CN108802834B - Underground target identification method based on joint inversion - Google Patents

Underground target identification method based on joint inversion Download PDF

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CN108802834B
CN108802834B CN201810150310.XA CN201810150310A CN108802834B CN 108802834 B CN108802834 B CN 108802834B CN 201810150310 A CN201810150310 A CN 201810150310A CN 108802834 B CN108802834 B CN 108802834B
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张晓娟
谢吴鹏
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Abstract

The invention provides an underground target identification method based on joint inversion, which comprises the following steps: establishing an observation coordinate system near an abnormal area, recording coordinates of a transmitting coil and a receiving coil, and performing fixed-point measurement to obtain a measurement area response of a secondary field; inputting parameters, observation coordinates and observation responses of a receiving and transmitting coil of the electromagnetic detection system; performing primary inversion by using a first optimization algorithm to obtain an inversion result; inputting the inversion result obtained in the step three into a second optimization algorithm for solving to obtain a final inversion result; and step five, performing information identification on the target to be detected according to the final inversion result obtained in the step four. In practical application, the method can be used as a method for accurately inverting underground target information and judging target characteristics.

Description

Underground target identification method based on joint inversion
Technical Field
The invention belongs to the technical field of underground target identification, and particularly relates to an underground target identification method based on joint inversion.
Background
The time domain electromagnetic method is an artificial source nondestructive detection method based on the electromagnetic induction principle, and transmits a primary field to the underground by using an ungrounded return line (magnetic source) or a grounded line source (galvanic couple source), and under the excitation of the primary field, induced eddy current excited in an underground conductor target generates an induced electromagnetic field which changes along with time. Due to the very low frequency of the transmitted signal, the time domain electromagnetic method satisfies the diffusion equation of the electromagnetic wave rather than the wave equation, the resolution ratio of the diffusion field is very low, so that the time domain electromagnetic method cannot be used for directly imaging the underground target, the underground target can only be subjected to inversion solution through a forward model, but the secondary field response of a general finite conductor cannot be used for solving the analytic solution, if a numerical calculation method of a finite element or finite difference is adopted, the calculated amount is too large, therefore, a dipole submodel is adopted to approximately equivalent the underground conductor target, the equivalent dipole strength of the target is obtained through inversion iteration, and the information of the target is further judged.
In the process of implementing the present invention, the applicant finds that the above prior art has the following technical defects:
in the inversion iteration process, the selection of the initial value is particularly important, and the final inversion result is directly influenced. The current common method is to give the approximate plane position of the underground target by using the actual measurement response, but the target burial depth, the inclination angle and the dipole strength can not be obtained by the actual measurement response, only guessed initial values can be given, the commonly used local optimal LM algorithm has very high requirements on the initial values, and if the initial values are not accurate, the inversion result finally solved has a very large difference from the actual value. However, the adoption of the global optimization DE algorithm is long in time consumption, and the global search capability and the local search capability of the DE algorithm are contradictory, so that the problem that the optimal solution cannot be converged exists.
On the basis of the inversion result, a fitting expression of the dipole intensity is obtained, but the fitting process needs to be substituted into all observation time points of the quadratic field response, including late signals which are very sensitive to noise, which causes a large amount of calculation and may fit inaccurate results.
Disclosure of Invention
In view of the above technical problems, the present invention aims to provide a subsurface target identification method based on joint inversion.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a subsurface target identification method based on joint inversion comprises the following steps:
establishing an observation coordinate system near an abnormal area, recording coordinates of a transmitting coil and a receiving coil, and performing fixed-point measurement to obtain a measurement area response of a secondary field;
inputting parameters, observation coordinates and observation responses of a receiving and transmitting coil of the electromagnetic detection system;
performing primary inversion by using a first optimization algorithm to obtain an inversion result;
inputting the inversion result obtained in the step three into a second optimization algorithm for solving to obtain a final inversion result;
and step five, performing information identification on the target to be detected according to the final inversion result obtained in the step four.
