UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Time‐Series Prediction Approaches to Forecasting Deformation in Sentinel‐1 InSAR Data

Hill, P; Biggs, J; Ponce‐López, V; Bull, D; (2021) Time‐Series Prediction Approaches to Forecasting Deformation in Sentinel‐1 InSAR Data. Journal of Geophysical Research: Solid Earth , 126 (3) , Article e2020JB020176. 10.1029/2020jb020176. Green open access

[thumbnail of Ponce Lopez_2020JB020176.pdf]
Preview
Text
Ponce Lopez_2020JB020176.pdf - Published Version

Download (3MB) | Preview

Abstract

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 LSTM compared to simple function extrapolation. We focus initially on forecasting seasonal signals and begin by characterising 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 ( < 1 month), whereas sinusoid extrapolation produces the lowest median RMSE values for longer predictions ( > 6 months). Machine learning methods (LSTM) perform less well. We then test the prediction methods on 2000 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 non‐periodic 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.

Type: Article
Title: Time‐Series Prediction Approaches to Forecasting Deformation in Sentinel‐1 InSAR Data
Open access status: An open access version is available from UCL Discovery
DOI: 10.1029/2020jb020176
Publisher version: https://doi.org/10.1029/2020jb020176
Language: English
Additional information: This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Keywords: InSAR, LSTM, Machine Learning, Ground Motion
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources
URI: https://discovery.ucl.ac.uk/id/eprint/10120443
Downloads since deposit
32Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item