Case Study: SWARM data analysis for precursor assessment

SWARM analysis using a data-driven approach Machine Learning

During a three month research sprint, an analysis of SWARM data was carried out to support existing activities which aim to identify possible earthquake precursors. To maintain an unbiased approach, unsupervised machine learning techniques were carried out to identify any possible correlations with earthquakes. In the timespan of the research activity it is has not yet been possible to confirm or deny the utility of SWARM data as earthquake precursors. However, a framework is being built which facilitates researchers to carry out similar, and more in-depth, analysis of multimission datasets, including SWARM, for precursor assessment of earthquakes and other phenomena, such as volcanic activity and lightning, using machine learning.

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