Towards structurally informed data collection for fold structural mapping

@inproceedings{CHAARANI_RM2021,
 abstract = { Structural data distributions usually reflect the path followed by geologists while mapping. In polydeformed terranes, geologists map and sample structural data either across structures or along form surfaces. Due to varying outcropping conditions, the collected structural data are either dense or sparse and may, for example, not capture the full wavelength of folds. This study aims to provide geologists with a structurally guided field sampling workflow for fold geometries. To do so, we assess the influence of structural data distributions on modelled 3D fold geometries using the new open-source modelling engine LoopStructural. LoopStructural models folds within a fold coordinate system (the fold frame). The axes of the fold frame correspond roughly to the axes of the finite strain ellipsoid associated with the local geometry of folds. The workflow of the experiments consisted of sampling a single synthetic noncylindrical fold: i) randomly, ii) across structures (across Sn+1), and iii) along the form surfaces of the folded foliation Sn. These datasets are then used to build 3D models. The recovery of the reference fold geometry is assessed by calculating the difference of the newly generated models and the reference model using fold geometrical characteristics such as the fold wavelength, asymmetry and tightness. We present results that show that form surface sampling is suitable for noncylindrical folds. We show that datasets with only few data points can recover well the reference geometry. These results open the way to build algorithms that can predict sampling locations for efficient structural data collection. },
 author = { Chaarani, Rabii AND Grose, Lachlan AND Ailleres, Laurent AND Laurent, Gautier AND Armit, Robin },
 booktitle = { 2021 RING Meeting },
 publisher = { ASGA },
 title = { Towards structurally informed data collection for fold structural mapping },
 year = { 2021 }
}