CN110531429A - A kind of time-domain electromagnetic data object inversion method based on supervision descent method - Google Patents
A kind of time-domain electromagnetic data object inversion method based on supervision descent method Download PDFInfo
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Abstract
The present invention provides a kind of time-domain electromagnetic data object inversion method based on supervision descent method, and process are as follows: training process: being arranged different learning samples, obtains its corresponding secondary field response as observing matrix；For each learning sample, it is iterated training, obtains the gradient decline matrix of entire training sample；Refutation process: carrying out inverting target secondary field response measurement near abnormal area, obtains the observation vector of inverting target；The initial parameter for setting inverting target carries out inverting using the observation vector that the gradient of entire training sample declines matrix and inverting target, obtains the actual parameter of inverting target.This method can accurately obtain the target component of buried target.
Description
Technical field
The present invention provides a kind of time-domain electromagnetic data object inversion method based on supervision descent method, belongs to buried target
Detection technique field.
Background technique
Time domain electromagnetic method is a kind of artificial source's lossless detection method established on the basis of electromagnetic induction principle, it is utilized
Earth-free loop line (magnetic source) or ground connection line source (Electric Dipole) are to underground transmitting primary field, under its excitation, in sub-surface conductors target
The induction field that the inductive loop motivated changes over time generation.Compared with frequency domain electromagnetic methods, time domain electromagnetic method has
Do not influenced by primary field, the advantages that signal-to-noise ratio is high, there is higher detection and resolution capability, thus time domain electromagnetic method at
For one of the important method of shallow underground target acquisition.Due to very low emission signal frequency, time domain electromagnetic method satisfaction is
The diffusion equation of electromagnetic wave rather than wave equation cause to utilize since the bulk effect in diffusion field makes its resolution ratio very low
Time domain electromagnetic method can not carry out direct imaging to buried target, can only carry out inverting to buried target by forward model and ask
Solution, but the response of the secondary field of general finite conductor can not acquire analytic solutions, according to the numerical value meter of finite element or finite difference
Calculation method, then calculation amount is excessively huge, therefore is changed come approximatively equivalent sub-surface conductors target by inverting using dipole model
In generation, obtains the equivalent dipole intensity of target, and further judgement obtains the information of target.In inverting iterative process, initial value
It chooses particularly important, directly affects final inversion result.Currently used method is to provide buried target using actual measurement response
General plan-position, but target buried depth, inclination angle and dipole strength can not be obtained by actual measurement response, can only provide conjecture
Initial value, and requirement of the local optimum LM algorithm generally used to initial value is very high, if initial value is inaccurate, what is finally solved is anti-
It drills result and differs quite big with practical.And using global optimization DE algorithm, time-consuming, and global search and the local search of DE algorithm
Ability be it is contradictory, equally exist the problem of cannot converging to optimal solution.
To sum up, there are following technological deficiencies for currently existing technology:
Due to very low electromagnetic induction signal frequency (usually tens to several hundred kHz), time domain electromagnetic method satisfaction is
The diffusion equation of electromagnetic wave rather than wave equation cause to utilize since the bulk effect in diffusion field makes its resolution ratio very low
Time domain electromagnetic method can not carry out direct imaging to buried target.Usually inverting is carried out to buried target by forward model to ask
Solution, but the response of the secondary field of general finite conductor can not acquire analytic solutions；According to the numerical value meter of finite element or finite difference
Calculation method, then calculation amount is excessively huge, is unfavorable for the real-time processing and analysis of data；Come according to forward models such as dipoles close
As equivalent sub-surface conductors target, the equivalent dipole intensity of target is obtained to judge target signature by inverting iteration,
The quality of inversion result usually has with the initial value of selection compared with Important Relations.In inverting iterative process, the selection of initial value is particularly important,
Directly affect final inversion result.Currently used method is to provide the general plane of buried target using actual observation response
Position, but target buried depth, inclination angle and dipole strength can not be obtained by actual measurement response, can only provide the initial value of conjecture, and
The inverting optimization algorithm used all has higher dependence to initial value to a certain extent, if initial value is inaccurate, what is finally solved is anti-
It drills result and differs quite big with practical.
Summary of the invention
In view of this, the present invention provides a kind of time-domain electromagnetic data object inversion method based on supervision descent method, it should
Method can accurately obtain the target component of buried target.
Realize that technical scheme is as follows:
A kind of time-domain electromagnetic data object inversion method based on supervision descent method, detailed process are as follows:
Training process:
Different learning samples is set, obtains its corresponding secondary field response as observing matrix；
For each learning sample, it is iterated training, obtains the gradient decline matrix of entire training sample；
Refutation process:
Inverting target secondary field response measurement is carried out near abnormal area, obtains the observation vector of inverting target；
The initial parameter for setting inverting target declines the observation of matrix and inverting target using the gradient of entire training sample
Vector carries out inverting, obtains the actual parameter of inverting target.
