ASSESSING CORONAL MASS EJECTION EFFECTS ON LEO ORBIT SCINTILLATIONS VIA STATISTICAL AI ANALYSIS OF GEO SATELLITE DATA
DOI:
https://doi.org/10.71146/kjmr982Keywords:
Satellite communications , GEO orbits, LEO, CME, AI , StatisticsAbstract
Coronal mass ejections (CMEs) pose a persistent and significant threat to orbital infrastructure, particularly by inducing ionospheric scintillations that disrupt Low Earth Orbit (LEO) satellite communications. This paper proposes a novel methodological framework to assess and predict localized LEO scintillations by applying statistical Artificial Intelligence (AI) to Geostationary Earth Orbit (GEO) satellite data. By utilizing the advanced vantage point of GEO satellites to capture upstream space weather precursors—such as magnetic field fluctuations and soft X-ray background enhancements—the proposed AI model hypothetically maps these signals to downstream LEO signal degradation. Through the integration of deep learning techniques on time-series telemetry, this work aims to transition space weather mitigation from reactive telemetry monitoring to proactive, predictive orbital management.
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Copyright (c) 2026 Dr Anum Ali, Adnan Zafar, Andrew Wells (Author)
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