The Experts below are selected from a list of 207 Experts worldwide ranked by ideXlab platform
Gerhard Bohrmann - One of the best experts on this subject based on the ideXlab platform.
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application of the automatic Seep location estimator asle with the use of contextual information for estimating offshore Oil Seeps
Remote Sensing Applications: Society and Environment, 2017Co-Authors: Gopika Suresh, Christian Melsheimer, Ian R Macdonald, Justus Notholt, Gerhard BohrmannAbstract:Offshore Oil Seeps occur in waters across the globe but locating them using in-situ methods requires a significant amount of time and cost. This motivated the design and implementation of an automatic method that can provide the location of offshore Oil Seeps from Synthetic Aperture Radar (SAR) data and resulted in the creation of the Automatic Oil Seep Location Estimator (ASLE). Designed and implemented in the open source programming language Python, it uses a rule-based decision classifier to automatically discriminate between offshore Oil slicks and other features visible in ocean SAR images. This paper reports how the detection and Seep location estimation results improve when contextual wind information is added during object classification in the ASLE. The optimisation is discussed by comparing the new set of results to an already validated dataset of ENVISAT images of the Black Sea. Additionally, the paper reports the results of testing the ASLE on a dataset of RADARSAT images and the offshore Oil Seeps estimated in the Gulf of Mexico. The efficiency of ASLE with respect to other existing algorithms is also discussed.
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establishing criteria to distinguish Oil Seep from methane Seep carbonates
Geology, 2016Co-Authors: Daniel Smrzka, Gerhard Bohrmann, Jennifer Zwicker, Andreas Klugel, Patrick Monien, Wolfgang Bach, Jorn PeckmannAbstract:Hydrocarbon Seeps harbor copious chemosynthesis-dependent life, the traces of which are preserved in the fossil record within authigenic carbonates. These environments are mostly characterized by Seepage of methane-rich fluids, yet numerous crude Oil–dominated Seeps have been discovered in recent years. Oil Seepage has a profound influence on the local fauna, but recognizing such Seeps in the rock record remains elusive. This study presents new geochemical data that will allow for a more confident identification of ancient Oil-Seep deposits. Geochemical data from modern and ancient Seep limestones reveal that Oil-dominated Seep carbonates are enriched in rare earth elements and uranium compared to their methane-dominated counterparts. These trace element patterns have the potential to serve as a basis for an improved understanding of the adaptation of chemosynthetic life to Oil Seepage, and to better constrain the marine carbon cycle in the geologic past.
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automatic estimation of Oil Seep locations in synthetic aperture radar images
IEEE Transactions on Geoscience and Remote Sensing, 2015Co-Authors: Gopika Suresh, Christian Melsheimer, Janhendrik Korber, Gerhard BohrmannAbstract:A framework for the automatic detection of natural Oil slicks and estimation of their associated Oil Seeps using synthetic aperture radar (SAR) images is presented, and the methodology used has been explained in detail. The designed detection system is the first automatic Oil Seep estimation system known to exist. The system detects Oil slicks in individual SAR images and estimates their origins on the sea surface. Spatial clustering of temporally recurrent slick origins is conducted in order to estimate the locations of the associated Oil Seeps on the sea floor. The system is implemented in the programming language Python and a direct rule-based approach is employed for the classification unit. A data set of 178 images of the Black Sea acquired by ENVISAT's Advanced Synthetic Aperture Radar was used to test the algorithm. In this paper, the methodology used to design the algorithm and the automatically estimated Oil Seep locations are reported. The efficiency of the system with respect to manual detection is discussed.
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natural Oil Seep location estimation in sar images using direct and contextual information
International Geoscience and Remote Sensing Symposium, 2014Co-Authors: Gopika Suresh, Christian Melsheimer, Georg Heygster, Gerhard BohrmannAbstract:The Automatic Oil Seep Location Estimator (ASLE) described in this paper is a system that can automatically estimate the locations of potential Oil Seeps using SAR images. The ASLE segments dark areas in SAR images, calculates direct features related to geometry and backscatter as well as contextual features like wind speed and direction for each dark object and uses them to classify the object as either a natural Oil slick or a look-alike. The classification scheme has been implemented using a rule-based approach. The slick origins are detected and clustered together spatially to detect the feeding Seep locations. A preliminary dataset of 25 images from ENVISAT's Advanced Synthetic Aperture Radar (ASAR) was used to test the algorithm. The results show the addition of contextual information helps reduce false positives in automatic slick detection.
