Polymetallic nodules abundance estimation using sidescan sonar: A quantitative approach using artificial neural network

Abstract

There is a high abundance of polymetallic nodules (PMN) scattered across the vast Clarion and Clipperton Fracture Zone (CCFZ) in the Pacific Ocean. These nodules possess high economic potential as they are rich in minerals such as manganese, nickel, copper and rare earth elements. Quantification of nodule coverage is important for economic feasibility studies and planning of effective exploitation strategies. Traditional methods for nodule quantification are highly labour and time intensive as they rely on freefall box corer measurements and/or image processing of seabed photographs. Using sidescan sonar data and geotagged photographs collected from an autonomous underwater vehicle (AUV) in our region of interest at CCFZ, we propose a novel technique based on artificial neural network (ANN) to estimate PMN abundance using texture variations from sidescan sonar data. Compared to an optical camera, the sidescan sonar provides a much larger area of coverage, which in effect can drastically increase the area surveyed by an AUV in a given amount of time. Till date, this is the first known published work to elaborate on a data-driven approach in estimating PMN abundance using sidescan sonar backscatter data. Our network yielded a test accuracy of 84%, which shows that it can be used as an effective tool in estimating nodule abundance from sidescan sonar. This approach allows faster evaluation of nodule abundance for future exploration without the need for an underwater camera.