CN111079285A - Full-tensor magnetic gradient data compensation optimization method and system - Google Patents
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Abstract
The invention provides a full-tensor magnetic gradient data compensation optimization method and system, aiming at each magnetic gradient component of full-tensor magnetic gradient data, adopting different experimental data under various working conditions, introducing relaxation coefficients and decision variables, and establishing constraint conditions of a model; establishing an objective function of a compensation optimization model according to the basic characteristics of passivity and non-rotation of the mathematical optimization model and the environmental magnetic field, and establishing a basic compensation optimization model by combining constraint conditions; and solving parameters of the compensation optimization model according to the no-load data, and calculating compensated loaded data according to the compensation model to obtain a compensation result of the full tensor magnetic gradient data. The method can effectively eliminate the platform interference in the full tensor magnetic gradient data and restore the true value of the data.
Description
Technical Field
The disclosure belongs to the field of magnetic detection, and relates to a full-tensor magnetic gradient data compensation optimization method and system.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
After the full tensor magnetic gradient data of the magnetic target is subjected to filtering processing, basic characteristics of passivity, derotation and the like of an environmental magnetic field are still difficult to meet, which shows that residual magnetic gradient interference still exists in the full tensor magnetic gradient data. The interference in the full tensor magnetic gradient data is comprehensively considered, and the interference is not only related to environmental interference, a platform structure and a detection mode, but also related to the working state of the working platform. When the platform itself changes due to material or other operating conditions, it also interferes with the measurements, requiring data compensation.
To the knowledge of the inventor, the current data compensation method generally uses a mechanism model to design a compensation algorithm based on the device structure of the detection device or based on the material. The method has the disadvantages that due to the complexity of environment, materials and working modes, all factors are difficult to comprehensively consider to establish an accurate mechanism model, the parameters of the model are difficult to determine, and the compensation parameters are difficult to be accurate.
Disclosure of Invention
The invention aims at solving the problems and provides a full tensor magnetic gradient data compensation optimization method and system, aiming at the platform interference in the full tensor magnetic gradient data, and from the data driving angle, a compensation model of the full tensor magnetic gradient data is established by combining the optimization method and the basic characteristics of an environmental magnetic field, so that the interference in the full tensor magnetic gradient data can be effectively eliminated, and the true value of the data is restored.
According to some embodiments, the following technical scheme is adopted in the disclosure:
a full-tensor magnetic gradient data compensation optimization method comprises the following steps:
aiming at each magnetic gradient component of full tensor magnetic gradient data, adopting different experimental data under various working conditions, introducing relaxation coefficients and decision variables, and establishing constraint conditions of a model;
establishing an objective function of a compensation optimization model according to the basic characteristics of passivity and non-rotation of the mathematical optimization model and the environmental magnetic field, and establishing a basic compensation optimization model by combining constraint conditions;
and solving parameters of the compensation optimization model according to the no-load data, and calculating compensated loaded data according to the compensation model to obtain a compensation result of the full tensor magnetic gradient data.
As an alternative embodiment, let Bx、By、BzRespectively representing magnetic sensor seatsThe scale is the magnitude of the magnetic field in x, y and z directions, i.e. the magnetic field vector B is (B)x,By,Bz). Then in the x-direction the magnetic gradient of the magnetic field vector B is: b isxx、Bxy、BxzIn the y direction, the magnetic gradients of the magnetic field vector B are: b isyx、Byy、ByzIn the z direction, the magnetic gradients of the magnetic field vector B are: b iszx、Bzy、Bzz。
As an alternative embodiment, the specific process of establishing the constraint condition of the compensation optimization model includes: under the condition of N groups of experimental data under M working conditions, nine magnetic gradient components B are establishedijThe compensation formula for (i, j ═ x, y, z) is:
wherein B isijmn、
Respectively representing the magnetic gradient data before and after compensation,
respectively represent working condition parameters of ImA positive compensation coefficient and a negative compensation coefficient,
representing positive and negative absolute compensation parameters of the magnetic gradient data, respectively.
