Akademik Veri Yönetim Sistemi
Tezin Türü: Yüksek Lisans
Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, Jeoloji Mühendisliği Bölümü, Türkiye
Tezin Onay Tarihi: 2007
Öğrenci: SERTAÇ AKAR
Danışman: MEHMET LÜTFİ SÜZEN
Özet:The aim of this thesis is to develop a methodology for detection of naturally occurring offshore oil slicks originating from hydrocarbon seeps using satellite remote sensing techniques. In this scope, Synthetic Aperture Radar (SAR) imagery has been utilized. Case study area was Andrusov High in the Central Black Sea. Hydrocarbon seepage from tectonic or stratigraphic origin at the sea floor causes oily gas plumes to rise up to the sea surface. They form thin oil films on the sea surface called oil slicks. Presence of seeps and surface oil slicks for the offshore basins is a trace of depleted oil traps. Spatial distribution of oil slicks is closely related to sea waves, dominant wind patterns and weathering factors. Even though, there are oil slick detection techniques available with optical remote sensing, laser fluorosensors, and hyperspectral remote sensing, the most efficient results can be obtained from active microwave sensors like synthetic aperture radar (SAR). SAR sensors simply measure the backscattered radiation from the surface and show the roughness of the terrain. Oil slicks dampen the sea waves creating dark patches in the SAR image. In this context an adapted methodology has been proposed, including three levels namely; visual inspection, image filtering and object based fuzzy classification. With visual inspection, targets have been identified and subset scenes have been created. Subset scenes have been categorized into 3 cases based on contrast difference of dark spots to the surroundings. Then object based classification has been utilized with the fuzzy membership functions defined by extracted features of layer values, shape and texture from segmented and filtered SAR subsets. As a result, oil slicks have been discriminated from look-alikes which are the phenomena resembling oil slicks. The overall classification accuracy obtained by averaging three different cases is 83 % for oil slicks and 77 % for look-alikes. The results of this study can considered to be a preliminary work and supplementary information for determining the best operational procedure of offshore hydrocarbon exploration.