CN108919363A - One kind is adaptively according to course Aeromagnetic data processing method - Google Patents
One kind is adaptively according to course Aeromagnetic data processing method Download PDFInfo
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- G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
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- G01V3/081—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation operating with magnetic or electric fields produced or modified by objects or geological structures or by detecting devices the magnetic field is produced by the objects or geological structures
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- G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
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Abstract
The present invention provides one kind adaptively according to course Aeromagnetic data processing method, and this method includes：Multiple calibration data of the aircraft in FOM calibration process are obtained as multiple training datas；The course type for obtaining each training data recalculates the corresponding mass center of its affiliated course type using the training data；Obtain multiple work datas to be processed；Successively classify to each work data by the sampling order of work data；For each section of work data of each course type, the distance vector that this section of work data is formed using the distance between mass center corresponding with the course type of each work data in this section of work data, according to sampling order, using first minimum point occurred in the distance vector and the last one minimum point as the starting point and cut off of this section of work data.Above-mentioned technology of the invention can automatically classify in bulk to Aeromagnetic data course, and all data segments in course needed for extracting reduce manual intervention degree.
Description
Technical field
The present invention relates to boat magnetic detection fields, more particularly to one kind is adaptively according to course Aeromagnetic data processing method.
Background technique
Aviation magnetic detection is a kind of one of the main means of Measurement of environmental magnetic field, is commonly used for geological prospecting, searches under water
The application fields such as rope.Magnetic air detection process is usually carried out according to the survey line of advance planning, therefore carries out data processing in the later period
When, it needs the data by remaining track not on survey line to clip, retains survey line data, further data are divided with facilitating
Analysis is operated at figure etc..This process generally requires manual intervention realization, to waste a large amount of energy, and inefficiency.
Summary of the invention
It has been given below about brief overview of the invention, in order to provide about the basic of certain aspects of the invention
Understand.It should be appreciated that this summary is not an exhaustive overview of the invention.It is not intended to determine pass of the invention
Key or pith, nor is it intended to limit the scope of the present invention.Its purpose only provides certain concepts in simplified form,
Taking this as a prelude to a more detailed description discussed later.
In consideration of it, the present invention provides one kind adaptively according to course Aeromagnetic data processing method, at least to solve existing boat
The problem of empty magnetic spy survey technology generally requires manual intervention, takes time and effort when removing track not in the data on survey line.
According to an aspect of the invention, there is provided one kind is adaptively according to course Aeromagnetic data processing method, this method packet
It includes：Multiple calibration data of the aircraft in FOM calibration process are obtained as multiple training datas, wherein each training data includes
Carried in the FOM calibration process by the aircraft three axis fluxgate magnetometers acquisition three component seismic data in along flute card
That X-axis of coordinate system and two components of Y-axis；Wherein, the FOM calibration process includes 4 orthogonal courses；It is randomly generated 4
Mass center, as the respective initial mass center in 4 orthogonal courses；For each training data, which is divided
Class to obtain course type belonging to the training data, and using the training data recalculates its affiliated course type pair
The mass center answered；Obtain multiple work datas to be processed, wherein each work data includes being flown in practical sortie operation by described
In the three component seismic data for the three axis fluxgate magnetometers acquisition that machine carries along the X-axis of cartesian coordinate system and two points of Y-axis
Amount；Successively classify to each work data by the sampling order of work data, and will continuously assign to same course type
Multiple work datas are as same section of work data under the course type；For each section of operation number of each course type
According to, using the distance between mass center corresponding with the course type of each work data in this section of work data, according to sample it is suitable
Sequence forms the distance vector of this section of work data, and first minimum point occurred in the distance vector is minimum with the last one
This section of work data is shorten to from first pole by value point respectively as the starting point and cut off of this section of work data
Small value point is to the corresponding data segment of the last one described minimum point.
Further, classify as follows to each training data to obtain course class belonging to the training data
Type：Calculate the training data and current 4 mass centers the distance between respectively, and by 4 mass centers with the training data it
Between course type corresponding to the smallest mass center of distance be determined as course type belonging to the training data.
Further, classify in the following way to each work data：It calculates the work data and current 4 mass centers is each
The distance between from, course type corresponding to the smallest mass center of distance between the work data is determined as the work data
Affiliated course type completes the classification to the work data.
Further, this method further includes：For each section of work data of each course type, if this section of work data
Length be less than predetermined threshold, then give up this section of work data.
Further, it for the distance vector of each section of work data of each course type, obtains as follows
Minimum point in the distance vector：
Further, it for the 2nd element in the distance vector to each element between second-to-last element, calculates
The front and back difference of the element determines that the element is to be somebody's turn to do if the preceding difference of the element is less than 0 and the rear difference of the element is greater than 0
A minimum point in distance vector.
