CN109269480A - The multibeam bathymetric data processing method of robust curved surface is assumed based on optimal more depth of waters - Google Patents
The multibeam bathymetric data processing method of robust curved surface is assumed based on optimal more depth of waters Download PDFInfo
- Publication number
- CN109269480A CN109269480A CN201811135258.7A CN201811135258A CN109269480A CN 109269480 A CN109269480 A CN 109269480A CN 201811135258 A CN201811135258 A CN 201811135258A CN 109269480 A CN109269480 A CN 109269480A
- Authority
- CN
- China
- Prior art keywords
- depth
- water
- curved surface
- robust
- optimal
- 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
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C13/00—Surveying specially adapted to open water, e.g. sea, lake, river or canal
- G01C13/008—Surveying specially adapted to open water, e.g. sea, lake, river or canal measuring depth of open water
Landscapes
- Life Sciences & Earth Sciences (AREA)
- Hydrology & Water Resources (AREA)
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Measurement Of Velocity Or Position Using Acoustic Or Ultrasonic Waves (AREA)
Abstract
The invention discloses a kind of multibeam bathymetric data processing method for assuming robust curved surfaces based on the optimal more depth of waters, mainly include the robust curved surface key parameter group joint based on typical depth of water feature preferably and the multibeam bathymetric data automatic filter based on optimal robust curved surface and the big step of result output two.By typical depth of water data decimation, reference water depth data set generation, the setting of key parameter group, robust surface experimental group generates, test data set generates and Comprehensive Comparison, and the joint for completing robust curved surface key parameter group is preferred；Parameter after preferably is applied to the original multi-beam bathymetric data collection that need to be handled, optimal robust curved surface is generated and carries out automatic filter, finally exported in the form of three classes depth of water performance data, complete the efficient, automatic of multi-beam Bathymetric Data, fine processing.Multibeam bathymetric data is tested after actual measurement, and this method has been obviously improved the efficiency and precision of database preparation.This method marine charting, marine information science and in terms of have important practical application value.
Description
Technical field
The present invention relates to the technical fields such as marine charting, marine information science and submarine science.
Background technique
Multibeam echosounding technology be developed in the 1960s, the technology be considered as from before 3800 restrict bathymetry with
Come, the most revolutionary progress of sea floor surreying.Multibeam echosounding technology has filled up people's recognizing roughly for sea-floor relief
Know, has greatly pushed the development of Marine Sciences and geoscience.
However, multibeam echosounding technology is while making us obtain High Accuracy and High Resolution bathymetric data, it is a large amount of to survey
Deep error also makes us pay no small processing time cost.Multibeam bathymetric data includes two class main errors: systematic error
And exceptional value.Systematic error (such as energy converter mounting shift angle error, tidal level error, Sound speed profile error) can be by being accordingly
Error compensation algorithm of uniting is eliminated；Exceptional value mainly resolves false seabed signal by multiple-beam system and causes, as in water body object,
Received lobe reflections etc. need to be identified and be rejected by manually or automatically algorithm.
Currently with the continuous upgrading of multibeam sounding system, depth measurement data volume be increased dramatically, such as the more waves of latest generation
Beam sounding system can produce the depth measurement point of up to ten million or even more than one hundred million orders of magnitude per hour.Traditional human-edited's processing method faces
The depth measurement exceptional value of such magnanimity, not only time and effort consuming, can also bring artificial subjective factor into.Therefore, how more to a new generation
It is current key points and difficulties technical problem that wave beam depth measurement data, which efficiently automatically process,.
The drawbacks of to overcome traditional artificial editing and processing method, domestic and foreign scholars open multi-beam Bathymetric Data automatic processing method
A large amount of research is opened up.Simplest method is to carry out automatic filter (MAR to data using depth and field angle threshold value thresholding
GEOD, 2015), more complicated method be then by gradient between detection depth of water point and angle (MAR GEOPHYS RES,
1996；MAR GEOD, 2016), establish local landform prior model (International Hydrographic Review,
2001；US Hydro,2007；Mapping journal, 2018) and based on Principle of Statistics simulation manual procedure (IEEE J
OCEANIC ENG,2003；MAR GEOPHYS RES, 2017) etc. means detect exceptional value.