As a further limitation, in order to avoid the system falling into a non-solution state, a relaxation parameter theta is introduced, namely, the relaxation parameter theta is compensated through a positive and negative term related to the working condition and falls within a controllable range after being compensated with the positive and negative terms:
in order to solve each parameter of the compensation formula, the values of the parameters are all positive numbers, that is:
As an alternative embodiment, the passivity of the ambient magnetic field is represented by: b isxx+Byy+B zz0; the derotation of the ambient magnetic field is represented by: b isij＝Bji,(i,j＝x,y,z)。
As an alternative embodiment, a compensation optimization model is established:
according to experimental no-load data
Parameter values of the compensation parameters, thereby obtaining a compensation formula based on the compensation parameters
Data B to be compensatedijmn(i, j ═ x, y, z) to obtain a compensation result
A full-tensor magnetic gradient data compensation optimization system, comprising:
a data determination module configured to determine nine component data of the full tensor magnetic gradient data participating in model parameter calculation and data compensation for the experimental data: b isxx、Bxy、Bxz、Byx、Byy、Byz、Bzx、Bzy、Bzz。
The optimization model building module is configured to build an objective function and model constraint conditions of the compensation optimization model according to the basic characteristics of passivity and non-rotation of the mathematical optimization model and the environmental magnetic field, so as to build a basic compensation optimization model;
and the data calculation module is configured to solve the compensation optimization model parameters according to the no-load data, calculate the compensated loaded data according to the compensation model and obtain the compensation result of the full tensor magnetic gradient data.
A computer readable storage medium having stored therein a plurality of instructions adapted to be loaded by a processor of a terminal device and to perform the steps of a full-tensor magnetic gradient data compensation optimization method.
A terminal device comprising a processor and a computer readable storage medium, the processor being configured to implement instructions; the computer readable storage medium is used for storing a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the steps of compensation optimization of the full-tensor magnetic gradient data.
Compared with the prior art, the beneficial effect of this disclosure is:
for the basic characteristics that the full tensor magnetic gradient data of the magnetic target still has platform interference and does not meet passive field tensor data after being subjected to filtering processing, the method analyzes the full tensor magnetic gradient data from the perspective of an optimization method, determines constraint conditions and objective functions mainly by using the basic theorem and rule of a magnetic field for non-frequency interference signals, establishes an optimization model of the magnetic gradient tensor, calculates compensation parameters, can effectively eliminate the interference in the full tensor magnetic gradient data, and restores the true value of the data. The method has wide application range and can be used for resource exploration of mineral deposits, petroleum and the like and research of geological structures and the like. Can also be used in various fields such as civil use, medicine and the like.
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The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
FIG. 1 is a flowchart of the present embodiment;
FIG. 2 is a comparison graph of a localized cloud before and after compensation of full tensor magnetic gradient data according to this embodiment.
The specific implementation mode is as follows:
the present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The invention provides a full tensor magnetic gradient data compensation optimization method and system, and particularly relates to a compensation optimization model which is established by introducing relaxation parameters according to N groups of experimental data of M working conditions and aiming at the basic characteristics that the full tensor magnetic gradient data of a magnetic target still has platform interference and cannot meet the full tensor magnetic gradient data of a magnetic field after being subjected to filtering processing, solving the optimal parameters of the model through the experimental data, and finally compensating the data according to the solved model so as to reduce the platform interference in the full tensor magnetic gradient data and restore the true value of the data. The method and the system can be applied to resource exploration such as mineral deposits and petroleum, research such as geological structures and the like, and can also be applied to military aspects such as detection of underground unexploded bombs or mines and other explosives, anti-submarine battles and the like.
As shown in fig. 1, the method specifically comprises the following steps:
(1) and (3) introducing relaxation coefficients theta and 36 decision variable parameters aiming at 9 magnetic gradient components of the full tensor magnetic gradient data by adopting N groups of experimental data under M working conditions, and establishing constraint conditions of the model.
(2) According to the general mathematical optimization model and the basic characteristics of passive field and irrotational field tensor data, a data-driven objective function is established, and a basic optimization compensation model is established by combining constraint conditions.
(3) And solving parameters of the optimization model according to the no-load data, and processing the full tensor magnetic gradient data to be compensated according to the compensation model to obtain a compensation result of the full tensor magnetic gradient data.
