Predicting peak ground acceleration of strong-motion earthquakes using variable snapshots of P-wave data with long short-term memory neural network

Conventional earthquake early warning systems (EEWS) rely on mathematical functions that utilize P-wave parameters extracted over a 3 s window to estimate peak ground acceleration (PGA). Advancements in the capabilities of deep neural networks to approximate universal functions, coupled with the availability of strong seismic event data, offer an unprecedented opportunity to evaluate the relationship between variable snapshots of P-wave data and the PGA of strong-motion earthquakes. This convergence of technology and data opens new avenues for research into utilizing smaller snapshots of P-wave in EEWS. Our study centers on the utilization of a long short-term memory (LSTM) neural network to model long dependencies within the P-wave of 1839 earthquakes recorded by the Kiyonshin Network (K-NET) for the prediction of PGA of S waves. Our methodology involves experiments that ultimately evaluate the network's performance on 4, 3, and 2 s of P-wave snapshots. Our findings indicate that there is sufficient information in 2 s of temporal accelerometer readings after the onset of P waves to predict PGA accurately with an LSTM network.