On the performance of sequential Bayesian update for database of diverse tsunami scenarios
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929409117650944 |
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| author | Nomura, Reika Vermare, Louise A. Hirao Fujita, Saneiki Rim, Donsub Moriguchi, Shuji LeVeque, Randall J. Terada, Kenjiro |
| author_facet | Nomura, Reika Vermare, Louise A. Hirao Fujita, Saneiki Rim, Donsub Moriguchi, Shuji LeVeque, Randall J. Terada, Kenjiro |
| contents | Although the sequential tsunami scenario detection framework was validated in our previous work, several tasks remain to be resolved from a practical point of view. This study aims to evaluate the performance of the previous tsunami scenario detection framework using a diverse database consisting of complex fault rupture patterns with heterogeneous slip distributions. Specifically, we compare the effectiveness of scenario superposition to that of the previous most likely scenario detection method. Additionally, how the length of the observation time window influences the accuracy of both methods is analyzed. We utilize an existing database comprising 1771 tsunami scenarios targeting the city Westport (WA, U.S.), which includes synthetic wave height records and inundation distributions as the result of fault rupture in the Cascadia subduction zone. The heterogeneous patterns of slips used in the database increase the diversity of the scenarios and thus make it a proper database for evaluating the performance of scenario superposition. To assess the performance, we consider various observation time windows shorter than 15 minutes and divide the database into five testing and learning sets. The evaluation accuracy of the maximum offshore wave, inundation depth, and its distribution is analyzed to examine the advantages of the scenario superposition method over the previous method. We introduce the dynamic time warping (DTW) method as an additional benchmark and compare its results to that of the Bayesian scenario detection method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_03631 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On the performance of sequential Bayesian update for database of diverse tsunami scenarios Nomura, Reika Vermare, Louise A. Hirao Fujita, Saneiki Rim, Donsub Moriguchi, Shuji LeVeque, Randall J. Terada, Kenjiro Computational Engineering, Finance, and Science Machine Learning Data Analysis, Statistics and Probability Geophysics Although the sequential tsunami scenario detection framework was validated in our previous work, several tasks remain to be resolved from a practical point of view. This study aims to evaluate the performance of the previous tsunami scenario detection framework using a diverse database consisting of complex fault rupture patterns with heterogeneous slip distributions. Specifically, we compare the effectiveness of scenario superposition to that of the previous most likely scenario detection method. Additionally, how the length of the observation time window influences the accuracy of both methods is analyzed. We utilize an existing database comprising 1771 tsunami scenarios targeting the city Westport (WA, U.S.), which includes synthetic wave height records and inundation distributions as the result of fault rupture in the Cascadia subduction zone. The heterogeneous patterns of slips used in the database increase the diversity of the scenarios and thus make it a proper database for evaluating the performance of scenario superposition. To assess the performance, we consider various observation time windows shorter than 15 minutes and divide the database into five testing and learning sets. The evaluation accuracy of the maximum offshore wave, inundation depth, and its distribution is analyzed to examine the advantages of the scenario superposition method over the previous method. We introduce the dynamic time warping (DTW) method as an additional benchmark and compare its results to that of the Bayesian scenario detection method. |
| title | On the performance of sequential Bayesian update for database of diverse tsunami scenarios |
| topic | Computational Engineering, Finance, and Science Machine Learning Data Analysis, Statistics and Probability Geophysics |
| url | https://arxiv.org/abs/2407.03631 |