In the step (1), the specific contents include:
(1-1) theoretically, in a measuring area without space current density, the magnetic field satisfies passivity and no rotation, namely, the divergence and the rotation of the magnetic field are both 0, namely
Through filtering and early compensation processing of a large amount of experimental data, it is found that the real data hardly satisfy two basic characteristics of passivity and irrotational property of the magnetic field, but a certain rule exists under the same experimental condition. Because of the different properties of the target magnetic field, we take the stable magnetic field as an example here to build an optimization model.
(1-2) to avoid the system falling into a no solution, a relaxation parameter θ is introduced. The choice of θ needs to be adjusted experimentally. And because the compensation parameters are related to experimental conditions and environments, corresponding data support is needed for different devices and different detection environments. The constraint conditions for establishing the model are as follows:
constraint (4) indicates that the nth set of unloaded magnetic gradient data component data B measured under the mth operating conditionijmnTheoretically, it should be equal to 0 after processing by filtering, etc., and fall within a small controllable range after compensation by positive and negative terms related to the operating condition and absolute compensation by the positive and negative terms.
For the solution, the values of these variables are all positive numbers:
description of the symbols
Subscripts:
i, j represents a certain coordinate axis direction, and i, j belongs to { x, y, z };
m represents the mth working condition, and M belongs to M;
n represents the nth set of magnetic field data, N belongs to N;
parameters are as follows:
BijLrepresents the derivative of the magnetic induction component in the i direction in the j direction, i.e. the magnetic gradient;
Bijmnrepresenting the nth group of magnetic gradients measured under the mth working condition under the no-load condition;
ILrepresenting the on-load working parameter;
Imrepresenting the mth working parameter under the condition of no load;
theta is a controllable relaxation parameter greater than 0;
decision variables:
The step (2) specifically comprises the following steps:
the optimization method is to find the best or best solution, or optimal value, for a particular problem. A general mathematical optimization model can be expressed as follows:
wherein f (x) is an objective function, gi(x) For constraint, x is n is a decision variable.
The optimization objective of the compensation optimization model for full tensor magnetic gradient data, considering the nature of the passive and derotation fields, requires:
Bxx+Byy+Bzz＝0
Bij＝Bji,(i,j＝x,y,z) (6)
namely: so that the full tensor magnetic gradient data, after compensation, satisfies the passivity and derotation characteristics of the magnetic field. The objective function of the specific model and the constraint condition form thereof are shown in formula (7), wherein 0.5 is a weighting coefficient:
for 9 components of the magnetic gradient tensor data, 36 decision variables are in total, and N groups of experimental data of M working conditions are adopted, so that the model scale is 9 × M × N constraint conditions.
In the step (3), the method specifically comprises the following steps:
and (3-1) solving the parameters of the model through the no-load data. Find BijL、
And the parameter values of the parameters are equal, wherein i, j is x, y and z.
And (3-2) estimating an estimated value of the full tensor magnetic gradient data through the solved model, and determining the compensated full tensor magnetic gradient data.
The embodiment provides a full-tensor magnetic gradient data compensation optimization method, which comprises the following steps: and (3) calculating parameters of the compensation model by using 10 groups of experimental data under 1 working condition, and compensating the belt load data and the no-load data. The no-load data is shown in table 1, the parameters of the calculation compensation model and the compensation results of the data with load are shown in table 2, and the compensation results of the data with no load are shown in table 3. The localized cloud images before and after compensation for the loaded full tensor magnetic gradient data are shown in figure 2.
