Haigen Zhou

Natural-source signals in airborne geomagnetic sounding (AGS) are characterized by weak amplitudes and broad frequency bandwidths. During flight, they are highly susceptible to low-frequency motion noise induced by platform attitude fluctuations, as well as various types of strong interference. These disturbances degrade data quality and subsequent imaging and interpretation. To address these issues, this paper proposes an adaptive AGS data denoising method based on ROCKET noise classification and an improved U-Net architecture. First, a Mamba module is incorporated to enhance the long-range temporal dependency modeling capability of the U-Net, enabling effective preliminary suppression of low-frequency motion noise. Subsequently, high-dimensional discriminative representations are constructed using ROCKET random convolutional features to efficiently identify and accurately classify strong interference noise types in the remaining data segments, including power-line interference, triangular-wave interference, and impulsive interference. Finally, noise-specific improved U-Net models with tailored network structures and optimized parameters are invoked according to the identified noise categories, achieving customized and refined noise suppression for different noise types. Experimental results are based on a large-scale synthetic dataset constructed from high-signal-to-noise-ratio field measurements, together with airborne survey data acquired in Baishan, Jilin Province. The results demonstrate that the proposed method significantly outperforms typical deep learning methods, as well as the non-adaptive conventional U-Net and non-adaptive improved U-Net in both time-domain and frequency-domain metrics. The time-domain metrics include signal-to-noise ratio, normalized cross-correlation, and reconstruction error, while the frequency-domain metrics include spectral mean squared error and logarithmic spectral distortion. Multi-line tipper magnitude imaging validation using field data indicates that the proposed approach can suppress artifacts, recover continuous electrical boundary features, and improve the reliability of imaging results. These results suggest that the proposed adaptive noise suppression method offers high accuracy, robustness, and good scalability, providing reliable technical support for high-quality processing and geological interpretation of AGS data.

Ground-airborne frequency-domain electromagnetic (GAFDEM) data are often severely degraded by complex multi-source noise, including broadband environmental interference, low-frequency motion disturbance, and non-stationary field contamination, which significantly reduce signal quality and hinder subsequent interpretation. This study develops an SNR-Stacking ensemble denoising framework for recovering effective electromagnetic responses under mixed noise conditions. The method combines multi-scale time–frequency feature extraction with heterogeneous base learners and introduces a physically interpretable signal-to-noise ratio (SNR)-guided adaptive weighting strategy within a two-stage stacking architecture. In the first stage, complementary predictors are fused according to their reconstruction reliability, and in the second stage a meta-learner is used to further refine the residual structure of the denoised signal. Experiments on simulated GAFDEM data show improved waveform reconstruction, spectral consistency, and signal preservation compared with conventional filtering baselines, individual learning models, and conventional stacking. Field application suggests that the simulation-trained model can suppress broadband and impulsive interference without field-data retraining while improving amplitude fidelity at representative frequencies. The reconstructed amplitudes remain consistently closer to the ground reference than the noisy observations, with retention ratios ranging from 0.957 to 0.991 at the analysed representative frequencies. These results support SNR-guided stacking as a compact and interpretable strategy for improving GAFDEM signal quality before quantitative interpretation.

GAFDEM is an efficient geophysical survey method, but complex terrain introduces multi-source, non-Gaussian, and non-stationary noise that degrades inversion quality. We propose a BO-LightGBM denoising framework that combines time-domain statistics with frequency-domain spectral features to learn a nonlinear noisy-to-clean mapping, without the need for complex deep networks. Bayesian optimization automatically tunes key hyperparameters to improve robustness across varying noise levels. Experiments on simulated signals and Qinghai field data show effective suppression of impulsive noise, reducing amplitudes to within ±0.1 μV, with a maximum noise reduction of 20–40 dB at 1–10 Hz and over 40 dB at 100–1000 Hz.

Airborne natural-source tipper detection is well suited for cold, high-altitude surveys, but strong airborne anthropogenic noise often distorts the data and reduces anomaly localization accuracy. This paper proposes a two-stage denoising method combining K-SVD dictionary learning and Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise. K-SVD adaptively removes structured noise, and ICEEMDAN further suppresses impulsive noise while mitigating mode mixing and preserving low-frequency components. Simulation data show that the proposed method effectively suppresses complex noise and outperforms traditional methods in spectral and imaging quality, offering reliable support for practical deployment in complex environments.

