www.sciencemag.org/344/6189/1265/suppl/DC1

Supplementary Materials for Dehydration melting at the top of the lower mantle Brandon Schmandt,* Steven D. Jacobsen,* Thorsten W. Becker, Zhenxian Liu, Kenneth G. Dueker *Corresponding author. E-mail: [email protected] (B.S.); [email protected] (S.D.J.) Published 13 June 2014, Science 344, 1265 (2014) DOI: 10.1126/science.1253358 This PDF file includes: Materials and Methods Figs. S1 to S4 References Captions for Additional Data tables S1 to S3 Other Supplementary Material for this manuscript includes the following: (available at www.sciencemag.org/344/6189/1265/suppl/DC1) Additional Data tables S1 to S3

Materials and Methods Common conversion point imaging We obtained three-component broadband records of earthquakes with mb >5.7 and distance 35-90° from the Incorporated Research Institutes for Seismology Data Management Center, and visually inspected them for coherent P arrivals on both the vertical and radial components. Selected waveforms were bandpass filtered between 50 and 0.5 s, and rotated into the P, SV, and SH coordinate system (31). Receiver functions are estimated with a multi-channel spectral deconvolution method (15,32) that assumes that intra-modal (P-to-P) scattering is both minimum phase and non-zero so that three-component (P, SV, SH) receiver functions are calculated. The method involves three main steps. First, the P-component spectra from all stations recording each earthquake source are stacked and fit with a smooth spline to form an initial estimate of the source spectra. Second, the smooth estimates of the source spectra are used as constraint equations in a log-spectral least-squares inversion to separate the amplitude spectra of the source and receiver functions (15,32). To exploit redundant sampling of similar source-receiver geometries, the sources recorded by each station are binned by ray parameter (0.0035 s/km) and back-azimuth (10°) so that a single estimate of the three-component receiver function is obtained for each bin. Because most stations operated for only 1-2 years the mean number of events per bin is 1.8, but bins that sample high seismicity regions often include >5 events. In the third step, the P-to-P scattering response (P-component receiver function) is assumed to be minimum phase and Kolmogorov spectral factorization is applied to construct a set of sequential all-pass filters that recover the phase spectra of the two transverse component (SV, SH) receiver functions. Here, we use only the SV component receiver functions, which are bandpass filtered between 50 s and 4.5 s. In total we use 111,820 receiver functions from 2,244 stations. For common conversion point (CCP) imaging the mantle is discretized into boxes that are 55 km wide and are 2 km thick. Receiver functions are spatially positioned in this image volume using P and S tomography models (16). To prevent artifacts in the lower mantle, the CCP image uses only the segment of the receiver function prior to the PP arrival. The image value in each box is a weighted average of all receiver functions with PDs ray paths that lie within RF1 of the box center point, where RF1 is the radius of the incident P wave first Fresnel zone. Receiver function weight is equal to unity within ½ RF1 and linearly decreases to zero at RF1. Typical RF1 dimensions at 500 – 800 km depth are 100 – 160 km, using a dominant period of 5 s. Negative amplitude Ps conversions from beneath the 660 are identified by locating points in the image volume between 670 – 820 km depth with magnitude >1.25% of the direct P-wave. Figure S1 shows comparisons of the locations sub-660 negative Ps conversions with both vertical mantle flow velocities and mantle shear velocities at 660 km depth. To estimate uncertainty in the CCP image in the top of the lower mantle we performed bootstrap re-sampling of the receiver functions contributing to each image point between 670-800 km (33). The bootstrap analysis used 200 realizations, each randomly re-sampling 100% of the receiver functions contributing to each image point. On average each image point has 530 receiver functions. In the 670-800 km depth interval of the CCP image the mean 95% confidence level is 1.14%. Strong suppression 2

