A novel multi-scale approach to fault network analysis and visualization: test case Franconian Platform (SE Germany)

Faults are essential to reconstruct geologic histories and their presence (or absence) in the subsurface plays a significant role for many societal issues, such as geothermal energy, carbon, hydrogen and hydrocarbon storage or nuclear waste deposition. In the area of the Permo-Mesozoic Franconian Platform (Northern Bavaria, SE Germany) the existence and properties of faults in the subsurface are poorly known due to limited exposure and lack of geophysical data coverage. To address this issue, we have constructed a new fault network based on stratigraphic offsets identified in a network of 18 cross sections. In this contribution, we present our workflow which allows the effective construction of an internally consistent cross-section network covering a study area of >30,000 km 2 , while representing the geology in high detail. Data input is provided by >3,500 (mostly shallow) wells and >250 high-resolution geological maps, complemented by geophysical surveys and field data. We demonstrate how fault segments and their attributes (e.g., fault type, dip direction, offset, certainty of the interpretation, etc.) can be inferred from the profiles, resulting iteratively in consistent profile and fault networks. While we highlight the non-uniqueness of the interpretation and its restriction to vertical offsets, we demonstrate advantages of the fault network, inferred through this pseudo-3D approach, compared to previously available datasets. We discuss the role of newly identified fault populations for Cretaceous inversion tectonics and Cenozoic rifting. Through the incorporation of basic measures of uncertainty and scale-dependency in the resulting fault model, quantification of the fault network can be achieved. This enables the comparison with independent datasets and different model realizations. • We present a new method for the construction of a 3d consistent fault network for study areas based on geological data. • We show how important it is to include scale dependencies in fault networks in order to achieve reliable results. • Through the incorporation of basic measures of uncertainty and scale-dependency in the resulting fault model, quantification of the fault network can be achieved.