Ground Subsidence Monitoring in a Mining Area Based on Mountainous Time Function and EnKF Methods Using GPS Data

Abstract

Ground subsidence is an important geomorphological phenomenon in mining areas. It is difficult to monitor and predict ground subsidence with high precision, especially in mountainous mining areas. Taking the mining workface of a mountainous coalfield in Taiyuan City, in the Shanxi Province of China as an example, this research selects five typical points from GPS observation data along the strike section. Based on the materials, the ground subsidence processes at these typical points are monitored and predicted using the mountainous time function method. Acquired from the mountains time function is a recurrence equation, which is regarded as the state equation, and the Ensemble Kalman (EnKF) method is conducted accordingly. Finally, the performance of the two methods is evaluated and compared using error curves and indexes. This research presents a recurrence equation based on the mountainous time function method and establishes the EnKF method for ground subsidence monitoring and prediction. Meanwhile, compared to the mountainous time function method, the values of the ME, MAE, RMSE and MAPE indexes are largely improved for the EnKF method. Hence, this research not only presents an effective method for ground subsidence monitoring in mountainous mining areas, but also provides theoretical support for safe coal mining and environmental protection.