Natural disasters such as earthquakes can have devastating effects, especially when they trigger secondary hazards such as landslides. Researchers have been exploring the use of Global Navigation Satellite System (GNSS) data to rapidly predict earthquake-triggered landslides, as demonstrated in a recent study on the 2022 Luding earthquake in China’s Sichuan Province.
The study, conducted by Kejie Chen and colleagues from the Southern University of Science and Technology, utilized GNSS data to develop methods for near real-time landslide prediction. Their models accurately identified approximately 80% of the landslide locations triggered by the Luding earthquake, showcasing the potential for using GNSS data in rapid landslide prediction.
The Luding earthquake, which occurred on September 5, 2022, resulted in over 6,000 landslides and caused significant damage to more than 3,500 square kilometers of the region. Chen noted that while the number of landslides was not unexpected given the area’s topography and seismic activity, the scale of destruction highlighted the importance of continuous monitoring and improved prediction models.
GNSS data played a crucial role in the study, as it measures the movement of the ground during an earthquake. Chen and colleagues had been previously exploring the use of GNSS data for earthquake source location and tsunami early warning, making it a natural choice for analyzing landslide prediction following the Luding earthquake.
The researchers developed an end-to-end GNSS prediction method, starting with constructing slip models of the event based on GNSS offset and displacement waveform data. They then utilized physics-based simulations to measure peak ground velocity, followed by using a machine learning algorithm to predict the spatial distribution of landslides. Training the prediction algorithm on six Chinese earthquakes with geological similarities to the Luding earthquake enhanced the accuracy of the predictions.
To further improve earthquake warning and response, the researchers suggested combining GNSS observations with data from low-cost accelerometers known as MEMS, which capture near-fault ground motion waveforms. By incorporating data from over 10,000 MEMS-based stations into a nationwide earthquake warning system in China, the researchers believe that the robustness and accuracy of landslide prediction can be significantly enhanced.
The study demonstrates the potential of using GNSS data for rapid earthquake-triggered landslide prediction. By refining and enhancing the methods developed in this study, researchers can improve the accuracy and timeliness of landslide predictions, ultimately helping to mitigate the impact of secondary hazards following seismic events. Further research and collaboration in this area are essential to advancing our understanding of landslide dynamics and improving disaster preparedness and response strategies.
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