Evaluating Physics-Informed Neural Network Performance for Seismic Discrimination between Earthquakes and Explosions

Abstract In this article, we evaluate adding a weak physics constraint, that is, a physics-based empirical relationship, to the loss function with a physics-informed manner in local distance explosion discrimination in the hope of improving the generalization capability of the machine learning (ML) model. We compare the proposed model with the two-branch model we previously developed, as well as with a pure data-driven model. Unexpectedly, the proposed model did not consistently outperform the pure data-driven model. By varying the level of inconsistency in the training data, we find this approach is modulated by the strength of the physics relationship. This result has important implications for how to best incorporate physical constraints in ML models.