The above multi-beam Bathymetric Data automatic processing method is all by certain criterion, by some depth measurement points labeled as " guarantor
Stay ", other depth measurement point identifications are " rejecting ", it may be said that these methods are all based on the principle of depth measurement point classification.The current side of rarely having
The core concept of processing is focused on final depth of water curved surface by method；And automatic processing method inside major parameter setting to it most
Whole treatment effect influence is very big, how to be also rarely reported according to survey area's features of terrain preferably one group of optimized parameter group.
Summary of the invention
For overcome the deficiencies in the prior art, the more of robust curved surface are assumed based on optimal more depth of waters the invention proposes a kind of
Wave beam depth measurement data processing method.
The present invention is achieved by following technical proposals:
A kind of multibeam bathymetric data processing method for assuming robust curved surface based on optimal more depth of waters, including it is based on typical water
The robust curved surface key parameter group joint of deep feature is preferably and the multibeam bathymetric data automatic filter based on optimal robust curved surface
And the big step of result output two；
The robust curved surface key parameter group joint based on typical depth of water feature is carried out first preferably, and step includes: (1) typical case
Bathymetric data is chosen, (2) reference water depth data set generation, and the setting of (3) key parameter group, (4) assume that robust is bent using more depth of waters
Face constructs method and generates robust surface experimental group, and (5) test data set generates, and (6) Comprehensive Comparison obtains optimal key parameter
Group；Multibeam bathymetric data automatic filter and result output based on optimal robust curved surface then are carried out, step includes: that (7) are defeated
Enter to combine preferred key parameter group, assumes that robust Surface Construction method generates optimal more depth of waters and assumes that robust is bent using more depth of waters
Face, (8) data filtering, (9) depth of water performance data generate.
More depth of waters assume that robust Surface Construction method includes the following steps:
(2.1) robust curved surface key parameter group is inputted
Wherein reshFor the resolution ratio of robust curved surface,For lock-on range proportionality coefficient,For minimum lock-on range,Proportionality coefficient is propagated for horizontal uncertainty,For bias estimation amount, h andIt is natural number；
(2.2) original multibeam bathymetric data point set to be processed is inputtedWhereinxi、yi、ziRespectively original multibeam echosounding pointPlan-position coordinate and depth
Value, i andIt is natural number, is calculated using formula (1)Horizontal uncertainty σihzWith vertical uncertainty σivert:
In formula, σ0For known positioning device measurement accuracy,For known multibeam sounding system sound
Coordinate shift measurement error of the center relative to positioning device center, σa、σR、σP、σr、σθ、σVRespectively known measurement
Ship course angle, roll angle, pitch angle, time delay and ship's speed measurement error,For known positioning device and multibeam sounding system
Between time delay error；σysvFor the measurement error of the known velocity of sound in the horizontal direction, a1…a9For known weight coefficient, σH、σDR、σWL、σzsvAngle, pitch angle, field angle, heave and induction are directed toward in respectively known ranging
Heave, the measurement error of tidal level and the velocity of sound in vertical direction；
(2.3) establishing spatial resolution is reshThe initial more depth of waters assume robust curved surface Gr={ gj}J=1, n, gjFor curved surface
Interior node, j and n are natural number； For robust curved surface node gjK-th interior of the depth of water is assumed
It calculates, j, k, n and m are natural number； For node gjInterior k-th of the depth of water is assumedThe depth of water
Value,For node gjInterior k-th of the depth of water is assumedIntensity value；
(2.4) it is calculated using formula (2)The depth measurement point radius of influence{ gj}J=1, nNode catch
Catch radius { rjCaptDist}J=1, n；It needs just to can be used formula (3) by its uncertainty σ simultaneously in the two radiusesihz
And σivertIt merges and is transmitted to the curved surface node { g near itj}J=1, nOn, after transmitting
In formula, σVertMaxIHO S-44 for known user selection measures depth of water uncertainty determined by grade,
μDistExpFor the proportionality coefficient that known uncertainty increases with distance and increases；
In formula, SdistFor known depth measurement pointWith node gjDistance；
(2.5) input is transmitted to node gjDepth of water pointSuch as gjThe interior no depth of water is it is assumed that then useEstablish first water
It is deep to assumeWhereinSuch as gjHas the depth of water it is assumed that then calculating gjInterior all depth of waters are false
