Detection of ionospheric disturbances with a sparse GNSS network in simulated near-real time Mw 7.8 and Mw 7.5 Kahramanmaras earthquake sequence.

ABSTRACT: On February 6, 2023 the Kahramanmaraş Earthquake Sequence caused significant ground shaking and catastrophic losses across south-central Türkiye and northwest Syria. These seismic events produced ionospheric perturbations detectable in Global Navigation Satellite System (GNSS) total electron content (TEC) measurements. This work aims to develop and incorporate a near-real-time (NRT) ionospheric disturbance detection method into JPL's GUARDIAN system. Our method uses a Long Short-Term Memory (LSTM) neural network to detect anomalous ionospheric behavior, such as co-seismic ionospheric disturbances among others. Our method detected an anomalous signature after the second Mw  7.5 earthquake at 10:24:48 UTC (13:24 local time) but did not alert after the first Mw  7.8 earthquake at 01:17:34 UTC

SUBMITTER: Luhrmann F 

PROVIDER: S-EPMC11839748 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature