CN115047523A - Seismic data dynamic correction method based on density clustering algorithm - Google Patents
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Abstract
The invention discloses a seismic data dynamic correction method based on a density clustering algorithm, which comprises the following steps: performing early-stage processing on the original seismic data to obtain CMP gather data; performing first density clustering on CMP gather data, and screening out all wave crests of all reflection interfaces; performing second density clustering on the screened peak data, and screening out peaks corresponding to each reflection interface; obtaining a dynamic correction time-speed pair corresponding to each reflection interface; and (5) constructing a fold line function and interpolating according to the time-speed pairs of all the reflection interfaces, and dynamically correcting the CMP gather data to obtain a corrected seismic gather. The method disclosed by the invention starts from data, and performs cluster analysis based on the characteristics of stronger signal energy, better continuity and the like at a reflection interface, so that dynamic correction can be performed more objectively and efficiently.
Description
Technical Field
The invention relates to the technical field of seismic data processing, in particular to a seismic data dynamic correction method based on a density clustering algorithm.
Background
In the multi-channel seismic data processing process, an important step is to perform dynamic correction, and the processing quality of the step is directly related to the final imaging effect. The key parameter of the kinetic correction is to find the kinetic correction "time-velocity" pair. The conventional calculation method comprises the steps of firstly carrying out speed scanning on a CMP gather to obtain a 'speed spectrum', namely calculating the superposition energy in a certain time and speed step length, then carrying out reasonable judgment by technical personnel according to the energy level of an energy cluster in the speed spectrum, the rationality of geological horizons and other factors, and manually picking up
The method has the defects of low efficiency, strong subjectivity and the like.
With the continuous improvement of computer computing power, automatic picking methods are developed gradually, but are still realized based on velocity spectrum, and can be mainly divided into two main methods: the first category is to simulate artificial recognition, the algorithm first matches all energy thresholds
And (4) all the data sets are included into one data set, then the data sets are judged one by one according to a set empirical rule (such as minimum or maximum speed, layer speed, trend closeness, minimum distance and the like), and finally the optimal solution is connected to form a speed curve. The second type is image recognition, in which the velocity spectrum is regarded as an image, and the highest matching degree with the recognition feature in the velocity spectrum is obtained by the image recognition method
And a velocity profile is formed. Such methods can be further broadly classified into 3 types:
(1) an objective function method, namely constructing an objective function by using an initial speed model, and automatically searching a global optimal solution to construct a speed curve, wherein a typical example is a Monte Carlo method;
(2) sample learning method-gathering part of a trace, verified
Learning a sample data input system, and then predicting time-speed pairs of other gathers to obtain a speed curve, wherein various artificial intelligence algorithms are used in the general method;
(3) feature clustering, which is a method of obtaining a velocity curve by analyzing the energy cluster features in the velocity spectrum and using an unsupervised clustering method such as K-means in combination with the velocity variation trend.
The methods are basically developed based on a velocity spectrum, and in a certain sense, the methods are used for simulating the processes of manual discrimination and pickup. On one hand, the existing method cannot completely simulate the comprehensive analysis process of a human, can only give out a judgment result according to a deterministic condition, is difficult to deal with the conditions of speed mutation, speed inversion and the like, and is easy to cause misjudgment; on the other hand, from the speed spectrum obtaining to the calculation or image recognition based on the spectrum data, the process lengthens the discrimination process, increases the calculation cost, introduces new variables, and is not beneficial to improving the operation efficiency and the discrimination precision. The method provided by the patent does not depend on a velocity spectrum, starts from data, and conducts clustering analysis based on the characteristics of stronger signal energy, better continuity and the like at a reflection interface, and is more objective and efficient.
