Robust Weakly Supervised Learning Prestack Multitrace Seismic Inversion

This is the authors' abstract. We don't add key points for this paper.

Prestack seismic inversion is one of the most commonly used methods to describe reservoirs. Deep learning (DL) has received wide attention because of its ability to handle complex nonlinear problems. The existing DL-based seismic inversion method adopts a single training dataset, which is prone to poor generalizability and geological artifact. In addition, field data are often difficult to transfer learning due to the lack of a training dataset. To partially overcome these problems, we improve the training dataset and network parameters, thus developing a new DL-based prestacked inversion framework. First, we generate a training dataset using a forward model and pretrain the network by adding labels to a portion of it as the training dataset. We use multitrace information for training and prediction to reduce the lateral geological artifact problem. For unlabeled or less-labeled datasets, we construct data residual loss terms and initial model constraints to achieve weakly supervised or even unsupervised learning, which solves the poor generalizability problem due to insufficient training datasets. Compared with previous data-driven approaches, the inverse framework in this article compensates for the lack of the training dataset, diminishes geological artifacts, and improves generalization and robustness. The effectiveness of the framework is verified by synthetic data from two different models and field data from S-fields, and good inversion results are obtained even with a low signal-to-noise ratio or lack of well data.