An Updated Seismicity View of the Dead Sea Transform Using an Early Warning Network

Abstract A network of strong-motion sensors typically used for early warning can be incorporated into an earthquake location workflow. By leveraging machine-learning-generated travel-time picks, validated through existing catalog information, stable and naturally coalescing locations are obtained. I apply this methodology to 6.5 yr of data from a dense array installed along the Dead Sea Transform (DST). In this time period, the seismogenic zone is, on average, deeper by about 4 km than previously estimated. In addition, new locations indicate depth localization of fault-related seismicity along the Dead Sea, Sea of Galilee, and Arava basins, as well as event migration toward the Faria fault system along a 70 km section of the DST. In the Northern Arava valley, event locations are surprisingly primarily perpendicular to mapped faults, indicating complex subsurface structures. Despite the limited period of analysis, these results may have significant implications for the tectonic understanding and seismic hazard assessment of the region.