ORIGINAL PAPER
Regression methods in predicting the abundance of nodules from seafloor images – a case study from the IOM area, Pacific Ocean
1
AGH University of Science and Technology
Submission date: 2023-01-19
Final revision date: 2023-03-27
Acceptance date: 2023-04-12
Publication date: 2023-06-12
Gospodarka Surowcami Mineralnymi – Mineral Resources Management 2023;39(2):5-36
KEYWORDS
nodule abundanceregression methodsnodule coverage of seafloorfraction distributionClarion-Clipperton Zone (CCZ)
TOPICS
Optimization of resources and reserves managementStrategy and methodology of mineral prospecting and exploration
ABSTRACT
The main source of information on the abundance of polymetallic nodules (APN) is the results of direct seafloor sampling, mainly using box corers. Due to the vast spread of nodule occurrence in the Pacific, the distances between successive sampling sites are significant. This makes it difficult to reliably estimate the nodule resources, especially in parts of the deposit with small areas corresponding to the areas scheduled for extraction in the short term (e.g. within one year). It seems justified to try to increase the accuracy of nodule resource estimates through the use of information provided by numerous photos of the ocean floor taken between sampling stations. In particular, the percentage of nodule coverage of the ocean floor (NC), the data on fraction distribution of nodules (FD) and the coverage of nodules with sediments (SC) are important here. In the presented study, three regression models were used to predict the nodule abundance from images: simple linear regression (SLR), multiple regression (MR), and general linear model (GLM). The GLM provides the most accurate prediction of nodule abundance (APN) due to the ability of this model to simultaneously take into account both quantitative variable (NC) and qualitative variables (FD, SC). The mean absolute errors of APN prediction are in the range of 1.0–1.7 kg/m2, which is 7–13% of the average nodule abundance determined for training or testing data sets. This result can be considered satisfactory for predicting the abundance in ocean floor areas covered only by photographic survey.
ACKNOWLEDGEMENTS
This research was financed from AGH University of Science and Technology grant no. 16.16.140.315.
METADATA IN OTHER LANGUAGES:
Polish
Metody regresji w szacowaniu zasobności konkrecji polimetalicznych ze zdjęć dna oceanicznego – studium przypadku z obszaru IOM, Ocean Spokojny
zasobność konkrecji, metody regresji, pokrycie konkrecjami dna oceanicznego, rozkład frakcji konkrecji, strefa Clarion-Clipperton (CCZ)
Podstawowym źródłem informacji o zasobności oceanicznych konkrecji polimetalicznych (APN) są wyniki bezpośredniego opróbowania dna najczęściej za pomocą próbników skrzynkowych. Z uwagi na ogromne rozprzestrzenienie wystąpień konkrecji w strefie Clarion-Clipperton na Pacyfiku odległości między kolejnymi stacjami opróbowania są znaczne. Utrudnia to wiarygodne oszacowanie zasobów konkrecji w oparciu o uśrednione zasobności konkrecji stwierdzone w próbnikach skrzynkowych, szczególnie w obszarach o relatywnie małych powierzchniach odpowiadających przykładowo obszarom planowanej, przyszłej eksploatacji w okresach rocznych. W tej sytuacji uzasadnione wydają się próby zwiększenia dokładności oszacowań zasobów konkrecji przez wykorzystanie informacji jakich dostarczają liczne zdjęcia dna oceanicznego wykonywane między stacjami opróbowania. Istotne są tu w szczególności procentowe pokrycie dna oceanicznego konkrecjami (NC), możliwe do ustalenia ze zdjęć dane dotyczące liczby, dane dotyczące rozkładu frakcji konkrecji (FD) oraz przysypanie konkrecji osadem (SC). W prezentowanych badaniach zastosowano trzy modele regresji: prostą regresję liniową (SLR), regresję wieloraką (MR) oraz ogólny model liniowy (GLM). GLM zapewnia najdokładniejsze przewidywanie zasobności konkrecji (APN) ze względu na zdolność tego modelu do jednoczesnego uwzględniania zarówno zmiennych ilościowych (NC), jak i zmiennych jakościowych (FD, SC). Średnie absolutne błędy predykcji mieszczą się w przedziale 1,0–1,7 kg/m2, co stanowi 7–13% średniej zasobności konkrecji określonej na podstawie opróbowania bezpośredniego w zbiorze danych (treningowym lub testowym). Wynik ten można uznać za satysfakcjonujący w praktyce prognozowania zasobności konkrecji w miejscach dna objętych jedynie rejestracją fotograficzną.
REFERENCES (46)
1.
Abramowski et al. 2021 – Abramowski, T., Urbanek, M., Baláž, P. 2021. Structural Economic Assessment of Polymetallic Nodules Mining Project with Updates to Present Market Conditions. Minerals 11(3), DOI: 10.3390/min11030311.
