Periodic Reporting for period 2 - EARLI (Detection of Early seismic signal using ARtificiaL Intelligence)
Período documentado: 2022-07-01 hasta 2023-12-31
EARLI aims at designing Artificial Intelligence (AI) algorithms to improve both the rapidity and accuracy of earthquake and tsunami early warning systems by extracting the information contained in recently identified low-amplitude signals (named PEGS for Prompt Elasto-Gravity Signals), which propagate at the speed of light. The second, more exploratory, objective of EARLI is to design AI algorithms to search for earthquake precursors in seismic and geodetic data.
In parallel, we designed a complementary early warning AI algorithm for Magnitude ≤ 8 earthquakes. This approach uses 3 seconds of "classical" seismic signal recorded by a single seismic station to rapidly estimate the earthquake location and magnitude (Lara et al., in review at JGR Solid Earth). We implemented this algorithm in the early warning system of Peru, currently in construction. We are now complementing the Peruvian system with the PEGS-based algorithm.
We stacked all the GPS data ever recorded before large earthquakes after projecting the measurements in the direction of expected motion assuming precursory slip on the fault. We found evidence that earthquakes likely start, on average, with a precursory phase of slow slip in the ~2 hours preceding large events (Bletery and Nocquet, Science, 2023).
In the second part of the project, we hope to lower the magnitude at which the PEGS-based algorithm starts working, we plan to incorporate it to the early warning system of Peru (as a complement to the other algorithm we already installed) and we aim to design a global version of this algorithm that could offer a global early warning cover.
We also plan to apply AI approaches to lower the noise in GPS data with the aim of identifying precursory signals at the scale of individual events.