Potential landslide origin of the Seram Island tsunami in Eastern Indonesia on 16 June 2021 following an Mw 5.9 earthquake.

Mohammad Heidarzadeh, Aditya R Gusman, Adi Patria, Bayu Triyogo Widyantoro

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A 51 cm tsunami amplitude was observed in Tehoru, Seram Island (Indonesia), following an Mw 5.9 earthquake. Such a relatively large tsunami is highly unexpected from this size earthquake. Our analyses showed that the tsunami was 15 times larger in Tehoru tide gauge station than that recorded on two other stations located nearby. These observations imply that the tsunami was most likely generated by a secondary source such as a submarine landslide that potentially occurred near Tehoru. Local people reported landslide activities around Tehoru following the earthquake. We conducted numerical modeling of the tsunami by considering the tectonic source and found that the resulting tsunami was only a few centimeters in Tehoru. Therefore, it is very likely that the earthquake was not responsible for the tsunami observed in Tehoru. By assuming that a submarine landslide was responsible for the tsunami, we applied spectral analysis and tsunami backward raytracing to gain information about the potential size and location of the landslide. Backward raytracing was also applied to identify the earthquake source of the tsunami. Numerical modeling of eight candidate landslide scenarios showed that a landslide with a length and a thickness of approximately 4 km and 50 m, respectively, was potentially responsible for the tsunami. We note that our results serve only as the first and preliminary estimates. Bathymetric surveys and high-resolution bathymetry data are essential to provide more detailed information about the landslide.
Original languageEnglish
Pages (from-to)2487-2498
Number of pages12
JournalBulletin of the Seismological Society of America (BSSA)
Issue number5
Early online date12 Aug 2022
Publication statusPublished - 31 Oct 2022

Bibliographical note

Funding: This research is funded by the Royal Society (the United Kingdom) grant number CHL/R1/180173. We also acknowledge funding from the Lloyd’s Tercentenary Research Foundation, the Lighthill Risk Network, and the Lloyd’s Register Foundation-Data Centric Engineering Programme of the Alan Turing Institute.


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