Advancing spatiotemporal forecasts of CO2 plume migration using deep learning networks with transfer learning and interpretation analysis

Accurate and timely forecasts of CO2 plume distribution throughout the injection and post-injection phases are crucial for detecting plume migration, assessing leakage risks, and supporting operational decisions in geologic carbon storage (GCS). Current convolutional neural network-based approaches...

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Bibliographic Details

Published in:	International journal of greenhouse gas control Vol. 132; no. 1
Main Authors:	Fan, Ming, Wang, Hongsheng, Zhang, Jing, Hosseini, Seyyed A., Lu, Dan
Format:	Journal Article
Language:	English
Published:	United States Elsevier Ltd 01.02.2024 Elsevier
Subjects:	Auto-Encoder Encoder-Decoder ConvLSTM networks Explainable machine learning Geologic carbon storage GEOSCIENCES MATHEMATICS AND COMPUTING Transfer learning Geologic carbon storage Encoder-Decoder ConvLSTM networks Transfer learning Auto-Encoder Explainable machine learning
ISSN:	1750-5836
Online Access:	Get full text
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