CN106990436B - The recognition methods of karst collapse col umn and device - Google Patents
The recognition methods of karst collapse col umn and device Download PDFInfo
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Abstract
The present invention provides a kind of recognition methods of karst collapse col umn and devices, it is related to the technical field of seismic data explanation, this method comprises: obtaining the data volume of the 3D seismic data in target subterranean formation area, wherein, the interface information of each rock stratum layer position in target subterranean formation area is carried in data volume, interface information includes the attribute information of subterranean strata and/or the structural information of subterranean strata；Data volume based on 3D seismic data extracts objective attribute target attribute body, wherein objective attribute target attribute body includes following at least two: chaos attribute body, dip angle attribute body, ant attribute volume；Karst collapse col umn is identified in target subterranean formation area based on the objective attribute target attribute body extracted.The present invention alleviates the lower technical problem of existing karst collapse col umn recognition methods accuracy of identification, has reached the technical effect for improving karst collapse col umn accuracy of identification.
Description
Technical field
The present invention relates to the technical field that seismic data is explained, recognition methods and dress more particularly, to a kind of karst collapse col umn
It sets.
Background technique
In Coal Exploration field, the presence of karst collapse col umn seriously affects safety of coal mines, efficient exploitation.Presently, there are utilize phase
Stem body, frequency, phase and neural network attribute are sliced by extracting along layer attribute, come the method for predicting karst collapse col umn intuitively to show
Show the developmental state of karst collapse col umn.
Although above-mentioned processing method achieves certain progress in the prediction of karst collapse col umn, also it is still within a kind of fuzzy
Forecast period cannot accurately determine karst collapse col umn position and boundary.Every kind of single attribute is certain for having portrayed for karst collapse col umn
Advantage and disadvantage.
Summary of the invention
The purpose of the present invention is to provide a kind of recognition methods of karst collapse col umn and devices, to alleviate existing karst collapse col umn identification
The lower technical problem of method accuracy of identification.
According to an aspect of an embodiment of the present invention, a kind of recognition methods of karst collapse col umn is provided, comprising: with obtaining target
The data volume of 3D seismic data in lower formation area, wherein the target subterranean formation area is carried in the data volume
In each rock stratum layer position interface information, the interface information include the target subterranean formation area attribute information and/or
The structural information of the target subterranean formation area；Data volume based on the 3D seismic data extracts objective attribute target attribute body,
In, the objective attribute target attribute body includes following at least two: chaos attribute body, dip angle attribute body, ant attribute volume；Based on extracting
The objective attribute target attribute body the target subterranean formation area identify karst collapse col umn.
Further, karst collapse col umn packet is identified in the target subterranean formation area based on the objective attribute target attribute body extracted
It includes: obtaining in advance as the weighted value of attribute body distribution every in the objective attribute target attribute body；Weight based on every attribute body
Value, merges at least two objective attribute target attribute bodies using neural network algorithm, the target category after being merged
Property body；The prediction of karst collapse col umn is carried out according to the objective attribute target attribute body after fusion, and in the target according to prediction result
The identification karst collapse col umn in lower formation area.
Further, the weighted value based on every attribute body, using neural network algorithm at least two mesh
Mark attribute volume is merged, and the objective attribute target attribute body after being merged includes: to calculate at least two target category
The weighted sum of property body, and using the result of the weighted sum as the objective attribute target attribute body after the fusion.
Further, in the case where the objective attribute target attribute body is ant attribute volume, based on the 3D seismic data
It includes: that passive ant attribute volume is extracted in the data volume of the 3D seismic data that data volume, which extracts objective attribute target attribute body, wherein
The passiveness ant attribute volume is used to indicate the discontinuity of the 3D seismic data；It is mentioned based on the passive ant attribute volume
Positive ant attribute volume is taken, and using the positive ant attribute volume as the ant attribute volume, wherein the positive ant category
Property body the discontinuity of the 3-d seismic data set can be indicated on the basis of the passive ant attribute volume.
Further, it includes: to obtain in advance that passive ant attribute volume is extracted in the data volume of the 3D seismic data
First group of ant body parameter being arranged, wherein first group of ant body parameter includes the first initial ant boundary, the first tracking
Deviation, the first illegal step number, the first legal step number, the first step-size in search, the first termination criteria；Based on first group of ant body
Parameter carries out ant to the data volume using ant tracing algorithm and tracks operation, obtains the institute comprising passive ant body attribute
State passive ant attribute volume.
Further, extracting positive ant attribute volume based on the passive ant attribute volume includes: that acquisition is pre-set
Second group of ant body parameter, wherein second group of ant body parameter include the second initial ant boundary, second tracking deviate,
Second illegal step number, the second legal step number, the second step-size in search, the second termination criteria；Based on second group of ant body parameter,
Operation is carried out to the passive ant attribute volume using ant tracing algorithm, is obtained described positive comprising positive ant body attribute
Ant attribute volume.
Further, it after the data volume for obtaining the 3D seismic data in target subterranean formation area, and is being based on
Before the data volume of the 3D seismic data extracts objective attribute target attribute body, the method also includes: to the 3D seismic data
Data volume be filtered, and the target is extracted based on the data volume of the 3D seismic data after filtering processing
Attribute volume.
