Yazbeck, Joe and John B. Rundle

Earthquakes are a common occurrence at The Geysers geothermal field in California which, over the years, have led to general land sinking in the area. In our study, we explore the correlation of geothermal data and seismicity and find that the injection rate exhibits a high correlation with the number of earthquakes. Additionally, we rely on satellite imagery which measures the extent of the land subsidence and develop a machine learning model that predicts future land subsidence, finding that it has a relatively low error rate. Moreover, we incorporate geothermal data into this model and find that it performs even better. All in all, this encourages the use of machine learning models in hazard mitigation in order to minimize the potential impacts that land subsidence might bring.Abstract The Geysers geothermal field in California is experiencing land subsidence due to the seismic and geothermal activities taking place. This poses a risk not only to the underlying infrastructure but also to the groundwater level which would reduce the water availability for the local community. Because of this, it is crucial to monitor and assess the surface deformation occurring and adjust geothermal operations accordingly. In this study, we examine the correlation between the geothermal injection and production rates as well as the seismic activity in the area, and we show the high correlation between the injection rate and the number of earthquakes. This motivates the use of this data in a machine learning model that would predict future deformation maps. First, we build a model that uses interferometric synthetic aperture radar (InSAR) images that have been processed and turned into a deformation time series using LiCSBAS, an open-source InSAR time series package, and evaluate the performance against a linear baseline model. The model includes both convolutional neural network (CNN) layers as well as long short-term memory (LSTM) layers and is able to improve upon the baseline model based on a mean squared error metric. Then, after getting preprocessed, we incorporate the geothermal data by adding them as additional inputs to the model. This new model was able to outperform both the baseline and the previous version of the model that uses only InSAR data, motivating the use of machine learning models as well as geothermal data in assessing and predicting future deformation at The Geysers as part of hazard mitigation models which would then be used as fundamental tools for informed decision making when it comes to adjusting geothermal operations.