Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Rapid tsunami inundation forecast using pre-computed earthquake scenarios and offshore data
https://orcid.org/0000-0003-1818-1754
Abstract We present an approach to enhance the development of Near-Field Tsunami Inundation Forecasting (NearTIF). We combine the inversion method with the original NearTIF to obtain a more accurate tsunami source, utilizing Ocean Bottom Pressure Gauge (OBPG) data. We use Mw 8.5 hypothetical earthquake located in the Java megathrust to assess the optimum time window of OBPG data for inversion with a smoothing factor. The 15-min time series data obtained a good-fit fault slip distribution, indicated by high correlation and low RMSE. Additionally, Green's function based on tsunami waveform predictions is the substitute for the low-resolution forward modeling part of the original NearTIF to acquire inundation forecasts with a reduced computational time. The 330 pre-computed tsunami scenario, using a non-linear numerical model, is employed for matching and shifting between the predicted and pre-computed waveform in the 15 virtual observation point along the Kulon Progo coast, the area of interest, using five hypothetical sources varying from Mw 8.5 to Mw 8.9. The Aida number (K) implies that the developed NearTIF gives accuracy in the acceptable range (0.6 < K < 1.4) in less than 2 min after the tsunami recorded in the OBPG. The technique presented in this paper could provide near real-time and accurate inundation forecast.
Highlights A combination of inversion method and original NearTIF to provide a rapid tsunami inundation forecast. Utilizing 15 minutes tsunami waveform at OBPG can obtain a reliable slip distribution for tsunami early warning purposes. Utilizing the Akaike Bayesian Information Criteria (ABIC) for the inversion process with a smoothing factor. Analysing the accuracy of tsunami inundation using Aida number (K) and its corresponding standard deviation (κ).
Rapid tsunami inundation forecast using pre-computed earthquake scenarios and offshore data
https://orcid.org/0000-0003-1818-1754
Abstract We present an approach to enhance the development of Near-Field Tsunami Inundation Forecasting (NearTIF). We combine the inversion method with the original NearTIF to obtain a more accurate tsunami source, utilizing Ocean Bottom Pressure Gauge (OBPG) data. We use Mw 8.5 hypothetical earthquake located in the Java megathrust to assess the optimum time window of OBPG data for inversion with a smoothing factor. The 15-min time series data obtained a good-fit fault slip distribution, indicated by high correlation and low RMSE. Additionally, Green's function based on tsunami waveform predictions is the substitute for the low-resolution forward modeling part of the original NearTIF to acquire inundation forecasts with a reduced computational time. The 330 pre-computed tsunami scenario, using a non-linear numerical model, is employed for matching and shifting between the predicted and pre-computed waveform in the 15 virtual observation point along the Kulon Progo coast, the area of interest, using five hypothetical sources varying from Mw 8.5 to Mw 8.9. The Aida number (K) implies that the developed NearTIF gives accuracy in the acceptable range (0.6 < K < 1.4) in less than 2 min after the tsunami recorded in the OBPG. The technique presented in this paper could provide near real-time and accurate inundation forecast.
Highlights A combination of inversion method and original NearTIF to provide a rapid tsunami inundation forecast. Utilizing 15 minutes tsunami waveform at OBPG can obtain a reliable slip distribution for tsunami early warning purposes. Utilizing the Akaike Bayesian Information Criteria (ABIC) for the inversion process with a smoothing factor. Analysing the accuracy of tsunami inundation using Aida number (K) and its corresponding standard deviation (κ).
Rapid tsunami inundation forecast using pre-computed earthquake scenarios and offshore data
https://orcid.org/0000-0003-1818-1754
Abstract We present an approach to enhance the development of Near-Field Tsunami Inundation Forecasting (NearTIF). We combine the inversion method with the original NearTIF to obtain a more accurate tsunami source, utilizing Ocean Bottom Pressure Gauge (OBPG) data. We use Mw 8.5 hypothetical earthquake located in the Java megathrust to assess the optimum time window of OBPG data for inversion with a smoothing factor. The 15-min time series data obtained a good-fit fault slip distribution, indicated by high correlation and low RMSE. Additionally, Green's function based on tsunami waveform predictions is the substitute for the low-resolution forward modeling part of the original NearTIF to acquire inundation forecasts with a reduced computational time. The 330 pre-computed tsunami scenario, using a non-linear numerical model, is employed for matching and shifting between the predicted and pre-computed waveform in the 15 virtual observation point along the Kulon Progo coast, the area of interest, using five hypothetical sources varying from Mw 8.5 to Mw 8.9. The Aida number (K) implies that the developed NearTIF gives accuracy in the acceptable range (0.6 < K < 1.4) in less than 2 min after the tsunami recorded in the OBPG. The technique presented in this paper could provide near real-time and accurate inundation forecast.
Highlights A combination of inversion method and original NearTIF to provide a rapid tsunami inundation forecast. Utilizing 15 minutes tsunami waveform at OBPG can obtain a reliable slip distribution for tsunami early warning purposes. Utilizing the Akaike Bayesian Information Criteria (ABIC) for the inversion process with a smoothing factor. Analysing the accuracy of tsunami inundation using Aida number (K) and its corresponding standard deviation (κ).
Rapid tsunami inundation forecast using pre-computed earthquake scenarios and offshore data
Weniza, Weniza (Autor:in) / Gusman, Aditya Riadi (Autor:in) / Puspito, Nanang Tyasbudi (Autor:in) / Rahayu, Harkunti Pertiwi (Autor:in) / Harig, Sven (Autor:in) / Hanifa, Nuraini Rahma (Autor:in) / Gunawan, Indra (Autor:in) / Nurokhim, Arif (Autor:in) / Setiawan, Yosi (Autor:in) / Sriyanto, Sesar Prabu Dwi (Autor:in)
Coastal Engineering ; 184
10.06.2023
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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