2.
A geological model of polymetallic nodule deposits in the Clarion-Clipperton Fracture Zone (Technical Study: No. 6), 2010. , ISA Technical Study. ISA (International Seabed Authority), Kingston, Jamaica.
3.
Alevizos et al. 2018 – Alevizos, E., Schoening, T., Köser, K., Snellen, M. and Greinert, J. 2018. Quantification of the fine-scale distribution of Mn-nodules: insights from AUV multi-beam and optical imagery data fusion. Biogeosciences Discussions 1–29, DOI: 10.5194/bg-2018-60.
4.
Anderson-Sprecher, R. 1994. Model Comparisons and R 2. The American Statistician 48, pp. 113–117, DOI: 10.1080/00031305.1994.10476036.
5.
Baláž, P. 2021. Results of the first phase of the deep-sea polymetallic nodules geological survey in the Interoceanmetal Joint Organization licence area (2001–2016). Mineralia Slovaca 53, pp. 3–36.
6.
Clarion-Clipperton Fracture Zone Exploration Areas for Polymetallic Nodules 2022. International Seabed Authority, January 2022 [Online:] https://www.isa.org.jm/map/cla... [Accessed: 2022-03-29].
7.
Ellefmo, S.L. and Kuhn, T. 2020. Application of Soft Data in Nodule Resource Estimation. Natural Resources Research 30, pp. 1069–1091, DOI: 10.1007/s11053-020-09777-2.
8.
Felix, D. 1980. Some Problems in Making Nodule Abundance Estimates from Seafloor Photographs. Marine Mining 2, pp. 293–302.
9.
Hahn, G. 1973. The coefficient of determination exposed! Chemical Technology 3, pp. 609–612.
10.
Handa, K. and Tsurusaki, K. 1981. Manganese Nodules: Relationship between Coverage and Abundance in the Northern Part of Central Pacific Basin. (No. 15), Deep Sea Mineral Resources Investigation in the Northern Part of Central Pacific Basin, January–March 1979 (GH79-1 Cruise), Ed. by Atsuyuki Mizuno. Geological Survey of Japan.
11.
Hein et al. 2020 – Hein, J.R., Koschinsky, A. and Kuhn, T. 2020. Deep-ocean polymetallic nodules as a resource for critical materials. Nature Reviews Earth & Environment 1, pp. 158–169, DOI: 10.1038/s43017-020-0027-0.
12.
Knobloch et al. 2017 – Knobloch, A., Kuhn, T., Rühlemann, C., Hertwig, T., Zeissler, K.-O. and Noack, S. 2017. Predictive Mapping of the Nodule Abundance and Mineral Resource Estimation in the Clarion-Clipperton Zone Using Artificial Neural Networks and Classical Geostatistical Methods. [In:] Sharma, R. (ed.), Deep-Sea Mining: Resource Potential, Technical and Environmental Considerations. Springer International Publishing, Cham, pp. 189–212, DOI: 10.1007/978-3-319-52557-0_6.
13.
Kotliński, R., 2009. Relationships Between Nodule Genesis And Topography In The Eastern Area Of The C-C region. [In:] Establishment of a geological model of polymetallic nodule deposits in the clarion-clipperton fracture zone of the equatorial North Pacific Ocean. Proceedings of the International Seabed Authority’s Workshop Held 13–20 May, 2003 in Nadi, Fiji. International Seabed Authority, Kingston, Jamaica, pp. 203–221.
14.
Kotliński et al. 2008 – Kotliński, R., Mucha, J. and Wasilewska, M. 2008. Deposits of polymetallic nodules in the Pacific: problems of their reserve estimation. Gospodarka Surowcami Mineralnymi – Mineral Resources Management 24, pp. 257–266.
15.
Kotliński, R. and Stoyanova, V. 2007. Buried and surface polymetallic nodule distribution in the eastern Clarion-Clipperton Zone: main distinctions and similarities. [In:] Advances in Geosciences, Advances in Geosciences. World Scientific Publishing Company, pp. 67–74. https://doi.org/10.1142/978981....
16.
Kuhn, T. and Rathke, M. 2017. Report on visual data acquisition in the field and interpretation for SMnN (No. Blue Mining Deliverable D1.31.), Blue Mining project. European Commission Seventh Framework Programme.
17.
Kuhn et al. 2011 – Kuhn, T., Rühlemann, C. and Wiedicke-Hombach, M.M. 2011. Development of Methods And Equipment For the Exploration of Manganese Nodules In the German License Area In the Central Equatorial Pacific. Presented at the Ninth ISOPE Ocean Mining Symposium, 19–24 June, The International Society of Offshore and Polar Engineers, Maui, Hawaii, USA, pp. 174–177.
