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Advanced Electromagnetic Sensors and Systems in Geophysics: Technology and Applications

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Dear Colleagues,

In recent years, with the accelerated advancement of the world's industrialization process, surface mineral resources have been gradually depleting, and the demand for deep resource exploration has become increasingly urgent. Geophysical methods are one of the main means for deep resource exploration. To achieve fine exploration of deep geological targets, increasingly stringent requirements are placed on detection systems centered on sensors. Key and frontier challenges in this field include how to more accurately detect weak deep-Earth signals, adapt to increasingly complex detection scenarios, reduce R&D, maintenance, and application costs without sacrificing performance, and achieve the integration of multiple observation modes at both the hardware and software levels to enhance result reliability.

This Special Issue focuses on addressing these key and frontier issues in the field of geophysical electromagnetic detection and highlighting their significance. This Issue aims to bring together original research and review articles on recent advances, technologies, solutions, applications, and emerging challenges in this field.

Potential topics include, but are not limited to the following:

advanced magnetic field sensor;
non-contact electric field sensor;
superconducting sensor;
integration of sensors with the carrying platform;
airborne/semi-airborne electromagnetic detection system;
marine electromagnetic detection system;
borehole electromagnetic detection system;
multi-modal detection system and information fusion.

Dr. Xin Wu
Prof. Dr. Bin Chen
Prof. Dr. Guoqiang Xue
Dr. Xiaodong Luan
Guest Editors

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21 pages, 6423 KB

Open AccessArticle

Time-Domain Airborne Electromagnetic Inversion with Gradient Guidance and Structural Enhancement

by Dajun Li, Yuan Gao, Yaoming Wang, Wei Su, Xingwang Li and Xuanlong Shan

Sensors 2026, 26(16), 5099; https://doi.org/10.3390/s26165099 - 12 Aug 2026

Viewed by 343

Abstract

Gradient-based inversion methods are widely used for time-domain airborne electromagnetic (AEM) data, but their results are commonly affected by the initial model and regularization-induced smoothing. These limitations are particularly evident when thin layers or alternating high- and low-resistivity structures need to be resolved. To address this problem, we propose a gradient-guided iterative enhancement (GGIE) inversion that combines a limited-iteration Gauss–Newton (GN) inversion with a lightweight U-Net. In each GGIE inversion iteration, the GN module first produces a coarse inverted model (IM) that preserves the main data-driven geoelectric trend but is still affected by regularization-induced smoothing. The trained U-Net then predicts a structurally enhanced model (PM) from the observed data and the IM. A data-misfit-guided adaptive approach is proposed to calculate the weight coefficients of the IM and PM and to construct an update model (UM). These coefficients are further smoothed by a momentum term so that the relative contributions of the IM and the PM are adjusted adaptively during the iterations. This design reduces error propagation from either component alone and dynamically balances learned structural enhancement with physics-based data consistency. The UM then serves as the initial model for the subsequent GN inversion. GGIE inversion is tested on synthetic data, and the results show that it is most beneficial for complex multilayer structures, for which it reduces the mean relative error and root mean squared error (RMSE) by 49.0% and 28.6%, respectively. Compared with the physics-informed neural network (PINN) baseline, GGIE inversion reduces the model relative error, log-domain RMSE, and data misfit by 19.4%, 7.0%, and 71.7%, respectively. Moreover, compared with U-Net alone, GGIE inversion reduces the data misfit by 85.4%. The proposed method is further applied to field data acquired from the Fox River area in Wisconsin, USA. The main advantage of GGIE inversion is its ability to resolve complex multilayered structures, thin layers, and sharp resistivity contrasts with improved accuracy and stability. Full article

(This article belongs to the Special Issue Advanced Electromagnetic Sensors and Systems in Geophysics: Technology and Applications)

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