ROSERS—A Deep Learning Framework for Earthquake Early Warning and Its Interpretation

Jawad Fayaz

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Earthquake early warning (EEW) systems are swiftly evolving from standalone physics-inferred methods (requiring computationally expensive inversions) to data-driven strategies for efficient earthquake hazard mitigation in real time. Besides being speedy in prediction, data-driven approaches such as artificial neural networks usually require minimal assumptions in the training and execution processes. This study discusses and attempts to interpret the data-driven EEW framework: ROSERS (Real-Time On-Site Estimation of Response Spectra) proposed by Fayaz and Galasso (2022). ROSERS aims to utilize the early non-damaging p-waves and the recording-site characteristics to predict the acceleration response spectrum (Sa(T) ) of the anticipated on-site ground motion waveform. The framework's efficacy is analyzed using an extensive database of ground motions, and it is observed that ROSERS leads to exceptional prediction power when implemented in a real-time backdrop. To provide a better interpretation of the framework, this study utilizes the concepts of explainable artificial intelligence (i.e., Shapley additive explanation, SHAP) to obtain insights into the decision-making process of the trained neural networks. Particularly, the cause-effect relationship of the computed latent variables and Sa(T) is explored. The analyses showcase that the two latent variables of the framework complement each other in capturing stiff short-period and flexible long-period Sa(T) thereby leading to excellent reconstruction power.

Original languageEnglish
Title of host publicationProceedings of 17th Symposium on Earthquake Engineering (Vol. 4)
EditorsManish Shrikhande, Pankaj Agarwal, P.C. Ashwin Kumar
PublisherSpringer Singapore
Number of pages14
ISBN (Electronic)9789819914593
ISBN (Print)9789819914586, 9789819914616
Publication statusPublished - 1 Jul 2023
Event17th International Symposium on Earthquake Engineering - Department of Earthquake Engineering, Indian Institute of Technology Roorkee, Roorkee, India
Duration: 14 Nov 202217 Nov 2022

Publication series

NameLecture Notes in Civil Engineering
Volume332 LNCE
ISSN (Print)2366-2557
ISSN (Electronic)2366-2565


Conference17th International Symposium on Earthquake Engineering
Abbreviated titleSEE 2022

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.


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