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Prétraitement optimal des images radar et modélisation des dérives de nappes d'hydrocarbures pour l'aide à la photo-interprétation en exploration pétrolière et surveillance environnementale

Abstract : This thesis deals with the preprocessing of radar images and their optimization for the analyzes in order to detect natural marine oil slicks (Sea surface Outbreak/SSO) as well as better determine their source location at the Sea Floor Source (SFS). We explained herein means, methods and difficulties encountered. This thesis consists of the following three distinct research axes represented by three submitted papers :1- A stochastic approach for pre-processing and improvement of C-band radar images to automatically detect oil slicks;2- A stochastic approach using a large quantity of radar images to evaluate the influence of wind speed and the different modes of the instrument (SAR) on the delectability of marine oil slicks ;3- Accurate location of the Sea Floor Source of marine hydrocarbon emissions using a new vertical drift model within the water column, applied to the northern Gulf of Mexico (southern USA).So first, we focused on the optimization of pre-processing and the improvement of C-band radar images by stochastic methods to automatically detect oil slicks. The proposed methodology includes three processing levels : preprocessing, thresholding, and binary cleaning. The first level consists of correcting the heterogeneity of the luminosity in the radar images resulting from the non-Lambertian reflection of the radar signal on the sea surface. The second level consists of a thresholding step which aims to produce dark objects as close as possible to the manually developed training data set. The third level consists of cleaning the output binary images from the noise residuals. Several preprocessing and cleaning methods have been tested and evaluated by a qualification engine that compares the objects automatically detected with the manual detection. Then, we focus in a second chapter in the evaluation of the influence of wind speed and instrument modes on the detection of oil slicks from radar images by using a stochastic approach. This study was dictated by the need to define the meteorological conditions capable for an optimal detection of oil slicks, from the radar images. The objective was to determine the wind speed range which optimizes the detection of oil slicks in all radar images using BigData and a stochastic approach. This work was also an opportunity to investigate the properties of the radar acquisition modes used in the detection of oil slicks. Thus, a 5-mode performance order is established (IW, APP, PRI, IMP and WSM) and shows that the IW (Sentinel-1) mode, with the best spatial resolution (greater than 5x20m) detects oil slicks at high wind speed. Finally, we focused on estimating the location of marine natural oil seeps sources using a new vertical drift model, applied in the Gulf of Mexico. Thus, we have developed a new method for detecting the source of oil seeps from natural sources on the seafloor according to the vertical drift model. Occurrences of oil seeps on the sea surface are generally offset from their sources on the seabed by several hundred meters or even kilometers. This deflection is dependent on the upward velocity of the oil and marine currents along the water column. In this study, the diameter of the droplets is not known to us a priori. To fill this gap, a new method called "the sources path" was applied herein that propose the Sea Floor Source taking into account the droplet size and the vertical drift within the water column before their Sea surface Outbreak (SSO).If these three studies can be taken independently of each other, they are firmly interconnected and complementary. They form a sort of process ranging from the optimization of the detection of an oil slick (the most appropriate means and tools for better detection) to the location of its source on the seafloor
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Zhour Najoui. Prétraitement optimal des images radar et modélisation des dérives de nappes d'hydrocarbures pour l'aide à la photo-interprétation en exploration pétrolière et surveillance environnementale. Géographie. Université Paris-Est, 2017. Français. ⟨NNT : 2017PESC1158⟩. ⟨tel-01764947⟩

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