Effective uncertainty visualization for aftershock forecast maps

Abstract

Abstract. Earthquake models can produce aftershock forecasts, which have recently been released to lay audiences following large earthquakes. While visualization literature suggests that displaying forecast uncertainty can improve how forecast maps are used, research on uncertainty visualization is missing from earthquake science. We designed a pre-registered online experiment to test the effectiveness of three visualization techniques for displaying aftershock forecast maps and their uncertainty. These maps showed the forecasted number of aftershocks at each location for a week following a hypothetical mainshock, along with the uncertainty around each location’s forecast. Three different uncertainty visualizations were produced: (1) forecast and uncertainty maps adjacent to one another; (2) the forecast map depicted in a color scheme, with the uncertainty shown by the transparency of the color; and (3) two maps that showed the lower and upper bounds of the forecast distribution at each location. Unlike previous experiments, we compared the three uncertainty visualizations using tasks that are systematically designed to address broadly applicable and user-generated communication goals. We compared task responses between participants using uncertainty visualizations and using the forecast map shown without its uncertainty (the current practice). Participants completed two map-reading tasks that targeted several dimensions of the readability of uncertainty visualizations. Participants then performed a comparative judgment task, which demonstrated whether a visualization was successful in reaching two key communication goals: indicating where many aftershocks and no aftershocks are likely (sure bets) and where the forecast is low but the uncertainty is high enough to imply potential risk (surprises). All visualizations performed equally well in the goal of communicating sure bet situations. But the visualization with lower and upper bounds was substantially better than the other designs at communicating surprises. These results have implications for the communication of forecast uncertainty both within and beyond earthquake science.

Enhancing the ETAS model: incorporating rate-dependent incompleteness, constructing a representative dataset, and reducing bias in inversions

“…Examples include (Omi et al 2015;Ebrahimian & Jalayer 2017;Shcherbakov et al 2019;Ross 2021;Schneider & Guttorp 2021;Shcherbakov 2021;Laub et al 2021;Ebrahimian et al 2022;Molkenthin et al 2022;Ross & Kolev 2022;Naylor et al 2023;Nishikawa & Nishimura 2023).…”