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A Data-Driven Approach to Deformation Forecasting: Machine Learning on InSAR Data    
  • Joe Yazbeck,
  • John B. Rundle
Joe Yazbeck

Corresponding Author:[email protected]

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John B. Rundle
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Abstract

Anthropogenic activities such as fluid injection, fluid extraction, mining, and hydraulic fracturing can all cause induced seismicity which can in turn result in land subsidence. This latter phenomenon is devastating to local infrastructure as well as underlying aquifers. It is for this reason that monitoring and predicting land deformation is of utmost importance. We relied on Interferometric Synthetic Aperture Radar (InSAR) images captured by Sentinel-1 to monitor deformation in the line-of-sight of the satellite. The Geysers geothermal field, where injection plays a direct role in induced seismicity, was used as the area of study and a deformation time series was built using LiCSBAS [1]. Two machine learning models (model A and model B) that included Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) layers were built to predict future deformation maps. The only difference between the models was the incorporation of geothermal injection and production data in model B. While both models outperformed a baseline linear model, it was model B that performed the best based on a mean squared error metric.
21 Dec 2023Submitted to ESS Open Archive
27 Dec 2023Published in ESS Open Archive