Extraction of velocity time series with an optimal temporal sampling from displacement observation networks

Today, more and more velocity observations are available online or on-demand. However, this amount of data is complex to analyze since velocity observations span different temporal baselines. Velocities obtained from a small temporal baseline are close to the derivative of the displacement but are more likely to be contaminated by noise. Velocities obtained from a long temporal baseline approximate the mean velocity between two dates but can be affected by temporal decorrelation. Having short and long temporal baselines provides a data redundancy that needs to be properly considered. In this article, we propose a method that aims to extract short-term velocity time series with a regular temporal sampling from all available displacement observations. The proposed method relies on a temporal inversion based on an improved temporal closure of the displacement observation network. Two criteria are proposed to determine the optimal temporal sampling to study short-term variations. To take the unequal data uncertainty into account, the temporal inversion is done by an Iterative Reweighted Least Square using a well-established weighting function, without preprocessing. The proposed method results in velocity time series with an optimal temporal sampling, an improved temporal coverage, reduced uncertainty and no redundancy. The studied area is the Kyagar glacier, in the North of the Karakoram range which is characterized by strong velocity variations originated from a glacier surge and additional seasonal variability.