Grouped Frequent Sequential patterns can be extracted in an unsupervised way from Image Time Series (ITS). Plotting the occurrence maps of these patterns allows to describe the dataset spatially and temporally while discarding random uncertainties. However these maps can be too numerous and a swap randomization ranking approach has been proposed recently to select the most promising patterns. This previous work experimented the technique on Satellite ITS, giving credit to the maps that are least likely to appear on a randomized ITS. In this paper, extraction and ranking of GFS patterns is performed on a motion field time series obtained by terrestrial photogrammetry over the Argentière glacier. The focus is extended to the maps that are most likely to occur on the randomized time series and the experiment is repeated thousand times to assess the stability of the ranking.