CN109614590A - A kind of research depositional environment is to the data mining algorithm of deep water water channel morphology influence - Google Patents
A kind of research depositional environment is to the data mining algorithm of deep water water channel morphology influence Download PDFInfo
- Publication number
- CN109614590A CN109614590A CN201910016257.9A CN201910016257A CN109614590A CN 109614590 A CN109614590 A CN 109614590A CN 201910016257 A CN201910016257 A CN 201910016257A CN 109614590 A CN109614590 A CN 109614590A
- Authority
- CN
- China
- Prior art keywords
- water channel
- influence
- depositional environment
- case
- depositional
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Physics (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- Operations Research (AREA)
- Probability & Statistics with Applications (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Algebra (AREA)
- Evolutionary Biology (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Bioinformatics & Computational Biology (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention discloses a kind of research depositional environments to the data mining algorithm of deep water water channel morphology influence, comprising the following steps: A, the depositional environment that different water channel cases are characterized using eigenmatrix；B, influence of the single governing factor for water channel characterization parameter is calculated according to statistical data；C, influence of the whole depositional environment for water channel characterization parameter is calculated according to COS distance formula；D, the form similarity of different water channels is calculated using lattice close-degree formula；The present invention analyzes DIFFERENT DEPOSITIONAL ENVIRONMENTS, to the influence degree of different characterization parameters, to obtain the relationship between depositional environment and water channel form by mathematical methods such as data minings.On engineering field, pass through the relationship between reservoir and water channel form and the relationship between the obtained form of this method and environment, depositional environment and deep-sea oil pool distribution are contacted, the reservoir property for evaluating and predicting not exploring deep water water channel in region is removed so as to the information by depositional environment.
Description
Technical field
The present invention relates to Geological Resources and Geological Engineering, a kind of research depositional environment is specifically related to deep water water channel form
The data mining algorithm of influence.
Background technique
Deep water water channel is important channel of the continental shelf to sea basin conveying deposit, while deep water water channel is also the weight for preserving oil gas
Want place.The depositional environment at deep-sea often determines the property of deposit in water channel, to influence in the channel system of different regions
The property of reservoir.In addition to the influence to reservoir, depositional environment can also cause tremendous influence to water channel form.Earthquake and seabed
The exploration means such as cable provide the largely quantized data about deep water water channel form for researcher.
In recent years, a large amount of case study goes to portray the geometric shape of water channel using characterization parameter (such as width, depth), and
It goes to describe different deep-water depositional environments using governing factor (such as continental shelf type, basin type).In addition, by statistical chart and
Linear regression goes to characterize the conventional method that different governing factors influence characterization parameter, can not clearly explain quantifying for the two
Relationship.In Geological Resources and Geological Engineering field, researcher often only analyzes single governing factor using this conventional method
Influence for water channel characterization parameter, however this method cannot embody influence of the whole depositional environment for characterization parameter,
Influence of the governing factor for water channel configuration cannot be showed.
Currently without can directly indicate different governing factors for the method for different water channel characterization parameters, more no analysis is whole
Method of the body depositional environment to water channel configuration.The quantitative relationship obscured between governing factor and water channel characterization parameter hinders
The research of depositional environment and water channel morphologic correlation.
Summary of the invention
In view of the above-mentioned problems, the present invention provides a kind of data mining calculation for studying depositional environment to deep water water channel morphology influence
Method, it is therefore intended that establish the quantitative relationship between characterization parameter and governing factor using the algorithm in data mining, and demarcate entirety
Quantitative relationship between depositional environment and water channel configuration feature.
