Real-Time Earthquake Magnitude Classification from Initial P-Waves: Models, Dataset, and Comparative Analysis for South Asia
For seismologists and disaster management agencies in South Asia, this work provides a practical deep learning solution for rapid magnitude estimation, though the improvement over existing methods is incremental.
This paper presents a comparative study of six machine learning models for earthquake magnitude classification using only the initial 7-second P-wave window from a single station, using a novel dataset of 7,318 events from South Asia. Their Transformer-based model achieved 76.23% standard accuracy and 81.56% adaptive accuracy with 4.8 ms inference latency, demonstrating viability for real-time early warning systems.
Rapid earthquake magnitude estimation is crucial for effective early warning systems that can save lives and reduce economic damage. In this paper, we present a comprehensive study of magnitude classification using only the vertical component of the initial 7-second P-wave window from a single station. We compare six machine learning approaches that range from traditional models to state-of-the-art deep learning architectures. We also curated a novel dataset of 7,318 earthquake events in South Asia. The dataset was categorized into five Richter-scale classes: slight (3.0-3.9), light (4.0-4.9), moderate (5.0-5.9), strong (6.0-6.9) and severe (>= 7.0). Our experiments show that deep learning models substantially outperform traditional approaches. Our Transformer based architecture achieved 76.23% standard accuracy and 81.56% adaptive accuracy with 4.8 ms inference latency. The adaptive-accuracy metric is introduced for the inherent uncertainty in magnitude estimation of near class boundaries. These results indicate that the attention mechanisms in Transformers combined with adaptive classification effectively capture the temporal dynamics of seismic signals. The architectural advantage facilitates promising generalization to rare high-magnitude events, despite the inherent data scarcity characteristic of seismic catalogs. The adaptive accuracy provides a more realistic assessment of model performance, and the result suggests viability for real-time deployment.