STRATICA: Stratigraphic Pattern Recognition & Paleoclimatic Temporal Reconstruction

A Physics-Informed AI Framework for Deep-Time Earth System Reconstruction, Stratigraphic Layer Intelligence, and Paleoclimatic Cycle Decoding via the Temporal Climate Integrity Index (TCI). STRATICA presents the first unified, multi-parameter Physics-Informed Neural Network framework for the systematic reconstruction, computational modeling, and temporal interpretation of Earth's stratigraphic record across 4.5 billion years. The framework integrates nine analytically independent stratigraphic and geochemical parameters into a single Temporal Climate Integrity Index (TCI), achieving paleoclimate classification accuracy of 96.2% across 47 sedimentary basins on 6 continents. Key Innovations: Temporal back-casting using Transformer-LSTM hybrid architectures Nine-parameter TCI composite metric Physics-Informed Neural Network with hard constraints Validated against 47 sedimentary basins, 12 IODP drill cores, 8 ice core records Applications: Deep-time climate analog mapping Mass extinction precursor detection Autonomous drill core analysis Real-time paleoclimate dashboard Principal Investigator: Samir Baladi (Ronin Institute) Status: Submitted to Nature Geoscience / Earth and Planetary Science Letters DOI: 10.5281/zenodo.18851076