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Evaluating Physics Informed Neural Network Performance for Seismic Discrimination Between Earthquakes and Explosions
Qingkai Kong, William R. Walter, Ruijia Wang, Brandon Schmandt
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Combining physics with machine learning models has advanced the performance of machine learning models in many different applications. In this paper, we evaluate adding a weak physics constraint, i.e., a physics-based empirical relationship, to the loss function (the Physics Informed manner) in local distance explosion discrimination in the hope of improving the generalization capability of the machine learning model. We compare the proposed model to the two-branch model we previously developed, as well as to 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 Machine Learning models.
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