Sea surface network optimization for tsunami forecasting in the near field: Application to the 2015 Illapel earthquake

Abstract

We propose a method for defining the optimal locations of a network of tsunameters in view of near real-time tsunami forecasting using sea surface data assimilation in the near and middle fields, just outside of the source region. The method requires first the application of the Empirical Orthogonal Functions (EOF) analysis to identify the potential initial locations, followed by an optimization heuristics that minimizes a cost-benefit function to narrow down the number of stations. We apply the method to a synthetic case of the 2015 Mw8.4 Illapel Chile earthquake, and show that it is possible to obtain an accurate tsunami forecast for wave heights at near coastal points, not too close to the source, from assimilating data from 3 tsunameters during 14 minutes, but with a minimum average time lag of nearly 5 minutes between simulated and forecasted waveforms. Additional tests, show that the time lag is reduced for tsunami sources that are located just outside of the area covered by the tsunameter network. The latter suggests that sea surface data assimilation from a sparse network of stations could be a strong complement for the fastest tsunami early warning systems based on pre-modeled seismic scenarios.