourier transform window function
Can not be optimal simultaneously with frequency resolution.
Below by practical application to doing with principle the step of the recognition methodss of seismic prospecting provided by the present invention
Further instruction.
With reference to shown in Fig. 2 to Fig. 6, RY depression Zhong Mou roads seismic signal is chosen as object of study, carry out thin
The work of layer identification.
With the phase information of earthquake poststack signal, then anomaly extracting and analysis are carried out to phase information, obtained
To the relevant information of reflection wave signal, display is extracted again to earthquake poststack signal, can preferably recognize thin layer
The reflection wave signal of generation simultaneously extracts the significant information such as the time of reflection wave signal, direction.Over the ground
Discrete Hilbert conversion is being carried out to original signal in shake signal processing, seismic signal phase place is being extracted again
Information and substep display, as shown in Figures 3 to 6.It can be seen that carrying out at substep to earthquake poststack signal
After reason, the earthquake poststack signal shown in Fig. 2 can not recognize where by process after (such as Fig. 5 and Fig. 6 institutes
Show) significantly abnormity point can be shown and the identification to thin layer has obvious effect.
The beneficial effect of technical scheme that the present invention is provided is：
(1) the seismic prospecting thin layer recognition methodss that the present invention is provided can be solved in exploration geophysics, thin layer
The problem that usually cannot be differentiated；
(2) the seismic prospecting thin layer recognition methodss that the present invention is provided are changed based on phase place criterion using Hilbert,
Process is reinterpreted to seismic facies position data, layer position Study of recognition is carried out, can be improved well to thin layer
Identification ability；
(3) the seismic prospecting thin layer recognition methodss that the present invention is provided add Fourier transformation using phase information,
A ripple space shared on a timeline can just be reduced, and be isolated and come；
(4) the seismic prospecting thin layer recognition methodss that the present invention is provided can eliminate to a certain extent aftershock to result
The interference for causing.
Presently preferred embodiments of the present invention is the foregoing is only, it is all the present invention's not to limit the present invention
Within spirit and principle, any modification, equivalent substitution and improvements made etc. should be included in the present invention's
Within protection domain.
Claims (11)
1. a kind of seismic prospecting thin layer recognition methodss, the seismic prospecting thin layer recognition methodss comprise the following steps：
(1) earthquake poststack signal is carried out into Hilbert transform to obtain the instantaneous phase of the earthquake poststack signal
Position；
(2) based on the instantaneous phase obtaining the when m- phase diagram of the earthquake poststack signal；
(3) carry out wavelet transformation based on the when m- phase diagram of the earthquake poststack signal to fold to obtain earthquake
M- scaling relation figure during signal reconstruction afterwards；
(4) abnormity point extraction is carried out based on m- scaling relation figure during the earthquake poststack signal reconstruction；
(5) information of reflection wave signal is obtained based on the abnormity point.
2. seismic prospecting thin layer recognition methodss according to claim 1, it is characterised in that
In step (3), the wavelet transformation is comprised the following steps：
A1 by the little wave profile of wavelet function and the earthquake poststack signal when m- phase diagram in song
The starting point alignment of line；
A2 calculate when the starting point is alignd the earthquake poststack signal when m- phase diagram in song
The approximation ratio of line and the little wave profile of the wavelet function, to obtain the first shifted wavelet conversion coefficient；
The little wave profile is moved a unit interval by a3 along time shafts direction, then repeat step
A1-a2 obtains the second shifted wavelet conversion coefficient；
A4 again moves the little wave profile one unit interval along the time shafts direction, then repeats
Step a1-a2 obtains the 3rd wavelet conversion coefficient, by that analogy, until the little wave profile covers complete institute
State earthquake poststack signal when m- phase diagram waveform whole length till, obtain it is all of translation it is little
Wave conversion coefficient；
A5 stretches the little wave profile on yardstick, then repeat step a1-a4, obtains the flat of all yardsticks
Move wavelet conversion coefficient；
A6 is based on all of shifted wavelet conversion coefficient and the shifted wavelet transformation series of all yardsticks
M- scaling relation figure when number obtains the earthquake poststack signal reconstruction.
3. seismic prospecting thin layer recognition methodss according to claim 2, it is characterised in that
In step a5, all of yardstick shifted wavelet conversion coefficient is obtained further comprising the steps of：
B1 a flexible unit on the first yardstick, then repeat step a1-a4 acquisition by the little wave profile
First shifted wavelet conversion coefficient of all of first yardstick on a timeline；
B2 on first yardstick stretches the little wave profile two units, then repeat step a1-a4
The second shifted wavelet conversion coefficient of all of first yardstick on a timeline is obtained, by that analogy, institute is obtained
State all of shifted wavelet conversion coefficient of little wave profile flexible not commensurate on the first yardstick；
B3 on second yardstick stretches the little wave profile one unit, then repeat step a1-a4
Obtain the first shifted wavelet conversion coefficient of all of second yardstick on a timeline；
B4 on second yardstick stretches the little wave profile two units, then repeat step a1-a4
The second shifted wavelet conversion coefficient of all of second yardstick on a timeline is obtained, by that analogy, will be all
Yardstick not commensurate of stretching respectively obtain the shifted wavelet conversion coefficient of all yardsticks.
