Role of Real-Time GNSS in Near-Field Tsunami Forecasting

Ohta, Yusaku; Inoue, Takuya; Koshimura, Shunichi; Kawamoto, Satoshi; Hino, Ryota

doi:10.20965/jdr.2018.p0453

Cited by 16 publications

(11 citation statements)

References 34 publications

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“…Furthermore, TVA represents a low-cost tool, ready to be implemented on existing high-rate GNSS real-time networks and to push forward the installation of new dense GNSS real-time networks. Finally, TVA main goal is to represent an additional resource to classic methods, such as tsunami forecast 62 , earthquake early warning for tsunami alert 63 , tsunami inundation simulation and damage estimation 64,65 ; in this context, the synergic use of all the available tools could really enhance the already existing tsunami warning systems.…”

Section: Discussionmentioning

confidence: 99%

GNSS total variometric approach: first demonstration of a tool for real-time tsunami genesis estimation

Ravanelli

Occhipinti

Savastano

et al. 2021

Sci Rep

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Global Navigation Satellite System (GNSS) is used in seismology to study the ground displacements as well as to monitor the ionospheric total electron content (TEC) perturbations following seismic events. The aim of this work is to combine these two observations in one real-time method based on the Total Variometric Approach (TVA) to include the GNSS real-time data stream in future warning systems and tsunami genesis estimation observing both, ground motion and TEC. Our TVA couples together the Variometric Approach for Displacement Analysis Stand-alone Engine (VADASE) with the Variometric Approach for Real-Time Ionosphere Observation (VARION) algorithms. We apply the TVA to the Mw 8.3 Illapel earthquake, that occurred in Chile on September 16, 2015, and we demonstrate the coherence of the earthquake ground shaking and the TEC perturbation by using the same GNSS data stream in a real-time scenario. Nominally, we also highlight a stronger kinetic energy released in the north of the epicenter and visible in both, the ground motion and the TEC perturbation detect at 30 s and around 9.5 min after the rupture respectively. The high spatial resolution of ionospheric TEC measurement seems to match with the extent of the seismic source. The GNSS data stream by TVA of both the ground and ionospheric measurement opens today new perspectives to real-time warning systems for tsunami genesis estimation.

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Section: Discussionmentioning

confidence: 99%

GNSS total variometric approach: first demonstration of a tool for real-time tsunami genesis estimation

Ravanelli

Occhipinti

Savastano

et al. 2021

Sci Rep

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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.…”

Section: Introductionmentioning

confidence: 97%

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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“…The finite fault models estimated by REGARD are helpful for obtaining the initial sea surface distribution for tsunami forecasting. The consortium led by Tohoku University has developed a real-time damage estimation system for tsunami inundation using the REGARD fault model as an initial tsunami source model (Musa et al 2018;Ohta et al 2018). This system is expected to be used for the initial response of the government when a disaster occurs.…”

Section: Introductionmentioning

confidence: 99%

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

Ohta

Hino

et al. 2022

Earth Planets Space

Self Cite

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This study proposes a new method for the uncertainty estimation of coseismic slip distribution on the plate interface deduced from real-time global navigation satellite system (GNSS) data and explores its application for tsunami inundation prediction. Jointly developed by the Geospatial Information Authority of Japan and Tohoku University, REGARD (REal-time GEONET Analysis system for Rapid Deformation monitoring) estimates coseismic fault models (a single rectangular fault model and slip distribution model) in real time to support tsunami prediction. The estimated results are adopted as part of the Disaster Information System, which is used by the Cabinet Office of the Government of Japan to assess tsunami inundation and damage. However, the REGARD system currently struggles to estimate the quantitative uncertainty of the estimated result, although the obtained result should contain both observation and modeling errors caused by the model settings. Understanding such quantitative uncertainties based on the input data is essential for utilizing this resource for disaster response. We developed an algorithm that estimates the coseismic slip distribution and its uncertainties using Markov chain Monte Carlo methods. We focused on the Nankai Trough of southwest Japan, where megathrust earthquakes have repeatedly occurred, and used simulation data to assume a Hoei-type earthquake. We divided the 2951 rectangular subfaults on the plate interface and designed a multistage sampling flow with stepwise perturbation groups. As a result, we successfully estimated the slip distribution and its uncertainty at the 95% confidence interval of the posterior probability density function. Furthermore, we developed a new visualization procedure that shows the risk of tsunami inundation and the probability on a map. Under the algorithm, we regarded the Markov chain Monte Carlo samples as individual fault models and clustered them using the k-means approach to obtain different tsunami source scenarios. We then calculated the parallel tsunami inundations and integrated the results on the map. This map, which expresses the uncertainties of tsunami inundation caused by uncertainties in the coseismic fault estimation, offers quantitative and real time insights into possible worst-case scenarios. Graphical Abstract

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Role of Real-Time GNSS in Near-Field Tsunami Forecasting

Cited by 16 publications

References 34 publications

GNSS total variometric approach: first demonstration of a tool for real-time tsunami genesis estimation

GNSS total variometric approach: first demonstration of a tool for real-time tsunami genesis estimation

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

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

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