Quasi real‐time fault model estimation for near‐field tsunami forecasting based on RTK‐GPS analysis: Application to the 2011 Tohoku‐Oki earthquake (<i>M</i>_w 9.0)

TLDR

Real‑time crustal deformation monitoring is crucial for rapid assessment of earthquake size and tsunami forecasting. The study develops an algorithm to detect and estimate static ground displacements from RTK‑GPS time series. The algorithm identifies permanent displacements by comparing short‑term and long‑term averages of RTK‑GPS data, applies this to 2011 Tohoku‑Oki data to invert for a fault model, and then uses the estimated model to synthesize tsunami waveforms with pre‑computed Green’s functions. The method achieves sufficient precision for baselines up to ~1,000 km, accurately estimates a Mw 8.7 fault model within five minutes of the 2011 Tohoku‑Oki earthquake, and produces tsunami waveforms that match observed arrival times and heights, demonstrating reliable rapid tsunami forecasting.

Abstract

Real‐time crustal deformation monitoring is extremely important for achieving rapid understanding of actual earthquake scales, because the measured permanent displacement directly gives the true earthquake size (seismic moment, M w ) information, which in turn, provides tsunami forecasting. We have developed an algorithm to detect/estimate static ground displacements due to earthquake faulting from real‐time kinematic GPS (RTK‐GPS) time series. The new algorithm identifies permanent displacements by monitoring the difference of a short‐term average (STA) to a long‐term average (LTA) of the GPS time series. We assessed the noise property and precision of the RTK‐GPS time series with various baseline length conditions and orbits and discerned that the real‐time ephemerides based on the International GNSS Service (IGS) are sufficient for crustal deformation monitoring with long baselines up to ∼1,000 km. We applied the algorithm to data obtained in the 2011 off the Pacific coast of Tohoku earthquake ( M w 9.0) to test the possibility of coseismic displacement detections, and further, we inverted the obtained displacement fields for a fault model; the inversion estimated a fault model with M w 8.7, which is close to the actual M w of 9.0, within five minutes from the origin time. Once the fault model is estimated, tsunami waveforms can be immediately synthesized using pre‐computed tsunami Green's functions. The calculated waveforms showed good agreement with the actual tsunami observations both in arrival times and wave heights, suggesting that the RTK‐GPS data by our algorithm can provide reliable rapid tsunami forecasting that can complement existing tsunami forecasting systems based on seismic observations.