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an automatic detection system for natural Oil Seep origin estimation in sar images
International Geoscience and Remote Sensing Symposium, 2013Co-Authors: Gopika Suresh, Christian Melsheimer, Gerhard Bohrmann, Georg Heygster, Janhendrik KorberAbstract:A framework for the automatic detection of natural Oil Seeps using Synthetic Aperture Radar (SAR) images, implemented in Python, is presented. Dark objects are detected using morphological thresholding. For each object, features are computed, which are used to classify the object as either a natural Oil slick or a look-alike. The classification scheme has been implemented using a rule-based approach. The slick origins are detected and clustered together spatially, in order to detect the Seep origin. A dataset of 122 images from ENVISAT's Advanced Synthetic Aperture Radar (ASAR) were used to test the algorithm. In this paper, only preliminary results are reported.
Gopika Suresh - One of the best experts on this subject based on the ideXlab platform.
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application of the automatic Seep location estimator asle with the use of contextual information for estimating offshore Oil Seeps
Remote Sensing Applications: Society and Environment, 2017Co-Authors: Gopika Suresh, Christian Melsheimer, Ian R Macdonald, Justus Notholt, Gerhard BohrmannAbstract:Offshore Oil Seeps occur in waters across the globe but locating them using in-situ methods requires a significant amount of time and cost. This motivated the design and implementation of an automatic method that can provide the location of offshore Oil Seeps from Synthetic Aperture Radar (SAR) data and resulted in the creation of the Automatic Oil Seep Location Estimator (ASLE). Designed and implemented in the open source programming language Python, it uses a rule-based decision classifier to automatically discriminate between offshore Oil slicks and other features visible in ocean SAR images. This paper reports how the detection and Seep location estimation results improve when contextual wind information is added during object classification in the ASLE. The optimisation is discussed by comparing the new set of results to an already validated dataset of ENVISAT images of the Black Sea. Additionally, the paper reports the results of testing the ASLE on a dataset of RADARSAT images and the offshore Oil Seeps estimated in the Gulf of Mexico. The efficiency of ASLE with respect to other existing algorithms is also discussed.
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automatic estimation of Oil Seep locations in synthetic aperture radar images
IEEE Transactions on Geoscience and Remote Sensing, 2015Co-Authors: Gopika Suresh, Christian Melsheimer, Janhendrik Korber, Gerhard BohrmannAbstract:A framework for the automatic detection of natural Oil slicks and estimation of their associated Oil Seeps using synthetic aperture radar (SAR) images is presented, and the methodology used has been explained in detail. The designed detection system is the first automatic Oil Seep estimation system known to exist. The system detects Oil slicks in individual SAR images and estimates their origins on the sea surface. Spatial clustering of temporally recurrent slick origins is conducted in order to estimate the locations of the associated Oil Seeps on the sea floor. The system is implemented in the programming language Python and a direct rule-based approach is employed for the classification unit. A data set of 178 images of the Black Sea acquired by ENVISAT's Advanced Synthetic Aperture Radar was used to test the algorithm. In this paper, the methodology used to design the algorithm and the automatically estimated Oil Seep locations are reported. The efficiency of the system with respect to manual detection is discussed.
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natural Oil Seep location estimation in sar images using direct and contextual information
International Geoscience and Remote Sensing Symposium, 2014Co-Authors: Gopika Suresh, Christian Melsheimer, Georg Heygster, Gerhard BohrmannAbstract:The Automatic Oil Seep Location Estimator (ASLE) described in this paper is a system that can automatically estimate the locations of potential Oil Seeps using SAR images. The ASLE segments dark areas in SAR images, calculates direct features related to geometry and backscatter as well as contextual features like wind speed and direction for each dark object and uses them to classify the object as either a natural Oil slick or a look-alike. The classification scheme has been implemented using a rule-based approach. The slick origins are detected and clustered together spatially to detect the feeding Seep locations. A preliminary dataset of 25 images from ENVISAT's Advanced Synthetic Aperture Radar (ASAR) was used to test the algorithm. The results show the addition of contextual information helps reduce false positives in automatic slick detection.
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an automatic detection system for natural Oil Seep origin estimation in sar images
International Geoscience and Remote Sensing Symposium, 2013Co-Authors: Gopika Suresh, Christian Melsheimer, Gerhard Bohrmann, Georg Heygster, Janhendrik KorberAbstract:A framework for the automatic detection of natural Oil Seeps using Synthetic Aperture Radar (SAR) images, implemented in Python, is presented. Dark objects are detected using morphological thresholding. For each object, features are computed, which are used to classify the object as either a natural Oil slick or a look-alike. The classification scheme has been implemented using a rule-based approach. The slick origins are detected and clustered together spatially, in order to detect the Seep origin. A dataset of 122 images from ENVISAT's Advanced Synthetic Aperture Radar (ASAR) were used to test the algorithm. In this paper, only preliminary results are reported.
