Investigating long-term subsidence at Medicine Lake Volcano, CA, using multitemporal InSAR

Long-term volcanic subsidence provides insight into inter-eruptive processes, which comprise the longest portion of the eruptive cycle. Ground based geodetic surveys of Medicine Lake Volcano (MLV), northern CA, document subsidence at rates of \({\sim }-\)10 mm/yr between 1954 and 2004. The long observation period plus the duration and stable magnitude of this signal presents an ideal opportunity to study long-term volcanic deformation, but this first requires accurate knowledge of the geometry and magnitude of the source. Best-fitting analytical source models to past leveling and GPS datasets show conflicting source parameters - primarily the model depth. To overcome this, we combine multiple tracks of InSAR data, each with a different look angle, to improve upon the spatial resolution of ground based measurements. We compare the results from InSAR to those of past geodetic studies, extending the geodetic record to 2011 and demonstrating that subsidence at MLV continues at \({\sim }-\)10 mm/yr. Using geophysical inversions, we obtain the best-fitting analytical source model - a sill located at 9–10 km depth beneath the caldera. This model geometry is similar to those of past studies, providing a good fit to the high spatial density of InSAR measurements, whilst accounting for the high ratio of vertical to horizontal deformation derived from InSAR and recorded by existing leveling and GPS datasets. We discuss possible causes of subsidence and show that this model supports the hypothesis that deformation at MLV is driven by tectonic extension, gravitational loading, plus a component of volume loss at depth, most likely due to cooling and crystallisation within the intrusive complex that underlies the edifice. Past InSAR surveys at MLV, and throughout the Cascades, are of variable success due to dense vegetation, snow cover and atmospheric artefacts. In this study, we demonstrate how InSAR may be successfully used in this setting by applying a suite of multi temporal analysis methods that account for atmospheric and orbital noise sources. These methods include: a stacking strategy based upon the noise characteristics of each dataset; pixel-wise rate-map formation (\(\pi \)-RATE); and persistent scatterer InSAR (StaMPS).