CN110989005B - Weak magnetic anomaly self-adaptive real-time detection method based on scalar magnetometer array - Google Patents

Weak magnetic anomaly self-adaptive real-time detection method based on scalar magnetometer array Download PDF

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CN110989005B
CN110989005B CN201911336725.7A CN201911336725A CN110989005B CN 110989005 B CN110989005 B CN 110989005B CN 201911336725 A CN201911336725 A CN 201911336725A CN 110989005 B CN110989005 B CN 110989005B
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magnetic anomaly
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CN110989005A (en
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樊黎明
赵维娜
王惠刚
刘星
刘建国
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Qingdao Research Institute Of Northwest Polytechnic University
Northwestern Polytechnical University
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    • G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
    • G01V3/08—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
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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 invention discloses a weak magnetic anomaly self-adaptive real-time detection method based on a scalar magnetometer array, which is used for solving the technical problem of low detection probability of the existing magnetic anomaly detection method. The technical scheme is that acquired magnetic field signals are decomposed into a plurality of Intrinsic Mode Functions (IMFs) through empirical mode decomposition, and the IMFs are reconstructed into two parts according to the properties of the IMFs: magnetic noise and magnetic anomaly signals. Due to the modal aliasing problem of empirical mode decomposition, the reconstructed magnetic anomaly signal contains a portion of the noise. And updating the probability density function of the noise in real time by using the reconstructed magnetic noise. Based on the updated probability density function, the corresponding geomagnetic entropy is calculated by using a moving window. And when the local magnetic entropy is smaller than the set threshold value, the detection of the magnetic anomaly is realized. The invention improves the probability of magnetic anomaly detection and improves the target detection capability under low signal-to-noise ratio.

