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 function analysis to identify the potential initial locations, followed by an optimization heuristic that minimizes a cost-benefit function to narrow down the number of stations. We apply the method to a synthetic case of the 2015 M_w8.4 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 three tsunameters during 14 min, but with a minimum average time lag of nearly 5 min 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-modelled seismic scenarios.

中文翻译：

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海表网络优化用于近海海啸预报：在2015年Illapel地震中的应用

考虑到使用源区域之外的近中场中的海面数据同化进行近实时海啸预报，我们提出了一种定义近海海啸网络的最佳位置的方法。该方法首先需要应用经验正交函数分析来识别潜在的初始位置，然后是一个优化启发式算法，该算法将成本收益函数最小化以缩小站点数量。我们将该方法应用于2015 M _w的合成案例 8.4 Illapel智利地震，并表明可以通过在14分钟内吸收来自三个tsunameter的数据来获得准确的海啸预报，以预测近海点附近，而不是离源头很近的海浪高度，但平均时差最小为模拟波形和预测波形之间的间隔为5分钟。其他测试表明，对于位于tsunameter网络覆盖区域之外的海啸源，时间延迟有所减少。后者表明，来自稀疏站网的海面数据同化可能是对基于预建模地震场景的最快海啸预警系统的有力补充。