Further, the process of repetitive exercise of the present invention are as follows:
Set the initial parameter m of training sample0, calculate the forward response d under the initial value0, enable initial value=0 of i；
Calculate Δ di=di-dobs, dobsIndicate the corresponding observation vector of sample, Δ mi=mobs-mi, mobsIndicate sample
Actual parameter；
It utilizesTo KiIt is iterated calculating；
Reach training maximum step number M or meets the setting condition of convergenceWhen,
I=i+1 is updated, iterative calculation, RMS are re-started0For the convergence threshold of setting；
K corresponding to all samplesiForm the gradient decline matrix K of entire training sample.
Further, the gradient of the present invention using entire training sample declines the observation vector of matrix and inverting target
Inverting is carried out, the actual parameter of inverting target is obtained are as follows:
If the initial value of inverting target component is m0, calculate the forward response d under the initial value0, and enable initial value=0 of i；
Calculate Δ di=di-dobs, dobsIndicate the corresponding observation vector of inverting target；
According to sample K obtained each in training processi, update mi=mi+ΔdiKi；
Judge whether that at least one meets the condition of convergenceOr reach maximum
Iterative steps M, RMS0For the convergence threshold of setting, if so, by minimum RMSiGinseng of the corresponding sample parameter as really target
Number mf, i=i+1 is otherwise enabled, update judgement is re-started.
Further, sample parameter of the present invention and target component representation are m:
M=[x0,y0,z0,α,β,k1,β1,γ1,k2,β2,γ2,k3,β3,γ3], wherein x0,y0,z0Respectively represent target
Three-dimensional coordinate, α, β respectively represent the land burial inclination angle of target, three-dimensional with three-dimensional doublet forward model approximate representation target
Current dipole parameters are respectively k1,β1,γ1,k2,β2,γ2,k3,β3,γ3.；
Further, the maximum iterative steps M=10 of the present invention.
Beneficial effect
(1) the method for the present invention is directed to the characteristics of traditional optimal method is vulnerable to initial value affecting, proposes with supervision descent method
Inverting is carried out to buried target, constantly updates iteration by the parameter that training set obtains under the premise of not needing prior information,
Until being optimal solution, the parameter of accurate object to be measured is obtained.
(2) the method for the present invention directly obtains global downward gradient from training data, avoids in optimization in inverting
Local minimum, do not need extra computation Jacob ian matrix and Hess i an matrix, greatly save operation time.
Detailed description of the invention
Fig. 1 is the flow chart of the method for the present invention；
Fig. 2 is that secondary field response measurement records coordinate schematic diagram in this example；
Fig. 3 is the learning process convergence curve of a sample in example；
Fig. 4 is the convergence curve of renewal process in example；
Fig. 5 is supervision descent method ((a): supervision compared with the dipole inversion result of tradition LM optimization algorithm in example
Descent method (b): traditional LM optimization algorithm)；
Fig. 6 is the Comparative result (a) that descent method and tradition LM optimal method are supervised in example: being positive respectively from left to right
Area's figure, the two residual plot (b) are surveyed in the inverting that the area Yan Ce figure, supervision descent method obtain: being from left to right respectively that forward modeling is surveyed area's figure, passed
Area's figure, the two residual plot are surveyed in the inverting that LM optimal method of uniting obtains).
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, the technical scheme in the embodiment of the invention is clearly and completely described.
A kind of time-domain electromagnetic data object inversion method based on supervision descent method of the present embodiment, as shown in Figure 1, specifically
Process are as follows:
Sample training:
(1) it is surveying in area, is placing different targets with different inclination angles in different coordinates respectively, generating a large amount of study
Sample；
(2) secondary field that one-point measurement obtains sample (in the area Ji Ce) near abnormal area is generated in target to respond, it will be every
Secondary field response splicing one row vector of composition for several measuring points that secondary measurement obtains is as observation vector, each learning sample
A corresponding row vector is obtained, the row vector composition observation matrix of multiple samples is as training data；
For the corresponding row vector of each sample, following process is executed by the way of concurrent operation:
(3) target component to be solved is set as m=[x0,y0,z0,α,β,k1,β1,γ1,k2,β2,γ2,k3,β3,γ3], wherein
x0,y0,z0The three-dimensional coordinate of target is respectively represented, α, β respectively represent the land burial inclination angle of target, with three-dimensional doublet forward modeling
Model approximate representation target, three-dimensional doublet parameter are respectively k1,β1,γ1,k2,β2,γ2,k3,β3,γ3, only with target sheet
Body property is related, such as size, shape and material etc..Select any initial value m0, calculate the forward response d under the initial value0, enable i
Initial value=0.
