Analysis of GPS-measured deformation associated with the 2004-2006 dome-building eruption of Mount St. Helens, Washington

Lisowski, M.; Dzurisin, Daniel; Denlinger, Roger P.; Iwatsubo, Eugene Y.

doi:10.3133/pp175015

Cited by 38 publications

(75 citation statements)

References 13 publications

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“…Volcanic deformation was detected at JRO1 at approximately the same time as the first seismic swarm, when the station began to move downward and radially inward towards the volcano consistent with the deflation of a mid-crustal magma reservoir (see Figure 4 and Lisowski et al [2008]). Deformation during the vent-clearing phase of the eruption (roughly the first 2-3 weeks) was rapid, at about 0.5 mm/day of inward motion, but slowed significantly ( Figure 4b) after an explosion on 5 October .…”

Section: Ground Deformation (Gps)mentioning

confidence: 92%

“…Here only minor rates of chamber influx (at the end of the eruption) are predicted; the histogram in Figure 7f shows few values over 0.1 m 3 /sec, with a peak only a little higher than 10 -4 m 3 /sec. This suggests that chamber influx may have been relatively minor and was not necessary to explain observed differences between extruded volume and inferred volume change at depth [e.g., Lisowski et al, 2008].…”

Section: Inversion Of Gps and Extrusion Datamentioning

confidence: 99%

“…[52] Although outside the scope of this work, by adding more stations (including campaign GPS sites) we could better resolve the source depth, volume change, and aspect ratio, as in Lisowski et al [2008] and Palano et al [2012]. Also, for consistency with other inversions we calculate net displacements using all syn-eruptive data (exclusive of ventclearing), but a significantly better signal-to-noise ratio can be obtained using only the first 1 year of data [Palano et al, 2012], which includes most of the deformation signal and less accumulated random walk (and other) error.…”

Section: Kinematic Ellipsoidal Magma Chambermentioning

confidence: 99%

“…[29] We obtained daily GPS solutions from the Pacific Northwest Geodetic Array (PANGA) and removed nonvolcanic deformation by correcting for seasonal effects, rigid-block rotation and uniform strain accumulation caused by convergence of the Juan de Fuca plate [e.g., Lisowski et al, 2008;Palano et al, 2012], and episodic slip events. GPS processing and noise estimation is described in section S1 of the supporting information; note that our approach here differs somewhat from that in Part 1.…”

Section: Ground Deformation (Gps)mentioning

confidence: 99%

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Bayesian inversion of data from effusive volcanic eruptions using physics‐based models: Application to Mount St. Helens 2004–2008

2013

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[1] Physics-based models of volcanic eruptions can directly link magmatic processes with diverse, time-varying geophysical observations, and when used in an inverse procedure make it possible to bring all available information to bear on estimating properties of the volcanic system. We develop a technique for inverting geodetic, extrusive flux, and other types of data using a physics-based model of an effusive silicic volcanic eruption to estimate the geometry, pressure, depth, and volatile content of a magma chamber, and properties of the conduit linking the chamber to the surface. A Bayesian inverse formulation makes it possible to easily incorporate independent information into the inversion, such as petrologic estimates of melt water content, and yields probabilistic estimates for model parameters and other properties of the volcano. Probability distributions are sampled using a Markov-Chain Monte Carlo algorithm. We apply the technique using GPS and extrusion data from the 2004-2008 eruption of Mount St. Helens. In contrast to more traditional inversions such as those involving geodetic data alone in combination with kinematic forward models, this technique is able to provide constraint on properties of the magma, including its volatile content, and on the absolute volume and pressure of the magma chamber. Results suggest a large chamber of >40 km 3 with a centroid depth of 11-18 km and a dissolved water content at the top of the chamber of 2.6-4.9 wt%.

