Magnetic anomaly detection on wavelet packet and support vector machine

With the problem of magnetic anomaly signal in low SNR, we using empirical mode decomposition (EMD) algorithm and Savitzky-Golay filter to solve weak magnetic anomaly signal in noisy environment detection problem. With the EMD, the weak magnetic anomaly signal can be decomposed into different numbers of sub-signals called intrinsic mode functions (IMFs). The IMFs include signals different frequency component from high to low. Recover the signal with IMFs which correlation coefficient is low with noise signal and smooth the signal with Savitzky-Golay filter. The proposed method is verified by simulation on computer. The present experimental results show that this method is effective to detect the weak magnetic anomaly and recover the original signal.

Magnetic anomaly detection (MAD) has been widely used for detecting the magnetic targets in many areas. In low signal-to-noise ratio (SNR) situations, the magnetic anomaly is usually buried in the magnetic noise, which leads to a decrease in the detection performance of traditional MAD methods. In order to improve the detection probability of MAD in low SNR, we propose an adaptive method of MAD using support vector machine (SVM). First, we design a detection model using SVM with radial basis function (RBF) kernel function. Meanwhile, we analyze the magnetic anomaly signal and determined the orthonormal basis function (OBF) energy and magnetic entropy as the features of the magnetic anomaly, which are as the input of the model. Then, the model is trained based on the data set with the magnetic signals. Finally, the trained model is applied to the raw magnetic signal to detect the anomaly. Compared to the traditional methods, the proposed method has a better performance and adaptive ability. The proposed method is validated by the real magnetic noise. The results show that the proposed method can reduce the case of false alarm and improve the detection performance in low SNR.

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.

Magnetic anomaly pole transformation converts magnetic field data into an equivalent response at the true magnetic pole, eliminating shifts and distortions, simplifying the interpretation of subsurface magnetic bodies, and improving data interpretation and inversion accuracy. However, the main challenge in magnetic anomaly pole transformation lies in the nonlinear nature of the signals, making traditional methods difficult to apply. The interaction between the shape, depth, and magnetic inclination of magnetic bodies, especially in high- and low-latitude regions, can distort the transformed signal, leading to unclear causal relationships. To address this, this article proposes a deep learning-based approach that automatically extracts high-dimensional features and establishes nonlinear mappings to enhance the correlation between magnetic anomaly signals and geological structures. Deep learning does not require explicit physical models and, through training with large datasets, demonstrates stronger robustness and accuracy, especially in areas where traditional methods fail. The proposed method is validated using both synthetic and measured data. Synthetic data simulates magnetic bodies of various shapes, depths, and magnetic inclinations, confirming the method’s stability and accuracy in handling complex nonlinear signals. The measured data evaluates its pole transformation advantages in typical ore deposit regions. The results indicate that the deep learning model significantly enhances the accuracy of pole transformation, particularly in areas with complex magnetic anomaly signals, effectively preventing signal distortion and demonstrating exceptional generalization capabilities.

Extraction of the magnetic anomaly signal is one of the difficulties in the magnetic anomaly detection as the weak features extracted are easily disturbed by strong background noise. To address this problem, a sparse feature extraction method based on the tunable Q-factor wavelet transform and overlapping group shrinkage is developed in this paper. Compared with the traditional wavelet transform, the proposed method has excellent characteristics, which can flexibly tune the Q-factor according to the oscillation characteristics of the useful features. In this way, the sparsity of extracted features can be induced more effectively. In addition, the non-convex overlapping group shrinkage can effectively extract weak features from signals with group property, enhancing the extraction accuracy of features. The practical experiment verifies the effectiveness of the proposed method in the magnetic anomaly detection.

Magnetic anomaly detection is a passive and effective approach for detecting magnetic targets. During detection using one scalar magnetometer, temporal magnetic variations will affect the detection performance. In order to reduce the influence of magnetic variations on detection, we focus on the analysis of magnetic gradient signals and obtain four orthonormal basis functions (OBFs) of gradient signal. Based on the set of OBFs, we propose an improved method using the magnetic gradient signal. Meanwhile, we can also obtain the bearing of the target relative to the survey line in the method. The proposed method is validated by simulation and real-world magnetic noise. The results show that the proposed method has a good detection performance with low signal to noise ratio. Therefore, the proposed method can improve the detection probability of weak magnetic anomalies and also determine the bearing of targets relative to the survey line.