of noise in the receiver function stacks results from the fact that each image point near the top of the lower mantle is sampled by >10 stations spanning an area >300 km wide at the surface. Consequently, noise (any energy that does not follow Ps conversion travel-time curves) is unlikely to stack coherently in the CCP image. Synthetic receiver functions Stacks of synthetic receivers functions created with processing identical to that used for observational data demonstrate that the negative Ps conversions imaged beneath the 660 are not artifacts of our seismic analysis (e.g. side-lobes). Additionally, synthetic receiver functions are used to estimate the magnitude and sharpness of the velocity decrease required to produce a -2% Ps conversion in the CCP image. Generation of synthetic receiver function stacks follows the methods of (34), except that here we show examples with and without noise added to the synthetic seismograms. Seismograms were calculated using the reflectivity method (35) for incident P-waves with a range of ray parameters corresponding to event distances of 35 – 90º. Identical deconvolution and filtering methods are used for processing the synthetic and observational seismograms into Ps receiver functions. The observed distribution of ray parameters is used to create a weighted stack of receiver functions that matches the ray parameter distribution of the observational data in order to facilitate comparison between synthetic stack amplitudes and the observational CCP image. After mapping the synthetic receiver functions to the depth domain, they are convolved with a 3 km half-width Gaussian to account for stack incoherence associated with inaccuracies of time-to-depth conversion in the observational CCP image (34). Figure S2 shows a comparison of synthetic receiver function stacks for mantle velocity models with and without a negative velocity gradient near the top of the upper mantle. A 2% amplitude negative Ps conversion is created by the model with a 2.6% shear velocity decrement linearly distributed over a depth interval of 14 km at the top of the low-velocity layer. Lack of an adjacent and deeper positive Ps conversion requires that the transition back to the reference velocity profile occur more gradually, over a depth interval of ≥ 35 km. A synthetic receiver function stack using seismograms with noise added shows that the negative arrival beneath the 660 is a resolvable feature with realistic noise levels (Figure S2C). We used white Gaussian noise with a standard deviation of 10% of the direct P amplitude. This noise level was chosen because stacks of the resulting synthetic receiver functions have a similar magnitude of uncertainty as in the observational CCP image at 670-800 km depth. The mean bootstrap-derived 95% confidence level in the synthetic stacks with added noise is 1.21% between 670-800 km depth, compared to 1.14% in the observational CCP image. Synthetic receiver function stacks used 530 traces, which is the mean number of receiver functions sampling CCP image points at 670-800 km depth. Mantle circulation computations Mantle circulation is inferred using a global, spherical, 3-D computation (36). Flow is assumed to follow a Newtonian fluid rheology and viscosity here varies only with depth. We then compute the contributions of large-scale currents induced by global plate motions and regional density (ρ) anomalies. The latter are inferred by scaling 3

tomographically imaged shear velocity (VS) anomalies to density with a constant scaling factor of d ln ρ/d ln VS = 0.2, with all details as in (19). This approach assumes that all tomographically mapped velocity anomalies are induced by temperature variations at homogenous composition. Compositional anomalies and depth-dependent mineral physics parameters will affect the inferred flow rates of such models (e.g. 22), as will radial and lateral viscosity variations (e.g. 19). However, we have experimented with a range of viscosity models (cf. 37), and we expect that the main effect on the geographic patterns of vertical transport arise from the different seismic tomography models, as shown in Figure 3. Large-scale patterns of flow appear robust among models and are broadly consistent with earlier findings (e.g. 21,22). Experimental methods The hydrous ringwoodite crystal used in this experiment was synthesized by (38) in the 5000 ton press at Bayerisches Geoinstitut, University of Bayreuth, Germany (run SZ0104). Starting materials consisted of natural Fo90 olivine, En90 orthopyroxene, hematite, silica, and brucite to give a bulk H2O content of 3 wt% H2O. Synthesis was carried out at 18 GPa and 1400 °C with a heating duration of approximately five hours. Electron microprobe analysis on the ringwoodite gives an Mg# [Mg/(Fe+Mg)] = 0.8975 where about 10% of the iron is ferric (38). FTIR spectroscopy gives the H2O content of 1.07 wt% H2O (38). New measurements of the H2O content were made by secondary ion mass spectrometry (SIMS) on a ringwoodite crystal from the same run (SZ0104) using the Cameca nanoSIMS 50L at the Carnegie Institution of Washington, Department of Terrestrial Magnetism. The nanoSIMS results show an average H2O content of 1.11(±0.06) wt% H2O from the average of nine measurements from rim-to-rim across a single crystal measuring 100 µm across. The SIMS calibration and sample measurement data are provided in Table S1. The FTIR and nanoSIMS measurements of H2O content of the ringwoodite used in this study are in agreement within 4%, and the value of 1.1(±0.1) wt% H2O is therefore reported as the sample water content. A single crystal of the hydrous Fo90 ringwoodite measuring ~100 µm across was polished using 3 and 0.5 µm diamond film into a parallel plate measuring ~20 µm thick. The sample was loaded into a tall symmetric diamond-anvil cell (DAC) fitted with 300 µm (culet size) type-IA diamond anvils. A rhenium gasket was pre-indented to ~35 µm thickness and used to load the sample between layers of KCl to act as a pressure transmitting medium as well as to act as a thermal insulator between the sample and diamond anvils during laser heating. The sample was compressed to ~35 GPa at room temperature before laser heating was conducted at Sector 13 (GSECARS) of the Advanced Photon Source, Argonne National Laboratory, beamline 13-ID-D. The sample was heated using a YAG laser in two different spots at 1600(±100) °C for approximately 5 minutes each (see spectra 2 and 3 in Figure 1). In-situ X-ray diffraction was carried out during the experiment to monitor the transition from single-crystal ringwoodite to polycrystalline perovskite plus (Mg,Fe)O. Following laser heating, the pressure was remeasured and found to be ~30 GPa. X-ray diffraction patterns from spot number 2 of Figure 1 are shown in Figure S3. X-ray diffraction data shown in Figure S3 are provided in Table S2. MAR345 images of the diffraction data are available upon request. Synchrotron-FTIR spectroscopy was carried out at the U2A beamline of the National Synchrotron Light Source, Brookhaven National Laboratory. Infrared spectra 4