Further, it for the distance vector of each section of work data of each course type, obtains as follows
Minimum point in the distance vector：For the 2nd element in the distance vector to each member between second-to-last element
Element calculates the front and back difference of the element, if the preceding difference of the element is less than 0 and the rear difference of the element is greater than 0, and this yuan
The value of element is less than the average value of each element in the distance vector, then determines that the element is a minimum in the distance vector
Point.
Further, for each section of work data of each course type, this section of operation number is obtained as follows
According to distance vector：Assuming that in this section of work data each work data arrive the course type respectively according to sampling order work as antecedent
The distance between heart is followed successively by dk+1、dk+2、……、dk+l, k=1,2 ..., Ks, KsIndicate one section that the course type correspondence includes
Or total number of segment of multistage work data, k indicate that this section of work data is the KsKth section in section work data, dk+pIndicate this
The distance between p-th of work data and the current mass center of course type in k sections of work datas, p=1,2 ..., l, l is should
The work data number that section work data includes；By vector (dk+1, dk+2... ..., dk+l) as this section of work data distance to
Amount.
Further, it for the distance vector of each section of work data of each course type, obtains as follows
Minimum point in the distance vector：For the corresponding each element d of k+2≤k+p≤k+l-1k+p, calculate dk+pPreceding difference
diffb k+p=dk+p-dk+p-1, calculate dk+pRear difference difff k+p=dk+p+1-dk+pIf diffb k+p< 0 and difff k+p> 0, sentences
Fixed element dk+pFor the distance vector (dk+1, dk+2... ..., dk+l) in a minimum point.
Further, it for the distance vector of each section of work data of each course type, obtains as follows
Minimum point in the distance vector：For the corresponding each element d of k+2≤k+p≤k+l-1k+p, calculate dk+pPreceding difference
diffb k+p=dk+p-dk+p-1, calculate dk+pRear difference difff k+p=dk+p+1-dk+pIf diffb k+p< 0 and difff k+p> 0, and
And dk+p≤mean(dk+1,dk+2,...,dk+l), then determine element dk+pFor the distance vector (dk+1, dk+2... ..., dk+l) in
A minimum point.
Of the invention is adaptive according to course Aeromagnetic data processing method, can be used for dividing magnetic airborne survey data automatically
Section, this method adaptively can divide flying quality according to flight course during navigating magnetic detection Data Post
Section, further to analyze, Cheng Tu.The present invention carries out according to the following steps：One, calibration flying quality is obtained；Two, each course is calculated
Mass center；Three, actual job data are obtained；Four, distance of each sampled point to each mass center, the corresponding class conduct of minimum range are calculated
The classification of the point；Five, of a sort data will continuously be assigned to as one section, cast out the too short data segment of length；Six, data are removed
Steering procedure in section；Seven, label is drawn.Method proposed by the present invention can greatly simplify magnetic airborne survey Data Post mistake
Journey saves labour turnover and handles the time, greatly improves efficiency of post treatment.
Adaptive flying quality can be segmented according to flight course, after magnetic airborne survey data can be greatly simplified
Treatment process saves labour turnover and handles the time, greatly improves efficiency of post treatment.
By the detailed description below in conjunction with attached drawing to highly preferred embodiment of the present invention, these and other of the invention is excellent
Point will be apparent from.
Detailed description of the invention
The present invention can be by reference to being better understood, wherein in institute below in association with description given by attached drawing
Have and has used the same or similar appended drawing reference in attached drawing to indicate same or similar component.Attached drawing is together with following detailed
Illustrate together comprising in the present specification and forming a part of this specification, and is used to that the present invention is further illustrated
Preferred embodiment and explain the principle of the present invention and advantage.In the accompanying drawings：
Fig. 1 is to schematically show an adaptive exemplary process according to course Aeromagnetic data processing method of the invention
Flow chart；
Fig. 2 is the schematic diagram for showing body coordinate system；
Fig. 3 is the schematic diagram for showing FOM calibration flight；
Fig. 4 is the processing stream for showing an adaptive preferred embodiment according to course Aeromagnetic data processing method of the invention
Cheng Tu；
Fig. 5 is to show different course (x, y) spot distribution figures；
Fig. 6 is the schematic diagram for the distance for showing certain segment data to its mass center.
It will be appreciated by those skilled in the art that element in attached drawing is just for the sake of showing for the sake of simple and clear,
And be not necessarily drawn to scale.For example, the size of certain elements may be exaggerated relative to other elements in attached drawing, with
Just the understanding to the embodiment of the present invention is helped to improve.
