arXiv Machine Learning By Shuang Wang, Xuben Wang, Fei Deng, Peifan Jiang, Jian Chen, Gianluca Fiandaca

OpenEM: Large-scale multi-structural 3D datasets for electromagnetic methods

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arXiv:2510. 21859v3 Announce Type: replace Abstract: Electromagnetic (EM) methods, owing to their efficiency and non-invasive nature, have become one of the most widely used techniques in geological exploration.

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arXiv Machine Learning
Aug 27

Continually learning neural-operator surrogate for three-dimensional airborne electromagnetic Bayesian inversion

The paper presents a continually learning neural‑operator surrogate for the three‑dimensional forward operator used in time‑domain airborne electromagnetic (AEM) Bayesian inversion. By training on successive geological priors and employing an ensemble‑disagreement validity check, the surrogate replaces the expensive forward solver, enabling the Markov chain Monte Carlo sampler to reproduce the full‑solver posterior with credible intervals within 2.6 % of the truth. Applied to the 2013 Capricorn TEMPEST survey, the surrogate inverts over two million soundings in seconds, making uncertainty‑quantified conductivity imaging at survey scale feasible for near real‑time mineral‑systems targeting.

By Jaehong Chung, Andrew Lockwood, Jef Caers