The present invention uses following technical solutions:
A kind of research depositional environment is to the data mining algorithm of deep water water channel morphology influence, comprising the following steps:
A, the depositional environment of different water channel cases: the depositional environment recorded according to water channel case is characterized using eigenmatrix
Governing factor, utilize the governing factor that does not occur in 0 expression case, the governing factor occurred indicated using 1, in this way, different
Water channel case can be indicated by eigenmatrix that 0 and 1 form；
B, influence of the single governing factor for water channel characterization parameter is calculated according to statistical data: calculates all water channels
The average value of case characterization parameter filters out the case of specific governing factor from all water channel cases, then to calculate these specific
Then the average value of governing factor case characterization parameter characterizes single governing factor by the difference of two average value and characterization is joined
Several influences；
C, influence of the whole depositional environment for water channel characterization parameter is calculated according to COS distance formula: according to step
The resulting all governing factors of B and calculate whole depositional environment for table using COS distance for the influence value of characterization parameter
Levy the influence value of parameter；
D, the form similarity of different water channels is calculated using lattice close-degree formula: according to characterization parameter in water channel case
Corresponding descriptive statistic parameter, and the form similarity between different water channel cases is calculated using lattice close-degree in fuzzy mathematics；
E, the environment similarity of different water channels is calculated using min-hash algorithm: according in step A for record not
With the eigenmatrix of case governing factor, the similarity of the whole depositional environment of min hashing computing difference water channel case is utilized.
Preferably, the water channel form of non-survey area: the numerical value being calculated by step C-E is predicted using depositional environment
As a result, recycling the water channel morphological feature of the non-survey area of combination supposition of governing factor.
The beneficial effects of the present invention are:
The invention discloses a kind of research depositional environments for the data mining algorithm of water channel morphology influence, which passes through
The mathematical methods such as data mining analyze DIFFERENT DEPOSITIONAL ENVIRONMENTS, the i.e. combination of governing factor, the influence journey to different characterization parameters
Degree, to obtain the relationship between depositional environment and water channel form.On engineering field, pass through the pass between reservoir and water channel form
System and the relationship between the obtained form of this method and environment, contact depositional environment and deep-sea oil pool distribution, so as to
Remove to evaluate and predict not exploring the reservoir property of deep water water channel in region with the information by depositional environment.
Detailed description of the invention
In order to illustrate the technical solution of the embodiments of the present invention more clearly, the attached drawing to embodiment is simply situated between below
It continues, it should be apparent that, the accompanying drawings in the following description merely relates to some embodiments of the present invention, rather than limitation of the present invention.
Fig. 1 is the schematic diagram data of all maximum measurement width of certain water channel case of the invention；
Fig. 2 is the schematic diagram of certain water channel case characterization parameter descriptive statistic value of the invention；
Fig. 3 is that the title of water channel profile characterization parameters of the present invention and form define schematic diagram.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
Attached drawing, the technical solution of the embodiment of the present invention is clearly and completely described.Obviously, described embodiment is this hair
Bright a part of the embodiment, instead of all the embodiments.Based on described the embodiment of the present invention, ordinary skill
Personnel's every other embodiment obtained under the premise of being not necessarily to creative work, shall fall within the protection scope of the present invention.
Unless otherwise defined, the technical term or scientific term that the disclosure uses should be tool in disclosure fields
The ordinary meaning for thering is the personage of general technical ability to be understood.The similar word meaning such as " comprising " or "comprising" used in the disclosure
Point out that element or object before the existing word are covered the element for appearing in the word presented hereinafter or object and its be equal, without
Exclude other elements or object."upper", "lower", "left", "right" etc. are only used for indicating relative positional relationship, when being described object
Absolute position change after, then the relative positional relationship may also correspondingly change.
Below with reference to embodiment, the present invention is further described.
The used governing factor type for indicating whole depositional environment of this method is as shown in table 1, which follows Stow etc.
People's classification proposed in 1996, is fully described from material resource, continental shelf, the construction of sea basin and global sea-level changes etc.
The concept of deep-water depositional environments.These governing factor information can be obtained from the work area background of different water channel cases.
