CN103901467A - Method for tracking positions of three-dimensional seismic data - Google Patents
Method for tracking positions of three-dimensional seismic data Download PDFInfo
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- CN103901467A CN103901467A CN201410100235.8A CN201410100235A CN103901467A CN 103901467 A CN103901467 A CN 103901467A CN 201410100235 A CN201410100235 A CN 201410100235A CN 103901467 A CN103901467 A CN 103901467A
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Abstract
The invention discloses a method for tracking positions of three-dimensional seismic data. The method comprises the steps that (A) extreme points are searched for in the three-dimensional seismic data; (B) the found extreme points are clustered to obtain cluster groups corresponding to all the extreme points and position fragments where the extreme points are located; (C) the position fragments which are adjacent in space and have the extreme points in the same cluster group are merged. By means of the method, automatic tracking of the positions can be achieved, manual interference is not needed in the overall process, and complete and continuous positions can be obtained.
Description
Technical field
The present invention relates to seismic prospecting data and explain field.More particularly, relate to the method for 3D seismic data tracing of horizons.
Background technology
Layer position explains that (tracing of horizons) is the important step that geologic information is explained.Explain and can detect underground structure by layer position, provide support for excavating subsurface reservoir.Be mainly to adopt artificial or semi-automatic trace horizon at present, current method is main or rely on artificial participation, and the problem of bringing is the artificial restriction that will inevitably be subject to subjective experience that participates in explaining; In the face of increasing geological data, the efficiency of manual interpretation is very low, can only, for minority zone of interest position provides explanation, become the bottleneck in seismic interpretation at every turn; And the precision of manual interpretation is not high, being difficult to provides basic data for follow-up accurate seismic data interpretation work.Along with continuous research and the improvement of tracing of horizons method, depend on computer implemented tracing of horizons algorithm and constantly propose, efficiency and the effect of tracking all improve to some extent.
But there are some problems in existing method, such as, under complicated geological environment, follow the trail of weak effect, the layer position obtaining is imperfect, needs artificial connection and fills, and can not realize full-automatic.There is at present a kind of conventional, simple and fast tracing of horizons method, implicit Markov (Markov) theoretical model of the method utilization carries out the tracking of echo time delay and extracts reflection horizon, the variation of the echo time delay of certain point of underground each medium layer is usually only relevant with more front time delay, irrelevant with more front Delay Variation, so just can utilize the known prior imformation of each point interface time delay above, by the relation of the implicit Markov chain of time delay between each point, detect down some echo time delays, thereby complete the tracking to echo time delay.The underground medium layer that but this method is only applicable to is simple in structure, smooth, echoed signal is strong, follows the trail of result when very weak for complicated a little interfacial structure (as cavity, subside etc.) or echoed signal very poor.
Summary of the invention
The object of the present invention is to provide a kind of method of 3D seismic data tracing of horizons.
An aspect of of the present present invention provides a kind of method of 3D seismic data tracing of horizons, comprises A) in 3D seismic data, search extreme point; B) extreme point finding is carried out to cluster, obtain the layer bit slice section at cluster corresponding to each extreme point group and each extreme point place; C) a spatially adjacent layer bit slice section for the extreme point that includes same cluster is merged.
Preferably, at step B) in, according to the clustering algorithm based on density, the extreme point finding is divided into different bunches, one bunch is equivalent to a layer bit slice section.
Preferably, at step B) in, according to the waveform similarity of extreme point, extreme point is carried out to cluster, obtain the cluster group that each extreme point is corresponding.
Preferably, step B) according to the waveform similarity of extreme point, extreme point is carried out to cluster step comprise, draw the wave shape eigen coefficient of each extreme point according to Chebyshev's way of fitting.
Preferably, step B) according to the waveform similarity of extreme point, extreme point is carried out to cluster step also comprise and comprising, set up mixed Gauss model according to wave shape eigen coefficient and carry out cluster.
Preferably, step B) according to the waveform similarity of extreme point, extreme point is carried out to cluster step also comprise and comprising, obtain the estimated value of the parameter of mixed Gauss model by maximum likelihood method, the estimated value of described parameter is carried out cluster for substitution mixed Gauss model to extreme point.
According to the present invention, can realize the automatic tracing to layer position, overall process, without manual intervention, can obtain complete, continuous layer position.
By part in ensuing description set forth the present invention other aspect and/or advantage, some will be clearly by descriptions, or can pass through enforcement of the present invention and learning.