Specific embodiment
Exemplary embodiment of the invention is described hereinafter in connection with attached drawing.For clarity and conciseness,
All features of actual implementation mode are not described in the description.It should be understood, however, that developing any this actual implementation
Much decisions specific to embodiment must be made during example, to realize the objectives of developer, for example, symbol
Restrictive condition those of related to system and business is closed, and these restrictive conditions may have with the difference of embodiment
Changed.In addition, it will also be appreciated that although development is likely to be extremely complex and time-consuming, to having benefited from the disclosure
For those skilled in the art of content, this development is only routine task.
Here, and also it should be noted is that, in order to avoid having obscured the present invention because of unnecessary details, in the accompanying drawings
Illustrate only with closely related apparatus structure and/or processing step according to the solution of the present invention, and be omitted and the present invention
The little other details of relationship.
The embodiment provides one kind adaptively according to course Aeromagnetic data processing method, and this method includes：It obtains
Multiple calibration data of the aircraft in FOM calibration process as multiple training datas, wherein each training data be included in it is described
Carried in FOM calibration process by the aircraft three axis fluxgate magnetometers acquisition three component seismic data in along cartesian coordinate
The X-axis of system and two components of Y-axis；Wherein, the FOM calibration process includes 4 orthogonal courses；4 mass centers are randomly generated, make
For the respective initial mass center in 4 orthogonal courses；For each training data, classify to the training data, to obtain
Course type belonging to the training data, and recalculate the corresponding matter of its affiliated course type using the training data
The heart；Obtain multiple work datas to be processed, wherein each work data includes being carried in practical sortie operation by the aircraft
Three axis fluxgate magnetometers acquisition three component seismic data in along the X-axis of cartesian coordinate system and two components of Y-axis；By work
The sampling order of industry data successively classifies to each work data, and the multiple operations that will continuously assign to same course type
Data are as same section of work data under the course type；For each section of work data of each course type, this is utilized
The distance between each work data mass center corresponding with the course type in section work data forms the section according to sampling order
The distance vector of work data makees occur in the distance vector first minimum point and the last one minimum point respectively
For the starting point and cut off of this section of work data, this section of work data is shorten to from first minimum point to institute
State the corresponding data segment of the last one minimum point.
Fig. 1 gives adaptive a kind of exemplary process flow 100 according to course Aeromagnetic data processing method of the invention.
After process flow 100 starts, step S110 is executed.
In step s 110, it is multiple in FOM (Figure of Merit, quality factor) calibration process to obtain aircraft
Calibration data is as multiple training datas, wherein each training data includes three axis carried in FOM calibration process by aircraft
Flux-gate magnetometer acquisition three component seismic data in along the X-axis of cartesian coordinate system and two components of Y-axis；Wherein, the school FOM
Quasi- process includes 4 orthogonal courses, respectively as j-th of course type, j=1, and 2,3,4.
FOM calibration flight usually can by such as fixed wing aircraft, helicopter or unmanned plane isodynamic instrument carrying platform Lai,
It in the present embodiment, such as can also be using aircraft (such as unmanned plane) conduct for being equipped with resultant field magnetometer and three-component magnetometer
One example of above-mentioned magnetometer carrying platform.Then, step S120 is executed.
Lateral shaft, longitudinal axis and vertical axis of X-axis, Y-axis and the Z axis of cartesian coordinate system respectively along aircraft.Such as Fig. 2 institute
Show, point O is coordinate origin, is equipped with resultant field magnetometer and three-component magnetometer.The edge respectively three axis X, Y, Z of cartesian coordinate system
Aircraft lateral shaft, longitudinal axis and vertical axis, N be the direction of north geographic pole, He is the direction in earth's magnetic field, three axis of aircraft and earth magnetism
The angle of field is respectively α, β, γ.
As shown in figure 3, FOM flight includes the aircraft in the flight in 4 orthogonal courses and bows on each course
It faces upward, roll, yaw the motor-driven of three types.The FOM flight of one group of standard needs to complete the flight in 4 orthogonal courses, each
Pitching, roll are carried out on course, yaws the motor-driven of three types, and amplitude is respectively ± 5 °, ± 5 °, ± 10 °, every kind of motor-driven progress
30 seconds, complete 3 groups.
It should be understood that FOM flight is not limited to track shown in Fig. 3, the FOM for being also possible to other conventional tracks flies
Row, but either any FOM flight, all generally comprise the flight in 4 courses, and this four courses are successively mutually orthogonal
's.For example, in Fig. 3, it can be using this section of A to B as the 1st class course, using this section of B to C as the 2nd class course, by C to D
This section is as the 3rd class course, and using this section of D to E as the 4th class course, the 1st class course is orthogonal with the 2nd class course, the 2nd class
Course is orthogonal with the 3rd class course, and the 3rd class course is orthogonal with the 4th class course, and the 4th class course is orthogonal with the 1st class course.
In the step s 120,4 mass centers are randomly generated, as the respective initial mass center in 4 orthogonal courses.Then, it executes
Step S130.
