Iyan E. Mulia 교수

Abstract We analyze the regional tsunami hazard along the Sea of Japan coast associated with 60 active faults beneath the eastern margin of the Sea of Japan. We generate stochastic slip distribution using a Monte Carlo approach at each fault, and the total number of required earthquake samples is determined based on convergence analysis of maximum coastal tsunami heights. The earthquake recurrence interval on each fault is estimated from observed seismicity. The variance parameter representing a

The world's largest and densest tsunami observing system gives us the leverage to develop a method for a real-time tsunami inundation prediction based on machine learning. Our method utilizes 150 offshore stations encompassing the Japan Trench to simultaneously predict tsunami inundation at seven coastal cities stretching ~100 km along the southern Sanriku coast. We trained the model using 3093 hypothetical tsunami scenarios from the megathrust (Mw 8.0-9.1) and nearby outer-rise (Mw 7.0-8.7) ear

Abstract Motivated by the unwarned tsunami disaster caused by the flank collapse of the Anak Krakatau volcano on 22 December 2018, we used a landslide tsunami model to explore potential tsunami observing and warning systems for the region. With the estimated volume of 0.24 km 3 and the relatively short duration (~3 to 5 min), the landslide of the volcanic edifice in the southwest sector triggered a tsunami of higher than 40 m in the vicinity. The tsunami, however, attenuated rapidly as it propag

Abstract We explore recent developments in computer science on deep learning to estimate high‐resolution tsunami inundation from a quick low‐resolution computation result. Deep network architecture is capable of storing large information acquired via a training/learning process by pairing low‐ and high‐resolution deterministic simulation results from precalculated hypothetical scenarios. In a real case, with a real‐time source estimate and linear simulation computed on a relatively low‐grid reso

Being situated on the major subduction zones in conjunction with a considerable number of submarine active faults and coastal volcanoes, Japan has a long history of catastrophic tsunami events. Consequently, enormous efforts in disaster mitigation, particularly in relation with tsunami hazards have been made across the country. It is of our interest to review the developments of tsunami observing systems in Japan, which may lead to a global implication beyond national boundaries. In this paper,

Abstract Historical records have proven that the southern coasts of Java, Indonesia, are prone to tsunamis. The existing tsunami observing system using bottom pressure gauges, also known as tsunameters, composes a global network but is too sparse for regional tsunami forecasts. The nearest tsunameter to Indonesia is located approximately 500‐km offshore, which is not very useful for the area, particularly for a tsunami source in eastern part of the Sunda megathrust. Here we propose a methodology

Abstract Recently, there are numerous tsunami observation networks deployed in several major tsunamigenic regions. However, guidance on where to optimally place the measurement devices is limited. This study presents a methodological approach to select strategic observation locations for the purpose of tsunami source characterizations, particularly in terms of the fault slip distribution. Initially, we identify favorable locations and determine the initial number of observations. These locations

Abstract The future Nankai Trough tsunami is one of the imminent threats to the Japanese coastal communities that could potentially cause a catastrophic event. As a part of the countermeasure efforts for such an occurrence, this study analyzes the efficacy of combining tsunami data assimilation (DA) and waveform inversion (WI). The DA is used to continuously refine a wavefield model whereas the WI is used to estimate the tsunami source. We consider a future scenario of the Nankai Trough tsunami

Unlike in the deep ocean, simulating the propagation of tsunamis in the coastal zone requires a non-linear model with a fine resolution bathymetry/topography to cope with the non-linearity and complex coastal morphology. Consequently, it raises considerable computational burdens that may not be suitable for a real-time tsunami forecasting. To overcome the issue, we utilize a precomputed tsunami database comprised of pairs of low- and high-resolution maximum tsunami elevations and flow depths ori

Modeling typhoon-induced storm surges requires 10-m wind and sea level pressure fields as forcings, commonly obtained using parametric models or a fully dynamical simulation by numerical weather prediction (NWP) models. The parametric models are generally less accurate than the full-physics models of the NWP, but they are often preferred owing to their computational efficiency facilitating rapid uncertainty quantification. Here, we propose using a deep learning method based on generative adversa

Abstract A large tsunamigenic earthquake of magnitude Mw 8.2 occurred on the Alaska-Aleutian subduction zone in July 2021. To reveal the characteristics of the event, we first applied spectral and wavelet analyses to the induced tsunami recorded both at the local and Pacific-wide sea level observation networks. Because the earthquake was relatively deep (∼30 km), the resultant maximum tsunami amplitudes were only ∼5 and ∼50 cm in the open ocean and coastal area respectively. However, owing to th

Abstract We propose a method for accurately estimating the initial tsunami source. Our technique is independent of the earthquake parameters, because we only use recorded tsunami waveforms and an auxiliary basis function, instead of a fault model. We first use the measured waveforms to roughly identify the source area using backward propagated travel times, and then infer the initial sea surface deformation through inversion analysis. A computational intelligence approach based on a genetic algo