Articles | Volume 15, issue 23
Biogeosciences, 15, 7347–7377, 2018

Special issue: Assessing environmental impacts of deep-sea mining...

Biogeosciences, 15, 7347–7377, 2018

Research article 13 Dec 2018

Research article | 13 Dec 2018

Quantitative mapping and predictive modeling of Mn nodules' distribution from hydroacoustic and optical AUV data linked by random forests machine learning

Iason-Zois Gazis et al.


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
ED: Publish subject to minor revisions (review by editor) (16 Oct 2018) by Daniel O. B. Jones
AR by Iason - Zois Gazis on behalf of the Authors (26 Oct 2018)  Author's response
ED: Publish subject to technical corrections (05 Nov 2018) by Daniel O. B. Jones
Short summary
The use of high-resolution hydroacoustic and optic data acquired by an autonomous underwater vehicle can give us detailed sea bottom topography and valuable information regarding manganese nodules' spatial distribution. Moreover, the combined use of these data sets with a random forest machine learning model can extend this spatial prediction beyond the areas with available photos, providing researchers with a new mapping tool for further investigation and links with other data.
Final-revised paper