In step s 130, for each training data, classify to the training data, to obtain the training data
Then affiliated course type recalculates the corresponding mass center of its affiliated course type using the training data.Then, it executes
Step S140.
For example, it is assumed that when first four mass center is respectively (a1, b1)、(a2, b2)、(a3, b3) and (a4, b4), if some training number
According toThe training data is obtained after being classifiedAffiliated course type is 1, that is,It is corresponding
Current mass center is (a1, b1), then recalculate the mass center of the 1st course type (j=1), can use the training when and its
The average value of the current mass center of affiliated course type is as new mass center, that is, willIt navigates as the 1st
To the newest mass center of type (j=1).
According to an implementation, for example, can classify each training data to obtain the training number as follows
According to affiliated course type：Calculate the training data and current 4 mass centers the distance between respectively, and will be in 4 mass centers
Course type corresponding to the smallest mass center of distance is determined as course type belonging to the training data between the training data.
In step S140, multiple work datas to be processed are obtained, wherein each work data is made including practical sortie
The X-axis and Y-axis along cartesian coordinate system in the three component seismic data for the three axis fluxgate magnetometers acquisition carried in industry by aircraft
Two components.Then, step S150 is executed.
In step S150, successively classify to each work data by the sampling order of work data, and will be continuous
Multiple work datas of same course type are assigned to as same section of work data under the course type.Then, step is executed
S160。
According to an implementation, can for example classify in the following way to each work data：Calculate the operation number
According to current 4 mass centers the distance between respectively, by course class corresponding to the smallest mass center of distance between the work data
Type is determined as course type belonging to the work data, completes the classification to the work data.
It in step S150, such as can also include following processing according to an implementation：For each course type
Each section of work data, if the length (data bulk for including) of this section of work data be less than predetermined threshold, give up this
Section work data.Predetermined threshold can for example be set based on experience value, for example be set as 10 or 20 etc..
In step S160, for each section of work data of each course type, using each in this section of work data
The distance between work data and the corresponding mass center of course type, formed according to sampling order at a distance from this section of work data to
Amount, then, using first minimum point occurred in the distance vector and the last one minimum point as this section of operation
The starting point and cut off of data, this section of work data is shorten to from first minimum point to the last one minimum point
Corresponding data segment.
According to an implementation, for the distance vector of each section of work data of each course type, such as can be with
The minimum point in the distance vector is obtained as follows：For the 2nd element in the distance vector to second-to-last member
Each element between element calculates the front and back difference of the element, if the preceding difference of the element is less than 0 and the rear difference of the element
Greater than 0, then determine that the element is a minimum point in the distance vector.
According to another implementation, for the distance vector of each section of work data of each course type, such as
The minimum point in the distance vector can be obtained as follows：For the 2nd element in the distance vector to inverse the 2nd
Each element between a element calculates the front and back difference of the element, if the preceding difference of the element is less than 0 and after the element
Difference be greater than 0, and the value of the element be less than the distance vector in each element average value, then determine the element be the distance to
A minimum point in amount.
In addition, according to an embodiment of the invention, be directed to each section of work data of each course type, such as can be according to
As under type obtains the distance vector of this section of work data：Assuming that each work data is according to sampling order point in this section of work data
The distance between the current mass center for being clipped to the course type is followed successively by dk+1、dk+2、……、dk+l, k=1,2 ..., Ks, KsIndicating should
The total number of segment for one or more snippets work data that type correspondence in course includes, k indicate that this section of work data is the KsDuan Zuoye number
Kth section in, dk+pIt indicates between p-th of the work data and the current mass center of course type in the kth section work data
Distance, p=1,2 ..., l, l are the work data number that this section of work data includes；By vector (dk+1, dk+2... ..., dk+l) conduct
The distance vector of this section of work data.
It in one example, can be according to as follows for the distance vector of each section of work data of each course type
Mode obtains the minimum point in the distance vector：For the corresponding each element d of k+2≤k+p≤k+l-1k+p, calculate dk+p's
Preceding difference diffb k+p=dk+p-dk+p-1, calculate dk+pRear difference difff k+p=dk+p+1-dk+pIf diffb k+p< 0 and difff k+p
> 0 determines element dk+pFor the distance vector (dk+1, dk+2... ..., dk+l) in a minimum point.
It in another example, can also be according to for the distance vector of each section of work data of each course type
As under type obtains the minimum point in the distance vector：For the corresponding each element d of k+2≤k+p≤k+l-1k+p, calculate
dk+pPreceding difference diffb k+p=dk+p-dk+p-1, calculate dk+pRear difference difff k+p=dk+p+1-dk+pIf diffb k+p< 0 and
difff k+p> 0, and dk+p≤mean(dk+1,dk+2,...,dk+l), then determine element dk+pFor the distance vector (dk+1,
dk+2... ..., dk+l) in a minimum point.