(4) Δ d is calculatedi=di-dobs, dobsIndicate the corresponding observation vector of sample, Δ mi=mobs-mi, mobsIndicate sample
Actual parameter；
(5) the Δ d calculated according to step (4)iWith Δ miTo KiIt is iterated calculating, iterative formula is
(6) K is acquirediAfterwards to miIt is updated mi=mi+ΔdiKi, maximum step number M or meet the condition of convergence when reaching trainingWhen, stop iteration, wherein miFor the target component of current step number, i=i is otherwise enabled
+ 1, calculate current miUnder forward response di, then return step (4)；
(7) gradient for obtaining entire learning sample declines matrix K, includes each learning sample in the matrix in each solution
The K that step number i is solvedi。
Refutation process:
(8) the secondary field response that one-point measurement inverting target near abnormal area is generated in target, obtains inverting target
Observation vector；
Set the initial value m of inverting target component0, calculate the forward response d under the initial value0, and enable initial value=0 of i；
(9) Δ d is calculatedi=di-dobs, dobsIndicate the corresponding observation vector of inverting target；
According to sample K obtained each in training processi, all it is updated mi=mi+ΔdiKi；
Judge whether that at least one meets the condition of convergenceOr reaches maximum and change
Ride instead of walk several M, mobsThe actual parameter for indicating sample, if so, by minimum RMSiParameter m of the corresponding sample parameter as targetf,
Otherwise i=i+1 is enabled, update judgement is re-started.
Example
In this example, by the time-domain electromagnetic data inversion method based on supervision descent method illustrated, to buried target
Quick and precisely identified.
Sample training process are as follows:
(1) survey area be 0.5m*0.5m, establish observation coordinate system, by 20*5*5 (cm) steel drum, 20*10*10 (cm) steel drum and
20*20*10 (cm) steel disk is placed at different coordinates with different angle respectively, generates a large amount of learning sample, sample number at any time
Amount is 2880, is a sample for example, 20*5*5 (cm) steel drum to be arranged in the position A for surveying area with certain inclination angle.
(2) in the secondary field response for surveying detection system interval 25cm one-point measurement sample object in area, referring to fig. 2, r is mesh
Mark position, rRFor ceoncentrically wound coil position, the secondary field response splicing of several measuring points in each measurement is formed one
A row vector carries out the measurement of secondary field response for each sample standard deviation, obtains different row vector composition observation matrixes and makees
For training data.
Following step (3)-(6) process is executed by the way of concurrent operation for each sample:
(3) target component to be solved is set as m=[x0,y0,z0,α,β,k1,β1,γ1,k2,β2,γ2,k3,β3,γ3],
In, x0,y0,z0The three-dimensional coordinate of target is respectively represented, α, β respectively represent the land burial inclination angle of target, just with three-dimensional doublet
Model approximate representation target is drilled, three-dimensional doublet parameter is respectively k1,β1,γ1,k2,β2,γ2,k3,β3,γ3, only and target
Nature is related, such as size, shape and material etc..Select any initial value m0, calculate the forward response d under the initial value0,
Enable initial value=0 of i.
(4) Δ d is calculatedi=di-dobs, dobsIndicate the corresponding observation vector of sample, Δ mi=mobs-mi, mobsIndicate sample
Actual parameter；
(5) the Δ d calculated according to step (4)iWith Δ miTo KiIt is iterated calculating, iterative formula is
(6) K is acquirediAfterwards to miIt is updated mi=mi+ΔdiKi, judge whether to reach training maximum step number M or meet to receive
Hold back conditionWherein M is the maximum step number 10 of training, if being off repeatedly
Generation, miFor the target component of current step number, otherwise the learning process convergence curve of one of sample enables i=i+1 referring to Fig. 3,
Calculate current miUnder forward response di, then return step (4).
(7) iteration that (6) reach greatest iteration step number is executed the step, the gradient decline matrix of entire learning sample is obtained
K, dimension are P × Q, and wherein P is the length of row vector in step (2), and Q is the length of vector m to be solved；Entire training sample
Shi Changwei 24.15s.
Refutation process are as follows:
(8) a large amount of test sample data, test sample 3840 are generated according to the method in (1).Choose test sample
Any test data concentrated carries out inverting, chooses 20*5*5 (cm) steel drum herein with α=0 °, β=50 ° (0.21,
0.3, -0.81) the secondary field response of m, selectes initial value m0, calculate the forward response d under the initial value0, enable initial value=0 of i.
