CN114325847A - Self-adaptive denoising method and platform for magnetic anomaly detection - Google Patents
Self-adaptive denoising method and platform for magnetic anomaly detection Download PDFInfo
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Abstract
The invention discloses a self-adaptive denoising method and a self-adaptive denoising platform for magnetic anomaly detection, which can denoise magnetic anomaly signals containing geomagnetic background noise according to different application scenes and improve the signal-to-noise ratio. The method comprises the following specific steps: 1) installing and adjusting instruments on site; 2) estimating the range of the field parameters according to the application scene; 3) determining parameters of a denoising algorithm according to the range of the field parameters; 4) starting a magnetic anomaly signal acquisition platform; 5) and obtaining the magnetic abnormal signal with high signal-to-noise ratio. The acquisition platform mainly comprises an upper computer, an acquisition device, a magnetic sensor and an external field power supply; instrument adjustments in the field include the orientation, level, and distance between the magnetic sensors. The method estimates the range of the motion speed of the magnetic target and the range of the nearest distance between the motion track and the magnetic sensor according to the field environment, then obtains the parameters of the denoising method according to the estimation range, and finally starts a magnetic anomaly signal acquisition platform to obtain the magnetic anomaly signal with high signal-to-noise ratio. The invention aims to provide a magnetic anomaly detection self-adaptive denoising method, which adjusts parameters of a denoising algorithm according to a use scene and improves the signal-to-noise ratio of an acquired signal.
Description
Technical Field
The invention relates to physical modeling, signal processing, target detection and weak signal denoising technologies, and belongs to the weak signal denoising technology.
Background
Magnetic anomaly detection is a passive detection method for detecting tiny changes of a magnetic field caused by a ferromagnetic target, and is suitable for most scenes. However, in practical applications, the magnetic anomaly signals are usually buried deeply in geomagnetic noise and are accompanied by platform interference noise and sensor intrinsic noise, so that the signal-to-noise ratio is very low. Therefore, before magnetic anomaly detection, weak magnetic anomaly signals generated by ferromagnetic targets need to be distinguished from various background noises, so that a weak signal denoising technology is very important for magnetic anomaly detection.
In recent years, due to the development of the magnetic sensor industry, the inherent noise of the sensor has been greatly reduced, and the platform interference noise can also be reduced through a calibration procedure, so that the geomagnetic environmental noise has become a main limiting factor of the detection range of magnetic anomaly detection. The main characteristics are as follows: 1) the magnetic anomaly signal magnetic field is much less noisy than the geomagnetic environment and decreases sharply with the third power of the distance, resulting in a limited detection range. 2) The ambient magnetic noise fluctuations have similarities with the target magnetic field characteristics, are typically a time-varying magnetic disturbance, have temporal coherence, and are distributed over the full frequency band of the magnetic anomaly signal.
In general, the magnetic anomaly signal denoising is used as a preprocessing process of magnetic anomaly detection, and the accuracy and the reliability of the magnetic anomaly detection are greatly improved. Therefore, preprocessing the magnetic anomaly signal deeply buried in the geomagnetic noise by using a weak signal denoising technique is indispensable in magnetic anomaly detection.
Disclosure of Invention
The invention aims to provide a self-adaptive denoising method and a self-adaptive denoising platform for magnetic anomaly detection, which can improve the signal-to-noise ratio of an original acquisition signal according to a scene fine adjustment algorithm and are used for subsequent magnetic anomaly detection.
In order to achieve the purpose, the invention provides the following technical scheme:
a self-adaptive denoising method and platform for magnetic anomaly detection specifically comprises the following steps:
step 1, installing and adjusting an instrument on site;
step 3, determining parameters of a denoising algorithm according to the range of the field parameters;
step 4, starting a magnetic anomaly signal acquisition platform;
and 5, obtaining the magnetic abnormal signal with high signal-to-noise ratio.
The instrument is installed and adjusted on site, the acquisition platform mainly comprises a magnetic anomaly signal acquisition platform consisting of an upper computer, an acquisition device, a magnetic sensor and an external field power supply, and the on-site adjustment of the instrument comprises the position, the position and the level of the magnetic sensor, the distance between the sensors and the like.
