Embracing Uncertainty in “Small Data” Problems: Estimating Earthquakes From Historical Anecdotes
Year: 2025
Type: article
Abstract: Seismic risk estimates are greatly improved with an increased understanding of historical (and pre‐historical) seismic events. Although Bayesian inference has been shown to provide reasonable estimates of the location and magnitude of historical earthquakes from anecdotal tsunamigenic evidence, the validity and robustness of such an approach has yet to be definitively demonstrated. Thus, in this article we present a careful analysis of the uncertainty inherent to this statistical recreation of historical seismic events. Using a priori estimates on the posterior and numerical approximations of the Hessian, we demonstrate that the 1852 Banda Sea earthquake and tsunami is well‐understood given certain explicit hypotheses. Using the same techniques we also find that the 1820 south Sulawesi event may best be explained by a dual fault rupture, best attributed to the Kalatoa fault potentially conjoining the Flores thrust and Walanae/Selayar fault.
DOI: 10.1029/2025jh000667
Authors: N. E. Glatt‐Holtz, R. A. Harris, A. J. Holbrook, J. Krometis, Y. Kurniawan, H. Ringer, J. P. Whitehead
Language: English
Subfield: Geophysics
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,500
Awards: CAREER: Data-Centric Evolutionary Contagion Models with Parallel and Quantum Parallel Computing, Stochastic Methods in Fluid Mechanics: Ergodic Properties, Statistical Sampling, and Uncertainty Quantification, XTRIPODS: Advances and novel applications of closed-loop data assimilation, and educational improvements to data science courses, Collaborative Research: Bayesian Inversion Approaches to Partial Differential Equations: Theory, Algorithm Development, and Applications, Collaborative Research: Data Assimilation for Turbulent Flows: Dynamic Model Learning and Solution Capturing, G5607414739, G6003153368, Collaborative Research: Bayesian Inversion Approaches to Partial Differential Equations: Theory, Algorithm Development, and Applications, Accelerating Bayesian Dimension Reduction for Dynamic Network Data with Many Observations
OpenAlex ID: W3172786989