Project Overview
This research explores fundamental challenges in computational representation of petrological knowledge. Unlike many scientific domains with standardized classification systems, rock classification in geosciences involves multiple valid interpretative frameworks that create unique computational challenges.
Research Context
This review paper evolved from challenges encountered in my applied machine learning work, where I observed significant gaps between computational classification approaches and the nuanced classification methods used by field geologists.
Key Insights
- Unlike minerals with standardized classification systems, rocks are classified through multiple legitimate schemes reflecting geological formation processes
- Identical mineral assemblages can yield different rock classifications depending on formation context, observation scale, and classification purpose
- Traditional computational approaches struggle with context-dependent classifications and hierarchical relationships across analytical scales
- Field geologists often recognize rock types without explicit knowledge of mineral compositions, creating translation challenges for computational systems
Collaboration Opportunities
I’m seeking collaborators with backgrounds in:
- Knowledge representation and ontology development
- Computational geology or petrological classification
- Scientific computing with experience in domains having multiple classification systems
This is an ongoing research effort, and I welcome discussions with potential collaborators interested in this interdisciplinary challenge.