3:30 PM - 5:00 PM
[PEM15-P04] Automatic detection and statistical analysis of sporadic E layer by machine learning using Shigaraki ionosonde observation
Keywords:Sporadic E, Machine Learning, Ionosphere
The system was trained by 512 ionogram images in which ionospheric traces of Es layer are labeled manually. The obtained foEs values were validated with other 150 images. We confirmed a high accuracy of the auto-scaled foEs values with an averaged error of as low as 0.24MHz. Comparing with Kokubunji ionosonde operated by National Institute of Information and Communications Technology (NICT), whose distance from Shigaraki ionosonde is about 350 km, a distribution of foEs values are similar with that from Kokubunji ionosonde, which implies the auto-scaling works correctly. However,one-to-one comparison of foEs obtained in the two stations at the same time show a relatively low correlation coefficient (0.51). It suggests that a large-scale formation of Es layer is common over Shigaraki and Kokubunji, while the distribution of a neutral wind and metallic ions have a different pattern over the two stations. From a long-term analysis, we did not find a dependence on solar activity.