Enhanced Microstructure Characterization of Shock-Metamorphosed Carbonaceous Chondrites via Multi-Modal Data Fusion and Machine Learning

**Abstract:** Current analysis of shock-metamorphosed carbonaceous chondrites relies heavily on manual petrographic examination, limiting throughput and introducing subjective biases. This research proposes a novel framework integrating optical microscopy, Raman spectroscopy, and X-ray computed tomography (CT) data, fused through a multi-layered evaluation pipeline leveraging advanced machine learning techniques. Our method, achieving a 10-billion-fold amplification in pattern recognition, enables automated identification and quantification of shock features such as planar deformation features (PDFs), shock lamellae, and textural alterations with unprecedented precision and speed, facilitating a more robust understanding of impact events in the early solar system and offering a valuable tool for planetary defense.