Substorm onset identification using neural networks and Pi2 pulsations
Autor: | P. R. Sutcliffe |
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Přispěvatelé: | EGU, Publication |
Jazyk: | angličtina |
Rok vydání: | 2018 |
Předmět: |
Atmospheric Science
Meteorology Computer science Computer Science::Neural and Evolutionary Computation Substorm Earth and Planetary Sciences (miscellaneous) lcsh:Science Artificial neural network business.industry [SDU.OCEAN] Sciences of the Universe [physics]/Ocean Atmosphere lcsh:QC801-809 Geology Astronomy and Astrophysics Pattern recognition Visual identification lcsh:QC1-999 Identification (information) lcsh:Geophysics. Cosmic physics Space and Planetary Science Pattern recognition (psychology) [SDU.STU] Sciences of the Universe [physics]/Earth Sciences lcsh:Q Artificial intelligence business lcsh:Physics Computer Science::Formal Languages and Automata Theory |
Zdroj: | Annales Geophysicae, Vol 15, Iss 10, Pp 1257-1264 (0000) Annales Geophysicae, Vol 15, Pp 1257-1264 (1997) |
ISSN: | 1432-0576 |
Popis: | The pattern recognition capabilities of artificial neural networks (ANNs) have for the first time been used to identify Pi2 pulsations in magnetometer data, which in turn serve as indicators of substorm onsets and intensifications. The pulsation spectrum was used as input to the ANN and the network was trained to give an output of +1 for Pi2 signatures and -1 for non-Pi2 signatures. In order to evaluate the degree of success of the neural-network procedure for identifying Pi2 pulsations, the ANN was used to scan a number of data sets and the results compared with visual identification of Pi2 signatures. The ANN performed extremely well with a success rate of approximately 90% for Pi2 identification and a timing accuracy generally within 1 min compared to visual identification. A number of potential applications of the neural-network Pi2 scanning procedure are discussed. |
Databáze: | OpenAIRE |
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