According to the technical scheme, the underground target identification method based on the joint inversion has at least one of the following beneficial effects:
(1) aiming at the characteristics that global optimization is time-consuming but accurate, local optimization convergence is fast but is easily influenced by an initial value, a joint optimization algorithm is provided for inverting the underground target, and an accurate inversion result can be obtained on the premise of not needing prior information;
(2) the invention provides a new algorithm for extracting target parameters, which does not need to fit the expression of a target dipole curve to extract parameters, has small calculation amount and can quickly determine the properties of the underground target.
Drawings
FIG. 1 is a flow chart of a method for identifying underground targets based on DE-LM joint inversion according to an embodiment of the present invention.
FIG. 2 is a schematic diagram of secondary field response measurement recording coordinates according to an embodiment of the present invention.
Fig. 3 is a diagram illustrating a variation rule of CR with iteration steps after improvement according to an embodiment of the present invention.
Fig. 4 is a diagram comparing convergence of the improved DE and the conventional DE according to the embodiment of the present invention.
FIG. 5 is a diagram illustrating the convergence comparison between the DE algorithm and the LM algorithm according to the embodiment of the present invention.
Fig. 6 is a dipole intensity diagram corresponding to three inversion methods.
FIG. 7 is a comparison of zone inversion plots for three algorithms.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to specific embodiments and the accompanying drawings.
The invention provides an underground target identification method based on joint inversion, which comprises the following steps:
establishing an observation coordinate system near an abnormal area, recording coordinates of a transmitting coil and a receiving coil, and performing fixed-point measurement to obtain a measurement area response of a secondary field;
inputting parameters, observation coordinates and observation responses of a receiving and transmitting coil of the electromagnetic detection system;
performing primary inversion by using a first optimization algorithm to obtain an inversion result;
inputting the inversion result obtained in the step three into a second optimization algorithm for solving to obtain a final inversion result;
and step five, performing information identification on the target to be detected according to the final inversion result obtained in the step four.
The first optimization algorithm used in the third step can be an improved Differential Evolution (DE) algorithm, a genetic algorithm, a simulated annealing algorithm, a tabu search algorithm, a particle swarm algorithm and an ant colony algorithm. The second optimization algorithm in step four may be the Levenberg-marquardt algorithm, the newton algorithm, the conjugate gradient algorithm.
The present invention will be described in detail below by taking the method for identifying underground targets based on DE-LM joint inversion as an example.
FIG. 1 is a flow chart of a method for identifying underground targets based on DE-LM joint inversion according to an embodiment of the present invention. As shown in FIG. 1, the underground target identification method based on DE-LM joint inversion comprises the following steps:
establishing an observation coordinate system near an abnormal area, recording coordinates of a transmitting coil and a receiving coil, and performing fixed-point measurement to obtain a measurement area response of a secondary field;
inputting parameters, observation coordinates and observation responses of a receiving and transmitting coil of the electromagnetic detection system;
performing primary inversion by using an improved Differential Evolution (DE) algorithm to obtain an inversion result;
step four, inputting the inversion result obtained in the step three into a Levenberg-Marquardt algorithm for solving to obtain a final inversion result;
and step five, performing information identification on the target to be detected according to the final inversion result obtained in the step four.
In step two, the parameters of the transceiver coil include: the size of the transmitting coil, the size of the transmitting current, the size of the receiving coil, the position relationship between the transmitting coil and the receiving coil and the like.
In the third step, an improved differential evolution algorithm is used for carrying out primary inversion to obtain a relatively accurate inversion result, wherein the inversion result comprises information such as a three-dimensional coordinate, an inclination angle and three-dimensional dipole strength of a target. When a time domain electromagnetic method is used for inverting an underground target, the number of parameters to be inverted is large, and the dipole strength difference is large in magnitude at early and late stages, so that the global search capability needs to be enhanced when the Differential Evolution (DE) algorithm is used for inversion, and once the approximate position of an optimal solution is located, the local search capability needs to be enhanced. While global and local search capabilities are contradictory. The cross probability CR of the original algorithm is a constant, if the value of CR is too large, premature convergence is easily caused at the beginning, and if the value of CR is too small, the local search capability at the late stage is poor. Thus reconstructing a cross probability factor
Figure RE-GDA0001815738670000041
Wherein G is the current iteration number of the differential evolution algorithm, and Gm is the maximum iteration number of the differential evolution algorithm.
