Multi-parameter Receiver Function Modeling: Application to the Subduction Zones of Cascadia and the Central Andes

Receiver functions are a powerful tool to image lithospheric stratigraphy. For flat lying structures, receiver functions can be stacked azimuthally to achieve high signal-to-noise ratios and h-κ-stacks allow to estimate the depth of interfaces (h) and P-to-S wave velocity ratio of the hanging layers (κ). For dipping layers, characteristic for the slab structure in a subduction zone forearc, these methods fail, because the moveout of phases arriving from different azimuths violates the basic assumptions of these methods.

We here present a simple routine to simultaneously search for the depth of the top of slab and of the oceanic Moho, for strike and dip of the downgoing slab, as well as for the S-wave velocities and the P‑to-S wave velocity ratios of multiple layers of the overriding and downgoing plates in subduction zone forearcs. Our approach is based on the recent Python port PyRaysum of Frederiksen and Bostock's classic (2000) code for modeling ray-theoretical plane body-wave propagation in dipping anisotropic media, and on SciPy's simulated annealing global parameter search.

We applied the routine to hundreds of azimuthally-dependent receiver function sections from the subduction zones of Cascadia (North America) and the central Andes (South America) and retrieved laterally coherent station measurements of the depth and orientation of the top of the subducting slab and the subducting Moho, with only weakly constrained seismic velocities. In Cascadia, we interpolated a regional slab model through fitting of regularized spline surfaces. Small scale structures that are not present in previous slab models can be resolved, e.g. under Olympic Peninsula (Cascadia) and Mejillones Peninsula (northern Chile). Where the receiver functions are more complex than can be accounted for by our model, the labeling of the modeled receiver function phases and comparison to the observed receiver functions allows us to confidently interpret the additional subsurface complexities and reconcile them with our interpretations.