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New neural-operator surrogate accelerates 3D AEM Bayesian inversion

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel neural-operator surrogate designed to accelerate Bayesian inversion for three-dimensional airborne electromagnetic (AEM) data. This surrogate model learns from Maxwell's equations and employs continual learning to adapt to various geological priors, enhancing its applicability across different case studies. By replacing the computationally expensive solver, the surrogate enables the inversion of millions of soundings in seconds, a task previously infeasible. This advancement promises to deliver uncertainty-quantified conductivity imaging at a survey scale, facilitating near real-time mineral exploration. AI

IMPACT Enables faster and more comprehensive geophysical surveys for mineral exploration.

RANK_REASON Publication of a new research paper detailing a novel method. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New neural-operator surrogate accelerates 3D AEM Bayesian inversion

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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jaehong Chung, Andrew Lockwood, Jef Caers ·

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

    arXiv:2608.25932v1 Announce Type: cross Abstract: Three-dimensional probabilistic inversion of time-domain airborne electromagnetic (AEM) data is limited by the cost of the forward solve. Even though one simulation takes only tens of seconds, a Bayesian inversion of a survey of m…

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