Other side according to an embodiment of the present invention additionally provides the identification device of karst collapse col umn, comprising: acquiring unit,
For obtaining the data volume of the 3D seismic data in target subterranean formation area, wherein carry the mesh in the data volume
The interface information of each rock stratum layer position in subterranean strata region is marked, the interface information includes the target subterranean formation area
The structural information of attribute information and/or the target subterranean formation area；Extraction unit, for being based on the 3D seismic data
Data volume extract objective attribute target attribute body, wherein the objective attribute target attribute body includes following at least two: chaos attribute body, inclination angle belong to
Property body, ant attribute volume；Recognition unit, for based on the objective attribute target attribute body extracted in the target subterranean formation area
Identify karst collapse col umn.
Further, the recognition unit includes: acquisition module, is in advance every kind in the objective attribute target attribute body for obtaining
The weighted value of attribute volume distribution；Fusion Module, for the weighted value based on every attribute body, using neural network algorithm pair
At least two objective attribute target attribute bodies are merged, the objective attribute target attribute body after being merged；Prediction module is used for basis
The objective attribute target attribute body after fusion carries out the prediction of karst collapse col umn, and according to prediction result in the target subterranean formation area
The interior identification karst collapse col umn.
Further, the Fusion Module includes: computational submodule, for calculating described at least two objective attribute target attributes
The weighted sum of body, and using the result of the weighted sum as the objective attribute target attribute body after the fusion.
In embodiments of the present invention, the data volume of the 3D seismic data in target subterranean formation area is obtained first；So
Afterwards, the data volume based on 3D seismic data extracts objective attribute target attribute body, wherein objective attribute target attribute body includes following at least two: mixed
Ignorant attribute volume, dip angle attribute body and ant attribute volume；Finally, being subside based on objective attribute target attribute body in the identification of target subterranean formation area
Column.In such a way that a variety of attribute volumes merge, karst collapse col umn position can be more accurately predicted and portray karst collapse col umn boundary, in turn
The lower technical problem of existing karst collapse col umn recognition methods accuracy of identification is alleviated, improves karst collapse col umn accuracy of identification to realize
Technical effect.
Detailed description of the invention
It, below will be to specific in order to illustrate more clearly of the specific embodiment of the invention or technical solution in the prior art
Embodiment or attached drawing needed to be used in the description of the prior art be briefly described, it should be apparent that, it is described below
Attached drawing is some embodiments of the present invention, for those of ordinary skill in the art, before not making the creative labor
It puts, is also possible to obtain other drawings based on these drawings.
Fig. 1 is a kind of flow chart of the recognition methods of karst collapse col umn according to an embodiment of the present invention；
Fig. 2 is a kind of schematic diagram of horizon slice for being optionally based on chaos attribute body according to an embodiment of the present invention；
Fig. 3 is a kind of schematic diagram of horizon slice for being optionally based on dip angle attribute body according to an embodiment of the present invention；
Fig. 4 is a kind of schematic diagram of horizon slice for being optionally based on ant attribute volume according to an embodiment of the present invention；
Fig. 5 is a kind of schematic diagram of horizon slice for being optionally based on fusion attribute volume according to an embodiment of the present invention；
Fig. 6 is the earth formation schematic diagram of the target subterranean formation area obtained using manual interpretation method；
Fig. 7 is a kind of flow chart of optionally recognition methods of karst collapse col umn according to an embodiment of the present invention；
Fig. 8 is a kind of schematic diagram of the identification device of karst collapse col umn according to an embodiment of the present invention.
Specific embodiment
Technical solution of the present invention is clearly and completely described below in conjunction with attached drawing, it is clear that described implementation
Example is a part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, ordinary skill
Personnel's every other embodiment obtained without making creative work, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that term " center ", "upper", "lower", "left", "right", "vertical",
The orientation or positional relationship of the instructions such as "horizontal", "inner", "outside" be based on the orientation or positional relationship shown in the drawings, merely to
Convenient for description the present invention and simplify description, rather than the device or element of indication or suggestion meaning must have a particular orientation,
It is constructed and operated in a specific orientation, therefore is not considered as limiting the invention.In addition, term " first ", " second ",
" third " is used for descriptive purposes only and cannot be understood as indicating or suggesting relative importance.
In the description of the present invention, it should be noted that unless otherwise clearly defined and limited, term " installation ", " phase
Even ", " connection " shall be understood in a broad sense, for example, it may be being fixedly connected, may be a detachable connection, or be integrally connected；It can
To be mechanical connection, it is also possible to be electrically connected；It can be directly connected, can also can be indirectly connected through an intermediary
Connection inside two elements.For the ordinary skill in the art, above-mentioned term can be understood at this with concrete condition
Concrete meaning in invention.
Embodiment one
According to embodiments of the present invention, a kind of embodiment of the recognition methods of karst collapse col umn is provided, it should be noted that attached
The step of process of figure illustrates can execute in a computer system such as a set of computer executable instructions, though also,
So logical order is shown in flow charts, but in some cases, it can be to be different from shown by sequence execution herein
Or the step of description.
