Tsunami height forecast from water-level data

Abstract

A methodology to forecast tsunami height based on real-time water-level data near the source is presented in this paper. The inverse method, which uses water level data to infer seismic source parameters, is extended to predict the tsunami waveforms away from the source. This study focuses on the Aleutian-Alaska source region and its potential threat to Hawaii. In the algorithm, the source region is divided into 41 sub-faults based on Johnson's (1999) analyses of major tsunamigenic earthquakes from 1938 to 1986. A linear longwave model is used to generate a database of synthetic mareograms at 14 water-level stations near the source and at six locations away from the source. Given tsunami signals at the water-level stations, a least-squares routine provides the expected waveforms near the Hawaiian Islands and a jackknife re-sampling scheme provides the confidence interval bounds of the predictions. The algorithm and the database are verified using actual water-level data of past tsunami events.