A Deep Attention Model for Onsite Estimation of Earthquake Epicenter Distance and Magnitude

The onsite early warning techniques that issue earthquake alerts based on the seismic response of single stations have proven to be quite successful in detecting damage. The magnitude of an earthquake and the epicentral distance are vital parameters for accessing the intensity of destruction at an observation point during an earthquake. This article presents a novel earthquake early warning (EEW) system model designed to predict epicentral distance. The proposed model synergizes the strengths of deep learning and shallow machine learning (ML) techniques, offering a new perspective on seismic event prediction. Specifically, the study introduces an architecture that uses a long short-term memory (LSTM) network with two attention mechanisms to extract high-level features from the initial 3 s of the primary waveform. These attention mechanisms are built for time- and feature-based dependencies. Global site-related features such as shear-wave velocities at various depths and station coordinates are also incorporated, enhancing the model’s predictive capacity. Following this, the scalable ML algorithm XGBoost is applied. The fusion of deep and shallow learning methods applied in the onsite prediction of epicentral distance makes this a significant contribution to the early detection of epicenter distance, azimuth, and focal depth. The next step is the single-station magnitude detection from the predicted epicenter distance, azimuth, focal depth, and other magnitude-dependent parameters. The study shows remarkable results for magnitude detection. The findings indicate the potential for improved prediction of earthquake parameters, contributing toward the ongoing goal of enhancing EEW systems and reducing the destructive impacts of seismic events.