Fig. 1 is a kind of flow chart of the recognition methods of karst collapse col umn according to an embodiment of the present invention, as shown in Figure 1, this method
Include the following steps:
Step S102 obtains the data volume of the 3D seismic data in target subterranean formation area, wherein take in data volume
Interface information with rock stratum layer each in target subterranean formation area position, interface information includes the attribute of target subterranean formation area
The structural information of information and/or target subterranean formation area.
In embodiments of the present invention, target subterranean formation area is the subterranean zone chosen in advance, in the subterranean region
It include the 3D seismic data of each rock stratum layer position in the data volume of the 3D seismic data in domain.
Specifically, it in the exploration process of geologic structure, first in shot point emission detection wave, then, is received in geophone station
Probing wave, wherein the propagation path of probing wave can be made by target by the launch angle of control detection wave launcher
Lower formation area.The attributes such as velocity of wave, amplitude, propagation time due to probing wave and material, density, structure by way of medium etc. are close
Cut phase is closed, thus, the interface information of subterranean strata can be carried by way of the probing wave in subterranean strata region.Therefore, of the invention real
It applies in example, above-mentioned 3D seismic data can select in field of seismic exploration common probing wave in target subterranean formation area
Amplitude, wherein the corresponding amplitude of the coordinate value of a three-dimensional space in target subterranean formation area.
Step S104, data volume based on 3D seismic data extract objective attribute target attribute body, wherein objective attribute target attribute body include with
Lower at least two: chaos attribute body, dip angle attribute body, ant attribute volume.
Step S106 identifies karst collapse col umn in target subterranean formation area based on the objective attribute target attribute body extracted.
Wherein, chaos attribute body is whether to lack inclination angle in measurement seismic volume, a kind of method of azimuth institutional framework,
That is chaos attribute body is able to reflect " confusion " degree of seismic data.Wherein, rock occurs to be crushed at karst collapse col umn development,
Severity of mixing up is higher.Therefore, the development position of karst collapse col umn can be substantially reflected in chaos attribute body.
In coal seam stability region, data inclination angle will not have greatly changed.It is not excessive to encounter the structure developments such as karst collapse col umn
Place, change dramatically can occur for inclination value, so dip angle attribute is sensitiveer to small construction.
In addition, ant attribute volume has tractability.Since seismic data has multi-solution, utilize different attribute body
Have the characteristics that subterranean strata different characteristic, a variety of attribute volumes are merged, can reduce the multi-solution of seismic data, effectively
Predict the position and boundary of karst collapse col umn.
In embodiments of the present invention, the data volume of the 3D seismic data in target subterranean formation area is obtained first；So
Afterwards, the data volume based on 3D seismic data extracts objective attribute target attribute body, wherein objective attribute target attribute body includes following at least two: mixed
Ignorant attribute volume, dip angle attribute body and ant attribute volume；Finally, being subside based on objective attribute target attribute body in the identification of target subterranean formation area
Column.In such a way that a variety of attribute volumes merge, karst collapse col umn position can be more accurately predicted and portray karst collapse col umn boundary, in turn
The lower technical problem of existing karst collapse col umn recognition methods accuracy of identification is alleviated, improves karst collapse col umn accuracy of identification to realize
Technical effect.
In an optional embodiment of the embodiment of the present invention, the 3-D seismics in target subterranean formation area are being obtained
After the data volume of data, the data volume of 3D seismic data can also be filtered, and be based on after filtering processing
3D seismic data data volume extract objective attribute target attribute body.
It specifically, in embodiments of the present invention, can in order to eliminate influence of the ambient noise to the precision of 3D seismic data
Median filter process is carried out with the data volume first to 3D seismic data, and based on the 3-D seismics after median filter process
The data volume of data extracts objective attribute target attribute body.
It should be noted that since the target in the recognition methods of karst collapse col umn provided in an embodiment of the present invention is to reach essence
Thin detection, still, the process of filtering can lose effective information while decaying noise, therefore, can be to 3-D seismics number
According to data carry out filtering appropriate.
After the data volume to 3D seismic data carries out median filter process, so that it may give 3D seismic data
Data volume extract objective attribute target attribute body, wherein objective attribute target attribute body includes following at least two: chaos attribute body, ant attribute volume and
Dip angle attribute body.
For example, the objective attribute target attribute body can be the fusion of chaos attribute body and ant attribute volume, it can also be chaos attribute
The fusion of body and dip angle attribute body can also be the fusion of ant attribute volume and dip angle attribute body, can also be chaos attribute body,
The fusion of ant attribute volume and dip angle attribute body.Preferably as one, objective attribute target attribute body can be chosen for chaos attribute
Body, the fusion of ant attribute volume and dip angle attribute body.
As an optional embodiment, can be extracted in the data volume of 3D seismic data by Petrel software mixed
Ignorant attribute volume, dip angle attribute body and ant attribute volume.
Petrel is a set of Visualization Modeling software based on windows platform, it collects seismic interpretation, construction is built
Mould, lithofacies modeling, oil reservoir model attributes and reservoir numerical simulation is shown and virtual reality is in one.Meanwhile Petrel is applied
Various advanced technologies, for example, powerful construction modeling technique, high accuracy three-dimensional gridding technology, certainty and randomness are heavy
Product phase model foundation technology, the rock physics modeling technique of science, the visualization of advanced three-dimensional computer and virtual reality technology.