For example, it is assumed that in certain section of work data
Distance vector (the d of (l work data of such as kth section work data)k+1, dk+2, dk+3, dk+4... ..., dk+l-2, dk+l-1, dk+l) in,
Determine dk+2、dk+4、…、dk+l-1For the minimum point successively occurred according to sampling order, and assume dk+1To dk+lAverage value be dmean,
And above-mentioned minimum point dk+2、dk+4、…、dk+l-1Respectively less than dmean, then this section of work data is foreshortened to from first minimum
Point dk+2To the last one minimum point dk+l-1Corresponding data segment, that is, shortening to：
In other words, in kth section work data
In, eliminate hashWith
Preferred embodiment 1
In the following, by an adaptive preferred embodiment according to course Aeromagnetic data processing method of the invention is described, Fig. 4 is given
The process flow of the preferred embodiment is gone out.
In the preferred embodiment, the three component seismic data in FOM calibration process data can be obtained first, record wherein x
Data with y-component areWith(i=1,2 ..., N) as training data, and trains the mass center in each course.
Wherein, the process of the mass center in each course of training can be for example carried out as follows：
1. 4 binary group (a are randomly generatedj,bj), j=1,2,3,4 are used as initial mass center；
2. the binary group in pair each training dataIt is calculated at a distance from each mass center according to (1) formula
di j；
3. j corresponding to the smallest distance is its classification；
4. the element mean value in every one kind is recalculated, as new mass center；
5. 2-4 is repeated, until having read all training datas.
Then, the three component seismic data in all work datas of certain sortie is read, wherein the data of x and y-component are record
With
For the binary group of each sampled point (i.e. each work data) in work dataIt calculates it and works as
The distance d of each mass center in preceding 4 mass centersi’ j, j=1,2,3,4, take j corresponding to its smallest distance to classify as it,
Obtain the classification to each sampled point in work data.
Certain a kind of sampled point will continuously be assigned to as one section, if the segment length (can be based on experience value less than threshold value Th
Setting), then give up the section.
Then, the hash of the data segment acquired by each is removed, can be carried out as follows：
1. obtaining the distance d that certain section of binary group corresponds to the mass center of classification to itk+1,dk+2,...,dk+l；
2. couple each k+2≤i'≤k+l-1 calculates its front and back difference diff according to (2), (3) formulab i'、difff i'；
3. if diffb i'< 0 and difff i'> 0, while di'≤mean(dk+1,dk+2,...,dk+l), it is minimum for recording the i'
Value point；
4. repeating 2, the 3 minimum point sequences for obtaining whole segment data, remember that first minimum point is ifirst, the last one pole
Small value point is ilast；
5. the course data section is shorten to sampling point number by ifirstTo ilastData；
6. pair all data segments repeat 1-5 step.
In this way, obtaining the work data section of all non-steerings of the sortie, segmentation is finished, and label is drawn.
Preferred embodiment 2
As previously mentioned, after executing the step S110 can 4 mass centers be randomly generated, be denoted as (a in the step s 120j,
bj), j=1,2,3,4, wherein (aj,bj) indicate 4 orthogonal courses in the corresponding mass center in jth class course.
For each training dataAccording toIt calculates the training data and works as
Preceding 4 mass centers the distance between respectively, by 4 mass centers between the training data boat corresponding to the smallest mass center of distance
It is determined as course classification belonging to the training data to classification, and recalculates its affiliated course classification pair using the training data
The mass center answered；Wherein, di jIndicate training dataThe distance between mass center corresponding with jth class course.
Then, work data to be processed is obtained, wherein the work data includes multiple binary groups
N' is the work data sum.
Then, it is determined that the course type of each work data, to obtain one or more snippets operation number of each course type
According to.
Then, for every section of work data of each course type, following processing is executed respectively：Utilize this section of work data
In the distance between each work data and the corresponding mass center of course classification, formed at a distance from such course according to sampling order
Vector；Then, to this section of work data, by first minimum point occurred in the distance vector and the last one minimum point
Respectively as the starting point and cut off of this section of work data, this section of work data is shorten to from first minimum
Point data segment corresponding with the last one described minimum point.
Wherein, the training of data mass center relies on boat magnetic compensation coefficient calibration phase data and completes.Carrying out boat magnetic detection process
In, it needs to compensate magnetic disturbance caused by magnetometer carrying platform.Compensation by one group of penalty coefficient with by being carried
The data inner product that generates of three axis fluxgates realize.And the process for solving this group of penalty coefficient is referred to as calibration process.One
It in the stage that secondary boat magnetic detection starts, is required to calibrate penalty coefficient.
