Uncovering implicit Seismogenic associated regions towards promoting urban resilience

Abstract

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 M_w 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.

Keywords

Earthquake pattern / Spatiotemporal association rule / Iran / Seismotectonic zoning / Apriori

Relevance to resilience

This study makes a significant contribution to the field of urban resilience by providing novel insights into the complex dynamics of earthquake occurrences. By identifying the implicit structure of seismogenic associated regions and uncovering the interplay between underlying processes, this research offers a foundation for urban resilience-based management.

The study pinpoints areas prone to earthquakes or with the potential to trigger seismic events in other regions, enabling policymakers to strategically invest in resilient infrastructure, building codes, and land-use planning. Furthermore, the model's ability to unveil implicit causal relationships between seismic regions provides crucial insights into earthquake propagation mechanisms. This knowledge can inform future research on earthquake prediction and early warning systems, ultimately contributing to the development of more resilient cities.

The model's practical value has been demonstrated through its successful application to earthquake events in Iran, guiding regulatory development and enhancing seismic hazard assessments. This research ultimately contributes to building more resilient cities capable of withstanding seismic disasters.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

CRediT authorship contribution statement

Roya Habibi: Writing - review & editing, Writing - original draft, Visualization, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Ali Asghar Alesheikh: Writing - review & editing, Supervision.

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

The authors would like to express their sincere gratitude to the personnel of the International Institute of Earthquake Engineering and Seismology (IIEES) in Iran, particularly those who provided the essential information used in this study. Their willingness to answer our questions and clarify any ambiguities in the dataset was invaluable.

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