Near-field GNSS for real-time tsunami early warning systems
Philippa Cowles1, Peter Clarke, Nigel Penna, Qiuhua Liang
1 School of Civil Engineering and Geosciences, Newcastle University
(philippa.cowles "at" newcastle.ac.uk)
Earthquake magnitude has traditionally been computed from seismometer data. Incorrect data due to the equipment’s limitations however leads to an initial underestimation of the magnitude of the earthquake, which therefore leads to an underestimation of the height of the resulting tsunami. The efficiency of a tsunami early warning system relies on the speed and accuracy of computing the earthquakes magnitude and resulting tsunami wave height. Recent advancements in the development of GNSS has made it possible for GNSS observations to be sampled frequently enough for use in seismic studies. Studies have suggested that a single estimate of vertical coseismic displacement from GNSS might be able to rapidly and reliably forecast the maximum height of the resulting tsunami wave due to their apparent linearised relationship. This project looks to provide a method for rapid fault determination based on real-time GNSS time series and can be split into the following objectives: (1) Investigate the main contributing factor to GNSS error, (multipath) and how this can be eliminated and/or mitigated.(2) Determine how soon after rupture initiation can the earthquake magnitude and/or seafloor displacement be estimated using real-time GNSS.(3) Determine the robustness of the linearised relationship between local vertical displacement and tsunami run-up. This will involve inverting for earthquake source parameters to establish fault geometry and modelling numerous tsunami scenarios using GNSS seafloor displacement data as a constraint to establish if a linearised relationship does exists. It is envisaged that this study will provide an improved GNSS positioning technique for earthquake magnitude determination and enhance current knowledge on the relationship between local vertical displacement and tsunami run-up. Both of which will strengthen the ability to reliably and accurately model future tsunami’s in real-time.
Poster Number: 16