were collected using a Bruker IFS 66v FTIR spectrometer and an IR-scopeII microscope attached with liquid nitrogen cooled MCT detector. The spatial resolution (10µm × 10µm) was adjusted using a knife-edge aperture. The IR synchrotron source was used to achieve not only the diffraction-limit performance but also the best signal-to-noise ratio possible under high spatial resolution. A KBr beamsplitter was used to cover the mid-IR region and a spectral resolution of 4 cm-1 applied to all spectra. FTIR spectra of the sample in all three spots shown in Figure 1 are provided in Table S3. Following FTIR measurements, the sample was ion-beam thinned for transmission electron microscopy measurements, conducted at Bayerisches Geoinstitut. To avoid damaging the silicate perovskite run product, we ion milled a portion of the heated spot numbered 2 in Figure 1A under liquid nitrogen using a shallow incident angle of ~12°, reduced the ion current to 0.5 mA and used a low acceleration voltage of 2.5 kV. The electron diffraction image of a single-crystal of silicate perovskite shown inset to Figure 1C demonstrates that the amorphous region did not form from breakdown of silicate perovskite. Bright field TEM and electron diffraction patterns taken from within the laser-heated spot number 2 of Figure 1 showed a mixture of (Mg,Fe)O and silicate perovskite, with amorphous (melt quench) between grains. The bright field TEM image from Figure 1C is reproduced in Fig S4, along with an additional image from a nearby location within spot 2 of Figure 1, also showing melt between grains of silicate perovskite and (Mg,Fe)O.

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Fig. S1. Comparison of variations in vertical flow velocity and mantle shear velocity near 660 km depth. A) The background color shows the vertical flow velocity at the boundary between the transition zone and lower mantle predicted by a numerical convection model using density structure inferred from the SH11-TX tomography model. White squares denote locations where the seismic CCP image detects velocity decreases with depth in the depth range of 670 – 800 km. B) The background color shows the mantle shear velocity variations near 660 km depth from the SH11-TX tomography model. C) The background color shows the vertical flow velocity predicted using the S40-RTS tomography model. D) The background color shows the mantle shear velocity variations near 660 km depth from the S40-RTS tomography model.

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Fig. S2 Synthetic receiver function example. A) Synthetic receiver functions were computed for a range of ray parameters, mapped to the depth domain, and stacked according to the distribution of ray parameters in the observationally based CCP image. A synthetic stack for a model without a sub-660 negative velocity gradient is shown in solid black. A synthetic stack for a velocity model with a 2.6% Vs decrease between 736-750 km is shown as the red dashed trace. B) The Vs model used to create the synthetic receiver function stack without a sub-660 negative velocity gradient is shown in black and the Vs model with a velocity decrease below 660 is shown in red dashed. C) Synthetic stacks of receiver functions with noise added and using the same velocity models as for the synthetic stacks shown in (A). 7

Fig. S3 X-ray diffraction patterns taken before and after laser heating hydrous ringwoodite to form perovskite and (Mg,Fe)O. The diffraction data were collected at the same position where FTIR was conducted (see spectrum 2 of Figure 1). Pressures were determined from the lattice parameters of the KCl pressure medium. Peaks are labeled as potassium chloride (KCl), ringwoodite (Rw), magnesiowuestite (Mw), perovskite (Pv), and brucite (Br). Only two peaks could not be indexed, at 2.23 and 2.60 Å, labeled with question marks.

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Fig. S4. Bright-field TEM images taken within the laser-heated spot number 2 shown in Figure 1A, where interpenetrating melt is observed between grains of silicate perovskite (pv) and (Mg,Fe)O (mw).

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Additional Data table S1 (separate file) NanoSIMS calibration and data file for hydrous ringwoodite SZ0104 Additional Data table S2 (separate file) X-ray diffraction data used in Figure S3. Additional Data table S3 (separate file) FTIR spectra of the sample used in Figure 1.

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