Fatbox: the fault analysis toolbox

Abstract. Understanding complex fault networks is essential for reconstructing their geological history, quantifying deformation in tectonically active regions, and assessing geohazards and resource potentials. Structure and evolution of fault networks are investigated using a range of methods, including numerical and analogue modelling, as well as the analysis of topographic data derived from satellite imagery. However, due to the high density and complexity of fault systems in many study areas or models, automated analysis remains a significant challenge, and fault interpretation is often performed manually. To address this limitation, we present Fatbox, the fault analysis toolbox, an open-source Python library that integrates semi-automated fault extraction with automated geometric and kinematic analysis of fault networks. The toolbox capabilities are demonstrated through three case studies on normal fault systems: (1) fault extraction and geometric characterization from GLO-30 topographic data in the Magadi-Natron Basin; (2) spatio-temporal tracking of fault development in vertical cross-sections of a forward numerical rift model; and (3) surface fault mapping and geometric evolution of an analogue rift model. By representing fault networks as graphs, Fatbox captures the complexity and variability inherent to fault systems. In time-dependent models, the toolbox enables temporal tracking of faults, providing detailed insights into their geometric evolution and facilitating high-resolution measurements of fault kinematics. Fatbox offers a versatile and scalable framework that enhances the efficiency, reproducibility, and precision of fault system analysis – opening new avenues for tectonic research.

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Publication Details

Published
2025-08-29
DOI
https://doi.org/10.5194/egusphere-2025-3989
Citations
2
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
Field-Weighted Citation Impact
2.48

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article

Fatbox: the fault analysis toolbox

Alessandro La Rosa, Pauline Gayrin, Thilo Wrona, John Naliboff et al.
2 citations
Industrial Vision Systems and Defect Detection
2.48
article

Fatbox: the fault analysis toolbox

Alessandro La Rosa, Pauline Gayrin, Thilo Wrona, John Naliboff, Sascha Brune, Derek Neuharth, Nicolas Molnar
article en
2 citations

Abstract

Abstract. Understanding complex fault networks is essential for reconstructing their geological history, quantifying deformation in tectonically active regions, and assessing geohazards and resource potentials. Structure and evolution of fault networks are investigated using a range of methods, including numerical and analogue modelling, as well as the analysis of topographic data derived from satellite imagery. However, due to the high density and complexity of fault systems in many study areas or models, automated analysis remains a significant challenge, and fault interpretation is often performed manually. To address this limitation, we present Fatbox, the fault analysis toolbox, an open-source Python library that integrates semi-automated fault extraction with automated geometric and kinematic analysis of fault networks. The toolbox capabilities are demonstrated through three case studies on normal fault systems: (1) fault extraction and geometric characterization from GLO-30 topographic data in the Magadi-Natron Basin; (2) spatio-temporal tracking of fault development in vertical cross-sections of a forward numerical rift model; and (3) surface fault mapping and geometric evolution of an analogue rift model. By representing fault networks as graphs, Fatbox captures the complexity and variability inherent to fault systems. In time-dependent models, the toolbox enables temporal tracking of faults, providing detailed insights into their geometric evolution and facilitating high-resolution measurements of fault kinematics. Fatbox offers a versatile and scalable framework that enhances the efficiency, reproducibility, and precision of fault system analysis – opening new avenues for tectonic research.

University of Pisa (IT), University of Potsdam (DE), New Mexico Institute of Mining and Technology (US), Deutsche Homöopathie-Union (DE), GFZ Helmholtz Centre for Geosciences (DE), FH Aachen (DE)
Deutsche Forschungsgemeinschaft
Openalex Percentile: Top 9%
Industrial Vision Systems and Defect Detection
2.48
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