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Slab tearing along a subducted oceanic plate joint beneath the Alaska Peninsula

Nature Geoscience volume 18, pages 801–807 (2025) Cite this article

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Abstract

Along-trench variations in various properties of subducted slabs have been inferred in subduction zones globally, yet the causes of these variations remain poorly understood. Along the Alaska Peninsula, the variation of slab (de)hydration and fluids shallower than 50-km depth cannot explain a seismicity gap below 150 km nor the abrupt changes in volcano density and arc orientation around Aniakchak volcano. Here we investigate the cause of these observations using seismic full-wave ambient noise tomography. Our 3D shear-wave velocity model reveals multiple high-velocity slab segments below about 50 km, with a much-lower-velocity segment broadly below Aniakchak. This low-velocity segment coincides with the inferred location of a subducted oceanic plate joint, where two sets of plate fabrics intersect, and with slab-normal fast directions in the asthenosphere indicated by seismic anisotropy. We infer that the subducted oceanic joint facilitated slab weakening and tearing, which has developed into a slab window below about 150 km, explaining the seismicity gap and slab-orthogonal mantle flow. Hence we suggest that oceanic plate joints may have an important role as potential weak zones in slabs and thus potentially influence subduction dynamics and seismicity.

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Fig. 1: Tectonic setting of the study area.
Fig. 2: Shear-wave velocity model.
Fig. 3: Oceanic plate joints along the Alaska Peninsula.
Fig. 4: Schematic model illustrating the interpreted slab tearing along a subducted oceanic plate joint.

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Data availability

The continuous seismic waveforms are available through the NSF SAGE Data Management Center (https://ds.iris.edu/ds/nodes/dmc/), operated by the EarthScope Consortium that is funded through the Seismological Facility for the Advancement of Geoscience (SAGE) Award of the National Science Foundation under Cooperative Agreement EAR-1724509. The OBS data used in this research were provided by instruments from the Ocean Bottom Seismic Instrument Center (https://obsic.whoi.edu), which is funded by the National Science Foundation. All seismic networks used are referenced in Supplementary Table 1. The velocity model is available via Zenodo at https://doi.org/10.5281/zenodo.13961013 (ref. 72) and through EarthScope Earth Model Collaboration at https://ds.iris.edu/ds/products/emc-aacse-fwant-vs2025/.

Code availability

The open-source Python toolbox SeisGo for extracting empirical Green’s functions from ambient noise is available via Zenodo at https://doi.org/10.5281/zenodo.5873724 (ref. 73). The Python scripts for waveform downloading and ambient noise cross correlations used in this study are archived via Zenodo at https://doi.org/10.5281/zenodo.13961013 (ref. 72). The full-wave ambient noise tomography codes are available via GitHub at https://github.com/xtyangpsp/FWANT and from the authors upon request.

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Acknowledgements

This work was supported by Purdue University start-up funding to X.Y. The funder had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We are grateful to the principal investigators and field teams for the EarthScope Transportable Array, the AACSE, and other flexible arrays within the study area that contributed data to this study. The computational resources used in this study were provided through the Purdue University Rosen Center for Advanced Computing. We thank N. Ruppert (Geophysical Institute, University of Alaska Fairbanks) for providing the full AACSE earthquake catalogue, J. Freymueller (Michigan State University) for providing a copy of ref. 13 and J. Delph (Purdue University) for constructive discussions.

Author information

Authors and Affiliations

  1. Department of Earth, Atmospheric and Planetary Sciences, Purdue University, West Lafayette, IN, USA

    Vincent Sassard & Xiaotao Yang

  2. State Key Laboratory of Lithospheric and Environmental Coevolution, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China

    Lijun Liu

  3. Department of Earth and Environmental Sciences, Michigan State University, East Lansing, MI, USA

    Julie Elliott

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  1. Vincent Sassard
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  2. Xiaotao Yang
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Contributions

V.S. contributed to the conceptualization, seismic imaging, formal analysis, interpretation and writing of the original manuscript. X.Y. contributed to the conceptualization, supervision, funding acquisition, methodology, interpretation and writing of the original manuscript. L.L. and J.E. contributed to the interpretation of seismic results and the reviewing and editing of the manuscript.

