Coupling physics-deep learning inversion

Speaker: Lu Zhang (Columbia University)

Date: 10/18/23

Abstract: In recent years, there is great interest in using deep learning to geophysical/medical data inversion. However, direct application of end-to-end data-driven approaches to inversion have quickly shown limitations in the practical implementation. Due to the lack of prior knowledge on the objects of interest, the trained deep learning neural networks very often have limited generalization. In this talk, we introduce a new methodology of coupling model-based inverse algorithms with deep learning for two typical types of inversion problems. In the first part, we present an offline-online computational strategy for coupling classical least-squares based computational inversion with modern deep learning based approaches for full waveform inversion (FWI) to achieve advantages that can not be achieved with only one of the components. An offline learning strategy is used to construct a robust approximation to the inverse operator and utilize it to design a new objective function for the online inversion with new datasets. In the second part, we present an integrated machine learning and model-based iterative reconstruction framework for joint inversion problems where additional data on the unknown coefficients are supplemented for better reconstructions. The proposed method couples the supplementary data with the partial differential equation (PDE) model to make the data-driven modeling process consistent with the model-based reconstruction procedure. The impact of learning uncertainty on the joint inversion results are also investigated.