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Abstract The lunar surface contains a wide variety of topographic shapes and features, each with different distributions and scales, and any analysis technique to objectively measure roughness must respect these qualities. Coarse‐graining is a naturally scale‐dependent filtering technique that preserves scale‐dependent symmetries and produces coarse elevation maps that gradually erase the smaller features from the original topography. In this study of the lunar surface, we present two surface variability metrics obtained from coarse‐graining lunar topography: fine elevation and coarse curvature. Both metrics are isotropic, deterministic, slope‐independent, and coordinate‐agnostic. Fine (detrended) elevation is acquired by subtracting the coarse elevation from the original topography and contains features that are smaller than the coarse‐graining length‐scale. Coarse curvature is the Laplacian of coarsened topography, and naturally quantifies the curvature at any scale and indicates whether a location is elevated or depressed relative to its neighborhood at that scale. We find that highlands and maria have distinct roughness characteristics at all length‐scales. Our topographic spectra reveal four scale‐breaks that mark characteristic shifts in surface roughness: 100, 300, 1,000, and 4,000 km. Comparing fine elevation distributions between maria and highlands, we show that maria fine elevation is biased toward smaller‐magnitude elevations and that the maria–highland discrepancies are more pronounced at larger length‐scales. We also provide local examples of selected regions to demonstrate that these metrics can successfully distinguish geological features of different length‐scales. 

Abstract We present the first in a series of dataset and model assessment products for investigating Africa’s lithosphere (ADAMA). This is a comprehensive catalog of short-period interstation surface-wave dispersion measurements and uncertainties. It is derived from processing continuous recordings of all publicly available three-component seismograms, spanning four decades, from ∼1372 stations, across 62 seismic networks deployed in and around the African continent. It includes Love- and Rayleigh-wave dispersion derived from frequency-domain ambient noise cross-correlation functions (NCFs). Phase and group dispersion, as well as their uncertainties, are then obtained with an iterative nonlinear waveform fitting of the NCFs, using a spectral element representation of a path-average a priori Earth model. Our catalog represents the following advances: (1) a large distribution of short period dispersion measurements: ∼114,000 interstation pairs at periods between 5 s and 40 s, (2) inclusion of uncertainties useful for regularization in continent-wide model building, (3) preliminary model assessments for different tectonic domains on the continent, and (4) an exemplary Love-wave phase velocity map obtained by Bayesian inversion revealing detailed features not previously detected. ADAMA will be used to prepare short-period, high-resolution dispersion maps, and for assessment and updates of widely used seismic velocity models of the crust across a diversity of terranes on the continent. 

Abstract 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 ofAfrica'sCrustEvaluated with ADAMA'sRayleighPhase 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) andmodified(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.