An AUV Based Method for Estimating Hectare-scale Distributions of Deep Sea Cobalt-rich Manganese Crust Deposits

Abstract

A method for estimating the volumetric distribution of Cobalt-rich Manganese Crusts (Mn-crusts) by combining multi modal sensor data collected using an Autonomous Underwater Vehicle (AUV) is described. The AUV calculates the thickness of Mn-crusts using a sub-bottom sonar and generates a 3D colour reconstruction of the seafloor using a light sectioning mapping system. The 3D map is classified into one of the 3 types of seafloor - crusts, sediments and nodules, using a machine learning classifier. The thickness measurements are made along a seafloor transect whereas the 3D maps have a width of ~1.5 m, depending on the AUV altitude. The thickness measurement is then extrapolated to areas not scanned by the sonar, by defining an area of influence which is the area over which the thickness of the Mn-crust is not expected to change significantly. Estimates for percentage coverage of the Mn-crust and mass of Mn-crust per unit area are determined along the AUV transect based on the extrapolated thickness. This method provides a novel approach to estimate the distribution of Mn-crusts over large areas.