Linking subduction parameters to the occurrence of slow slip events using machine learning on a global scale

Slow-slip events (SSEs) are episodic fault slip phenomena that involve the gradual and aseismic release of tectonic stress, bridging the gap between the rapid rupture of regular earthquakes and the steady sliding along fault interfaces. SSEs are common in megathrusts, having been observed in most of the well geodetically-instrumented subduction margins worldwide, both on the shallow plate interface (less than 10 km depth) and on the deeper plate interface (25–60 km). We explore the relations between the occurrence of SSEs and various subduction parameters along megathrusts at a global scale. Using a parametric approach, we applied three Machine Learning (ML) algorithms to predict the presence (or absence) of shallow and deep SSEs, modeling it as a nonlinear function of subduction variables. The subduction parameters considered include subducting plate age and roughness, sediment thickness, slab dip, convergence rate and azimuth, distance to the nearest ridge or plate boundary, maximum observed magnitude, b-value and earthquake rates, among others. We then employed Shapley Additive exPlanations (SHAP) on the ML outcomes, to identify the most influential factors associated with SSE occurrence. Preliminary analysis and previous studies suggest that plate age, slab dip, and b-value are among the most critical variables. These observations point to the possibility that the frictional properties of the subducting plate, which influence plate coupling and stress levels, may play a key role in controlling the occurrence of shallow, deep, or both types of SSEs. Our study provides valuable insights into the complex, nonlinear processes governing SSEs on a global scale and highlights regions where previously undetected SSEs may be occurring.