This study introduces an alternate spatial-temporal modeling-assisted InSAR time-series analysis method for mine subsidence mapping, aiming to address the large deformation gradients and decorrelation issues. The approach employs the iterative Modeling-Assisted Phase Unwrapping (MA-PU) algorithm for spatial phase unwrapping, and integrates it with temporal models to derive the deformation time-series. Four different inversion procedures are implemented to derive the time-series based on pixel types. The MA-PU method’s effectiveness in handling phase gradients is validated with simulations and real data analysis, showing superiority over non-model-based methods. The incorporation with temporal modeling and time-series inversion demonstrates advantages over time-series inversion alone in dealing with rank-deficiency issues within subsidence zones. The approach is applied to the West Cliff Colliery, Australia, using 23 ALOS-1 data. Results have been compared with GNSS data for validation. Obtained accuracy is approximately 16 mm, with a correlation of 0.99 between the two measurements, showing generally better performances compared to other methods. The comparison result suggest that this approach provides a more robust solution for monitoring mine subsidence in complex scenarios.

中文翻译：

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将空间建模辅助的 InSAR 相位展开与时间分析相结合，以进行高级矿井沉降时间序列绘图


本研究介绍了一种交替时空建模辅助的InSAR时间序列分析方法用于矿井沉陷测绘，旨在解决大变形梯度和去相关问题。该方法采用迭代建模辅助相位展开（MA-PU）算法进行空间相位展开，并将其与时间模型集成以导出变形时间序列。实施四种不同的反演程序来根据像素类型导出时间序列。 MA-PU 方法处理相位梯度的有效性通过仿真和实际数据分析得到验证，显示出相对于非基于模型的方法的优越性。时间建模和时间序列反演的结合在处理沉降区内的等级不足问题方面比单独的时间序列反演具有优势。该方法使用 23 个 ALOS-1 数据应用于澳大利亚 West Cliff 煤矿。结果已与 GNSS 数据进行比较以进行验证。获得的精度约为 16 毫米，两次测量之间的相关性为 0.99，与其他方法相比，通常表现出更好的性能。比较结果表明，该方法为复杂场景下的矿井沉降监测提供了更稳健的解决方案。