New research by Ming Zhang, Colin G. Farquharson, and Eric Attias, published in Geophysics, introduces a three-dimensional electromagnetic (EM) inversion method designed to better resolve geological boundaries. Conventional smoothness-regularized inversion can blur sharp resistivity contrasts. The new approach incorporates EM field-gradient information into a Gauss-Newton inversion framework, using it to adjust regularization across the model. This strengthens boundary definition while...

New research by Yanyi Wang, Xavier Garcia, Eric Attias, Boyao Zhang, Fansheng Xiong, Jianxin Liu, and Zhenwei Guo is now available online in Computers & Geosciences. Building on the work presented at EGU 2026, the study develops a physics-constrained implicit neural representation (INR) approach for two-dimensional magnetotelluric (MT) inversion. The method represents subsurface resistivity as a continuous neural field and compares its predictions with observed MT data using a...

At EGU General Assembly 2026 in Vienna, Central South University Ph.D. student Yanyi Wang presented a novel physics-driven deep learning approach for 2-D magnetotelluric (MT) inversion. This collaborative project with E. Attias (OCEEMlab) and Xavier Garcia (CSIC-ICM) leverages implicit neural representations (INRs) to advance geophysical inversion techniques. Unlike traditional methods that rely on manually tuned penalty terms such as Tikhonov or Occam regularization to stabilize inversion...