Sarah E Cook - One of the best experts on this subject based on the ideXlab platform.
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the fossil Oil Seep in mupe bay dorset a myth investigated
Marine and Petroleum Geology, 1993Co-Authors: Jennifer A Miles, Christopher J Downes, Sarah E CookAbstract:Abstract The story of the Mupe Bay boulder bed has assumed mythical proportions in the British Oil industry. It is supposed to demonstrate a Seep which was active at the time of deposition of the lowermost Wealden sandstones, indicating that the Lias source rocks, offshore to the south, had already entered the Oil window at that time. The boulder bed contains sandstone clasts which are apparently Oil-cemented, in a sandstone matrix which shows lighter Oil staining. The boulders were obviously soft when deposited, as they were deformed during deposition. This study suggests that there is no difference in the maturity or source of the Oils from the clasts and the host channel sand; that there is no significant difference in the diagenetic sequence or in the authigenic mineral assemblage of the two sandstones. The conclusion is that the clasts are not contemporaneous ‘Oil-cemented’ boulders as previously supposed. The only differences noted in this study between the clasts and their host sandstone is in their grain sizes and the degree of biodegradation of the Oils. This difference in biodegradation can logically be ascribed to the difference in permeability associated with the change in grain size, and the resulting penetration of meteoric water. The source of the Oil in both of the sandstones can be correlated to the Lias. Aromatic biological markers for thermal maturity, resistant to biodegradation, indicate a maturity equivalent to about 1% vitrinite reflectance or higher for both Oils. These conclusions remove the constraint of early Wealden generation of Oil previously imposed on burial history reconstructions for the timing of Oil generation and migration in south Dorset. It is further proposed that Osmington/Bran Point-Lulworth Cove-Mupe Bay-Worbarrow Bay area was the site of a large Oilfield, with reservoirs within the Wealden and extending down to the Bencliff Grits (and Bridport Sandstones?), of the same order of size as the Wytch Farm accumulation. This Oilfield was uplifted and breached during the erosion which followed Tertiary compression.
John Agard - One of the best experts on this subject based on the ideXlab platform.
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free living nematodes from a natural Oil Seep at la brea trinidad and tobago
Living World Journal of the Trinidad and Tobago Field Naturalists' Club, 2012Co-Authors: Judith Gobin, John Agard, Azad Mohammed, R M WarwickAbstract:The La Brea Oil Seep in Trinidad is reportedly one of the largest natural Oil Seeps in the world. As part of a larger environmental survey of this Seepage site, the free-living marine nematodes of the meiofauna were studied. Samples were col- lected using 60 mm corers at 10 stations at the Seep site, between Point Courbarill and Point Rouge west of the Trinidad Pitch Lake. The nematode fauna was represented by 16 families and 32 species. Five families: Chromadoridae, Comesomatidae, Linhomoeidae, Monohysteridae and Ethmolaimidae, comprised approximately 75% of the total abundance. The species diversity (H’) was 3.09 with a range of 1.39 to 2.83 between stations. Given the uniqueness of this ecosystem the average taxonomic distinctness index of biodiversity (D+) was applied to the nematode data and comparisons were made with other locations in the UK and Chile. Taxonomic distinctness (D+) values for La Brea was determined to be 73.28, with the lowest value at station 6 (64.81). Although nematodes are relatively abundant in the sediment samples from La Brea, their extremely low taxonomic distinctness is indicative of a stressed environment.
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the occurrence of nadph ferrihemoprotein reductase in corbula caribea from a natural Oil Seep at la brea trinidad
Marine Pollution Bulletin, 2004Co-Authors: Azad Mohammed, John AgardAbstract:Abstract Corbula caribea is the most common non-polychaete macrofaunal organism identified at a large natural Oil Seep at La Brea in south Trinidad. It is hypothesized that these animals may possess (NADPH-ferrihemoprotein reductase) a component of the Mixed Function Oxygenase system (MFO), which may allow them to ameliorate the potentially deleterious effects resulting from exposure to the high levels of petroleum hydrocarbons within this environment. This study was designed to determine whether organisms from the Seep site showed greater enzyme activity when compared to organisms from a non-Seep reference site. NADPH-ferrihemoprotein reductase activity was determined by incubating 10 μm cryostat sections with nitro-blue tetrazolium. The reaction product was determined by visual assessment and quantified by measuring the relative mean stain intensity. The intense staining, indicative of enzyme activity was evident in the digestive epithelia of Seep animals. Observations indicated that organisms from the Seep showed more intense staining, indicating greater enzyme activity, when compared to animals from a non-Seep reference site. The relative stain intensity of NADPH-ferrihemoprotein reductase determined for organisms from the Seep was 61.30. This was significantly higher than the stain intensity determined for organisms from the non-Seep reference site (7.11). This supported visual assessments, which suggested that the Seep organisms showed higher enzyme activity than organisms from the non-Seep site. The results suggest that NADPH-ferrihemoprotein reductase may be present in Corbula caribea from the Seep site and not in those from the non-Seep site. It is possible that this enzyme may contribute to these animals ability to tolerate chronic exposure to petroleum hydrocarbons and offer then a selective advantage for survival the Seep environment.
