How do we predict and track earthquakes and tsunamis?

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Predicting and Tracking Earthquakes and Tsunamis

Kinematic Earthquake Source Inversion and Tsunami Prediction

Kinematic Source Models and Tsunami Runup Prediction
Rapid near-source earthquake source modeling is crucial for accurate tsunami predictions. Traditional methods relying solely on strong motion data face limitations due to instrumental offsets and magnitude saturation. However, integrating high-rate GPS and strong motion data can overcome these issues. This approach, supplemented with offshore wave measurements from seafloor pressure sensors and GPS-equipped buoys, enhances the accuracy of earthquake source imaging and tsunami predictions. For instance, the 2011 Mw9.0 Tohoku-Oki earthquake model demonstrated that incorporating offshore data significantly improves the correlation between stress drop distribution and aftershock locations, leading to more accurate tsunami inundation and runup predictions.

Multi-Array Back-Projection for Tsunami Warning

Rapid Tsunami Predictions Using Seismic Networks
The development of dense strong-motion networks and seismic array processing has enabled rapid tsunami predictions through the back-projection (BP) approach. The multi-array local BP method (MLBP) merges local BP results from individual arrays into a single image of the rupture process, providing tsunami predictions within seven minutes of the earthquake's origin time. Case studies, such as the 2003 Mw 8.1 Tokachi-oki and the 2011 Mw 9.0 Tohoku earthquakes, show that this method accurately resolves rupture zones and predicts tsunami wave amplitudes and arrival times with minimal errors, making it effective for early warning systems.

Coupled Earthquake and Tsunami Modeling

Comparing Modeling Methods for Tsunami Generation
Numerical modeling is essential for interpreting data and assessing hazards from tsunamis generated by offshore earthquakes. Traditional two-step methods with one-way coupling between earthquake and tsunami models may not capture the full wavefield, including ocean acoustic and seismic waves. Advanced methods, such as the fully-coupled method, simultaneously solve for earthquake rupture, seismic waves, and ocean response, providing a more comprehensive understanding of the wavefield. This method, implemented in the 3D open-source code SeisSol, offers a more accurate assessment of earthquake and tsunami hazards.

GPS-Based Systems for Real-Time Earthquake and Tsunami Warnings

Real-Time Earthquake Source Determination Using GPS
GPS-based systems can significantly enhance real-time earthquake source determination and tsunami warning capabilities. By inverting the spatial pattern, magnitude, and timing of permanent GPS station displacements, these systems can predict the 3D displacement field of the ocean bottom, providing initial conditions for tsunami models. The effectiveness of this approach is contingent on the deployment of sufficient near-field GPS stations, which can leverage existing GPS networks for rapid and accurate tsunami warnings.

Probabilistic Tsunami Forecasting

Early Warning with Probabilistic Tsunami Forecasting (PTF)
Tsunami warning centers face high uncertainty immediately after an earthquake. Probabilistic Tsunami Forecasting (PTF) addresses this by explicitly treating data and forecast uncertainties, allowing for alert level definitions based on predefined conservatism levels. PTF's accuracy improves as more data become available, as demonstrated by hindcasting events like the 2010 M8.8 Maule and 2003 M6.8 Zemmouri-Boumerdes tsunamis. This method provides reliable forecasts across various magnitudes and earthquake types.

Near-Field Tsunami Models and Rapid Source Inversions

Forecasting Tsunamis with Land and Ocean-Based Observations
Near-field tsunami models face challenges due to limitations in traditional seismological instrumentation and the nonuniqueness of source inversions. However, combining land-based GPS and strong-motion sensors with offshore shallow water wave gauge data can significantly improve earthquake source models and tsunami forecasts. This approach was validated using data from the 2011 Mw 9.0 Tohoku-oki earthquake, demonstrating the potential for timely and accurate tsunami intensity forecasts.

Real-Time Tsunami Data Assimilation

Assimilating S-Net Pressure Gauge Records
The 2016 Fukushima earthquake highlighted the effectiveness of real-time tsunami data assimilation using offshore pressure gauges from the Seafloor Observation Network for Earthquakes and Tsunamis (S-net). By processing S-net records with real-time detection algorithms, accurate tsunami wave forecasts were achieved, demonstrating the practical application of data assimilation for early warning systems.

Artificial Neural Networks for Tsunami Forecasting

Data-Driven Tsunami Forecasting with ANN
Artificial Neural Networks (ANN) offer a data-driven approach for efficient tsunami forecasting. By training on pre-computed tsunami scenarios, ANNs can provide near-instantaneous predictions of maximum tsunami heights and arrival times. Validation tests confirm the accuracy of ANN-based forecasts, making them a valuable tool for real-time tsunami warning systems.

Conclusion

Predicting and tracking earthquakes and tsunamis involve a combination of advanced modeling techniques, real-time data assimilation, and innovative technologies like GPS and artificial neural networks. Integrating land-based and offshore observations, leveraging dense seismic networks, and employing probabilistic forecasting methods enhance the accuracy and timeliness of tsunami warnings, ultimately improving disaster preparedness and response.