IfWithDifference, determine closestThe most suitable depth of water assumeMake
It is calculated with formula (4)WithNormalization difference en；
(2.6) Bayesian Factor B is calculated using formula (5)n, work as BnWhen > 0.135, operating procedure (2.7)；Work as Bn≤0.135
When, use depth of water pointIn node gjThe new depth of water is inside established to assumeWherein Operating procedure (2.8)；
(2.7) formula (6) are combined, usedThe depth of water is updated to assumeThe updated most suitable depth of water is assumed
(2.8) judge whether alsoCalculating is had neither part nor lot in, if so, then return step (2.4), if nothing, operating procedure
(2.9)；
(2.9) it is calculated using formula (7)Intensity value
In formula, εcurrentForThe depth of water point number for inside including；εnextForInside include
Depth of water point number；
(2.10) right by the size for assuming intensityIt is ranked up, determines that the optimal depth of water of each node is false
If generating more depth of waters assumes robust curved surface Gfinal。
It is specific as follows that the robust curved surface key parameter group based on typical depth of water feature combines preferred step:
(3.1) typical depth of water data decimation: in Multibeam Data collectionInterior, selection can characterize area's Water Depth Information
The typical morphologic region of feature exports the original multibeam bathymetric data point set in the area as parameter optimization test areaAnd it uses
Depth measurement dot density analytic approach obtainsOptimal grid resolution ratio resbest；
(3.2) it reference water depth data set generation: is handled using traditional artificial Editing MethodObtain reference water depth point setWith reference water depth curved surface
(3.3) key parameter group is arranged: settingGroup curved surface key parameter groupWherein resh=resbest,The setting range of value is 0.1-20,The setting range of value is 0-2,The setting range of value is
0.05-5,The setting range of value is 0.1-10；
(3.4) assume that robust Surface Construction method generates robust surface experimental group using more depth of waters: willWith
It is input in step (2.1)~(2.10), generatesThe more depth of waters of group assume robust surface experimental group
(3.5) test bathymetric data collection generates: withFor filtered reference curved surface, filter window size is set
It is 1.5 times of curved surface standard deviation, it is rightAutomatic filter is carried out, filtered test depth of water point set is obtainedAnd Kriging depth of water Surface Construction method is used, establish corresponding test depth of water curved surface collection
(3.6) Comprehensive Comparison: withOn the basis of, deep point of relieving oedema or abdominal distension through diuresis or purgation compares itself and test depth of water point setDifference；WithOn the basis of, itself and test depth of water curved surface collection are compared by curved surface node
Difference；Two kinds of control methods of comprehensive analysis as a result, according to the maximum principle of similitude, obtain optimal key parameter group
ωOptimal。
Described multibeam bathymetric data automatic filter and result output step based on optimal robust curved surface are specific as follows:
(4.1) preferred key parameter group is combined in input, and it is optimal more to assume that robust Surface Construction method generates using more depth of waters
The depth of water assumes robust curved surface: willAnd ωOptimalIt is input in step (2.1)~(2.10), generates optimal more depth of waters and assume
Robust curved surface
(4.2) data filtering: withFor filtered reference curved surface, 1.5 that filter window size is standard deviation are set
Times, it is rightAutomatic filter is carried out, filtered depth of water point set is obtainedUse Kriging depth of water Surface Construction
Method establishes whole district's depth of water curved surface
(4.3) depth of water performance data generates: setting isobath space DIsobath, obtained using isobath generating algorithmCorresponding isobath vector VIsobath；With the output of regular grid formatWith the output of vector dot formatV is exported with line of vector formatIsobath, obtain end result dataAnd VIsobath。
Beneficial effects of the present invention:
The present invention is proposed and realizes the multibeam bathymetric data processing method for being assumed robust curved surface based on optimal more depth of waters.
The advantage of this method is that processing the efficient, automatic of multibeam bathymetric data, tractability；The core concept of method focuses on finally
Depth of water curved surface, be conducive to the expression of final depth of water performance data；And this method can be according to preferably one group of area's features of terrain of survey most
Excellent parameter group ensure that the accuracy of algorithm parameter selection；This method is in multi-beam exploration with Data processing with important
Engineering application value.The present invention can be widely used in marine charting, marine information science and submarine science.
