References:
Q. Brissaud, and E. Astafyeva. Near-real-time detection of co-seismic ionospheric disturbances using machine learning. Geophysical Journal International (2022). https://doi.org/10.1093/gji/ggac167
Our paper has finally been accepted in Geophysical Journal International. See below for a quick summary and here for more details: See blog post
Undersea earthquakes can cause large displacements of the ocean floor. Such displacements affect the ocean surface level and can generate tsunamis with tragic consequences for coastal communities. In order to prevent such disasters, efficient near-real-time tsunami monitoring techniques are essential. Fortunately, in addition to tsunamis, large earthquake excite strong inaudible sound waves, called infrasound, which can propagate up to the ionosphere, an electrically charged atmospheric layer between 150-250 km altitude. Since the acoustic waves excited by earthquakes can be detected within 15min of a seismic event, their ionospheric measurement enables us to characterize an earthquake’s tsunami potential in Near-Real-Time (NRT). However, the detection of such waves is time consuming as it relies on human experts. To automatize this procedure, we propose the first machine-learning model to detect and classify earthquake signals in the ionosphere in NRT. This model paves the way for future NRT assessment of the tsunamigenic potential of earthquakes from the ionosphere.