New Research could Lead to Earlier Tsunami Warnings

New Research could Lead to Earlier Tsunami Warnings

A tsunami is one of nature’s most powerful and destructive forces. It is a series of waves caused by the ocean’s large and sudden displacement. Tsunamis pose a serious threat to both people and property. Tsunamis are typically caused by large earthquakes beneath or near the ocean floor, but they can also be caused by landslides, volcanic activity, specific types of weather, and near-Earth objects. Tsunami waves are often barely visible in the deep ocean, but they can travel at speeds up to 500 mph, faster than a jet plane. They slow down and grow in height as they approach shallow water near land, and currents intensify.

A new method of detecting mega earthquakes that uses deep-learning models developed at Los Alamos National Laboratory to detect the gravity waves they generate can estimate earthquake magnitude in real time and provide earlier warning of tsunamis.

“Our model enables real-time estimation of earthquake magnitude using data that is routinely treated as noise and can immediately be transformative for tsunami early warning,” said Bertrand Rouet-Leduc, a scientist in Los Alamos’ Geophysics group.

Rapid and accurate magnitude estimation for large earthquakes is critical for mitigating the risk of strong shaking and tsunamis. Standard early warning systems based on seismic waves are incapable of rapidly estimating the size of large earthquakes; the systems rely on estimating earthquake magnitude directly from the shaking caused by the earthquake. These systems are unable to differentiate between magnitude 8 and magnitude 9 earthquakes, despite the latter being 30 times more energetic and destructive.

Our model enables real-time estimation of earthquake magnitude using data that is routinely treated as noise and can immediately be transformative for tsunami early warning.

Bertrand Rouet-Leduc

Important distinctions possible

In new research, published May 11 in Nature,a research team found that a long-theorized gravity wave associated with very large earthquakes can also be used for earthquake early warning. Unlike seismic-based early warning, gravity-based early warning does not saturate with magnitude, meaning that gravity-based earthquake early warning can immediately distinguish between magnitude 8 and 9 earthquakes.

Other current approaches rely on GPS to estimate earthquake magnitude. While this approach provides better estimations than seismic-based earthquake early warning, it is also subject to large uncertainties and latency.

New research could provide earlier warning of tsunamis

PEGS approach more accurate for larger earthquakes

A tsunami warning system (TWS) detects tsunamis ahead of time and issues warnings to prevent loss of life and property damage. It consists of two equally important components: a network of sensors to detect tsunamis and a communications infrastructure to issue timely warnings to allow evacuation of coastal areas. There are two types of tsunami warning systems: international and regional.

The recently discovered, speed-of-light Prompt Elasto-Gravity Signals approach raised hopes for overcoming these limitations, but had never been tested for earthquake early warning. In comparison to current methods, the PEGS approach to detection becomes more accurate for larger earthquakes.

The researchers demonstrated that PEGS can be used in real time to track earthquake growth and magnitude immediately after it reaches a certain size. The team created a deep-learning model that takes advantage of the information carried by PEGS and recorded by regional broadband seismometers in Japan.

The team demonstrated the first instance of instantaneous tracking of an earthquake source on real data after training the deep-learning model on a database of synthetic waveforms augmented with empirical noise measured on the seismic network.

This model, when combined with real-time data, can alert communities much sooner if a subduction mega earthquake generates a tsunami large enough to breach existing seawalls and endanger coastal populations.