On the use of tsunami-source data for high-resolution fault imaging of offshore earthquakes
[摘要] The source imaging for offshore earthquakes using terrestrial geodetic data has a limited estimation performance due to the low data resolution. One approach to overcome this limitation is the use of seafloor geodetic data. In this study, we focus on tsunami-source data, which is the spatial distribution of vertical crustal displacements above the source area and can be derived from tsunami waveform records. We evaluate how the use of this spatial seafloor geodetic data improves the estimation of a rectangular fault model. Here, the fault model of the 2016 off-Fukushima earthquake in Japan, which was a shallow intraplate earthquake (Mw 7.0), was estimated by three inversions: terrestrial Global Navigation Satellite System (GNSS) data only, tsunami-source data only, and a combination of the GNSS data and tsunami-source data. A Bayesian inversion approach was used to understand the distribution of the estimated fault parameters and their relationship. The results indicated that the terrestrial GNSS data have a low resolution for the analysis of the offshore earthquake, which resulted in a biased solution with large uncertainty. Conversely, the use of tsunami-source data significantly improved the resolution and reliability of source imaging and reduced the dependency among fault parameters. These results suggested that the high-spatial-resolution information of tsunami source is a powerful tool in source imaging of offshore shallow earthquakes. Moreover, the combined use of the two different geodetic data leads to a more robust estimation of fault parameters. We believe that the use of tsunami-source data is useful, not only for the post hoc source analysis, but also for estimating an earthquake rupture area just after a large earthquake, where GNSS data are currently used.Graphical Abstract
[发布日期] 2023-08-06 [发布机构]
[效力级别] [学科分类]
[关键词] Tsunami source;Seafloor geodetic data;Terrestrial GNSS data;Estimation of rectangular fault;Bayesian inversion [时效性]