Abstract

It is increasingly common for seismic networks to operate multiple independent automatic algorithms to characterise earthquakes in real-time, such as in earthquake early warning (EEW) or even standard network practice. Commonly used methods to select the best solution at a given time are simple and use ad hoc rules. An absolute measure of how well a solution (event origin and magnitude) matches the observations by the goodness-of-fit between the observed and predicted envelopes is a robust and independent metric to select optimal solutions. We propose such a measure that is calculated as a combination of amplitude and cross-correlation fit. This metric can be used to determine when a preferred solution reaches an appropriate confidence level for alerting, or indeed to compare two (or more) different event characterisations directly. We demonstrate that our approach can also be used to suppress false alarms commonly seen at seismic networks. Tests using the 10 largest earthquakes in Switzerland between 2013 and 2020, and events that caused false alarms demonstrate that our approach can effectively prefer solutions with small errors in location and magnitude, and can clearly identify and discard false origins or incorrect magnitudes, at all time scales, starting with the first event characterisation.

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