If objective attribute target attribute body is ant attribute volume, ant category can be extracted using mode described in following step
Property body:
Step S1 extracts passive ant attribute volume, wherein passive ant attribute volume in the data volume of 3D seismic data
For indicating the discontinuity of 3D seismic data；
Step S2 extracts positive ant attribute volume based on passive ant attribute volume, and using positive ant attribute volume as ant
Ant attribute volume, wherein positive ant attribute volume can further indicate that 3-D seismics number on the basis of passive ant attribute volume
According to the discontinuity of body.
In embodiments of the present invention, ant can be carried out using data volume of the ant tracing algorithm to above-mentioned 3D seismic data
Ant tracking, successively obtains passive ant attribute volume and positive ant attribute volume, wherein ant attribute volume is for indicating above-mentioned three-dimensional
The discontinuity of seismic data.
Specifically, there is the data volume progress ant of the 3D seismic data of mutation to value first with ant tracing algorithm
Tracking, obtains passive ant attribute volume；Then, the passive ant attribute volume that ant tracing algorithm has mutation to value is continued with
Ant tracking is carried out, positive ant attribute volume is obtained.
Wherein, the search capability of passive ant attribute volume is weaker, therefore, has for the karst collapse col umn compared with macrotectonics good
Reflection；The search capability of positive ant attribute volume is stronger, has good reflection for the karst collapse col umn compared with little structure.
Therefore, in embodiments of the present invention, operation is tracked by ant twice to carry out the data volume of 3D seismic data
Tracing algorithm has effectively taken into account the large-scale detection with small-sized karst collapse col umn, scientifically and rationally reaches the effect of accurate prediction karst collapse col umn
Fruit.
Optionally, passive ant attribute volume is extracted in the data volume of 3D seismic data to include the following steps:
Step S11 obtains pre-set first group of ant body parameter, wherein first group of ant body parameter includes first
Initial ant boundary, the first tracking deviation, the first illegal step number, the first legal step number, the first step-size in search, first terminate mark
It is quasi-；
Step S12 is based on first group of ant body parameter, carries out ant tracking fortune to data volume using ant tracing algorithm
It calculates, obtains the passive ant attribute volume comprising passive ant body attribute.
Optionally, positive ant attribute volume is extracted based on passive ant attribute volume to include the following steps:
Step S21 obtains pre-set second group of ant body parameter, wherein second group of ant body parameter includes second
Initial ant boundary, the second tracking deviation, the second illegal step number, the second legal step number, the second step-size in search, second terminate mark
Standard, and opposite second group of ant body parameter, the ant body of second group of ant body parametric configuration are positive ant body；
Step S22 is based on second group of ant body parameter, is transported using ant tracing algorithm to passive ant attribute volume
It calculates, obtains the positive ant attribute volume comprising positive ant body attribute.
Since ant tracing algorithm has tractability, it include multiple control parameters, example in ant tracing algorithm
Such as, initial ant boundary, tracking deviate, illegal step number, legal step number, step-size in search, the parameters such as termination criteria.For different
Survey area can test a variety of different parameter combinations, be suitble to current survey area (that is, target subterranean rock stratum area with determination
Domain) trace parameters, and carry out the extraction of ant attribute volume.
In an optional embodiment, the value on above-mentioned first initial ant boundary can be chosen for 7 sampling points, and first
The value that tracking deviates can be chosen for 2 sampling points, and the value of the first illegal step number can be chosen for 1, the first legal step number
Value can be chosen for 3, and the value of the first step-size in search can be chosen for 3 sampling points, and the value of the first termination criteria can be selected
It is taken as 5%.
It is passive ant attribute volume by the ant attribute volume that the parameter combination obtains, passive ant attribute volume is suitable for general
Compared with the identification of the characteristics of karst collapse of macrotectonics in the case of, have universality good excellent the identification of the karst collapse col umn of macrotectonics
Point.
The value on above-mentioned first initial ant boundary can be chosen for 5 sampling points, and the value that the first tracking deviates can be selected
2 sampling points are taken as, the value of the first illegal step number can be chosen for 2, and the value of the first legal step number can be chosen for 2, first
The value of step-size in search can be chosen for 3 sampling points, and the value of the first termination criteria can be chosen for 10%.
It is positive ant attribute volume by the ant attribute volume that the parameter combination obtains, positive ant attribute volume is suitable for general
Compared with the identification of the characteristics of karst collapse of little structure in the case of, have universality good excellent the identification of the karst collapse col umn of little structure
Point.
Chaos attribute body is being determined through the above way, after ant attribute volume and dip angle attribute body, so that it may will be upper
At least two stated in three attribute bodies are merged, and objective attribute target attribute body is obtained.