Description

Weak magnetic anomaly self-adaptive real-time detection method based on scalar magnetometer array
Technical Field
The invention relates to a magnetic anomaly detection method, in particular to a weak magnetic anomaly self-adaptive real-time detection method based on a scalar magnetometer array.
Background
The document "Magnetic and detection using entropy filter, Measurement science and technology,2008, Vol19(4), 045205" discloses a Magnetic anomaly detection method based on geomagnetic entropy. The method is a magnetic anomaly detection method constructed based on a mechanism that magnetic noise mode changes are caused by magnetic anomalies generated by a magnetic target. The method takes magnetic field background noise as a stable random process, takes a probability density function as a prior condition, and takes geomagnetic entropy as a criterion for magnetic anomaly detection. In the detection process, the method has low algorithm complexity and simple calculation process, and can meet the requirement of real-time detection. In the method described in the document, when the environmental condition changes or the detection is based on the moving platform, the probability density function of the background magnetic noise as the prior condition is used as the prior condition, the acquisition of the probability density function of the background magnetic noise as the prior condition becomes difficult, and the detection performance of the background magnetic noise is affected due to inaccuracy of the probability density function, so that the detection probability of the method is reduced.
Disclosure of Invention
In order to overcome the defect of low detection probability of the existing magnetic anomaly detection method, the invention provides a weak magnetic anomaly self-adaptive real-time detection method based on a scalar magnetometer array. The method decomposes an acquired magnetic field signal into a plurality of Intrinsic Mode Functions (IMFs) through empirical mode decomposition, and reconstructs the IMFs into two parts according to the properties of the IMFs: magnetic noise and magnetic anomaly signals. Due to the modal aliasing problem of empirical mode decomposition, the reconstructed magnetic anomaly signal contains a portion of the noise. And updating the probability density function of the noise in real time by using the reconstructed magnetic noise. Based on the updated probability density function, the corresponding geomagnetic entropy is calculated by using a moving window. And when the local magnetic entropy is smaller than the set threshold value, the detection of the magnetic anomaly is realized. The invention improves the probability of magnetic anomaly detection and improves the target detection capability under low signal-to-noise ratio.
The technical scheme adopted by the invention for solving the technical problems is as follows: a weak magnetic anomaly self-adaptive real-time detection method based on a scalar magnetometer array is characterized by comprising the following steps:
(a) carrying out self-adaptive decomposition on the acquired magnetic field difference value by using empirical mode decomposition to obtain a plurality of inherent mode functions and a residual error term
Figure BDA0002331137880000011
In the formula, ci(t) denotes an i-th natural mode function component, and r (t) denotes a residual component. d represents the number of layers of empirical mode decomposition;
(b) according to the property of the inherent mode function, respectively reconstructing magnetic noise and magnetic abnormal signals:
Figure BDA0002331137880000021
wherein n (t) represents reconstructed magnetic noise, and s (t) represents reconstructed magnetic anomaly signal;
(c) statistically analyzing and updating the probability density function p (-) of the reconstructed magnetic noise N (t);
(d) setting the length L of a sliding window, and calculating the geomagnetic entropy of the reconstructed magnetic anomaly signal S (t) according to the updated noise probability density function:
Figure BDA0002331137880000022
in the formula, i represents the ith time.
The invention has the beneficial effects that: the method decomposes an acquired magnetic field signal into a plurality of Intrinsic Mode Functions (IMFs) through empirical mode decomposition, and reconstructs the IMFs into two parts according to the properties of the IMFs: magnetic noise and magnetic anomaly signals. Due to the modal aliasing problem of empirical mode decomposition, the reconstructed magnetic anomaly signal contains a portion of the noise. And updating the probability density function of the noise in real time by using the reconstructed magnetic noise. Based on the updated probability density function, the corresponding geomagnetic entropy is calculated by using a moving window. And when the local magnetic entropy is smaller than the set threshold value, the detection of the magnetic anomaly is realized. According to the invention, the acquired magnetic field difference value is subjected to adaptive decomposition by adopting empirical mode decomposition to obtain a plurality of intrinsic mode functions and a residual error item, a magnetic noise part is reconstructed by utilizing the intrinsic mode functions obtained by decomposition, and the approximate probability density function is obtained by carrying out statistical analysis on the constructed magnetic noise, so that the key problem that the magnetic noise probability density function is difficult to obtain in the magnetic anomaly detection process due to the change of external environmental conditions or based on a motion platform is solved, the probability of magnetic anomaly detection is improved, and the target detection capability under the condition of low signal to noise ratio is improved.
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
Drawings
FIG. 1 is a flow chart of the weak magnetic anomaly adaptive real-time detection method based on the scalar magnetometer array.
FIG. 2 is a schematic diagram of object detection of a one-dimensional magnetometer array according to the method of the present invention.
FIG. 3 is a magnetic anomaly generated by a distant target.
FIG. 4 is a diagram showing the results of weak magnetic anomaly detection by the method of the present invention.
Detailed Description
Reference is made to fig. 1-4. The weak magnetic anomaly self-adaptive real-time detection method based on the scalar magnetometer array specifically comprises the following steps:
1. and (5) collecting the magnetic field difference value.
Two magnetometers are adopted to form a one-dimensional scalar magnetic sensor array. Sensor spacing in array BxThe magnetic sensor used a CS-L optical pump magnetometer with a sensitivity of 0.6pT at 1 m. The magnetic target moves in a horizontal plane and has a magnetic moment of (0,0,25) A.m2. The target reaches the end position (180,30, -1) m in a uniform motion manner from the starting position (-180,30, -1) m.
2. And (5) reconstructing magnetic noise and magnetic abnormal signals.
Carrying out self-adaptive decomposition on the acquired magnetic field difference value by using empirical mode decomposition, wherein the decomposition layer number d is 8, and obtaining 8 inherent mode functions and 1 residual error term:
Figure BDA0002331137880000031
in the formula, ci(t) denotes an i-th natural mode function component, and r (t) denotes a residual component. d represents the number of layers of empirical mode decomposition.
According to the property of the natural modal function, the first 4 terms of the natural modal function are used for reconstructing magnetic noise, and the last 4 terms are used for reconstructing magnetic anomaly signals:
Figure BDA0002331137880000032
wherein n (t) represents reconstructed magnetic noise, and s (t) represents reconstructed magnetic anomaly signal;
3. the probability density function of the magnetic noise is updated.
Statistically analyzing and updating the probability density function p (-) of the reconstructed magnetic noise N (t);
4. and (4) computing the geomagnetic entropy.
Setting the length L of a sliding window, and calculating the geomagnetic entropy of the reconstructed magnetic anomaly signal S (t) according to the updated noise probability density function p (·):
Figure BDA0002331137880000041
wherein i represents the ith time;
5. and detecting the magnetic anomaly.
And judging whether the magnetic anomaly is detected or not through the threshold value T. When I (S)i) < T, a magnetic anomaly exists, i.e., a magnetic target exists; when I (S)i) T or more, no magnetic anomaly, i.e. no magnetic target.

Claims (1)

1. A weak magnetic anomaly self-adaptive real-time detection method based on a scalar magnetometer array is characterized by comprising the following steps:
(a) carrying out self-adaptive decomposition on the acquired magnetic field difference value by using empirical mode decomposition to obtain a plurality of inherent mode functions and a residual error term
Figure FDA0002331137870000011
In the formula, ci(t) denotes the ith natural mode function component, and r (t) denotes the residual component; d represents the number of layers of empirical mode decomposition;
(b) according to the property of the inherent mode function, respectively reconstructing magnetic noise and magnetic abnormal signals:
Figure FDA0002331137870000012
wherein n (t) represents reconstructed magnetic noise, and s (t) represents reconstructed magnetic anomaly signal;
(c) statistically analyzing and updating the probability density function p (-) of the reconstructed magnetic noise N (t);
(d) setting the length L of a sliding window, and calculating the geomagnetic entropy of the reconstructed magnetic anomaly signal S (t) according to the updated noise probability density function:
Figure FDA0002331137870000013
in the formula, i represents the ith time.
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