TABLE 1 partial Idle magnetic gradient tensor initial data
| Bxx | Bxy | Bxz | Byx | Byy | Byz | Bzx | Bzy | Bzz |
| 0.344 | 0.344 | 187.83 | -187.835 | 0.344 | 0.344 | 187.8 | 0.344 | 0.344 |
| 0.341 | 0.341 | 187.83 | -187.836 | 0.341 | 0.341 | 187.836 | 0.341 | 0.341 |
| 0.338 | 0.338 | 187.83 | -187.836 | 0.338 | 0.338 | 187.836 | 0.338 | 0.338 |
| 0.336 | 0.336 | 187.83 | -187.836 | 0.336 | 0.336 | 187.836 | 0.336 | 0.336 |
| 0.333 | 0.333 | 187.83 | -187.836 | 0.333 | 0.333 | 187.836 | 0.333 | 0.333 |
| 0.330 | 0.330 | 187.836 | -187.836 | 0.330 | 0.330 | 187.836 | 0.330 | 0.330 |
| 0.328 | 0.328 | 187.836 | -187.836 | 0.328 | 0.328 | 187.836 | 0.328 | 0.328 |
| 0.325 | 0.325 | 187.836 | -187.836 | 0.325 | 0.325 | 187.836 | 0.325 | 0.325 |
| 0.322 | 0.322 | 187.836 | -187.836 | 0.322 | 0.322 | 187.836 | 0.322 | 0.322 |
| 0.319 | 0.319 | 187.837 | -187.837 | 0.319 | 0.319 | 187.837 | 0.319 | 0.319 |
TABLE 2 parameters and compensation results for load data
TABLE 3 No-load data Compensation results
| Bxx | Bxy | Bxz | Byx | Byy | Byz | Bzx | Bzy | Bzz |
| 0.0151 | 0.0151 | -0.0134 | -0.0134 | 0.0151 | 0.0151 | 0.0146 | 0.0091 | 0.0151 |
| 0.0125 | 0.0125 | -0.0136 | -0.0136 | 0.0125 | 0.0125 | 0.0144 | 0.0065 | 0.0125 |
| 0.0099 | 0.0099 | -0.0137 | -0.0137 | 0.0099 | 0.0099 | 0.0143 | 0.0039 | 0.0099 |
| 0.0073 | 0.0073 | -0.0138 | -0.0138 | 0.0073 | 0.0073 | 0.0142 | 0.0013 | 0.0073 |
| 0.0046 | 0.0046 | -0.0139 | -0.0139 | 0.0046 | 0.0046 | 0.0141 | -0.0014 | 0.0046 |
| 0.0019 | 0.0019 | -0.014 | -0.014 | 0.0019 | 0.0019 | 0.014 | -0.0041 | 0.0019 |
| -0.0008 | -0.0008 | -0.0142 | -0.0142 | -0.0008 | -0.0008 | 0.0138 | -0.0068 | -0.0008 |
| -0.0036 | -0.0036 | -0.0143 | -0.0143 | -0.0036 | -0.0036 | 0.0137 | -0.0096 | -0.0036 |
| -0.0064 | -0.0064 | -0.0144 | -0.0144 | -0.0064 | -0.0064 | 0.0136 | -0.0124 | -0.0064 |
| -0.0093 | -0.0093 | -0.0145 | -0.0145 | -0.0093 | -0.0093 | 0.0135 | -0.0153 | -0.0093 |
The present disclosure also provides the following product examples:
a full-tensor magnetic gradient data compensation optimization system, comprising:
a data determination module configured to determine nine component data of the full tensor magnetic gradient data participating in model parameter calculation and data compensation for the experimental data: b isxx、Bxy、Bxz、Byx、Byy、Byz、Bzx、Bzy、Bzz。
The optimization model building module is configured to build an objective function of the compensation optimization model according to the basic characteristics of passivity and non-rotation of the mathematical optimization model and the environmental magnetic field, and the basic compensation optimization model is built by combining constraint conditions;
and the data calculation module is configured to solve the compensation optimization model parameters according to the no-load data, calculate the compensated loaded data according to the compensation model and obtain the compensation result of the full tensor magnetic gradient data.
As a possible embodiment, an objective function of an optimization model and constraint conditions thereof are established for the passivity and the non-rotation basic characteristics of the full tensor magnetic gradient data.
As a possible embodiment, the optimal model parameters are solved by using multiple groups of no-load data, data compensation is carried out on the full tensor magnetic gradient data according to the model, and the compensation effect is verified.
A computer readable storage medium having stored therein a plurality of instructions adapted to be loaded by a processor of a terminal device and to perform the steps of a full-tensor magnetic gradient data compensation optimization method.
A terminal device comprising a processor and a computer readable storage medium, the processor being configured to implement instructions; the computer readable storage medium is used for storing a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the steps of compensation optimization of the full-tensor magnetic gradient data.
As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Although the present disclosure has been described with reference to specific embodiments, it should be understood that the scope of the present disclosure is not limited thereto, and those skilled in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the present disclosure.