In an optional embodiment of the embodiment of the present invention, based on the objective attribute target attribute body extracted in target subterranean rock
Layer region identification karst collapse col umn includes the following steps:
Step S1061 is obtained in advance as the weighted value of attribute body distribution every in objective attribute target attribute body；
Step S1062, based on the weighted value of every attribute body, using neural network algorithm at least two objective attribute target attribute bodies
It is merged, the objective attribute target attribute body after being merged；
Wherein, the weighted value based on every attribute body carries out at least two objective attribute target attribute bodies using neural network algorithm
Fusion, the objective attribute target attribute body after merge include: the weighted sum of at least two objective attribute target attribute bodies of calculating, and by weighted sum
As a result as the objective attribute target attribute body after fusion.
Step S1063 carries out the prediction of karst collapse col umn according to the objective attribute target attribute body after fusion, and according to prediction result in mesh
It marks and identifies karst collapse col umn in subterranean strata region.
For example, selection chaos attribute body and ant attribute volume are to be merged, the objective attribute target attribute body after being merged.This
When, the weight A1 distributed in advance for chaos attribute body is obtained, and acquisition is in advance the weight A2 of ant attribute volume distribution,
In, A1+A2=1.Then, the weighted sum of chaos attribute body and ant attribute volume is calculated, and using the result of weighted sum as fusion
Objective attribute target attribute body later.
In another example choosing chaos attribute body, ant attribute volume and dip angle attribute body are merged, the mesh after being merged
Mark attribute volume.At this point, preparatory chaos attribute body is obtained, weight B1, B2 and B3 of ant attribute volume and the distribution of dip angle attribute body,
In, B1+B2+B3=1.Then, chaos attribute body is calculated, the weighted sum of ant attribute volume and dip angle attribute body, and by weighted sum
Result as fusion after objective attribute target attribute body.
The embodiment of the present invention is illustrated below in conjunction with Fig. 2 to Fig. 6.
Fig. 2 is a kind of schematic diagram of horizon slice for being optionally based on chaos attribute body according to an embodiment of the present invention.Fig. 3
It is a kind of schematic diagram of horizon slice for being optionally based on dip angle attribute body according to an embodiment of the present invention.Fig. 4 is according to this hair
A kind of schematic diagram of horizon slice for being optionally based on ant attribute volume of bright embodiment.Fig. 5 is according to an embodiment of the present invention
A kind of schematic diagram for the horizon slice being optionally based on fusion attribute volume, wherein the objective attribute target attribute body chosen herein is ant category
Property body, dip angle attribute body and chaos attribute body fusion after attribute volume.Fig. 6 is the target that is obtained using manual interpretation method
The earth formation schematic diagram in subterranean strata region.
Fig. 2 and Fig. 3 are compared with Fig. 4 respectively it is found that relative to merely using chaos attribute body, alternatively, making merely
With dip angle attribute body, identify that karst collapse col umn position can more clearly from be identified by subsideing columnar region based on ant attribute volume.
Fig. 4 and Fig. 5 are compared it is found that using fusion after objective attribute target attribute body identification karst collapse col umn when, can be more
The position of accurate identification karst collapse col umn, and the boundary of identification karst collapse col umn.
Fig. 5 and Fig. 6 are compared it is found that No. 1 region, No. 2 regions shown in Fig. 6, No. 3 regions, No. 4 regions are being schemed
Have in 5 and show well, and give finer display in Fig. 5, is not shown in addition, being also shown in Fig. 6 in Fig. 5
The some small karst collapse col umns come, the technical problem big there are subjectivity relative to manual interpretation method, using the embodiment of the present invention
The obtained result of optional embodiment it is more accurate and reliable.Wherein, the region of irregular loop coil mark is sunken in Fig. 6
Fall the region of column.
Embodiment two
Fig. 7 is a kind of flow chart of optionally recognition methods of karst collapse col umn according to an embodiment of the present invention, as shown in fig. 7,
This method comprises the following steps:
Step S701 loads 3D seismic data, wherein carries in 3D seismic data every in target subterranean formation area
The interface information of a rock stratum layer position, interface information includes attribute information and/or the target subterranean rock stratum of target subterranean formation area
The structural information in region；
Step S702, is filtered 3D seismic data；
It specifically, in embodiments of the present invention, can in order to eliminate influence of the ambient noise to the precision of 3D seismic data
Median filter process is carried out with the data volume first to 3D seismic data, and based on the 3D seismic data after filtering processing
Data volume extract objective attribute target attribute body.
Step S703, the data volume based on the 3D seismic data after filtering processing extract chaos attribute body, and inclination angle belongs to
Property body and ant attribute volume；
After the data volume to 3D seismic data carries out median filter process, so that it may based on 3D seismic data
Data volume extract objective attribute target attribute body, wherein objective attribute target attribute body includes following at least two: chaos attribute body, ant attribute volume and
Dip angle attribute body.
It should be noted that can extract chaos attribute body using mode described in above-described embodiment one, inclination angle belongs to
Property body and ant attribute volume, in this regard, repeating no more.
Step S704, by chaos attribute body, dip angle attribute body and ant attribute volume are merged, the mesh after being merged
Mark attribute volume；
It should be noted that can using mode described in step S1061 in above-described embodiment one and step S1062 come
By chaos attribute body, dip angle attribute body and ant attribute volume are merged, the objective attribute target attribute body after being merged, in this regard, not
It repeats again.
Step S705 subsides columnar region using the objective attribute target attribute body identification after fusion；
It should be noted that can described in the step S1063 in above-described embodiment one by the way of subside to identify
Columnar region, in this regard, repeating no more.
