Statistical Approaches to Leak Detection for Geological

Geological sequestration has been proposed as a way to remove CO 2 from the atmosphere by injecting it into deep saline aquifers. Detecting leaks to the atmosphere will be important for ensuring safety and effectiveness of storage. However, a standard set of tools for monitoring does not yet exist. The basic problem for leak detection- and eventually for the inverse problem of deter-mining where and how big a leak is given measurements- is to detect shifts in the mean of atmospheric CO 2 data. Because the data are uncertain, statistical approaches are nec-essary. The traditional way to detect a shift would be to apply a hypothesis test, such as Z- or t-tests, directly to the data. These methods implicitly assume the data are Gaussian and independent. Analysis of atmospheric CO 2 data suggests these assumptions are often poor. The data are characterized by a high degree of variability, are non-Gaussian, and exhibit obvious systematic trends. Simple Z- or t-tests will lead to higher false positive rates than desired by the operator. Therefore Bayesian methods and methods for handling autocorrelation will be needed to control false positives