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August 7, 2024 – Researchers have taken advantage of the extensive data gathered from the devastating magnitude 6.8 earthquake that struck Luding County in China's Sichuan Province on September 5, 2022, to investigate whether Global Navigation Satellite System GNSS observations could facilitate rapid prediction of landslides triggered by earthquakes.
Kejie Chen of Southern University of Science and Technology and colleagues published their findings, which explore near real-time GNSS landslide prediction methods in Seismological Research Letters. Theiraccurately predicted approximately 80 of the landslides triggered by the Luding earthquake in a remarkable timeframe, about 40 minutes following its occurrence.
The destructive September 2022 earthquake on the southeastern section of the Xianshuihe Fault caused over 6,000 landslides across an area of 35,000 square kilometers. While the scale and impact were not entirely unforeseen due to the region's geographical features and seismic history, they provided new insights into risk assessment.
The volume of co-seismic landslides triggered by the Luding earthquake was substantial yet not unprecedented given its topography and seismic activity, explned Chen. However, the extent of destruction and specific locations affected underscored the importance of continuous monitoring and enhanced predictive.
GNSS data capture ground movement during earthquakes. Prior to the Luding event, Chen and colleagues had been developing methods for locating earthquake sources and tsunami early warning systems with GNSS information.
Seismic ruptures inland, particularly in mountnous Chinese regions like this area, primarily cause landslides as their mn cascading seismic hazard, stated Chen. Our research has centered on refining and developing methodologies for landslide prediction using GNSS data. The Luding earthquake provided an opportune case study to evaluate and adapt our strategies agnst co-seismic landslides.
The researchers developed a comprehensive approach that starts with the construction of slipbased on GNSS offset and displacement waveform data, followed by physics-based simulations employing thoseto determine peak ground velocity.
Finally, Chen and colleagues utilized peak ground velocity in combination with algorith forecast the spatial distribution of potential landslide occurrences for the event. They trned this algorithm using six Chinese earthquakes with magnitudes ranging from 6.1 to 8.0 that were geologically similar to Luding's quake.
One improvement avenue could be integrating GNSS observations with data from low-cost accelerometers known as MEMS Micro-Electro-Mechanical Systems captured by a nationwide earthquake warning system recently expanded by China, which now boasts over 10,000 such stations. This combined approach would boost the method's reliability and accuracy through complementary data.
By merging both data types, we can create a more robust monitoring system for landslide prediction, Chen sd. GNSS data validates and refines predictions made from MEMS data, ensuring comprehensive oversight.
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Seismic Hazard Assessment Techniques Real time GNSS landslide prediction methods Luding earthquake landslide analysis Global Navigation Satellite System observations Chinas landslide warning system development Earthquake triggered co seismic landslides forecasting