14 citations as recorded by crossref.

1. Comparative analysis of deep learning and traditional airborne electromagnetic data processing: A case study M. Asif et al. https://doi.org/10.1190/geo2024-0282.1
2. DREMnet: An Interpretable Denoising Framework for Semi-Airborne Transient Electromagnetic Signal S. Wang et al. https://doi.org/10.1109/TGRS.2026.3680901
3. Inverse geo-electromagnetic modeling: a systematic review and bibliometric assessment O. Castillo-Reyes et al. https://doi.org/10.3389/feart.2025.1645896
4. Two-stage modeling of ionospheric TEC for earthquake precursors: forecasting with LSTM, GRU, RNN and classification via Random Forest based on the 2023 Türkiye earthquakes C. Budak https://doi.org/10.1016/j.asr.2025.12.018
5. A multi-task learning network based on the Transformer network for airborne electromagnetic detection imaging and denoising Y. Liu et al. https://doi.org/10.1093/jge/gxae054
6. Characterization and determination of soil unsaturated hydraulic conductivity by integrating time-lapse geophysical data with hydrogeological measurements C. Zou et al. https://doi.org/10.1016/j.geoderma.2026.117742
7. Constraints on Water-Rich Areas in Huangling Coal Mine Using High-Resolution Semi-Airborne Electromagnetic Imaging C. Su et al. https://doi.org/10.1109/TGRS.2025.3548078
8. Automated Transient Electromagnetic Data Processing for Ground-Based and Airborne Systems by a Deep Learning Expert System M. Asif et al. https://doi.org/10.1109/TGRS.2022.3202304
9. Data Science and Machine Learning in Geo-Electromagnetics: A Review Q. Huang et al. https://doi.org/10.1007/s10712-025-09904-9
10. High-Resolution Hybrid-Dimensional Inversion of Transient Electromagnetic Data for Water Hazard Detection in Coal Mines C. Su et al. https://doi.org/10.1109/TGRS.2024.3447285
11. Model-Driven and Data-Driven Inversion in Geophysical Exploration Y. Wang et al. https://doi.org/10.1134/S0965542525701805
12. A Deep Learning Estimation for Probing Depth of Transient Electromagnetic Observation L. Gan et al. https://doi.org/10.3390/app14167123
13. A Deep Learning Approach for Transient Electromagnetic Data Denoising, Inversion and Uncertainty Analysis With Monte Carlo Dropout Technique Y. Zhu et al. https://doi.org/10.1111/1365-2478.70069
14. Inversion of Audio Magnetotelluric Data Based on Residual Mixed Density Network With an Estimation of Posterior Distribution Probabilities Z. Liu et al. https://doi.org/10.1109/TGRS.2025.3595583