4. seismic prospecting thin layer recognition methodss according to claim 3, it is characterised in that
In step b4, all yardsticks are all of value in default flexible threshold value.
5. seismic prospecting thin layer recognition methodss according to claim 4, it is characterised in that
Obtained based on judgment threshold set in advance on m- scaling relation figure in the earthquake poststack signal reconstruction
The abnormity point.
6. seismic prospecting thin layer recognition methodss according to claim 5, it is characterised in that
The information of the reflection wave signal includes the direction letter of the temporal information of reflection wave signal and reflection wave signal
Breath.
7. seismic prospecting thin layer recognition methodss according to claim 6, it is characterised in that
The temporal information of the reflection wave signal is the temporal information that the reflection wave signal is reached.
8. seismic prospecting thin layer recognition methodss according to claim 7, it is characterised in that
The Time-energy of m- scaling relation figure and the earthquake poststack signal during earthquake poststack signal reconstruction
Graph of a relation data on a timeline are each other one-to-one relationship.
9. seismic prospecting thin layer recognition methodss according to claim 8, it is characterised in that
Extract described anti-by Mathematical Method based on m- scaling relation figure during the earthquake poststack signal reconstruction
The directional information of ejected wave signal.
10. the seismic prospecting thin layer recognition methodss according to any one of claim 1-9, it is characterised in that
In step (1), the method for obtaining the instantaneous phase comprises the following steps：
The earthquake poststack signal is carried out into the Hilbert transform, the solution that the earthquake overlaps signal is obtained
Analysis signal；
The instantaneous phase is obtained based on the analytic signal.
11. seismic prospecting thin layer recognition methodss according to claim 10, it is characterised in that
The expression formula of the instantaneous phase is：
Wherein, φ (t) is the instantaneous phase；
F (t) is the earthquake poststack signal；
For the corresponding Hilbert transform of the earthquake poststack signal.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107831541A (en) * | 2017-11-17 | 2018-03-23 | 中国石油天然气集团公司 | Thin strate recognition methods and device based on high density VSP data |
| CN109581477A (en) * | 2017-09-29 | 2019-04-05 | 中国石油化工股份有限公司 | Predict the method and system of seismic reflector |
| CN111880222A (en) * | 2020-09-17 | 2020-11-03 | 东北大学 | Shearlet transformation-based seismic image minor fault identification enhancement method |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101359356A (en) * | 2007-08-03 | 2009-02-04 | 联想(北京)有限公司 | Method and system for deleting or isolating computer virus |
| CN101545983A (en) * | 2009-05-05 | 2009-09-30 | 中国石油集团西北地质研究所 | Multiattribute frequency division imaging method based on wavelet transformation |
| WO2009136387A2 (en) * | 2008-05-09 | 2009-11-12 | Total Sa | Combining seismic data sets with overlapping bandwidths |
| CN102508295A (en) * | 2011-10-28 | 2012-06-20 | 西安交通大学 | Earthquake stratum thickness variation analysis method |
| CN103969685A (en) * | 2014-04-23 | 2014-08-06 | 长江大学 | Method for processing thin interbed seismic signals |
| US8976624B2 (en) * | 2006-05-07 | 2015-03-10 | Geocyber Solutions, Inc. | System and method for processing seismic data for interpretation |
-
2015
- 2015-10-26 CN CN201510703106.2A patent/CN106610506B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8976624B2 (en) * | 2006-05-07 | 2015-03-10 | Geocyber Solutions, Inc. | System and method for processing seismic data for interpretation |
| CN101359356A (en) * | 2007-08-03 | 2009-02-04 | 联想(北京)有限公司 | Method and system for deleting or isolating computer virus |
| WO2009136387A2 (en) * | 2008-05-09 | 2009-11-12 | Total Sa | Combining seismic data sets with overlapping bandwidths |
| CN101545983A (en) * | 2009-05-05 | 2009-09-30 | 中国石油集团西北地质研究所 | Multiattribute frequency division imaging method based on wavelet transformation |
| CN102508295A (en) * | 2011-10-28 | 2012-06-20 | 西安交通大学 | Earthquake stratum thickness variation analysis method |
| CN103969685A (en) * | 2014-04-23 | 2014-08-06 | 长江大学 | Method for processing thin interbed seismic signals |
Non-Patent Citations (1)
| Title |
|---|
| 李枝文 等: "Hilbert变换与小波变换在探地雷达资料处理中的应用", 《工程地球物理学报》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109581477A (en) * | 2017-09-29 | 2019-04-05 | 中国石油化工股份有限公司 | Predict the method and system of seismic reflector |
| CN109581477B (en) * | 2017-09-29 | 2020-08-25 | 中国石油化工股份有限公司 | Method and system for predicting seismic reflection interface |
| CN107831541A (en) * | 2017-11-17 | 2018-03-23 | 中国石油天然气集团公司 | Thin strate recognition methods and device based on high density VSP data |
| CN111880222A (en) * | 2020-09-17 | 2020-11-03 | 东北大学 | Shearlet transformation-based seismic image minor fault identification enhancement method |
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