Detailed description of the invention
Fig. 1 is flow chart of the invention；
Fig. 2 is that more depth of waters in the embodiment of the present invention assume robust Surface Construction method flow chart；
Fig. 3 is that each main measurement error in the embodiment of the present invention is not horizontal and vertical to original multibeam bathymetric data true
Surely contribution amount is spent；
Fig. 4 is that the depth measurement point radius of influence, the node in the embodiment of the present invention capture radius and uncertainty fusion transmitting is shown
It is intended to；
Fig. 5 is the selection schematic diagram that the foundation that the depth of water in the embodiment of the present invention is assumed is assumed with the most suitable depth of water；
Fig. 6 is that the depth of water in the embodiment of the present invention is assumed to update schematic diagram；
Fig. 7 is Multibeam Data collection and typical morphologic region schematic diagram in the embodiment of the present invention；
Fig. 8 is that the depth measurement dot density analytic approach in the embodiment of the present invention obtains optimal grid resolution ratio schematic diagram；
Fig. 9 is that the traditional artificial Editing Method in the embodiment of the present invention handles to obtain reference water depth data set；
Figure 10 is the curved surface filtering schematic diagram in the embodiment of the present invention；
Figure 11 is the three classes depth of water performance data in the embodiment of the present invention；
Figure 12 is that the method for the present invention and human-edited's method timeliness compare.
Specific embodiment
The present invention is described further with reference to the accompanying drawings and examples.
Referring to attached drawing 1, a kind of multibeam bathymetric data processing method for assuming robust curved surface based on optimal more depth of waters is special
Sign is, combines including the robust curved surface key parameter group based on typical depth of water feature preferably and based on the more of optimal robust curved surface
Wave beam depth measurement data automatic filter and the big step of result output two；It is crucial that the robust curved surface based on typical depth of water feature is carried out first
Preferably, step includes: (1) typical depth of water data decimation, (2) reference water depth data set generation, (3) key parameter to parameter group joint
Group setting, (4) assume that robust Surface Construction method generates robust surface experimental group using more depth of waters, and (5) test data set generates,
(6) Comprehensive Comparison obtains optimal key parameter group；Then carry out the multibeam bathymetric data based on optimal robust curved surface certainly
Dynamic filtering and result output, step include: that preferred key parameter group is combined in (7) input, assume robust curved surface using more depth of waters
Building method generates optimal more depth of waters and assumes that robust curved surface, (8) data filtering, (9) depth of water performance data generate.
Wherein more depth of waters hypothesis robust Surface Construction methods are all utilized in step (4) and (7), and it is false that attached drawing 2 illustrates more depth of waters
If the key step of robust Surface Construction method, comprising:
(a) robust curved surface key parameter group is inputted
(b) original multibeam bathymetric data point set is inputtedIt is calculated using formula (1)'s
Horizontal uncertainty and vertical uncertainty.
(c)
In formula, σ0For known positioning device measurement accuracy,For known multibeam sounding system sound
Coordinate shift measurement error of the center relative to positioning device center, σa、σR、σP、σr、σθ、σVRespectively known measurement
Ship course angle, roll angle, pitch angle, time delay and ship's speed measurement error,For known positioning device and multibeam sounding system
Between time delay error；σysvFor the measurement error of the known velocity of sound in the horizontal direction, a1…a9For known weight coefficient, σH、σDR、σWL、σzsvRespectively known ranging is directed toward angle, pitch angle, field angle, heaves and lure
Lead the measurement error of heave, tidal level and the velocity of sound in vertical direction.
Attached drawing 3 illustrates each main measurement error pairThe contribution amount of horizontal and vertical uncertainty；
(d) establishing spatial resolution is reshThe initial more depth of waters assume robust curved surface Gr。
(e) it is calculated using formula (2)The depth measurement point radius of influence{ gj}J=1, nNode
Capture radius { rjCaptDist}J=1, n；
In formula, σVertMaxIHO S-44 for known user selection measures depth of water uncertainty determined by grade,
μDistExpFor the proportionality coefficient that known uncertainty increases with distance and increases.
Referring to attached drawing 4,It needs just to can be used formula (3) by its uncertainty σ simultaneously in the two radiusesihz
And σivertIt merges and is transmitted to the curved surface node { g near itj}J=1, nOn,It is transmitted to node { gj}J=1, nWater afterwards
Deep point
In formula, SdistFor known depth measurement pointWith node gjDistance.