In certain scenarios, such as detection of unexploded ordnance (UXO), submarines, and intruders, magnetic anomaly detection (MAD) is an effective method due to the magnetic field advantages of small operating power, strong penetrability, and strong anti-interference ability. However, variability in the background geomagnetic field and low signal-to-noise ratio (SNR) in MAD cannot be avoided. In this article, we propose a MAD method employing an improved orthogonal basis function (OBF) for the magnetic tensor contraction decomposition. The orthogonal basis decomposition approach makes the best use of modeling the MAD output signal, whereas the magnetic tensor contraction aid in avoiding the background field problem. Furthermore, by increasing the baseline length of the magnetic gradiometer, the SNR can also be improved in some manner. A dual magnetic gradiometer framework is introduced to fulfill the demand of the magnetic anomaly localization. From our simulation results, the proposed method improves the SNR of the MAD raw signal and is capable of detecting and locating the magnetic anomaly. Moreover, field test has been carried out to evaluate the performance of the proposed method.

To solve the problem that the performance of the detector in traditional aeromagnetic anomaly detection is bad, a multiscale wavelet energy accumulator (MWEA) for energy accumulation of coarse resolution approximation and detailed edge information of signal is proposed for aeromagnetic anomaly signal detection under low signal-to-noise ratio (SNR) conditions. Based on the wavelet transform theory, the magnetic anomaly signal denoising is performed firstly and the purified magnetic anomaly signal is decomposed by Mallat flow-type wavelet decomposition (MFWD). Then the energy of coarse resolution approximation and detailed information decomposed at the different scales is accumulated by the designed MWEA. Finally the energy signal is determined by a given statistical detection threshold. The detection performance of the multiscale wavelet energy accumulation detector (MWEAD) is analyzed by using the measurement magnetic noise data and the magnetic dipole target signal. The results show that the detection probability of the MWEAD is still superior to the traditional detection method in the case of low SNR.

Quantitative online detection of microcracks in long-distance oil and gas pipelines is an international problem, and the effective detection method is still lacking. In this paper, a mathematical model of non-uniform distribution of crack magnetic charges is established based on the stress distribution laws of pipeline cracks under internal pressure. The weak magnetic signal characteristics of pipeline cracks with different sizes are analyzed. The internal pressure increasing factor of weak magnetic signals are extracted to analyze the corresponding relationship between crack size and weak magnetic signals. The experimental study of the X70 pipeline is carried out. The results show that the axial component of the weak magnetic signal at the crack has a maximum value near the tip, and a minimum value appears in the middle of the crack. The internal pressure increasing factor is introduced to quantify the weak magnetic signal, the crack is in a safe state (not expanding) when the internal pressure increasing factor is positive, the weak magnetic signal has a linear relationship with the crack size. However, the crack is in a dangerous state when the internal pressure increasing factor is negative, and the pipeline crack will expand as the internal pressure increases.

The recognition of submarine cable magnetic anomaly (SCMA) signals is a challenging task in magnetic signal data processing. In this study, a multi-task convolutional neural network (MTCNN) model is proposed to simultaneously recognize abnormal signals and locate abnormal regions. The residual block is added to the shared feature backbone to improve the ability of the network to extract high-level features and maintain the gradient stability of the model in the training process. The long short-term memory (LSTM) block is added to the classification branch task to learn the internal relationship of the magnetic anomaly time series, so as to improve the network’s ability to recognize magnetic anomalies. Our proposed model can accurately recognize the SCMA signals collected in the East China Sea and the South China Sea. The classification accuracy and the ability to locate the abnormal regions are close to the manual labeling of human analysts. The newly developed model can help analysts reduce the probability of missing and misjudging submarine cable magnetic anomalies, improve the efficiency and accuracy of interpretation, and could even be deployed to an unmanned platform to realize the automatic detection of SCMAs.

