BayMoDe - Bayesian Monitoring Design

Project Department: Uni Research Climate (group: Biogeochemistry) period: 01.01.16 - 31.12.19

About the project

With the current plans to utilize the large theoretical storage capacity in the North Sea there is a need for economical monitoring technologies that will either assure storage integrity or detect adverse effects or unwanted release events over large areas. This is also required by international regulations and agreements.

BayMoDe addresses the design and operation of monitoring programs aiming to detecting anomalies in the marine environment. The suggested probabilistic approach offers quantification of uncertainties in the program, and this uncertainty is minimised during design. This further reduces the chance of false alarms that will accelerate the cost significantly.

This approach will automatically filter out any outliers in a time series; a single leak indication will not automatically sound the alarm but rather increase our awareness by increasing our belief that a leak is on going. Subsequent measurements might reduce or increase our awareness, only when the number of indications reaches a threshold will the extra resources be mobilized.

To test the ability and usefulness of Bayes theorem in the context of environmental monitoring we aim to design a data analysis framework, including monitoring design capabilities, in which the Bayesian approach is the core data treatment. The three main building blocks in the framework will be a probabilistic map of potential leak locations, environmental baseline statistics, and predictions of leak footprint characteristics. The former two will be part of a site characterization, while the latter will in addition depend on characteristics of seeps.

Even though the focus here is on seafloor monitoring, the approach has the potential to simplify documentation of uncertainty in all monitoring methods. As such the method might accelerate implementation of large-scale storage projects through better procedures for designing and maintaining monitoring programs.

cp: 2017-12-14 04:16:35