Abstract
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 - e.g., 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
reflect back as S-waves). A higher resolution method using P-to-S
scattered waves is preferred but often obscured by multiply reflected
waves trapped in a shallow layer, limiting the visibility of deeper
boundaries. Here, we address the interference problem and re-evaluate
upper mantle stratification using filtered 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 of the
polarity-filtered Ps-RFs into distinct depth-based clusters, clearly
distinguishes three discontinuity types: (1) intra-lithosphere
discontinuity with no base, (2) intra-lithosphere discontinuity with a
top and bottom boundary (3) transitional and 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.