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dc.contributor.authorKvammen, Andreas
dc.contributor.authorVierinen, Juha-Pekka
dc.contributor.authorHuyghebaert, Devin Ray
dc.contributor.authorRexer, Theresa
dc.contributor.authorSpicher, Andres
dc.contributor.authorGustavsson, Björn Johan
dc.contributor.authorFloberg, Jens
dc.date.accessioned2024-09-25T07:44:29Z
dc.date.available2024-09-25T07:44:29Z
dc.date.issued2024-06-03
dc.description.abstractMillions of ionograms are acquired annually to monitor the ionosphere. The accumulated data contain untapped information from a range of locations, multiple solar cycles, and various geomagnetic conditions. In this study, we propose the application of deep convolutional neural networks to automatically classify and scale high-latitude ionograms. A supervised approach is implemented and the networks are trained and tested using manually analyzed oblique ionograms acquired at a receiver station located in Skibotn, Norway. The classification routine categorizes the observations based on the presence or absence of E− and F-region traces, while the scaling procedure automatically defines the E− and F-region virtual distances and maximum plasma frequencies. Overall, we conclude that deep convolutional neural networks are suitable for automatic processing of ionograms, even under auroral conditions. The networks achieve an average classification accuracy of 93% ± 4% for the E-region and 86% ± 7% for the F-region. In addition, the networks obtain scientifically useful scaling parameters with median absolute deviation values of 118 kHz ±27 kHz for the E-region maximum frequency and 105 kHz ±37 kHz for the F-region maximum O-mode frequency. Predictions of the virtual distance for the E− and F-region yield median distance deviation values of 6.1 km ± 1.7 km and 8.3 km ± 2.3 km, respectively. The developed networks may facilitate EISCAT 3D and other instruments in Fennoscandia by automatic cataloging and scaling of salient ionospheric features. This data can be used to study both long-term ionospheric trends and more transient ionospheric features, such as traveling ionospheric disturbances.en_US
dc.identifier.citationKvammen, Vierinen, Huyghebaert, Rexer, Spicher, Gustavsson, Floberg. NOIRE-Net–a convolutional neural network for automatic classification and scaling of high-latitude ionograms. Frontiers in Astronomy and Space Sciences. 2024;11en_US
dc.identifier.cristinIDFRIDAID 2280138
dc.identifier.doi10.3389/fspas.2024.1289840
dc.identifier.issn2296-987X
dc.identifier.urihttps://hdl.handle.net/10037/34854
dc.language.isoengen_US
dc.publisherFrontiers Mediaen_US
dc.relation.journalFrontiers in Astronomy and Space Sciences
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2024 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleNOIRE-Net–a convolutional neural network for automatic classification and scaling of high-latitude ionogramsen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's license is described as Attribution 4.0 International (CC BY 4.0)