Christian Melsheimer - One of the best experts on this subject based on the ideXlab platform.
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application of the automatic Seep location estimator asle with the use of contextual information for estimating offshore Oil Seeps
Remote Sensing Applications: Society and Environment, 2017Co-Authors: Gopika Suresh, Christian Melsheimer, Ian R Macdonald, Justus Notholt, Gerhard BohrmannAbstract:Offshore Oil Seeps occur in waters across the globe but locating them using in-situ methods requires a significant amount of time and cost. This motivated the design and implementation of an automatic method that can provide the location of offshore Oil Seeps from Synthetic Aperture Radar (SAR) data and resulted in the creation of the Automatic Oil Seep Location Estimator (ASLE). Designed and implemented in the open source programming language Python, it uses a rule-based decision classifier to automatically discriminate between offshore Oil slicks and other features visible in ocean SAR images. This paper reports how the detection and Seep location estimation results improve when contextual wind information is added during object classification in the ASLE. The optimisation is discussed by comparing the new set of results to an already validated dataset of ENVISAT images of the Black Sea. Additionally, the paper reports the results of testing the ASLE on a dataset of RADARSAT images and the offshore Oil Seeps estimated in the Gulf of Mexico. The efficiency of ASLE with respect to other existing algorithms is also discussed.
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automatic estimation of Oil Seep locations in synthetic aperture radar images
IEEE Transactions on Geoscience and Remote Sensing, 2015Co-Authors: Gopika Suresh, Christian Melsheimer, Janhendrik Korber, Gerhard BohrmannAbstract:A framework for the automatic detection of natural Oil slicks and estimation of their associated Oil Seeps using synthetic aperture radar (SAR) images is presented, and the methodology used has been explained in detail. The designed detection system is the first automatic Oil Seep estimation system known to exist. The system detects Oil slicks in individual SAR images and estimates their origins on the sea surface. Spatial clustering of temporally recurrent slick origins is conducted in order to estimate the locations of the associated Oil Seeps on the sea floor. The system is implemented in the programming language Python and a direct rule-based approach is employed for the classification unit. A data set of 178 images of the Black Sea acquired by ENVISAT's Advanced Synthetic Aperture Radar was used to test the algorithm. In this paper, the methodology used to design the algorithm and the automatically estimated Oil Seep locations are reported. The efficiency of the system with respect to manual detection is discussed.
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natural Oil Seep location estimation in sar images using direct and contextual information
International Geoscience and Remote Sensing Symposium, 2014Co-Authors: Gopika Suresh, Christian Melsheimer, Georg Heygster, Gerhard BohrmannAbstract:The Automatic Oil Seep Location Estimator (ASLE) described in this paper is a system that can automatically estimate the locations of potential Oil Seeps using SAR images. The ASLE segments dark areas in SAR images, calculates direct features related to geometry and backscatter as well as contextual features like wind speed and direction for each dark object and uses them to classify the object as either a natural Oil slick or a look-alike. The classification scheme has been implemented using a rule-based approach. The slick origins are detected and clustered together spatially to detect the feeding Seep locations. A preliminary dataset of 25 images from ENVISAT's Advanced Synthetic Aperture Radar (ASAR) was used to test the algorithm. The results show the addition of contextual information helps reduce false positives in automatic slick detection.
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an automatic detection system for natural Oil Seep origin estimation in sar images
International Geoscience and Remote Sensing Symposium, 2013Co-Authors: Gopika Suresh, Christian Melsheimer, Gerhard Bohrmann, Georg Heygster, Janhendrik KorberAbstract:A framework for the automatic detection of natural Oil Seeps using Synthetic Aperture Radar (SAR) images, implemented in Python, is presented. Dark objects are detected using morphological thresholding. For each object, features are computed, which are used to classify the object as either a natural Oil slick or a look-alike. The classification scheme has been implemented using a rule-based approach. The slick origins are detected and clustered together spatially, in order to detect the Seep origin. A dataset of 122 images from ENVISAT's Advanced Synthetic Aperture Radar (ASAR) were used to test the algorithm. In this paper, only preliminary results are reported.