(f) input is transmitted to node gjDepth of water pointReferring to attached drawing 5 (a) part, such as gjThe interior no depth of water is it is assumed that then make
WithFirst depth of water is established to assumeWhereinReferring to attached drawing 5 (b) part, such as gj
Has the depth of water it is assumed that then calculating gjInterior all depth of waters are assumedWithDifference, determine closestIt is most suitable
Heshui is assumed deeplyIt is calculated using formula (4)WithNormalization difference en。
(g) Bayesian Factor B is calculated using formula (5)n.Work as BnWhen > 0.135, operating procedure (g)；Work as Bn≤0.135
When, use depth of water pointIn node gjThe new depth of water is inside established to assumeWherein Operating procedure (h).
(h) depth of water point is used in conjunction with formula (6) referring to attached drawing 6The depth of water is updated to assumeUpdated most suitable water
It is deep to assume
(i) judge whether that there are also depth measurement pointsCalculating is had neither part nor lot in, if so, then return step (d), if nothing, operation step
Suddenly (i).
(j) it is calculated using formula (7)Intensity value
In formula, εcurrentForThe depth of water point number for inside including；εnextForInside include
Depth of water point number.
(k) right by the size for assuming intensityIt is ranked up, determines the optimal depth of water of each node it is assumed that generating more water
It is deep to assume robust curved surface Gfinal。
Robust curved surface key parameter group based on typical depth of water feature combines preferred step are as follows:
(1) typical depth of water data decimation: referring to attached drawing 7, in Multibeam Data collectionInterior, selection can characterize the area
The typical morphologic region of Water Depth Information feature exports the original multibeam bathymetric data point set in the area as parameter optimization test areaReferring to attached drawing 8, obtain having 99.5% grid when it is 1m that grid resolution ratio, which is arranged, using depth measurement dot density analytic approach
Node meets the threshold requirement of minimum depth point quantity, thereforeOptimal grid resolution ratio resbest=1m.
(2) it reference water depth data set generation: referring to attached drawing 9, is handled using traditional artificial Editing MethodObtain reference water
Deep point setWith reference water depth curved surface
(3) key parameter group is arranged: 20000 groups of curved surface key parameter group { ω of settingh}H=1,20000, wherein resh=
resbest=1；The setting range of value is 0.1-20, and setting step-length is 1；The setting range of value is 0-
2, setting step-length is 0.2；The setting range of value is 0.05-5, and setting step-length is 0.5；The setting range of value is
0.1-10, setting step-length are 1.
(4) assume that robust Surface Construction method generates robust surface experimental group using more depth of waters:, will referring to attached drawing 2With
{ωh}H=1,20000It is input in step (a)~(j), generates the depth of water more than 20000 groups and assume robust surface experimental group
(5) test data set generates: referring to attached drawing 10, withFor filtered reference curved surface, setting filtering
Window size is 1.5 times of curved surface standard deviation, rightAutomatic filter is carried out, filtered depth of water point set is obtainedAnd Kriging depth of water Surface Construction method is used, establish corresponding depth of water curved surface collection
(6) Comprehensive Comparison: withOn the basis of, relieve oedema or abdominal distension through diuresis or purgation deep point comparison its withDifference
Not；WithOn the basis of, by curved surface node compare its withDifference；Two kinds of comprehensive analysis to analogy
Method as a result, according to the maximum principle of similitude, obtain optimized parameter group ωOptimal=(1,0.5,0.8,2,4), completes to be based on
The robust curved surface key parameter group joint of typical depth of water feature is preferred.
Multibeam bathymetric data automatic filter and result output, step based on optimal robust curved surface include:
(7) preferred key parameter group is combined in input, assumes that robust Surface Construction method generates optimal more water using more depth of waters
It is deep to assume robust curved surface:, will referring to attached drawing 2And ωoptimalIt is input in step (a)~(j), it is false to generate optimal more depth of waters
If robust curved surface
(8) data filtering: referring to attached drawing 10, withFor filtered reference curved surface, setting filter window size is standard
1.5 times of difference, it is rightCarry out automatic filter, the depth of water point set after obtaining automatic filterUse the Kriging depth of water
Surface Construction method establishes whole district's depth of water curved surface
(9) depth of water performance data generates: referring to attached drawing 11, isobath space D is arrangedIsobath=1m is raw using isobath
It is obtained at algorithmCorresponding isobath vector VIsobath；With the output of regular grid formatWith vector dot format
OutputV is exported with line of vector formatIsobath, obtain end result dataAnd VIsobath。
(10) referring to attached drawing 12, it compared the method for the present invention and traditional artificial Editing Method handle data set required time.