When the search is started, CR is small and is kept for a period of time, so that the diversity of the population and the global search capability are ensured; then the small is transited to the large, and finally the small is slowly close to 1, so that the later convergence speed and the local searching capability are ensured.
Setting a three-dimensional position range, an inclination angle range and a three-dimensional dipole intensity range of a target, and performing primary inversion by using an improved DE algorithm to obtain a relatively accurate inversion result.
Although the reconstruction cross probability factor overcomes the premature convergence problem to a certain extent, the reconstruction cross probability factor still cannot accurately converge to an optimal solution, and the actual requirement cannot be met due to long inversion time consumption. When the LM algorithm is used for solving, although the local optimal solution is easy to converge when the optimal solution is far away, the convergence speed is very high when the initial value of the local optimal solution is near the global optimal solution. And substituting the inversion result in the third step into a Levenberg-Marquardt (LM) algorithm to obtain a final inversion result.
In the fifth step, the following substeps are specifically included:
and S1, carrying out parameter synthesis on the dipole strength to obtain characteristic information about the target to be measured, such as size, attenuation rate, symmetry, axial ratio and the like.
And (4) carrying out parameter synthesis on the dipole strength of the inversion result in the fourth step to obtain characteristic information Size, Decay, Symmetry and Ratio about the target. The targets are different in size, magnetic fluxes generated by the primary field passing through the targets are different, eddy currents generated in the targets are different in size (corresponding to different equivalent magnetic dipole strengths) after the targets are turned off, and induced voltages generated by the receiving coils are different. The larger the target, the greater the equivalent magnetic dipole strength, and the greater the induced voltage. Thus the Size of the target is characterized by Size
Figure RE-GDA0001815738670000051
t1Representing the center instant of the first time window.
Different target materials to be detected have different attenuation rates of internal eddy currents (corresponding to different slopes of different equivalent magnetic dipole attenuation curves). The larger the conductivity of the target to be detected is, the slower the attenuation rate of the equivalent magnetic dipole is. The target Decay rate is therefore denoted by Decay,
Figure RE-GDA0001815738670000052
tnthe center time of the late sampling time window.
The target shapes are different, and the distribution of the eddy current inside the target shapes is different. The more symmetrical the target, the more symmetrical the eddy current distribution, and the more similar the equivalent magnetic dipole characteristics. Therefore, the target symmetry is characterized by the proportional relationship between the magnetic dipole attenuation characteristic curves. Symmetry represents the degree of Symmetry of the object,
Figure RE-GDA0001815738670000053
the larger the length-to-axis ratio of the target is, the larger the difference of eddy current distribution excited by the primary field when the target is irradiated in different directions is, and the larger the difference between the attenuation characteristics of the equivalent magnetic dipole is. However, when the geometric relationship of the target is characterized by the proportional relationship between the dipole intensities, the different characteristics of the response caused by different materials of the target are considered firstly.
The target axial Ratio is expressed by Ratio,
Figure RE-GDA0001815738670000054
Figure RE-GDA0001815738670000061
and S2, comprehensively judging the target to be detected according to the obtained characteristic information.
The specific embodiment is as follows:
in the embodiment, the underground target is identified by the underground target identification method based on the DE-LM joint inversion.
The measuring area is 5m by 5m, an observation coordinate system is established, a steel barrel with the size of 20 x 10(cm) is placed at the coordinates (2.5, 2.5, -1.5), and the inclination angles of alpha and beta are both 0 degrees. The electromagnetic detection system measures the secondary field response of the target at fixed points 50cm apart, see fig. 2.
Inputting the parameters of a receiving and transmitting coil of an electromagnetic detection system, wherein a square transmitting coil 1m x 1m, a transmitting current 6A and a square receiving coil 0.5m x 0.5m are concentrically and coplanarly placed with the transmitting coil.
When a time domain electromagnetic method is used for inverting an underground target, the number of parameters to be inverted is large, and the dipole strength difference is large in magnitude at early and late stages, so that the global search capability needs to be enhanced when the Differential Evolution (DE) algorithm is used for inversion, and once the approximate position of an optimal solution is located, the local search capability needs to be enhanced. While global and local search capabilities are contradictory. The cross probability CR of the original algorithm is a constant, if the value of CR is too large, premature convergence is easily caused at the beginning, and if the value of CR is too small, the local search capability at the late stage is poor. Thus reconstructing a cross probability factor
Figure RE-GDA0001815738670000062
As shown in fig. 3, when a search is started, CR is small and kept for a period of time, ensuring the diversity of the population and the global search capability; then the small is transited to the large, and finally the small is slowly close to 1, so that the later convergence speed and the local searching capability are ensured.
