Search

The investigation of upper mantle structure beneath the US has revealed a growing diversity of discontinuities within, across, and underneath the sub‐continental lithosphere. As the complexity and variability of these detected discontinuities increase—for example, velocity increase/decrease, number of layers and depth—it is hard to judge which constraints are robust and which explanatory models generalize to the largest set of constraints. Much work has been done to image discontinuities of interest using S‐waves that convert to P‐waves (or top‐side reflected SS waves). A higher resolution method using P‐to‐S scattered waves is preferred but often obscured by multiply reflected waves trapped in a shallower layer, limiting the visibility of deeper boundaries. Here, we address the interference problem and re‐evaluate upper mantle stratification using filtered P‐to‐S receiver functions (Ps‐RFs) interpreted using unsupervised machine‐learning. Robust insight into upper mantle layering is facilitated with CRISP‐RF: Clean Receiver‐Function Imaging using Sparse Radon Filters. Subsequent sequencing and clustering organizes the polarity‐filtered Ps‐RFs into distinct depth‐based clusters. We find three types of upper mantle stratification beneath the old and stable continental US: (a) intra‐lithosphere discontinuities (paired or single boundary), (b) transitional discontinuities (single boundary or with a top layer), and (c) sub‐lithosphere discontinuities. Our findings contribute a more nuanced understanding of mantle discontinuities, offering new perspectives on the nature of upper mantle layering beneath continents. Plain Language Summary: Early investigations of the mantle rocks in the US indicate intricate layering. However, uncertainties remain regarding the origins of these structures. Here, we re‐examine mantle rock stratification using a fine‐resolution approach. We use short waves that improve our ability to identify the depth of thin layers and sharp transitions in rock properties. Until now, these methods haven't been used due to interference with waves trapped in the near‐surface layers. We address this problem with machine learning and the CRISP‐RF (Clean Receiver Function Images Using Sparse Radon‐Filters) method. CRISP‐RF filters out the waves trapped in the crust and machine learning reveals spatially coherent patterns. Underneath the stable continents, we find evidence for different types of rock layering: (a) reflectors within cold stiff rocks (b) reflectors at depth ranges where the rocks become warmer and flow more readily, and (c) reflectors at depths farther down in the upper mantle. Our approach enables the test of hypotheses about the origins of upper mantle layering beneath continents. Key Points: Upper mantle stratification is constrained using Clean Receiver‐function Imaging using Sparse Radon Filters and machine learningStratification is classified into intra‐lithospheric, transitional and sub‐lithosphericHigh‐resolution constraints allow the evaluation of different causal models [ABSTRACT FROM AUTHOR]