Real-Time Earthquake Magnitude Classification from Initial P-Waves: Models, Dataset, and Comparative Analysis for South Asia
arXiv:2605.22836v1 Announce Type: cross Abstract: 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
The proliferation of advanced machine learning techniques and the increasing availability of seismic data are enabling more sophisticated real-time analysis for disaster mitigation.
Rapid and accurate earthquake magnitude classification directly impacts the effectiveness of early warning systems, crucial for reducing casualties and economic disruption in seismically active regions.
The ability to classify earthquake magnitude from initial P-waves within seconds improves the speed and precision of disaster response, potentially leading to earlier emergency actions and infrastructure protection.
- · Emergency services
- · Populations in earthquake-prone regions
- · AI/ML developers in geophysics
- · Disaster preparedness organizations
- · Legacy seismic monitoring systems
- · Regions without advanced AI integration
Faster and more reliable earthquake early warnings become possible.
Improved warnings lead to reduced physical damage and human casualties, fostering greater societal resilience in vulnerable areas.
The success of AI in geophysical hazard prediction drives further investment in AI for other natural disaster forecasting and mitigation efforts globally.
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Read at arXiv cs.LG