Intelligent Technologies for the Detection of Hydrocarbons in the Maritime Environment

Project location: SPAIN
Project start date: February 2004 - Project end date: January 2006
Project number: 2003-62
Beneficiary: FundaciĆ²n Arao

Project Background

Introduction
The ARAO Foundation is an organization with of general interest for Galicia. It was incorporated as a non-profit organization in public deed dated December 30th 2002, in an act chaired by the President of Xunta de Galicia (Galician Regional Government). Prior to it, on December 19th, the Consello da Xunta (Galician Government Cabinet) had passed the Articles of Incorporation.

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The purpose of the Fundación ARAO is to channel and manage all the aids granted in an altruistic way in order to mitigate the effects caused by the sinking of the oil tanker "Prestige" and to carry out the environmental recovery of Galicia.

On April 2003, the Board of Trustees of the Arao Foundation approved its Action Plan, which determines the Purpose and Objectives of the Foundation. One of these objectives is the scientific research in the recovery of coastal ecosystems and prevention measures.
The Arao Foundation has signed a cooperation agreement with the following universities: University of Santiago de Compstela, Univeristy of A Coruña, Politécnica of Madrid, and Federico II of Naples to undertake this research project.

We believe that the organizational capacity of these entities, as well as their highly recognized expertise in all aspects of scientific research, are the best assurance of our capability to successfully complete this project.

Background information
On Nov. 19th 2002, a single-hull oil tanker, The Prestige, split in two and sunk 150 miles off the coast of Galicia, Spain. The Prestige was carrying some 77.000 tons of heavy crude oil at the time. It is estimated that 63.000 tons of this poisonous cargo was spilled onto the sea and carried by wind and sea currents to the Galician coast, causing the worst environmental disaster in Spain's history.

This situation made painfully evident the need for the detection of oil slicks at sea, as the Spanish Government was trying to neutralize them before they reached the coast. At the same time the efficient management of clean-up crews and materials on the shoreline depended on accurate information about the situation and direction of the oil slicks at sea.
In this case the detection of oil slicks was done by visually analyzing pictures of the area taken by earth-orbiting satellites. The efficacy of this system was seriously limited by several problems:

  • The system did not track the directional movement of oil slicks in real time.
  • It created false positive readings, making it difficult to organize clean-up operations in the shoreline.
  • It did not allow for the detection of oil under the water's surface.
  • It did not provide any information on the nature of the oil spilled.
How the project will address the problem

The proposal contemplates the development of two alternative methods for the detection of hydrocarbons in the maritime environment, the deep study of its characteristics and indications of applicability.

1. Detection based on radar signals.
The operation of radar is based on the transmission of radio waves that are reflected by the objects found in their path. The energy reflected by said objects is scattered in multiple directions and part of it is returned in the direction of the radar, this is called backscattering. The quantity of energy scattered in the direction of the radar depends on the object in question, this characteristic permits the utilization of radars to obtain images of the terrestrial surface: registering the differences of intensity of the echoes received across the width of the beam which the antenna displaces along the surface.
In a radar image the degree of detail or resolution depends on the ability of the radar to separate objects that are positioned next to each other. This ability depends; on the one hand, on the characteristics of the pulses of radiation emitted by the radar and, on the other hand, on the width of the beam. Because of this, to enlarge the resolution of a radar image, we can compress and modulate the pulses of radiation, or diminish the width of the beam. To obtain resolutions sufficiently fine to recognize the forms of the objects at a long distance, Given that the width of the beam is inversely proportional to the size of the antenna, it would be necessary the use of extremely large antennae. To solve this problem the Synthetic Aperture Radar (SAR) has been developed. SAR can take advantage of the mobility of radar on board of airplanes or satellites to coherently sum several successive signals. This way it can obtain the equivalent to an array of an antenna of many meters of length and a beam half the width.

Currently, SAR devices are utilized on board of satellites to obtain images of the surface of the terrestrial globe, since, due to the conditions of propagation of the waves of radio, the images of radar can be taken during the day or during the night and in all types of weather conditions. The relatively recent commercial availability of this type of images has permitted his use in the detection of phenomena related to the maritime environment, such as displacement of ice mass, sea currents, and changes of vegetation. (The Spatial Agency European has devices SAR on board of three of his satellites, the ERS-1, the ERS-2 and the ENVISAT, which can obtain images of the Spanish coasts during approximately 4 minutes, and every 3 days)

