Detailed structural analysis of digital outcrops: A learning example from the Kermanshah-Qulqula radiolarite basin, Zagros Belt, Iran

A digital outcrop example and associated structural analysis of highly deformed sedimentary strata from the Zagros Belt of Iran is presented. By providing this site in open-access, downloadable format, we aim to make this excellent outcrop exposure accessible to a wide range of geoscientists. Digital data extraction techniques are used to constrain structural interpretations and cross section orientation, as well as kinematic restorations of interpreted structures. Structural analysis protocols provided here are well-suited to learning outcomes associated with digital cross section construction, interpretation and restoration. Complex deformation at the study locality and associated uncertainties in horizon and fault mapping yield interpretation and structural restoration results that are likely non-unique. Interpretation uncertainties are discussed in the context of geoscience education, with specific reference to the need for considering and assessing data quality and underlying geological assumptions. Our workflow and results can be used to bridge the gap between field-based training at undergraduate level and the proficiency in 3D digital environments required of professional geoscientists. By using digital outcrops to achieve learning outcomes, knowledge of underlying geological processes and practical skills in digital data handling and treatment can be effectively communicated to future geoscientists within the virtual environment.

Automated summary

This study presents a digital outcrop example and associated structural analysis of highly deformed sedimentary strata from the Zagros Belt of Iran. By providing open-access digital data, the authors aim to make this outcrop exposure accessible to a wide range of geoscientists and bridge the gap between field-based training and proficiency in 3D digital environments required of professional geoscientists. Discussions on interpretation uncertainties and the importance of considering data quality and geological assumptions contribute to geoscience education.