Uncovering implicit Seismogenic associated regions towards promoting urban resilience

Earthquakes pose a significant threat to urban environments,highlighting the need for enhanced seismic resilience.To improve understanding of earthquake dynamics and the interplay of seismic activity across space,this study introduces a novel approach for identifying associated regions that exhibit interdependence seismic behavior,revealing a network structure of earthquake interplays.This model was applied to earthquakes exceeding 3.0 Mw in Iran(1976–2023),using a 1°×1°grid.Monthly and seasonal timespans were evaluated to capture potential short-term and long-term interactions.The model revealed a network of interdependent seismic regions in southern and southwestern Iran,predominantly located within the Zagros belt.Notably,the strongest associations were observed between spatial units 45 and 36,located approximately 6°apart in southern Iran.These units exhibited significant association in both monthly and seasonal scenarios,with support values of 0.28 and 0.65,and average confidence values of 0.58 and 0.84,respectively.The second significant bilateral relation was detected between neighboring spatial units 22 and 36,with support values of 0.26 and 0.59,and average confidence values of 0.57 and 0.80,respectively.The recognized structure was compared to the established seismotectonic zoning.This network aligns with established seismotectonic provinces,particularly in the seasonal scenario.The model also identified potential interactions between distinct zones in the monthly scenario,highlighting areas where urban development strategies might need reevaluation.Additionally,the analysis revealed implicit causal relationships between spatial units,pinpointing areas susceptible to or influencing seismic activities elsewhere.These results contribute to a deeper understanding of crustal structure,earthquake propagation,and the potential for seismic activity to trigger earthquakes in nearby or distant areas.This knowledge is crucial for developing effective strategies to build earthquake-resilient cities.

Voxel modeling and association of ubiquitous spatiotemporal information in natural language texts

The ubiquitous spatiotemporal information extracted from Internet texts limits its application in spatiotemporal association and analysis due to its unstructured nature and uncertainty.This study uses ST-Voxel modeling to solve the problem of structured modeling and the association of ubiquitous spatiotemporal information in natural language texts.It provides a new solution for associating ubiquitous spatiotemporal information on the Internet and discovering public opinion.The main contributions of this paper include:(1)It proposes a convolved method for ST-Voxel,which solves the voxel modeling problem of unstructured and uncertain spatiotemporal objects and spatiotemporal relation in natural language texts.Experiments show that this method can effectively model 5 types of spatiotemporal objects and 16 types of uncertain spatiotemporal relation founded in texts;(2)It realizes the unknown event discovery based on voxelized spatiotemporal information association.Experiments show that this method can effectively solve the aggregation of ubiquitous spatiotemporal information in multi-natural language texts,which is conducive to discovering spatiotemporal events.The selection of convolution parameters in voxel modeling is also discussed.A parameter selection method for balancing the discovery capability and discovery accuracy of spatiotemporal events is given.