Hyper-Resolution Paleoenvironmental Reconstruction Using Multi-modal Deep Learning for Permian-Triassic Extinction Event Simulation

**Abstract:** This paper introduces a novel framework for creating high-resolution paleoenvironmental simulations of the Permian-Triassic extinction event (PTE), the largest known mass extinction in Earth's history. Leveraging multi-modal deep learning, we integrate diverse geochemical, paleontological, and geological datasets to reconstruct past climate, ocean chemistry, and biotic conditions with unprecedented detail. Our innovative approach, termed the "Geo-Chronos Deep Learning Engine" (GCDLE), achieves a 10x improvement in spatial resolution and temporal fidelity compared to traditional paleoclimate models, facilitating a more accurate understanding of the triggering mechanisms and cascading impacts of the PTE. This technology offers direct applicability to predicting future climate change impacts and informing mitigation strategies.