Claims (9)
1. A full-tensor magnetic gradient data compensation optimization method is characterized by comprising the following steps: the method comprises the following steps:
aiming at each magnetic gradient component of full tensor magnetic gradient data, adopting different experimental data under various working conditions, introducing relaxation coefficients and decision variables, and establishing constraint conditions of a model;
establishing an objective function of a compensation optimization model according to the basic characteristics of passivity and non-rotation of the mathematical optimization model and the environmental magnetic field, and establishing a basic compensation optimization model by combining constraint conditions;
and solving parameters of the compensation optimization model according to the no-load data, and calculating compensated loaded data according to the compensation model to obtain a compensation result of the full tensor magnetic gradient data.
2. The full-tensor magnetic gradient data compensation optimization method as set forth in claim 1, wherein: let Bx、By、BzThe magnetic field vector B (B) represents the magnitude of the magnetic field in the x, y, and z directions in the magnetic sensor coordinate system, respectivelyx,By,Bz) (ii) a Then in the x-direction the magnetic gradient of the magnetic field vector B is: b isxx、Bxy、BxzIn the y direction, the magnetic gradients of the magnetic field vector B are: b isyx、Byy、ByzIn the z direction, the magnetic gradients of the magnetic field vector B are: b iszx、Bzy、Bzz。
3. The full-tensor magnetic gradient data compensation optimization method as set forth in claim 1, wherein: the specific process of establishing the constraint condition of the compensation optimization model comprises the following steps: under the condition of N groups of experimental data under M working conditions, nine magnetic gradient components B are establishedijThe compensation formula for (i, j ═ x, y, z) is:
wherein B isijmn、
Respectively representing the magnetic gradient data before and after compensation,
respectively represent working condition parameters of ImA positive compensation coefficient and a negative compensation coefficient,
representing positive and negative absolute compensation parameters of the magnetic gradient data, respectively.
4. The full-tensor magnetic gradient data compensation optimization method as set forth in claim 3, wherein: in order to avoid the system falling into a non-solution state, a relaxation parameter theta is introduced, namely the relaxation parameter theta is compensated through a positive term and a negative term related to the working condition and falls within a controllable range after being compensated with the positive term and the negative term in an absolute mode:
in order to solve each parameter of the compensation formula, the values of the parameters are all positive numbers, that is:
5. The full-tensor magnetic gradient data compensation optimization method as set forth in claim 1, wherein: the passivity of the ambient magnetic field is represented as: b isxx+Byy+Bzz0; the derotation of the ambient magnetic field is represented by: b isij＝Bji,(i,j＝x,y,z)。
6. The full-tensor magnetic gradient data compensation optimization method as set forth in claim 1, wherein: establishing a compensation optimization model:
according to experimental no-load data
Parameter values of the compensation parameters, thereby obtaining a compensation formula based on the compensation parameters
Data B to be compensatedijmn(i, j ═ x, y, z) to obtain a compensation result
7. A full-tensor magnetic gradient data compensation optimization system is characterized in that: the method comprises the following steps:
a data determination module configured to determine, for experimental data, a number of nine components of full tensor magnetic gradient data participating in model parameter calculation and data compensationAccording to the following steps: b isxx、Bxy、Bxz、Byx、Byy、Byz、Bzx、Bzy、Bzz。
The optimization model building module is configured to build an objective function of the compensation optimization model according to the basic characteristics of passivity and non-rotation of the mathematical optimization model and the environmental magnetic field, and the basic compensation optimization model is built by combining constraint conditions;
and the data calculation module is configured to solve the compensation optimization model parameters according to the no-load data, calculate the compensated loaded data according to the compensation model and obtain the compensation result of the full tensor magnetic gradient data.
8. A computer-readable storage medium characterized by: stored with instructions adapted to be loaded by a processor of a terminal device and to perform the steps of a method for full-tensor magnetic gradient data compensation optimization as claimed in any one of claims 1 to 6.
9. A terminal device is characterized in that: the system comprises a processor and a computer readable storage medium, wherein the processor is used for realizing instructions; a computer readable storage medium for storing a plurality of instructions adapted to be loaded by a processor and for performing the steps of compensation optimization of full-tensor magnetic gradient data as recited in any one of claims 1-6.
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