The field parameter range is estimated according to the detection scene, and the field parameter range comprises a range of estimating the motion speed of the magnetic target object and a range of estimating the nearest distance between the motion track of the magnetic target object and the magnetic sensor.
And determining parameters of a denoising algorithm according to the field parameter range, wherein the parameters comprise the range of the motion speed of the magnetic target object, the range of the nearest distance and the equivalent model of the magnetic dipole, and the frequency range of the magnetic abnormal signal is determined.
The starting of the magnetic anomaly signal acquisition platform comprises the steps of configuring field parameters in a magnetic anomaly acquisition program, enabling a heat engine of the acquisition platform to achieve a stable working state and the like.
The magnetic anomaly signal with high signal-to-noise ratio is obtained by removing the high-frequency component and the low-frequency component of the magnetic anomaly signal and leaving the signal component in the frequency band range determined by the self-adaptive denoising algorithm, so that the magnetic anomaly signal with high signal-to-noise ratio is obtained.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a schematic view of an exemplary collection platform of the method of the present invention.
Fig. 3 is a schematic diagram of an example improved wavelet packet algorithm of the method of the present invention.
Detailed Description
The following describes the technical solution of the present invention in detail by taking an improved wavelet packet algorithm and a singular value decomposition algorithm as an example of a denoising algorithm with reference to the accompanying drawings and the detailed embodiments.
As shown in fig. 1, the specific steps are as follows:
step 1, installing and adjusting an instrument on site;
step 3, determining parameters of a denoising algorithm according to the range of the field parameters;
step 4, starting a magnetic anomaly signal acquisition platform;
and 5, obtaining the magnetic abnormal signal with high signal-to-noise ratio.
The field installation and adjustment instrument comprises a magnetic anomaly signal acquisition platform which is built by using a three-axis fluxgate sensor Mag 690-FL100, a MCMAG30F multi-channel data acquisition device, an external field power supply, an upper computer and the like, and the adjustment instrument comprises the steps of adjusting the geographical position of the magnetic sensors, the distance between the magnetic sensors and ensuring the position level of the magnetic sensors, as shown in FIG. 2.
The field parameter range is estimated according to the detection scene, and the field parameter range comprises the range of estimating the motion speed of the magnetic target object, namely the slowest speed vminAnd the fastest speed vmaxThe CPA is judged according to the range of the closest distance between the motion track of the magnetic target object and the magnetic sensormin，CPAmax。
The method comprises the steps of substituting a speed range and a nearest distance range into an empirical formula of a frequency domain range of an estimated magnetic anomaly signal obtained by combining a magnetic dipole equivalent model and an actually measured signal, estimating the frequency domain range of the magnetic anomaly signal generated by a field magnetic target, inputting the frequency domain range into an improved wavelet packet algorithm, adaptively removing noise signals with the lowest frequency and the highest frequency to obtain the magnetic anomaly signal in the corresponding frequency domain range, and performing secondary denoising on the obtained magnetic anomaly signal by using a singular value decomposition algorithm to obtain the magnetic anomaly signal with high signal-to-noise ratio.
The improved wavelet packet algorithm formula is as follows:
compared with the conventional wavelet packet decomposition and single node reconstruction algorithm, the improved wavelet packet decomposition and single node reconstruction algorithm has the advantages that an operator C for removing the redundant frequency components after convolution with a filter H or H and an operator D for removing the redundant frequency components after convolution with a filter G or G are added, and the calculation formulas of the two operators are as follows:
let x (n) represent 2jThe sub-band wavelet packet coefficients of low frequencies on a scale,
then the calculation formula of operator C is
In the formula, NjIs shown in (2)jA data length of the scale; k is 0, 1, …, Nj-1；n＝0，1，…，Nj-1；
Is the output of operator C.
The calculation formula of operator D is
In the formula, NjIs shown in (2)jA data length of the scale; k is 0, 1, …, Nj-1；n＝0，1，…，Nj-1；
Is the output of operator D. The purpose of operators C and D is to remove the unwanted frequency components after convolution.
