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<p>This study proposes a stochastic approach to assess the future tsunami potential globally, focusing on the inter-arrival time of tsunami events in the 14 most active tsunamigenic zones. Two methods, conditional probability (CP) and the total probability theorem (TPT), were employed to estimate tsunami potential. For CP, the occurrence of a tsunami over time was determined by selecting the best-fit stochastic model (Gamma, Lognormal, Weibull, or log-logistic distributions) based on the time of the last earthquake in each zone. In contrast, TPT calculated the probability as the product of the tsunami-to-earthquake ratio ‘r’ and the CP of the time zone [P (E)]. However, direct comparison between the probabilities obtained by the two methods proved challenging due to the maximum value ‘r’ can attain for a region in TPT. In contrast, CP values may extend to 1 over a longer period. The study evaluates the probability of future tsunami occurrences under two conditions: mixed tsunamis (any wave height) and tsunamis with wave heights exceeding 3&#xa0;m. Zones with higher likelihoods of tsunami occurrences for both scenarios are identified and highlighted. The obtained probabilities were validated against respective zone threshold probabilities. Additionally, TPT-derived probabilities for tsunamis under mixed conditions reveal higher likelihoods in Zone 1 (Colombia, Ecuador, Brazil), succeeded by Zone 7 (Japan, China, North Korea), and Zone 11 (Solomon Islands, Vanuatu, New Caledonia). Conversely, the probability of tsunamis with wave heights exceeding 3&#xa0;m is higher in Zone 3 (Cuba, Jamaica, Haiti), followed by Zone 9 (Malaysia, Brunei, Australia), and Zone 1 (Colombia, Ecuador, Brazil). These variations stem primarily from spatial changes in the global tsunami-to-earthquake ratio across distributed zones. This study contributes to a more comprehensive understanding of future tsunami potential, aiding decision-makers and planners in taking informed actions to mitigate potential risks.</p>

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Forecasting of high-potential tsunami occurrences across the globe

  • 期刊论文

  • 发表日期：

    2025年1月30日

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  • Soham Biswas,
  • Arjun Sil

摘要

This study proposes a stochastic approach to assess the future tsunami potential globally, focusing on the inter-arrival time of tsunami events in the 14 most active tsunamigenic zones. Two methods, conditional probability (CP) and the total probability theorem (TPT), were employed to estimate tsunami potential. For CP, the occurrence of a tsunami over time was determined by selecting the best-fit stochastic model (Gamma, Lognormal, Weibull, or log-logistic distributions) based on the time of the last earthquake in each zone. In contrast, TPT calculated the probability as the product of the tsunami-to-earthquake ratio ‘r’ and the CP of the time zone [P (E)]. However, direct comparison between the probabilities obtained by the two methods proved challenging due to the maximum value ‘r’ can attain for a region in TPT. In contrast, CP values may extend to 1 over a longer period. The study evaluates the probability of future tsunami occurrences under two conditions: mixed tsunamis (any wave height) and tsunamis with wave heights exceeding 3 m. Zones with higher likelihoods of tsunami occurrences for both scenarios are identified and highlighted. The obtained probabilities were validated against respective zone threshold probabilities. Additionally, TPT-derived probabilities for tsunamis under mixed conditions reveal higher likelihoods in Zone 1 (Colombia, Ecuador, Brazil), succeeded by Zone 7 (Japan, China, North Korea), and Zone 11 (Solomon Islands, Vanuatu, New Caledonia). Conversely, the probability of tsunamis with wave heights exceeding 3 m is higher in Zone 3 (Cuba, Jamaica, Haiti), followed by Zone 9 (Malaysia, Brunei, Australia), and Zone 1 (Colombia, Ecuador, Brazil). These variations stem primarily from spatial changes in the global tsunami-to-earthquake ratio across distributed zones. This study contributes to a more comprehensive understanding of future tsunami potential, aiding decision-makers and planners in taking informed actions to mitigate potential risks.