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Continental-scale probabilistic resistivity imaging of Australia using deep learning: Implications for geology, groundwater, and critical minerals — arXiv:2609.19516

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.19516
License (as archived): CC0-1.0
Archived: 2026-10-10T06:04:26.958Z
SHA-256 of the archived text: 9225668b366c60d05983746f9c5a5ffd6c0080018719748ff6b091ad70d8e48d
Stable URL of this page: https://metraipolis.com/documento/dc_8f46db2fa2ea

How to cite

Continental-scale probabilistic resistivity imaging of Australia using deep learning: Implications for geology, groundwater, and critical minerals — arXiv:2609.19516 — arXiv (en), https://arxiv.org/abs/2609.19516 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_8f46db2fa2ea (Metraipolis archive dc_8f46db2fa2ea, sha256 9225668b366c60d05983746f9c5a5ffd6c0080018719748ff6b091ad70d8e48d)

BibTeX

@misc{dc8f46db2fa2ea,
  title = {Continental-scale probabilistic resistivity imaging of Australia using deep learning: Implications for geology, groundwater, and critical minerals — arXiv:2609.19516},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.19516},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_8f46db2fa2ea; sha256 9225668b366c60d05983746f9c5a5ffd6c0080018719748ff6b091ad70d8e48d},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.19516. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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 m. The model is derived from probabilistic inversion of more than 26.6 million airborne electromagnetic (AEM) soundings acquired along 352,600 line-kilometers from the AusAEM program. We develop a deep learning-based probabilistic inversion framework using invertible neural networks (INNs). To accommodate differences among AEM surveys, we trained 11 networks using a common set of synthetic resistivity models and the same training strategy, enabling consistent integration of inversion results across Australia. The entire inversion was completed in only 4.37 GPU hours, overcoming long-standing computational barriers to continental-scale probabilistic geophysical imaging. The resulting resistivity model exhibits clear spatial patterns that closely align with Australia's major geological provinces, sedimentary basins, aquifer and mineralizing systems. We further define the depth of investigation using posterior resistivity distributions to support uncertainty-informed geological interpretation. The resistivity model together with quantified uncertainty provides new physical constraints on regolith thickness, basin architecture, paleochannel and mineralized systems across Australia, serving as a foundation for interpreting continental-scale electrical structure and its geological, hydrological, and mineral system implications.

Tema degli esploratori: terra, clima ed energia (retro-catalogo arXiv: annunci del 2026-09-18 in physics.geo-ph).
Categorie arXiv: Geophysics (physics.geo-ph).
Pagina del lavoro: https://arxiv.org/abs/2609.19516 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-10T05:48:11.137Z dalla pagina https://arxiv.org/catchup/physics.geo-ph/2026-09-18?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).