Executive Summary

While earthquake monitoring and early-warning systems have advanced considerably in recent decades, predicting whether a given earthquake will generate a tsunami—and estimating the scale of such a tsunami—remains an incomplete science. Despite widespread deployment of seismic and oceanic sensors, there remain critical gaps in understanding the geophysical parameters, rupture dynamics, and seabed displacement characteristics that determine tsunamigenic potential. This white paper outlines the aspects of earthquake science that require deeper study to improve tsunami risk forecasting, hazard assessment, and emergency response.

1. Introduction

Tsunamis are among the most devastating natural hazards, often triggered by undersea earthquakes. The 2004 Indian Ocean tsunami and the 2011 Tōhoku event demonstrated that even with earthquake detection systems in place, the ability to predict tsunami occurrence, arrival times, and destructive potential remains limited. Not all undersea earthquakes cause tsunamis, and even when they do, the size and extent can vary dramatically. Determining whether an earthquake is tsunamigenic requires understanding a complex interplay of seismic, geological, and oceanographic factors.

2. Current Understanding and Limitations

2.1 Known Primary Factors

Current tsunami models rely heavily on a small set of measurable earthquake parameters:

Magnitude: Tsunamis are generally generated by large earthquakes (Mw ≥ 7.0), but magnitude alone is insufficient to determine tsunamigenic potential. Focal Depth: Shallow-focus earthquakes (< 50 km) are more likely to produce tsunamis because they displace the seafloor more effectively. Fault Type: Thrust (reverse) faulting, common at subduction zones, is the primary generator of large tsunamis. Location: Offshore events, particularly those along active subduction zones, pose the highest risk.

2.2 Key Unknowns

Despite these parameters, significant uncertainty remains because:

Many large earthquakes fail to generate significant tsunamis. Some moderate-magnitude earthquakes unexpectedly trigger destructive tsunamis. Current models often underestimate or overestimate tsunami heights and inundation distances.

3. Aspects Requiring Deeper Understanding

3.1 Seafloor Displacement Dynamics

The extent, direction, and distribution of vertical seafloor displacement during fault rupture is the primary driver of tsunami formation. Key unknowns include:

Spatial heterogeneity of slip: Ruptures often occur unevenly along the fault plane, creating complex uplift and subsidence patterns. Role of splay faults: Secondary faults branching from the main fault may significantly contribute to vertical displacement. Seafloor topography interactions: Seamounts, ridges, and sediment layers can amplify or dampen wave generation.

3.2 Rupture Propagation Characteristics

The way an earthquake rupture propagates influences both the efficiency and direction of tsunami generation:

Rupture velocity: Faster ruptures may produce sharper vertical displacement, altering tsunami amplitude. Directional bias: Ruptures propagating toward deep water may produce different wave patterns compared to those directed toward coastlines. Duration of rupture: Long-duration ruptures often produce more sustained seafloor displacement.

3.3 Sediment Mobilization and Submarine Landslides

Some of the most destructive tsunamis result not directly from fault movement but from massive submarine landslides triggered by shaking:

Slope stability thresholds: Understanding when seismic shaking will cause slope failure in continental margins remains imprecise. Hybrid events: Earthquakes may cause both direct tectonic uplift and secondary landslides, combining two sources of wave energy. Sediment composition: Clay-rich sediments may fail differently than sandy or silty layers.

3.4 Coupled Earthquake-Tsunami Modeling

Current early-warning systems often treat earthquake and tsunami modeling separately. Integration is needed to:

Link real-time seismic source modeling with hydrodynamic tsunami simulations. Incorporate non-fault displacement sources into tsunami forecasting. Improve rapid inversion algorithms that estimate slip distribution within minutes of rupture detection.

3.5 Role of “Tsunami Earthquakes”

Some earthquakes, termed tsunami earthquakes, produce unusually large tsunamis for their magnitude because of slow rupture speeds and enhanced vertical displacement:

Mechanics of slow slip: Slow rupture allows more efficient ocean displacement despite lower seismic energy release. Detection challenges: Their relatively low seismic wave amplitude often delays or obscures detection in standard seismic networks. Trigger thresholds: Determining how much slow slip is required for significant wave generation remains unresolved.

4. Advancing Detection and Prediction

4.1 Denser Seafloor Monitoring

Installing ocean-bottom seismometers, pressure sensors, and GPS-Acoustic systems along subduction zones can:

Directly measure vertical displacement. Detect slow ruptures and splay fault activity in near real-time. Provide early displacement data to improve forecasts before waves reach shore.

4.2 Machine Learning and Pattern Recognition

By analyzing large datasets of past earthquakes and tsunamis:

Machine learning can identify subtle correlations between rupture parameters and tsunami generation. Models can learn from both successful and failed tsunami events to refine predictive criteria.

4.3 Integrated Early Warning Systems

Future systems must:

Combine seismic, geodetic, and hydrodynamic data streams. Issue adaptive warnings that adjust in real time as more information becomes available. Factor in submarine landslide probability as part of initial hazard assessment.

5. Policy and Research Recommendations

Expand Subduction Zone Instrumentation Prioritize installation of dense, multi-sensor arrays in known tsunamigenic regions such as the Cascadia subduction zone, the Sunda trench, and the Kuril–Kamchatka trench. Fund High-Resolution Rupture Imaging Invest in techniques like rapid finite-fault inversion, seismic back-projection, and real-time seafloor displacement imaging. Integrate Landslide and Sediment Dynamics into Tsunami Models Create coupled geotechnical–hydrodynamic models that assess earthquake-triggered landslide probability. Improve Global Data Sharing Facilitate near-instantaneous sharing of seismic, geodetic, and oceanographic data between nations. Advance Tsunami Earthquake Recognition Develop rapid classification tools to identify slow ruptures within minutes of origin time.

6. Conclusion

Understanding whether an earthquake will generate a tsunami requires going beyond magnitude and location. The critical determinants include the distribution and velocity of seafloor displacement, the triggering of submarine landslides, the complex interactions of rupture direction with seabed topography, and the role of slow-slip mechanisms. Future progress depends on improved seafloor instrumentation, integrated modeling, machine learning-based pattern recognition, and comprehensive global data sharing. Without these advancements, early warning systems will continue to struggle with false alarms and missed events, leaving coastal populations vulnerable to one of the ocean’s most devastating hazards.