SeismoNet: A proximal policy optimization-based earthquake early warning system using dilated convolution layers and online data augmentation

Abstract

In seismic safety, Earthquake Early Warning (EEW) systems are indispensable for mitigating earthquake hazards. These systems strive to quickly evaluate earthquake magnitudes to determine which events warrant immediate alerts. Despite their importance, conventional approaches to rapid earthquake classification face significant hurdles, particularly with the skewed data distribution where instances requiring alerts are far less common. This study proposes an EEW method, called SeismoNet, that leverages a 7-second snapshot of three-dimensional seismic waveforms from the China Earthquake Network Center (CENC). SeismoNet incorporates a trio of dilated convolution layers to distill essential features for effective classification, addressing the critical challenge of data imbalance. Our novel integration of the Off-policy Proximal Policy Optimization (Off-policy PPO) algorithm enriches the model by rewarding accurate classifications, refining its sensitivity to less frequent yet critical seismic events. The training approach is conceptualized as a sequence of decision-making steps, with each seismic sample treated as a unique scenario. The model, acting as a decision-making agent, is rewarded or penalized based on its ability to differentiate between less and more common events. To further enhance classification accuracy, we employ a generative adversarial network (GAN) for dynamic data augmentation and a regularization technique to prevent common pitfalls like mode collapse and training instability. The efficacy of SeismoNet is underscored by its superior performance metrics, including an F-measure of 88.14% and a geometric mean of 90.66%, outperforming existing deep-learning solutions, traditional algorithms, and transfer learning methods. Our exhaustive evaluations, including ablation studies that remove key components like dilated convolutions and PPO, affirm the vital contribution of these elements to SeismoNet’s exceptional capability in EEW. SeismoNet can successfully issue timely warnings for significant seismic events, allowing for essential preventative measures and significantly reducing the potential impact on these communities.