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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]

Africa's continental crust hosts a variety of geologic terrains and is crucial for understanding the evolution of its longest‐lived cratons. However, few of its seismological models are yet to incorporate the largest continent‐wide noise dispersion data sets. Here, we report on new insights into Africa's crustal architecture obtained using a new data set and model assessment product, ADAMA, which comprises a large ensemble of short‐period surface wave dispersion measurements: 5–40 s. We construct a continent‐wide model of Africa's Crust Evaluated with ADAMA's Rayleigh Phase maps (ACE‐ADAMA‐RP). Dispersion maps, and uncertainties, are obtained with a probabilistic approach. This model update, and a crustal taxonomy derived from unsupervised machine learning, reveals that the architecture of Africa's crust can be classified into two main types: primitive (C1: faster velocities with little gradients) and modified (C2–C4: slower velocities in the shallow crust with more pronounced gradients). The Archean shields are "primitive," showing little variation or secular evolution. The basins, orogens, and continental margins are "modified" and retain imprints of surface deformation. The crustal taxonomy is obtained without a‐priori geological information and differs from previous classification schemes. While most of our reported features are robust, probabilistic modeling suggests caution in the quantitative interpretations where illumination is compromised by low‐quality measurements, sparse coverage or both. Future extension of our approach to other complementary seismological and geophysical data sets—for example, multimode earthquake dispersion, receiver functions, gravity, and mineral physics, will enable continent‐wide lithospheric modeling that extends resolution to the upper mantle. Plain Language Summary: The rocks that constitute Africa's crust record the history of different geological periods. We produce a map, for the entire continent, of how fast shear waves travel within these rocks. We obtain this map from ambient noise surface wave vibrations. The ambient noise surface waves are generated from ocean and atmospheric waves that couple with the solid Earth. There are two types: Rayleigh and Love waves and they travel at different speeds for different wavelengths. This property is called dispersion and it is used to tell how fast the shear wave speeds travel within the subsurface rocks. Constructing the final map from ambient noise surface waves requires the solution of a computational imaging problem. We solve the most challenging computational task with a probabilistic approach—using random sampling—and this enables us to also construct associated error maps. The new maps of Africa's crust show new features that have important implications for subsurface geology of the continent. Key Points: A continent‐wide s‐velocity model of Africa's crust is constructed using probabilistic modeling of the largest catalog of dispersion measurementsA crustal taxonomy, derived with unsupervised machine learning, reveals that Africa's crust is one‐third primitive and two‐thirds modifiedArchean shields are primitive and show no secular evolution; basins, orogens, and margins are modified, retaining imprints of deformation [ABSTRACT FROM AUTHOR]

The stable continents have a puzzling structure. Recent seismological studies have revealed a marked drop in seismic velocity at middle-lithosphere depths, but a generally small velocity drop at the lithosphere-asthenosphere boundary. The mid-lithosphere discontinuity has previously been attributed to changes in composition and/or crystal alignment (anisotropy) caused by metasomatic alteration, as well as to partial melting and/or accretion of intruded materials that occurred after the formation of the continents. We show that these models cannot easily explain the global presence of a large seismic velocity drop in the middle lithosphere and a small velocity change at the lithosphere-asthenosphere boundary. These models are also difficult to reconcile with long-term continental stability and, in particular, observations of nearly depth-invariant ages of rocks in the continental lithosphere that do not support the notion of late alteration events. Instead, we propose an elastically accommodated grain-boundary sliding model that predicts a substantial velocity drop at the mid-lithosphere discontinuity and a weak seismic signal at the lithosphere-asthenosphere boundary, as observed, without invoking late-stage modifications to the lithosphere. In this model, the mid-lithosphere discontinuity is a general feature of the stable continents, the precise depth of which depends primarily on temperature and water content. Consequently, the depth of the mid-lithosphere discontinuity may provide clues to the evolution of continents. [ABSTRACT FROM AUTHOR]