Logo des Repositoriums
Zur Startseite
  • English
  • Deutsch
Anmelden
  1. Startseite
  2. SuUB
  3. Dissertationen
  4. Identification of seafloor provinces - specific applications at the deep-sea HÃ ¥kon Mosby Mud Volcano and the North Sea
 
Zitierlink URN
https://nbn-resolving.de/urn:nbn:de:gbv:46-diss000103267

Identification of seafloor provinces - specific applications at the deep-sea HÃ ¥kon Mosby Mud Volcano and the North Sea

Veröffentlichungsdatum
2006-03-24
Autoren
Jerosch, Kerstin  
Betreuer
Schlüter, Michael  
Gutachter
Schulz, Horst D.  
Zusammenfassung
The identification of distinct provinces is currently an emphasis of marine research geosciences. Typological approaches for the HÃ ¥kon Mosby Mud Volcano and the North Sea combining geological, biological and chemical properties are accomplished by geostatistical, multivariate statistical, and GIS techniques. Besides scientific needs seafloor provinces support management decisions related to upcoming economic use of the seafloor and bear up to model spatio-temporal connections and changes of coastal regions.Submarine mud volcanoes are considered as source locations for methane indicated by unique communities as Beggiatoa and pogonophorans. They signify graduated CH4 consumption of microbial consortia (sulphate-reducing bacteria and anaerobic methane-oxidising archaea). The quantification of the habitat areas identified by indicator kriging is thus important for understanding the global methane cycle.Kriging methods were also applied for selected parameters for the North Sea creating surface maps from measured data as an assumption for multivariate statistics like Classification and Regression Trees.
Schlagwörter
HÃ ¥kon Mosby Mud Volcano

; 

North Sea

; 

seafloor provinces

; 

AOM indicating communities

; 

GIS

; 

kriging

; 

area quantification

; 

video mosaics

; 

automatical image analysis

; 

CART
Institution
Universität Bremen  
Fachbereich
Fachbereich 05: Geowissenschaften (FB 05)  
Dokumenttyp
Dissertation
Zweitveröffentlichung
Nein
Sprache
Englisch
Dateien
Lade...
Vorschaubild
Name

00010326.pdf

Size

39.47 MB

Format

Adobe PDF

Checksum

(MD5):1723df4fe6b1a53a47027917dc61793e

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science

  • Datenschutzbestimmungen
  • Endnutzervereinbarung
  • Feedback schicken