Optimal deployment of seafloor observation network for tsunami data assimilation in the South China Sea

We propose an optimal deployment scheme of the Seafloor Observation Network (SON) in the South China Sea (SCS), aiming at early warning of potential tsunami hazards based on the data assimilation approach. The SON is composed of offshore bottom pressure gauges (OBPGs). We design the initial location of OBPGs along the iso-baths of 500, 1000, and 2000 m. The energy distribution of the tsunami wavefield based on Empirical Orthogonal Function (EOF) analysis is used as a reference. Then, synthetic tsunamis generated by stochastic slip sources along the Manila Trench are adopted to evaluate the performance of the SON. The OBPGs at a depth of 1000 m can make an accurate early warning based on tsunami data assimilation at a reasonable engineering cost. Finally, based on the quantitative evaluation, the optimal deployment scheme of the SON is suggested. Our results indicate that at least three stations are required to cover the coast along southern China to forecast the tsunami in the SCS successfully. We note that our method could also be applied to other regions to design the SON against potential tsunami hazards.