Methodological reconstruction of historical seismic events from anecdotal accounts of destructive tsunamis: a case study for the great 1852 Banda arc mega-thrust earthquake and tsunami

TL;DR

This paper presents a Bayesian framework to reconstruct historical seismic events from anecdotal tsunami records, demonstrated on the 1852 Banda arc earthquake, aiding seismic hazard assessment in data-sparse regions.

Contribution

The study introduces a Bayesian inversion method to estimate earthquake parameters from historical tsunami accounts, applicable to pre-instrumental seismic events.

Findings

Estimated earthquake magnitude near 8.8 Mw

Reconstructed rupture zone in the northeastern Banda arc

Predicted epicenters align with seismic gaps

Abstract

We demonstrate the efficacy of a Bayesian statistical inversion framework for reconstructing the likely characteristics of large pre-instrumentation earthquakes from historical records of tsunami observations. Our framework is designed and implemented for the estimation of the location and magnitude of seismic events from anecdotal accounts of tsunamis including shoreline wave arrival times, heights, and inundation lengths over a variety of spatially separated observation locations. As an initial test case we use our framework to reconstruct the great 1852 earthquake and tsunami of eastern Indonesia. Relying on the assumption that these observations were produced by a subducting thrust event, the posterior distribution indicates that the observables were the result of a massive mega-thrust event with magnitude near 8.8 Mw and a likely rupture zone in the north-eastern Banda arc. The distribution of predicted epicentral locations overlaps with the largest major seismic gap in the region as indicated by instrumentally recorded seismic events. These results provide a geologic and seismic context for hazard risk assessment in coastal communities experiencing growing population and urbanization in Indonesia. In addition, the methodology demonstrated here highlights the potential for applying a Bayesian approach to enhance understanding of the seismic history of other subduction zones around the world.