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Qualitative and quantitative analysis of seismic waveforms sensitive to the core–mantle boundary (CMB) region reveal the presence of ultralow-velocity zones (ULVZs) that have a strong decrease in compressional (P) and shear (S) wave velocity, and an increase in density within thin structures. However, understanding their physical origin and relation to the other large-scale structures in the lowermost mantle are limited due to an incomplete mapping of ULVZs at the CMB. The SKS and SPdKS seismic waveforms is routinely used to infer ULVZ presence, but has thus far only been used in a limited epicentral distance range. As the SKS/SPdKS wavefield interacts with a ULVZ it generates additional seismic arrivals, thus increasing the complexity of the recorded wavefield. Here, we explore utilization of the multi-scale sample entropy method to search for ULVZ structures. We investigate the feasibility of this approach through analysis of synthetic seismograms computed for PREM, 1-, 2.5-, and 3-D ULVZs as well as heterogeneous structures with a strong increase in velocity in the lowermost mantle in 1- and 2.5-D. We find that the sample entropy technique may be useful across a wide range of epicentral distances from 100° to 130°. Such an analysis, when applied to real waveforms,more »could provide coverage of roughly 85% by surface area of the CMB.« less

SUMMARY Horizontal slowness vector measurements using array techniques have been used to analyse many Earth phenomena from lower mantle heterogeneity to meteorological event location. While providing observations essential for studying much of the Earth, slowness vector analysis is limited by the necessary and subjective visual inspection of observations. Furthermore, it is challenging to determine the uncertainties caused by limitations of array processing such as array geometry, local structure, noise and their effect on slowness vector measurements. To address these issues, we present a method to automatically identify seismic arrivals and measure their slowness vector properties with uncertainty bounds. We do this by bootstrap sampling waveforms, therefore also creating random sub arrays, then use linear beamforming to measure the coherent power at a range of slowness vectors. For each bootstrap sample, we take the top N peaks from each power distribution as the slowness vectors of possible arrivals. The slowness vectors of all bootstrap samples are gathered and the clustering algorithm DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is used to identify arrivals as clusters of slowness vectors. The mean of slowness vectors in each cluster gives the slowness vector measurement for that arrival and the distribution of slowness vectorsmore »in each cluster gives the uncertainty estimate. We tuned the parameters of DBSCAN using a data set of 2489 SKS and SKKS observations at a range of frequency bands from 0.1 to 1 Hz. We then present examples at higher frequencies (0.5–2.0 Hz) than the tuning data set, identifying PKP precursors, and lower frequency by identifying multipathing in surface waves (0.04–0.06 Hz). While we use a linear beamforming process, this method can be implemented with any beamforming process such as cross correlation beamforming or phase weighted stacking. This method allows for much larger data sets to be analysed without visual inspection of data. Phenomena such as multipathing, reflections or scattering can be identified automatically in body or surface waves and their properties analysed with uncertainties.« less

Ultralow-velocity zones (ULVZs) at the core–mantle boundary (CMB) represent some of the most preternatural features in Earth’s mantle. These zones most likely contain partial melt, extremely high iron content ferropericlase, or combinations of both. We analyzed a new collection of 58,155 carefully processed and quality-controlled broadband recordings of the seismic phase SPdKS in the epicentral distance range from 106° to 115°. These data sample 56.9% of the CMB by surface area. From these recordings we searched for the most anomalous seismic waveforms that are indicative of ULVZ presence. We used a Bayesian approach to identify the regions of the CMB that have the highest probability of containing ULVZs, thereby identifying sixteen regions of interest. Of these regions, we corroborate well-known ULVZ existence beneath the South China Sea, southwest Pacific, the Samoa hotspot, the southwestern US/northern Mexico, and Iceland. We find good evidence for new ULVZs beneath North Africa, East Asia, and north of Papua New Guinea. We provide further evidence for ULVZs in regions where some evidence has been hinted at before beneath the Philippine Sea, the Pacific Northwest, and the Amazon Basin. Additional evidence is shown for potential ULVZs at the base of the Caroline, San Felix and Galapagosmore »hotspots.« less