Biogeosciences
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Articles | Volume 15, issue 23
Articles | Volume 15, issue 23
https://doi.org/10.5194/bg-15-7347-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/bg-15-7347-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 23
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, Timm Schoening, Evangelos Alevizos, and Jens Greinert

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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.
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