Physics-Constrained Seismic Impedance Inversion Based on Deep Learning

Yuqing Wang

doi:10.1109/LGRS.2021.3072132

Physics-Constrained Seismic Impedance Inversion Based on Deep Learning

2022-01-01

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DOI：10.1109/LGRS.2021.3072132

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Deep learning has been widely adopted in seismic inversion. One of the major obstacles when adopting deep learning in seismic inversion is the demand for labeled data sets. There are mainly two approaches to address this problem. One is to generate massive numbers of synthetic data and then transfer the trained model to real data. The other is to introduce theoretical constraints and reduce the parameter spaces of deep learning. In this letter, we propose a physics-constrained seismic impedance inversion method based on deep learning. Robinson convolution model is adopted to model the seismic forward process and provide theoretical constraints for the inversion process. Bilateral filtering is further combined to constrain the spatial continuity of the inversion results. The experimental results on both synthetic examples and real examples demonstrate that the proposed method can effectively improve the prediction accuracy and the spatial continuity of the inversion results.

Keywords:

Impedance

Data models

Training

Convolution

Computational modeling

Estimation

Deep learning

Convolutional neural network (CNN)

deep learning

physics-constrained model

seismic inversion

Physics-Constrained Seismic Impedance Inversion Based on Deep Learning

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