Time-Series Prediction Approaches to Forecasting Deformation in Sentinel-1 InSAR Data
Time-Series Prediction Approaches to Forecasting Deformation in Sentinel-1 InSAR Data
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DOI：
10.1029/2020jb020176
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发表时间：
2021-03-01
影响因子：
3.9
通讯作者：
Bull, D.
中科院分区：
文献类型：
--
作者：
Hill, P.;Biggs, J.;Bull, D.
现在通常可以从卫星 InSAR 获得位移时间序列，并用于标记异常地面运动，但尚未进行预测。我们测试传统时间序列预测方法（例如 SARIMA）和监督机器学习方法（例如长短期记忆 (LSTM)）与简单函数外推法的比较。我们最初专注于预测季节性信号，并首先使用正弦曲线拟合、季节性分解和自相关函数来表征时间序列。我们发现这三种措施具有广泛的可比性，但识别出不同类型的季节性特征。我们用它来选择一组具有高度季节性特征的 310 个点，并在 1-9 个月的预测窗口内测试所选的三种预测方法。考虑短期预测（6 个月）时，SARIMA 的总体 RMSE 中值最低。机器学习方法 (LSTM) 的表现较差。然后，我们在具有一系列季节性的 2,000 个随机选择的点上测试预测方法，发现常数函数的简单外推法总体上比任何更复杂的时间序列预测方法表现得更好。季节性和 RMSE 之间的比较表明，随着季节性的增加，性能略有改善。这项概念验证研究展示了 InSAR 数据时间序列预测的潜力，但也强调了将这些技术应用于非周期信号或单个测量点的局限性。我们预计未来的发展，特别是更短的时间尺度，将具有广泛的潜在应用，从基础设施稳定性到火山喷发。
Time series of displacement are now routinely available from satellite InSAR and are used for flagging anomalous ground motion, but not yet forecasting. We test conventional time series forecasting methods such as SARIMA and supervised machine learning approaches such as long short-term memory (LSTM) compared to simple function extrapolation. We focus initially on forecasting seasonal signals and begin by characterizing the time-series using sinusoid fitting, seasonal decomposition, and autocorrelation functions. We find that the three measures are broadly comparable but identify different types of seasonal characteristic. We use this to select a set of 310 points with highly seasonal characteristics and test the three chosen forecasting methods over prediction windows of 1-9 months. The lowest overall median RMSE values are obtained for SARIMA when considering short term predictions (6 months). Machine learning methods (LSTM) perform less well. We then test the prediction methods on 2,000 randomly selected points with a range of seasonalities and find that simple extrapolation of a constant function performed better overall than any of the more sophisticated time series prediction methods. Comparisons between seasonality and RMSE show a small improvement in performance with increasing seasonality. This proof-of-concept study demonstrates the potential of time-series prediction for InSAR data but also highlights the limitations of applying these techniques to nonperiodic signals or individual measurement points. We anticipate future developments, especially to shorter timescales, will have a broad range of potential applications, from infrastructure stability to volcanic eruptions.