Seismic Intensity Estimation Using Multilayer Perceptron for Onsite Earthquake Early Warning

Abstract

Automated early preventive measures are necessary to reduce secondary hazards in case of high-intensity earthquakes, as these have the potential to damage civil structures over a larger distance from the epicentre. The warning in case of a low-intensity earthquake can, however, disrupt human life and cause unnecessary losses. Hence, in this article, a Multilayer Perceptron-classifier is modeled to provide ensuing severity-based warning by predicting the possibility of the onsite intensity exceeding a pre-trained Peak Ground Acceleration threshold related to damaging intensities in the MMI scale. The supervised learning of the classifier utilized seismic features extracted from the strong-motion signal after the onset of the p-wave. A stratified differential feature-window resampling is applied, which enhanced the F-score of intensity class prediction from 79% to 91%. The trained model is realized on a Faster Than Real-Time (FTRT) simulation environment and tested with sliding windows for estimation of earthquake intensity classes for early warning, predicting with 89% accuracy. A seismic dataset consisting of around eight thousand non-earthquake seismic events from the National Capital Region, India, is generated using a fleet of developed seismic sensing nodes (SSNs) over a period of four years. The cross-dataset validation on this generated dataset showed 95.98% accuracy. A novel Factor of Early Warning (FoEW) metric is introduced to measure the effectiveness of warning by normalizing the achieved lead time with the corresponding maximum warning time. Mean FoEW exceeded 85% for the earthquakes with moderate to heavy damage potentials, i.e., MMI ≥ VI, suitable for onsite earthquake early warning.