Affiliation:
1. Department of Earth and Environmental Sciences University of Rochester Rochester NY USA
2. Department of Electrical and Computer Engineering University of Rochester Rochester NY USA
3. Georgen Institute of Data Sciences University of Rochester Rochester NY USA
4. Department of Geosciences Princeton University Princeton NJ USA
5. Seismological Laboratory California Institute of Technology Pasadena CA USA
Abstract
AbstractAfrica'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.
Funder
National Science Foundation
Publisher
American Geophysical Union (AGU)
Subject
Geochemistry and Petrology,Geophysics