1 control parameter information list of table
It is a kind of research depositional environment for water channel morphology influence data mining algorithm, comprising the following steps:
A, the depositional environment of different water channel cases is characterized using eigenmatrix: as shown in table 2, being had recorded in tables of data multiple
The corresponding governing factor of water channel case depositional environment, the corresponding case information of entry in table.It will be controlled present in case
Factor is expressed as 1, the expression 0 being not present, so as to obtain the feature vector being made of 1 and 0.This feature vector characterization
The governing factor combination of different cases, eigenmatrix corresponding to table 2 are expressed as follows:
Each horizontally-arranged data correspond to the combination of different governing factors in case in matrix.
The statistical form of the different case governing factors of table 2
B, influence of the single governing factor for water channel characterization parameter is obtained according to statistical data: as shown in Figure 1, opening up in figure
The numerical value (there are 273 data in figure) of all maximum measurement width in a water channel case are shown, these parameters are from earthquake work area
Middle measure along the seismic profile of water channel flow direction obtains；The maximum measurement width of water channel case is by this 273 sectional parameters in Fig. 1
Average value W indicate, average value W's is expressed as follows:As shown in table 3, the width numerical value of case one is in table
The average value of 273 width datas in 224, i.e. Fig. 1, altogether including the mean breadth numerical value of multiple water channels in table 3.
The statistical form of the different case channel widths of table 3
| Case 1 | Case 2 | Case 3 | Case 4 | Case 5 | Case 6 | Case 7 | Case 8 | … | |
| Width W | 224 | 235 | 321 | 567 | 489 | 190 | 241 | 389 | … |
As shown in table 3, a certain characterization parameter of each case is counted, by taking width W as an example, if sharing m case, then all cases
Example width average value WmeanIt is expressed as follows:
The width average value W of all cases in table 3meanFor 324, (partial data is unlisted in table, lists the case of part
Example width average value WmeanFor 352.25).
Filter out the case of a certain specific governing factor (jointly owned a certain governing factor in the case of part), such as table
Shown in 4.1: the width value of all passive continental margin cases shares 4 passive continental margins (n=4 in formula 2), calculates every
The width F of a passive continental marginn(each data i.e. in table 4.1) and width average value Wmean(being here 324)) difference, then
Passive continental margin is expressed as R to channel width influence valuemIt is as follows:
The calculated R of data institute in table 4.1mIt is 15.The corresponding widths affect value of passive continental margin in table 4.2 is
Calculate resulting 15.According to formula (1) and (2), influence of other governing factors for channel width can also be found out, finally may be used
To obtain all governing factors as shown in table 4.2 for the influence value of channel width.In addition to width, counted involved in the step
Calculation further includes influence of the governing factor to other characterization parameters, such as the gradient, depth, is only illustrated with width in specification
It is bright.
The width value statistical form of 4.1 passive continental margin case of table
| There is the case code name of passive continental margin | Case 3 | Case 6 | Case 9 | Case 10 |
| Width | 334 | 339 | 340.1 | 342.1 |
Influence value statistical form of 4.2 governing factor of table for channel width
C, influence of the whole depositional environment for water channel characterization parameter is calculated according to COS distance formula: according to step
Eigenmatrix in A, all governing factors in some available case, if the corresponding eigenmatrix value of governing factor is P；
Further according to governing factors different in step B for the influence value of characterization parameter, it is set as R, n governing factor is shared, by remaining
Whole depositional environment is expressed as follows the influence value X of characterization parameter in certain available case of chordal distance:
By choosing different water channel cases, shadow of the depositional environment to water channel characterization parameter in available each case
It rings.