Corresponding authors

Correspondence to Vincent Sassard or Xiaotao Yang.

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Nature Geoscience thanks Melissa Moore, Kevin Ward and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Stefan Lachowycz, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 Comparison of shear wave velocity and surface wave anisotropy.

Shear-wave velocity model from this study at 98 km depth is overlain by surface wave anisotropy at 100 km from ref. 57. Figure created with M_Map58.

Extended Data Fig. 2 Velocity model plotted with color-blind-friendly colormap.

Depth slices (a,b) and cross-sections (c-f) of the velocity model as in Fig. 2 using the Lapaz scientific colormap as described by ref. 74. Figure created with M_Map58.

Extended Data Fig. 3 Recovered checkerboard models with different horizontal block sizes.

The model recovery results are selected here to show the minimum resolvable horizontal scales of the velocity anomalies. The size of each cell in the checkerboard is 66 km (a), 110 km (b), 154 km (c), and 198 km (d), at the depths of 51, 98, 123, and 150 km, respectively.

Extended Data Fig. 4 Resolution of 20-km thick layers.

The panels on the left are the input models (a, c, e, and g). The panels on the right are the recovery velocity anomalies (b, d, f, and h). The input models are homogeneous layers with vertically alternating velocity perturbations.

Extended Data Fig. 5 Resolution of 40-km thick layers.

The panels on the left are the input models (a, c, e, and g). The panels on the right are the recovery velocity anomalies (b, d, f, and h). The input models are homogeneous layers with vertically alternating velocity perturbations.

Extended Data Fig. 6 Recovery test of a dipping slab with constant thickness.

The panels on the left are depth slices of the input model (a, c, and e). The panels on the right are depth slices of the recovered velocity anomalies (b, d, and f). In the input model, a dipping slab with a 10% velocity anomaly is induced while the rest of the medium has a -10% velocity anomaly. The color scale shows the percentage of amplitude recovery compared to the input model.

Extended Data Fig. 7 Cross-sections of a dipping slab recovery test with constant thickness.

The cross-sections are at the same locations as those in Fig. 2c-e in the main text. The panels on the left are the input models (a, c, and e). The panels on the right are the recovery velocity anomalies (b, d, and f). The color scale shows the percentage of amplitude recovery compared to the input model.

Extended Data Fig. 8 Global distribution of major oceanic plate joints and bathymetric highs.

Major global oceanic plate oceanic joints are visually identified based on seafloor magnetic anomalies13,25,44,45 (solid red lines) and seafloor age42 (dashed orange lines). The red triangle indicates the slab window revealed in this study. The dark blue bars are the approximate locations of major bathymetric highs coincident with identified oceanic plate joints. Major plate boundaries (thin green lines) are shown for reference. Figure created with Generic Mapping Tools56.

Extended Data Fig. 9 Ray coverage at different period bands.

Colors correspond to the number of rays counted per cell. Cells with less than 5 rays were masked out. The different period bands shown here are 50-100 s (a), 40-70 s (b), 30-50 s (c), 20-40 s (d), 15-30 s (e), 10-20 s (f), 7.5-15 s (g), and 5-10 s (h).

Extended Data Fig. 10 Improvements of phase delay measurements.

Comparison of Rayleigh wave phase delays calculated using the initial model (gray) and the final model (red) after six iterations. The different period bands shown here are 50-100 s (a), 40-70 s (b), 30-50 s (c), 20-40 s (d), 15-30 s (e), 10-20 s (f), 7.5-15 s (g), and 5-10 s (h).

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Sassard, V., Yang, X., Liu, L. et al. Slab tearing along a subducted oceanic plate joint beneath the Alaska Peninsula. Nat. Geosci. 18, 801–807 (2025). https://doi.org/10.1038/s41561-025-01749-6

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