The Importance of GNSS Data in Rapid Prediction of Earthquake-Triggered Landslides

In the wake of the devastating 2022 magnitude 6.8 earthquake in Luding County in China’s Sichuan Province, researchers have delved into the realm of using Global Navigation Satellite System (GNSS) observations for swift prediction of earthquake-triggered landslides. The findings from their report in Seismological Research Letters shed light on the potential of GNSS data in providing near real-time landslide prediction, offering hope for more efficient disaster response in the future.

The Luding Earthquake and Landslides

The Luding earthquake on 5 September 2022 unleashed a series of over 6,000 landslides across the region, causing widespread destruction over 3,500 square kilometers of land. The impact of the landslides following the earthquake was significant, underscoring the vulnerability of the area to seismic events. The aftermath of the Luding earthquake prompted researchers like Kejie Chen and colleagues to explore the use of GNSS data for predicting landslides in the event of earthquakes, particularly in mountainous regions like Luding County.

Chen and his team devised an end-to-end GNSS prediction method to anticipate the occurrence and distribution of landslides post-earthquake. This method involved constructing slip models based on GNSS offset and displacement waveform data, followed by physics-based simulations to estimate peak ground velocity. Furthermore, a machine learning algorithm was employed to predict the spatial distribution of potential landslides using the peak ground velocity data. By training the prediction algorithm on six Chinese earthquakes with geological similarities to Luding, the researchers aimed to enhance the accuracy and reliability of their landslide prediction models.

Integration of MEMS Data

To further bolster their prediction method, Chen and colleagues suggested integrating data from low-cost accelerometers known as Micro-Electro-Mechanical Systems (MEMS) with GNSS observations. By combining these two data sources, researchers can improve the robustness and precision of landslide prediction models, ultimately facilitating more effective earthquake warning and response strategies. China’s recent initiative to incorporate over 10,000 MEMS-based stations in a nationwide earthquake warning system aligns with this approach, emphasizing the value of utilizing diverse data streams for enhanced disaster preparedness.

As demonstrated by the Luding earthquake case study, the utilization of GNSS data for rapid landslide prediction has showcased promising results. With advancements in modeling techniques and computing capabilities, the timeline for predicting earthquake-triggered landslides can be further shortened, optimizing emergency response efforts in high-risk areas. Continuous monitoring and refinement of prediction models based on real-time data insights are crucial for mitigating the impact of seismic events on vulnerable regions like Luding County.

The integration of GNSS data in earthquake-triggered landslide prediction signifies a pivotal advancement in disaster management practices. The research conducted by Kejie Chen and colleagues highlights the significance of leveraging innovative technologies to enhance the timeliness and accuracy of forecasting landslides post-earthquake. By harnessing the power of GNSS observations and complementary data sources like MEMS, researchers are paving the way for more effective disaster response strategies and heightened resilience in earthquake-prone regions.