In embodiments of the present invention, the data volume of the 3D seismic data in target subterranean formation area is obtained first；So
Afterwards, the data volume based on 3D seismic data extracts objective attribute target attribute body, wherein objective attribute target attribute body includes following at least two: mixed
Ignorant attribute volume, dip angle attribute body and ant attribute volume；Finally, being subside based on objective attribute target attribute body in the identification of target subterranean formation area
Column.In such a way that a variety of attribute volumes merge, karst collapse col umn position can be more accurately predicted and portray karst collapse col umn boundary, in turn
The lower technical problem of existing karst collapse col umn recognition methods accuracy of identification is alleviated, improves karst collapse col umn accuracy of identification to realize
Technical effect.
Embodiment three
The embodiment of the invention also provides a kind of identification device of karst collapse col umn, the identification device of the karst collapse col umn is mainly used for holding
The recognition methods of karst collapse col umn provided by row above content of the embodiment of the present invention, below to karst collapse col umn provided in an embodiment of the present invention
Identification device do specific introduction.
Fig. 8 is a kind of schematic diagram of the identification device of karst collapse col umn according to an embodiment of the present invention, as shown in figure 8, this is subside
The identification device of column specifically includes that acquiring unit 81, extraction unit 82 and recognition unit 83, in which:
Acquiring unit 81, for obtaining the data volume of the 3D seismic data in target subterranean formation area, wherein data
The interface information of each rock stratum layer position in target subterranean formation area is carried in body, interface information includes target subterranean formation area
Attribute information and/or target subterranean formation area structural information；
In embodiments of the present invention, target subterranean formation area is the region chosen in advance, the three-dimensional in the region
It include the 3D seismic data of each layer of position in the data volume of seismic data.
Specifically, it in the exploration process of geologic structure, first in shot point emission detection wave, then, is received in geophone station
Probing wave, wherein the propagation path of probing wave can be made by target by the launch angle of control detection wave launcher
Lower formation area.The attributes such as velocity of wave, amplitude, propagation time due to probing wave and material, density, structure by way of medium etc. are close
Cut phase is closed, thus, the interface information of subterranean strata can be carried by way of the probing wave in subterranean strata region.Therefore, of the invention real
It applies in example, above-mentioned 3D seismic data can select in field of seismic exploration common probing wave in target subterranean formation area
Amplitude, wherein the corresponding amplitude of the coordinate value of a three-dimensional space in target subterranean formation area.
Extraction unit 82 extracts objective attribute target attribute body for the data volume based on 3D seismic data, wherein objective attribute target attribute body
Including following at least two: chaos attribute body, dip angle attribute body, ant attribute volume；
Recognition unit 83, for identifying karst collapse col umn in target subterranean formation area based on the objective attribute target attribute body extracted.
Wherein, chaos attribute body is whether to lack inclination angle in measurement seismic volume, a kind of method of azimuth institutional framework,
That is chaos attribute body is able to reflect " confusion " degree of seismic data.Wherein, rock occurs to be crushed at karst collapse col umn development,
Severity of mixing up is higher.Therefore, the development position of karst collapse col umn can be substantially reflected in chaos attribute body.
In coal seam stability region, data inclination angle will not have greatly changed.It is not excessive to encounter the structure developments such as karst collapse col umn
Place, change dramatically can occur for inclination value, so dip angle attribute is sensitiveer to small construction.
In addition, ant attribute volume has tractability.Since seismic data has multi-solution,, have with different attribute body
There is the characteristics of subterranean strata different characteristic, a variety of attribute volumes are merged, can reduce the multi-solution of seismic data, it is effectively pre-
Measure the position and boundary of karst collapse col umn.
In embodiments of the present invention, the data volume of the 3D seismic data in target subterranean formation area is obtained first；So
Afterwards, the data volume based on 3D seismic data extracts objective attribute target attribute body, wherein objective attribute target attribute body includes following at least two: mixed
Ignorant attribute volume, dip angle attribute body and ant attribute volume；Finally, being subside based on objective attribute target attribute body in the identification of target subterranean formation area
Column.In such a way that a variety of attribute volumes merge, karst collapse col umn position can be more accurately predicted and portray karst collapse col umn boundary, in turn
The lower technical problem of existing karst collapse col umn recognition methods accuracy of identification is alleviated, improves karst collapse col umn accuracy of identification to realize
Technical effect.
Optionally, recognition unit includes: acquisition module, for obtaining in advance as attribute body distribution every in objective attribute target attribute body
Weighted value；Fusion Module, for the weighted value based on every attribute body, using neural network algorithm at least two target categories
Property body is merged, the objective attribute target attribute body after being merged；Prediction module, for according to the objective attribute target attribute body after fusion into
The prediction of row karst collapse col umn, and karst collapse col umn is identified in target subterranean formation area according to prediction result.
Optionally, Fusion Module includes: computational submodule, for calculating the weighted sum of at least two objective attribute target attribute bodies, and
Using the result of weighted sum as the objective attribute target attribute body after fusion.
