GIS와 확률방법을 이용한 북동태평양에서 망간단괴 개발 적지선정 연구

GIS와 확률방법을 이용한 북동태평양에서 망간단괴 개발 적지선정 연구
Alternative Title
Suitable Site Investigation for Manganese Nodule Development in the Northeastern Pacific using GIS and Probability Methods
고영탁; 형기성; 박정기; 지상범; 김기현
KIOST Author(s)
Ko, Young Tak(고영탁)Hyeong, Ki Seong(형기성)Chi, Sang Bum(지상범)
Publication Year
The northeastern Pacific shows the highest nodule abundance among the oceans in the world, and thus has been drawing international attention for deep-sea manganese nodule development. The objectives of this study are to construct database using geostatistics and geographic information system (GIS), to quantify rating value using probability and statistical methods, to suggest suitable sites for manganese nodule development, and to verify results using three statistical approaches. The copper and nickel contents of the nodules were determined by chemical analysis. Factors related to slope, aspect, water depth, and topography were obtained from multibeam echo sounding data. The transparent layer thickness was determined using a subbottom profiler. The probability method was used to calculate each factor's rating, and the layers for different factors were summed to produce the development potential index (DPI). Result maps were constructed for manganese nodules development. The distribution pattern of DPI showed to be controlled of nodule abundance and metal contents. Verification was performed by comparing the results with sampling data in three ways: success and prediction rate, linear regression, and test of independence. Verification was conducted separately for the south and north sectors because of their difference in geologic and geochemical properties, sediment type, nodule type, and nodule genesis. First, the success and prediction rate verifications were conducted by dividing the block type, sector, success rate, and prediction rate. Second, the regression analysis was conducted to search for the mathematical relationships between nodule abundance and DPI. As the results of two verifications, the success rate turns out to be the most suitable model in terms of area and cumulative frequency, and in terms of slope and R2 for block N1. On the other hand, the prediction rate is the most suitable model for block N3. In general, success rate is more suitable mo
Bibliographic Citation
Deep-Sea Minerals and Mining 2008, pp.15 - 16, 2008
RWTH Aachen University
RWTH Aachen University
Related Researcher
Research Interests

deep sea mineral resources,deep sea sediments,심해저 광물자원,심해저 퇴적물,심해 환경

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