Automatic Detection of Interplanetary Coronal Mass Ejections in Solar Wind In Situ Data
Abstract Interplanetary coronal mass ejections (ICMEs) are one of the main drivers for space weather disturbances. In the past, different approaches have been used to automatically detect events in existing time series resulting from solar wind in situ observations. However, accurate and fast detect...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-10-01
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| Series: | Space Weather |
| Online Access: | https://doi.org/10.1029/2022SW003149 |
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| author | H. T. Rüdisser A. Windisch U. V. Amerstorfer C. Möstl T. Amerstorfer R. L. Bailey M. A. Reiss |
| author_facet | H. T. Rüdisser A. Windisch U. V. Amerstorfer C. Möstl T. Amerstorfer R. L. Bailey M. A. Reiss |
| author_sort | H. T. Rüdisser |
| collection | DOAJ |
| description | Abstract Interplanetary coronal mass ejections (ICMEs) are one of the main drivers for space weather disturbances. In the past, different approaches have been used to automatically detect events in existing time series resulting from solar wind in situ observations. However, accurate and fast detection still remains a challenge when facing the large amount of data from different instruments. For the automatic detection of ICMEs we propose a pipeline using a method that has recently proven successful in medical image segmentation. Comparing it to an existing method, we find that while achieving similar results, our model outperforms the baseline regarding training time by a factor of approximately 20, thus making it more applicable for other datasets. The method has been tested on in situ data from the Wind spacecraft between 1997 and 2015 with a True Skill Statistic of 0.64. Out of the 640 ICMEs, 466 were detected correctly by our algorithm, producing a total of 254 false positives. Additionally, it produced reasonable results on datasets with fewer features and smaller training sets from Wind, STEREO‐A, and STEREO‐B with TSSs of 0.56, 0.57, and 0.53, respectively. Our pipeline manages to find the start of an ICME with a mean absolute error (MAE) of around 2 hr and 56 min, and the end time with a MAE of 3 hr and 20 min. The relatively fast training allows straightforward tuning of hyperparameters and could therefore easily be used to detect other structures and phenomena in solar wind data, such as corotating interaction regions. |
| format | Article |
| id | doaj-art-846dd11a714e45939d32bd8c1e880300 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2022-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-846dd11a714e45939d32bd8c1e8803002025-01-14T16:30:48ZengWileySpace Weather1542-73902022-10-012010n/an/a10.1029/2022SW003149Automatic Detection of Interplanetary Coronal Mass Ejections in Solar Wind In Situ DataH. T. Rüdisser0A. Windisch1U. V. Amerstorfer2C. Möstl3T. Amerstorfer4R. L. Bailey5M. A. Reiss6Know‐Center GmbH Graz AustriaKnow‐Center GmbH Graz AustriaSpace Research Institute Austrian Academy of Sciences Graz AustriaSpace Research Institute Austrian Academy of Sciences Graz AustriaSpace Research Institute Austrian Academy of Sciences Graz AustriaZentralanstalt für Meteorologie und Geodynamik Vienna AustriaSpace Research Institute Austrian Academy of Sciences Graz AustriaAbstract Interplanetary coronal mass ejections (ICMEs) are one of the main drivers for space weather disturbances. In the past, different approaches have been used to automatically detect events in existing time series resulting from solar wind in situ observations. However, accurate and fast detection still remains a challenge when facing the large amount of data from different instruments. For the automatic detection of ICMEs we propose a pipeline using a method that has recently proven successful in medical image segmentation. Comparing it to an existing method, we find that while achieving similar results, our model outperforms the baseline regarding training time by a factor of approximately 20, thus making it more applicable for other datasets. The method has been tested on in situ data from the Wind spacecraft between 1997 and 2015 with a True Skill Statistic of 0.64. Out of the 640 ICMEs, 466 were detected correctly by our algorithm, producing a total of 254 false positives. Additionally, it produced reasonable results on datasets with fewer features and smaller training sets from Wind, STEREO‐A, and STEREO‐B with TSSs of 0.56, 0.57, and 0.53, respectively. Our pipeline manages to find the start of an ICME with a mean absolute error (MAE) of around 2 hr and 56 min, and the end time with a MAE of 3 hr and 20 min. The relatively fast training allows straightforward tuning of hyperparameters and could therefore easily be used to detect other structures and phenomena in solar wind data, such as corotating interaction regions.https://doi.org/10.1029/2022SW003149 |
| spellingShingle | H. T. Rüdisser A. Windisch U. V. Amerstorfer C. Möstl T. Amerstorfer R. L. Bailey M. A. Reiss Automatic Detection of Interplanetary Coronal Mass Ejections in Solar Wind In Situ Data Space Weather |
| title | Automatic Detection of Interplanetary Coronal Mass Ejections in Solar Wind In Situ Data |
| title_full | Automatic Detection of Interplanetary Coronal Mass Ejections in Solar Wind In Situ Data |
| title_fullStr | Automatic Detection of Interplanetary Coronal Mass Ejections in Solar Wind In Situ Data |
| title_full_unstemmed | Automatic Detection of Interplanetary Coronal Mass Ejections in Solar Wind In Situ Data |
| title_short | Automatic Detection of Interplanetary Coronal Mass Ejections in Solar Wind In Situ Data |
| title_sort | automatic detection of interplanetary coronal mass ejections in solar wind in situ data |
| url | https://doi.org/10.1029/2022SW003149 |
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