Joint Inversion

Joint inversion is a technique that simultaneously reconstructs multiple subsurface properties from different geophysical datasets, aiming for more accurate and robust estimations than inverting each dataset individually. Current research heavily utilizes deep learning architectures, such as convolutional neural networks and Fourier neural operators, to improve the efficiency and accuracy of joint inversion, particularly for challenging problems like monitoring CO2 sequestration. This approach enhances the interpretation of geophysical data, leading to improved subsurface imaging and monitoring capabilities with applications in various fields, including reservoir characterization and environmental monitoring.