Disclosure of Invention
In order to solve the technical problems, the invention provides a seismic data dynamic correction method based on a density clustering algorithm, which does not depend on a velocity spectrum, but performs clustering analysis based on the characteristics of stronger signal energy, better continuity and the like at a reflection interface from data per se, and can perform dynamic correction more objectively and efficiently.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a seismic data dynamic correction method based on a density clustering algorithm comprises the following steps:
firstly, carrying out early-stage processing on original seismic data to obtain CMP gather data;
secondly, performing first density clustering on the CMP gather data: firstly, counting the distance between two sampling points in each channel of the seismic data, and calculating the average number; then, performing first density clustering by taking the average number as a scanning radius, and screening out all wave crests at all reflection interfaces;
thirdly, performing secondary density clustering on the screened peak data: firstly, counting the distance between two front sampling points and two rear sampling points in the screened peak data, and solving an average number; secondly, performing second density clustering by taking the average number as a scanning radius, and screening out wave crests corresponding to all reflection interfaces;
step four, calculating a dynamic correction time-speed pair: selecting each wave crest corresponding to the 1 st reflection interface, taking the two-way travel time of the 1 st wave crest as the self-excitation and self-receiving time of the reflection interface, and marking as T1 0 The hyperbolic curve fitting is performed on each trace of peak data of the reflection interface to obtain the dynamic correction speed of the reflection interface, which is recorded as V1 NMO To obtain a kinematically corrected time-velocity pair (T1) of the reflective interface 0 ,V1 NMO ) (ii) a By analogy, the dynamic correction time-speed pair (Tj) corresponding to each reflection interface is obtained 0 ,Vj NMO ) J represents the jth reflective interface;
step five, obtaining the seismic gather after dynamic correction: correcting time-velocity pairs (Tj) with corresponding dynamics of all reflective interfaces 0 ,Vj NMO ) And constructing a fold line function and performing interpolation, and performing dynamic correction on the CMP gather data to obtain a seismic gather after dynamic correction.
In the scheme, the specific method of the step one is as follows:
firstly, removing various noises;
secondly, performing spherical diffusion compensation on the data;
finally, the CMP gather data is extracted with the common center point as the main key.
In the above scheme, in the second step, the scanning radius eps of the first density cluster 1 The calculation formula is as follows:
in the formula, N sample The number of sampling points for a trace of seismic data,
is the abscissa of the ith and (i +1) th sample points,
the ordinate of the ith and (i +1) th sample points.
In the above scheme, in the second step, when density clustering is performed for the first time, the minimum number miniPts of the classes is 5, and the distance between the sampling points corresponding to the reflection interface is significantly greater than the distance between the sampling points of the non-reflection interface, so that the sampling points of the non-reflection interface can be classified into several classes because the distance between the sampling points is smaller than the scanning radius, and the peaks at the reflection interface can be classified into a single class because the distance between the sampling points is greater than the scanning radius, and therefore, the peaks corresponding to all the reflection interfaces are screened out.
In the above scheme, in step three, the scanning radius eps of the second density cluster 2 The calculation formula is as follows:
in the formula, N peak The total number of peak sampling points obtained by a first density clustering method in a seismic data,
the abscissa of the ith and (i +1) th peak sample points,
the ordinate of the ith and (i +1) th peak sample points.
In the above scheme, in the third step, when performing density clustering for the second time, the minimum number miniPts of the classes is 5, and the characteristic that there is a significant distance between the reflecting interfaces is utilized to separate the wave peaks corresponding to the reflecting interfaces, that is, the number of the classes is consistent with the number of the reflecting interfaces.
In the scheme, the specific method of the step four is as follows:
selecting each wave crest of the 1 st reflection interface, and performing hyperbolic fitting on all wave crest data of the 1 st reflection interface according to the formula (3) to obtain the dynamic correction speed V1 of the reflection interface NMO ：
In the formula, t1 i For the two-way travel of the ith sampling point in all the peak data corresponding to the extracted 1 st reflection interface, T1 0 Self-excited self-acceptance time of the 1 st reflecting interface, X1 i The offset of the ith sample point in all the peak data corresponding to the 1 st reflecting interface, V1 NMO And the corresponding dynamic correction speed of the 1 st reflecting interface is obtained.
In the scheme, the concrete method of the step five is as follows:
(1) dynamic correction time-speed pairs (Tj) corresponding to each reflective interface according to self-excitation self-collection time 0 ,Vj NMO ) Sorting, each two adjacent groups (Tj) 0 ,Vj NMO ) Constructing a straight-line function, and forming a broken-line function by all time-velocity pairs as shown in formula (4):
in the formula (I), the compound is shown in the specification,
representing the rate of kinetic correction at the ith sample point,
representing the dynamic correction time, Vj, of the ith sample point NMO Representing the motion correction speed corresponding to the jth reflecting interface, V (j +1) NMO Represents the corresponding dynamic correction speed of the j +1 th reflection interface, T (j +1) 0 Is the self-excited self-receiving time of the (j +1) th reflection interface, Tj 0 The self-excitation and self-collection time of the jth reflecting interface is obtained;
(2) calculating the dynamic correction time-speed pairs of all sampling points on the CMP gather according to the above polygonal line function
(3) Calculating the dynamic correction dt of each sampling point by the formula (5) i ：
In the formula (dt) i Representing the amount of motional correction, x, at the ith sample point i Represents the offset of the ith sampling point;
(4) let the two-way travel time t of each sampling point i And dt i Making a difference, and calculating a new two-way travel time t 'of a sampling point i' i To complete dynamic correction：
t′ i ＝t i -dt i (6)
In the formula (II), t' i Representing the time of the double-trip travel after the i-th sample point dynamic correction, t i Representing the time of the two-way trip before the i-th sample point is dynamically corrected.