Airborne electromagnetic detection systems are highly susceptible to low-frequency motion-induced noise, which significantly degrades the extraction of weak geological signals. Conventional signal processing methods alone are often insufficient to suppress mechanically induced vibration noise, resulting in signal distortion and reduced detection reliability. To address this limitation, this study proposes an active noise suppression strategy that integrates mechanical vibration isolation with advanced signal processing. A pneumatic vibration isolation platform based on a cable-driven parallel robot (CDPR) architecture is developed to achieve precise orientation correction and effective vibration isolation. The system employs kinematic modeling and a servo-controlled pneumatic cylinder driven by a proportional directional valve to enable accurate dynamic regulation. Numerical simulations conducted in the Advanced Modeling and Simulation Environment (AMESim), combined with proportional–integral–derivative (PID) control, demonstrate that piston displacement overshoot is constrained within 0.2 mm. Furthermore, targeted filtering techniques are applied to enhance signal quality. Experimental results show that the response time for continuous step input is 0.18–0.2 s, with a steady-state error below 0.3 mm, confirming robust control performance. The proposed framework provides an effective low-noise solution for airborne electromagnetic detection and can improve survey reliability in deep resource exploration.

The Ground-Airborne Frequency-Domain Electromagnetic (GAFDEM) method utilizes a ground-based transmitter and an airborne receiver, offering high transmission power and efficient data acquisition. It is particularly suited for rapid subsurface exploration in geologically complex regions. However, current UAV-based coil-type EM systems suffer from high background noise and significant measurement variability, which hinder accurate field measurements under large transmitter–receiver offsets. To overcome these challenges, we developed a novel UAV-compatible three-component GAFDEM system (JLU-3D-II) based on magnetic-core sensor technology. A quantitative evaluation method based on repeated flight experiments was proposed and implemented. The system’s performance was assessed through laboratory tests and field repeatability experiments. Indoor tests demonstrated that JLU-3D-II achieves nearly a 30-fold reduction in noise within the 10–1000 Hz frequency band, compared to air-core coil sensors of equivalent weight. Field experiments showed that the root mean square relative error of the vertical magnetic component across repeated flights decreased from 18% to below 2%, indicating a substantial improvement in measurement accuracy. Compared to the typical operational radius (approximately 1.5 km) of mainstream international UAV-based EM systems, this experiment achieved more than double the effective detection distance, verifying the system’s adaptability and data stability in long-offset detection scenarios.

The Ground-Airborne Frequency Domain Electromagnetic Method (GAFDEM) utilizes multi-frequency signals to detect underground electrical structures. However, its non-stationary characteristics and spectral aliasing issues make it challenging to accurately capture the amplitude and phase of time-varying electromagnetic responses. To address the problems of insufficient frequency resolution and poor resistance to adjacent frequency interference in traditional methods, this paper proposes a Frequency-domain Masking and Hilbert-transform based Joint Amplitude-Phase Extraction Method (FM-HAPE). First, a binary frequency-domain masking mechanism with adaptive relative bandwidth adjustment is introduced in FM-HAPE, which dynamically controls the mask width at different frequency points, thereby enabling precise isolation of the target frequency band energy and robust suppression of frequency drift. Second, a symmetric zero-phase FIR filtering technique based on the Hilbert Transform is presented, which effectively eliminates group delay errors. Lastly, an adaptive unwrapping method based on the local phase change slope is proposed to enhance the preservation of phase continuity in noisy environments. In addition, accurate decoupling of instantaneous amplitude and phase is achieved by FM-HAPE without the need for window function constraints. Simulation results show that under adjacent-frequency interference of ±1–5 Hz and a signal-to-noise ratio of 5 dB, FM-HAPE achieves an average amplitude RMSE of 0.026 μV and an average phase RMSE of about 0.1°, demonstrating strong anti-interference capability. Field data further indicate that FM-HAPE effectively reduces amplitude and phase variability compared with traditional methods, while producing smoother and more continuous pseudo-depth imaging results. This method provides a new solution for high-precision parameter extraction of non-stationary multi-frequency signals in deep electromagnetic exploration.