Setting the x, y coordinate range [0, 2 ] of the target]Z coordinate range [ -2, 0]Angle of inclination of alpha, beta [0, 180 ]]Three-dimensional dipole intensity range [10-1，102]The improved DE algorithm is used for carrying out primary inversion to obtain a relatively accurate inversion result, the improved DE algorithm is compared with the inversion result of the traditional DE algorithm, as shown in Table 1, and the convergence curves of the two methods are shown in FIG. 4.
TABLE 1 comparison of inversion results of improved DE versus conventional DE
Figure RE-GDA0001815738670000071
And substituting the accurate inversion result into a Levenberg-Marquardt (LM) algorithm to obtain a final inversion result, wherein the convergence curves of the three inversion algorithms are shown in a table 2, the convergence curves of the three inversion algorithms are shown in a figure 5, and the DE-LM combined inversion algorithm has the fastest convergence and the minimum error. Dipole intensity graphs corresponding to the three inversion algorithms are shown in fig. 6, the first dipole intensity obtained by the DE-LM joint inversion algorithm is greater than the second dipole intensity and the third dipole intensity, the second dipole intensity and the third dipole intensity are approximately equal, the dipole model closest to the steel barrel can reflect the characteristics of the barrel-shaped target, the difference of the second dipole and the third dipole inverted by the DE algorithm is increased at the later stage, and the three dipoles inverted by the LM algorithm cannot correctly reflect the barrel-shaped characteristics of the target; in fig. 7, the first column is the measured area actual measurement response, the second column is the measured area inversion graph of the inversion algorithm of the three algorithms (LM, DE, and DE-LM algorithms in sequence from top to bottom), and the third column is the residual graph of the two algorithms.
TABLE 2 comparison of inversion results for three algorithms
Figure RE-GDA0001815738670000072
Performing parameter synthesis on the three-dimensional dipole intensity of the final inversion result to obtain characteristic information Size, Decay, Symmetry and Ratio about the target, wherein,
the Size indicates the Size of the object,
Figure RE-GDA0001815738670000081
t1representing the center time of the first time window;
decay represents the target Decay rate and,
Figure RE-GDA0001815738670000082
tnthe center time of the late sampling time window.
Symmetry represents the degree of Symmetry of the object,
Figure RE-GDA0001815738670000083
the Ratio indicates a target axial Ratio,
Figure RE-GDA0001815738670000084
Figure RE-GDA0001815738670000085
according to the formula, the parameters of the steel barrel of 20 × 10(cm) are calculated as Size: 78.99, Decay: 0.0247, Symmetry: 0.0109, Ratio is 2.56.
Analyzing the parameters according to the result of the calculation, wherein the Size is 78.99, and the target is a larger object; decay is 0.0247, which shows that the target per se attenuates faster and the conductivity is smaller; symmetry is 0.0109, which indicates that the object is an axisymmetric object; ratio is 2.56, and the description is directed to a cylindrical object. Therefore, the underground target identification method based on the DE-LM joint inversion can well reflect the property of the underground target.
Up to this point, the present embodiment has been described in detail with reference to the accompanying drawings. From the above description, those skilled in the art should clearly recognize that the method for identifying subsurface targets based on joint inversion of the present invention. In practical application, the method can be used as a method for accurately inverting underground target information and judging target characteristics.
It is to be noted that, in the attached drawings or in the description, the implementation modes not shown or described are all the modes known by the ordinary skilled person in the field of technology, and are not described in detail. Furthermore, the above definitions of the various elements and methods are not limited to the particular structures, shapes or arrangements of parts mentioned in the examples, which may be easily modified or substituted by one of ordinary skill in the art, for example:
size may be replaced with a target Size or target Size; decay may be replaced with a target Decay rate; symmetry can be replaced by target Symmetry; the Ratio may be replaced with a target long to short axis Ratio.
