Magnetic Anomaly Detection Research Articles

An adaptive weighted magnetic anomaly detection based on full magnetic gradient orthonormal basis function

An adaptive weighted magnetic anomaly detection based on full magnetic gradient orthonormal basis function

Self-Supervised Marine Noise Learning with Sparse Autoencoder Network for Generative Target Magnetic Anomaly Detection

As an effective physical field feature to perceive ferromagnetic targets, magnetic anomaly is widely used in covert marine surveillance tasks. However, its practical usability is affected by the complex marine magnetic noise interference, making robust magnetic anomaly detection (MAD) quite a challenging task. Recently, learning-based detectors have been widely studied for the discrimination of magnetic anomaly signal and achieve superior performance than traditional rule-based detectors. Nevertheless, learning-based detectors require abundant data for model parameter training, which are difficult to access in practical marine applications. In practice, target magnetic anomaly data are usually expensive to acquire, while rich marine magnetic noise data are readily available. Thus, there is an urgent need to develop effective models to learn discriminative features from the abundant marine magnetic noise data for newly appearing target anomaly detection. Motivated by this, in this paper we formulate MAD as a single-edge detection problem and develop a self-supervised marine noise learning approach for target anomaly classification. Specifically, a sparse autoencoder network is designed to model the marine noise and restore basis geomagnetic field from the collected noisy magnetic data. Subsequently, reconstruction error of the network is used as a statistical decision criterion to discriminate target magnetic anomaly from cluttered noise. Finally, we verify the effectiveness of the proposed approach on real sea trial data and compare it with seven state-of-the-art MAD methods on four numerical indexes. Experimental results indicate that it achieves a detection accuracy of 93.61% and has a running time of 21.06 s on the test dataset, showing superior MAD performance over its counterparts.

Dual-Vector Aeromagnetic Survey-Based Gradient Imaging and Detection of Target Magnetic Anomaly

Dual-Vector Aeromagnetic Survey-Based Gradient Imaging and Detection of Target Magnetic Anomaly

Improving Stability and Generalization of Magnetic Anomaly Detection Using Deep Convolutional Siamese Neural Networks

Improving Stability and Generalization of Magnetic Anomaly Detection Using Deep Convolutional Siamese Neural Networks

Integrated Low Electromagnetic Interference Design Method for Small, Fixed-Wing UAVs for Magnetic Anomaly Detection

Integrated Low Electromagnetic Interference Design Method for Small, Fixed-Wing UAVs for Magnetic Anomaly Detection

Airborne magnetic anomaly detection based on Bi-stable stochastic resonance system

In order to address the problem with low signal-to-noise ratio (SNR) detection for airborne magnetic anomaly detection (MAD), an adaptive detection method based on bi-stable stochastic resonance (SR) system is proposed. The method adopts a series structure, S-SR, effectively improving detection speed. Allowing for the difficulty in parameter selection for the SR system, this method can achieve the adaptive selection of system parameters. On this basis, the effect of different sampling frequencies in the Runge–Kutta method on the numerical model of the SR system is investigated. Finally, the simulation and experiment analysis of MAD methods are performed to draw a comparison between the S-SR, orthogonal basis functions (OBF) decomposition, and high-order crossing (HOC) methods. According to the simulation and experiment results, the S-SR method outperforms the OBF and HOC methods in the outcome and range of detection under the same conditions.

Structural Design and Parameter Optimization of Magnetic Gradient Tensor Measurement System.

Magnetic anomaly detection (MAD) technology based on the magnetic gradient tensor (MGT) has broad application prospects in fields such as unexploded ordnance detection and mineral exploration. The difference approximation method currently employed in the MGT measurement system introduces measurement errors. Designing reasonable geometric structures and configuring optimal structural parameters can effectively reduce measurement errors. Based on research into differential MGT measurement, this paper proposes three simplified planar MGT measurement structures and provides the differential measurement matrix. The factors that affect the design of the baseline distance of the MGT measurement system are also theoretically analyzed. Then, using the magnetic dipole model, the error analysis of the MGT measurement structures is carried out. The results demonstrate that the planar cross-shaped structure is optimal, with the smallest measurement error, only 3.15 × 10-10 T/m. Furthermore, employing the control variable method, the impact of sensor resolution constraints, noise level, target magnetic moment, and detection distance on the design of the optimal baseline distance of the MGT measurement system is simulated and verified. The results indicate that the smaller the target magnetic moment, the farther the detection distance, the lower the magnetometer resolution, the greater the noise, and the greater the baseline distance required. These conclusions provide reference and guidance for the construction of the MGT measurement system based on triaxial magnetometers.

Determinants of Maximum Magnetic Anomaly Detection Distance.

