In the past two decades, unmanned aerial vehicles (UAVs) have been used in many scientific research fields for various applications. In particular, the use of UAVs for magnetic surveys has become a hot spot and is expected to be actively applied in the future. A considerable amount of literature has been published on the use of UAVs for magnetic surveys, however, how to choose the platform and reduce the interference of UAV to the collected data have not been discussed systematically. There are two primary aims of this study: (1) To ascertain the basis of UAV platform selection and (2) to investigate the characteristics and suppression methods of UAV magnetic interference. Systematic reviews were performed to summarize the results of 70 academic studies (from 2005 to 2021) and outline the research tendencies for applying UAVs in magnetic surveys. This study found that multi-rotor UAVs have become the most widely used type of UAVs in recent years because of their advantages such as easiness to operate, low cost, and the ability of flying at a very low altitude, despite their late appearance. With the improvement of the payload capacity of UAVs, to use multiple magnetometers becomes popular since it can provide more abundant information. In addition, this study also found that the most commonly used method to reduce the effects of the UAV’s magnetic interference is to increase the distance between the sensors and the UAV, although this method will bring about other problems, e.g., the directional and positional errors of sensors caused by erratic movements, the increased risk of impact to the magnetometers. The pros and cons of different types of UAV, magnetic interference characteristics and suppression methods based on traditional aeromagnetic compensation and other methods are discussed in detail. This study contributes to the classification of current UAV applications as well as the data processing methods in magnetic surveys.

Despite the increased attention that has been given to the unmanned aerial vehicle (UAV)-based magnetic survey systems in the past decade, the processing of UAV magnetic data is still a tough task. In this paper, we propose a novel noise reduction method of UAV magnetic data based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), permutation entropy (PE), correlation coefficient and wavelet threshold denoising. The original signal is first decomposed into several intrinsic mode functions (IMFs) by CEEMDAN, and the PE of each IMF is calculated. Second, IMFs are divided into four categories according to the quartiles of PE, namely, noise IMFs, noise-dominant IMFs, signal-dominant IMFs, and signal IMFs. Then the noise IMFs are removed, and correlation coefficients are used to identify the real signal-dominant IMFs. Finally, the wavelet threshold denoising is applied to the real signal-dominant IMFs, the denoised signal can be obtained by combining the signal IMFs and the denoised IMFs. Both synthetic and field experiments are conducted to verify the effectiveness of the proposed method. The results show that the proposed method can eliminate the interference to a great extent, which lays a foundation for the further interpretation of UAV magnetic data.

Noise suppression is essential in time-domain electromagnetic (TDEM) data processing and interpretation. TDEM data are typically in broadband signal, which makes it difficult to separate the signal in the whole frequency band. The conventional methods tend to process data trace by trace, ignoring the lateral continuity between channels. This paper proposes a workflow based on multivariate variational mode decomposition (MVMD) and multivariate detrended fluctuation analysis (MDFA) to deal with the noise in 2-D TDEM data. The proposed method initially employs MVMD to decompose TDEM signals into a series of intrinsic mode functions (IMFs). Subsequently, MDFA is used to calculate the scaling exponent of each IMF, facilitating the selection of signal-dominant IMFs. Finally, the signal IMFs are summed up to reconstruct the TDEM signal. Both simulation and field results demonstrate that, by considering the lateral continuity of data across channels, the proposed method is more effective at noise removal than other single-channel data processing techniques.

With the development of electromagnetic detection technology, the inversion of electromagnetic induction (EMI) response based on a 3-D orthogonal dipole model can provide an estimation of parameters of a high-conductivity target, such as the target’s position, orientation, and shape. However, the traditional inversion methods suffer from several limitations including relying on the initial value, easy to fall into the local optimal solution, and high computational complexity. To overcome these disadvantages, in this letter, we propose a deep learning (DL) inversion method of subsurface target EMI response based on deep neural network (DNN) architecture, which is combined with adaptive moment estimation (Adam) optimization algorithm and learning rate attenuation strategy to improve the model accuracy. Datasets are obtained from forward modeling in different target parameters. By limiting the range of target parameters, errors caused by the nonuniqueness of inversion results are avoided. Through simulation and field experiments, we verify the performance of this method. The experimental results show that compared with the traditional inversion algorithms, the inversion accuracy is higher and the inversion speed is three to four orders of magnitude faster.

Due to the advantages of being nondestructive, rapid, and convenient, the electromagnetic detection method has attracted growing interest in the field of shallow subsurface detection. With the rapid development of unmanned aerial vehicle (UAV) technology, the use of the UAV platform for measurement can not only improve work efficiency but also avoid the significant losses that may be caused by humans working in dangerous areas. Therefore, we propose a broadband frequency domain electromagnetic system AFEM-3 based on a UAV platform for shallow subsurface targets detection (within less than 2 m). The sensor head adopts a concentric planar coil structure with a high spatial resolution, and a bucking coil connected in reverse series with the transmitting coil is used to suppress the primary field at the receiving coil. We designed a transmitting module based on unipolar frequency multiplication sinusoidal pulse width modulation technology that can generate multi-frequency arbitrary combination transmitting waveforms with low total harmonic distortion. It can also be matched to a variety of different transmitter coils by using the same hardware circuit. In addition, the global navigation satellite system and inertial measurement unit are integrated on the sensor head. The measurement response value, position, and attitude information can be displayed in real-time through the host computer. Through the static experiment of a standard coil, we verified the consistency between the AFEM-3 system with the theory. The performance of the system was evaluated through field experiments. The experimental results show that the system can effectively detect multiple metal targets in shallow subsurface areas. For different metal targets, the AFEM-3 system can provide obvious frequency domain characteristics.

The time-domain electromagnetic (TDEM) method is acknowledged for its simplicity in setup and non-intrusive detection capabilities, particularly within shallow subsurface detection methodologies. However, extant TDEM systems encounter constraints when detecting intricate topographies and hazardous zones. The rapid evolution in unmanned aerial vehicle (UAV) technology has engendered the inception of UAV-based time-domain electromagnetic systems, thereby augmenting detection efficiency while mitigating potential risks associated with human casualties. This study introduces the UAV-TDEM system designed explicitly for discerning shallow subsurface targets. The system comprises a UAV platform, a host system, and sensors that capture the electromagnetic response of the area while concurrently recording real-time positional data. This study also proposes a processing technique rooted in robust local mean decomposition (RLMD) and approximate entropy (ApEn) methodology to address noise within the original data. Initially, the RLMD decomposes the original data to extract residuals alongside multiple product functions (PFs). Subsequently, the residual is combined with various PFs to yield several cumulative sums, wherein the approximate entropy of these cumulative sums is computed, and the resulting output signals are filtered using a predetermined threshold. Ultimately, the YOLOv8 (You Only Look Once version 8) network is employed to extract anomalous regions. The proposed denoising method can process data within one second, and the trained YOLOv8 network achieves an accuracy rate of 99.0% in the test set. Empirical validation through multiple flight tests substantiates the efficiency of UAV-TDEM in detecting targets situated up to 1 m below the surface. Both simulated and measured data corroborate the proposed workflow’s effectiveness in mitigating noise and identifying targets.