Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A Parameter-Free Spectral Clustering Approach to Coherent Structure Detection in Geophysical Flows

Version 1 : Received: 20 November 2020 / Approved: 24 November 2020 / Online: 24 November 2020 (09:25:02 CET)

A peer-reviewed article of this Preprint also exists.

Filippi, M.; Rypina, I.I.; Hadjighasem, A.; Peacock, T. An Optimized-Parameter Spectral Clustering Approach to Coherent Structure Detection in Geophysical Flows. Fluids 2021, 6, 39. Filippi, M.; Rypina, I.I.; Hadjighasem, A.; Peacock, T. An Optimized-Parameter Spectral Clustering Approach to Coherent Structure Detection in Geophysical Flows. Fluids 2021, 6, 39.

Journal reference: Fluids 2021, 6, 39
DOI: 10.3390/fluids6010039

Abstract

In Lagrangian dynamics, the detection of coherent clusters can help understand the organization of transport by identifying regions with coherent trajectory patterns. Many clustering algorithms, however, rely on user-input parameters, requiring a priori knowledge about the flow and making the outcome subjective. Building on the conventional spectral clustering method of Hadjighasem et al (2016), a new parameter-free spectral clustering approach is developed that automatically identifies parameters and does not require any user-input choices. A noise-based metric for quantifying the coherence of the resulting coherent clusters is also introduced. The parameter-free spectral clustering is applied to two benchmark analytical flows, the Bickley Jet and the asymmetric Duffing oscillator, and to a realistic, numerically-generated oceanic coastal flow. In the latter case, the identified model-based clusters are tested using observed trajectories of real drifters. In all examples, our approach succeeded in performing the partition of the domain into coherent clusters with minimal inter-cluster similarity and maximum intra-cluster similarity. For the coastal flow, the resulting coherent clusters are qualitatively similar over the same phase of the tide on different days and even different years, whereas coherent clusters for the opposite tidal phase are qualitatively different.

Subject Areas

parameter-free spectral clustering; Lagrangian Coherent Structures; clusters; geophysical flows; unsupervised machine learning

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.