Accompanying drawing explanation
By the detailed description of carrying out below in conjunction with accompanying drawing, above and other objects of the present invention, feature and advantage will become apparent, wherein:
Fig. 1 illustrates a kind of according to an embodiment of the invention process flow diagram of method of 3D seismic data tracing of horizons;
Fig. 2 illustrates and includes according to an embodiment of the invention the extreme point of same cluster group and the exemplary plot of two adjacent layer bit slice sections spatially.
Fig. 3 illustrates the sectional view of the result of the full tracing of horizons in certain work area according to an embodiment of the invention.
Embodiment
Below, describe in detail with reference to the accompanying drawings according to exemplary embodiment of the present invention.
Fig. 1 illustrates a kind of according to an embodiment of the invention process flow diagram of method of 3D seismic data tracing of horizons.
As shown in Figure 1, in step 101, in 3D seismic data, search extreme point.
Described extreme point can comprise maximum point and maximum point, and maximum point is the crest in seismic waveshape, and minimum point is the trough in seismic waveshape.Because the lookup method is here prior art, do not describe in detail at this.
In step 102, the extreme point finding is carried out to cluster, obtain the layer bit slice section at cluster corresponding to each extreme point group and each extreme point place.
Preferably, clustering method can comprise two kinds, and the first is: by the clustering algorithm based on density, the extreme point finding is divided into different bunches, one bunch is equivalent to a layer bit slice section; The second is: according to the waveform similarity of extreme point, extreme point is carried out to cluster, obtain the cluster group that each extreme point is corresponding.
The density connectedness of the clustering algorithm utilization layer position based on density can be found the layer position of arbitrary shape fast.Its basic thought is: for the each extreme point in a layer position, the object comprising in its given radius neighborhood Eps can not be less than a certain given minimal amount Minpts.Eps and Minpts are preset value.The maximum set that a complete layer position is defined as to the connected extreme point of density, as long as the density (number of extreme point) of neighborhood exceedes Minpts, expansion is just continued in layer position.
According to the clustering algorithm based on density, the extreme point finding is divided into the method step of different bunches as follows: choose arbitrarily an extreme point p; Be not less than minPts if the extreme value comprising in the neighborhood of extreme point p is counted, find out the extreme point comprising in its field as one bunch, and this extreme point of mark P is for accessed; Process and allly in this bunch be not marked as accessed extreme point with the identical method of last step, thereby to bunch expanding; Repeat above-mentioned three steps, until all extreme points are all processed.
Utilize said method the extreme point finding in step 101 can be divided into different bunches, one bunch is equivalent to a layer bit slice section.Because the layer position that the factors such as tomography cause is discontinuous, same layer position may be divided into one or even multiple fragment.
The step of according to the waveform similarity of extreme point, extreme point being carried out to cluster in step 102, in order to simplify Wave data, reduces calculated amount, and this step can comprise, draws the wave shape eigen coefficient of each extreme point according to Chebyshev's way of fitting.By fitting coefficient b
j(j=0,1,2 ... N) vector [b of composition
0, b
1..., b
n] as the wave shape eigen coefficient of extreme point, N is matching exponent number.
Can carry out cluster to the wave shape eigen coefficient of extreme point obtained above according to the various algorithms that can carry out cluster to data sample point.Because the waveform character of the extreme point of same layer position meets Gaussian distribution, therefore, preferably, step 102 can also comprise step, sets up mixed Gauss model carry out cluster according to wave shape eigen coefficient.The wave shape eigen coefficient of supposing all extreme points is proper vector b={b
k, b
kprobability density function can be expressed as:
Wherein
proper vector b
kmarginal distribution, n
arelevant to Chebyshev's matching exponent number, be proper vector b
kdimension.C
k∈ 1,2 ..., n
cthe class mark of presentation class, c
kthe set of composition is c, is always divided into n
cclass, n
cbeing the input parameter of limited gauss hybrid models algorithm, is the valuation about single Gaussian distribution number in model,
represent the average of Gaussian distribution,
represent corresponding variance, the numbering that k is extreme point, ε represents the sum of extreme point.
Preferably, adopt the maximum Likelihood of greatest hope (EM) Algorithm for Solving above formula, obtain with minor function:
with
be respectively u, the estimated value of ∑ and c, they are mutually related each other, obtain corresponding estimated value by the continuous iteration of EM algorithm.The estimated value substitution mixed Gauss model of above-mentioned each parameter is carried out to cluster to extreme point, can obtain the cluster group that each extreme point is corresponding.
