Sensing prior constraints in deep neural networks for solving exploration geophysical problems.

ABSTRACT: One of the key objectives in geophysics is to characterize the subsurface through the process of analyzing and interpreting geophysical field data that are typically acquired at the surface. Data-driven deep learning methods have enormous potential for accelerating and simplifying the process but also face many challenges, including poor generalizability, weak interpretability, and physical inconsistency. We present three strategies for imposing domain knowledge constraints on deep neural networks (DNNs) to help address these challenges. The first strategy is to integrate constraints into data by generating synthetic training datasets through geological and geophysical forward modeling and properly encoding prior knowledge as part of the input fed into the DNNs. The second strategy is to d

SUBMITTER: Wu X 

PROVIDER: S-EPMC10265955 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature