Extended Abstract
Introduction
of the Alpine–Himalayan orogenic belt, shaped by active deformation, folding, and the presence of major strike-slip and thrust fault systems. These processes contribute significantly to the development of surface ruptures, which serve as key geomorphic indicators of crustal stress release and active tectonics. Despite the importance of this region from a neotectonic and seismic-hazard perspective, quantitative and spatially explicit assessments of surface rupture potential remain scarce. Most previous studies have focused either on fault geometry, morphotectonic indices, or seismicity patterns in isolation, while the integrated and probabilistic modelling of rupture susceptibility has received limited attention.
Recognizing this gap, the present study develops a spatial–probabilistic framework that integrates multi-source geological, seismological, and geomorphological datasets to predict potential zones of surface rupture within the Kopeh Dagh structural domain. The proposed framework employs a Weighted Linear Combination (WLC) model complemented by Monte Carlo simulation to quantify uncertainty and assess the stability of the model under varying input conditions. By combining these methods, the study aims to provide a robust, reproducible, and spatially coherent evaluation of rupture potential across diverse lithological units and structural environments. Ultimately, this work contributes to a more comprehensive understanding of the tectonic behavior of Kopeh Dagh and enhances regional hazard assessment.
Materials and Methods
The methodological framework is based on a systematic integration of spatial datasets representing seismic, structural, lithological, and geomorphological variables. Earthquake data—including magnitude, depth, and epicentral coordinates—were collected from reliable seismic catalogs and used to model the radius of influence based on empirical magnitude–rupture relationships. Fault density was computed through kernel density estimation, capturing the spatial clustering of active fault traces. Lithological sensitivity was classified according to the mechanical properties of rock units, distinguishing brittle formations from weaker, more deformable sediments. Geomorphological indices such as slope, curvature, and landform type were extracted from high-resolution DEMs to represent surface instability and morphological predisposition to rupture.
All datasets were standardized to a common scale and projected into a uniform coordinate system. A Weighted Linear Combination (WLC) model was then applied, incorporating expert-defined weights (0.40 for earthquake influence, 0.30 for fault density, 0.20 for lithology, and 0.10 for geomorphology). This produced an initial rupture-potential index ranging from 0 (very low potential) to 1 (very high potential).
To address uncertainty—an inherent component of tectonic and geomorphic processes—Monte Carlo simulation with 1000 iterations was implemented. In each iteration, the weights assigned to input variables were perturbed according to a normal distribution (σ = 0.05), enabling the evaluation of model sensitivity and probabilistic variation. This approach allowed the identification of zones where minor changes in input parameters resulted in significant shifts in potential rupture values, thereby highlighting structurally complex or poorly constrained areas. Model performance and stability were evaluated through the coefficient of variation (CV) and cross-validation metrics, including R² and RMSE.
Results and Discussion
The integrated model demonstrates that tectonic factors overwhelmingly dominate the spatial distribution of surface rupture potential in the Kopeh Dagh region. Among the variables, earthquake magnitude exhibits the strongest correlation with rupture potential (r = 0.85), followed by fault density (r = 0.73). This confirms that areas exposed to higher seismic energy release and greater structural segmentation are inherently more susceptible to rupture propagation. Lithological properties and geomorphological characteristics, while influential, play a secondary reinforcing role rather than acting as primary controls.
Spatial analysis reveals that the highest rupture-potential zones are concentrated in the central and western parts of Kopeh Dagh, where active tectonic deformation, dense fault networks, and moderate-to-large seismic events coincide. These areas correspond closely with previously documented neotectonic activity and align with regional patterns of distributed deformation.
Monte Carlo simulation results further validate the robustness of the model. The mean potential value across simulations is 0.51, with an average standard deviation of 0.18 and a low coefficient of variation (CV = 0.11). This indicates that the model is relatively insensitive to moderate fluctuations in weighting schemes, and the resulting spatial patterns remain stable across iterations. Areas exhibiting elevated CV values correspond to structurally intricate fault intersections, reflecting known complexities in fault kinematics and stress interactions.
The strong agreement between modeled rupture potential and observed seismic–structural patterns is further supported by the high R² value (0.89) obtained during cross-validation. This suggests that the model not only captures the statistical relationships among variables but also succeeds in reproducing the spatial behavior of rupture-prone zones. Overall, the findings underscore the necessity of incorporating probabilistic methods when assessing tectonic hazards in regions where geological heterogeneity and data quality may introduce uncertainty.
Conclusion
This study provides a novel and reliable framework for modeling and mapping surface rupture potential in tectonically active regions. The findings highlight the dominant role of tectonic factors, particularly earthquake magnitude and fault density, in determining surface rupture risk. The model’s ability to integrate uncertainty through Monte Carlo simulation enhances its predictive power, making it a valuable tool for future studies in tectonically active regions. The results can inform risk management strategies and contribute to the development of disaster mitigation plans in high-risk areas. It is recommended that future research focus on incorporating higher-resolution data and more accurate field measurements to further improve the model's accuracy and reliability.