EGU General Assembly 2021
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Semi-automated fault extraction and structural analysis from DEM data of the Magadi and Natron basins, East African Rift System

Pauline Gayrin1, Thilo Wrona2, Sascha Brune2, Simon Riedl3, and Tim Hake3
Pauline Gayrin et al.
  • 1Université Grenoble Alpes, Department of Geosciences PhITEM, France
  • 2German Research Centre for Geosciences GFZ, Potsdam, Germany
  • 3University of Potsdam, Institute of Geosciences, Germany

Continental rifts show surface expressions of deep crustal processes, such as faulting and volcanism. The East African Rift System (EARS) is one of the most prominent examples of an active continental rift driven by tectonics and magmatism. Nonetheless, we still struggle to quantify the amount of extension due to these processes on a kyr- to Myr-time-scale. In particular, the distribution of extension within low-offset normal fault networks within rift basin interiors is challenging to determine.

To address these issues, we develop a semi-automated workflow to extract normal faults from the TanDEM-X science DEM data (12 m horizontal resolution, 0.4 m average height error) of the Magadi-Natron Region of the Eastern branch of the EARS, limited to the north by the Suswa caldera (1.15°S) and to the south by Gelai and Oldoinyo Lengai volcanoes (2.75°S). This data allows us to quantify brittle surface deformation that occurred since the last deposition of widespread volcanic lavas in  these basins. Our workflow consists of five steps: (1) gradient calculation, (2) thresholding, (3) skeletonization, (4) Hough transformation, and (5) clustering. Because the surface faults appear as topographic discontinuities, we first calculate the gradient of the DEM to detect them. Then we use an adaptive threshold (Otsu) to distinguish faults from unfaulted areas. Next, we skeletonize the threshold to extract line segments and perform a Hough transformation to determine the orientation of these segments. Finally, we use a density-based clustering algorithm (DBSCAN) to group these segments into faults. This algorithm is considering proximity between the segment, similarity in dip and strike direction.

A strike analysis applied on the fault data of the whole basin shows four main directions from distinct fault populations. Each direction cluster corresponds to a geological layer and a time interval. For example, the azimuth N20°, corresponds to present and recent rift direction, mostly on the ~1Myr old Magadi trachyte. A direction of N170° is mostly represented in earlier,  Mio-Pliocene volcanic units of the rift. Moreover, we derive the fault displacement distribution throughout the basin.This allows us to calculate the total extension of each geological unit and to compute the overall amount of extension of the region during geologically recent times.

We provide a new high-resolution fault map that depicts strike direction and throw even of small-offset normal faults. This characterization helps us increase our understanding of recent brittle deformation within the Magadi-Natron region and thus the propagation of rifting in the eastern branch of the East African Rift System.

How to cite: Gayrin, P., Wrona, T., Brune, S., Riedl, S., and Hake, T.: Semi-automated fault extraction and structural analysis from DEM data of the Magadi and Natron basins, East African Rift System, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10695,, 2021.

Corresponding displays formerly uploaded have been withdrawn.