109 minutes total used times of the method for the present invention, it is more to show that the method for the present invention can effectively improve at 876 minutes total used times of human-edited's method
The treatment effeciency of beam data is up to 8 times.
Claims (4)
1. a kind of multibeam bathymetric data processing method for assuming robust curved surface based on optimal more depth of waters, which is characterized in that including
Robust curved surface key parameter group joint based on typical depth of water feature is preferably and the multibeam echosounding number based on optimal robust curved surface
According to automatic filter and the big step of result output two；The robust curved surface key parameter group joint based on typical depth of water feature is carried out first
It is preferred that step includes: (1) typical depth of water data decimation, (2) reference water depth data set generation, the setting of (3) key parameter group, (4)
Assume that robust Surface Construction method generates robust surface experimental group using more depth of waters, (5) test data set generates, (6) Comprehensive Correlation
Analysis, obtains optimal key parameter group；Then carry out multibeam bathymetric data automatic filter based on optimal robust curved surface and at
Fruit output, step include: that preferred key parameter group is combined in (7) input, assume that robust Surface Construction method generates using more depth of waters
Optimal more depth of waters assume that robust curved surface, (8) data filtering, (9) depth of water performance data generate.
2. the method as described in claim 1, which is characterized in that more depth of waters assume that robust Surface Construction method includes following
Step:
(2.1) robust curved surface key parameter group is inputted
Wherein reshFor the resolution ratio of robust curved surface,For lock-on range proportionality coefficient,For minimum lock-on range,Proportionality coefficient is propagated for horizontal uncertainty,For bias estimation amount, h and n are natural number；
(2.2) original multibeam bathymetric data point set to be processed is inputtedWhereinxi、yi、ziRespectively original multibeam echosounding pointPlan-position coordinate and depth
Value, i and m are natural number, are calculated using formula (1)Horizontal uncertainty σihzWith vertical uncertainty σivert:
In formula, σ0For known positioning device measurement accuracy,For in known multibeam sounding system acoustics
Coordinate shift measurement error of the heart relative to positioning device center, σa、σR、σP、σr、σθ、σVRespectively known surveying vessel boat
To angle, roll angle, pitch angle, time delay and ship's speed measurement error,Between known positioning device and multibeam sounding system
Time delay error；σysvFor the measurement error of the known velocity of sound in the horizontal direction, a1...a9For known weight coefficient, σH、σDR、σWL、σzsvAngle, pitch angle, field angle, heave and induction are directed toward in respectively known ranging
Heave, the measurement error of tidal level and the velocity of sound in vertical direction；
(2.3) establishing spatial resolution is reshThe initial more depth of waters assume robust curved surface Gr={ gj}J=1, n, gjFor in curved surface
Node, j and n are natural number； For robust curved surface node gjK-th interior of the depth of water is assumed to calculate,
J, k, n and m are natural number； For node gjInterior k-th of the depth of water is assumedWater depth value,For node gjInterior k-th of the depth of water is assumedIntensity value；
(2.4) it is calculated using formula (2)The depth measurement point radius of influence { riInflu}I=1, m{ gj}J=1, nNode capture
Radius { rjCaptDist}J=1, n；It needs just to can be used formula (3) by its uncertainty σ simultaneously in the two radiusesihz
And σivertIt merges and is transmitted to the curved surface node { g near itj}J=1, nOn, after transmitting
In formula, σVertMaxIHO S-44 for known user selection measures depth of water uncertainty determined by grade, μDistExpFor
Known uncertainty increases with distance and the proportionality coefficient of increase；
In formula, SdistFor known depth measurement pointWith node gjDistance；
(2.5) input is transmitted to node gjDepth of water pointSuch as gjThe interior no depth of water is it is assumed that then useEstablish first depth of water vacation
IfWhereinSuch as gjHas the depth of water it is assumed that then calculating gjInterior all depth of waters are assumedWithDifference, determine closestThe most suitable depth of water assumeIt uses
Formula (4) calculatesWithNormalization difference en；
(2.6) Bayesian Factor B is calculated using formula (5)n, work as BnWhen > 0.135, operating procedure (2.7)；Work as BnWhen≤0.135,
Use depth of water pointIn node gjThe new depth of water is inside established to assumeWherein Operating procedure (2.8)；