If by three axis fluxgates, three components collected being respectively x, y, z in one group of calibration data, then sample each time
Generate one group of triple (xi,yi,zi), i=1,2 ..., N, N are sampling number.As shown in Fig. 2, fluxgate collected three
A component may be considered projection of the earth's magnetic field in three reference axis of aircraft axes, thus x and y will follow vector into
The corresponding variation of row, and z is then unrelated with course variation, therefore, a pair of of binary group (x is only considered when calculatingi,yi),
That is, binary group (the x in calibration datai,yi) it is used as training dataAnd N is training data sum.
As shown in figure 3, the calibration flight of standard is referred to as FOM and flies, it is motor-driven comprising 3 kinds on 4 orthogonal courses, therefore
This group of data can be divided into 4 sections.Each section of binary group distributing position will have apparent difference, because aircraft transforms to just
Coordinate system also rotates with 90 ° when handing over course, and great variety will occur for the value of x and y.
Fig. 5 illustratively illustrates the (x of one group of truthful datai,yi) (be equivalent to) distribution scattergram.It is different
(x, the y) binary group generated on course is more significantly distinguished in different location, which can be used to course carry out area
Point.
The point in each course be can be seen that by integrated distribution and a certain region, and relatively far apart with other regions.If therefore
The mass center in each region is calculated, then can be covered all course datas by means of a radius, thus to course carry out area
Point.
Centroid calculation for example can be by handling realization as follows：
1. 4 mass centers, respectively (a is randomly generatedj,bj), j=1,2,3,4；(aj,bj) indicate in 4 orthogonal courses
The corresponding mass center of j-th of course type；
2. repeating following step：
A. for each training dataIt is calculated at a distance from each mass center：
B. j corresponding to the smallest distance is its classification；
C. the element mean value in every one kind is recalculated, as new mass center.
4 mass center (a after final updatedj,bj) it is training gained.
By above-mentioned training, the mass center of the binary pair of the composition of two components corresponding to four orthogonal courses is obtained.It connects
Get off in measurement data (being equivalent to work data described above), if aircraft flies along one of course, gained (x,
Y) (i.e. described above) will be close apart from its correspondence mass center, and it is far apart from other mass centers.But aircraft is turning
In the flat winged section that some course can be also assigned to a part of data in the process, this partial data needs to remove.
The method applied in the present invention is, using distance vector, obtain distance vector first minimum value and last
A minimum value, starting point and cut off as the course.
IfBelong to certain a kind of binary group for one section, shares l sampled point (i.e.
L work data point), k expression kth segment data, such as k=1,2,3 ..., it is assumed thatThis
The corresponding course classification of segment data is jth0Class course (j0It can be 1,2,3 or 4), and assume jth0The mass center in class course isMeanwhile if the distance vector of this section is dk+1,dk+2,...,dk+l, wherein dk+1It indicatesWith described in it
Course mass centerThe distance between, dk+2It indicatesWith course mass center described in itsBetween away from
From ... ..., dk+lIt indicatesWith course mass center described in itsThe distance between.When due to flight, it is transferred to the boat
To with produce during the course generated binary group apart from the course and correspond to mass center closer, the course can be divided into.Cause
This, distance vector d will from the distant to the near, and the distance of certain segment data to its mass center as shown in Fig. 6 meets first to reduce to be increased afterwards
Rule, by select minimum point can get rid of turn in data segment, but need to pay attention to circle in a minimum
Caused influence.
First minimum point of d and the position of the last one minimum point should belong to the course data section, and non-turn
Process.But sometimes due to airflow influence, aircraft generated shake in flight course may be such that the distance for turning to section
Vector is not smooth downwards, but generates one such as the minimum point in Fig. 6 circle.It adjusts the distance to be segmented in order to avoid the situation and produce
It is raw to influence, minimum should be selected to be less than point of this section apart from mean value when calculating minimum point.
Specific practice is described as follows：
1. couple each k+2≤i '≤k+l-1 calculates front and back difference
diffb i’=di’-di’-1 (2)
difff i’=di’+1-di’ (3)
2. if diffb i’< 0 and difff i’> 0, while di’≤mean(dk+1,dk+2,...,dk+l), it is minimum for recording the i'
Value point；
3. repeating 1, the 2 minimum point sequences for obtaining whole segment data, remember that first minimum point is ifirst, the last one pole
Small value point is ilast；
4. the course data section is shorten to sampling point number by ifirstTo ilastData.
As can be seen from the above description, method proposed by the invention can be adaptive according to flight course to flying quality
It is segmented, magnetic airborne survey Data Post process can be greatly simplified, the time is saved labour turnover and handle, after greatly improving
Treatment effeciency.