The operation flow of improving wavelet packet transformation is shown in fig. 3: operator C is used for removing the redundant frequency components after convolution with filter H or H, operator D is used for removing the redundant frequency components after convolution with filter G or G, x 2 represents isolated point sampling, and ↓2represents isolated point zero insertion, the result is subjected to Fourier transform after the signal is convolved with the wavelet filter once, then the spectrum value of the redundant frequency in the spectrum is set to zero, the spectrum after zero setting is subjected to inverse Fourier transform, and the obtained result replaces the result of convolution with the wavelet filter and continues to be decomposed or reconstructed.
The starting of the magnetic anomaly signal acquisition platform comprises the steps of configuring field parameters in a magnetic anomaly acquisition program, enabling a heat engine of the acquisition platform to achieve a stable working state and the like.
The magnetic anomaly signal with high signal-to-noise ratio is obtained by removing the high-frequency component and the low-frequency component of the magnetic anomaly signal and leaving the signal component in the frequency band range determined by the self-adaptive denoising algorithm, so that the magnetic anomaly signal with high signal-to-noise ratio is obtained.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (6)
1. A self-adaptive denoising method and platform for magnetic anomaly detection comprise the following steps:
step 1, installing and adjusting an instrument on site;
step 2, estimating the range of the field parameters according to the detection scene;
step 3, determining parameters of a denoising algorithm according to the range of the field parameters;
step 4, starting a magnetic anomaly signal acquisition platform;
and 5, obtaining the magnetic abnormal signal with high signal-to-noise ratio.
2. The adaptive denoising method and platform for magnetic anomaly detection according to claim 1, wherein: the instrument is installed and adjusted on site in the step 1, the acquisition platform mainly comprises a magnetic anomaly signal acquisition platform consisting of an upper computer, an acquisition device, a magnetic sensor and an external field power supply, and the on-site adjustment of the instrument comprises the azimuth, the position and the level of the magnetic sensor, the distance between the sensors and the like.
3. The adaptive denoising method and platform for magnetic anomaly detection according to claim 1, wherein: and 2, estimating the field parameter range according to the detection scene, wherein the field parameter range comprises a range of estimating the motion speed of the magnetic target object and a range of estimating the closest distance between the motion track of the magnetic target object and the magnetic sensor.
4. The adaptive denoising method and platform for magnetic anomaly detection according to claim 1, wherein: and 3, determining parameters of the denoising algorithm according to the field parameter range, wherein the parameters comprise a magnetic target object motion speed range, a magnetic target object closest distance range and a magnetic dipole equivalent model, and determining the frequency range of the magnetic abnormal signal.
5. The adaptive denoising method and platform for magnetic anomaly detection according to claim 1, wherein: and 4, starting the magnetic anomaly signal acquisition platform, configuring field parameters in a magnetic anomaly acquisition program, and enabling a heat engine of the acquisition platform to reach a stable working state and the like.
6. The adaptive denoising method and platform for magnetic anomaly detection according to claim 1, wherein: and 5, obtaining the magnetic abnormal signal with the high signal-to-noise ratio, wherein the step comprises removing the high-frequency component and the low-frequency component of the magnetic abnormal signal, and leaving the signal component in the frequency band range determined by the self-adaptive denoising algorithm, so that the magnetic abnormal signal with the high signal-to-noise ratio is obtained.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110599425A (en) * | 2019-09-17 | 2019-12-20 | 青岛海月辉科技有限公司 | Wavelet parameter selection method suitable for ACFM signal wavelet denoising |
| CN113359192A (en) * | 2021-06-04 | 2021-09-07 | 北京中科导控科技有限公司 | Weak magnetic anomaly target detection and positioning method |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110599425A (en) * | 2019-09-17 | 2019-12-20 | 青岛海月辉科技有限公司 | Wavelet parameter selection method suitable for ACFM signal wavelet denoising |
| CN113359192A (en) * | 2021-06-04 | 2021-09-07 | 北京中科导控科技有限公司 | Weak magnetic anomaly target detection and positioning method |
Non-Patent Citations (1)
| Title |
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| S. TSUNOMURA，T. TOKUMOTO: "Man-made electromagnetic noises causing difficulty in geomagnetic and geoelectric observations in city area", 《BIOMEDICINE & PHARMACOTHERAPY》 * |
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