Constraining Mechanisms of Volcanic Subsidence at Lassen Volcanic Center, CA, Using InSAR

  • Amy Laura ParkerEmail author
Part of the Springer Theses book series (Springer Theses)


Lassen Volcanic Center, southern Cascades, has subsided in recent decades, but the onset, temporal evolution, and cause of subsidence remain unconstrained. Here we use multiple sets of InSAR data, each corrected using the North American Regional Reanalysis atmospheric model, to determine the temporal and spatial characteristics of deformation between 2004 and 2010. ENVISAT data shows that the rate of subsidence at LaVC has decreased over time from 13 ± 2.7 mm/yr between 2004 and 2007 to 8 ± 1.6 mm/yr between 2007 and 2010. Time-series analysis shows that this may be due to either a decrease in the rate of subsurface volume change or an increase in source depth. Comparing these observations to past geodetic studies suggests that the onset of deformation was in the early 1990s. Combining multiple tracks of InSAR data, we find that the ratio of horizontal to vertical displacements is high, and geophysical inversions suggest that subsidence is driven by a point source located at \(\sim \)9 km depth. This source geometry, and the temporal evolution of deformation, contrasts to steady subsidence observed at nearby Medicine Lake Volcano since the 1950s. We evaluate possible causes of subsidence in light of hydrothermal and seismic activity at LaVC, and suggest that deformation is driven by changes in the distribution of magmatic/hydrothermal fluids and/or magma cooling, in addition to tectonic extension. Lassen Volcanic Center was one of >10 volcanic/geothermal centers to experience triggered seismicity after the 1992 M\(_w\)7.3 Landers earthquake, but further constraints are required to assess whether the earthquake had a role in the onset of recent subsidence.


Hydrothermal System Ground Deformation Source Depth Tectonic Extension Prolate Ellipsoid 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Authors and Affiliations

  1. 1.Department of Spatial SciencesCurtin UniversityPerthAustralia

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