Space-time outlier identification in a large ground deformation data set

A novel application for outlier detection is in ground deformation monitoring. During any type of underground construction in urban settings, sensors are placed on the ground surface to monitor the vertical displacement with the goal of ensuring that there is no substantial heaving or settling of the ground. As a result, a large spatial-temporal data set is produced, but the sensors are often very sensitive, and spurious readings are commonly observed, resulting in both random and systematic outliers. In this work, we present a novel, fast spatial-temporal quality control procedure that is designed to remove these spurious readings prior to subsequent ground deformation monitoring. First, a robust kriging model is applied to the spatial ground deformations at each time point to remove systematic errors; next, an exponential moving average model is applied to the time series of ground deformations at each station to remove random outliers. A case study using ground deformation data when four subway tunnels are bored under a railyard in Queens, New York is used to illustrate the methodology. Methods used to construct outlier bounds are described, and the accuracy of our outlier detection approach is evaluated by calculating the percentages of outliers detected in an introduced artificial outlier set.