The integration of Markov chain models with geologic stratigraphic simulations marks a significant step forward in sedimentary basin modeling. This study focuses on modeling the complexities of facies deposition influenced by sea level variations and geologic processes within sedimentary basins. Combining mathematical precision with geologic insights, the approach simulates and visualizes the formation and evolution of sedimentary basins, moving beyond traditional deterministic models. The methodology employs Markov chain models to capture the stochastic nature of geologic phenomena, simulating transitions between various geologic states, such as deformation, tilting, faulting, and erosion, over geologic timescales. Transition matrices within this probabilistic framework determine the likelihood of shifting from one geologic state to another. A synthetic sea level curve, developed using a sine function, mimics the periodic nature of sea level changes and their impact on facies deposition. A significant finding is the enhanced predictive capability of the Markov chain approach in geologic modeling. By embracing stochastic processes, the study reveals a deeper understanding of inherent uncertainties and variabilities in geologic formations. This insight is crucial for resource exploration and environmental studies, providing a nuanced perspective on sedimentation and structural transformations. The simulations yield various subsurface model realizations, enabling extensive evaluation of geologic uncertainties and demonstrating the model's adaptability to different geologic events. Finally, the study exemplifies how integrating mathematical models with geologic processes leads to improved understanding and visualization of complex geologic systems. It highlights the potential of interdisciplinary approaches in yielding innovative solutions and deeper insights into earth's subsurface complexities.

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