<![CDATA[Earthquake occurrences exhibit complex spatial patterns influenced by geological structures such as faults, subduction zones, volcanic activity, and soil characteristics. Conventional approaches often assume homogeneous intensity, which may fail to capture spatial variability and underlying heterogeneity in seismic processes. Therefore, this study aims to model and compare earthquake occurrences in the Nusa Tenggara region during 2010–2025 using the Homogeneous Poisson Process (HPP), Non-Homogeneous Poisson Process (NHPP), and a Mixture Model-based Poisson process to identify the most appropriate modelling framework. The analysis is conducted using a spatial grid approach (10×15 cells), incorporating geophysical covariates including distances to faults, subduction zones, volcanoes, and soil conditions. Parameter estimation is performed within a Bayesian framework using Markov Chain Monte Carlo (MCMC) methods with consistent settings across all models. The results show that the NHPP model captures spatial variability better than HPP, with subduction distance, volcanic activity, and soil characteristics identified as significant factors, while fault distance is not statistically significant. However, the mixture model provides substantially improved model fit, revealing the presence of two latent components that represent different seismic patterns, with estimated proportions of 84.6% and 15.4%, respectively. Based on model comparison using the Widely Applicable Information Criterion (WAIC), the mixture model yields the lowest value (866.05), indicating superior predictive performance. In conclusion, incorporating both spatial non-homogeneity and latent heterogeneity leads to a more flexible and accurate representation of earthquake occurrences. It is recommended that future studies consider more advanced spatial or hierarchical modelling approaches to further enhance predictive accuracy]]>
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