Although the embodiment according to limited quantity describes the present invention, above description, the art are benefited from
It is interior it is clear for the skilled person that in the scope of the present invention thus described, it can be envisaged that other embodiments.Additionally, it should be noted that
Language used in this specification primarily to readable and introduction purpose and select, rather than in order to explain or limit
Determine subject of the present invention and selects.Therefore, without departing from the scope and spirit of the appended claims, for this
Many modifications and changes are obvious for the those of ordinary skill of technical field.For the scope of the present invention, to this
Invent done disclosure be it is illustrative and not restrictive, it is intended that the scope of the present invention be defined by the claims appended hereto.
Claims (9)
1. one kind is adaptively according to course Aeromagnetic data processing method, which is characterized in that this method includes：
Multiple calibration data of the aircraft in FOM calibration process are obtained as multiple training datas, wherein each training data packet
Include carried in the FOM calibration process by the aircraft three axis fluxgate magnetometers acquisition three component seismic data in along flute
The X-axis of karr coordinate system and two components of Y-axis；Wherein, the FOM calibration process includes 4 orthogonal courses；
4 mass centers are randomly generated, as the respective initial mass center in 4 orthogonal courses；
For each training data,
Classify to the training data, to obtain course type belonging to the training data, and
The corresponding mass center of its affiliated course type is recalculated using the training data；
Obtain multiple work datas to be processed, wherein each work data includes being taken in practical sortie operation by the aircraft
Carry three axis fluxgate magnetometers acquisition three component seismic data in along the X-axis of cartesian coordinate system and two components of Y-axis；
Successively classify to each work data by the sampling order of work data, and will continuously assign to same course type
Multiple work datas are as same section of work data under the course type；
For each section of work data of each course type,
Using the distance between mass center corresponding with the course type of each work data in this section of work data, according to sample it is suitable
Sequence forms the distance vector of this section of work data,
Using first minimum point occurred in the distance vector and the last one minimum point as this section of work data
Starting point and cut off, this section of work data shorten to the last one is minimum from first minimum point to described
The corresponding data segment of value point.
2. according to claim 1 adaptively according to course Aeromagnetic data processing method, which is characterized in that each training data
Classify as follows to obtain course type belonging to the training data：
Calculate the training data and current 4 mass centers the distance between respectively, and
By course type corresponding to the smallest mass center of distance is determined as the training number between the training data in 4 mass centers
According to affiliated course type.
3. according to claim 1 or 2 adaptively according to course Aeromagnetic data processing method, which is characterized in that each work
Industry data are classified in the following way：
Calculate the work data and current 4 mass centers the distance between respectively, will between the work data the smallest matter of distance
Course type corresponding to the heart is determined as course type belonging to the work data, completes the classification to the work data.
4. according to any one of claim 1-3 adaptively according to course Aeromagnetic data processing method, which is characterized in that should
Method further includes：
Give up for each section of work data of each course type if the length of this section of work data is less than predetermined threshold
This section of work data.
5. adaptive according to course Aeromagnetic data processing method described in any one of -4 according to claim 1, which is characterized in that right
In the distance vector of each section of work data of each course type, the minimum in the distance vector is obtained as follows
Point：
For the 2nd element in the distance vector to each element between second-to-last element,
The front and back difference of the element is calculated, if the preceding difference of the element is less than 0 and the rear difference of the element is greater than 0, determining should
Element is a minimum point in the distance vector.
6. adaptive according to course Aeromagnetic data processing method described in any one of -4 according to claim 1, which is characterized in that right
In the distance vector of each section of work data of each course type, the minimum in the distance vector is obtained as follows
Point：
For the 2nd element in the distance vector to each element between second-to-last element,
The front and back difference of the element is calculated, if the preceding difference of the element is less than 0 and the rear difference of the element is greater than 0, and this yuan
The value of element is less than the average value of each element in the distance vector, then determines that the element is a minimum in the distance vector
Point.
7. according to claim 1 to 6 adaptively according to course Aeromagnetic data processing method, which is characterized in that needle
To each section of work data of each course type, the distance vector of this section of work data is obtained as follows：
Assuming that each work data is arrived according to sampling order respectively between the current mass center of the course type in this section of work data
Distance is followed successively by dk+1、dk+2、……、dk+l, k=1,2 ..., Ks, KsIndicate one or more snippets work that the course type correspondence includes
Total number of segment of industry data, k indicate that this section of work data is the KsKth section in section work data, dk+pIndicate the kth section operation
The distance between p-th of work data and the current mass center of course type in data, p=1,2 ..., l, l are this section of operation number
According to comprising work data number；
By vector (dk+1, dk+2... ..., dk+l) distance vector as this section of work data.