D, the form similarity of different water channels is calculated using lattice close-degree formula: calculating the characterization parameter of different cases
Descriptive statistic numerical value, as shown in Fig. 2, indicating in figure is certain characterization parameter in different numberical ranges in certain water channel case
The frequency of occurrences；Such as first cake chart in Fig. 2, case has the numerical value of 273 maximum measurement width, between 100-250m
Numerical value accounting be about 96%.Characterization parameter type and form involved in Fig. 2 are defined as number shown in Fig. 3
4. 1. parameter, the not shown area of section refers to the area of water channel U or V-shaped section in Fig. 3.Such as (the different case water of table 5
Road characterization parameter statistical form) shown in, the descriptive statistic numerical value of each water channel case is counted, the numerical value corresponding diagram 2 of case 1 in table
(maximum measurement width w1, maximum measurement width w2, maximum measurement width w3, minimum measurement are wide for statistical data in middle cake chart
Spend w1, minimum measurement width w2, minimum measurement width w3, maximum measuring depth D1, maximum measuring depth D2, maximum measuring depth
D3).It should also include minimum measuring depth D1 in table 5, minimum measuring depth D2, minimum measuring depth D3, cross-sectional area A 0, transversal
The parameters such as area A1.
The different case water channel characterization parameter statistical forms of table 5
If the characterization parameter item of case 1 is A (xo), the characterization parameter item of case 2 is B (xo), the range of o is 1 to 9, i.e. table
The sequence of characterization parameter in 5.The apposition x in fuzzy mathematics is calculated, is expressed as follows:
X=max { min [A (x1),B(x1)],min[A(x2),B(x2)],min[A(x3),B(x3)],...,min[A
(xn),B(xn)]} (4)
Then, inner product y is calculated, is expressed as follows:
Y=min { max [A (x1),B(x1)],max[A(x2),B(x2)],max[A(x3),B(x3)],...,max[A
(xn),B(xn)]} (5)
Finally by inner product x and apposition y, lattice close-degree L is calculated, expression formula is as follows:
Lattice close-degree expresses the configuration similarity of two water channels, by this method, can be in the hope of all water channels
The form similarity of case.
E, the environment similarity of different water channels is calculated using min-hash algorithm: according to the resulting feature square of step A
Battle array can learn the governing factor combination of two cases.Pass through min-hash algorithm, it is assumed that the governing factor that two cases have
Number be J, i.e. matrix value in two cases is all 1；The governing factor that only one case has is K, i.e. case
Matrix value is 1, another is 0, then calculates the expression formula of resulting depositional environment similarity E are as follows:
Depositional environment similarity by min-hash algorithm, between available all water channel cases.
Utilize depositional environment to predict the water channel form of non-survey area: according to the resulting configuration similarity of step D with
And the resulting depositional environment similarity of step E, it is similar can to learn whether similar depositional environment development can develop into form
Deep water water channel.It is compared by multiple cases, if it is similar to find that certain depositional environment (i.e. similar governing factor combination) can generate
Water channel form, i.e. configuration similarity L and depositional environment similarity E be both greater than 0.8 (80% similarity degree, the value according to
Accuracy required by concrete engineering determines, can be 85%, 90% etc.), then determine that this depositional environment can generate specifically
Water channel form；Then resulting depositional environment is calculated to characterization parameter disturbance degree X by step C, makes a concrete analysis of this deposition ring
Border can generate those similar morphological features actually.If the depositional environment of zone of ignorance can generate specific water channel form with certain
When environment is similar, the shape for speculating water channel in zone of ignorance can be gone by the form in water channel case known under this depositional environment
State feature recommends the potential morphological feature of zone of ignorance water channel to researcher that is, by similar depositional environment.
The above described is only a preferred embodiment of the present invention, be not intended to limit the present invention in any form, though
So the present invention has been disclosed as a preferred embodiment, and however, it is not intended to limit the invention, any technology people for being familiar with this profession
Member, without departing from the scope of the present invention, when the technology contents using the disclosure above make a little change or modification
For the equivalent embodiment of equivalent variations, but anything that does not depart from the technical scheme of the invention content, according to the technical essence of the invention
Any simple modification, equivalent change and modification to the above embodiments, all of which are still within the scope of the technical scheme of the invention.
