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

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Geoscience Frontiers 2024年第5期15卷 182-196页

作者： Jawad Fayaz Carmine Galasso Department of Computer Science University of ExeterUK Department of Civil Environmentaland Geomatic EngineeringUniversity College London(UCL)LondonUK

Earthquakes pose significant risks globally,necessitating effective seismic risk mitigation strategies like earthquake early warning(EEW)***,developing and optimizing such systems requires thoroughly understanding their internal procedures and coverage *** 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 *** 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 *** is retrained and tested on a dataset of around 11,000 unprocessed japanese subduction ground ***-of-fit testing shows that the ROSERS framework achieves good performance on this database,especially given the peculiarities of the subduction seismic *** trained DNN and LVs are then interpreted using game theory-based Shapley additive explanations to establish cause-effect ***,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 *** 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 *** information can assist end-users in strategically placing sensors,minimizing blind spots,and reducing errors from regional extrapolation.

关键词： Earthquake early warning systems Spatial regression Neural networks japanese subduction Explainable artificial intelligence

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