Offshore Landslide Hazard Curves From Mapped Landslide Size Distributions

Abstract

We present a method to calculate landslide hazard curves along offshore margins based on size distributions of submarine landslides. The method utilizes 10 different continental margins that were mapped by high-resolution multibeam sonar with landslide scar areas measured by a consistent Geographic Information System procedure. Statistical tests of several different probability distribution models indicate that the lognormal model is most appropriate for these siliciclastic environments, consistent with an earlier study of the U.S. Atlantic margin , https://doi.org/10.1016/j.margeo.2008.08.007). Parameter estimation is performed using the maximum likelihood technique, and confidence intervals are determined using likelihood profiles. Pairwise comparison of size distributions for the 10 margins indicates that the U.S. Atlantic and Queen Charlotte margins are different than most other margins. These margins represent end-members, with the U.S. Atlantic margin having the highest mean scar area and the Queen Charlotte margin the lowest. We demonstrate that empirical, offshore landslide hazard curves can be developed from the landslide size distributions, if the duration of mapped landslide activity is known. This study indicates that the shape parameter of the size distribution is similar among all 10 margins, and thus, the shape of the hazard curves is also similar. Significant differences in hazard curves among the margins are therefore related to differences in mean sizes and, potentially, differences in the duration of landslide activity.

On the landslide tsunami uncertainty and hazard

“…Therefore, prognostic analysis of landslide tsunamis is far less developed than for earthquakes, and hence, we have a more limited understanding of landslide tsunami hazards (Harbitz et al 2014a;Geist and Parsons 2014). A first reason for this is due to lack of knowledge related to the landslide occurrence frequency, with just a few limited records of comprehensive landslide statistics covering landslide volumes across several orders of magnitude (Blikra et al 2005;Geist and ten Brink 2019;Lane et al 2016;Urgeles and Camerlenghi 2013). Hence, the statistics describing the landslide recurrence is more poorly constrained than for earthquakes and sometimes even nonexisting.…”

Landslides in the Upper Submarine Slopes of Volcanic Islands: The Central Azores

“…If it is higher than 0.1, the observed data fit the tested distribution, whereas a p-value equal to or less than 0.1 suggests that the data are unlikely to follow the distribution. These tests have been applied in other submarine landslide studies (Casas et al, 2016;Geist & ten Brink, 2019).…”

On the Inference of Tsunami Uncertainties From Landslide Run‐Out Observations

“…Despite the presence of several historical submarine landslide tsunamis, it is likely that the occurrence of tsunamis due to submarine landslides in the past is largely under‐reported. Morphological observations available from previous submarine investigations (e.g., Brune et al., 2010; Chaytor et al., 2009; Gamboa et al., 2021, 2022; Geist & ten Brink, 2019; Twichell et al., 2009; Urgeles & Camerlenghi, 2013) reveal occurrence of many large landslides that are likely tsunami‐genic due their size (e.g., Løvholt et al., 2017). Most of these landslides have not yet been investigated with respect to their tsunamigenic potential.…”