Estimate of tsunami source using optimized unit sources and including dispersion effects during tsunami propagation: The 2012 Haida Gwaii earthquake

Abstract

We apply a genetic algorithm to find the optimized unit sources using dispersive tsunami synthetics to estimate the tsunami source of the 2012 Haida Gwaii earthquake. The optimal number and distribution of unit sources gives the sea surface elevation similar to that from our previous slip distribution on a fault using tsunami data, but different from that using seismic data. The difference is possibly due to submarine mass failure in the source region. Dispersion effects during tsunami propagation reduce the maximum amplitudes by up to 20% of conventional linear longwave propagation model. Dispersion effects also increase tsunami travel time by approximately 1 min per 1300 km on average. The dispersion effects on amplitudes depend on the azimuth from the tsunami source reflecting the directivity of tsunami source, while the effects on travel times depend only on the distance from the source.

Green's Function‐Based Tsunami Data Assimilation: A Fast Data Assimilation Approach Toward Tsunami Early Warning

“…The propagation matrix F corresponds to the tsunami propagation model. Generally, the simple 2‐D Linear Long‐Wave (LLW) tsunami model is adopted (Gusman, Mulia, et al, ; Maeda et al, ). In the assimilation step, …”

“…However, the tsunami wavefield in the entire modeling domain needs to be repeatedly calculated during the assimilation process (Figure a). To deal with this problem, Gusman, Mulia, et al () updated the tsunami forecast every 10 min, and a new wavefield was created at intervals of 1 s.…”

“…Another example is the Seafloor Observation Network for Earthquakes and Tsunamis (S‐net), which is a new network of cabled pressure gauges that is being installed around the Japan Trench (Uehira et al, ; Yamamoto et al, ). In the Cascadia subduction zone, the Cascadia Initiative Community Experiment deployed an array of pressure gauges on the seafloor (Incorporated Research Institutions for Seismology Ocean Bottom Seismograph Instrument Pool, ; Sheehan et al, ); although this network did not send data in real time, the recorded tsunami data can be used to evaluate tsunami forecasting methods (Gusman, Mulia, et al, ).…”

“…The tsunami wavefield, including tsunami height and velocities in two directions, can be estimated on the basis of real‐time data from tsunameters (Maeda et al, ), where the tsunami wavefield is forecasted directly from real‐time observational data and does not require precalculated scenarios from a tsunami source model. This tsunami data assimilation method has the advantage that it is free from the uncertainties associated with the source estimation and showed high performance on tsunami forecasting of the 2012 Haida Gwaii earthquake (Gusman, Mulia, et al, ). On the other hand, the data assimilation has a relatively high computational load, as it is necessary to run numerical simulations to obtain the tsunami wavefield.…”

Adaptive Tsunami Source Inversion Using Optimizations and the Reciprocity Principle

“…Such locations can be determined using an optimization method. The same technique has been applied to several tsunami events such as the 2011 Tohoku‐oki tsunami (Mulia & Asano, ), the 2012 Haida Gwaii tsunami (Gusman et al, ), and the 2017 Tehuantepec tsunami (Gusman et al, ). The method provides advantages over the conventional tsunami source inversion with equidistant unit sources, but it requires the computation of synthetic waveforms for each iteration.…”