Sequential Bayesian Update to Detect the Most Likely Tsunami Scenario Using Observational Wave Sequences

Nomura, Reika; Fujita, Saneiki; Galbreath, Joseph M.; Otake, Yu; Moriguchi, Shuji; Koshimura, Shunichi; LeVeque, Randall J.; Terada, Kenjiro

doi:10.1029/2021jc018324

Cited by 3 publications

(1 citation statement)

References 49 publications

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“…In line with Nomura et al. (2022), we note that a t contains scenario‐specific and time‐dependent information, which is a key factor for tsunami risk assessment in their framework. On the other hand, the mode matrix Φ r represents the common spatial features for all scenarios.…”

Section: Methodssupporting

confidence: 79%

Optimization of a Tsunami Gauge Configuration for Pseudo‐Super‐Resolution of Wave Height Distribution

Fujita,

Nomura,

Moriguchi

et al. 2024

Earth and Space Science

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In this study, we present an optimization method for determining a cost‐effective sparse configuration for tsunami gauges to realize the reconstruction of high‐resolution wave height distribution throughout the target region based on the concept of super‐resolution. This optimization method consists of three procedures. First, we generate time series data of tsunami wave heights at synthetic gauges by conducting numerical simulations of various earthquake and tsunami scenarios at the target site. Next, we apply proper orthogonal decomposition to the synthetic tsunami data to extract the spatial features of the wave height distribution. Finally, according to these spatial features, an optimization process is performed to determine a sparse configuration of synthetic gauges. In the optimization, the optimal gauges are sequentially selected from the set of synthetic gauges to reconstruct the wave height distribution with the highest accuracy. Targeting hypothetical Nankai Trough earthquakes and tsunamis, we determine the optimal configuration near Shikoku and demonstrate the wave height reconstruction capability of the approach by comparing the performance of networks with optimally and randomly placed gauges. The results indicate that coastal gauges contribute more to improving the reconstruction accuracy and that a configuration with 21 optimal gauges has satisfactory performance. In addition, we assess the performance of the existing NOWPHAS network installed in the Shikoku region and find that the reconstruction performance of the existing network is equivalent to that of the optimal gauge network.

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Section: Methodssupporting

confidence: 79%

Optimization of a Tsunami Gauge Configuration for Pseudo‐Super‐Resolution of Wave Height Distribution

Fujita,

Nomura,

Moriguchi

et al. 2024

Earth and Space Science

Self Cite

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Scenario Superposition Method for Real‐Time Tsunami Prediction Using a Bayesian Approach

Fujita,

Nomura,

Moriguchi

et al. 2024

JGR Oceans

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In this study, we propose a scenario superposition method for real‐time tsunami wave prediction. In the offline phase, prior to actual tsunami occurrence, hypothetical tsunami scenarios are created, and their wave data are decomposed into spatial modes and scenario‐specific coefficients by the singular value decomposition. Then, once an actual tsunami event is observed, the proposed method executes an online phase, which is a novel contribution of this study. Specifically, the predicted waveform is represented by a linear combination of training scenarios consisting of precomputed tsunami simulation results. To make such a prediction, a set of weight parameters that allow for appropriate scenario superposition is identified by the Bayesian update process. At the same time, the probability distribution of the weight parameters is obtained as reference information regarding the reliability of the prediction. Then, the waveforms are predicted by superposition with the estimated weight parameters multiplied by the waveforms of the corresponding scenarios. To validate the performance and benefits of the proposed method, a series of synthetic experiments are performed for the Shikoku coastal region of Japan with the subduction zone of the Nankai Trough. All tsunami data are derived from numerical simulations and divided into a training data set used as scenario superposition components and a test data set for an unknown real event. The predicted waveforms at the synthetic gauges closest to the Shikoku Islands are compared to those obtained using our previous prediction method incorporating sequential Bayesian updating.

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Comparative Performance of Scenario Superposition by Sequential Bayesian Update for Tsunami Risk Evaluation

Nomura,

Hirao-Vermare,

Fujita

et al. 2024

JDR

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This study aims to evaluate the performance of the previous sequential Bayesian update for synthesizing tsunami scenarios in a diverse database consisting of complex fault rupture patterns with heterogeneous slip distributions. We utilize an existing database comprising 1771 tsunami scenarios targeting the city of Westport (WA, U.S.), which includes synthetic wave height records and inundation distributions resulting from a fault rupture in the Cascadia subduction zone. After preprocessing the training dataset according to the developed framework, Bayesian updates are performed sequentially to evaluate the probability that each training scenario is a test case. In addition to detecting the scenario with the highest probability, i.e., the most likely scenario, we synthesize the scenario by the weighted mean of all the learning scenarios by their probabilities. The accuracies of tsunami risk evaluation based on both resultant scenarios are evaluated from the maximum offshore wave, inundation depth, and its distribution. The results of the cross-validation with five different testing/training datasets showed that the weighted mean scenario has almost comparable performance to that of the most likely scenario. Additionally, the sequential Bayesian update improves the accuracy of both methods if a 3–4 minute observation time window is given, and has an advantage over the benchmark results provided by dynamic time warping with full-time series data.

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Sequential Bayesian Update to Detect the Most Likely Tsunami Scenario Using Observational Wave Sequences

Cited by 3 publications

References 49 publications

Optimization of a Tsunami Gauge Configuration for Pseudo‐Super‐Resolution of Wave Height Distribution

Optimization of a Tsunami Gauge Configuration for Pseudo‐Super‐Resolution of Wave Height Distribution

Scenario Superposition Method for Real‐Time Tsunami Prediction Using a Bayesian Approach

Comparative Performance of Scenario Superposition by Sequential Bayesian Update for Tsunami Risk Evaluation

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