EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Ambient noise waveform imaging of Yellowstone's magmatic system

Ross Maguire1, Min Chen1, Brandon Schmandt2, Chengxin Jiang3, Justin Wilgus2, and Jiaqi Li2
Ross Maguire et al.
  • 1Michigan State University, Computational Mathematics, Science, and Engineering, East Lansing, Michigan, United States of America (
  • 2University of New Mexico, Earth and Planetary Sciences, Albuquerque, New Mexico, United States of America
  • 3Australian National University, Research School of Earth Sciences, Canberra, Australia

Understanding important characteristics of Yellowstone's magmatic system such as the melt fraction, composition, and geometric organization of melt are critical for improving our knowledge of volcanic processes and assessing the potential for future eruptions.  While previous tomographic images have provided much insight into the magmatic system, imaging results are complicated by an incomplete understanding of how large crustal magmatic systems affect seismic waveforms. In particular, tomographic studies based on asymptotic methods may underestimate the seismic wave speed anomaly of the magma reservoir because first arriving energy may be diffracted around strong low wave speed anomalies. Here, we present a high-resolution shear wave speed model of Yellowstone’s crust and uppermost mantle structure, based on the most up to date dataset of ambient noise correlation functions from broadband stations deployed in the Yellowstone region over the past two decades. This model serves as the starting point for an adjoint inversion, which has potential to improve resolution by incorporating more accurate sensitivity kernels based on realistic wave propagation physics. We discuss our adjoint tomography methodology and present the first model iterations. Continued iterations promise to sharpen features in the model which can provide new inferences into the present state of Yellowstone’s magmatic system.

How to cite: Maguire, R., Chen, M., Schmandt, B., Jiang, C., Wilgus, J., and Li, J.: Ambient noise waveform imaging of Yellowstone's magmatic system, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13437,, 2021.


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