Optionally, extraction unit includes: the first extraction module, for the case where objective attribute target attribute body is ant attribute volume
Under, passive ant attribute volume is extracted in the data volume of 3D seismic data, wherein passive ant attribute volume is for indicating three-dimensional
The discontinuity of seismic data；Second extraction module, for extracting positive ant attribute volume based on passive ant attribute volume, and will
Positive ant attribute volume is as ant attribute volume, wherein positive ant attribute volume can be on the basis of passive ant attribute volume
Indicate the discontinuity of 3-d seismic data set.
Optionally, the first extraction module is used for: obtaining pre-set first group of ant body parameter, wherein first group of ant
Ant body parameter includes the first initial ant boundary, the first tracking deviation, the first illegal step number, the first legal step number, the first search
Step-length, the first termination criteria；Based on first group of ant body parameter, ant tracking fortune is carried out to data volume using ant tracing algorithm
It calculates, obtains the passive ant attribute volume comprising passive ant body attribute.
Optionally, the second extraction module is used for: obtaining pre-set second group of ant body parameter, wherein second group of ant
Ant body parameter includes the second initial ant boundary, the second tracking deviation, the second illegal step number, the second legal step number, the second search
Step-length, the second termination criteria；Based on second group of ant body parameter, passive ant attribute volume is transported using ant tracing algorithm
It calculates, obtains the positive ant attribute volume comprising positive ant body attribute.
Optionally, the device further include: filter unit, for obtaining the 3-D seismics number in target subterranean formation area
According to data volume after, and based on 3D seismic data data volume extract objective attribute target attribute body before, to 3D seismic data
Data volume be filtered, and based on the data volume of the 3D seismic data after filtering processing extract objective attribute target attribute body.
The recognition methods of karst collapse col umn provided by the embodiment of the present invention and the computer program product of device, including store
The computer readable storage medium of program code, the instruction that said program code includes can be used for executing in previous methods embodiment
The method, specific implementation can be found in embodiment of the method, and details are not described herein.
It is apparent to those skilled in the art that for convenience and simplicity of description, the system of foregoing description
It with the specific work process of device, can refer to corresponding processes in the foregoing method embodiment, details are not described herein.
In addition, in the description of the embodiment of the present invention unless specifically defined or limited otherwise, term " installation ", " phase
Even ", " connection " shall be understood in a broad sense, for example, it may be being fixedly connected, may be a detachable connection, or be integrally connected；It can
To be mechanical connection, it is also possible to be electrically connected；It can be directly connected, can also can be indirectly connected through an intermediary
Connection inside two elements.For the ordinary skill in the art, above-mentioned term can be understood at this with concrete condition
Concrete meaning in invention.
It, can be with if the function is realized in the form of SFU software functional unit and when sold or used as an independent product
It is stored in a computer readable storage medium.Based on this understanding, technical solution of the present invention is substantially in other words
The part of the part that contributes to existing technology or the technical solution can be embodied in the form of software products, the meter
Calculation machine software product is stored in a storage medium, including some instructions are used so that a computer equipment (can be a
People's computer, server or network equipment etc.) it performs all or part of the steps of the method described in the various embodiments of the present invention.
And storage medium above-mentioned includes: that USB flash disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), arbitrary access are deposited
The various media that can store program code such as reservoir (RAM, Random Access Memory), magnetic or disk.
Finally, it should be noted that embodiment described above, only a specific embodiment of the invention, to illustrate the present invention
Technical solution, rather than its limitations, scope of protection of the present invention is not limited thereto, although with reference to the foregoing embodiments to this hair
It is bright to be described in detail, those skilled in the art should understand that: anyone skilled in the art
In the technical scope disclosed by the present invention, it can still modify to technical solution documented by previous embodiment or can be light
It is readily conceivable that variation or equivalent replacement of some of the technical features；And these modifications, variation or replacement, do not make
The essence of corresponding technical solution is detached from the spirit and scope of technical solution of the embodiment of the present invention, should all cover in protection of the invention
Within the scope of.Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. a kind of recognition methods of karst collapse col umn characterized by comprising
Obtain the data volume of the 3D seismic data in target subterranean formation area, wherein the mesh is carried in the data volume
The interface information of each rock stratum layer position in subterranean strata region is marked, the interface information includes the target subterranean formation area
The structural information of attribute information and/or the target subterranean formation area；
Data volume based on the 3D seismic data extracts objective attribute target attribute body, wherein the objective attribute target attribute body include with down toward
It is two kinds few: chaos attribute body, dip angle attribute body, ant attribute volume；
Karst collapse col umn is identified in the target subterranean formation area based on the objective attribute target attribute body extracted；
Wherein, it includes: using ant tracing algorithm to taking that data volume based on the 3D seismic data, which extracts objective attribute target attribute body,
Value has the data volume of the 3D seismic data of mutation to carry out ant tracking, obtains passive ant attribute volume；Ant is continued with to chase after
Track algorithm has the passive ant attribute volume of mutation to carry out ant tracking value, obtains positive ant attribute volume.