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Section: Ground Deformation (Gps)mentioning

confidence: 92%

Section: Inversion Of Gps and Extrusion Datamentioning

confidence: 99%

Section: Kinematic Ellipsoidal Magma Chambermentioning

confidence: 99%

Section: Ground Deformation (Gps)mentioning

confidence: 99%

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Bayesian inversion of data from effusive volcanic eruptions using physics‐based models: Application to Mount St. Helens 2004–2008

2013

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“…Alternatively, InSAR data could provide the short spatial scale surface deformation such as every coherence points within SAR images practically lacking in GPS data (Wei et al, 2010). In particular, the use of GPS devices in volcanoes is limited since the antenna is easily damaged by eruptions and weather conditions may affect data collection Lisowski et al, 2008). When comparing it to the InSAR technique s relatively longer data collection cycle, the GPS method is superior in that it can measure surface displacements at the time of eruption and collect three-dimensional locational information unlike InSAR that provides the LOS(Line-OfSight) directions of the displacement only (Larson et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Sustainable Surface Deformation Related with 2006 Augustine Volcano Eruption in Alaska Measured Using GPS and InSAR Techniques

Lee¹,

Kim²,

Lee³

2016

Journal of the Korean Society of Surveying, Geodesy, Photogramm

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Augustine volcano, located along the Aleutian Arc, is one of the most active volcanoes in Alaska and nearby islands, with seven eruptions occurring between 1812 and 2006. This study monitored the surface displacement before and after the most recent 2006 eruption. For analysis, we conducted a time-series analysis on data observed at the permanent GPS(Global Positioning System) observation stations in Augustine Island between 2005 and 2011. According to the surface displacement analysis results based on GPS data, the movement of the surface inflation at the average speed of 2.3 cm/year three months prior to the eruption has been clearly observed, with the post-eruption surface deflation at the speed of 1.6 cm/year. To compare surface displacements measurement by GPS observation, ENVISAT(Environmental satellite) radar satellite data were collected between 2003 and 2010 and processed the SBAS(Small Baseline Subset) method, one of the time-series analysis techniques using multiple InSAR(Interferometric Synthetic Aperture Radar) data sets. This result represents 0.97 correlation value between GPS and InSAR time-series surface displacements. This research has been completed precise surface deformation using GPS and time-series InSAR methods for a detection of precursor symptom on Augustine volcano. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Volcanic tremor masks its seismogenic source: Results from a study of noneruptive tremor recorded at Mount St. Helens, Washington

Denlinger

Moran

2014

JGR Solid Earth

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. This episode was remarkable both because no explosion followed, and because seismicity abruptly stopped following the episode. This sequence motivated us to consider a model for volcanic tremor that does not involve energetic gas release from magma but does involve movement of conduit magma through extension on its way toward the surface. We found that the tremor signal was composed entirely of Love and Rayleigh waves and that its spectral bandwidth increased and decreased with signal amplitude, with broader bandwidth signals containing both higher and lower frequencies. Our modeling results demonstrate that the forces giving rise to this tremor were largely normal to conduit walls, generating hybrid head waves along conduit walls that are coupled to internally reflected waves. Together these form a crucial part of conduit resonance, giving tremor wavefields that are largely a function of waveguide geometry and velocity. We find that the mechanism of tremor generation fundamentally masks the nature of the seismogenic source giving rise to resonance. Thus multiple models can be invoked to explain volcanic tremor, requiring that information from other sources (such as visual observations, geodesy, geology, and gas geochemistry) be used to constrain source models. With concurrent GPS and field data supporting rapid rise of magma, we infer that tremor resulted from drag of nearly solid magma along rough conduit walls as magma was forced toward the surface.

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Analysis of GPS-measured deformation associated with the 2004-2006 dome-building eruption of Mount St. Helens, Washington

Abstract: Detecting far-field

Cited by 38 publications

References 13 publications

Bayesian inversion of data from effusive volcanic eruptions using physics‐based models: Application to Mount St. Helens 2004–2008

Bayesian inversion of data from effusive volcanic eruptions using physics‐based models: Application to Mount St. Helens 2004–2008

Sustainable Surface Deformation Related with 2006 Augustine Volcano Eruption in Alaska Measured Using GPS and InSAR Techniques

Volcanic tremor masks its seismogenic source: Results from a study of noneruptive tremor recorded at Mount St. Helens, Washington

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