CN108629364A - Non-gaussian type submarine geomorphy kind identification method based on multi-fractal spectrum signature - Google Patents
Non-gaussian type submarine geomorphy kind identification method based on multi-fractal spectrum signature Download PDFInfo
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Abstract
The invention discloses a kind of non-gaussian type submarine geomorphy kind identification method based on multi-fractal spectrum signature.Include the following steps：1) depth distribution skewness and kurtosis is calculated according to seabed depth measurement data, judges whether landform is non-gaussian type landform；2) the multi-fractal spectrum signature of non-gaussian type landform is calculated；3) using multi-fractal spectrum signature as original variable, application factor analysis method extracts geomorphologic factor；4) according to geomorphologic factor, geomorphic type grader is designed using support vector machines；5) the landform depth distribution skewness and kurtosis for calculating landforms to be identified, judges the non-Gaussian system of landform, calculates the multi-fractal spectrum signature and its geomorphologic factor of non-gaussian type landform, and geomorphic type is identified using the grader of design.The present invention has many advantages, such as that method is simple, calculation amount is small, recognition accuracy is high, saves manpower.The present invention is suitable for non-gaussian type submarine geomorphy type identification.
Description
Technical field
The present invention relates to the technical fields such as marine charting, ocean engineering, Marine oil and gas resource, and in particular to one kind is based on more
The non-gaussian type submarine geomorphy kind identification method of multifractal spectrum feature.
Background technology
Landforms are the general names of the various rolling shapes of earth surface.The type of seafloor topography include ancient stream channel, scour trough,
Submarine canyon, deep water water channel, seamount, carbonate platform, cliff, landslide etc..Geomorphological Classification is submarine geomorphy research and drawing
Basis, landform shape can reflect the inner link of morphogenesis type and origin cause of formation control form, be to carry out deep layer Geo knowledge digging
The key of pick.
Multibeam sounding system can detect wide covering, high-resolution seafloor topography fine-feature.The system uses
Strip-type measures, and can measure the water depth value of dozens or even hundreds of seabed measured points in the plane vertical with course-and-bearing simultaneously,
Or all standing depth of water band of an one fixed width, it can accurately and quickly measure the submarine target in the one fixed width of course line
Size, shape and height change.
Landforms parameter is the number description to landforms, for characterizing the spatial distribution characteristic of landforms.There are many landforms parameter, no
It is understood with subject and field and is classified also different.In current research, landforms parameter relates generally to micro-parameter and macroscopic view
Parameter two major classes.Micro-parameter is described and reflection be specific location geomorphic feature.Common micro-parameter mainly has slope
Degree, slope aspect, length of grade, planar curvature, profile curvature etc..Macroparameter is described and reflection is landforms in large area are special
Sign.Common macroparameter mainly has poor landform depth standards, terrain variance entropy, terrain roughness, elevation coefficient of variation etc..
20 th century laters, French mathematician Mandelbrot propose Concept of Fractal to indicate complex process or figure.Point
Shape is the general name to the definite meaning but not self similarity figure of characteristic length, structure.Fractal structure is widely present in certainly
In right boundary, for example, coastline, snowflake, trees, cloud etc. all have fractal structure.Multi-fractal is to space self similarity
Property or the two dimension of statistical self-similarity or one kind of three dimensional object are estimated.Multifractal Structure has non-Gaussian system, multi-fractal
Spectrum can reflect the complexity of object structure.
Seafloor topography is more complicated, currently, a large amount of geomorphic type analysis limited to the understanding of submarine geomorphy type
It relies primarily on and is accomplished manually, by the observation and experience of technical staff come Division identification.This method can make full use of technology
The knowledge of personnel, flexibility is good, but technical staff is needed to have abundant Geo knowledge and observation judgement experience, has prodigious
Subjectivity, there are poor in timeliness, labor intensity is big the shortcomings of.Especially because seafloor data has magnanimity rank, rely solely on
The artificial ability of technical staff can not much undertake the processing task of mass data.
