Electrical resistivity tomography (ERT) inversion is fundamentally constrained by the nonlinear and ill-posed nature of the problem, as well as the intrinsic limitations of measurement instruments. Conventional inversion methods, relying primarily on smoothness constraints, often oversmooth subsurface structures and bl...
Yin-Peng Li, Xiang-Hao Liu, Xiang Qiu et al.· IEEE Transactions on Instrum...· 0 citations
While full-waveform inversion (FWI) offers high-resolution ground penetrating radar (GPR) imaging, it is hampered by prohibitive computational costs and intrinsic sensitivity to initial models. Conversely, deep learning provides efficiency but often lacks physical constraints, leading to structural artifacts under nois...
Meijia Huang, Xiang Qiu, Yan-Qi Wu et al.· IEEE Transactions on Geoscie...· 0 citations
Electrical resistivity tomography (ERT) inversion is inherently nonlinear and ill-posed, and conventional algorithms often suffer from limited resolution due to their dependence on initial models and regularization. Recent deep learning (DL)-based approaches have shown potential for resolution enhancement but purely da...
Yinpeng Li, Xianghao Liu, Yanqi Wu et al.· IEEE Transactions on Geoscie...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.