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

Umesh Neettiyath, B. Thornton, M. Sangekar, Yuya Nishida, K. Ishii, Takumi Sato, A. Bodenmann, T. Ura
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引用次数: 2

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.
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基于AUV的深海富钴锰结壳矿床公顷尺度分布估算方法
介绍了一种利用自主水下航行器(AUV)采集的多模态传感器数据,估算富钴锰结壳(mn -crust)体积分布的方法。AUV使用水下声纳计算mn地壳的厚度,并使用光切片映射系统生成海底的3D彩色重建。3D地图使用机器学习分类器将海底分为地壳、沉积物和结核三种类型中的一种。厚度测量是沿着海底样带进行的,而3D地图的宽度约为1.5米,具体取决于水下航行器的高度。然后,通过定义一个影响区域,将厚度测量外推到声纳未扫描的区域,该影响区域是预计锰地壳厚度不会发生显着变化的区域。根据外推的厚度,沿AUV样带确定mn地壳的覆盖率百分比和单位面积的mn地壳质量。该方法提供了一种估算大面积锰壳分布的新方法。
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