Ground–airborne frequency-domain electromagnetic method (GAFEM) is a geophysical exploration technique capable of detecting subsurface electrical structures over large areas and at great depths in complex terrains. Tipper divergence imaging, one of the principal GAFEM interpretation approaches, offers high lateral resolution and rapid imaging speed. However, conventional tipper divergence imaging relies on magnetic field data generated by orthogonal dual-source excitation, which is difficult—or in extreme cases impossible—to deploy in complex terrains, and it also fails to delineate the vertical boundaries of anomalies effectively. In this paper, we propose a tipper apparent resistivity-united-gradient imaging method with single-source excitation. The proposed approach combines lateral and vertical spatial gradients of tipper apparent resistivity derived from a single-source excitation to achieve high-resolution detection of subsurface electrical anomalies. We first present the derivation of the theoretical formulation underpinning the proposed imaging method. We then apply this approach to GAFEM datasets generated from three‐dimensional simulation models—incorporating anomalies at varying depths, anomalies whose resistivities closely match the background, and multiple coexisting anomalies—to further analyze the method’s three‐dimensional imaging characteristics. We further assess the effect of background resistivity estimation errors and noise interference on the imaging method. Finally, field experiments validate the method’s practical applicability. Compared with conventional tipper-based techniques, our method requires only a single-source excitation, overcoming the challenge of orthogonal dual-source deployment and enhancing feasibility in field surveys; moreover, the tipper apparent resistivity-united-gradien t strategy resolves the inability of conventional divergence imaging to identify vertical anomaly boundaries, thus offering a novel pathway for precise geophysical exploration.

In the semi-airborne frequency-domain electromagnetic (SAFEM) method, the total magnetic intensity (TMI) is derived from the vector synthesis of triaxial inductive magnetic sensors. However, non-orthogonal errors in the sensors introduce measurement inaccuracies. Traditional calibration techniques are unsuitable for induction magnetic sensors because they require either static magnetic field measurements or complex alternating current reference fields. We propose a novel, high-precision calibration method based on an improved particle swarm optimization (IPSO) algorithm that circumvents the need for an external physical reference to address this challenge. This method establishes a mathematical model of the non-orthogonal error, transforming the calibration into an optimization problem that aims to minimize the root mean square error (RMSE) of the TMI. Simulation results demonstrate that the proposed IPSO algorithm exhibits higher convergence accuracy and stronger noise immunity compared with the standard PSO. Experiments with a physical measurement system further validated the method’s efficacy: after calibration, the maximum absolute error and the RMSE of the TMI were significantly reduced by 82.84% and 87.64%, respectively. This study provides a critical technical solution for the complex, multi-parameter error calibration of alternating current vector magnetometers, ensuring the acquisition of high-precision TMI data for SAFEM systems.

Ground-airborne frequency domain electromagnetic (GAFDEM) is an important tool for mineral resource exploration and geological surveys in complex mountainous areas. The rapid and high-precision calculation of background resistivity is the foundation for high-resolution imaging. Due to the influence of attitude noise, the current accuracy of background resistivity calculations is not high. Furthermore, when the measurement area is large and the terrain is complex, the need to establish a terrain model for the measurement area leads to a decrease in efficiency. To solve the above problems, this paper proposes a method to calculate the background resistivity using the total magnetic intensity. Firstly, the magnetic field component is analyzed for its ability to identify anomalies, then the effects of transmitting frequency and source-receiver distance parameters are analyzed, and finally, simulation experiments and field experiments are conducted. Simulation experiments show that the method of calculating background resistivity using total magnetic intensity can accurately calculate the background resistivity of the double anomaly model and the model with topographic relief compared with the current method. Field experiments show that compared with the current method, the total magnetic intensity method of calculating background resistivity is not affected by attitude noise, and the results of background resistivity calculation are consistent with the real results with higher accuracy. The method in this paper supports GAFDEM to realize high-resolution and fast exploration of underground structures.