Xiaotian Liu

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.

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.