Machine Learning for Fast and Reliable Source-Location Estimation in Earthquake Early Warning

Abstract

We develop a random forest (RF) model for
rapid earthquake location with an aim to
assist earthquake early warning (EEW)
systems in fast decision making. This system
exploits P-wave arrival times at the first five
stations recording an earthquake and
computes their respective arrival time
differences relative to a reference station (i.e.,
the first recording station). These differential
P-wave arrival times and station locations are
classified in the RF model to estimate the
epicentral location. We train and test the
proposed algorithm with an earthquake
catalog from Japan. The RF model predicts
the earthquake locations with a high
accuracy, achieving a Mean Absolute Error
(MAE) of 2.88 km. As importantly, the
proposed RF model can learn from a limited
amount of data (i.e., 10% of the dataset) and
much fewer (i.e., three) recording stations
and still achieve satisfactory results (MAE)