The maximum detection distance is usually the primary concern of magnetic anomaly detection (MAD). Intuition tells us that larger object size, stronger magnetization and finer measurement resolution guarantee a further detectable distance. However, the quantitative relationship between detection distance and the above determinants is seldom studied. In this work, unmanned aerial vehicle-based MAD field experiments are conducted on cargo vessels and NdFeB magnets as typical magnetic objects to give a set of visualized magnetic field flux density images. Isometric finite element models are established, calibrated and analyzed according to the experiment configuration. A maximum detectable distance map as a function of target size and measurement resolution is then obtained from parametric sweeping on an experimentally calibrated finite element analysis model. We find that the logarithm of detectable distance is positively proportional to the logarithm of object size while negatively proportional to the logarithm of resolution, within the ranges of 1 m~500 m and 1 pT~1 μT, respectively. A three-parameter empirical formula (namely distance-size-resolution logarithmic relationship) is firstly developed to determine the most economic sensor configuration for a given detection task, to estimate the maximum detection distance for a given magnetic sensor and object, or to evaluate minimum detectable object size at a given magnetic anomaly detection scenario.

Adaptive Magnetic Disturbance Suppression of a Shaking Buoy and Magnetic Anomaly Detection

Adaptive Magnetic Disturbance Suppression of a Shaking Buoy and Magnetic Anomaly Detection

Investigation into flow-induced internal corrosion direct assessment in small-diameter dry gas fluctuating pipeline

Small-diameter undulating natural gas pipelines with low gas transmission capacity do not have internal inspection conditions to grasp the internal corrosion status of the pipeline. This study focuses on a small-diameter undulating dry natural gas gathering and transportation pipeline, the internal corrosion mechanism of the pipeline is studied through corrosion products, and the specific location of the internal corrosion sensitive pipe segment is determined based on multiphase flow simulation and PMDT(pipeline non-contact magnetic anomaly detection); the excavation inspection is ultimately used to evaluate the internal status of the pipeline. The results demonstrate that the internal corrosion mechanism of ZL (ZI Lai) pipeline is O2 and CO2 synergistic corrosion in which O2 plays a dominant role, while the increasing flow rate will aggravate corrosion; The maximum general corrosion rate and critical inclination along the pipeline are 0.0218 mm/a and 18.4°, respectively. Furthermore, twelve corrosion sensitive segments have been identified, including two II-level and six III-level magnetic anomaly segments. The internal corrosion defects are localized at the 3–9o’clock position of the pipeline, with a maximum general corrosion rate not exceeding 0.0228 mm/a. Furthermore, the pipeline failure pressure of 12.27 MPa surpasses the maximum allowable working pressure of 8.84 MPa, ensuring its continued operational safety. The corrosion protection measures of reducing the water content of natural gas, adding vapor corrosion inhibitor and regularly detecting magnetic anomaly pipeline are developed.

Ferromagnetic Target Detection in the Ocean Using Drone-based Magnetic Anomaly Detection

Magnetic anomaly signals from the ferromagnetic targets such as ships in the sea are measured by drone-based magnetic anomaly detection. A quantum magnetometer is suspended from the drone by 4 strings. Flight altitude and speed of drone are 100 m and 5 m/s, respectively. We obtain magnetic anomaly signals of few nT from the ships clearly. We analyze the signal characteristics by the ferromagnetic target through simulation using COMSOL multiphysics.

3D Anisotropic Magnetoresistance sensor for steel health monitoring

Electromagnetic sensors are widely used for safety and maintenance inspection purposes of ferromagnetic materials used in aerospace, construction and transport industry. In this paper, a portable device based on an Anisotropic Magnetoresistance (AMR) sensor for steel health monitoring is presented. The principle of operation of this device is the detection of magnetic anomalies of ferromagnetic materials, such as steel, due to stresses, corrosion, or other kind of defections. For this purpose, a cost-effective, low-consumption and high-sensitivity device has been developed, consisting of a sensor, a magnetic yoke and a microcontroller. The AMR sensor, is placed above the material under test, giving the ability of contactless measurement of the magnetic flux density on 3 axes. Additionally, a yoke, consisting of two iron poles and a permanent magnet between them, is used in this implementation, in order to create a closed magnetic circuit with the steel sample, enhancing the measurements. Then, a microcontroller with the appropriate electronic components receives the acquired data, enabling their real-time analysis and visualization of the measurements through a personal computer, smartphone, etc. Moreover, a 3D-printed enclosure has been designed and constructed, which both protects the sensor and maintains the desired position and alignment with the samples. The device can be portable due to an integrated Li-Po battery and its Wi-Fi capabilities. It can be used to determine the location of the defective spots or zones of the material under inspection, facilitating its treatment through rehabilitation methods that can be applied on the affected area. Both single-point and scanning mode monitoring are possible, making it suitable for many testing scenarios. Furthermore, its ease of use and portability render it accessible to a wide range of users, and appropriate for on-site measurements. The initial measurements using the device on test samples, demonstrate its reliability and accuracy in steel health monitoring.

