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