The Geological Survey of Israel has upgraded and expanded the national Israeli Seismic Network (ISN), with more than 110 stations country-wide, as part of the implementation of a governmental decision to build a national Earthquake Early Warning (EEW) system named TRUAA. This upgraded seismic network exhibits a high station density and fast telemetry. The stations are distributed mainly along the main fault systems, the Dead Sea Transform, and the Carmel-Zfira Fault, which may potentially produce M w 7.5 earthquakes. The system has recently entered a limited operational phase, allowing for initial performance estimation. Real-time performance during eight months of operation (41 earthquakes) matches expectations. Alert delays (interval between origin-time and Earthquake Early Warning alert time) are reduced to as low as 3 s, and source parameter errorstatistics are within expected values found in previous works using historical data playbacks. An evolutionary alert policy is implemented based on a magnitude threshold of Mw 4.2 and peak ground accelerations exceeding 2 cm/s 2 . A comparison between different ground motion prediction equations (GMPE) is presented for earthquakes from Israel and California using median ground motion prediction equations values. This analysis shows that a theoretical GMPE produced the best agreement with observed ground motions, with less bias and lower uncertainties. The performance of this GMPE was found to improve when an earthquake specific stress drop is implemented.

Abstract. The use of distributed acoustic sensing (DAS) presents unique advantages for earthquake monitoring compared with standard seismic networks: spatially dense measurements adapted for harsh environments and designed for remote operation. However, the ability to determine earthquake source parameters using DAS is yet to be fully established. In particular, resolving the magnitude and stress drop is a fundamental objective for seismic monitoring and earthquake early warning. To apply existing methods for source parameter estimation to DAS signals, they must first be converted from strain to ground motions. This conversion can be achieved using the waves' apparent phase velocity, which varies for different seismic phases ranging from fast body waves to slow surface and scattered waves. To facilitate this conversion and improve its reliability, an algorithm for slowness determination is presented, based on the local slant-stack transform. This approach yields a unique slowness value at each time instance of a DAS time series. The ability to convert strain-rate signals to ground accelerations is validated using simulated data and applied to several earthquakes recorded by dark fibers of three ocean-bottom telecommunication cables in the Mediterranean Sea. The conversion emphasizes fast body waves compared to slow scattered waves and ambient noise and is robust even in the presence of correlated noise and varying wave propagation directions. Good agreement is found between source parameters determined using converted DAS waveforms and on-land seismometers for both P and S wave records. The demonstrated ability to resolve source parameters using P waves on horizontal ocean-bottom fibers is key for the implementation of DAS-based earthquake early warning, which will significantly improve hazard mitigation capabilities for offshore earthquakes, including those capable of generating tsunami.

ABSTRACT Currently available earthquake early warning systems employ region-specific empirical relations for magnitude determination and ground-motion prediction. Consequently, the setting up of such systems requires lengthy calibration and parameter tuning. This situation is most problematic in low seismicity and/or poorly instrumented regions, where the data available for inferring those empirical relations are scarce. To address this issue, a generic approach for real-time magnitude, stress drop, and ground-motion prediction is introduced that is based on the omega-squared model. This approach leads to the following approximate expressions for seismic moment: M0∝RT0.5Drms1.5/Vrms0.5, and stress drop: Δτ∝RT0.5Arms3/Vrms2, in which R is the hypocentral distance; T is the data interval; and Drms, Vrms, and Arms are the displacement, velocity, and acceleration root mean squares, respectively, which may be calculated in the time domain. The potential of these relations for early warning applications is demonstrated using a large composite data set that includes the two 2019 Ridgecrest earthquakes. A quality parameter is introduced that identifies inconsistent earthquake magnitude and stress-drop estimates. Once initial estimates of the seismic moment and stress drop become available, the peak ground velocity and acceleration may be estimated in real time using the generic ground-motion prediction equation of Lior and Ziv (2018). The use of stress drop for ground-motion prediction is shown to be critical for strong ground accelerations. The main advantages of the generic approach with respect to the empirical approach are that it is readily implementable in any seismic region, allows for the easy update of magnitude, stress drop, and shaking intensity with time, and uses source parameter determination and peak ground motion predictions that are subject to the same model assumptions, thus constituting a self-consistent early warning method.