Unsupervised learning on large collections of high-resolution trajectories

Rath, Willi¹ , Trahms, C.^{1, 2} , Handmann, P.¹ , Wölker, Y.^{1, 2}

The Lagrangian perspective on Ocean currents describes trajectories of individual virtual or physical particles which move passively or semi-actively with the Ocean currents. The analysis of such trajectory data offers insights about pathways and connectivity within the Ocean. To date, studies using trajectory data typically identify pathways and connections between regions of interest in a manual way. Hence, they are limited in their capability in finding previously unknown structures, since  the person analyzing the data set can not foresee them. An unsupervised approach to trajectories could allow for using the potential of such collections to a fuller extent.

This study aims at identifying and subsequently quantifying pathways based on collections of millions of simulated Lagrangian trajectories. It develops a stepwise multi-resolution clustering approach, which substantially reduces the computational complexity of quantifying similarity between pairs of trajectories and it allows for parallelized cluster construction.

It is found that the multi-resolution clustering approach makes unsupervised analysis of large collections of trajectories feasible. Moreover, it is demonstrated that for selected example research questions the unsupervised results can be applied.