Automated Detection of Slow Slip Events from InSAR: Application to the North Anatolian Fault

The growing volume of InSAR time series offers new opportunities to systematically detect transient aseismic deformation, but identifying low-amplitude slow slip events (SSEs) remains challenging due to noise and limited temporal resolution. Here, we adapt the geodetic matched filter, originally developed for GNSS data, to InSAR displacement time series in the context of shallow strike-slip faults. The method relies on correlations between physics-based templates and relative displacement time series constructed between pixels located on either side of the fault, enhancing the signal-to-noise ratio and mitigating atmospheric artifacts. Using synthetic experiments with realistic noise, we quantify detection thresholds and show that SSEs with magnitudes as small as Mw 4–4.5 can be reliably detected at shallow depths. A validation strategy based on spatial coherence and weighted stacking of displacement time series significantly reduces false detections. We apply this approach to the Izmit and Ismetpasa segments of the North Anatolian Fault using multi-level processed InSAR datasets. The method successfully retrieves previously documented SSEs and shows that advanced post-processing improves detection capability. Detected events have magnitudes Mw 4.0–4.3, shallow depths (< 2–4 km), and durations of days to weeks, consistent with independent geodetic observations. These results demonstrate that physics-based template matching provides a robust and scalable framework for automatic SSE detection in InSAR time series.