How to use the Earth's gravitational field to detect earthquakes

How to use the Earth's gravitational field to detect earthquakes

For a short time in 2011, just after the failure of two tectonic plates off the east coast of Japan, gravity faltered. The gravitational field of the Earth is the result of a distribution of matter; when huge volumes of land and water suddenly shift, as happens with an earthquake, this distribution changes. The forces that keep the moon close, maintain the density of the atmosphere, and keep our feet on the ground have realigned. The whole world snapped, seconds before the seismic waves came and Japan really shook.

Not that anyone has noticed. Even the strongest tremors, such as the 2011 Tohoku earthquake, have an imperceptible effect on gravity. But for seismologists accustomed to hearing the sounds of the Earth up close, these changes have long been a tempting possibility: a fleeting seismic signal, propagating across the globe at the speed of light. In recent years, scientists have been sifting through data from large earthquakes for signs of these gravitational perturbations, which are elusive and still quite controversial in the field of seismology. But with the help of more sensitive tools and better computer models, researchers have begun to track them down.

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Arrow Scientists are now getting closer to putting this data to good use. In an article published in Nature, some researchers describe a seismic warning system that relies only on signals derived from gravity. They tested their model on seismic data from the Tohoku earthquake, finding that the system was able to accurately detect the quake about eight seconds earlier than previous methods, providing a better estimate of its enormous range. The research represents a feasibility test linked to a single event, but it also aims to verify whether in the future the method could gain valuable seconds to the early warning systems. "We are demonstrating that it is indeed a signal and that it can be used - explains Andrea Licciardi, seismologist at the Université Côte-d'Azur, in France, who was in charge of the research -. These data were not even looked at, but they are comparable, if not better, than existing signals. "

The main signals currently available are P waves, seismic ripples that occur when rock compresses and vibrates due to a sudden shock. When these waves reach the seismic stations, software quickly identifies the origin of the earthquake and estimates its size. The goal is to alert the population before the ascending and descending movements of the S waves occur, a slower type of jolt that often causes the greatest damage. In recent years, advances in instrumentation and algorithms have led to the development of faster and more reliable alarm systems. But P-waves typically travel at a few kilometers per second, which is an obstacle to the speed of detection.

Since they travel at the speed of light, gravitational perturbations are faster. "It's faster than any other method we have today," explains Martin Vallée, a seismologist at the University of Paris, who worked on detecting the signals. These perturbations are also much less powerful than P waves, which makes it harder to distinguish them from seismologists' worst enemy: noise. The din of the earth is constant, a chorus of small events generated by human beings, seismic tremors and turbulence in the air and in the ocean, which make it extremely difficult to perceive the first hints of a great earthquake. Seismologists need a signal that clearly indicates what is about to happen. If the noise is perceived in the wrong way, the millions of people who inhabit the cities could pour into the streets to seek shelter without any reason.

The debate among seismologists For decades, seismologists have been discussing the possibility of detecting these phenomena in a clear way. There are tools for observing gravitational waves directly, such as the massive Ligo structures in Louisiana and Washington state, but these are primarily useful to astronomers and are not a practical method for detecting the small shifts caused by earthquakes. The fluctuations are instead observed indirectly by seismometers, which detect the response of the Earth to counteract the displacement of the mass. The problem is that gravity changes and elastic reactions cancel each other out, producing a very weak signal, known as prompt elastogravity signal, or Pegs.

The seismic waves of a large earthquake are easy to see. Think of the classic image of a seismograph, in which the pencil traces the waves on the paper when the shock arrives. Even for very trained eyes, Pegs are only squiggles, and are indistinguishable from noise. It is difficult to prove their presence. In 2017, the first identifications of Pegs in the Tohoku seismic data were contested by other seismologists.

Over time, however, researchers have been able to observe more earthquakes around the world. "I am convinced that the theory is correct," says Maarten de Hoop, a computational seismologist at Rice University who was not involved in the research. Driven in part by the controversy surrounding the early surveys, de Hoop sought to mathematically prove whether gravitational fluctuations are observable or not. The key, he says, is to examine the data in the early moments of the earthquake, before the P waves reach the sensors. At that point, the two forces "do not cancel out completely", which means that in theory there is a signal that can be detected in the midst of the noise. However, it is still unclear whether seismologists are actually able to separate the two phenomena.

THE new study The new research offers a first confirmation in this sense, explains de Hoop. One thing clear is that current instruments are only able to distinguish gravitational signals from noise during the strongest earthquakes, with magnitudes greater than 8.0, such as earthquakes that hit places like Japan, Alaska and Chile. Since these massive earthquakes are rare, Licciardi's team created a series of data on hypothetical earthquakes, adding the seismic noise found at stations across Japan. Noise was used to train a machine learning algorithm that could detect the onset of an earthquake and estimate its size.

WiredLeaks, how to send us an anonymous report When the researchers applied the model to the data In real time recorded by sensors during the Tohoku earthquake, it took about fifty seconds to provide accurate detection, beating recent cutting-edge methods, including GPS-based ones that measure ground movement immediately after an earthquake. An eight-second difference may seem insignificant, but "it's very much in the context of early warning," Licciardi notes, especially in scenarios like the Tohoku earthquake, in which coastal residents were only given a few minutes to evacuate earlier. of the arrival of the tsunami.

Researchers also point out that the new model was more accurate in estimating the size of the earthquake, a key aspect for predicting the extent of a tsunami. In Japan, in 2011, the first estimates indicated an earthquake with a magnitude of less than 8.0, suggesting a much smaller wave.

The method is still far from becoming a practically applicable technique. According to Thomas Heaton, a seismologist at CalTech, the primary focus of early warning systems must be to make alerts more effective by testing existing methods so that when an alarm is raised, people hear it and know how to react. "Our problem is not the sensors, but how to get the data out of the system and tell people what to do," he adds.

However, de Hoop, who describes himself as "enthusiastic" about the new job, points out that the system represents a roadmap to improve existing methods with better data and machine learning techniques. The key to making this method work even with the most common and limited earthquakes will be figuring out how to lower the magnitude threshold to detect gravitational signals, which may require sensors that can directly track changes in the gravitational field. "I think there is a great deal of information and work to be done," explains de Hoop.

This article originally appeared on US.

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