As sensitive magnetic sensors, superconducting quantum interference devices (SQUIDs) can be applied to measure weak magnetic field signals. Biomagnetism, measurement of magnetic signals from human body, especially at low frequency range below few kHz, is one of the important applications of SQUIDs, and biomagnetic technology can be applied for the functional study of human brain and heart. KRISS has been developing both low-noise SQUIDs and biomagnetic measurement technology. The SQUIDs are low-temperature Nb double relaxation oscillation SQUID, and high-temperature YBCO SQUID. We are applying SQUID technology for measuring magnetoencephalography signals and magnetocardiography signals. Using the high sensitivity of SQUID, we are developing nuclear magnetic resonance technology for imaging of brain anatomy at very low magnetic field, and direct imaging of brain electrical activity.

For searching and detecting near-field unknown ferromagnetic targets, four automatic search algorithms are proposed based on magnetic anomaly information from any position on planes or in space. Firstly, gradient search algorithms and enhanced gradient search algorithms are deduced using magnetic modulus anomaly information and magnetic vector anomaly information. In each algorithm, there are plane search forms and space search forms considering different practical search situations. Then the magnetic anomaly space data of typical magnetic source of oblique magnetization are forwardly simulated by ANSYS MAXWELL software. The plane distributions of some variables are numerically computed and the search destinations of different algorithms are predicted. Four automatic search algorithms are applied to simulate search paths on three characteristic orthogonal planes and in whole solution space. The factor affecting the performance of algorithms is analyzed. Features of each algorithm in different conditions are analyzed and suitable applications are discussed and verified by the experiment. The results show that proposed search algorithms require few prior information and have real-time performance for searching and tracking magnetic anomaly target.

YBCO thick film fr-SQUID for detection of weak magnetic field signals

It is known to all that the wide monitoring of geomagnetic anomalies requires geomagnetic detection equipment to be of low cost, low power, and high stability. On the basis of the weak magnetic signal detection technology, a weak magnetic signal acquisition system with low-cost magnetic data acquisition, data processing, and data analysis is established by using a three-dimensional SEN-R65 magnetic sensor, magnetic signal acquisition chip PNI 11096, and P89C668 chips. Firstly, the hardware of a low-cost geomagnetic data acquisition system is designed, and the corresponding data processing and analysis programs are written. Based on the designed hardware circuit and data processing for functions such as acquisition, transmission, storage, and real-time display of geomagnetic signals, it is found that the developed instrument meets the expectations of the low-cost geomagnetic data acquisition system designed in this paper, indicating that the system is applicable.

The northernmost spreading centre of the world, the Gakkel Ridge, is also an end‐member in terms of global spreading velocities. Models show that full spreading rates vary between 1.3 and 0.63 mm yr−1 along the almost 1800 km long ridge system in the Central Arctic Ocean. The western part of the ridge was investigated in great detail by a two‐ship expedition in summer 2001. The complete data sets and the modelling of the seismic refraction and aeromagnetic experiments gathered during this expedition are shown in this study. The magnetic signals along the dense (2 km spacing) aeromagnetic flight lines acquired at the same time show a good correlation between high amplitudes and a shallowing of the rift valley and the presence of large volcanic constructions at the rift shoulders. The magnetic anomalies rapidly fade out east and west of these centres of focused magmatism. This might indicate that the basaltic layer producing the magnetic anomaly thins away from the volcanic centres. A continuous magnetic anomaly is observed along the rift valley west of 3°30′E, consistent with increasing and more robust magmatism. The crustal thickness along the Gakkel Ridge varies greatly. Beneath some of the centres of focused magmatism, the oceanic crust thickens up to 3.5 km. In the amagmatic segments in between the crust thins to 1.4–2.9 km. This observation is also valid for the Western Volcanic Zone west of 3°30′E, where despite the stronger magnetic anomaly the crust does not significantly thicken. The strength of the magnetic anomaly along the rift valley is thus not a reliable indicator of crustal thickness beneath the Gakkel Ridge. The data show that the crustal thickness does not change dramatically across 3°30′E. Only the occurrence of a large elongate volcanic ridge significantly influences this parameter. More frequent volcanic eruptions along such ridges are most likely responsible for the basalts found in the westernmost part of the Gakkel Ridge. In the non‐transform segments some seismic stations indicate that mantle rocks are exposed at the seafloor, with no indication of the presence of a basaltic cover or normal oceanic crust. Both the seismic and magnetic data support models in which the uppermost basaltic cover is responsible for the magnetic anomaly in the rift valley.