(9) Δ d is calculatedi=di-dobs, dobsIndicate the corresponding observation vector of inverting target；
(10) m is updatedi=mi+ΔdiKiRe-start iteration update；
Meet the m of the condition of convergence for first in iterative processiPosition as target；
Do not meet the condition of convergence also when all training samples all reach maximum train epochs 10, at this time by 2880 training
It is the smallest in sampleCorresponding miPosition as target；
The process of above-mentioned steps (9) (10) is to execute parallel to all learning samples (quantity 2880), such as first
A learning sample executes, at this time KiFor the K obtained when first training sample trainingi。
This example stops iteration when reaching maximum train epochs 10, convergence curve referring to fig. 4, when inverting a length of 7.5s, mesh
Mark final argument mfIn position and inclination angle result and tradition LM optimal method inversion result comparison referring to table 1, target sheet
Body dipole decay characteristics curve and tradition LM optimal method inversion result are compared referring to Fig. 5, as seen from the figure, under supervision
The dipole decay characteristics curve of drop method is consistent with target signature, hence it is evident that better than the inversion result of traditional LM optimal method.Just
The area Yan Ce figure surveys area's figure and residual plot referring to Fig. 6, by scheming with the inverting that supervision descent method and tradition LM optimal method obtain
It can obtain, it is of slight difference to survey area and forward modeling survey area with the inverting of supervision descent method, and uses the inverting of tradition LM optimal method
It surveys area and differs quite big with forward modeling survey area's result.
1 SDM of table and traditional inversion result compare
Fig. 5 is supervision descent method ((a): supervision compared with the dipole inversion result of tradition LM optimization algorithm in example
Descent method (b): traditional LM optimization algorithm).
Fig. 6 is the Comparative result (a) that descent method and tradition LM optimal method are supervised in example: being positive respectively from left to right
Area's figure, the two residual plot (b) are surveyed in the inverting that the area Yan Ce figure, supervision descent method obtain: being from left to right respectively that forward modeling is surveyed area's figure, passed
Area's figure, the two residual plot are surveyed in the inverting that LM optimal method of uniting obtains).
In conclusion the above is merely preferred embodiments of the present invention, being not intended to limit the scope of the present invention.
All within the spirits and principles of the present invention, any modification, equivalent replacement, improvement and so on should be included in of the invention
Within protection scope.
Claims (5)
1. a kind of time-domain electromagnetic data object inversion method based on supervision descent method, which is characterized in that detailed process are as follows:
Training process:
Different learning samples is set, obtains its corresponding secondary field response as observing matrix；
For each learning sample, it is iterated training, obtains the gradient decline matrix of entire training sample；
Refutation process:
Inverting target secondary field response measurement is carried out near abnormal area, obtains the observation vector of inverting target；
The initial parameter for setting inverting target declines the observation vector of matrix and inverting target using the gradient of entire training sample
Inverting is carried out, the actual parameter of inverting target is obtained.
2. the time-domain electromagnetic data object inversion method according to claim 1 based on supervision descent method, which is characterized in that
The process of the repetitive exercise are as follows:
Set the initial parameter m of training sample0, calculate the forward response d under the initial value0, enable initial value=0 of i；
Calculate Δ di=di-dobs, dobsIndicate the corresponding observation vector of sample, Δ mi=mobs-mi, mobsIndicate the true ginseng of sample
Number；
It utilizesTo KiIt is iterated calculating；
Reach training maximum step number M or meets the setting condition of convergenceWhen, update i
=i+1 re-starts iterative calculation, RMS0For the convergence threshold of setting；
K corresponding to all samplesiForm the gradient decline matrix K of entire training sample.
3. the time-domain electromagnetic data object inversion method according to claim 2 based on supervision descent method, which is characterized in that
The gradient using entire training sample declines matrix and the observation vector of inverting target carries out inverting, obtains inverting target
Actual parameter are as follows:
If the initial value of inverting target component is m0, calculate the forward response d under the initial value0, and enable initial value=0 of i；
Calculate Δ di=di-dobs, dobsIndicate the corresponding observation vector of inverting target；
According to sample K obtained each in training processi, update mi=mi+ΔdiKi；
Judge whether that at least one meets the condition of convergenceOr reach greatest iteration
Step number M, RMS0For the convergence threshold of setting, if so, by minimum RMSiCorresponding sample parameter is as the parameter for being really target
mf, i=i+1 is otherwise enabled, update judgement is re-started.
4. according to claim 1 or the 3 time-domain electromagnetic data object inversion methods based on supervision descent method, feature exist
In the sample parameter and target component representation are m:
M=[x0,y0,z0,α,β,k1,β1,γ1,k2,β2,γ2,k3,β3,γ3], wherein x0,y0,z0Respectively represent the three-dimensional of target
Coordinate, α, β respectively represent the land burial inclination angle of target, with three-dimensional doublet forward model approximate representation target, three-dimensional dipole
Subparameter is respectively k1,β1,γ1,k2,β2,γ2,k3,β3,γ3。
5. the time-domain electromagnetic data object inversion method based on supervision descent method according to Claims 2 or 3, maximum to change
Ride instead of walk several M=10.
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Application publication date: 20191203 |