SAEDNEWS: Beneath the Earth lies a complex network of faults where large pieces of rock move past, over, under, or through each other.
According to SaedNews, these movements occur at a speed equivalent to the growth of a fingernail, until finally, with a violent jolt, the Earth shakes. Inevitably, devastating earthquakes will occur, and there is nothing that can be done to prevent them. However, understanding where these earthquakes are more likely to happen and what magnitude they might release can help communities prepare. In other words, the best way to mitigate the impact of these disasters is to create underground maps that specify each fault and monitor their behavior.
To create underground maps, researchers use seismometers on the Earth's surface—small devices that detect and record various vibrations, including earthquakes. Seismic waves are somewhat similar to music. By identifying the notes and changes in their rhythm, scientists can understand which "underground instruments" generated these waves, where they are located, and how they behave during fractures. However, this work has been very difficult, often slow, and sometimes inaccurate. The data was too vast for experts to analyze on their own. These small earthquakes produce very subtle seismic waves that even the most skilled seismologists might struggle to distinguish from seismic data. In 2017, however, scientists had a brilliant idea. They observed that machine learning programs could process massive sets of images, so why not use a similar approach in seismology?
As machine learning programs advance, they may reveal much more about the Earth's subsurface. This technology can also increase the speed and accuracy of early warning systems for earthquakes—nearly automated programs that predict threats in the initial moments after an earthquake starts and send emergency messages to people before seismic waves arrive. This technology will join the growing set of AI tools for disaster reduction. For example, in Tokyo, AI software scans images from cameras positioned at high points in the city to detect fires and building collapses and immediately notify authorities.
Using AI, earthquakes can be indirectly analyzed and predicted with greater accuracy. However, it should be noted that these methods are still under development and are more useful for identifying patterns and reducing associated risks than for precise earthquake prediction.
Machine learning algorithms can identify small and imperceptible earthquakes that traditional data cannot detect. These earthquakes reveal fault behavior patterns.
AI can process large volumes of seismic data quickly and accurately, identifying fault networks and their characteristics.
Using historical data, AI algorithms can simulate future fault behavior and identify areas with a high likelihood of earthquakes.
This technology can increase the speed and accuracy of early warning systems for earthquakes, allowing people and authorities to take necessary actions before seismic waves arrive.
Complex underground networks, such as molten rock movements or pressure in sensitive faults, can be identified by AI, which can signal potential earthquake occurrences.