Underwater target correlation detection and location method based on normalized magnetic moment

For the dynamic magnetic detection platform represented by airborne magnetic anomaly detection, the magnetic field measured by the vector magnetometer is easily affected by the rapid attitude change. Therefore, traditional anti-submarine aircraft are equipped with scalar magnetometers that are less affected by changes in the carrier attitude. However, the scalar magnetometer can only measure the total magnetic field signal, and its measurement information is single. It contains a time-varying environmental magnetic field signal, including the geomagnetic field, which is difficult to effectively reject. This brings great difficulties to the single real-time location of a magnetic anomaly target. This paper proposes a target correlation detection method based on the normalized magnetic moment that can effectively solve the above problems, considering the rotational invariance of magnetic anomaly measurements for dynamic magnetic exploration platforms and the efficient detection methods required for the single real-time positioning of targets. Correlation detection based on the normalized magnetic moment characteristic function and characteristic time search based on the correlation detection are included. Simulation and experimental results verify the single-point real-time positioning ability of the improved detection method within an effective operating distance. This method has strong usability for actual magnetic anomaly detection of underwater targets.

Physics-Enhanced TinyML for Real- Time Detection of Ground Magnetic Anomalies

Physics-Enhanced TinyML for Real- Time Detection of Ground Magnetic Anomalies

Magnetic anomaly detection of moving objects

Magnetometry is commonly used to detect and locate stationary as well as moving objects made of magnetic materials. The problem consists of detecting and determining any anomaly in the geomagnetic field generated by the presence and/or motion of a magnetic object, with the latter presenting a more challenging case. The data collected by a sensor or an array of sensors are treated by common signal and data processing techniques or by the objective base function (OBF) optimization methods which is the method employed in this work. A novel fluxgate sensor developed by the Sensors Laboratory is used to collect magnetic field data in an appropriately designed setup in the laboratory. The data are generated by a weakly magnetized object moving at a constant velocity and various distances from the sensor. The OBF method is used to construct the energy detection criterion. Results show that the energy value decreases at longer distances but the object is clearly detected even when the signal is buried in background noise. The performance of the overall system is promising for detecting and tracking moving objects in adverse environments such as marine traffic.

Magnetic Anomaly Detection Method Based on Multifeatures and Support Vector Machine

Magnetic Anomaly Detection Method Based on Multifeatures and Support Vector Machine

Magnetic Anomaly Detection Based on Attention-Bi-LSTM Network

Magnetic Anomaly Detection Based on Attention-Bi-LSTM Network

Development of a high-sensitivity orthogonal fluxgate sensor

Fluxgate magnetometers are one of the most common magnetometers to use as they offer a reliable solution for magnetic anomaly detection. These magnetometers are used in room temperature, exhibiting high sensitivity, making them ideal for several applications. In this paper, a new fluxgate magnetometer design is presented, that is based on a sinusoidal signal for the excitation of the magnetic core and offers improved sensitivity and low power consumption. The developed device is able to operate as a standalone fluxgate sensor based on the orthogonal topology, consisting of one sensing coil, an amorphous magnetic wire core, as well as a microcontroller along with the required electronics for its operation and signal process.

A stochastic resonance etection algorithm based on orthonormalized basis function for magnetic anomaly detection.

To address the problem that the performance of the detector in airborne magnetic anomaly detection (MAD) is terrible, a stochastic resonance (SR) detection algorithm based on orthonormalized basis function (OBF-SR) is proposed for MAD under low signal-to-noise ratio conditions. The signal contaminated by noise is first preprocessed by the OBF method, where the sum of the three components in the OBF space is selected as the SR system input. Then, a parallel SR system with different initial states is designed to detect the signal. Finally, the simulation analysis of MAD methods is performed to draw a comparison between the OBF-SR method, the typical SR method, and the OBF method. The results show that the OBF-SR method outperforms the SR and OBF methods in the detection probability and detection range under the same conditions.

Magnetic Anomaly Detection Based on a Compound Tri-Stable Stochastic Resonance System.

In the case of strong background noise, a tri-stable stochastic resonance model has higher noise utilization than a bi-stable stochastic resonance (BSR) model for weak signal detection. However, the problem of severe system parameter coupling in a conventional tri-stable stochastic resonance model leads to difficulty in potential function regulation. In this paper, a new compound tri-stable stochastic resonance (CTSR) model is proposed to address this problem by combining a Gaussian Potential model and the mixed bi-stable model. The weak magnetic anomaly signal detection system consists of the CTSR system and judgment system based on statistical analysis. The system parameters are adjusted by using a quantum genetic algorithm (QGA) to optimize the output signal-to-noise ratio (SNR). The experimental results show that the CTSR system performs better than the traditional tri-stable stochastic resonance (TTSR) system and BSR system. When the input SNR is -8 dB, the detection probability of the CTSR system approaches 80%. Moreover, this detection system not only detects the magnetic anomaly signal but also retains information on the relative motion (heading) of the ferromagnetic target and the magnetic detection device.