Challenges in fault detection using ambient noise in urban environments: Distinguishing structural scattering from source-induced artifacts through moveout and back-scattering analysis

Accurately mapping buried fault structures is essential for assessing seismic hazards and characterizing subsurface complexity. Many small or concealed faults lack surface expression or recorded seismicity, creating hidden risks that are difficult to identify with traditional methods. Consequently, there is a vital need for reliable detection tools in diverse environments, ranging from geothermal fields to urban centers. The analysis of back-scattered surface waves from ambient seismic noise has emerged as a promising method to fill this gap. However, while synthetic models often exhibit distinct V-shaped patterns in virtual source gathers, applying these techniques to complex geological settings like the Southern Vienna Basin presents significant practical challenges.

We conducted an comprehensive, gather-by-gather analysis of virtual shot gathers from a dense linear nodal array. By manually inspecting binned plots across multiple frequency bands, we identified distinct wavefield anomalies at suspected fault locations. Preliminary results reveal two distinct wavefield anomalies: a transition from normal moveout to nearly flat moveout in the 0.5–1 Hz range, and a transition of energy from the causal to the acausal time lag in the 2–3 Hz range at suspected fault locations.

While these "kinks" and moveout of surface waves appear to correlate with known geological faults, their proximity to urban infrastructure introduces significant interpretive ambiguity. At this preliminary stage, it remains unclear whether these features represent true structural back-scattering or are artifacts induced by localized anthropogenic noise sources acting as stationary phase points. This study highlights the inherent difficulties in urban seismic imaging and underscores the necessity of distinguishing between structural scattering and source-induced artifacts to reliably identify hidden fault risks.