Articles | Volume 11, issue 2
Solid Earth, 11, 419–436, 2020
https://doi.org/10.5194/se-11-419-2020
Solid Earth, 11, 419–436, 2020
https://doi.org/10.5194/se-11-419-2020

Research article 31 Mar 2020

Research article | 31 Mar 2020

Towards plausible lithological classification from geophysical inversion: honouring geological principles in subsurface imaging

Jérémie Giraud et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Jeremie Giraud on behalf of the Authors (04 Feb 2020)  Author's response    Manuscript
ED: Publish subject to minor revisions (review by editor) (11 Feb 2020) by Michal Malinowski
AR by Jeremie Giraud on behalf of the Authors (13 Feb 2020)  Author's response    Manuscript
ED: Publish as is (14 Feb 2020) by Michal Malinowski
ED: Publish as is (17 Feb 2020) by Susanne Buiter(Executive Editor)
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Short summary
We propose a methodology for the identification of rock types using geophysical and geological information. It relies on an algorithm used in machine learning called self-organizing maps, to which we add plausibility filters to ensure that the results respect base geological rules and geophysical measurements. Application in the Yerrida Basin (Western Australia) reveals that the thinning of prospective greenstone belts at depth could be due to deep structures not seen from surface.