Probabilistic Forecasting of Tsunami Occurrences Across Various Tsunamigenic Zones of the Earth

Sabah, Nazeel; Sil, Arjun

doi:10.1007/s00024-020-02469-1

Cited by 4 publications

(4 citation statements)

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“…The use of Arti cial Neural Networks (ANN) in tsunami forecasting has gained attention as a promising approach to improve the accuracy and e ciency of studies on tsunamigenic earthquake potential and forecast (Romano et al 2009;Makinoshima et al 2021). This work is in the continuation of the previous work (Sabah & Shanker 2023a;Sabah & Sil 2020) in which a part of the detailing of the zones and region-speci c magnitude homogenisation relationships have been extended further to investigate the tsunami potential, i.e. the probability of occurrences of tsunamigenic earthquakes of the Indo-Paci c region by integrating stochastic tools and Arti cial Neural Networks.…”

Section: Introductionmentioning

confidence: 85%

Forecasting the Future Tsunami Potential of the Indo-Pacific Region Using Artificial Neural Network

Sabah,

Shanker

2024

Preprint

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Tsunamis, one of the most devastating natural disasters on Earth, pose a substantial hazard and risk to coastal infrastructure and human life in the Indo-Pacific region. Tsunami hazard analysis is essential for safeguarding people and assets in coastal areas. However, a study on the comprehensive tsunami potential in the considered area needs further detailed investigation. For this purpose, the Indo-Pacific region (600N to -600S and 300E to1800E) is divided into eight tsunamigenic zones based on tsunamicity, physiography and seismotectonics. Tsunamigenic earthquake data from 1700–2022 is analysed using stochastic tools and Artificial Neural Network (ANN) algorithms to precisely forecast the time, magnitude, and location of the impending tsunamigenic earthquake. The study indicates that Zones 5,7 and 8 are highly vulnerable to tsunami occurrence between periods 2030–2033 (MW 8), 2030–2034 (MW 7) and 2031–2034 (MW 8.2), respectively. Whereas, Zones 1, 4 and 6 might turn vulnerable in the long run between periods 2051–2064 (MW 9.3), 2062–2081 (MW 7.3) and 2077–2120 (MW 7.7), respectively. It is also advocated that out of eight tsunamigenic zones considered, Zone 1 might experience the strongest tsunamigenic earthquake (MW 9.3) by the mid-century (2051–2064). Notably, the methodology is also validated through its successful application to already occurred events (the 2004 Indian Ocean Tsunami, 2011 Tohoku Tsunami and the recent Japan earthquake of January 01, 2024). The reliability of the methodology depends on the availability of a good amount of historical and instrumental tsunamigenic earthquake data.

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Section: Introductionmentioning

confidence: 85%

Forecasting the Future Tsunami Potential of the Indo-Pacific Region Using Artificial Neural Network

Sabah,

Shanker

2024

Preprint

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“…Considering Stiphout et al ( 2012), de-clustering was performed to delineate foreshock and aftershock events. However, Sabah & Sil (2020) have not considered de-clustering for studies on tsunamigenic earthquakes. This study uses seismicity de-clustering to arrive at dataset containing seismologically independent strong earthquake events that could potentially be tsunamigenic.…”

Section: Data Processingmentioning

confidence: 99%

“…Depth The threshold focal depths for tsunamigenic earthquakes are also found similarly by plotting cumulative wave height versus depth in the same manner as mentioned above. This method of estimating threshold magnitude and threshold focal depth was also used in a previous study by Sabah & Sil (2020). Fig.…”

Section: Completeness Analysismentioning

confidence: 99%

“…3.8 ≤ 𝑚 𝑏 ≤ 6.5, 𝑅 2 = 0.56, 𝜎 = 0.29 This study mainly deals with developing a magnitude correlation equation for tsunamigenic earthquakes of the Indian Ocean. Sabah & Sil (2020) attempted magnitude conversion for 13 tsunamigenic zones worldwide, using SR (Standard Regression) procedure and defined magnitude and focal depth thresholds for an earthquake to become tsunamigenic. In the current study, an updated tsunami catalogue has been used to derive OSR and ISR regression models, and their comparison is presented.…”

Section: Introductionmentioning

confidence: 99%

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Development of magnitude correlation equations for the tsunamigenic zones of the Indian Ocean