18.
Lipton et al. 2021 – Lipton, I., Nimmo, M. and Stevenson, I. 2021. Technical Report: NORI Area D Clarion Clipperton Zone Mineral Resource Estimate (No. 319002). Deep Green Metals Inc.
19.
Machida et al. 2021 – Machida, S., Sato, T., Yasukawa, K., Nakamura, K., Iijima, K., Nozaki, T. and Kato, Y. 2021. Visualisation method for the broad distribution of seafloor ferromanganese deposits. Marine Georesources & Geotechnology 39, pp. 267–279, DOI: 10.1080/1064119X.2019.1696432.
20.
Minerals: Polymetallic Nodules | International Seabed Authority [Online:] https://www.isa.org.jm/explora... [Accessed: 2022-04-04].
21.
Mucha, J. and Wasilewska-Błaszczyk, M. 2020. Estimation Accuracy and Classification of Polymetallic Nodule Resources Based on Classical Sampling Supported by Seafloor Photography (Pacific Ocean, Clarion-Clipperton Fracture Zone, IOM Area). Minerals 10, DOI: 10.3390/min10030263.
22.
Mucha et al. 2013 – Mucha, J., Wasilewska-Błaszczyk, M., Kotliński, R.A. and Maciąg, L. 2013. Variability and Accuracy of Polymetallic Nodules Abundance Estimations in the IOM Area – Statistical and Geostatistical Approach. [In:] Proceedings of Tenth ISOPE Ocean Mining and Gas Hydrates Symposium. Presented at the Tenth ISOPE Ocean Mining and Gas Hydrates Symposium, International Society of Offshore and Polar Engineers, Szczecin, Poland, pp. 27–31.
23.
Park et al. 1996 – Park, C.-Y., Chon, H.-T. and Kang, J.-K. 1996. Correction of nodule abundance using image analysis technique on manganese nodule deposit. Economic and Environmental Geology 29, pp. 429–437.
24.
Park et al. 1999 – Park, C.-Y., Park, S.-H., Kim, C.-W., Kang, J.-K. and Kim, K.-H. 1999. An Image Analysis Technique for Exploration of Manganese Nodules. Marine Georesources & Geotechnology 17, pp. 371–386, DOI: 10.1080/106411999273684.
25.
Piper et al. 1979 – Piper, D.Z., Leong, K. and Cannon, W.F. 1979. Manganese Nodule and Surface Sediment Compositions: DOMES Sites A, B, And C. [In:] Bischoff, J.L., Piper, D.Z. (eds.), Marine Geology and Oceanography of the Pacific Manganese Nodule Province, Marine Science. Springer US, Boston, MA, pp. 437–473, DOI: 10.1007/978-1-4684-3518-4_13.
26.
Polymetallic Nodules | International Seabed Authority 2022. Polymetallic Nodules | International Seabed Authority. URL [Online:] https://www.isa.org.jm/documen... [Accessed: 2022-04-26].
27.
Schoening et al. 2017 – Schoening, T., Jones, D.O.B. and Greinert, J. 2017. Compact-Morphology-based poly-metallic Nodule Delineation. Scientific Reports 7, DOI: 10.1038/s41598-017-13335-x.
28.
Sharma, R. 2017. Assessment of Distribution Characteristics of Polymetallic Nodules and Their Implications on Deep-Sea Mining. [In:] Sharma, R. (ed.), Deep-Sea Mining: Resource Potential, Technical and Environmental Considerations. Springer International Publishing, Cham, pp. 229–256, DOI: 10.1007/978-3-319-52557-0_8.
29.
Sharma, R. 1993. Quantitative estimation of seafloor features from photographs and their application to nodule mining. Marine Georesources & Geotechnology 11, pp. 311–331, DOI: 10.1080/10641199309379926.
30.
Sharma, R., 1989. Computation of Nodule Abundance From Seabed Photos. Presented at the 21st Annual Offshore Technology Conference, Offshore Technology Conference, Houston, Texas, pp. 201–212, DOI: 10.4043/6062-MS.
31.
Sharma et al. 2013 – Sharma, R., Khadge, N.H. and Jai Sankar, S. 2013. Assessing the distribution and abundance of seabed minerals from seafloor photographic data in the Central Indian Ocean Basin. International Journal of Remote Sensing 34, pp. 1691–1706, DOI: 10.1080/01431161.2012.725485.
32.
STATGRAPHICS 19® Centurion 2022. Program for statistical analysis, data visualization and predictive analytics.
33.
Sterk, R. and Stein, J.K. 2015. Seabed Mineral Deposits: An Overview of Sampling Techniques and Future Developments. [In:] Deep Sea Mining Summit. Presented at the Deep Sea Mining Summit, Aberdeen, Scotland, p. 29.
