Separating Injection‐Driven and Earthquake‐Driven Induced Seismicity by Combining a Fully Coupled Poroelastic Model With Interpretable Machine Learning

In areas of induced seismicity, earthquakes can be triggered by stress changes due to fluid injection and static deformation from fault slip. Here we present a method to distinguish between injection‐driven and earthquake‐driven triggering of induced seismicity by combining a calibrated, fully coupled, poroelastic stress model of wastewater injection with interpretation of a machine learning algorithm trained on both earthquake catalog and modeled stress features. We investigate seismicity from Paradox Valley, Colorado as an ideal test case: a single, high‐pressure injector that has induced thousands of earthquakes since 1991. Using feature importance analysis, we find that injection‐driven earthquakes are approximately 225% of the total catalog but act as background events that can trigger subsequent aftershocks. Injection‐driven events also have distinct spatiotemporal clustering properties with a larger b‐value, closer proximity to the well, and earlier occurrence in the injection history. Generalization of our technique can help characterize triggering processes in other regions where induced seismicity occurs.