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

Giraud, Jérémie; Lindsay, Mark; Jessell, Mark; Ogarko, Vitaliy

doi:https://doi.org/10.5194/se-11-419-2020

Articles | Volume 11, issue 2

Solid Earth, 11, 419–436, 2020

https://doi.org/10.5194/se-11-419-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Solid Earth, 11, 419–436, 2020

https://doi.org/10.5194/se-11-419-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 11, issue 2

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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Final revised paper (published on 31 Mar 2020)
Preprint (discussion started on 11 Nov 2019)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Some statistical concerns for an otherwise strong manuscript', Tom Horrocks, 12 Dec 2019
- AC1: 'Answer to RC1', Jeremie Giraud, 04 Feb 2020
RC2: 'Review of Towards geologically reasonable lithological classification from integrated geophysical inverse modelling: methodology and application case', Anonymous Referee #2, 20 Dec 2019
- AC2: 'Answer to RC2', Jeremie Giraud, 04 Feb 2020

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)

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.