Through the technical scheme, the seismic data dynamic correction method based on the density clustering algorithm has the following beneficial effects:
based on the energy characteristics of signals at the reflecting interfaces, the invention separates and obtains the peak data corresponding to each reflecting interface through twice Density Clustering (DBSCAN) and then respectively carries out hyperbolic curve fitting to obtain the dynamic correction time-speed pair (Tj) corresponding to each reflecting interface 0 ,Vj NMO ) Thereby achieving dynamic correction of the CMP gather. The method is different from the conventional method in that the method does not depend on a velocity spectrum, completely starts from data, has higher picking efficiency and better noise-resistant effect, and is more suitable for automatic and industrial production.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below.
FIG. 1 is a schematic flow chart of a seismic data dynamic correction method based on a density clustering algorithm disclosed in an embodiment of the present invention. (ii) a
FIG. 2 is simulated seismic data used in an embodiment of the invention;
FIG. 3 is CMP gather data obtained after processing simulated seismic data;
FIG. 4 is the results of a first density-based clustering;
FIG. 5 is a diagram of class 5 extraction from the first clustering results;
FIG. 6 is the results of a second density-based clustering;
FIG. 7 shows the result of extracting and fitting the peak data corresponding to the reflective layer;
FIG. 8 is a kinetic correction velocity profile;
FIG. 9 is a common midpoint gather after kinetic correction.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
The invention provides a seismic data dynamic correction method based on a density clustering algorithm, which comprises the following steps as shown in figure 1:
firstly, carrying out early-stage processing on original seismic data to obtain CMP gather data;
the specific method comprises the following steps:
firstly, various noises are removed, and the signal-to-noise ratio is improved to the maximum extent;
secondly, performing spherical diffusion compensation on the data;
finally, CMP gather data is extracted with a Common Midpoint (CMP) as a primary key.
Secondly, performing first density clustering on the CMP gather data: the density-based clustering method needs to determine two key parameters, one of which is the scanning radius eps, and the other of which is the minimum inclusion point miniPts of the class, wherein eps must be obtained according to actual data, and as the parameter miniPts has no significant influence on clustering, an algorithm default value of 5 is selected in the embodiment of the invention, namely, each class has at least 5 points.
In the first clustering of the trace set data, firstly, the distance between two sampling points in the same trace is counted in sequence, namely a two-dimensional plane is constructed by taking the offset distance and the two-way travel time as the horizontal axis and the vertical axis, each sampling point is taken as a plane point, and the value of the horizontal coordinate of each sampling point is equal to the sum of the offset distance and the amplitude (recorded as the sum of the offset distance and the amplitude)
) The ordinate of the travel time is equal to the sampling point on a double-travel time scale (marked as
) (ii) a Then calculating the plane distance between each sampling point, and solving the average distance (formula (1)); then taking the average distance as the scanning radius eps 1 And carrying out first clustering on each data. Because the amplitude value of the corresponding sampling point at the reflecting interface is significantly higher than that of the sampling point at the non-reflecting interface, that is, the distance between the sampling points at the reflecting interface is significantly greater than that of the sampling points at the non-reflecting interface, the sampling points at the non-reflecting interface can be classified into several categories because the distance between the sampling points is smaller than the scanning radius, and the peak sampling points at each reflecting interface can be classified into a single category because the distance between the sampling points is greater than the scanning radius, so that the peaks corresponding to all the reflecting interfaces can be screened out.
The method comprises the following specific steps:
(1) determining the scanning radius eps 1 ：
In the formula, N sample The number of sampling points for a trace of seismic data,
the abscissa of the ith and (i +1) th sample points,
the ordinate of the ith and (i +1) th sample points.
