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The goal of earthquake early warning (EEW) is to issue an alert before the damaging seismic waves of an earthquake hit a given exposure. We develop a framework to evaluate the EEW performance in a loss-based context. We use warning time as a key performance indicator and determine statistics of warning time by loss severity to assess the rate and consistency with which an EEW system can deliver timely alerts. In the second part of this work, we develop a Genetic Algorithm approach to optimize an existing sensor network by proposing sites for new stations to enhance the EEW performance in damaging earthquakes. We demonstrate this framework for Switzerland using 2000 realizations of a 50-yr-long stochastic earthquake catalog, which samples the earthquake rate forecast of the Swiss Hazard Model in space and time. For each of the almost 24k earthquake scenario ruptures (5.0 ≤ M ≤ 7.4), we predict shaking intensities and losses (here, fatalities and injuries) at the largest Swiss cities. We find that the current Swiss Seismic Network could provide positive warning times to the affected sites for about 80% of very damaging earthquakes (≥100 fatalities) and for around 85% of earthquakes with ≥10 fatalities. Warning times of >5 s could be achieved for about 40%-55% of very damaging earthquakes and >10 s for about 35%. For around 50% of events with ≥1 fatality (≥10 injuries), EEW could provide >15 s of warning. The greatest benefit of EEW is expected in Zürich, where the population density and, consequently, absolute long-term expected losses are highest and the warning times for damaging earthquakes are often long (>15 s). Densifying the Swiss Seismic Network with additional stations can increase warning times in selected scenarios by up to 5 s. However, because it is already very dense (7 ± 5 km interstation distance), decreasing data latencies (currently ∼2 s) may be more important. [ABSTRACT FROM AUTHOR]

In anticipation of the upcoming 2018 InSight (Interior exploration using Seismic Investigations, Geodesy and Heat Transport) Discovery mission to Mars, we calibrate magnitude scales for marsquakes that incorporate state-of-the-art knowledge on Mars interior structure and the expected ambient and instrumental noise. We regress magnitude determinations of 2600 randomly distributed marsquakes, simulated with a spectral element method for 13 published 1D structural models of Mars' interior. The continuous seismic data from InSight will be returned at 2 samples per second. To account for this limited bandwidth as well as for the expected noise conditions on Mars, we define and calibrate six magnitude scales: (1) local Mars magnitude MMaL at a period of 3 s for marsquakes at distances of up to 10°; (2) P-wave magnitude mMab; (3) S-wave magnitude mMabS each defined at a period of 3 s and calibrated for distances from 5° to 100°; (4) surface-wave magnitude MMas defined at a period of 20 s, as well as (5) moment magnitudes MMaFB; and (6) MMaF computed from the low-frequency (10-100 s) plateau of the displacement spectrum for either body waves or body and surface waves, respectively; we calibrate scales (4)-(6) for distances from 5° to 180°. We regress stable calibrations of the six scales with respect to the seismic moment magnitude at Mw 5.5 by correcting filtered phase amplitudes for attenuation with distance and source depth. Expected errors in epicentral distance and in source depth (25% and 20 km, respectively) translate into magnitude errors of 0.1-0.3 units. We validate our magnitude relations with an independent test dataset of 2600 synthetic marsquakes (1.0=Mw=7.0), for which seismograms are superimposed on the realistic noise predicted by the InSight noise model. Marsquakes with Mw<3.0 and epicentral distances of Δ>15° are expected to be hidden in the Mars background noise and will likely not be detectable. [ABSTRACT FROM AUTHOR]