A deep learning prediction model for on-site earthquake early warning system in India

IF 1.3 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Seismology Pub Date : 2026-04-17 DOI:10.1007/s10950-026-10395-3

Bhargavi Podili, Pavan Mohan Neelamraju, Jahnabi Basu, S. T. G. Raghukanth

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Abstract

The increased success of earthquake early warning systems (EEWS) worldwide has warranted the need to explore the same for earthquake prone regions such as the active Himalayas of India. While network-based EEWS has been successfully implemented, the scope for on-site EEWS in the region has yet to be investigated. The current study presents a deep learning model, which is aimed at enabling the on-site EEWS in India. The model is developed for predicting broadband spectral acceleration of the expected ground motion using the initial P-wave portion of the seismic recording. By utilizing ground motion parameters derived from the first 3 s of the P-wave, the model can provide rapid and accurate predictions of the associated spectral acceleration between periods 0.01 s and 4.0 s. The research leverages data from multiple seismic networks in the Western Himalayas, encompassing 689 ground motion records from 124 earthquakes with magnitudes ranging from M_w 3.0 to 7.8. The model's performance is evaluated through rigorous sensitivity analyses and validation procedures, demonstrating its potential for real-time application in EEWS.

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印度地震现场预警系统的深度学习预测模型

世界范围内地震早期预警系统（EEWS）的日益成功，证明有必要在地震易发地区（如印度活跃的喜马拉雅山脉）探索同样的预警系统。虽然基于网络的EEWS已经成功实施，但该地区现场EEWS的范围还有待调查。目前的研究提出了一种深度学习模型，旨在实现印度的现场EEWS。该模型是为了利用地震记录的初始纵波部分预测预期地面运动的宽带频谱加速度而开发的。利用纵波前3 s的地震动参数，该模型可以快速准确地预测周期为0.01 s到4.0 s之间的相关谱加速度。该研究利用了西喜马拉雅地区多个地震台网的数据，包括124次地震的689次地面运动记录，震级从3.0到7.8兆瓦不等。该模型的性能通过严格的灵敏度分析和验证程序进行评估，展示了其在EEWS中实时应用的潜力。

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来源期刊

Journal of Seismology 地学-地球化学与地球物理

CiteScore

3.30

自引率

6.20%

发文量

审稿时长

3 months

期刊介绍： Journal of Seismology is an international journal specialising in all observational and theoretical aspects related to earthquake occurrence. Research topics may cover: seismotectonics, seismicity, historical seismicity, seismic source physics, strong ground motion studies, seismic hazard or risk, engineering seismology, physics of fault systems, triggered and induced seismicity, mining seismology, volcano seismology, earthquake prediction, structural investigations ranging from local to regional and global studies with a particular focus on passive experiments.