Academic Journal

A Fusion of Geothermal and InSAR Data with Machine Learning for Enhanced Deformation Forecasting at the Geysers

التفاصيل البيبلوغرافية
العنوان: A Fusion of Geothermal and InSAR Data with Machine Learning for Enhanced Deformation Forecasting at the Geysers
المؤلفون: Joe Yazbeck, John B. Rundle
المصدر: Land, Vol 12, Iss 11, p 1977 (2023)
بيانات النشر: MDPI AG, 2023.
سنة النشر: 2023
المجموعة: LCC:Agriculture
مصطلحات موضوعية: hazard mitigation, machine learning, geothermal reservoirs, neural networks, InSAR, Agriculture
الوصف: 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.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2073-445X
Relation: https://www.mdpi.com/2073-445X/12/11/1977; https://doaj.org/toc/2073-445X
DOI: 10.3390/land12111977
URL الوصول: https://doaj.org/article/b343dffcc2f847b4bba39b9a2650acde
رقم الانضمام: edsdoj.b343dffcc2f847b4bba39b9a2650acde
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:2073445X
DOI:10.3390/land12111977