KF-Outlined Recognition Model to Rebuild the Geological Topology Change of Metro Tunnel Face

In this article, a spatiotemporal recognition model was proposed to nondestructively map the geological condition of metro tunnel, avoiding the low generality problem of the existing machine learning (ML) methods operating on the correlative dynamic parameters of inaccuracy theory. Specifically, first, a temporal estimator based on weighted least square method (WLSM) is designed to calculate stratum thickness from posterior estimations, and its static weight as input control corrects the direction and speed of geology evolution according to observation, copying with non-Gaussian noises while avoiding the oversimplification error of extended Kalman filter (EKF). Second, a spatial observer based on geomechanical theory simulates ladder-dimensional unscented Kalman filter (UKF), matching the nonlinear geological models while reducing the computation and initialization complexity of UKF through ladder-dimension sigma sampling points. Third, the input control parameter is refined by a back propagation neural network (BPNN) improved to reduce learning time through exponentially extending the learning rate and constraining the initial learning values. Finally, theory proofs and experiment comparisons are conducted to verify its accuracy and cost.