SignificanceWe present three possible strategies to effectively incorporate geological and/or geophysical constraints into deep neural networks (DNNs). They help address the main challenges of poor generalizability, weak interpretability, and physical inconsistency that are commonly faced when applying DNNs to solve geophysical problems. In discussing each of the three strategies, we provide examples of applications to demonstrate their effectiveness.