Probabilistic Landslide-Generated Tsunamis in the Indus Canyon, NW Indian Ocean, Using Statistical Emulation

Abstract

The Indus Canyon in the northwestern Indian Ocean has been reported to be the site of numerous submarine mass failures in the past. This study is the first to investigate potential tsunami hazards associated with such mass failures in this region. We employed statistical emulation, i.e. surrogate modelling, to efficiently quantify uncertainties associated with slump-generated tsunamis at the slopes of the canyon. We simulated 60 slump scenarios with thickness of 100-300 m, width of 6-10.5 km, travel distances of 500-2000 m and submergence depth of 250-450 m. These scenarios were then used to train the emulator and predict 500,000 trial scenarios in order to study probabilistically the tsunami hazard over the near field. Due to narrow-deep canyon walls and the shallow continental shelf in the adjacent regions (\100 m water depth), the tsunami propagation has a unique pattern as an ellipse stretched in the NE-SW direction. The results show that the most likely tsunami amplitudes and velocities are approximately 0.2-1.0 m and 2.5-13 m/s, respectively, which can potentially impact vessels and maritime facilities. We demonstrate that the emulator-based approach is an important tool for probabilistic hazard analysis since it can generate thousands of tsunami scenarios in few seconds, compared to days of computations on High Performance Computing facilities for a single run of the dispersive tsunami solver that we use here.

Faster Than Real Time Tsunami Warning with Associated Hazard Uncertainties

“…By providing an approximation of the simulator, along with uncertainties in its approximation to validate quality, the GP emulator allows for gains of orders of magnitude in computational costs. Tsunami GP emulation has supplied ranges of prediction for tsunamis generated by earthquakes and landslides over the North Atlantic, the Western Indian Ocean and Cascadia (Salmanidou et al, 2017;Guillas et al, 2018;Salmanidou et al, 2019;Gopinathan et al, 2020). We choose the GP emulator due to its versatility and our efficiently parallelized Multiple-Output Gaussian Process emulator (MOGP) 3 code.…”

Probabilistic quantification of tsunami current hazard using statistical emulation

“…(v) We display more spatial variations for maximum velocity compared to wave heights around the port and their complementary behaviour for the aggregate of 1 million scenarios (Figures 6,7,11,12,13,and 14). ).…”

“…Derivations and exact equations can be found in Beck and Guillas [52]. GP emulation has been instrumental in successfully quantifying uncertainties in tsunami heights generated by landslides over the North Atlantic and the Western Indian Ocean as well as earthquakes over Cascadia [13,[53][54][55]. We use the the efficiently implemented Multiple-Output Gaussian Process emulator (MOGP) 5 for emulation.…”

“…Due to the considerable computational complexity of each simulation of coastal tsunami currents, such a probabilistic endeavour is made feasible by essentially replacing the numerical tsunami model by a statistical surrogate: the emulator. To our knowledge, this is the first time that Gaussian process (GP) emulation has been marshalled to generate future earthquake-generated tsunami currents; it has been employed only once in the past for currents, for a single source of landslide-generated tsunamis with considerable benefits in terms of computational costs and hazard assessment [13]. Here, with a design of only 300 full-fledged training runs, we fit an emulator to rapidly predict the impact of 1 million plausible tsunamis at prescribed locations.…”

Probabilistic, high-resolution tsunami predictions in northern Cascadia by exploiting sequential design for efficient emulation

“…We propose using a statistical surrogate approach, also called emulation, in which one approximates simulation outputs of interest as a function of the scenario parameter space. Such approaches have been implemented for uncertainty quantification of tsunami hazard at various settings (Sraj et al, 2014;Salmanidou et al, 2017Salmanidou et al, , 2019Guillas et al, 2018;Denamiel et al, 2019;Snelling et al, 2020;Gopinathan et al, 2021;Giles et al, 2021).…”