Abstract:
Deep learning (DL) models can exhibit powerful feature extraction ability once trained on massive, diverse labeled datasets. However, such datasets are typically lacking ...Show MoreMetadata
Abstract:
Deep learning (DL) models can exhibit powerful feature extraction ability once trained on massive, diverse labeled datasets. However, such datasets are typically lacking in geophysics, limiting the performance of DL models on solving geophysical problems. The high complexity and diversity of field data in the inference step further constrains the generalizability of DL models trained on only limited data. To address these challenges, incorporating geophysical knowledge as constraints has proven effective. In seismic interpretation, seismic attributes provide quantified geophysical insights into subsurface situation and can be used as constraints to enhance the performance of DL models. We consider the geophysically meaningful attributes as feature channels and propose optimal ways to select and incorporate suitable seismic attributes as constraints on DL models, with a focus on seismic fault interpretation. Specifically, we utilize U-Net as the DL architecture and explore three different ways of incorporating seismic attributes at the input, encoder, and decoder stages. Extensive field data applications show that seismic attributes, as features of seismic data carrying prior information, can compensate for the limitations of DL models caused by the lack of training datasets and the diversity of inference data in geophysics. This is reflected in improved accuracy, generalizability, and geological consistency of predictions by DL models with constraints. It is also discovered that simplified DL models with seismic attribute constraints outperform the complex DL models without such constraints. This further demonstrates that incorporating seismic attributes as constraints allows for the simplification of DL models while improving the prediction accuracy.
Published in: IEEE Transactions on Geoscience and Remote Sensing ( Volume: 62)