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Si-Hong Wu

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Open access Oct 2026

CURIE: A continental-scale probabilistic magnetotelluric reference model of USArray data for the contiguous United States

Magnetotellurics (MT) provides critical constraints on Earth’s electrical conductivity structure and thereby illuminates lithosphere and mantle processes. The EarthScope USArray MT program provides a unique opportunity to investigate large-scale tectonic and compositional variations across the contiguous United State...

Si-Hong Wu, Jia-Jia Sun, Di-Kun Yang et al. · 0 citations
Review Sep 2026

Continental-scale probabilistic resistivity imaging of Australia using deep learning: Implications for geology, groundwater, and critical minerals

Electrical resistivity is a fundamental physical property that provides powerful constraints on subsurface structure, lithology, fluid distribution, and mineralization. We present a continental-scale electrical resistivity model of Australia's shallow crust with quantified uncertainty, extending to depths of up to ~650...

Si-Hong Wu, Jia-Jia Sun, Jie-Fu Chen · 0 citations
Oct 2026

Demystifying and unifying ELBO for Probabilistic Deep Learning Inversion

Variational inference has emerged as a computationally efficient framework for solving Bayesian inverse problems. Rather than relying on random sampling in high-dimensional model spaces, it reformulates probabilistic inference as an optimization problem, marking a fundamental shift from sampling-based to optimization...

Jia-Jia Sun, Si-Hong Wu · 0 citations
Open access Sep 2026

Probabilistic inversion of electromagnetic data using stein variational gradient descent

Geophysical inversion is inherently non-unique, and efficient uncertainty quantification remains a major challenge for large-scale applications. Conventional Bayesian approaches, such as Markov chain Monte Carlo (McMC), provide rigorous posterior estimates but are often computationally prohibitive, while deterministi...

Si-Hong Wu, Jia-Jia Sun · 0 citations

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