Integrated Environmental Monitoring; taking environmental data in to decision making processes

Environmental monitoring related to the offshore petroleum industry has traditionally been carried out through field campaigns, with a delay of receiving processed data on typically 9 months. Typical parameters measured are physical parameters e.g. grain size and total organic material (TOM), chemical parameters e.g. heavy metals and hydrocarbons and biological parameters e.g. number of species and species diversity. For reporting of status on chemical polluted areas and area of impacted fauna, this is sufficient methodology. However, this approach is not suited if possible impacts, or even the possibility of preventing possible impact, is the purpose for linking environmental monitoring to the industry's day-to-day operations. Broadening the definition of environmental monitoring to include leaks and discharge control, there is a need for real-time data and more automated analysis of raw data for identification, mapping and monitoring purposes of object of interest (OOI). The focus of the PhD project is on the overall infrastructure, including selection of parameters to measure, sensor platforms and strategies for interpretation of these, needed to provide decision makers with sufficient data in due time. Starting to get vast amount of real-time or continuous measured data, both industry and the scientific community is still struggling with optimising the use of these data and to utilise the opportunities of enhanced knowledge that can be obtained from combining multi sensor data. According to the Norwegian Research Council is one of the most important challenges within the area of climate -, environment - and ocean research the need for increased used, availability and harmonisation of data (Forskningsrådet 2012). A more optimal use of environmental data also requires a continuous development of, and investment in, novel analytical tools. Further, this increased opportunity of understanding the environmental processes and impact need to be 'translated' to right level of details and delivered to decision makes in due time.

The project consists of the four following parts: Part 1 Automatic image analysis and machine learning Main Aim: For proper analysis and use of pictures, there is a need for a system to help humans to pinpoint/select issues of interest to be further inv estigated. This could be 1) specific objects of interest, e.g. coverage of corals or leaks of hydrocarbons or 2) changes in patterns of vast amount of data on one geographical location e.g. gradually present or absent of specific species. Part 2 Combini ng multi sensor data for optimal benefit of information Main Aim: is to use one or several of the identified case studies in the IEM project and the AUR-LAB cruises to identify which key-environmental variables and parameters that should be measured in g iven situations (1-3 described below), and use multivariate analysis to interpret the results with respect to the source for observed change and the co-variance between variables and parameters. Part 3 How to use processed data into a decision making pr ocesses? Main Aim: The focus in this part will be on finding the optimal way of presenting the data types, or a set of these, described in part 1 and 2. Furthermore, as basis for environmental management the aim is to identify what kind of data different decision makers needs and if some of the data need to be shared between the different decision makers. Part 4 Piloting process Main Aim: The focus in this part is to apply Part 1-3 on an actual offshore case. This part will also be a validation of the s olutions chosen in part 1-3. The pilot will be specified by the IEM project. I addition to reflecting key issues identified as success factors in the IEM project, it is also believed that the different parts will give valid knowledge and experiences on how to optimise and use environmental data in management in general.