It is also noted that the illustrations herein may provide examples of parameters that include particular values, but that these parameters need not be exactly equal to the corresponding values, but may be approximated to the corresponding values within acceptable error tolerances or design constraints. Directional phrases used in the embodiments, such as "upper", "lower", "front", "rear", "left", "right", etc., refer only to the direction of the attached drawings and are not intended to limit the scope of the present invention. In addition, unless steps are specifically described or must occur in sequence, the order of the steps is not limited to that listed above and may be changed or rearranged as desired by the desired design. The embodiments described above may be mixed and matched with each other or with other embodiments based on design and reliability considerations, i.e., technical features in different embodiments may be freely combined to form further embodiments.
In summary, the invention provides a subsurface target identification method based on joint inversion. The method can obtain an accurate inversion result without prior information, does not need to fit an expression extraction parameter of a target dipole curve, has small calculation amount, and can quickly determine the property of the underground target.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (4)

1. A subsurface target identification method based on joint inversion comprises the following steps:
establishing an observation coordinate system near an abnormal area, recording coordinates of a transmitting coil and a receiving coil, and performing fixed-point measurement to obtain a measurement area response of a secondary field;
inputting parameters, observation coordinates and observation responses of a receiving and transmitting coil of the electromagnetic detection system;
performing primary inversion by using a first optimization algorithm to obtain an inversion result;
inputting the inversion result obtained in the step three into a second optimization algorithm for solving to obtain a final inversion result;
fifthly, identifying information of the target to be detected according to the final inversion result obtained in the fourth step;
wherein the first optimization algorithm is as follows: an improved Differential Evolution (DE) algorithm, a genetic algorithm, a simulated annealing algorithm, a tabu search algorithm, a particle swarm algorithm or an ant colony algorithm;
the second optimization algorithm is as follows: a Levenberg-Marquardt LM algorithm, a Newton algorithm, or a conjugate gradient algorithm;
wherein, step five includes the following substeps:
s1, carrying out parameter synthesis on the three-dimensional dipole strength to obtain characteristic information about the underground target; the characteristic information includes: size, attenuation rate, symmetry, axial ratio;
s2, comprehensively judging the underground target according to the obtained characteristic information;
the size of the underground target is
Figure FDA0002683746990000011
t1Indicating the central time of the first time window, Li(t1) (i ═ 1, 2, and 3), each of which indicates a time t at the center of the first time window1The strength of the equivalent dipole 1, the strength of the dipole 2 and the strength of the dipole 3 of the underground target;
attenuation rate of subsurface target of
Figure FDA0002683746990000012
tnSampling the central time of the time window at the later period;
the symmetry of the subsurface target being
Figure FDA0002683746990000013
s.t { p, q }, is e {1, 2, 3 }; n is the number of time windows, Lp(tj) Is shown at time tjThe strength of the equivalent dipole p; l isq(tj) Is shown at time tjThe strength of the equivalent dipole q is equivalent, and p is not equal to q;
the axial ratio of the subsurface target is calculated by the following formula:
Figure FDA0002683746990000021
Figure FDA0002683746990000022
{p，q，m}∈{1，2，3}，
wherein L ism(tj) Is shown at time tjThe strength of an equivalent dipole m, wherein m belongs to {1, 2, 3}, and p is not equal to q is not equal to m, n is the number of time windows, Lp(tj) Is shown at time tjStrength, L, of equivalent dipole pq(tj) Is shown at time tjThe strength of the equivalent dipole q.
2. A subsurface target identification method as claimed in claim 1 wherein the transceiver coil parameters include: the size of the transmitting coil, the size of the transmitting current, the size of the receiving coil and the position relation between the transmitting coil and the receiving coil.
3. The underground target recognition method of claim 1, wherein the cross probability factor of the improved Differential Evolution (DE) algorithm is expressed as:
Figure FDA0002683746990000023
wherein G is the current iteration number of the differential evolution algorithm, and Gm is the maximum iteration number of the differential evolution algorithm.
4. A subsurface target identification method as claimed in claim 1 wherein the information in the final inversion result comprises: three-dimensional coordinates, dip angle, three-dimensional dipole intensity of the subsurface target.
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