Interpretability and spatial efficacy of a deep-learning-based on-site early warning framework using explainable artificial intelligence and geographically weighted random forests

Abstract

Earthquakes pose significant risks globally, necessitating effective seismic risk mitigation strategies like earthquake early warning (EEW) systems. However, developing and optimizing such systems requires thoroughly understanding their internal procedures and coverage limitations. This study examines a deep-learning-based on-site EEW framework known as ROSERS (Real-time On-Site Estimation of Response Spectra) proposed by the authors, which constructs response spectra from early recorded ground motion waveforms at a target site. This study has three primary goals: (1) evaluating the effectiveness and applicability of ROSERS to subduction seismic sources; (2) providing a detailed interpretation of the trained deep neural network (DNN) and surrogate latent variables (LVs) implemented in ROSERS; and (3) analyzing the spatial efficacy of the framework to assess the coverage area of on-site EEW stations. ROSERS is retrained and tested on a dataset of around 11,000 unprocessed Japanese subduction ground motions. Goodness-of-fit testing shows that the ROSERS framework achieves good performance on this database, especially given the peculiarities of the subduction seismic environment. The trained DNN and LVs are then interpreted using game theory-based Shapley additive explanations to establish cause-effect relationships. Finally, the study explores the coverage area of ROSERS by training a novel spatial regression model that estimates the LVs using geographically weighted random forest and determining the radius of similarity. The results indicate that on-site predictions can be considered reliable within a 2–9 km radius, varying based on the magnitude and distance from the earthquake source. This information can assist end-users in strategically placing sensors, minimizing blind spots, and reducing errors from regional extrapolation.

Keywords

Earthquake early warning systems / Spatial regression / Neural networks / Japanese subduction / Explainable artificial intelligence

CRediT authorship contribution statement

Wenbiao Li: Conceptualization, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing. Jun Wang: Investigation, Software. Chengzao Jia: Funding acquisition, Supervision. Shuangfang Lu: Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Writing – review & editing. Junqian Li: Methodology, Resources, Supervision. Pengfei Zhang: Investigation, Validation. Yongbo Wei: Investigation. Zhaojing Song: Investigation. Guohui Chen: Investigation. Nengwu Zhou: Investigation.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work was funded by the National Natural Science Foundation of China (Grant Nos. 42302170, 42302160), the Innovation Platform for Academicians of Hainan Province (YSPTZX202301), the National Postdoctoral Innovative Talent Support Program (Grant No. BX20220062), the National Science Foundation of Heilongjiang Province of China (Grant No. YQ2023D001), the Project of Sanya Yazhou Bay Science and Technology City (Grant No. SCKJ-JYRC-2023-01), CNPC Innovation Found (Grant No. 2022DQ02-0104).

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