In step 103, a spatially adjacent layer bit slice section for the extreme point that includes same cluster group is merged.
In the layer bit slice section obtaining according to the clustering algorithm based on density, may exist the layer position of causing due to factors such as tomographies discontinuous, same layer position is divided into one or even multiple fragment, by a spatially adjacent layer bit slice section for the extreme point that includes same cluster group is merged, the fragment of same layer position is merged obtain large, complete layer like this.
Fig. 2 includes the extreme point of same cluster group and the exemplary plot of two adjacent layer bit slice sections spatially, in two layer bit slice sections, has two clustering cluster d based on Density Clustering
xand d
y, they are continuous layer bit slice sections in space, in Fig. 2, stain is the extreme point that same cluster is rolled into a ball that belongs to obtaining according to extreme point waveform character cluster.
Fig. 3 illustrates the sectional view of the result of the full tracing of horizons in certain work area according to an embodiment of the invention.According to the present invention, can realize the automatic tracing to layer position, overall process is without manual intervention.As shown in Figure 3, the layer position in sectional view is complete, continuously.
Although specifically shown with reference to its exemplary embodiment and described the present invention, but it should be appreciated by those skilled in the art, in the case of not departing from the spirit and scope of the present invention that claim limits, can carry out the various changes in form and details to it.
Claims (6)
1. a method for 3D seismic data tracing of horizons, comprising:
A) in 3D seismic data, search extreme point;
B) extreme point finding is carried out to cluster, obtain the layer bit slice section at cluster corresponding to each extreme point group and each extreme point place;
C) by include the extreme point of same cluster group and spatially an adjacent layer bit slice section merge.
2. method according to claim 1, wherein, at step B) in, according to the clustering algorithm based on density, the extreme point finding is divided into different bunches, one bunch is equivalent to a layer bit slice section.
3. method according to claim 1, wherein, at step B) in, according to the waveform similarity of extreme point, extreme point is carried out to cluster, obtain the cluster group that each extreme point is corresponding.
4. method according to claim 3, wherein, step B) according to the waveform similarity of extreme point, extreme point is carried out to cluster step comprise, draw the wave shape eigen coefficient of each extreme point according to Chebyshev's way of fitting.
5. method according to claim 4, wherein, step B) according to the waveform similarity of extreme point, extreme point is carried out to cluster step also comprise and comprising, set up mixed Gauss model according to wave shape eigen coefficient and carry out cluster.
6. method according to claim 5, wherein, step B) according to the waveform similarity of extreme point, extreme point is carried out to cluster step also comprise and comprising, obtain the estimated value of the parameter of mixed Gauss model by maximum likelihood method, the estimated value of described parameter is carried out cluster for substitution mixed Gauss model to extreme point.
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CN104199096A (en) * | 2014-09-12 | 2014-12-10 | 吉林大学 | Extraction method and device of horizons of seismic data cube |
CN104199092A (en) * | 2014-08-31 | 2014-12-10 | 电子科技大学 | Multi-level framework based three-dimensional full-horizon automatic tracking method |
CN105182410A (en) * | 2015-09-02 | 2015-12-23 | 电子科技大学 | Seismic data layered characteristic reinforcement drafting method |
CN105182411A (en) * | 2015-09-10 | 2015-12-23 | 中国石油天然气集团公司 | Method and device used for determining pre-stack seismic horizon |
CN104181596B (en) * | 2014-08-27 | 2017-01-11 | 中国石油集团东方地球物理勘探有限责任公司 | Geologic horizon automatic tracking method and device |
CN111796324A (en) * | 2019-04-09 | 2020-10-20 | 中国石油天然气股份有限公司 | Seismic all-horizon tracking method and device |
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CN104181596B (en) * | 2014-08-27 | 2017-01-11 | 中国石油集团东方地球物理勘探有限责任公司 | Geologic horizon automatic tracking method and device |
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CN111796324A (en) * | 2019-04-09 | 2020-10-20 | 中国石油天然气股份有限公司 | Seismic all-horizon tracking method and device |
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CN111796324B (en) * | 2019-04-09 | 2023-02-10 | 中国石油天然气股份有限公司 | Seismic all-horizon tracking method and device |
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