Invention content
The present invention determines whether landform depth distribution has non-Gaussian system according to the kurtosis and the degree of bias of sea-floor relief, and extraction is non-
The space structure feature for dividing shape spectrum signature characterization landform of Gaussian landform, application factor analysis method extraction reflection geomorphic type
Geomorphologic factor, geomorphic type grader is established by support vector machines, realizes geomorphic type identification.The multifractal spectra of extraction
Feature includes poor multi-fractal spectrum width, multi-fractal spectrum peak, minimax fractal dimension, capacity dimension and correlation dimension.Tool
Have the advantages that method is simple, calculation amount is small, recognition accuracy is high, save manpower.Know suitable for non-gaussian type submarine geomorphy type
Not.
The present invention includes the following steps：
(1) non-gaussian type landform judges：
Basis respectivelyThe seabed for calculating region to be identified is deep
The degree of bias m of degree1, kurtosis m2If meeting m1＞ 0 and m2＞ 0, then region landform to be identified is non-gaussian type landform, wherein ziFor
The seabed depth value after normalization in region to be identified at ith measurement point,For seabed depth average value, n is
Points are measured in region to be identified；
(2) multifractal spectra feature calculation：
Respectively according to Δ α=αmax-αmin、f(α(q))max=max (f (α (q))), Δ f=f (αmax)-f(αmin) and Dq=
α (q) * q-f (α (q))/(1-q) calculates multi-fractal spectrum width Δ α, multi-fractal spectrum peak f (α (q))max, minimax divide shape
Dimension difference Δ f, dimension Dq, wherein D when q=0qFor capacity dimension, D when q=2qFor correlation dimension, max (f (α (q))) expressions take
The maximum value of f (α (q)), α (q) and f (α (q)) are respectively the Hausdor dimensions of landform depth distribution in region to be identified, αmaxFor
Maximum singularity exponents, αminFor minimum singularity exponents, f (α (q)) is distribution density function, αmax、αmin, α (q) and f (α
(q)) the Chhabza algorithms of application enhancements are calculated, and q is scale parameter, are set in Chhabza algorithms；
(3) the geomorphologic factor extraction based on multi-fractal spectrum signature：
Original variable is Δ α, f (α (q))max, Δ f and Dq, application factor analysis method extract original variable it is public because
Sub- fi, the variance contribution ratio of i=1,2,3 ... ..., t, each factor are λi, i=1,2,3 ... ..., t, if meetingThen
fi, i=1,2 ... ..., j are the geomorphologic factor based on multi-fractal spectrum signature, wherein t is common factor number, fi、λi, t according to
Factorial analysis determines that θ is threshold value, is set in program parameter, and j is geomorphologic factor number；
(4) geomorphic type classifier design：
According to geomorphologic factor fi, i=1,2 ... ..., j determine landforms type sorter using support vector machines；
(5) geomorphic type identifies：
Calculate the skewness and kurtosis value m of the landform depth of landforms to be identified1And m2If meeting m1＞ 0 and m2＞ 0, then calculate
The multi-fractal spectrum signature and its geomorphologic factor value f of landforms to be identifiedi, i=1,2 ... ..., j, point obtained according to step (4)
Class device determines the type of landforms to be identified.
Description of the drawings
Fig. 1 (a) to Fig. 1 (e) is respectively the landform of submarine plateau, coombe, landslide, protuberance, water channel this 5 kinds of geomorphic types
Figure；
Fig. 2 is that the degree of bias and the kurtosis of the depth distribution of 5 kinds of landforms cross figure；
Fig. 3 (a) to Fig. 3 (f) is that the multi-fractal spectrum signature of non-gaussian type landforms in the present embodiment crosses figure；
Fig. 4 is the variance contribution ratio figure of 5 common factors of non-gaussian type 5 multi-fractal spectrum signatures of geomorphic type；
Fig. 5 is the cumulative proportion in ANOVA of 5 common factors of non-gaussian type 5 multi-fractal spectrum signatures of geomorphic type
Figure；
Fig. 6 is non-gaussian type geomorphic type grader in the present embodiment；
Fig. 7 is the recognition result of non-gaussian type geomorphic type in the present embodiment.
Specific implementation mode
The present embodiment calculates kurtosis and the degree of bias value of sea-floor relief to determine landform according to multi-beam sounding survey data
Whether depth distribution has non-Gaussian system, extracts the multi-fractal spectrum signature and its geomorphologic factor of non-gaussian type landform, passes through branch
It holds vector machine and establishes geomorphic type grader, realize the identification of seabed coombe, landslide, water channel this 3 kinds of non-gaussian type geomorphic types.
