Probabilistic Near-Field Tsunami Source and Tsunami Run-up Distribution Inferred From Tsunami Run-up Records in Northern Chile

Understanding a tsunami source and its impact is vital to assess a tsunami hazard. Thanks to the efforts of the tsunami survey teams, high-quality tsunami run-up data exist for contemporary events. Still, it has not been widely used to infer a tsunami source and its impact mainly due to the computational burden of the tsunami forward model. In this study, we propose a TRRF-INV (Tsunami Run-up Response Function-based INVersion) model that can provide probabilistic estimates of a near-field tsunami source and tsunami run-up distribution from a small number of run-up records. We tested the TRRF-INV model with synthetic tsunami scenarios in northern Chile and applied it to the 2014 Iquique, Chile, tsunami event as a case study. The results demonstrated that the TRRF-INV model can provide a reasonable tsunami source estimate to first order and estimate tsunami run-up distribution well. Moreover, the case-study results agree well with the United States Geological Survey report and the global Centroid Moment Tensor solution. We also analyzed the performance of the TRRF-INV model depending on the number and the uncertainty of run-up records. We believe that the TRRF-INV model has the potential for supporting accurate hazard assessment by (1) providing new insights from tsunami run-up records into the tsunami source and its impact, (2) using the TRRF-INV model as a tool to support existing tsunami inversion models, and (3) estimating a tsunami source and its impact for ancient events where no data other than estimated run-up from sediment deposit data exist.

Automated summary

In this study, a new tsunami model TRRF-INV is proposed to provide probabilistic estimates of a tsunami source and run-up distribution from a small number of run-up records. The model was tested in synthetic scenarios and a case study, showing reasonable estimation of tsunami source and run-up distribution. The TRRF-INV model has the potential to support accurate hazard assessment and provide new insights into tsunami source and impact.