The first prediction model in the world was developed for the landslide that revolve around the earthquake, which started a new era in the prevention of risk

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(A) Cosmic landslides in stocks of dairy belts and peripheral animals. (B) The expanded view of the 38th. Moderate (warm plus arid); Equalist. Beach balls indicate the dynamics of primary seismic rupture.

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Credit: © Science China Press

The landslides that operate the earthquake pose a great secondary risk, and it is often responsible for tens of thousands of deaths and billions of dollars in economic losses every year. However, the rapid recognition of the place where the landslides will be a challenge: the sensing of the dimension of the past failure can be but it depends on the images free of the cloud that may not be available during the critical hours after the earthquake.

To overcome these restrictions, the Professor Xuanmei team for fans at the main state laboratory for the prevention of Geohazard and the Protection of the Geological Environment (Chengdu University of Technology) collected the most comprehensive global database for Kusini landslides so far, 398,698 events that have been appointed across 38 of the heavenly cars since 1970. The landslides strictly have to integrate the detection of remote sensing change and a manual review of experts to ensure the accuracy of the borders and temporal consistency.

Based on this inventory, the researchers ranked a group of 17 topographical, geological, geological, urban and zipper indicators. The spatial and statistical analysis revealed that the Earth accelerates the peak, slope and inferiority are the main engines of kuisi landslide on a global scale, with the secondary impact of relief and terrain roughness. Moreover, they divided the global stocks into the Pacific and Pulby seismic belts, each divided into cold, moderate and level climatic areas, revealing distinctive dominant controls in each region and providing a basis for improving regional model parameters.

Another basic innovation is the prediction model that depends on the multi -scale tavnati decline network reinforced by the spatial interest units of the channel. This architecture allows the generation and focus of the most discriminatory features through multi -dimensional inputs, and to achieve rapid reasoning for the occurrence of landslide. In this work, two typical variables were trained and tested.

(A) Regional models, which are seized for local climatic and tectonic areas (for example, tropical and moderate areas), offer the highest accuracy where adequate training samples are provided.

(B) The global model guarantees a strong performance in the cold and cold regions by taking advantage of the full diversity of global events.

Using the validity of one of the 38 events, the frame has constantly made a spatial accuracy exceeding 82 % and processing each scenario in less than one minute on Tesla V100 graphics processing units.

“Our deep learning model allows the provision of probability maps close to time to the time of land collapse immediately after an earthquake without any previous signs,” says Professor Van. “This ability can direct the first respondents and risk managers to the most at risk areas during the decisive early hours.”

Professor John Yansen, co -author of the Czech Academy of Sciences, emphasizes the operational capabilities: “By integrating our typical outputs with population conversations and infrastructure, we can estimate the societies at risk at seconds, before the images become highly accurate.”

Looking at the future, the team plans to integrate rainfall, final tremors in the model, and move towards the unified “multi -step” early warning system. They also explore publishing via platforms based on the groom and integration with air sensors and the uninhabited land to increase the reduction of time between the occurrence of the earthquake and the risk prediction.

“This work represents a shift in the form of maps of the capacity retrospectively to the pre -emptive prediction and prediction of the landslides that caused earthquakes,” says Professor Hakan Tanias, co -author of the University of Tontete. “It opens the door to support the real -time decision on a global scale in seismic risk areas.”

By mixing a unique global database of terrestrial collapses, strict mechanical analysis, and developed deep learning, this research sets the basis for tools for reducing geophysard risk from the next generation, which transforms how societies are ready and respond to the consequences of the destructive major earthquakes and respond to them.

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See the article:

Deep learning can predict earthquakes around earthquakes

https://doi.org/10.1093/nsr/nwaf179

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National Science Review

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