Yampi Shelf, Browse Basin, North-West Shelf, Australia: a test-bed for constraining hydrocarbon migration and seepage rates using combinations of 2D and 3D seismic data and multiple, independent remote sensing technologies

O'Brien, Geoffrey; Lawrence, G. M.; Williams, Alan; Glenn, K.; Barrett, A. G.; Lech, Megan; Edwards, Dianne; Cowley, R.; Boreham, Christopher J.; Summons, Roger E.

doi:10.1016/j.marpetgeo.2004.10.027

Cited by 48 publications

(36 citation statements)

References 14 publications

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“…When vast areas of the sea surface have been polluted, we can use remote sensing technology to do a real time, high speed, macro monitoring of the polluted regions. Studies on the condition and process of oil spills on sea surfaces are the subject of much attention worldwide [2][3][4].…”

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confidence: 99%

Study on the hyperspectral polarized reflection characteristics of oil slicks on sea surfaces

et al. 2011

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This study is concerned with the need for remote sensing techniques for the monitoring of oil-slick pollution on sea surfaces and the effects of oil-slick pollution on the sea. We used Daqing crude oil, Jilin crude oil, heavy oil and seawater from Dalian Bay to simulate oil-slick pollution on the sea surface and obtained multi-angle hyperspectral polarized reflectance information, from which we calculated the polarization for different kinds of crude oil, and oil-slicks of different thicknesses. By comparing the degree of polarization between oil-slicks of different thickness, and sea water, it was found that in the case of thin oil-slicks, the degree of polarization of seawater is higher than that of oil slicks with wavelengths of between 400-1000 nm. However, there was little difference at a wavelength of 785 nm. At angles of incident of 20° and 30° (the viewing angle equals the incident angle), it was easy to distinguish the changes of oil-slick thickness by the degree of polarization at 785 and 880 nm in the near-infrared band. The crude oil showed its inherent polarized characteristic as the oil-slick thickness increased to a certain degree. That is to say, the polarization of the seawater was higher than that of the oil-slick in the visible light range, but less than that in the near-infrared band. As the incident angle changed from 40° to 50°, the degree of polarization of seawater increased, and it was higher than that for the oil-sick between wavelengths of 400-1000 nm at an angle of incidence of 50°. This research on the polarized characteristics of an oil-slick on a sea surface brings new scientific techniques to the monitoring of sea-surface pollution by remote sensing. Oil from offshore spills is one of the common marine pollutants; it causes serious pollution of the marine environment and has harmful effects on marine life. Oil spills on the sea surface can obstruct the ocean-atmosphere heat exchange and influence the absorption, transmission and reflection of light waves by the sea surface [1]. When vast areas of the sea surface have been polluted, we can use remote sensing technology to do a real time, high speed, macro monitoring of the polluted regions. Studies on the condition and process of oil spills on sea surfaces are the subject of much attention worldwide [2][3][4].With the rapid development of remote sensing technology, researchers are focusing on using hyperspectral remote-*Corresponding authors

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confidence: 99%

Study on the hyperspectral polarized reflection characteristics of oil slicks on sea surfaces

et al. 2011

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“…The oil slick detection and mapping were often used for the hydrocarbon exploration activities and emergency response to oil spill incidents in the world places such as Gulf of Mexico (Friedman et al 2002) and South Caspian Sea (Ivanov & Vostokov et al 2004; *Corresponding author. Email: emil.bayramov@yahoo.com Ivanov & Fang et al 2004;Zatyagalova et al 2007;Ivanov & Zatyagalova, 2008;Ivanov et al 2012), Australian Shelf (O'Brein et al 2005) and Santa Barbara Channel, California (Leifer et al 2006). Oil spill satellite detection was also used during the Lebanese oil spill pollution crisis (Coppini et al 2011) during the hostilities between Israel and Lebanon back in 2006, as well for routine monitoring of illegal oil slicks from maritime traffic (Zodiatis et al 2012).…”

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confidence: 99%

“…Possibilities for the detection of oil slicks depend on radar polarization modes, height of the waves, the amount of oil that has been released, slick nature, the speed of the wind and type of oil (Espedal 1998;Kotova et al 1998;Espedal & Wahl 1999;Gade et al 2000;Girard-Ardhuin et al 2003;O'Brein et al 2005).…”