34.
Szamałek et al. 2016 – Szamałek, K., Damrat, M., Frydel, J., Kaulbarsz, D., Kramarska, R., Relisko-Rybak, J., Mucha, J. and Wasilewska-Błaszczyk, M. 2016. Technical report on the Interoceanmetal Joint Organization polymetallic nodules project in the Pacific Ocean Clarion-Clipperton Fracture Zone (No. 501-2.4/2-15). Polish Geological Institute – National Research Institute, Warsaw, Poland.
35.
Technical Report Summary, TOML Mineral Resource, Clarion Clipperton Zone, Pacific Ocean (No. AMC Project 321012), 2021. DeepGreen Metals Inc.
36.
Tsune, A. 2021. Quantitative Expression of the Burial Phenomenon of Deep Seafloor Manganese Nodules. Minerals 11, DOI: 10.3390/min11020227.
37.
Tsune, A. 2015. Effects of Size Distribution of Deep-Sea Polymetallic Nodules on the Estimation of Abundance Obtained from Seafloor Photographs using Conventional Formulae. [In:] Proceedings of Eleventh Ocean Mining and Gas Hydrates Symposium. Presented at the Eleventh Ocean Mining and Gas Hydrates Symposium, International Society of Offshore and Polar Engineers, Kona, Big Island, Hawaii, USA, p. 7.
38.
Tsune, A. and Okazaki, M. 2014. Some Considerations about Image Analysis of Seafloor Photographs for Better Estimation of Parameters of Polymetallic Nodule Distribution. [In:] Proceedings of The Twenty-Fourth International Ocean and Polar Engineering Conference, International Society of Offshore and Polar Engineers, 5–20 June 2014. Busan, Korea, pp. 72–77.
39.
Wasilewska-Błaszczyk, M. and Mucha, J. 2020. Possibilities and Limitations of the Use of Seafloor Photographs for Estimating Polymetallic Nodule Resources – Case Study from IOM Area, Pacific Ocean. Minerals 10, DOI: 10.3390/min10121123.
40.
Wasilewska-Błaszczyk, M. and Mucha, J. 2021. Application of General Linear Models (GLM) to Assess Nodule Abundance Based on a Photographic Survey (Case Study from IOM Area, Pacific Ocean). Minerals 11, DOI: 10.3390/min11040427.
41.
Wong et al. 2019 – Wong, L.J., Pallayil, V., Muthuvel, P., Amudha, K., Kalyan, B., Vishnu, H., Atmanand, M.A. and Chitre, M., 2019. Acoustic Backscattering Properties of Polymetallic Nodules from the Indian Ocean Basin: Results from a Laboratory Measurement. Presented at the 2019 IEEE Underwater Technology (UT). DOI: 10.1109/UT.2019.8734442.
42.
Wong et al. 2020 – Wong, L.J., Kalyan, B., Chitre, M. and Vishnu, H., 2020. Acoustic Assessment of Polymetallic Nodule Abundance Using Sidescan Sonar and Altimeter. IEEE J. Oceanic Eng. 1–11, DOI: 10.1109/JOE.2020.2967108.
43.
Yang et al. 2020 – Yang, Y., He, G., Ma, J., Yu, Z., Yao, H., Deng, X., Liu, F. and Wei, Z., 2020. Acoustic quantitative analysis of ferromanganese nodules and cobalt-rich crusts distribution areas using EM122 multibeam backscatter data from deep-sea basin to seamount in Western Pacific Ocean. Deep Sea Research Part I: Oceanographic Research Papers 161, DOI: 10.1016/j.dsr.2020.103281.
44.
Yoo et al. 2015 – Yoo, C.M., Hyeong, K., Kim, H.J., Chi, S.-B., Kang, J.K., 2015. Image analyses for estimation of polymetallic nodule abundance in Korean Contract Area., in: 44th Underwater Mining Conference (Expanded Abstract). Presented at the 44th Underwater Mining Conference, Florida, USA, pp. 1–2.
45.
Yoo et al. 2018 – Yoo, C.M., Joo, J., Lee, S.H., Ko, Y., Chi, S.-B., Kim, H.J., Seo, I. and Hyeong, K. 2018. Resource Assessment of Polymetallic Nodules Using Acoustic Backscatter Intensity Data from the Korean Exploration Area, Northeastern Equatorial Pacific. Ocean Science Journal 53, pp. 381–394, DOI: 10.1007/s12601-018-0028-9.
46.
Yu, G. and Parianos, J. 2021. Empirical Application of Generalized Rayleigh Distribution for Mineral Resource Estimation of Seabed Polymetallic Nodules. Minerals 11, DOI: 10.3390/min11050449.
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