Automatic Detection and Classification of ICMEs in Solar Wind Data

Hannah Ruedisser - Know Center, Graz // University of Graz, Graz, Austria; Andreas Windisch - Know Center, Graz // Department of Physics, Washington University in St. Louis, MO 63130, USA, Austria; Ute Amerstorfer - Space Research Institute, Austrian Academy of Sciences, Graz, Austria; Tanja Amerstorfer - Space Research Institute, Austrian Academy of Sciences, Graz, Austria; Christian Moestl - Space Research Institute, Austrian Academy of Sciences, Graz, Austria; Rachel Louise Bailey - Conrad Observatory, ZAMG, Vienna, Austria

Session: Interplanetary CMEs and Solar Particle Events

Abstract

Interplanetary coronal mass ejections (ICMEs) are one of the main drivers for space weather disturbances. In the past, different machine learning approaches have been used to automatically detect events in existing time series resulting from solar wind in situ data. However, classification, early detection and ultimately forecasting still remain challenges when facing the large amount of data from different instruments. We attempt to further enhance existing Convolutional Neural Network (CNN) models through extending their possibilities to process data from multiple spacecraft. Additionally, we make an effort to extend the previously binary classification problem to a multiclass classification, to also include Corotating Interaction Regions (CIRs) into the range of detectable phenomena. Ultimately, we aspire to explore the suitability of several other methods used in time series forecasting, such as Long Short-Term Memory Recurrent Neural Networks (LSTMs), in order to pave the way for the elaboration of an early warning system.