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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. 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. 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 Upper mantle stratification is constrained using Clean Receiver‐function Imaging using Sparse Radon Filters and machine learning Stratification is classified into intra‐lithospheric, transitional and sub‐lithospheric High‐resolution constraints allow the evaluation of different causal models

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. The Earth, in large portions, is covered in oceans, sediments, and glaciers. Because of the large differences in rock properties between these layers and the solid Earth underneath, waves get trapped in them which hampers the successful investigation of deeper layers. This is often referred to as “reverberation,” waves that “echo” or “sing” again and again. In this study, we propose a systematic approach to detect and eliminate this “singing” effect using only observed data, without any knowledge of the local geological structure. Our approach extends the autocorrelation analysis, a widely used method to detect repeating patterns in time series, and also takes advantage of the homomorphic analysis, a popular technique in speech and audio processing. We demonstrate the effectiveness of our approach using both human‐generated and real seismic data, collected from complex geosettings around the world, including in the oceans to the south of the Alaska Peninsula, sedimentary basin in northeast China, and Antarctica. On glaciers where the reverberation comes from the complicated coupled effect of ice and sediment, homomorphic analysis is preferable in detecting the signature of reverberation. We expect that our technique will greatly improve the imaging of deep Earth structure across a wide variety of conditions. A data‐driven approach using autocorrelation and homomorphic analysis is proposed to detect the signature of reverberations in seismic dataThe proposed approach can be applied to various geological settings where reverberations are present; that is, sediments, oceans, and glaciersSignal enhancement using either approach is best in single‐layer systems, while homomorphic analysis is preferred for ice‐sediment systems A data‐driven approach using autocorrelation and homomorphic analysis is proposed to detect the signature of reverberations in seismic data The proposed approach can be applied to various geological settings where reverberations are present; that is, sediments, oceans, and glaciers Signal enhancement using either approach is best in single‐layer systems, while homomorphic analysis is preferred for ice‐sediment systems

While the receiver function technique has been successfully applied to high‐resolution imaging of sharp discontinuities within and across the lithosphere, it suffers from severe limitations when applied to seafloor seismic recordings. This is because the water and sediment layer could strongly influence the receiver function traces, making detection and interpretation of crust and mantle layering difficult. This effect is often referred to as the singing phenomena in marine environments. We demonstrate, using analytical and synthetic modeling, that this singing effect can be reversed using a selective dereverberation filter tuned to match the elastic property of each layer. We apply the dereverberation filter to high‐quality earthquake records collected from the NoMelt seismic array deployed on normal, mature Pacific seafloor. An appropriate filter designed using the elastic properties of the underlying sediments, obtained from prior studies, greatly improves the detection of Ps conversions from the Moho (∼8.6 km) and from a sharp discontinuity (<∼5 km) across the lithosphere asthenosphere transition (∼72 km). Sensitivity tests show that the dereverberation filter is mostly sensitive to the two‐way travel time of the shear wave in sediment and is robust to seismic noise and small errors in the sediment properties. Our analysis suggests that selectively filtering out the sediment reverberations from ocean seismic data could make inferences on subsurface structure more robust. We expect that this study will enable high‐resolution receiver function imaging of the oceanic plate across the growing ocean bottom seismic arrays being deployed in the global oceans. A dereverberation filter is proposed to eliminate the sediment reverberation effects on receiver functions of ocean bottom seismological dataThe proposed filter is proven effective and robust to small errors in sediment properties using synthetic modelingA Moho of 8.6 km depth and a sharp discontinuity of the lithosphere asthenosphere transition of 72 km depth is observed A dereverberation filter is proposed to eliminate the sediment reverberation effects on receiver functions of ocean bottom seismological data The proposed filter is proven effective and robust to small errors in sediment properties using synthetic modeling A Moho of 8.6 km depth and a sharp discontinuity of the lithosphere asthenosphere transition of 72 km depth is observed