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Statistical Properties of Distribution of Manganese Nodules in Indian and Pacific Oceans and Their Applications in Assessing Commonality Levels and in Exploration Planning

  • T. R. P. SinghEmail author
  • M. Sudhakar
Chapter

Abstract

This chapter presents results of analysis of available statistical data on distribution of manganese (polymetallic) nodules on the seafloor of Indian and Pacific Oceans. It is concluded that the nodule fields of varying sizes within each of the two oceans share common distribution patterns. More importantly, the study brings out striking similarity between the distribution characteristics of nodule fields in the two oceans in terms of coefficients of variation of nodule abundance, variographic parameters including similar but high level of nugget coefficients and the unimodal lognormal frequency distribution of abundance values. The computations of estimation variances for study areas in Indian and Pacific Oceans establish the constancy of the product of variance of error and the area of nodule field under given conditions. Finally, since nodule abundance forms the governing parameter for exploration planning as brought out by the data, estimation variances for selected sizes of nodule fields have been computed for varying sampling grids for both the oceans. It is concluded that a given sampling grid of say, 0.15° over a given size of nodule field of 75,000 km2, produces an identical estimation error of less than ±10% of the respective mean abundance values in Indian as well as Pacific Oceans.

Abstract

It is well known that manganese nodules containing copper, nickel, cobalt and manganese occur extensively in the Clarion-Clipperton Fracture Zone (CCZ) in the Equatorial North Pacific Ocean as well as Central Indian Ocean Basin (CIOB) in the Indian Ocean (Cronan 1980; Siddiquie et al. 1978). Currently, a large number of State or State-sponsored agencies are registered as the contractors with International Seabed Authority (ISA) in the CCZ while India is the only contractor engaged in exploration for nodules in CIOB. Extensive sampling of nodules for measurement of abundance (kg/m2) and metal grades leading to estimation of nodule resources have been carried out in CCZ and CIOB, in addition to bathymetric surveys. This chapter, however, is restricted to the study of sampling data originating from exploration by a large number of contractors and other agencies. As the original raw sampling data are not available for CCZ or for CIOB in public domain, summary statistical data including the variographic models have been used for this study to make some meaningful inferences on the statistical properties of the distribution of nodules in the two oceans. Variographic parameters allow prediction of estimation accuracies for given sizes of nodule fields as also planning of exploration density for achieving pre-specified estimation accuracies.

Notes

Acknowledgements

The authors gratefully acknowledge the Secretary, Ministry of Earth Sciences, Govt. of India for his overall encouragement and support. The authors also thankfully acknowledge the approval of the International Sea Bed Authority to use relevant data and map. Special thanks to Dr. HS Mandal for supporting the graphics.

References

  1. Hennigar HF, RE Dick, Foell EJ (1986) Derivation of abundance estimates for manganese nodule deposits; Grab sampler recoveries to ore reserve. In: Offshore technology conference, OTC 5237, Houston, May 1986, 5 pGoogle Scholar
  2. ISA (2009) Workshop on the results of a project to develop a geological model of polymetallic nodule deposits in the Clarion-Clipperton zone International Seabed Authority, Kingston, 14–17 Dec 2009Google Scholar
  3. ISA (2010) A geological model of polymetallic nodule deposits in the Clarion-Clipperton fracture zone, 2010. Technical Study: No. 6. eBook by the International Seabed Authority, Kingston. ISBN: 978-976-95268-2-2, p 211Google Scholar
  4. Jauhari P, Kodagali VN, Sankar SJ (2001) Optimum sampling interval for evaluating ferromanganese nodule resources in the Central Indian Ocean. Geo-Mar Lett 21:176–182CrossRefGoogle Scholar
  5. Koch GS Jr, Link RF (1971) The coefficient of variation—a guide to the sampling of ore deposits. Econ Geol 66:293–301CrossRefGoogle Scholar
  6. Nautilus Minerals: Press Release (Nov 2012) Nautilus Minerals Defines 410 million tonne inferred mineral resource. http://www.nautilusminerals.com
  7. Singh TRP (1978) Estimation accuracy in relation to control spacing in sampling of ore deposits: Graphic solutions. J Math Geol 10(6):691–697CrossRefGoogle Scholar
  8. Singh TRP (1980) An investigation on the relationship among the models of covariances function used in Geology. Indian J Earth Sci 7(2):109–118Google Scholar
  9. Singh TRP, Sudhakar M (2015) Estimating potential of additional mine sites for polymetallic nodules in Pacific and Indian Oceans. Int J Earth Sci Eng 8(4):1938–1941Google Scholar
  10. Sudhakar M (1993) Geostatistical analysis of polymetallic nodules from the Indian Ocean and a comparative studies from the Pacific Ocean deposits. Unpublished Ph.D. thesis, Indian School of Mines, Dhanbad, p 287Google Scholar
  11. United Nations (1982) United Nations Convention on Law of the Sea (UNCLOS). United Nations, New YorkGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Ministry of Earth SciencesNew DelhiIndia

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