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SUMMARY SS-precursor imaging is used to image sharp interfaces within Earth’s mantle. Current SS-precursor techniques require tightly bandpassed signals (e.g. 0.02–0.05 Hz), limiting both vertical and horizontal resolutions. Higher frequency content would allow for the detection of finer structure in and around the mantle transition zone (MTZ). Here, we present a new SS-precursor deconvolution technique based on multiple-taper correlation (MTC). We show that applying MTC to SS-precursor deconvolution can increase the frequency cut-off up to 0.5 Hz, which potentially sharpens vertical resolution to ∼10 km. Furthermore, the high-pass frequency can be lowered (≪ 0.01 Hz), allowing more long-period energy to be included in the calculation, to better constrain the signal and reduce side lobes. Our method is benchmarked on full-waveform synthetic seismograms computed via AxiSEM3D for the PREM 1-D Earth model. We apply our novel MTC-SS-precursor deconvolution to ∼7000 seismograms recorded at broad-band borehole sensors of the Global Seismographic Network with source–receiver bounce points in the North-Central Pacific Ocean. The MTZ in this region appears to be thin, which agrees with previous results. We do not observe the 520-km discontinuity in our SS-precursor estimates. Additionally, we detect a low-velocity zone above the MTZ to the north of the Hawaiian Islands that has previously been inferred from asymmetry in side lobe amplitudes. Our high-frequency analysis demonstrates this feature to be a sharp interface (≤ 10-km thickness), rather than a thick wave speed gradient. 

Abstract Surface-wave arrival angles are an important secondary set of observables to constrain Earth’s 3D structure. These data have also been used to refine information on the alignments of horizontal seismometer components with the geographic coordinate system. In the past, particle motion has been inspected and analyzed on single three-component seismograms, one at a time. But the advent of large, dense seismic networks has made this approach tedious and impractical. Automated toolboxes are now routinely used for datasets in which station operators cannot determine the orientation of a seismometer upon deployment, such as conventional free-fall ocean bottom seismometers. In a previous paper, we demonstrated that our automated Python-based toolbox Doran–Laske-Orientation-Python compares favorably with traditional approaches to determine instrument orientations. But an open question has been whether the technique also provides individual high-quality measurements for an internally consistent dataset to be used for structural imaging. For this feasibility study, we compared long-period Rayleigh-wave arrival angles at frequencies between 10 and 25 mHz for 10 earthquakes during the first half of 2009 that were recorded at the USArray Transportable Array—a component of the EarthScope program. After vigorous data vetting, we obtained a high-quality dataset that compares favorably with an arrival angle database compiled using our traditional interactive screen approach, particularly at frequencies 20 mHz and above. On the other hand, the presence of strong Love waves may hamper the automated measurement process as currently implemented.