Regression methods in predicting the abundance of nodules from seafloor images – a case study from the IOM area, Pacific Ocean

• Autorzy: Wasilewska-Błaszczyk Monika , Mucha Jacek

Identyfikatory

DOI

10.24425/gsm.2023.145886

Warianty tytułu

PL

Metody regresji w szacowaniu zasobności konkrecji polimetalicznych ze zdjęć dna oceanicznego – studium przypadku z obszaru IOM, Ocean Spokojny

• Języki publikacji: EN

Abstrakty

EN

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.

PL

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.

Słowa kluczowe

EN

nodule abundance; regression method; polymetallic nodules; seafloor; fraction distribution; Clarion-Clipperton zone

PL

zasobność konkrecji; metoda regresji; konkrecje polimetaliczne; dno oceaniczne; strefa Clarion-Clipperton

• Wydawca: Instytut Gospodarki Surowcami Mineralnymi i Energią PAN ; Komitet Zrównoważonej Gospodarki Surowcami Mineralnymi PAN
• Czasopismo: Gospodarka Surowcami Mineralnymi
• Rocznik: 2023
• Tom: T. 39, z. 2
• Strony: 5--36
• Opis fizyczny: Bibliogr. 46 poz., rys., tab., wykr.

Twórcy

autor

Wasilewska-Błaszczyk Monika

• AGH University of Science and Techology, Kraków, Poland

• https://orcid.org/0000-0003-0042-9782

autor

Mucha Jacek

• AGH University of Science and Technology, Kraków, Poland

• https://orcid.org/0000-0001-6366-083X

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)