(2) Eps determined by equation (1) 1 Performing first DBSCAN clustering for key parameters, with eps 1 The value of (2) is input into a clustering algorithm as a scanning radius, and the strong wave peak is separated by utilizing the characteristics that the amplitude value of the wave peak at the reflecting interface is stronger and the distance between sampling points at the strong wave peak is larger, so that all the wave peaks corresponding to all the reflecting interfaces can be separated.
Thirdly, performing secondary density clustering on the screened peak data: in the previous step, although the peak data of all interfaces are successfully separated, the peak data corresponding to each interface is needed, so that the data needs to be clustered twice. According to the seismic wave propagation theory, in the same CMP gather, the wave crests of the same reflection interface are distributed along a hyperbolic curve shape, and the longitudinal distance (namely, the two-way travel time difference) of the wave crest sampling points at different reflection interfaces is obviously greater than the transverse distance (namely, the plane distance between two sampling points) of the wave crest sampling points at the same reflection interface, so that the screened wave crest data are more convergent in the aspect of point distance, and are more beneficial to density-based clustering.
Continuing the two-dimensional plane constructed at the previous time, firstly, sequentially counting the distance between the front sampling point and the rear sampling point of each channel of peak data, and solving the average distance; then the average is used as the scanning radius eps 2 Keeping the minimum contained point miniPts of the class to be 5, performing secondary DBSCAN clustering, and separating the wave crest corresponding to each reflection interface by utilizing the characteristic that the obvious distance exists between the reflection interfaces. The method comprises the following specific steps:
(1) determining the scanning radius eps 2 ：
In the formula, N peak The total number of peak sampling points obtained by a first density clustering method in a seismic data,
the abscissa of the ith and (i +1) th peak sample points,
the ordinate of the ith and (i +1) th peak sample points.
(2) Eps determined by equation (2) 2 And performing secondary DBSCAN clustering for the key parameters, keeping the minimum contained point number (miniPts) to be 5 unchanged, and obtaining the wave crest corresponding to each reflection interface.
Step four, solving the dynamic correction time-speed pair corresponding to each reflection interface: selecting each wave crest corresponding to the 1 st reflection interface, and taking the double-journey travel time of the 1 st wave crest as the self-excitation and self-receiving time of the reflection interfaceM, is marked as T1 0 The hyperbolic curve fitting is performed on each trace of peak data of the reflection interface to obtain the dynamic correction speed of the reflection interface, which is recorded as V1 NMO To obtain a kinematically corrected time-velocity pair (T1) of the reflective interface 0 ,V1 NMO )。
The specific method comprises the following steps:
selecting each wave crest of the 1 st reflection interface, and performing hyperbolic fitting on all wave crest data of the 1 st reflection interface according to the formula (3) to obtain the dynamic correction speed V1 of the reflection interface NMO ：
In the formula, t1 i For the two-way travel of the ith sampling point in all the peak data corresponding to the extracted 1 st reflection interface, T1 0 Self-excited self-absorption time of the 1 st reflecting interface, X1 i The offset of the ith sample point in all the peak data corresponding to the 1 st reflecting interface, V1 NMO And the corresponding dynamic correction speed of the 1 st reflecting interface is obtained.
By analogy, the dynamic correction time-speed pair (Tj) of each reflection interface is obtained 0 ,Vj NMO ) And j represents the jth reflective interface.
Step five, obtaining a corrected seismic gather: time-velocity pairs (Tj) with dynamic correction of all reflective interfaces 0 ,Vj NMO ) And constructing a fold line function and performing interpolation, and performing dynamic correction on the CMP gather data to obtain a corrected seismic gather.
The specific method comprises the following steps:
(1) dynamic correction time-speed pairs (Tj) corresponding to each reflective interface according to self-excited self-receiving time 0 ,Vj NMO ) Sorted in ascending order, with two adjacent groups (Tj) 0 ,Vj NMO ) Constructing a straight-line function, and forming a broken-line function by all time-speed pairs as shown in formula (4):
in the formula (I), the compound is shown in the specification,
representing the rate of kinetic correction at the ith sample point,
representing the dynamic correction time of the ith sample point, Vj NMO Representing the motion correction speed corresponding to the jth reflecting interface, V (j +1) NMO Represents the corresponding dynamic correction speed of the j +1 th reflection interface, T (j +1) 0 Is the self-excited self-receiving time of the (j +1) th reflection interface, Tj 0 The self-excitation and self-collection time of the jth reflecting interface is obtained;
(2) calculating the dynamic correction time-speed pairs of all sampling points on the CMP gather according to the above polygonal line function
(3) Calculating the dynamic correction dt of each sampling point by the formula (5) i ：
In the formula (dt) i Representing the amount of motional correction, x, at the ith sample point i Represents the offset of the ith sampling point;
(4) let the two-way travel time t of each sampling point i And dt i Making a difference, and solving a new two-way travel time t 'of the ith sampling point' i And completing dynamic correction:
t′ i ＝t i -dt i (6)
of formula (II) to' i Representing the time of the double-trip travel after the i-th sample point dynamic correction, t i Representing the time of the two-way trip before the i-th sample point is dynamically corrected.
