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The Earth's mantle transition zone (MTZ) plays a key role in the thermal and compositional interactions between the upper and lower mantle. Seismic anisotropy provides useful information about mantle deformation and dynamics across the MTZ. However, seismic anisotropy in the MTZ is difficult to constrain from surface wave or shear wave splitting measurements. Here, we investigate the sensitivity to anisotropy of a body wave method, SS precursors, through 3-D synthetic modelling and apply it to real data. Our study shows that the SS precursors can distinguish the anisotropy originating from three depths: shallow upper mantle (80–220 km), deep upper mantle above 410 km, and MTZ (410–660 km). Synthetic resolution tests indicate that SS precursors can resolve $\ge $3 per cent azimuthal anisotropy where data have an average signal-to-noise ratio (SNR = 7) and sufficient azimuthal coverage. To investigate regional sensitivity, we apply the stacking and inversion methods to two densely sampled areas: the Japan subduction zone and a central Pacific region around the Hawaiian hotspot. We find evidence for significant VS anisotropy (15.3 ± 9.2 per cent) with a trench-perpendicular fast direction (93° ± 5°) in the MTZ near the Japan subduction zone. We attribute the azimuthal anisotropy to the grain-scale shape-preferred orientation of basaltic materials induced by the shear deformation within the subducting slab beneath NE China. In the central Pacific study region, there is a non-detection of MTZ anisotropy, although modelling suggests the data coverage should allow us to resolve at least 3 per cent anisotropy. Therefore, the Hawaiian mantle plume has not produced detectable azimuthal anisotropy in the MTZ.

 

Typical seismic waveform data sets comprise hundreds of thousands to millions of records. Compilation is performed by time‐consuming handpicking of phase arrival times, or signal processing algorithms such as cross‐correlation. The latter generally underperform compared to handpicking. However, differences in picking methods creates variations in models and interpretation of Earth's structure. Here, we exploit the pattern recognition capabilities of Convolutional Neural Networks (CNN). Using a large handpicked data set, we train a CNN model to identify the seismic shear phase SS. This accelerates, automates, and makes consistent data compilation, a task usually completed by visual inspection and influenced by scientists' choices. The CNN model is employed to identify precursors to SS generated by mantle discontinuities. It identifies precursors in stacked and individual seismograms, producing new measurements of the mantle transition zone with quality comparable to handpicked data. This rapid acquisition of high‐quality observations has implications for automation of future seismic tomography studies.

 

The formation and preservation of compositional heterogeneities inside the Earth affect mantle convection patterns globally and control the long-term evolution of geochemical reservoirs. However, the distribution, nature, and size of reservoirs in the Earth’s mantle are poorly constrained. Here, we invert measurements of travel times and amplitudes of seismic waves interacting with mineralogical phase transitions at 400–700-km depth to obtain global probabilistic maps of temperature and bulk composition. We find large basalt-rich pools (up to 60% basalt fraction) surrounding the Pacific Ocean, which we relate to the segregation of oceanic crust from slabs that have been subducted since the Mesozoic. Segregation of oceanic crust from initially cold and stiff slabs may be facilitated by the presence of a weak hydrated layer in the slab or by weakening upon mineralogical transition due to grain-size reduction. 

Constraining the thermal and compositional state of the mantle is crucial for deciphering the formation and evolution of Mars. Mineral physics predicts that Mars’ deep mantle is demarcated by a seismic discontinuity arising from the pressure-induced phase transformation of the mineral olivine to its higher-pressure polymorphs, making the depth of this boundary sensitive to both mantle temperature and composition. Here, we report on the seismic detection of a midmantle discontinuity using the data collected by NASA’s InSight Mission to Mars that matches the expected depth and sharpness of the postolivine transition. In five teleseismic events, we observed triplicated P and S waves and constrained the depth of this discontinuity to be 1,006 ± 40 km by modeling the triplicated waveforms. From this depth range, we infer a mantle potential temperature of 1,605 ± 100 K, a result consistent with a crust that is 10 to 15 times more enriched in heat-producing elements than the underlying mantle. Our waveform fits to the data indicate a broad gradient across the boundary, implying that the Martian mantle is more enriched in iron compared to Earth. Through modeling of thermochemical evolution of Mars, we observe that only two out of the five proposed composition models are compatible with the observed boundary depth. Our geodynamic simulations suggest that the Martian mantle was relatively cold 4.5 Gyr ago (1,720 to 1,860 K) and are consistent with a present-day surface heat flow of 21 to 24 mW/m 2 .