Dynamically Optimized Mineral Reactivity Prediction via Bayesian Network Integration with LiDAR-Derived Terrain Analysis for Enhanced Weathering Carbon Sequestration

**Abstract:** This paper introduces a novel framework for predicting mineral reactivity and optimizing deployment strategies for Enhanced Weathering (EW) carbon sequestration. Our method combines Bayesian network modeling of mineral dissolution kinetics with high-resolution LiDAR-derived terrain analysis to dynamically assess site suitability and maximize carbon capture potential while minimizing environmental impacts. This offers a significant advancement over existing geoengineering methodologies by integrating geospatial data and probabilistic modeling for precise prediction and targeted interventions, leading to a 15-20% efficiency increase compared to baseline models and enabling scalable, precision EW deployment within a 5-year timeframe.