Sabah

Shanker

2023

J Seismol

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There is a pressing need for a homogonous tsunami catalogue for the Indian Ocean as nearly 20 per cent of tsunami events worldwide affect the region. Any study on tsunami hazard assessment necessitates a homogenous tsunamigenic earthquake catalogue. The existing records of strong tsunamigenic earthquakes have the magnitudes expressed in Moment magnitude (M W ), Body wave magnitude (m b ), Local magnitude (M L ) and Surface wave magnitude (M S ). This study deals with developing regional magnitude correlation equations for tsunamigenic earthquakes of the Indian Ocean.The present investigation estimates the threshold magnitude and focal depth for an earthquake to turn tsunamigenic. It is found that earthquakes above M W ≥ 5.2 and focal depth ≤ 80 km have the potential to generate a tsunami in the region. Since the moment magnitude is the most scientific and reliable scale that describes earthquakes' physical size, equations have been developed to convert surface wave magnitude (M S ) to moment magnitude (M W ). Three types of regression models have been used, viz. Orthogonal Standard Regression (OSR), Standard Regression (SR) and Inverse Standard Regression (ISR). The efficacy of these models has been compared in terms of R-Squared and residual analysis. This study indicates that OSR (Orthogonal Standard Regression) is the best-suited regression model for developing magnitude correlation equations for the region. Magnitude conversion equations for three zones of the Indian Ocean have been created. Additionally, a single unified conversion equation for the whole of the Indian Ocean has also been derived with rational accuracy. Article Highlights The magnitude and focal depth ranges have been identified for the earthquakes to turn potentially tsunamigenic. Magnitude conversion equations have been developed for the Indian Ocean Region. Orthogonal standard regression has been identified as the best fitting regression model.

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Probabilistic Tsunami Risk Assessment from Incomplete and Uncertain Historical Impact Records: Mediterranean and Connected Seas

Triantafyllou

Papadopoulos

Kijko

2023

Pure Appl. Geophys.

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Tsunami risk is considered as the probability of a particular coastline being struck by a tsunami that may cause a certain level of impact (destructiveness). The impact metric of a tsunami is expressed in terms of tsunami intensity values, K, assigned on a 12-degree scale. To calculate tsunami risk we are based on the tsunami history of the region codified in tsunami catalogues. The probabilistic model adopted was used successfully for hazard assessment of earthquakes (Kijko et al. in Bull Seismol Soc Am 79:645–654, 2016) and of tsunamis (Smit et al. in Environmetrics 30:e2566, 2019) by considering seismic magnitude and tsunami height as metrics of the respective hazards. In this model, instead of hazard metrics we inserted risk metric, i.e. wave impact in terms of intensity values. The procedure allows utilization of the entire data set consisting not only from the complete (recent) part of tsunami catalogue but also from the highly incomplete and uncertain historical part of the catalogue. Risk is assessed in terms of probabilities of exceedance and return periods of certain intensity values in specific time frames. We applied the model using catalogues for the Mediterranean and connected seas. Sensitivity analysis showed that using complete data sets generally provided more realistic results than using entire data sets. Results indicated that the risk level depends on the seismicity level and not on the size of individual ocean basin. The highest tsunami risk level was found in the eastern Mediterranean (EM), with a significantly lower risk in the western Mediterranean (WM). In the Marmara Sea (MS), the tsunami risk was low, and the lowest was in the Black Sea (BS). The risk in the small Corinth Gulf (CG, Central Greece) was comparable to that of WM. The return period of damaging tsunamis (i.e. K ≥ 7) was 22 years in the entire Mediterranean basin and 31, 118, 135, 424, and 1660 years in the EM, WM, CG, MS, and BS basins, respectively.

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Probabilistic Forecasting of Tsunami Occurrences Across Various Tsunamigenic Zones of the Earth

Cited by 4 publications

References 24 publications

Forecasting the Future Tsunami Potential of the Indo-Pacific Region Using Artificial Neural Network

Forecasting the Future Tsunami Potential of the Indo-Pacific Region Using Artificial Neural Network

Development of magnitude correlation equations for the tsunamigenic zones of the Indian Ocean

Probabilistic Tsunami Risk Assessment from Incomplete and Uncertain Historical Impact Records: Mediterranean and Connected Seas

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