In order to verify the effectiveness and the practicability of the invention, the seismic data with 3 reflection interfaces are simulated and dynamically corrected by taking the actual geological condition of a certain sea area as an example. The specific treatment process comprises the following steps:
first, the simulated seismic data shown in fig. 2 is denoised to obtain a CMP gather for dynamic correction, as shown in fig. 3, the total data is 18 traces, and the recording length is close to 1.972 seconds.
Then, each data is divided into 5 classes by a clustering algorithm based on density, as shown in FIG. 4, respectively
+、×、
Five shape labels. Key parameter eps of clustering therein 1 The value is the distance average of each sampling point, about 0.007774, and the minimum contained point (miniPts) selects the DBSCAN algorithm default value 5;
thirdly, extracting the 5 th class data (figure 5) in the above steps, and carrying out the second density-based clustering to obtain 3 clustering results, wherein the results are respectively expressed by +, ×,
Three shape markers, as shown in FIG. 6, correspond to exactly 3 reflecting interfaces, wherein the key parameter eps of the clustering 2 Taking the average distance of the sampling points as about 5.81421, and keeping the minimum contained point number (miniPts) to be 5;
fourthly, extracting data class by class, taking the peak extreme value of each channel and each class as a representative value, performing hyperbolic fitting, and obtaining a dynamic correction time-velocity pair (Tj) corresponding to each reflection interface as shown in FIG. 7 0 ,Vj NMO ) The fitting results are (0.67,1504.307), (0.97,1692.861), (1.21,1890.532);
finally, the velocity profile (fig. 8) is constructed using the dynamic correction time-velocity pairs for each reflecting interface (i.e., the three sets of time-velocity pairs obtained in the fourth step, identified by blocks in fig. 8), and the CMP gather of fig. 1 is dynamically corrected to obtain the gather data of fig. 9.
From the view of the dynamic correction effect, the in-phase axis is nearly a straight line, and the dynamic correction effect is good. As with manual pickup, the far track produces large distortion and only the far track data needs to be cut off at a later stage.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. A seismic data dynamic correction method based on a density clustering algorithm is characterized by comprising the following steps:
firstly, carrying out early-stage processing on original seismic data to obtain CMP gather data;
secondly, performing first density clustering on the CMP gather data: firstly, counting the distance between two sampling points in each channel of the seismic data, and calculating the average number; then, performing first density clustering by taking the average number as a scanning radius, and screening out all wave crests at all reflection interfaces;
thirdly, performing secondary density clustering on the screened peak data: firstly, counting the distance between two front sampling points and two rear sampling points in the screened peak data, and solving an average number; secondly, performing second density clustering by taking the average number as a scanning radius, and screening out wave crests corresponding to all reflection interfaces;
step four, calculating a dynamic correction time-speed pair: selecting each wave crest corresponding to the 1 st reflection interface, taking the two-way travel time of the 1 st wave crest as the self-excitation and self-receiving time of the reflection interface, and marking as T1 0 The hyperbolic curve fitting is performed on each trace of peak data of the reflection interface to obtain the dynamic correction speed of the reflection interface, which is recorded as V1 NMO To obtain a kinematically corrected time-velocity pair (T1) of the reflective interface 0 ,V1 NMO ) (ii) a By analogy, each reflection boundary is obtainedFacing a corresponding dynamic correction time-velocity pair (Tj) 0 ,Vj NMO ) J represents the jth reflective interface;
step five, obtaining the seismic gather after dynamic correction: correcting time-velocity pairs (Tj) with corresponding dynamics of all reflective interfaces 0 ,Vj NMO ) And constructing a fold line function and performing interpolation, and performing dynamic correction on the CMP gather data to obtain a seismic gather after dynamic correction.