Claims (2)
1. a kind of research depositional environment is to the data mining algorithm of deep water water channel morphology influence, which is characterized in that including following step
It is rapid:
A, the depositional environment of different water channel cases is characterized using eigenmatrix: according to the control for the depositional environment that water channel case is recorded
Factor processed indicates the governing factor occurred using 1, in this way, different water using the governing factor not occurred in 0 expression case
Road case can be indicated by the eigenmatrix that 0 and 1 form；
B, influence of the single governing factor for water channel characterization parameter is calculated according to statistical data: calculates all water channel cases
The average value of characterization parameter filters out the case of specific governing factor from all water channel cases, then calculates these specific controls
Then the average value of factor case characterization parameter characterizes single governing factor for characterization parameter by the difference of two average values
It influences；
C, influence of the whole depositional environment for water channel characterization parameter is calculated according to COS distance formula: according to step B institute
All governing factors for the influence value of characterization parameter, and calculate whole depositional environment using COS distance and characterization joined
Several influence values；
D, the form similarity of different water channels is calculated using lattice close-degree formula: corresponding according to characterization parameter in water channel case
Descriptive statistic parameter, and calculate the form similarity between different water channel cases using lattice close-degree in fuzzy mathematics；
E, the environment similarity of different water channels is calculated using min-hash algorithm: according in step A for record not accomplice
The eigenmatrix of example governing factor, utilizes the similarity of the whole depositional environment of min hashing computing difference water channel case.
2. a kind of research depositional environment according to claim 1 is to the data mining algorithm of deep water water channel morphology influence,
It is characterized in that, the water channel form of non-survey area: the numerical result being calculated by step C-E is predicted using depositional environment,
The combination of governing factor is recycled to speculate the water channel morphological feature of non-survey area.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910016257.9A CN109614590B (en) | 2019-01-08 | 2019-01-08 | Data mining method for researching influence of deposition environment on form of deepwater channel |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910016257.9A CN109614590B (en) | 2019-01-08 | 2019-01-08 | Data mining method for researching influence of deposition environment on form of deepwater channel |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109614590A true CN109614590A (en) | 2019-04-12 |
| CN109614590B CN109614590B (en) | 2022-03-25 |
Family
ID=66016349
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910016257.9A Active CN109614590B (en) | 2019-01-08 | 2019-01-08 | Data mining method for researching influence of deposition environment on form of deepwater channel |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109614590B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102243678A (en) * | 2011-07-19 | 2011-11-16 | 北京师范大学 | Method for analyzing sand bodies in reservoirs based on inversion technique of sedimentary dynamics |
| CN103971102A (en) * | 2014-05-21 | 2014-08-06 | 南京大学 | Static gesture recognition method based on finger contour and decision-making trees |
| CN104063059A (en) * | 2014-07-13 | 2014-09-24 | 华东理工大学 | Real-time gesture recognition method based on finger division |
| CN104619791A (en) * | 2012-05-24 | 2015-05-13 | 麻省理工学院 | Apparatus with a liquid-impregnated surface |
| CN107609759A (en) * | 2017-08-29 | 2018-01-19 | 广州海洋地质调查局 | A kind of seabed engineering geology of exploiting ocean natural gas hydrates influences evaluation method |
| CN108829717A (en) * | 2018-05-07 | 2018-11-16 | 西南石油大学 | A kind of Database Systems and method carrying out the quantitative analysis of deep water water channel configuration and morphological Simulation based on seismic data |
-
2019
- 2019-01-08 CN CN201910016257.9A patent/CN109614590B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102243678A (en) * | 2011-07-19 | 2011-11-16 | 北京师范大学 | Method for analyzing sand bodies in reservoirs based on inversion technique of sedimentary dynamics |
