We present recently developed computational techniques that exploit spectral geometry of the energy spectra for differential energy flux over MAVEN data sets. The goal is to enable a large scale automated discovery detection of statistical analysis of ion trails in the Martian atmosphere. Specifically, we present a case study across a diverse portfolio of azimuthal (φ) and polar (θ) angles over the same time frame and demonstrate that angular separation helps us to distinguish between individual ion escape processes. We discuss the performance of our algorithms regarding precision of detection as well as interpretation of ion trail geometry. The potential of this algorithm is that it can enable large-scale statistical analysis that connects to individual ion trails with unique angular trajectories automatically detected across potentially hundreds of thousands of case studies.