In order to improve the accuracy of the surface dynamic prediction model in mining areas with thick unconsolidated layers and improve Knothe time function, the influence coefficient was firstly changed into the coefficient in exponential form, and the influence coefficient of unconsolidated layer was added. Then, a subsidence basin prediction model for mining under thick unconsolidated layers was established. Next, the model was combined with the improved Knothe function, thus constructing a new mining subsidence prediction model. The new subsidence prediction model was applied in 1414 (1) working face in Huainan mining area. The results showed that the integrated model could better reflect the subsidence process, and the prediction values and the measured values agreed well.

Incorrect unwrapping of dense interferometric fringes caused by large gradient displacements make 12 it difficult to measure mining subsidence using conventional Interferometric Synthetic Aperture Radar (InSAR). 13This paper presents a Range Split Spectrum Interferometry assisted Phase Unwrapping (R-SSIaPU) method for 14 the first time. The R-SSIaPU method takes advantage of (i) the capability of Range Split Spectrum 15Interferometry of measuring surface displacements with large spatial gradients, and (ii) the capability of 16 conventional InSAR of being sensitive to surface displacements with limited spatial gradients. Both simulated 17and real experiments show that the R-SSIaPU method can monitor large gradient mining-induced surface 18 movements with high precision. In the case of the Tangjiahui mine, the R-SSIaPU method agreed with GPS 19 with differences of approximately 4.2 cm, whilst conventional InSAR deviated from GPS with differences of 20 nearly 1 m. The R-SSIaPU method makes phase unwrapping less challenge, especially in the cases with large 21 surface displacements. In addition to mining subsidence, it is believed that the R-SSIaPU method can be used 22 to monitor surface displacements caused by landslides, earthquakes, volcanic eruptions, and glacier movements. 23 24 25

By relying on the advantages of a uniform site distribution and continuous observation of the Continuously Operating Reference Stations (CORS) system, real-time high-precision Global Navigation Satellite System/Precipitable Water Vapor (GNSS/PWV) data interpretation can be carried out to achieve accurate monitoring of regional water vapor changes. The study of the atmospheric water vapor content and distribution changes is the basis for the realization of rainfall forecasting and water vapor circulation research. Such research can provide data support for the effective forecasting of regional precipitation in megacities and the construction of a more sensitive flood prevention and warning system. Nowadays, a single model is often adopted for GNSS/PWV time series. This makes it challenging to match the high randomness characteristic of water vapor change. This study proposes a hybrid model that takes into account the linear and nonlinear aspects of water vapor data by using complete empirical mode decomposition (CEEMDAN) of adaptive noise, differential autoregressive integrated moving average (ARIMA), and the long-short-term memory network (LSTM). The CEEMDAN is used to decompose the water vapor data series. Then, the high- and low-frequency data are modeled separately, reducing the sequence’s complexity and non-stationarity. In selecting the prediction model, we use the ARIMA model for the high-frequency series and the ARIMA–GWO–LSTM ensemble model for the low-frequency sub-series and residual series. The model is verified using GNSS/PWV time series data collected at the Hong Kong CORS station in July 2021. The results show the following: (1) The LSTM model optimized by the grey wolf optimization algorithm (GWO) is comparable with the single LSTM model in the low-frequency sequence prediction process, and the error items are reduced by 30% after calculation. (2) During the process from CEEMDAN decomposition to the use of the combination model for prediction, the accuracy evaluation indexes of the station increase by more than 20%. The interpolation method can accurately determine the regional water vapor spatial variation, which is of practical significance for local rainfall forecasting. High-frequency data obtained by CEEMDAN decomposition demonstrate the dramatic changes in water vapor before and after the rainfall, which can provide ideas for improving the accuracy of rainfall forecasting.

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