Exploratory Data Analysis of Synthetic Aperture Radar (SAR) Measurements to Distinguish the Sea Surface Expressions of Naturally-Occurring Oil Seeps from Human-Related Oil Spills in Campeche Bay (Gulf of Mexico)

Abstract

An Exploratory Data Analysis (EDA) aims to use Synthetic Aperture Radar (SAR) measurements for discriminating between two oil slick types observed on the sea surface: naturally-occurring oil seeps versus human-related oil spills-the use of satellite sensors for this task is poorly documented in scientific literature. A long-term RADARSAT dataset (2008)(2009)(2010)(2011)(2012)) is exploited to investigate oil slicks in Campeche Bay (Gulf of Mexico). Simple Classification Algorithms to distinguish the oil slick type are designed based on standard multivariate data analysis techniques. Various attributes of geometry, shape, and dimension that describe the oil slick Size Information are combined with SAR-derived backscatter coefficients-sigma-(σ o ), beta-(β o ), and gamma-(γ o ) naught. The combination of several of these characteristics is capable of distinguishing the oil slick type with~70% of overall accuracy, however, the sole and simple use of two specific oil slick's Size Information (i.e., area and perimeter) is equally capable of distinguishing seeps from spills. The data mining exercise of our EDA promotes a novel idea bridging petroleum pollution and remote sensing research, thus paving the way to further investigate the satellite synoptic view to express geophysical differences between seeped and spilled oil observed on the sea surface for systematic use.

Refined Analysis of RADARSAT-2 Measurements to Discriminate Two Petrogenic Oil-Slick Categories: Seeps versus Spills

“…We developed a comprehensive Exploratory Data Analysis (EDA) to reveal hidden information contained in the satellite-derived measurements and to refine the analysis to discriminate slicks by category, as proposed in our earlier studies [19][20][21][22]. The design of our EDA focuses on a data-driven scheme to investigate possible ways to improve the seep-spill discrimination with the simplest possible analysis and the lowest satellite-imaging cost.…”

“…We report on analyses to refine the ability to discriminate the petrogenic oil-slick category (seeps versus spills) proposed in our previous investigations [19][20][21][22]. Exploiting the same dataset, but with expanded Data Processing Segments, we extend our earlier studies onto a firmer basis.…”

“…The available literature covering the subject of identifying oil slicks at the surface of the ocean using space-borne surveillance systems, for the most part, does not address the petrogenic oil-slick category discrimination: telling apart the oil-slick sea surface expression in relation to their source, thus considering oil seeps (i.e., natural oil seepages from a hydrocarbon reservoirs) versus oil spills (i.e., mineral oil spillages from man-made activities) [19][20][21][22]. The seep-spill discrimination mostly regards two points of view: economic and environmental.…”

“…While the former deals with the discovery of new oil exploration frontiers in finding the presence of active petroleum systems, the latter is capable of improving the relationship between the oil-and gas-related industry and environmental organizations (and society as a whole) by reducing any origin uncertainty about the oil slick source (i.e., naturally-occurring seeps versus man-made spills). A third point of view is the one of the remote sensing community, in which if a certain methodology is capable of discriminating oil from oil using microwave measurements acquired from space [19][20][21][22], it might be plausible to say that such methodology can also be applied to differentiate oil from look-alike features in SAR imagery. This framework scientifically strengthens the other two points of view.…”

“…Notwithstanding the relative neglect of research projects on the use of satellite sensors for the discrimination of the oil-slick category, Carvalho [19] showed it is feasible to use SAR-derived measurements for seep-spill discrimination-see also [20][21][22]. These authors have used a series of Multivariate Data Analysis Techniques to devise a novel idea to discriminate the oil-slick category while studying seeps and spills observed on the surface of the ocean in the Gulf of Mexico off the Mexican coast in the Campeche Bay region (Figure 1).…”