Tsunami databases

Goff, James

doi:10.1016/b978-0-12-815686-5.00005-5

Cited by 3 publications

(2 citation statements)

References 44 publications

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“…The information on the tsunamigenic earthquakes in the region was taken from the Global Historical Tsunami Database (http://www.ngdc.noaa.gov/hazard/tsu_db.shtml), which has a separate section on Mediterranean tsunamis. A description of the data base along with other tsunami data bases and the principles underlying their creation can be found in (Goff 2020). This data base contains parameters of tsunamigenic earthquakes (date, geographic coordinates, magnitude, and depth of focus) and characteristics of the tsunamis themselves (intensity and runup).…”

Section: The Tsunamigenic Earthquakes In Greecementioning

confidence: 99%

Local Tsunamigenic Sources In Greece, Identified By Pattern Recognition

O.V

A.I.

2022

Preprint

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This study is an attempt to determine potential tsunamigenic morphostructural nodes in mainland Greece using pattern recognition algorithms. The earthquakes that have produced local tsunamis in the region were confined to morphostructural nodes whose locations were found by morphostructural zoning. The recognition problem consisted in separating all nodes in the region into the tsunamigenic class and the non-tsunamigenic class based mainly on the geomorphologic parameters of the nodes. The data on tsunamigenic earthquakes in Greece for training the Cora-3 algorithm were taken from the GHTD global historical catalog of tsunamigenic events (http://www.ngdc.noaa.gov/hazard/tsu_db.shtml). The recognition procedure resulted in determining 27 tsunamigenic nodes, with most of these being situated in the southern tip of the Peloponnese Peninsula, as well as in the gulfs of Corinth and Patras. Three tsunamigenic nodes were identified in the area of the Malian Gulf on the Aegean coast of Greece. According to the relevant literature, most local tsunamis in Greece were initiated by submarine slides and slumps due to earthquakes. According to the characteristic geomorphologic features derived in this study, the tsunamigenic nodes are situated in settings of contrasting relief characterized by steep slopes. This favors submarine landslides when subjected to earthquake excitation. The results reported in this paper form a basis for developing a methodology to be used in long-term tsunami hazard assessment, supplying information on local potential tsunamigenic sources required for tsunami regionalization of coastal areas in Greece.

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Section: The Tsunamigenic Earthquakes In Greecementioning

confidence: 99%

Local Tsunamigenic Sources In Greece, Identified By Pattern Recognition

O.V

A.I.

2022

Preprint

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“…The Sunda megathrust has generated numerous tsunamigenic earthquakes: in the past 120 years, 12 earthquakes along this boundary caused wave heights greater than 1 m [9]. Historical data on tsunamis in Indonesia can be retrieved and studied from world tsunami databases [10]- [12]. In addition to historical data, there is the potential for a tsunami that may occur in the future.…”

Section: Introductionmentioning

confidence: 99%

Tsunami Database Development in the Sunda Arc Indonesia to Support Early Warning through Artificial Intelligence Technology

Wibowo,

Hendriyono,

Khoirunnisa

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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The Sunda Arc-Indonesia is very vulnerable to tsunamis. There have been at least 55 tsunamis from 416â€“2018. Tsunami in the Sunda Arc is classified as a near-field tsunami with an arrival time of < 30 minutes after the earthquake. Meanwhile, the BMKG issued a warning within 5 minutes after the earthquake; therefore, speed in giving warnings is very vital. Artificial intelligence is an alternative technology that can quickly predict a tsunami's height and arrival time. For developing this technology, adequate quality and quantity of data and information on tsunamis are needed. Therefore, this study was conducted to build a tsunami database based on the results of simulations and numerical modeling of multiple scenarios from hypothetical and historical earthquake sources. This study used the open-source TUNAMI F1 model. This model simulates the propagation of tsunami waves using a linear equation. This study obtained 465 hypothetical earthquake sources, 534 historical earthquake sources, and 9,990 datasets from tsunami model simulation results. Each dataset contains ten information. Based on the 8.2 magnitude earthquake scenario, the potential tsunami hazard is 3â€“47 m with an estimated arrival time of < 30 minutes. An earthquake < 7 Mw can trigger a tsunami, especially an earthquake that is shallow and close to the coast, even though the tsunami height is < 0.5m. This data will be used to train an artificial intelligence-based tsunami prediction system. The artificial intelligence-based tsunami prediction system is expected to be used to strengthen the Indonesia tsunami early warning system (InaTEWS).

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Local tsunamigenic sources in Greece, identified by pattern recognition

Новикова

Gorshkov

2022

Nat Hazards

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Tsunami databases

Cited by 3 publications

References 44 publications

Local Tsunamigenic Sources In Greece, Identified By Pattern Recognition

Local Tsunamigenic Sources In Greece, Identified By Pattern Recognition

Tsunami Database Development in the Sunda Arc Indonesia to Support Early Warning through Artificial Intelligence Technology

Local tsunamigenic sources in Greece, identified by pattern recognition

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