Modeling Magma System Evolution During 2006–2007 Volcanic Unrest of Atka Volcanic Center, Alaska

Zhan, Y.; Gregg, P. M.; Lü, Zhong

doi:10.1029/2020jb020158

Cited by 8 publications

(13 citation statements)

References 73 publications

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“…To understand the controls on the local stress field within Augustine's edifice, we build a series of numerical models (similar to Zhan et al, 2021) with a wide range of tectonic stresses, edifice loading via different densities, and dike overpressures. We modeled a 30 × 30 × 25 km box centered on Augustine Volcano (Figure S4 in Supporting Information S1).…”

Section: Stress Field Modelingmentioning

confidence: 99%

Earthquakes Indicated Stress Field Change During the 2006 Unrest of Augustine Volcano, Alaska

Zhan

Roman

Mével

et al. 2022

Geophysical Research Letters

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The crustal stress field around a volcano is affected by many competing and temporally varying factors, such as gravitational loading, tectonic processes, pre-existing structures, and pressurizing and ascending magmas or hydrothermal fluids. Volcano tectonic (VT) earthquakes, indicating shear ruptures, often provide early warnings of volcanic unrest and reveal stress changes due to magma or hydrothermal fluid (Roman & Cashman, 2006). Unfortunately, the absolute magnitude of stresses triggering the volcano tectonic (VT) seismicity cannot be measured directly. However, the relative influence of different sources, such as dike opening and tectonic processes, on the stress field can be inferred from the stress orientation or its changes (Bonafede & Danesi, 1997). For example, a ∼90° rotation of the fault plane solution (FPS) P-axis from regional maximum compression is sometimes observed when a dike opens (Fukuyama et al., 2001;Roman et al., 2021), indicating a possible reorientation in the local stress field. From numerical modeling, the influence of tectonically-and magmatically generated stress fields on the local stress field can be quantified (Roman & Heron, 2007;Vargas-Bracamontes & Neuberg, 2012). However, the effect of gravitational loading due to volcanic edifices is usually ignored, and a systematic study integrating numerical models with seismicity and geodetic data from an active volcanic system is needed.Geophysical monitoring of the 2006 eruption of Augustine Volcano in Cook Inlet, Alaska (Figure 1) provides a wealth of data with which to study the stress field changes during its unrest and subsequent eruption. Starting on 30 April 2005, the number of volcano tectonic (VT) earthquakes generally increased from several to >1,000 per

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Section: Stress Field Modelingmentioning

confidence: 99%

Earthquakes Indicated Stress Field Change During the 2006 Unrest of Augustine Volcano, Alaska

Zhan

Roman

Mével

et al. 2022

Geophysical Research Letters

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“…Our model configuration contains several simplifications, including the assumption that the free surface above the magmatic system is flat; the work of Albino et al (2018) demonstrates that failure overpressures can be significantly affected by including the topographic stress field due to a volcanic edifice. Failure analyses of island or submarine volcanoes should account for the ambient stress field due to the surrounding or overlying water mass, which also impacts the onset of reservoir failure (Cabaniss et al, 2020;Satow et al, 2021;Zhan et al, 2021). Additionally, the models used here assume that the crustal rock is competent and does not contain pre-existing fractures, therefore neglecting the role of pre-existing weaknesses along or near to the reservoir wall, which can act to reduce failure thresholds for both elastic and thermo-viscoelastic models, or govern where such failure may predominantly occur.…”

Section: Modeling Considerationsmentioning

confidence: 99%

“…The stress field induced by a deforming reservoir under a given overpressure, and hence its conditions for failure, can be impacted by many factors, including mechanical layering (e.g., Long & Grosfils, 2009), gravitational and edifice loading (e.g., Albino et al., 2018; Grosfils, 2007), surface load variations (e.g., Albino et al., 2010; Satow et al., 2021), and local pore pressure gradients (e.g., Albino et al., 2018; Rozhko et al., 2007), among others. Recent studies incorporate statistical routines to assimilate multiple geodetic observations (e.g., Bato et al., 2017; Zhan & Gregg, 2017) in order to provide constraints on previous eruptive activity (Albright et al., 2019), or to outline potential forecast scenarios (Zhan et al., 2019, 2021), highlighting the capacity for (pseudo‐)real‐time modeling of the stress fields induced by deforming magmatic systems. To date, the majority of these analyses, along with studies of specific volcanoes (e.g., Browning et al., 2015), assume elastic behavior.…”

Section: Introductionmentioning

confidence: 99%

Rheological Controls on Magma Reservoir Failure in a Thermo‐Viscoelastic Crust

et al. 2022

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As volcanoes undergo unrest, understanding the conditions and timescales required for magma reservoir failure, and the links to geodetic observations, are critical when evaluating the potential for magma migration to the surface and eruption. Inferring the dynamics of a pressurized magmatic system from episodes of surface deformation is heavily reliant on the assumed crustal rheology, typically represented by an elastic medium. Here, we use Finite Element models to identify the rheological response to reservoir pressurization within a temperature‐dependent Standard Linear Solid viscoelastic (“thermo‐viscoelastic”) domain. We assess the mechanical stability of a deforming reservoir by evaluating the overpressures required to initiate brittle failure along the reservoir wall, and the sensitivity to key parameters. Reservoir inflation facilitates compression of the ductile wall rock, due to the non‐uniform crustal viscosity, impacting the temporal evolution of the induced tensile stress. Thermo‐viscoelasticity enables a deforming reservoir to sustain greater overpressures prior to failure, compared to elastic analyses. High‐temperature (e.g., mafic) reservoirs fail at lower overpressures compared to low‐temperature (e.g., felsic) reservoirs, producing smaller coincident displacements at the ground surface. The impact of thermo‐viscoelasticity on reservoir failure is significant across a wide range of overpressure loading rates. By resisting mechanical failure on the reservoir wall, thermo‐viscoelasticity impacts dyke nucleation and formation of shear fractures. Numerical models may need to incorporate additional processes that act to promote failure, such as regional stresses (e.g., topographic and tectonic), external triggers (e.g., earthquake stress drops), or pre‐existing weaknesses along the reservoir wall.

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“…Volcanic risk assessment and eruption forecasting necessitates the characterization of the nature of unrest and the discrimination between magmatic and hydrothermal contributions (Jasim et al., 2015; Rouwet et al., 2014; Todesco & Berrino, 2005). Multi‐parameter geophysical studies help to identify driving mechanisms and source properties behind volcanic unrest, especially when interpretations of field observations are combined with data modeling (e.g., Gottsmann, Flynn, & Hickey, 2020; Gottsmann et al., 2008; Hickey et al., 2016; Rinaldi et al., 2011; Wauthier et al., 2016; Zhan et al., 2021). Joint ground displacement and gravity change time series have, for example, been used at several volcanoes to interrogate enigmatic unrest processes (Coco, Gottsmann, et al., 2016; Currenti & Napoli, 2017; Gottsmann, Biggs, et al., 2020; Zhan et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

“…. Multi-parameter geophysical studies help to identify driving mechanisms and source properties behind volcanic unrest, especially when interpretations of field observations are combined with data modeling (e.g., Gottsmann, Flynn, & Hickey, 2020;Gottsmann et al, 2008;Hickey et al, 2016;Rinaldi et al, 2011;Wauthier et al, 2016;Zhan et al, 2021). Joint ground displacement and gravity change time series have, for example, been used at several volcanoes to interrogate enigmatic unrest processes Gottsmann, Biggs, et al, 2020;Zhan et al, 2019).…”

mentioning

confidence: 99%