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confidence: 99%

Modelling of oil spill frequency, leak sources and contamination probability in the Caspian Sea using multi-temporal SAR images 2006–2010 and stochastic modelling

Bayramov¹,

Buchroithner²

2015

Geomatics, Natural Hazards and Risk

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The main goal of this research was to detect oil spills, to determine the oil spill frequencies and to approximate oil leak sources around the Oil Rocks Settlement, the Chilov and Pirallahi Islands in the Caspian Sea using 136 multi-temporal ENVISAT Advanced Synthetic Aperture Radar Wide Swath Medium Resolution images acquired during 2006À2010. The following oil spill frequencies were observed around the Oil Rocks Settlement, the Chilov and Pirallahi Islands: 2À10 (3471.04 sq km), 11À20 (971.66 sq km), 21À50 (692.44 sq km), 51À128 (191.38 sq km). The most critical oil leak sources with the frequency range of 41À128 were observed at the Oil Rocks Settlement. The exponential regression analysis between wind speeds and oil slick areas detected from 136 multitemporal ENVISAT images revealed the regression coefficient equal to 63%. The regression model showed that larger oil spill areas were observed with decreasing wind speeds. The spatiotemporal patterns of currents in the Caspian Sea explained the multi-directional spatial distribution of oil spills around Oil Rocks Settlement, the Chilov and Pirallahi Islands. The linear regression analysis between detected oil spill frequencies and predicted oil contamination probability by the stochastic model showed the positive trend with the regression coefficient of 30%.

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“…However, SAR is useful particularly for searching large areas and observing oceans and seas at night and at cloudy weather conditions. Possibilities for the detection of oil slicks depend on radar polarization modes, height of the waves, the amount of oil that has been released, slick nature, the speed of the wind and type of oil (Espedal et al 1999;Espedal 1998;Girard-Ardhuin et al 2003;O'Brein et al 2005;Kotova et al 1998;Gade et al 2000). The discrimination of oil slicks from oil spill look-alikes produced by oceanic and atmospheric processes is one of the most complex issues in the process of oil slick detection from SAR images.…”

Section: Introductionmentioning

confidence: 99%

Detection of Oil Pollution Hotspots and Leak Sources Through the Quantitative Assessment of the Persistence and Temporal Repetition of Regular Oil Spills in the Caspian Sea Using Remote Sensing and Gis

Bayramov

Buchroithner

Bayramov

2015

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The main goal of this research was to detect oil spills, to determine the oil spill frequencies and to approximate oil leak sources around the Oil Rocks Settlement, the Chilov and Pirallahi Islands in the Caspian Sea using 136 multi-temporal ENVISAT Advanced Synthetic Aperture Radar Wide Swath Medium Resolution Images acquired during 2006-2010. The following oil spill frequencies were observed around the Oil Rocks Settlement, the Chilov and Pirallahi Islands: 2-10 (3471.04 sq. km.), 11-20 (971.66 sq. km.), 21-50 (692.44 sq. km.), 51-128 (191.38 sq. km.). The most critical oil leak sources with the frequency range of 41-128 were observed at the Oil Rocks Settlement. The exponential regression analysis between wind speeds and oil slick areas detected from 136 multi-temporal ENVISAT images revealed the regression coefficient equal to 63%. The regression model showed that larger oil spill areas were observed with decreasing wind speeds. The spatiotemporal patterns of currents in the Caspian Sea explained the multi-directional spatial distribution of oil spills around Oil Rocks Settlement, the Chilov and Pirallahi Islands. The linear regression analysis between detected oil spill frequencies and predicted oil contamination probability by the stochastic model showed the positive trend with the regression coefficient of 30%.

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Yampi Shelf, Browse Basin, North-West Shelf, Australia: a test-bed for constraining hydrocarbon migration and seepage rates using combinations of 2D and 3D seismic data and multiple, independent remote sensing technologies

Cited by 48 publications

References 14 publications

Study on the hyperspectral polarized reflection characteristics of oil slicks on sea surfaces

Study on the hyperspectral polarized reflection characteristics of oil slicks on sea surfaces

Modelling of oil spill frequency, leak sources and contamination probability in the Caspian Sea using multi-temporal SAR images 2006–2010 and stochastic modelling

Detection of Oil Pollution Hotspots and Leak Sources Through the Quantitative Assessment of the Persistence and Temporal Repetition of Regular Oil Spills in the Caspian Sea Using Remote Sensing and Gis

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