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On August 7, 2024, Kejie Chen of the Southern University of Science and Technology and co-authors published a study in Seismological Research Letters that utilized Global Navigation Satellite System GNSS observations to predict landslides triggered by an earthquake with magnitude 6.8 that occurred in Luding County, Sichuan Province, China on September 5, 2022.
Chen and colleagues have developed methods for near-real-time landslide prediction using GNSS data, which revealed a success rate of approximately 80 for identifying locations triggered by the Luding earthquake. This means that within about forty minutes after the event, theirwere able to complete rapid landslide predictions for the region affected by this significant seismic activity.
The Luding earthquake on the southeastern part of the Xianshuihe Fault resulted in over 6000 landslides covering an area of nearly 3500 square kilometers. This extensive damage underscores the unique risks associated with earthquakes in mountnous areas, particularly those prone to landslides following large-scale seismic events.
Previously known for its susceptibility to landslides, especially following major quakes, Luding County experienced a notable but not unprecedented number of co-seismic landslides due to its geography and seismic history, explned Chen. The magnitude and extent of destruction prompted us to refine our GNSS-based landslide prediction.
Chen's team has been researching how GNSS data can be utilized for earthquake source location and tsunami early warning systems. The Luding earthquake provided a vital opportunity to evaluate the efficiency and effectiveness of their methods in dealing with co-seismic landslides.
Our primary focus is on refining our techniques using GNSS observations, Chen explned. In inland earthquakes, especially those occurring in mountnous regions like China's Sichuan Province, landslides often pose the most significant seismic threat. The Luding earthquake offered an ideal case study to test and modify our landslide prediction strategies.
To improve their method, the researchers are proposing a combination of GNSS data with near-fault ground motion waveforms captured by low-cost accelerometers known as MEMS micro-electromechanical systems. As part of China's nationwide earthquake warning system, over 10,000 such MEMS-based stations have recently been incorporated.
Integrating both types of data will enhance the reliability and precision of landslide predictions, Chen stated. By cross-referencing GNSS observations with information from MEMS sensors, we m to create a more comprehensive monitoring system that can better inform earthquake warnings and emergency responses.
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