(2.7) formula (6) are combined, usedThe depth of water is updated to assumeThe updated most suitable depth of water is assumed
(2.8) judge whether alsoCalculating is had neither part nor lot in, if so, then return step (2.4), if nothing, operating procedure
(2.9)；
(2.9) it is calculated using formula (7)Intensity value
In formula, εcurrentForThe depth of water point number for inside including；εnextForThe depth of water for inside including
Point number；
(2.10) right by the size for assuming intensityIt is ranked up, determines the optimal depth of water of each node it is assumed that raw
Robust curved surface G is assumed at more depth of watersfinal。
3. method according to claim 2, which is characterized in that the robust curved surface key ginseng based on typical depth of water feature
It is specific as follows that array combines preferred step:
(3.1) typical depth of water data decimation: in Multibeam Data collectionInterior, selection can characterize area's Water Depth Information feature
Typical morphologic region as parameter optimization test area, export the original multibeam bathymetric data point set in the areaAnd use depth measurement
Dot density analytic approach obtainsOptimal grid resolution ratio resbest；
(3.2) it reference water depth data set generation: is handled using traditional artificial Editing MethodObtain reference water depth point setWith
Reference water depth curved surface
(3.3) key parameter group is arranged: setting n group curved surface key parameter group { ωh}H=1, n, wherein resh=resbest,The setting range of value is 0.1-20,The setting range of value is 0-2,The setting range of value is
0.05-5,The setting range of value is 0.1-10；
(3.4) assume that robust Surface Construction method generates robust surface experimental group using more depth of waters: will{ ωh}H=1, nInput
Into step (2.1)~(2.10), generates the more depth of waters of n group and assume robust surface experimental group
(3.5) test bathymetric data collection generates: withFor filtered reference curved surface, setting filter window size is curved surface
1.5 times of standard deviation, it is rightAutomatic filter is carried out, filtered test depth of water point set is obtainedAnd make
With Kriging depth of water Surface Construction method, corresponding test depth of water curved surface collection is established
(3.6) Comprehensive Comparison: withOn the basis of, deep point of relieving oedema or abdominal distension through diuresis or purgation compares itself and test depth of water point set
Difference；WithOn the basis of, itself and test depth of water curved surface collection are compared by curved surface nodeDifference；It is comprehensive
Analyze two kinds of control methods as a result, according to the maximum principle of similitude, obtain optimal key parameter group ωOptimal。
4. method according to claim 2, which is characterized in that the multibeam bathymetric data based on optimal robust curved surface
Automatic filter and result output step are specific as follows:
(4.1) preferred key parameter group is combined in input, assumes that robust Surface Construction method generates optimal more depth of waters using more depth of waters
Assuming that robust curved surface: willAnd ωOptimalIt is input in step (2.1)~(2.10), generates optimal more depth of waters and assume robust
Curved surface
(4.2) data filtering: withFor filtered reference curved surface, 1.5 times that filter window size is standard deviation are set, it is rightAutomatic filter is carried out, filtered depth of water point set is obtainedUsing Kriging depth of water Surface Construction method, establish
Whole district's depth of water curved surface
(4.3) depth of water performance data generates: setting isobath space DIsobath, obtained using isobath generating algorithmIt is right
The isobath vector V answeredIsobath；With the output of regular grid formatWith the output of vector dot formatWith vector
Line format exports VIsobath, obtain end result dataAnd VIsobath。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811135258.7A CN109269480B (en) | 2018-09-27 | 2018-09-27 | Multi-beam sounding data processing method based on optimal multi-depth hypothesis robust curved surface |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811135258.7A CN109269480B (en) | 2018-09-27 | 2018-09-27 | Multi-beam sounding data processing method based on optimal multi-depth hypothesis robust curved surface |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109269480A true CN109269480A (en) | 2019-01-25 |
| CN109269480B CN109269480B (en) | 2020-07-07 |
Family
ID=65197964
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811135258.7A Active CN109269480B (en) | 2018-09-27 | 2018-09-27 | Multi-beam sounding data processing method based on optimal multi-depth hypothesis robust curved surface |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109269480B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113697054A (en) * | 2021-10-27 | 2021-11-26 | 北京星天科技有限公司 | Data processing method and device and electronic equipment |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102749622A (en) * | 2012-07-03 | 2012-10-24 | 杭州边界电子技术有限公司 | Multiwave beam-based depth-sounding joint inversion method for sound velocity profile and seafloor topography |