| CN104619791A (en) * | 2012-05-24 | 2015-05-13 | 麻省理工学院 | Apparatus with a liquid-impregnated surface |
| CN103971102A (en) * | 2014-05-21 | 2014-08-06 | 南京大学 | Static gesture recognition method based on finger contour and decision-making trees |
| CN104063059A (en) * | 2014-07-13 | 2014-09-24 | 华东理工大学 | Real-time gesture recognition method based on finger division |
| CN107609759A (en) * | 2017-08-29 | 2018-01-19 | 广州海洋地质调查局 | A kind of seabed engineering geology of exploiting ocean natural gas hydrates influences evaluation method |
| CN108829717A (en) * | 2018-05-07 | 2018-11-16 | 西南石油大学 | A kind of Database Systems and method carrying out the quantitative analysis of deep water water channel configuration and morphological Simulation based on seismic data |
Non-Patent Citations (3)
| Title |
|---|
| BEIBEI LIU 等: "Sedimentary characteristics and controls of a retreating, coarse-grained fan-delta system in the Lower Triassic, Mahu Depression, northwestern China", 《GEOLOGICAL JOURNAL》 * |
| 付超 等: "南海北部神狐海域峡谷层序结构差异与控制因素", 《现代地质》 * |
| 林煜 等: "深水浊积朵叶储层构型模式研究", 《天然气地球科学》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109614590B (en) | 2022-03-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11836976B2 (en) | Method for recognizing seawater polluted area based on high-resolution remote sensing image and device | |
| Ghiasi-Freez et al. | Semi-automated porosity identification from thin section images using image analysis and intelligent discriminant classifiers | |
| CN112347823B (en) | Deposition phase boundary identification method and device | |
| Fortin et al. | Species’ geographic ranges and distributional limits: pattern analysis and statistical issues | |
| CN109800863B (en) | Logging phase identification method based on fuzzy theory and neural network | |
| De Bruin et al. | Soil-landscape modelling using fuzzy c-means clustering of attribute data derived from a Digital Elevation Model (DEM) | |
| Houston et al. | Adaptive-window indicator kriging: A thresholding method for computed tomography images of porous media | |
| Wang et al. | Improved pore structure prediction based on MICP with a data mining and machine learning system approach in Mesozoic strata of Gaoqing field, Jiyang depression | |
| Samet et al. | Fuzzy rule-based image segmentation technique for rock thin section images | |
| Wang et al. | Automatic mapping of lunar landforms using DEM-derived geomorphometric parameters | |
| Sun et al. | Magnetization clustering inversion—Part 1: Building an automated numerical optimization algorithm | |
| CN106442122A (en) | Method for detecting ductile section percentage of fracture of steel material in drop weight tear test based on image segmentation and identification | |
| Callihan et al. | Topographic expressions of lunar graben | |
| Coenen et al. | Semi-supervised segmentation of concrete aggregate using consensus regularisation and prior guidance | |
| Silvero et al. | Protocol for the use of legacy data and magnetic signature on soil mapping of São Paulo Central West, Brazil | |
| CN109614590A (en) | A kind of research depositional environment is to the data mining algorithm of deep water water channel morphology influence | |
| Leblanc et al. | Numerical clustering of soil series using profile morphological attributes for potato | |
| Corsaro et al. | Geochemical pattern classification of recent volcanic products from Mt. Etna, Italy, based on Kohonen maps and fuzzy clustering | |
| CN112464997B (en) | Multi-sensor collaborative monitoring method and system based on fuzzy-greedy search decision | |
| CN111881125B (en) | Real-time cleaning method and system for offshore non-combat target | |
| Hyland et al. | Automated area segmentation for ocean bottom surveys | |
| CN111475685B (en) | Oil gas exploration method and device, storage medium and electronic equipment | |
| Beirami et al. | An introduction to wireline log analysis by integration of ascendant hierarchical clustering and k-nearest neighbor methods for permeability prediction using conventional well logs and core data | |
| Korobiichuk et al. | Approach to determination of parameters of probability density function of object attributes recognition in space photographs is considered within statistical method | |
| Klementiev et al. | Method for detection of the vessel trace anomalies in the sea surface images based on analysis of color gradient correlations |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |