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A sharp volatile-rich cap to the Yellowstone magmatic system

Nature volume 640, pages 962–966 (2025)Cite this article

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Abstract

The stability of hazardous volcanic systems is strongly influenced by the uppermost magma storage depth and volatile exsolution1,2,3. Despite abundant evidence for an upper crustal magma reservoir beneath Yellowstone caldera4,5,6,7, its depth and the properties at its top have not been well constrained. New controlled-source seismic imaging illuminates a sharp reflective cap of the magma reservoir approximately 3.8 km beneath the northeastern caldera. Magma ascent to such low pressure is expected to drive volatile exsolution and potentially localized accumulation of bubbles near the top of the reservoir8,9, but this process typically remains hidden in contemporary volcanic systems. P-wave and P-to-S-wave reflections from the sharp top of the Yellowstone magma reservoir indicate that a mixture of supercritical fluid and magma fills the pore space at the cap of the approximately 3–8-km-deep low-shear-velocity layer imaged by seismic tomography6,7. The results are consistent with partial retention of bubbles exsolved from an upper crustal reservoir with ongoing magma supply from a volatile-enriched mantle source. Bubble accumulation can eventually lead to reservoir instability2,8, but the bubble volume fraction seismically estimated at the top of the reservoir today is lower than typical estimates of pre-eruptive conditions for rhyolites1,10,11, and measurements of the hydrothermal system document high fluxes of magmatic volatiles escaping to the surface12,13,14,–15. We infer that the magma reservoir is in a stable state of efficient bubble ascent into the hydrothermal system on the basis of estimates that it is a crystal-rich (less than 30% porosity) reservoir for which dynamic modelling favours channelized bubble escape that prevents instability8.

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Fig. 1: Map of the northeastern Yellowstone caldera survey location and evidence of magma reservoir reflections.
Fig. 2: 2D reflectivity imaging of the magma reservoir cap.
Fig. 3: Estimating fluid type and porosity in the magma reservoir cap.

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Data availability

All data used in this study are available in raw form via EarthScope Data Services as Assembled Data 22-028 (https://ds.iris.edu/mda/22-028/) and as processed source gathers along with the imaging code via GitHub at https://github.com/chenglongduan/PS_adjoint_imaging_2D.

Code availability

The adjoint imaging code can be accessed via GitHub at https://github.com/chenglongduan/PS_adjoint_imaging_2D. All the 2D synthetic seismograms used in this paper were generated by SOFI2D time-domain finite-difference elastic wave simulation code (https://gitlab.kit.edu/kit/gpi/ag/software/sofi2d). The rock physics models were generated using the ElasticC open source code available via GitHub at https://github.com/michpaulatto/ElasticC. Data analysis and figures can be reproduced via GitHub at https://github.com/chenglongduan/reproduce_YS2024_UNM.

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Acknowledgements

Nascent ideas for this project arose from discussions of magma reservoir depth uncertainties at the 2019 Cooperative Institute for Dynamic Earth Research Summer Program supported by National Science Foundation (NSF) grant no. EAR-1664595. Most seismic instruments in the project were borrowed from the Seismological Facility for the Advancement of Geoscience facilities operated by the EarthScope Consortium, which is supported by NSF EAR-1851048. Additional instruments were obtained from facilities at the University of Utah and University of Texas at El Paso (thanks to J. Chaput and M. Karplus). Dawson Geophysical staff J. Prozeller and B. Peterson provided professional field support for the vibration source in an atypical survey. Data were collected under Yellowstone research permit YELL-2020-SCI-8146. This work used Stampede3 at Texas Advanced Computing Center (TACC) through allocation EES240056 from the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) programme, which is supported by US National Science Foundation grant nos. 2138259, 2138286, 2138307, 2137603 and 2138296. Data collection, analysis and synthesis were supported by NSF grants EAR-1950328, EAR-1950331, EAR-1753362 and EAR-2342525, and the Brinson Foundation.

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Author notes

  1. Chenglong Duan

    Present address: Department of Earth, Environmental and Planetary Sciences, Rice University, Houston, TX, USA

Authors and Affiliations

  1. Department of Earth and Planetary Sciences, University of New Mexico, Albuquerque, NM, USA

    Chenglong Duan, Wenkai Song, Tobias Fischer & Lindsay Lowe Worthington

  2. Department of Earth, Environmental and Planetary Sciences, Rice University, Houston, TX, USA

    Brandon Schmandt

  3. Department of Geology and Geophysics, University of Utah, Salt Lake City, UT, USA

    Jamie Farrell & Fan-Chi Lin

  4. Department of Sustainable Earth Systems Sciences and Department of Physics, University of Texas at Dallas, Richardson, TX, USA

    David Lumley

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Contributions

C.D, W.S., B.S., J.F., L.W., F.L. and T.F conceptualized the study. C.D., W.S., D.L. and B.S. were responsible for the methodology and software. Formal analysis was carried out by C.D. and W.S. The original draft was written by C.D., W.S. and B.S. C.D., W.S., B.S., J.F., T.F., D.L., L.W. and F.L. reviewed and edited the manuscript. The study was supervised by B.S.

