Ambient noise tomography across Mount St. Helens using a dense seismic array
KAUST Grant NumberOCRF-2014-CRG3- 2300
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AbstractWe investigated upper crustal structure with data from a dense seismic array deployed around Mount St. Helens for 2 weeks in the summer of 2014. Interstation cross correlations of ambient seismic noise data from the array were obtained, and clear fundamental mode Rayleigh waves were observed between 2.5 and 5 s periods. In addition, higher-mode signals were observed around 2 s period. Frequency-time analysis was applied to measure fundamental mode Rayleigh wave phase velocities, which were used to invert for 2-D phase velocity maps. An azimuth-dependent traveltime correction was implemented to mitigate potential biases introduced due to an inhomogeneous noise source distribution. Reliable phase velocity maps were only obtained between 3 and 4 s periods due to limitations imposed by the array aperture and higher-mode contamination. The phase velocity tomography results, which are sensitive to structure shallower than 6 km depth, reveal an ~10–15% low-velocity anomaly centered beneath the volcanic edifice and peripheral high-velocity anomalies that likely correspond to cooled igneous intrusions. We suggest that the low-velocity anomaly reflects the high-porosity mixture of lava and ash deposits near the surface of the edifice, a highly fractured magmatic conduit and hydrothermal system beneath the volcano, and possibly a small contribution from silicate melt.
CitationWang Y, Lin F-C, Schmandt B, Farrell J (2017) Ambient noise tomography across Mount St. Helens using a dense seismic array. Journal of Geophysical Research: Solid Earth 122: 4492–4508. Available: http://dx.doi.org/10.1002/2016jb013769.
SponsorsWe thank Ivan Koulakov, Yehuda Ben-Zion, and Michael Ritzwoller for their constructive comments, which helped to improve this paper. We thank Jing Li and Gerard Schuster for their discussion on the topography effect on surface wave propagation. All waveform data used in this study can be downloaded from the IRIS Data Management Center. This work was supported by National Science Foundation (NSF) grant CyberSEES-1442665 and the King Abdullah University of Science and Technology (KAUST) under award OCRF-2014-CRG3- 2300. Collection and analysis of the node array data was supported by NSF grants 1445937 (B.S.) and 1520875 (B.S.).