InSAR Modeling and Deformation Prediction for Salt Solution Mining Using a Novel CT‐PIM Function

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Abstract

Deformation prediction for a salt solution mining area is essential to mining environmental protection. The combination of Synthetic Aperture Radar Interferometry (InSAR) technique with Probability Integral Method (PIM) has proven to be powerful in predicting mining‐induced subsid-ence. However, traditional mathematical empirical models (such as linear model or linear model combined with periodical function) are mostly used in InSAR approaches, ignoring the under-ground mining mechanisms, which may limit the accuracy of the retrieved deformations. Inaccurate InSAR deformations will transmit an unavoidable error to the estimated PIM parameters and the forward predicted subsidence, which may induce more significant errors. Besides, theoretical con-tradictory and non‐consistency between InSAR deformation model and future prediction model is another limitation. This paper introduces the Coordinate‐Time (CT) function into InSAR deformation modeling. A novel time‐series InSAR model (namely, CT‐PIM) is proposed as a substitute for traditional InSAR mathematical empirical models and directly applied for future dynamic pre-diction. The unknown CT‐PIM parameters can be estimated directly via InSAR phase observations, which can avoid the error propagation from the InSAR‐generated deformations. The new approach has been tested by both simulated and real data experiments over a salt mine in China. The root mean square error (RMSE) is determined as ±10.9 mm, with an improvement of 37.2% compared to traditional static PIM prediction method. The new approach provides a more robust tool for the forecasting of mining‐induced hazards in salt solution mining areas, as well as a reference for en-suring the environment protection and safety management.

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Xing, X., Zhang, T., Chen, L., Yang, Z., Liu, X., Peng, W., & Yuan, Z. (2022). InSAR Modeling and Deformation Prediction for Salt Solution Mining Using a Novel CT‐PIM Function. Remote Sensing, 14(4). https://doi.org/10.3390/rs14040842

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