Corresponding authors

Correspondence to Chenglong Duan or Brandon Schmandt.

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Extended data figures and tables

Extended Data Fig. 1 Three-component source gathers.

(a) Map view of the source (red star) and receiver (circles) locations. (b) Vertical (Z) component source gather. (c) Radial (R) component source gather. (d) Transverse (T) component source gather.

Extended Data Fig. 2 Average PSDs for the velocity traces and STA/LTA traces.

The decrease in frequency content of the time series improves spatial coherence of signals and enables adequate sampling for full wavefield imaging methods. The vibration source sweep function extended from 6–30 Hz, but the truck-mounted source could not reach peak force of ~220,000 N until frequencies >10 Hz.

Extended Data Fig. 3 Predicted reflection amplitude variations as a function of offset.

(a) Normalized PzP reflection amplitude for a 3.8 km deep interface with a velocity drop corresponding to 14% total porosity filled be a mixture (50/50) of supercritical H2O and rhyolite melt. (b) Normalized PzS reflection amplitude.

Extended Data Fig. 4 Velocity analysis.

(a) B-spline basis functions (n = 3), which ensure a smooth 1-D velocity model suitable for adjoint reflection imaging and Kirchhoff migration. (b) 1-D P-wave velocity (Vp) and S-wave velocity (Vs) profiles (Vp/Vs = 1.82). (c) First P-wave arrival travel time (Tp) fit between observed and synthetic data, with RMS misfit shown in units of seconds. (d-f) Synthetic PzP and PzS reflection travel times overlain on the stacked STA/LTA images for Z, R and T components, respectively.

Extended Data Fig. 5 Kirchhoff PzP and PzS imaging results.

(a, d) Using velocity time series inputs. (b, e) Using envelope time series inputs after filtering the velocity seismograms from 6 to 30 Hz. (c, f) Using STA/LTA time series inputs.

Extended Data Fig. 6 Adjoint PzP and PzS imaging results.

(a, d) Using velocity time series inputs. (b, e) Using envelope time series inputs after filtering the velocity seismograms from 6 to 30 Hz. (c, f) Using STA/LTA time series inputs.

Extended Data Fig. 7 Point spread function resolution analysis using STA/LTA input data.

(a) True point spread function scattering model and source-receiver geometry. (b) Point spread function of PzP scattering. (c) Point spread function of PzS scattering. Elastic synthetic seismograms were calculated and then processed into STA/LTA functions for the test.

Extended Data Fig. 8 Two-layer model synthetic test using STA/LTA input data.

(a) True model. The contrast between the top high-velocity layer and bottom low-velocity layer occurs at 3.8 km depth. (b) PzP reflection image. (c) PzS reflection image. Elastic synthetic seismograms were calculated and then processed into STA/LTA functions for the test.

Extended Data Fig. 9 Supercritical fluid and melt fraction modeling using self-consistent effective elastic media theory.

(a) Bulk modulus for 0%, 5%, 10%, 25%, 50% and 100% scH2O fraction. (b) Relative P and S wave velocity perturbations versus porosity for 50% scH2O and 50% melt mixture at pressure of 100 MPa, temperature of 700 °C and aspect ratio of 0.15. (c) Relative P and S wave velocity perturbations versus porosity for pure rhyolite melt. The elastic properties of scH2O-melt mixtures and rhyolite solid matrix are shown in Supplementary Table 1.

Extended Data Fig. 10 Predicted PzS/PzP ratios and Chi-squared misfits associated with different gas-melt mixture fractions.

(a) 50% scH2O and 50% melt. (b) 25% scH2O and 75% melt. (c) 10% scH2O and 90% melt. (d) 5% scH2O and 95% melt. The black dots with error bars are observed PzS/PzP ratios with standard deviations. (e) Chi-squared misfit values for 5%, 10%, 25% and 50% scH2O fractions within the same total porosity of 0.14.

Supplementary information

Supplementary Information

Supplementary Figs. 1–7 and Tables 1–6.

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Duan, C., Song, W., Schmandt, B. et al. A sharp volatile-rich cap to the Yellowstone magmatic system. Nature 640, 962–966 (2025). https://doi.org/10.1038/s41586-025-08775-9

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