The multi-fractal spectrum signature of extraction includes that multi-fractal spectrum width, multi-fractal spectrum peak, minimax fractal dimension be poor, capacity dimension
Number and correlation dimension.
Specific identification step is as follows：
(1) non-gaussian type landform judges：
Basis respectivelyThe seabed for calculating region to be identified is deep
The degree of bias m of degree1, kurtosis m2If meeting m1＞ 0 and m2＞ 0, then region landform to be identified is non-gaussian type landform, wherein ziFor
The seabed depth value after normalization in region to be identified at ith measurement point,For seabed depth average value, n is
Points are measured in region to be identified.
In the present embodiment, seabed depth data are obtained by multi-beam method measurement, and Fig. 1 (a) to Fig. 1 (e) is respectively sea
Base frame, coombe, landslide, protuberance, water channel this 5 kinds of geomorphic types topographic map.
Fig. 2 is that the degree of bias and the kurtosis of the depth distribution of 5 kinds of landforms cross figure.The depth of 5 kinds of landforms in the present embodiment point
The degree of bias of cloth is all higher than zero, and whole tablelands and the kurtosis for swelling landforms are respectively less than zero, and the kurtosis of whole coombe landforms is all higher than zero,
The kurtosis of part landslide and water channel landforms is more than zero.Therefore, the tableland in the present embodiment and protuberance landforms all do not have non-height
This property；Whole coombes, part landslide and water channel landforms have non-Gaussian system, can extract its multi-fractal spectroscopic eigenvalue.
(2) multifractal spectra feature calculation：
Respectively according to Δ α=αmax-αmin、f(α(q))max=max (f (α (q))), Δ f=f (αmax)-f(αmin) and Dq=
α (q) * q-f (α (q))/(1-q) calculates multi-fractal spectrum width Δ α, multi-fractal spectrum peak f (α (q))max, minimax divide shape
Dimension difference Δ f, dimension Dq, wherein D when q=0qFor capacity dimension, D when q=2qFor correlation dimension, max (f (α (q))) expressions take
The maximum value of f (α (q)), α (q) and f (α (q)) are respectively the Hausdor dimensions of landform depth distribution in region to be identified, αmaxFor
Maximum singularity exponents, αminFor minimum singularity exponents, f (α (q)) is distribution density function, αmax、αmin, α (q) and f (α
(q)) the Chhabza algorithms of application enhancements are calculated, and q is scale parameter, are set in Chhabza algorithms.
Fig. 3 is that the multi-fractal spectrum signature of non-gaussian type landforms in the present embodiment crosses figure.The multifractal spectra being related to is special
Sign includes poor multi-fractal spectrum width, multi-fractal spectrum peak, minimax fractal dimension, capacity dimension, correlation dimension.
(3) the geomorphologic factor extraction based on multi-fractal spectrum signature：
Original variable is Δ α, f (α (q))max, Δ f and Dq, application factor analysis method extract original variable it is public because
Sub- fi, the variance contribution ratio of i=1,2,3 ... ..., t, each factor are λi, i=1,2,3 ... ..., t, if meetingThen
fi, i=1,2 ... ..., j are the geomorphologic factor based on multi-fractal spectrum signature, wherein t is common factor number, fi、λi, t according to
Factorial analysis determines that θ is threshold value, is set in program parameter, and j is geomorphologic factor number.
In the present embodiment, the common factor number of the original variable of non-gaussian type landforms is t=5, and Fig. 4 is non-gaussian type landforms
The variance contribution ratio of 5 common factors of 5 multi-fractal spectrum signatures of type is distributed, and Fig. 5 is non-gaussian type geomorphic type more than 5
The cumulative proportion in ANOVA of 5 common factors of multifractal spectrum feature.In the present embodiment, θ=50% obtains 2 geomorphologic factors.
(4) geomorphic type classifier design：
According to geomorphologic factor fi, i=1,2 ... ..., j determine landforms type sorter using support vector machines.
In the present embodiment, 2 geomorphologic factors are extracted, Fig. 6 is non-gaussian type geomorphic type grader in the present embodiment.
