arXiv Machine Learning

Interpretable Deep Learning Paradigm for Airborne Transient Electromagnetic Inversion

arXiv:2503. 22214v2 Announce Type: replace Abstract: The extraction of geoelectric structural information from airborne transient electromagnetic (ATEM) data primarily involves data processing and inversion.

arXiv Machine Learning
Sep 2

Coordinate-Residual Physics-Driven Neural Network for Inverse Scattering Imaging

The paper introduces a coordinate-residual physics-driven neural network (CRPDNN) for 3‑D electromagnetic inverse scattering. CRPDNN models the unknown contrast distribution using normalized spatial coordinates and a residual convolutional network, optimizing parameters by enforcing consistency between measured and predicted scattered fields. It eliminates the need for preliminary reconstruction, achieving lower relative error and significant speedups compared to existing methods, while maintaining stability under noisy measurements and showing promise in practical imaging experiments.

By Yutong Du, Zicheng Liu, Bo Qi, Yali Zong, Peixian Han