| CN103400405A (en) * | 2013-08-01 | 2013-11-20 | 国家海洋局第二海洋研究所 | Multi-beam bathymetric chart construction method based on seabed digital depth model feature extraction |
-
2018
- 2018-09-27 CN CN201811135258.7A patent/CN109269480B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102749622A (en) * | 2012-07-03 | 2012-10-24 | 杭州边界电子技术有限公司 | Multiwave beam-based depth-sounding joint inversion method for sound velocity profile and seafloor topography |
| CN103400405A (en) * | 2013-08-01 | 2013-11-20 | 国家海洋局第二海洋研究所 | Multi-beam bathymetric chart construction method based on seabed digital depth model feature extraction |
Non-Patent Citations (1)
| Title |
|---|
| 王海栋: "多波束系统测深异常处理理论与方法研究", 《中国优秀硕士学位论文全文数据库 基础科学辑 （月刊）》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113697054A (en) * | 2021-10-27 | 2021-11-26 | 北京星天科技有限公司 | Data processing method and device and electronic equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109269480B (en) | 2020-07-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11733381B2 (en) | Sound velocity profile inversion method based on inverted multi-beam echo sounder | |
| CN103292792B (en) | Actual measurement SVP reconstruction method suitable for submarine detection and pseudo-landform processing | |
| CN108613645B (en) | A kind of Pb-Zn deposits absorbing well, absorption well surveying on sludge thickness method based on parameter Estimation | |
| CN109858523B (en) | Shallow sea sound velocity profile inversion method based on neural network and ray theory | |
| CN102109495A (en) | Method for classifying types of mixed seabed sediment based on multi-beam sonar technology | |
| CN110058245B (en) | Low-frequency active towed linear array sonar shallow sea detection efficiency evaluation method based on cloud model | |
| Nosal et al. | Sperm whale three-dimensional track, swim orientation, beam pattern, and click levels observed on bottom-mounted hydrophones | |
| Chakraborty et al. | Sea-floor classification using multibeam echo-sounding angular backscatter data: A real-time approach employing hybrid neural network architecture | |
| CN117198330B (en) | Sound source identification method and system and electronic equipment | |
| Zhao et al. | From 10 m to 11000 m, automatic processing multi-beam bathymetric data based on pgo method | |
| CN116125386A (en) | Intelligent positioning method and system for underwater vehicle with enhanced sparse underwater acoustic ranging | |
| Baron et al. | Hydrophone array optimization, conception, and validation for localization of acoustic sources in deep-sea mining | |
| CN116660996B (en) | Drifting type shallow sea local earth sound parameter prediction method based on deep learning | |
| CN108572349B (en) | Sound source depth setting method based on model calculation under deep sea environment | |
| CN109269480A (en) | The multibeam bathymetric data processing method of robust curved surface is assumed based on optimal more depth of waters | |
| CN109632258A (en) | A kind of internal wave of ocean acoustic detection method that the transmitting-receiving based on vector sensor is isolated | |
| CN107942316A (en) | Concentrate suspension movement velocity method of estimation in a kind of water based on multibeam sonar beamformer output signal | |
| CN117368924A (en) | Active target depth resolution method based on vertical subarray echo cross spectrum phase diagram Laplacian norm | |
| CN112305502A (en) | Water surface and underwater sound source binary discrimination method based on array invariants | |
| TANG et al. | Application of LVQ neural network combined with the genetic algorithm in acoustic seafloor classification | |
| CN113126029B (en) | Multi-sensor pulse sound source positioning method suitable for deep sea reliable acoustic path environment | |
| CN114861400A (en) | Method for drawing high-precision underwater topography and sludge thickness double-interface distribution map | |
| CN111965601A (en) | Underwater sound source passive positioning method based on nuclear extreme learning machine | |
| Zeng et al. | An improved forward-looking sonar 3D visualization scheme of underwater objects | |
| Preston et al. | Acoustic classification of submerged aquatic vegetation |
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 | ||
| CB02 | Change of applicant information |
Address after: Hangzhou City, Zhejiang province 310012 Xihu District Baochu Road No. 36 Applicant after: SECOND INSTITUTE OF OCEANOGRAPHY, MNR Address before: Hangzhou City, Zhejiang province 310012 Xihu District Baochu Road No. 36 Applicant before: THE SECOND INSTITUTE OF OCEANOGRAPHY, SOA |
|
| CB02 | Change of applicant information | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |