Near-real-time detection of co-seismic ionospheric disturbances using machine learning

Abstract

Tsunamis generated by large earthquake-induced displacements of the ocean floor can lead to tragic consequences for coastal communities. Measurements of co-seismic ionospheric disturbances (CIDs) offer a unique solution to characterize an earthquake's tsunami potential in near-real-time (NRT) since CIDs can be detected within 15 min of a seismic event. However, the detection of CIDs relies on human experts, which currently prevents the deployment of ionospheric methods in NRT. To address this critical lack of automatic procedure, we designed a machine-learning-based framework to (1) classify ionospheric waveforms into CIDs and noise, (2) pick CID arrival times and (3) associate arrivals across a satellite network in NRT. Machine-learning models (random forests) trained over an extensive ionospheric waveform data set show excellent classification and arrival-time picking performances compared to existing detection procedures, which paves the way for the NRT imaging of surface displacements from the ionosphere.