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====== Detection and classification of ICMEs in in situ solar wind data ======

===== Description of the science case =====

In general, the term space weather describes disturbances of the near-Earth environment. Such disturbances can lead to failures of satellites affecting communication and navigation, increase the radiation affecting manned space flight and air traffic, and can lead to power outages. The scientific community tries to understand the processes that lead to intense disturbances of the Earth's magnetic field, so-called geomagnetic storms.

Coronal mass ejections (CMEs) are the dominant triggers of geomagnetic storms at the Sun. The interaction of CMEs with the Earth's magnetosphere that often causes strong geomagnetic storms. An interplanetary CME (ICME) is the whole in situ structure disturbing the quiet solar wind, i.e.~the shock, sheath, solar wind pile-up, compression regions, magnetic field structure, driver gas, and ejecta wake.

One of the main problems when identifying ICMEs in in situ observations is the fact that not every ICME exhibits all of the aforementioned features: some have shocks and sheath regions, some don't; some show a clear rotation of the magnetic field and have a magnetic cloud, some don't; sometimes two CMEs interact during their propagation from the Sun to the Earth and then the signature can also look different; etc.

Another problem lies in the duration of the ICME signature. Roughly speaking, the average duration of an ICME lies around 21 hours, but there are ICMEs that last only 4 hours, and some that exhibit signatures for even more than 48 hours.

===== Main aim =====

The main aim of this science case is to develop/implement an algorithm to automatically detect ICME signatures in in situ solar wind data.

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 (e.g., Nguyen et al., 2019). However, classification, early detection and ultimately forecasting still remain challenging when faced with the large amount of data from different instruments. While CNNs are often used to discover objects or patterns in images or data series, there are two main problems when facing our specific task: high duration variability and a rather ambiguous definition of start and end time.

===== Description of the machine learning problem and our approach =====

The first step in this science case was the **reimplementation of a model proposed by Nguyen et al. (2019)**, which had previously been tested on WIND data and achieved a maximum [[:glossary#recall|recall]] and [[:glossary#precision|precision]] of around 84%.

After the reimplementation of this model, the model was tested on STEREO-A and STEREO-B data as well as on WIND data. All three contain less variables than the original data set used by Nguyen et al. At a similar [[:glossary#recall|recall]] as for the original set, the [[:glossary#precision|precision]] for all three datasets was only around 30% and the [[:glossary#accuracy|accuracy]] in delivering start and end times was limited.

The next step was to align all three data sets in order to process more training data for a combined model. It was tested on held out datasets for WIND, STEREO-A and STEREO-B. Surprisingly, this did not sufficiently improve performance and lead us to explore other approaches.

Starting from the reimplementation, a **post processing step based on YOLO v5** (ultralytics) was investigated, in order to improve performance. Even though first results seemed promising, the idea was later discarded due to unsatisfactory results and the laborious pipeline. Since the ultimate goal is an explicit and widely applicable pipeline, it was decided to abandon the general approach of using multiple basic [[:glossary#neural_network|neural networks ]]and the similarity measure used by Nguyen et al. (2019) completely and **compose it as a segmentation problem** instead.

We proposed a pipeline using a **UNet ** (Ronneberger et al., 2015) including residual blocks, squeeze and excitation blocks, Atrous Spatial Pyramidal Pooling (ASPP) and attention blocks, similar to the **<nowiki>ResUNet++</nowiki>** (Jha et al., 2019), for the automatic detection of ICMEs. Comparing it to our first results, we find that our model outperforms the baseline regarding GPU usage, training time and robustness to missing [[:glossary#feature|features]], thus making it more usable for other data sets, as well as the three aligned data sets. The relatively fast training allows straightforward tuning of [[:glossary#hyperparameters|hyperparameters]]. Our proposed pipeline can be used for any time series segmentation problem. The straightforward implementation allows a simple extension to a [[:glossary#multi-class_classification|multi-class classification]] problem and paves the way to include corotating interaction regions into the range of detectable phenomena within our pipeline. Furthermore, we hope to apply our model to similar problems in the future.

Results of this science case were presented at the {{:wiki:esws2020-iwf_presentation.pdf|ESWS 2020}}, at {{:wiki:egu2021-ruedisser_etal.pdf|EGU21}}, at {{:wiki:esww2021-ruedisser_presentation.pdf|ESWW 2021}}, at {{:wiki:agu21_icme_ruedissser.pdf|AGU21}}, and at {{:wiki:mlhelio22_ruedisser_etal.pdf|ML-Helio 2022}}. This ML pipeline was presented in a [[https://github.com/epn-ml/EPSC2021-ICME-workshop|workshop at EPSC2021]] and is, together with a [[:tutorials_icme|tutorial]], available on our [[https://github.com/epn-ml/|GitHub repository]]. **A publication was submitted to and accepted by the journal "Space Weather".**

**References: **

* Nguyen, G., et al. (2019), Automatic Detection of Interplanetary Coronal Mass Ejections from In Situ Data: A Deep Learning Approach, Astrophys. J. 874, 145, doi:10.3847/1538-4357/ab0d24\\
* Jha, D., et al. (2019), Resunet++: An advanced architecture for medical image segmentation, arXiv e-prints, arXiv:1911.07067\\
* Ronneberger, O., et al. (2015), U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab N., Hornegger J., Wells W., Frangi A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science, vol 9351. Springer, Cham. [[https://doi.org/10.1007/978-3-319-24574-4_28|https://doi.org/10.1007/978-3-319-24574-4_28]]