One of the many applications of SAR signals has been the detection of oil slicks at sea: a spot of fuel in the marine surface produces a drastic decrease of the waves capillaries, this reduces the electromagnetic backscattering in the areas where the fuel is present and generates dark zones in the SAR images. Unfortunately, these dark zones can be due to other phenomena, such as the lack of wind, and, therefore, is necessary to resort to methods that solve these false positive readings. This can be done by means of the characteristics of the backscattering of the radar.
In spite of the availability of SAR images, is still very difficult to obtain the images jointly with information about the physical and chemical characteristics of the fuel, as well as the hydrodynamic characteristics of the sea environment at the moment the image is taken. That it is why, for the study of the relationship between said characteristics and the characteristics of the electromagnetic echoes received by SAR, is necessary the use of SAR signal simulators.
For this work a SAR-OCEAN interaction simulator, developed in the University of Naples, will be used. This simulator takes into account the non-linear hydrodynamic effect of the movement of water particles. This model has been validated with real SAR images for the detection of oil. However it has not been verified for the totality of the effects of backscattering. In this project we intend to verify the reliability of said simulator, through the characteristics of the backscattering of the signal, for the detection of oil slicks, to determine their thickness, and found out some of their characteristics. We will build a radar, whose answer of backscattering will be identical to that of a SAR system, to validate the results obtained by the simulator. Once the results are validated the simulator will be used to provide the examples needed by the artificial intelligence systems "to learn". This will allow us to obtain a device that will permit the identification of oil slicks in real time, as well as to obtain information on certain characteristics of the fuel.

The following activities will take place:
A. Characterization of the hydrocarbons and simulation of their backscattering characteristics
B. Construction of a radar system and measure of backscattering in a controlled environment
C. Generation of samples for the design of an artificial intelligence system
D. Verification of the system obtained



Characterization of hydrocarbons and simulation of their backscattering characteristics
We will measure the physical and chemical characteristics of diverse types of hydrocarbons. This will serve for the simulation SAR-OCEAN. By means of this simulation the backscattering characteristics of each type of hydrocarbon will be obtained.

Construction of a radar system and measure of backscattering in a controlled environment
A radar system will be built that will allow us to measure the real characteristics of the backscattering of hydrocarbons in the previous section. In figure 1 a diagram of the system is shown (PA: Power Amplifier and LNA: Low Noise Amplifier)

To measure the signal of backscattering from oil slicks we will use the Complex for the Simulation of the Marine Environment that the University of A Coruña has available in the CITEEC (Centre for Technological Innovation in Building and Civil Engineering). These installations, including the necessary support on the part of the technical personal, are available for scientific research purposes. Besides, the Head Technician of the CITEEC, Dr. Juan Rabuñal Dopico belongs to the research team from the University of A Coruña involved in this project. This complex has at its disposal, among others installations, a hydrodynamics experimentation dock and a swell producing channel.

The data obtained from measuring the radar signal will be compared with the data of the simulation previously obtained.

Generation of the necessary samples to build an artificial intelligence system.
By means of the simulator we will generate a set of statistical parameters and/or image properties for the design of an artificial intelligence system. From these data two systems of recognition and classification of patterns will be established, one based on Neural Networks and another a Knowledge Based System, to discriminate the areas of the ocean with spots of petroleum from the ones that are clean.

From the available information we will develop a system of rules that will encompass and interrelate the different parameters measured by the sensors. These rules will be the ones that, applied to the data observed, will indicate the existence of petroleum spots in a specific area of the ocean. To build this collection of rules we will take advantage of another area of Artificial Intelligence: Evolutionary Computation - a set of techniques that refers to the study of certain heuristics based on the principles of natural evolution. Finally, if necessary, to refine the behaviour of the system, we will proceed with the design and development of an Expert System.


2. Detection based on fluorescence
Fluorescence is a phenomenon by which certain materials react to a luminous stimulus with another emission of light in a wavelength level greater than of the original. If the material impacted by the radiation provides an appreciable fluorescence, as is the case with hydrocarbons, the spectrum of frequencies and the timing of the decline of the fluorescence are unmistakable characteristics of the nature of the material.

In figure 2 we show a graphic example of the fluorescent answer of a specific substance to the stimulus of a very narrow-band light emission (usually a pulsed laser beam). The substance reacts returning the energy distributed in a wider band, with certain predominant wavelengths. Furthermore, the answer persists beyond the stimulus, decaying gradually during a period of time known as Declining Time. Nevertheless, as it is seen in figure 2, the Declining Time is not uniform in the entire array of wavelengths of the fluorescence but, to the contrary, the fluorescence persists more or less according to the situation in said array.

The spectrum of the intensity of the fluorescence and the spectrum of the Declining Time constitute characteristic "signatures" of the substance subjected to the stimulus. There are very few substances that fluoresce in the natural environment, but the ones that do provide a unique identifying "signature". For this reason, fluorescence is the physical effect that offers greater technological possibilities to locate and analyse oil slicks in coastal areas and at sea, even at certain depths.

