Image analysis of seafloor photographs for estimation of deep-sea minerals

• Published in: Geo-marine letters Vol. 30; no. 6; pp. 617 - 626
• Main Authors: Sharma, Rahul, Sankar, S. Jai, Samanta, Sudeshna, Sardar, A. A., Gracious, D.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer-Verlag 01-12-2010; Springer Nature B.V
• Subjects: Deep sea; Earth and Environmental Science; Earth Sciences; Geology; Image processing systems; Marine; Minerals; Ocean bottom; Ocean floor; Spatial distribution; Technical Paper; Water depth; ISW, Central Indian Ocean; Manganese Nodule; Grey Shade; Compute Estimate; Central Indian Ocean; Ferromanganese Crust
• ISSN: 0276-0460; 1432-1157
• DOI: 10.1007/s00367-010-0205-z

Factors such as non-uniform illumination of seafloor photographs and partial burial of polymetallic nodules and crusts under sediments have prevented the development of a fully automatic system for evaluating the distribution characteristics of these minerals, necessitating the involvement of a user input. A method has been developed whereby spectral signatures of different features are identified using a software ‘trained’ by a user, and the images are digitized for coverage estimation of nodules and crusts. Analysis of >20,000 seafloor photographs was carried out along five camera transects covering a total distance of 450 km at 5,100–5,300 m water depth in the Central Indian Ocean. The good positive correlation (R
2
 > 0.98) recorded between visual and computed estimates shows that both methods of estimation are highly reliable. The digitally computed estimates were ∼10% higher than the visual estimates of the same photographs; the latter have a conservative operator error, implying that computed estimates would more accurately predict a relatively high resource potential. The fact that nodules were present in grab samples from some locations where photographs had nil nodule coverage emphasises that nodules may not always be exposed on the seafloor and that buried nodules will also have to be accounted for during resource evaluation. When coupled with accurate positioning/depth data and grab sampling, photographic estimates can provide detailed information on the spatial distribution of mineral deposits, the associated substrates, and the topographic features that control their occurrences. Such information is critical for resource modelling, the selection of mine sites, the designing of mining systems and the planning of mining operations.