2. The seismic data dynamic correction method based on the density clustering algorithm as claimed in claim 1, wherein the concrete method of the first step is as follows:
firstly, removing various noises;
secondly, performing spherical diffusion compensation on the data;
finally, the CMP gather data is extracted with the common center point as the main key.
3. The method for dynamically correcting seismic data based on density clustering algorithm as claimed in claim 1, wherein in the second step, the scanning radius eps of the first density clustering 1 The calculation formula is as follows:
in the formula, N sample The number of sampling points for a trace of seismic data,
the abscissa of the ith and (i +1) th sample points,
the ordinate of the ith and (i +1) th sample points.
4. The method as claimed in claim 1, wherein in the second step, when density clustering is performed for the first time, the minimum point miniPts of the class is 5, and the distance between the sampling points corresponding to the reflecting interface is significantly greater than that of the sampling points corresponding to the non-reflecting interface, so that the sampling points of the non-reflecting interface can be classified into several classes because the distance between the points is smaller than the scanning radius, and the peaks at the reflecting interface can be classified into a single class because the distance between the points is greater than the scanning radius, and therefore, the peaks corresponding to all the reflecting interfaces are screened out.
5. The method for dynamically correcting seismic data based on density clustering algorithm as claimed in claim 1, wherein in the third step, the scanning radius eps of the second density clustering 2 The calculation formula is as follows:
in the formula, N peak The total number of peak sampling points obtained by a first density clustering method in a seismic data,
the abscissa of the ith and (i +1) th peak sample points,
the ordinate of the ith and (i +1) th peak sample points.
6. The method for dynamically correcting seismic data based on the density clustering algorithm as claimed in claim 1, wherein in step three, in the second density clustering, the minimum contained point number miniPts of the class is 5, and the characteristic that the significant distance exists between the reflection interfaces is utilized to separate the wave peaks corresponding to the reflection interfaces, namely the class number is consistent with the reflection interface number.
7. The seismic data dynamic correction method based on the density clustering algorithm as claimed in claim 1, wherein the concrete method of the fourth step is as follows:
selecting each wave crest of the 1 st reflection interface, and performing hyperbolic fitting on all wave crest data of the 1 st reflection interface according to the formula (3) to obtain the dynamic correction speed V1 of the reflection interface NMO ：
In the formula, t1 i For the two-way travel of the ith sampling point in all the peak data corresponding to the extracted 1 st reflection interface, T1 0 Self-excited self-absorption time of the 1 st reflecting interface, X1 i The offset of the ith sample point in all the peak data corresponding to the 1 st reflecting interface, V1 NMO And correcting the speed for the motion corresponding to the 1 st reflecting interface.
8. The seismic data dynamic correction method based on the density clustering algorithm as claimed in claim 1, wherein the concrete method of the fifth step is as follows:
(1) dynamic correction time-speed pairs (Tj) corresponding to each reflection interface according to self-excitation self-receiving time 0 ,Vj NMO ) Sorting, each two adjacent groups (Tj) 0 ,Vj NMO ) Constructing a straight-line function, and forming a broken-line function by all time-velocity pairs as shown in formula (4):
in the formula, v inmo Representing the rate of kinetic correction at the ith sample point,
representing the dynamic correction time, Vj, of the ith sample point NMO Represents the corresponding dynamic correction speed of the jth reflecting interface, V (j)+1) NMO Represents the dynamic correction speed corresponding to the j +1 th reflection interface, T (j +1) 0 Is the self-excited self-receiving time of the (j +1) th reflection interface, Tj 0 The self-excitation and self-collection time of the jth reflecting interface is obtained;
(2) calculating the dynamic correction time-speed pairs of all sampling points on the CMP gather according to the broken line function
(3) Calculating the dynamic correction dt of each sampling point by the formula (5) i ：
In the formula (dt) i Representing the amount of motional correction, x, at the ith sample point i Represents the offset of the ith sampling point;
(4) let the two-way travel time t of each sampling point i And dt i Making a difference, and calculating a new two-way travel time t 'of a sampling point i' i And completing dynamic correction:
t′ i ＝t i -dt i (6)
in the formula (II), t' i Representing the time of the double-trip travel after the i-th sample point dynamic correction, t i Representing the time of the two-way trip before the i-th sample point is dynamically corrected.
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