From Observation to Understanding: The Lithotectonic Framework as Foundation for Europe's Digital Geological Infrastructure

Geological mapping stands at a methodological crossroads. While traditional chronostratigraphic and lithostratigraphic approaches are effective at documenting observable rock patterns and temporal sequences, modern geological applications increasingly demand maps that directly relate to geological processes and events. The Lithotectonic Framework (LTF), developed within the GSEU project (grant 101075609), revisits lithotectonic concepts from the 1970s with the first rigorous theoretical framework. Complementing parallel European initiatives (doi.org/10.1051/bsgf/2022017; doi.org/10.31223/X5RT28), it organizes geological knowledge based on our understanding of Earth's history, rather than from observed rock age or lithological composition only.

The LTF's boundary-first principle defines geological units based on the events that created them, producing maps that reflect uniform geological histories. Consider the Paris Basin and North Sea Basin that are chronostratigraphically continuous, but lithotectonically distinct: the former is linked to post-Variscan subsidence, and the latter to Atlantic rifting. This event-based approach complements traditional mapping methods: chronostratigraphy provides robust temporal correlation, lithostratigraphy captures compositional variation, while LTF reveals the tectonic and sedimentary processes that shaped Europe's geology. The framework is equally applicable to polydeformed basement and sedimentary sequences, offering a systematic treatment of overprinting relationships through a hierarchical structure.

Beyond cartographic advantages, LTF's conceptual foundation unlocks transformative digital capabilities. By describing geology conceptually rather than descriptively, its hierarchical structure translates directly into semantic knowledge systems. Unlike traditional geological databases that catalogue and describe map features, LTF knowledge bases formally encode the theoretical relationships between geological entities. This enables dynamic visualizations, such as temporal "undressing" to expose deeper or earlier geological levels, thematic extraction for applied research, and crucially, machine-assisted geological reasoning. Preliminary testing demonstrates that LTF's conceptual structure enables AI systems to reason correctly about novel geological questions, outperforming geologists unfamiliar with the framework.

The paradigm shift is profound: geological mapping evolves from producing static maps with implicit knowledge to dynamic knowledge bases, where maps become interactive visualizations of deeper insights. Traditional geological mapping discovered that rocks form traceable patterns across continents, leading to the realization that geology records Earth's history. The LTF builds upon that foundation, introducing a self-organizing framework – where structure emerges from conceptual principles – that transforms geological knowledge from implicit expertise into explicit, queryable infrastructure. For Europe's geological community, this is not a replacement but an evolution: a digital geological infrastructure that preserves the strengths of traditional mapping while unlocking computational capabilities essential for modern Earth science applications.