Uncovering implicit Seismogenic associated regions towards promoting urban resilience

•Developed a novel model to uncover hidden associations between earthquake occurrences across space and time.•Enhanced our understanding of earthquake dynamics, leading to improved risk assessment and disaster preparedness.•Identified regions particularly susceptible to or influential on earthquake activity, providing valuable insights for targeted mitigation strategies.•Provided a framework for identifying homogeneous tectonic subregions, comparing with previous seismotectonic provinces.•Uncovered potential causal relationships between earthquake occurrences. 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 ea