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SUMMARY Long-period underside SS wave reflections have been widely used to furnish global constraints on the presence and depth of mantle discontinuities and to document evidence for their origins, for example, mineral phase-transformations in the transition zone, compositional changes in the mid-mantle and dehydration-induced melting above and below the transition zone. For higher-resolution imaging, it is necessary to separate the signature of the source wavelet (SS arrival) from that of the distortion caused by the mantle reflectivity (SS precursors). Classical solutions to the general deconvolution problem include frequency-domain or time-domain deconvolution. However, these algorithms do not easily generalize when (1) the reflectivity series is of a much shorter period compared to the source wavelet, (2) the bounce point sampling is sparse or (3) the source wavelet is noisy or hard to estimate. To address these problems, we propose a new technique called SHARP-SS: Sparse High-Resolution Algorithm for Reflection Profiling with SS waves. SHARP-SS is a Bayesian deconvolution algorithm that makes minimal a-priori assumptions on the noise model, source signature and reflectivity structure. We test SHARP-SS using real data examples beneath the NoMelt Pacific Ocean region. We recover a low-velocity discontinuity at a depth of $$\sim 69 \pm 4$$ km which marks the base of the oceanic lithosphere, consistent with previous work derived from surface waves, body wave conversions, and ScS reverberations. We anticipate high-resolution fine mantle stratification imaging using SHARP-SS at locations where seismic stations are sparsely distributed. 

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—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. 

ABSTRACT The receiver function (RF) is a widely used crustal imaging technique. In principle, it assumes relatively noise-free traces that can be used to target receiver-side structures following source deconvolution. In practice, however, mode conversions and reflections may be severely degraded by noisy conditions, hampering robust estimation of crustal parameters. In this study, we use a sparsity-promoting Radon transform to decompose the observed RF traces into their wavefield contributions, that is, direct conversions, multiples, and incoherent noise. By applying a crustal mask on the Radon-transformed RF, we obtain noise-free RF traces with only Moho conversions and reflections. We demonstrate, using a synthetic experiment and a real-data example from the Sierra Nevada, that our approach can effectively denoise the RFs and extract the underlying Moho signals. This greatly improves the robustness of crustal structure recovery as exemplified by subsequent H−κ stacking. We further demonstrate, using a station sitting on loose sediments in the Upper Mississippi embayment, that a combination of our approach and frequency-domain filtering can significantly improve crustal imaging in reverberant settings. In the presence of complex crustal structures, for example, dipping Moho, intracrustal layers, and crustal anisotropy, we recommend caution when applying our proposed approach due to the difficulty of interpreting a possibly more complicated Radon image. We expect that our technique will enable high-resolution crustal imaging and inspire more applications of Radon transforms in seismic signal processing. 

SUMMARY Seismic interrogation of the upper mantle from the base of the crust to the top of the mantle transition zone has revealed discontinuities that are variable in space, depth, lateral extent, amplitude and lack a unified explanation for their origin. Improved constraints on the detectability and properties of mantle discontinuities can be obtained with P-to-S receiver function (Ps-RF) where energy scatters from P to S as seismic waves propagate across discontinuities of interest. However, due to the interference of crustal multiples, uppermost mantle discontinuities are more commonly imaged with lower resolution S-to-P receiver function (Sp-RF). In this study, a new method called CRISP-RF (Clean Receiver-function Imaging using SParse Radon Filters) is proposed, which incorporates ideas from compressive sensing and model-based image reconstruction. The central idea involves applying a sparse Radon transform to effectively decompose the Ps-RF into its underlying wavefield contributions, that is direct conversions, multiples, and noise, based on the phase moveout and coherence. A masking filter is then designed and applied to create a multiple-free and denoised Ps-RF. We demonstrate, using synthetic experiment, that our implementation of the Radon transform using a sparsity-promoting regularization outperforms the conventional least-squares methods and can effectively isolate direct Ps conversions. We further apply the CRISP-RF workflow on real data, including single station data on cratons, common-conversion-point stack at continental margins and seismic data from ocean islands. The application of CRISP-RF to global data sets will advance our understanding of the enigmatic origins of the upper mantle discontinuities like the ubiquitous mid-lithospheric discontinuity and the elusive X-discontinuity. 

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 The Earth, in large portions, is covered in oceans, sediments, and glaciers. High‐resolution body wave imaging in such environments often suffers from severe reverberations, that is, repeating echoes of the incoming scattered wavefield trapped in the reverberant layer, making interpretation of lithospheric layering difficult. In this study, we propose a systematic data‐driven approach, using autocorrelation and homomorphic analysis, to solve the twin problem of detection and elimination of reverberations without a priori knowledge of the elastic structure of the reverberant layers. We demonstrate, using synthetic experiments and data examples, that our approach can effectively identify the signature of reverberations even in cases where the recording seismic array is deployed in complex settings, for example, using data from (a) a land station sitting on Songliao basin, (b) an ocean bottom station in the fore‐arc setting of the Alaska amphibious community seismic experiment, and (c) a station deployed on ice‐sediment strata in the glaciers of Antarctica. The elimination of the reverberation is implemented by a frequency domain filter whose parameters are automatically tuned using seismic data alone. On glaciers where the reverberating sediment layer is sandwiched between the lithosphere and an overlying ice layer, homomorphic analysis is preferable in detecting the signature of reverberation. We expect that our technique will see wide application for high‐resolution body wave imaging across a wide variety of conditions. 

Abstract West Africa continues to host a growing number of low and intermediate‐magnitude earthquakes (M2‐5) along its passive margins, and its continental interior. Earthquake activity in these regions raises the need to comprehend the causes and the tectonic controls of the seismicity. Unfortunately, such studies are rare. Here, we apply single‐station inversion techniques to constrain fourteen focal mechanisms, computed after compiling a set of high‐quality waveforms. We describe the connection between seismicity, the contemporary stress field, anthropogenic activity and Holocene fault scarps in the region. Our results indicate transpressive stresses acting on the inherited brittle structures in the passive margins. We also observe a compressive regime in the intracontinental failed rifts. We attribute the seismicity to the reactivation of “weak” faults in the Neoproterozoic and Mesozoic failed rifts, the passive transform structures, and the intracratonic Precambrian brittle shear zones. 

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.