2. the method according to claim 1, wherein based on the objective attribute target attribute body extracted in the target
Subterranean strata region recognition karst collapse col umn includes:
It obtains in advance as the weighted value of attribute body distribution every in the objective attribute target attribute body；
Based on the weighted value of every attribute body, at least two objective attribute target attribute bodies are melted using neural network algorithm
It closes, the objective attribute target attribute body after being merged；
The prediction of karst collapse col umn is carried out according to the objective attribute target attribute body after fusion, and according to prediction result in the target subterranean
The identification karst collapse col umn in formation area.
3. according to the method described in claim 2, it is characterized in that, the weighted value based on every attribute body, using nerve
Network algorithm merges at least two objective attribute target attribute bodies, and the objective attribute target attribute body after being merged includes:
The weighted sum of the described at least two objective attribute target attribute bodies is calculated, and using the result of the weighted sum as the fusion
The objective attribute target attribute body afterwards.
4. the method according to claim 1, wherein the case where the objective attribute target attribute body is ant attribute volume
Under, the data volume based on the 3D seismic data extracts objective attribute target attribute body and includes:
Passive ant attribute volume is extracted in the data volume of the 3D seismic data, wherein the passiveness ant attribute volume is used
In the discontinuity for indicating the 3D seismic data；
Positive ant attribute volume is extracted based on the passive ant attribute volume, and using the positive ant attribute volume as the ant
Ant attribute volume, wherein the positive ant attribute volume can indicate the three-dimensional on the basis of the passive ant attribute volume
The discontinuity of seismic data cube.
5. according to the method described in claim 4, it is characterized in that, extracting passiveness in the data volume of the 3D seismic data
Ant attribute volume includes:
Obtain pre-set first group of ant body parameter, wherein first group of ant body parameter includes the first initial ant
Boundary, the first tracking deviation, the first illegal step number, the first legal step number, the first step-size in search, the first termination criteria；
Based on first group of ant body parameter, ant is carried out to the data volume using ant tracing algorithm and tracks operation, is obtained
To the passive ant attribute volume comprising passive ant body attribute.
6. according to the method described in claim 4, it is characterized in that, extracting positive ant category based on the passive ant attribute volume
Property body includes:
Obtain pre-set second group of ant body parameter, wherein second group of ant body parameter includes the second initial ant
Boundary, the second tracking deviation, the second illegal step number, the second legal step number, the second step-size in search, the second termination criteria；
Based on second group of ant body parameter, operation is carried out to the passive ant attribute volume using ant tracing algorithm, is obtained
To the positive ant attribute volume comprising positive ant body attribute.
7. the method according to claim 1, wherein obtaining the 3-D seismics number in target subterranean formation area
According to data volume after, and based on the data volume of the 3D seismic data extract objective attribute target attribute body before, the method is also
Include:
The data volume of the 3D seismic data is filtered, and based on the 3-D seismics number after filtering processing
According to data volume extract the objective attribute target attribute body.
8. a kind of identification device of karst collapse col umn characterized by comprising
Acquiring unit, for obtaining the data volume of the 3D seismic data in target subterranean formation area, wherein the data volume
The middle interface information for carrying each rock stratum layer position in the target subterranean formation area, the interface information include the target
The structural information of the attribute information of lower formation area and/or the target subterranean formation area；
Extraction unit extracts objective attribute target attribute body for the data volume based on the 3D seismic data, wherein the objective attribute target attribute
Body includes following at least two: chaos attribute body, dip angle attribute body, ant attribute volume；
Recognition unit, for identifying karst collapse col umn in the target subterranean formation area based on the objective attribute target attribute body extracted；
Wherein, extraction unit is used for: having the data volume progress of the 3D seismic data of mutation to value using ant tracing algorithm
Ant tracking, obtains passive ant attribute volume；Continue with the passive ant attribute volume that ant tracing algorithm has mutation to value
Ant tracking is carried out, positive ant attribute volume is obtained.
9. device according to claim 8, which is characterized in that the recognition unit includes:
Module is obtained, for obtaining in advance as the weighted value of attribute body distribution every in the objective attribute target attribute body；
Fusion Module, for the weighted value based on every attribute body, using neural network algorithm at least two mesh
Mark attribute volume is merged, the objective attribute target attribute body after being merged；
Prediction module, for carrying out the prediction of karst collapse col umn according to the objective attribute target attribute body after fusion, and according to prediction result
The karst collapse col umn is identified in the target subterranean formation area.
10. device according to claim 9, which is characterized in that the Fusion Module includes:
Computational submodule, for calculating the weighted sum of the described at least two objective attribute target attribute bodies, and by the knot of the weighted sum
Fruit is as the objective attribute target attribute body after the fusion.
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| CN109782345A (en) * | 2019-01-07 | 2019-05-21 | 中国石油天然气股份有限公司 | Method for predicting reservoir stratum by using seismic attributes |
| CN110727025A (en) * | 2019-08-21 | 2020-01-24 | 中国石油化工股份有限公司 | Hidden fault recognition method |
| CN112230281A (en) * | 2020-09-17 | 2021-01-15 | 陕西省煤田地质集团有限公司 | Earthquake method for quickly identifying collapse column |
| CN113640876B (en) * | 2021-07-09 | 2023-05-30 | 中国煤炭地质总局地球物理勘探研究院 | Method for carrying out fine recognition on collapse column by utilizing chaos attribute |
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