Automatic detection of sudden commencements using neural networks
The aim of this work is to develop an automatic system to detect sudden commencements (SCs). SCs are produced by a sudden increase of solar wind dynamic pressure and are detected simultaneously everywhere on the ground (Araki, 1994). Since 1975, Ebro Observatory is responsible to elaborate the list...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2013 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:20.500.14342/5402 |
| Acceso en línea: | http://hdl.handle.net/20.500.14342/5402 https://doi.org/10.5047/eps.2012.12.011 |
| Access Level: | acceso abierto |
| Palabra clave: | Sudden commencements Automatic detection Neural networks |
| Sumario: | The aim of this work is to develop an automatic system to detect sudden commencements (SCs). SCs are produced by a sudden increase of solar wind dynamic pressure and are detected simultaneously everywhere on the ground (Araki, 1994). Since 1975, Ebro Observatory is responsible to elaborate the list of SC, following the morphological rules given by Mayaud (1973). Nowadays, this task is still done manually and presents some difficulties; the most worrying one is the decreasing number of observatories who collaborate with this task because most of them opted for the installation of unattended observatories. Hence, the necessity of an alternative method to continue the service becomes a urgency. The automatic method presented in this work is based on neural network analysis. To succeed with neural networks, we did a previous work of characterization and parameterization of SCs by statistical analysis. In this way, we focused on the determination of the appropriate parameters to be used as the inputs of the network which resulted to be: slope, change of magnetic activity and difference of the levels before and after the jump. We worked with X component and also with Y component. An important criteria introduced in this work is the necessary coherence of the results obtained with this new automatic method with those obtained with the manual method and reported in the old list of SC. Finally, the neural network is able to recognize the SC pattern successfully, but now this is achieved in a non-manned way. A robust quasi-real-time detection can be undertaken in the future. |
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