8. according to claim 7 adaptively according to course Aeromagnetic data processing method, which is characterized in that for each course
The distance vector of each section of work data of type, obtains the minimum point in the distance vector as follows：
For the corresponding each element d of k+2≤k+p≤k+l-1k+p,
Calculate dk+pPreceding difference diffb k+p=dk+p-dk+p-1,
Calculate dk+pRear difference difff k+p=dk+p+1-dk+p,
If diffb k+p< 0 and difff k+p> 0 determines element dk+pFor the distance vector (dk+1, dk+2... ..., dk+l) in one
A minimum point.
9. according to claim 7 adaptively according to course Aeromagnetic data processing method, which is characterized in that for each course
The distance vector of each section of work data of type, obtains the minimum point in the distance vector as follows：
For the corresponding each element d of k+2≤k+p≤k+l-1k+p,
Calculate dk+pPreceding difference diffb k+p=dk+p-dk+p-1,
Calculate dk+pRear difference difff k+p=dk+p+1-dk+p,
If diffb k+p< 0 and difff k+p> 0, and dk+p≤mean(dk+1,dk+2,...,dk+l), then determine element dk+pFor
The distance vector (dk+1, dk+2... ..., dk+l) in a minimum point.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113049004A (en) * | 2021-02-28 | 2021-06-29 | 哈尔滨工业大学 | Automatic assessment method and device for aeromagnetic compensation calibration quality |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080129289A1 (en) * | 2006-12-05 | 2008-06-05 | Alto Stemmer | Multi-coil magnetic resonance data acquisition and image reconstruction method and apparatus using blade-like k-space sampling |
| CN103294895A (en) * | 2013-05-08 | 2013-09-11 | 西北工业大学 | Flight path and air line classifying method based on evidence reasoning |
| CN104236549A (en) * | 2014-09-16 | 2014-12-24 | 上海航海仪器有限责任公司 | Course sending equipment and course sending method |
| CN105301666A (en) * | 2015-11-05 | 2016-02-03 | 哈尔滨工业大学 | Self-adaptive adjustment method of aeromagnetic interference compensation coefficient |
| CN105425304A (en) * | 2015-11-03 | 2016-03-23 | 哈尔滨工业大学 | Compensation method for airplane aeromagnetic interference |
| CN105469114A (en) * | 2015-11-25 | 2016-04-06 | 大连理工大学 | Method of increasing K-means convergence speed |
| CN105675006A (en) * | 2015-12-30 | 2016-06-15 | 惠州市德赛西威汽车电子股份有限公司 | Road deviation detection method |
| CN106228552A (en) * | 2016-07-20 | 2016-12-14 | 湖南文理学院 | Gray level image rectangular histogram fast partition method based on mediation K mean cluster |
| CN106959471A (en) * | 2017-04-21 | 2017-07-18 | 中国科学院电子学研究所 | Boat magnetic compensation method based on the non-linear boat total field gradient compensation model of magnetic |
| CN107816989A (en) * | 2017-10-13 | 2018-03-20 | 中国船舶重工集团公司七五0试验场 | Underwater robot course data treating method and apparatus |
-
2018
- 2018-08-07 CN CN201810891450.2A patent/CN108919363B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080129289A1 (en) * | 2006-12-05 | 2008-06-05 | Alto Stemmer | Multi-coil magnetic resonance data acquisition and image reconstruction method and apparatus using blade-like k-space sampling |
| CN103294895A (en) * | 2013-05-08 | 2013-09-11 | 西北工业大学 | Flight path and air line classifying method based on evidence reasoning |
| CN104236549A (en) * | 2014-09-16 | 2014-12-24 | 上海航海仪器有限责任公司 | Course sending equipment and course sending method |
| CN105425304A (en) * | 2015-11-03 | 2016-03-23 | 哈尔滨工业大学 | Compensation method for airplane aeromagnetic interference |
| CN105301666A (en) * | 2015-11-05 | 2016-02-03 | 哈尔滨工业大学 | Self-adaptive adjustment method of aeromagnetic interference compensation coefficient |
| CN105469114A (en) * | 2015-11-25 | 2016-04-06 | 大连理工大学 | Method of increasing K-means convergence speed |
| CN105675006A (en) * | 2015-12-30 | 2016-06-15 | 惠州市德赛西威汽车电子股份有限公司 | Road deviation detection method |
| CN106228552A (en) * | 2016-07-20 | 2016-12-14 | 湖南文理学院 | Gray level image rectangular histogram fast partition method based on mediation K mean cluster |
| CN106959471A (en) * | 2017-04-21 | 2017-07-18 | 中国科学院电子学研究所 | Boat magnetic compensation method based on the non-linear boat total field gradient compensation model of magnetic |
| CN107816989A (en) * | 2017-10-13 | 2018-03-20 | 中国船舶重工集团公司七五0试验场 | Underwater robot course data treating method and apparatus |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113049004A (en) * | 2021-02-28 | 2021-06-29 | 哈尔滨工业大学 | Automatic assessment method and device for aeromagnetic compensation calibration quality |
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