Scientists at Japan’s Nagoya University have used Japan’s extensive network of GNSS receivers to create the first 3D images of atmospheric disturbances caused by the 2024 Noto Peninsula Earthquake. Their results show sound wave disturbance patterns in unique 3D detail and provide new insights into how earthquakes generate these waves.
The team published the results in the journal Earth, Planets and Space (May 29, DOI 10.1186/s40623-025-02211-y).
Japan has one of the densest GNSS networks in the world, with more than 4,500 receivers spread across the country. The network helps with precise location tracking and can also detect changes in the ionosphere.
A research team led by Dr. Weizheng Fu and Professor Yuichi Otsuka from Nagoya University’s Institute for Space-Earth Environmental Research (ISEE) has captured the detailed 3D structure of electron density changes in the ionosphere after the 7.5 magnitude Noto Peninsula Earthquake that occurred on January 1, 2024, in Ishikawa Prefecture, Japan.
When satellite signals travel through the ionosphere, they slow down because the radio waves interact with electrically charged particles. By measuring how much the signals slow down, scientists can calculate how many electrons are in the signals’ path and map the total electron content. Mapping these electrons allows them to effectively probe and monitor the state of the ionosphere.
About 10 minutes after the earthquake, the sound waves it generated traveled upward through the atmosphere and reached the ionosphere (60-1000 km above Earth). This created ripple disturbances similar to throwing a stone in a pond.
To build a 3D model of wave patterns, the researchers used a technique called “tomography” — similar to how CT scans create 3D images of the human body. They collected data on electron numbers from thousands of receivers tracking signals from satellites at different angles. By tracking their 3D models at different times after the earthquake, they created a time series of how electron density changed.
Sound waves generated from entire fault lines, not single points
South of the epicenter, the researchers observed a tilted sound wave pattern that gradually became more vertical over time. When an earthquake creates sound waves that travel upward through the atmosphere, the upper parts of the waves move faster than the lower parts. This makes the wave front lean or tilt as it moves. Over time, the tilted pattern gradually straightens into a more vertical alignment.
The researchers produced the first detailed 3D visualization of how the tilt angle changes over time during a seismic event. They tracked how the tilted wave patterns gradually straightened in unprecedented detail.
Earthquakes do not create atmospheric waves from just one spot, but rather from multiple points along the entire fault as different sections rupture over time. This explains why the atmospheric disturbances observed, such as tilted waves, were more complex than previous simpler models had predicted.
Previous models assumed all sound waves came from a single point at the earthquake’s center. While this matched some of their observations, it could not explain the complex, uneven wave patterns they saw in their 3D images.
To understand this, they included data from multiple wave sources along the fault line in their model, assuming that some parts of the fault generated waves about 30 seconds after the initial rupture. The results better matched their real-world observations and showed that earthquakes do not create atmospheric waves from just one spot, but rather from multiple points along the entire fault as different sections rupture over time. This explains why the atmospheric disturbances observed, such as tilted waves, were more complex than previous simpler models had predicted.
“By including multiple distributed sources and time delays, our improved modeling provides a more accurate representation of how these waves propagate through the upper atmosphere,” Professor Otsuka said.
“Disturbances in the ionosphere can interfere with satellite communications and location accuracy. If we understand these patterns better, we could improve our ability to protect sensitive technologies during and after earthquakes and enhance early warning systems for similar natural events,” lead author Weizheng Fu added.
Moving forward, the researchers are working on applying their model to other natural events such as volcanic eruptions, tsunamis, and severe weather events.