In the water, part of the energy of the radiation from the stimulus is absorbed by a vibratory state of the water molecule, and consequently, the radiation is returned displaced to a higher wavelength (Raman line of the spectrum). For example, with a stimulus light beam at 308 nm, the Raman Line appears in the 344 nm. While with a stimulus at 337 nm, said line is situated in the 381 nm. The intensity of the emission in the Raman line is proportional to the number of water molecules that the original beam finds in its path. Therefore, said intensity is an indication of the depth to which the light beam has penetrated the water. On the other hand, the suitable stimulus for hydrocarbons is found in the ultraviolet, between 300 and 340 nm. The corresponding fluorescence is established between 400 and 650 nm. with peak intensities in the proximity of 480 nm. Consequently, fluorescence of hydrocarbons is clearly distinguished from that of other natural substances, such as water (around 380 nm.)
The detection by means of fluorosensors could be carried out in two different modalities, air or underwater. In any case the instrument embarked should be capable to explore perpendicular to the path followed by the vehicle transporting it, as indicated in figure 3. This way it would sweep a wide stripe of terrain, enabling the efficient acquisition of information. The transporting vehicle should be fitted with a positioning system in order to be able to determine the exact situation of the oil slick.

The fluorosensor would consist essentially of the following elements: a transmitter of UV light, inside the band between 300 and 340 nm, and a receiver followed by a spectrometer. Additionally, a data acquisition system will digitalize and register all the measurements. The fluorosensor, thus described, would obtain the radiation intensity spectrum that, according to experimental results, is sufficient to detect the presence and nature of the contaminants. Nevertheless, it would be convenient to obtain the temporary answer of, at least, a pair of sub-bands of the spectrum, since this would complete the characterization and would reinforce the certainty of the measure.

The following activities are projected:
A. Development of a experimental fluorosensor
B. Development of the experimental environment
C. Characterization of the different contaminants
D. Techniques for the processing and recognition of the signals
E. Conclusions and draft of a fuorosensor

Experimental fluorosensor.
The experimental sensor will be one of relative low power, which will permit us to carry out experimentations in a controlled environment. Its foreseeable structure is illustrated in figure 4.

The subdivisión of the band to be explored, in narrow bands by means of filters, does not only cheapens the cost in comparison with the use of a spectrometer of greater resolution, but also allows the data acquisition system to capture the intensity in each one of the lines, and their Declining Time.

The instrument thus constituted will have a double function: the characterization of the substances we want to detect and the experimentation itself.

Experimental Environment
The experimental environment will consist of an enclosure of adequate size for the experimental sensor. It should allow for a flexible and exhaustive experimentation. This way the preparation and elimination of the experimental cases, as well as their clean up will be easily done. The same infrastructure as the one used for the detection based on SAR signals (that in the CITEEC) will be used. The extrapolation of experimental data, as well as the scaling of the instrument itself, do not present any difficulties. Hence the experimentation could be guaranteed as certain. However, in the final phase of the experimentation field test will be carried out in a real environment, in order to properly contrast the entire process.


Characterization of the contaminant
The characterization of the contaminants will be carried out from a model in which we will introduce all substances whose characterization is required. The intensity and Decline Time of each one of the sub-bands or lines of the spectrum will be measured. All of it will be contrasted with the available data in the bibliography.

Processing and recognition
Once the caption of signals by means of the experimental sensor has been made possible, it is necessary to process and analyse them; since we not only intend the detection of specific substances, but to extract all possible information from the signals provided by the sensor.
It is necessary to process the signal to separate the information from the noise, as the disruptions that can affect the signals are of multiple origins. It is necessary to study the nature of the noise, and employ filtering techniques adapted to each type of noise once they have been analysed and characterized. Concerning the information to be extracted, what we intend to find is:

  • The presence of oil
  • The nature of the same
  • The depth to which it is found, if submerged
  • Its "format".

We define the "format" of the contaminant as everything related to its quantification and qualification. That is, to distinguish between a compact, extensive oil slick and a fragmented one, in the form of "crackers"; to know the size of the "crackers", and to determine the thickness of the layer.


The extraction of the information required is not a trivial aspect of this project, and because of this we intend to employ artificial intelligence procedures. Nevertheless, a first analysis permits us to speculate that the depth at which the contaminant is found will be related to the Raman line. We also theorise that the fragmentation and the average size will be related to the average intensity of the characteristic lines of the spectrum, and that the depth of the layer will be related to the temporal answer.
With all these data, and through the reproduction of different cases in the experimental environment, we will perform the training of a neuronal network, which will permit the extraction of knowledge, as well as the employment of techniques based on fuzzy logic.


Anticipated achievements or outcomes of the project
The new system will be able to detect and track oil spills in real time, detect oil spilled by passing ships during illegal tanker clean-up operations, it will allow for the detection of oil at a certain depth in the water, and it will be able to discern between three different types of oil.

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