(5) geomorphic type identifies：
Calculate the skewness and kurtosis value m of the landform depth of landforms to be identified1And m2If meeting m1＞ 0 and m2＞ 0, then calculate
The multi-fractal spectrum signature and its geomorphologic factor value f of landforms to be identifiedi, i=1,2 ... ..., j, point obtained according to step (4)
Class device determines the type of landforms to be identified.
Fig. 7 is the identification result of non-gaussian type geomorphic type in the present embodiment.
Claims (1)
1. a kind of non-gaussian type submarine geomorphy kind identification method based on multi-fractal spectrum signature, feature includes following specific
Step：
(1) non-gaussian type landform judges：
Basis respectivelyCalculate the seabed depth in region to be identified
Degree of bias m1, kurtosis m2If meeting m1＞ 0 and m2＞ 0, then region landform to be identified is non-gaussian type landform, wherein ziTo wait knowing
The seabed depth value after normalization in other region at ith measurement point,For seabed depth average value, n is to wait knowing
Points are measured in other region；
(2) multifractal spectra feature calculation：
Respectively according to Δ α=αmax-αmin、f(α(q))max=max (f (α (q))), Δ f=f (αmax)-f(αmin) and Dq=α (q) *
Q-f (α (q))/(1-q) calculates multi-fractal spectrum width Δ α, multi-fractal spectrum peak f (α (q))max, minimax fractal dimension it is poor
Δ f, dimension Dq, wherein D when q=0qFor capacity dimension, D when q=2qFor correlation dimension, max (f (α (q))) expressions take f (α
(q)) maximum value, α (q) and f (α (q)) are respectively the Hausdor dimensions of landform depth distribution in region to be identified, αmaxFor maximum
Singularity exponents, αminFor minimum singularity exponents, f (α (q)) is distribution density function, αmax、αmin, α (q) and f (α (q)) answer
It is calculated with improved Chhabza algorithms, q is scale parameter, is set in Chhabza algorithms；
(3) the geomorphologic factor extraction based on multi-fractal spectrum signature：
Original variable is Δ α, f (α (q))max, Δ f and Dq, the common factor f of application factor analysis method extraction original variablei, i
=1, the variance contribution ratio of 2,3 ... ..., t, each factor are λi, i=1,2,3 ... ..., t, if meetingThen fi, i=
1,2 ... ..., j are the geomorphologic factor based on multi-fractal spectrum signature, wherein t is common factor number, fi、λi, t is according to Factor minute
Analysis determines that θ is threshold value, is set in program parameter, and j is geomorphologic factor number；
(4) geomorphic type classifier design：
According to geomorphologic factor fi, i=1,2 ... ..., j determine landforms type sorter using support vector machines；
(5) geomorphic type identifies：
Calculate the skewness and kurtosis value m of the landform depth of landforms to be identified1And m2If meeting m1＞ 0 and m2＞ 0 is then calculated and is waited knowing
The multi-fractal spectrum signature and its geomorphologic factor value f of other landformsi, i=1,2 ... ..., j, the grader obtained according to step (4)
Determine the type of landforms to be identified.
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| CN110147802A (en) * | 2019-05-13 | 2019-08-20 | 安徽工业大学 | The inertinite classification method and system of trend fluction analysis are gone based on multi-fractal |
| CN111475910A (en) * | 2019-12-30 | 2020-07-31 | 水利部交通运输部国家能源局南京水利科学研究院 | Detection and statistics method for estuary salt and fresh water mixing degree based on fractal theory |
| CN112598792A (en) * | 2020-12-21 | 2021-04-02 | 武汉大学 | Multi-fractal quantification method and system for terrain complexity in three-dimensional scene |
| CN112907615A (en) * | 2021-01-08 | 2021-06-04 | 中国石油大学(华东) | Submarine landform unit contour and detail identification method based on region growing |
| CN114782211A (en) * | 2022-05-16 | 2022-07-22 | 广州海洋地质调查局 | Method and system for acquiring information of sea and mountain distribution range |
| CN116188964A (en) * | 2023-01-09 | 2023-05-30 | 中国海洋大学 | Method for carrying out real-time identification on submarine landslide by utilizing multi-beam image |
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| CN116188964B (en) * | 2023-01-09 | 2024-01-26 | 中国海洋大学 | Method for carrying out real-time identification on submarine landslide by utilizing multi-beam image |
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