Rapid and quantitative uncertainty estimation of coseismic slip distribution for large interplate earthquakes using real-time GNSS data and its application to tsunami inundation prediction

Ohno, Keitaro; Ohta, Yusaku; Hino, Ryota; Koshimura, Shunichi; Musa, Akihiro; Abe, Takashi; Kobayashi, Hiroaki

doi:10.1186/s40623-022-01586-6

Cited by 4 publications

(1 citation statement)

References 47 publications

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“…In this paper, we show that equally good forecasts can be made using only a few minutes of data from an existing network of Global Navigation Satellite System (GNSS) stations. Tsunami warning centers are already starting to incorporate this data in performing earthquake magnitude estimates, and it has been shown that the use of GNSS data can have great benefits, particularly for near-field forecasting (Crowell et al, 2018;Ohno et al, 2022;Ohta et al, 2018;Williamson et al, 2020). We show that this can be taken further by training Convolutional Neural Networks (CNNs) to forecast the tsunami waveforms directly from the GNSS waveforms.…”

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confidence: 97%

Tsunami Early Warning From Global Navigation Satellite System Data Using Convolutional Neural Networks

Rim

Baraldi

Liu

et al. 2022

Geophysical Research Letters

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Accurate tsunami early warning allows for more effective emergency planning, thereby mitigating the human and economic toll. However, constructing a rapid forecast model is challenging for several reasons. One is that the underlying physical processes are governed by partial differential equations whose solution requires substantial computation that cannot be performed in a short timeframe. Furthermore, determining the proper initial conditions for the differential equations requires solving the earthquake source inversion problem, which itself holds significant uncertainty due to the lack of direct observations. The current US warning system relies on early estimates of earthquake location and magnitude from seismic data, coupled with direct tsunami observations from Deep Ocean Assessment and Reporting of Tsunamis sensors (DART;Titov et al., 2005) in the deep ocean and coastal tide gauges. The sparsity of such sensors limits the amount of data one can collect on the tsunami directly. Moreover, one has to wait for the tsunami to reach these sensors, which can be hours after the earthquake.In previous work (Liu et al., 2021a), hereafter referred to as Liu21, we explored machine learning (ML) techniques to forecast tsunami waveforms at two "forecast gauges" in the Puget Sound (denoted Gauges 901 and 911) shown in Figure 1. The forecasts were based on synthetic tsunami observations from Cascadia Subduction Zone (CSZ) events at an hypothetical "observation gauge" (denoted Gauge 702) in the Strait of Juan de Fuca. This ML approach avoids the need for real-time source inversion and tsunami simulation. We showed that several hours of tsunami waveforms at the forecast gauges could be forecast from shorter time series at the observation gauge, but it still requires 30-60 min of observed data after the tsunami reaches the observation gauge.

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confidence: 97%

Tsunami Early Warning From Global Navigation Satellite System Data Using Convolutional Neural Networks

Rim

Baraldi

Liu

et al. 2022

Geophysical Research Letters

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Real-time modeling of transient crustal deformation through the quantification of uncertainty deduced from GNSS data

Ohno,

Ohta,

Takamatsu

et al. 2024

Earth Planets Space

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We propose a new method for real-time uncertainty monitoring of earthquake and volcano source models using data from the global navigation satellite system (GNSS) and explore its application concerning observation station placement. The Geospatial Information Authority of Japan operates two main types of GNSS earth observation network system (GEONET) coordinates for crustal deformation monitoring on different time scales: post-processing analysis values and real-time GEONET analysis system for rapid deformation monitoring (REGARD). REGARD uses the Markov Chain Monte Carlo (MCMC) method termed real-time automatic uncertainty estimation of a coseismic single rectangular model using GNSS data (RUNE) for single rectangular fault model estimation to handle uncertainty. Thus far, no GNSS monitoring system can automatically detect transient crustal deformation events, such as volcanic activity and earthquake swarms, on timescales of a day or less. We extended RUNE and developed a core program for a new monitoring system for earthquake and volcanic source models and their uncertainties. Our program achieved automatic and stable MCMC utilization for rectangular fault, dike, Mogi, and spheroid models by increasing the computational speed, improving search efficiency, and adjusting hyperparameters. The program automatically determines the standard deviation of the likelihood function assuming a normal distribution with weights for each observation station. The calculation time was within 15 s for 1 × 106 samples on a standard 1U server. We assessed the reliability of the developed method using synthetic and observed GNSS data from the 2015 Sakurajima volcanic event. The results were consistent with the assumed model and previous studies and indicated an advantage in automatically quantifying uncertainty in a short computation time. Based on MCMC samples, we developed a new visualization algorithm to indicate areas on a map in which the number of observation stations should be expanded. We assessed the reliability using data from the 2023 Noto Peninsula earthquake [Mj 6.5]. The results indicate that the algorithm is helpful in studying the placement of stations. The above model extensions and their application are essential to achieve a rapid quantitative understanding of disaster events near urban areas and for utilizing this information in emergency response activities. Graphical Abstract

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Tsunami Early Warning from Global Navigation Satellite System Data using Convolutional Neural Networks

Rim¹,

Baraldi²,

Liu³

et al. 2022

Preprint

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We investigate the potential of using Global Navigation Satellite System (GNSS) observations to directly forecast full tsunami waveforms in real time. We train convolutional neural networks (CNNs) to use less than 9 minutes of GNSS data to forecast the full tsunami waveforms over 6 hours at select locations, and obtain accurate forecasts on a test dataset. Our training and test data consists of synthetic earthquakes and associated GNSS data generated for the Cascadia Subduction Zone (CSZ) using the MudPy software, and corresponding tsunami waveforms in Puget Sound computed using GeoClaw. We use the same suite of synthetic earthquakes and waveforms as in earlier work where tsunami waveforms were used for forecasting, and provide a comparison. We also explore varying the number of GNSS stations, their locations, and their observation durations.

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Rapid and quantitative uncertainty estimation of coseismic slip distribution for large interplate earthquakes using real-time GNSS data and its application to tsunami inundation prediction

Cited by 4 publications

References 47 publications

Tsunami Early Warning From Global Navigation Satellite System Data Using Convolutional Neural Networks

Tsunami Early Warning From Global Navigation Satellite System Data Using Convolutional Neural Networks

Real-time modeling of transient crustal deformation through the quantification of uncertainty deduced from GNSS data

Tsunami Early Warning from Global Navigation Satellite System Data using Convolutional Neural Networks

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