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Hooper, D. M.; Mattioli, Glen

doi:10.1023/a:1008130605558

Cited by 17 publications

(3 citation statements)

References 36 publications

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“…The change in backazimuth direction from the dome towards SSE was observed four more times during the high-amplitude infrasonic phase, suggesting a pulsating nature of the flow. The mean velocity of the flow's propagation front varied between 30 m/s and 75 m/s, in good agreement with theoretical modelling of non-explosive gravitational PFs at SHV [Wadge et al, 1998;Hooper and Mattioli, 2001] and with other estimates for the velocity of PFs at Unzen volcano [Yamasato, 1997] and Mount St. Helens [McEwen and Malin, 1989].…”

Section: Tracking the Propagation Of Pyroclastic Frontssupporting

confidence: 85%

Observation of infrasonic and gravity waves at Soufrière Hills Volcano, Montserrat

Ripepe

Angelis

Lacanna

et al. 2010

Geophysical Research Letters

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The sudden ejection of material during an explosive eruption generates a broad spectrum of pressure oscillations, from infrasonic to gravity waves. An infrasonic array, installed at 3.5 km from the Soufriere Hills Volcano has successfully detected and located, in real‐time, the infrasound generated by several pyroclastic flows (PF) estimating mean flow speeds of 30–75 m/s. On July 29 and December 3, 2008, two differential pressure transducers, co‐located with the array, recorded ultra long‐period (ULP) oscillations at frequencies of 0.97 and 3.5 mHz, typical of atmospheric gravity waves, associated with explosive eruptions. The observation of gravity waves in the near‐field (<6 km) at frequencies as low as about 1 mHz is unprecedented during volcanic eruptions.

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Section: Tracking the Propagation Of Pyroclastic Frontssupporting

confidence: 85%

Observation of infrasonic and gravity waves at Soufrière Hills Volcano, Montserrat

Ripepe

Angelis

Lacanna

et al. 2010

Geophysical Research Letters

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“…Hsü (1975), Sheridan (1979), and Malin & Sheridan (1982) first used the energy-line or energy-cone concept (which is defined by H/L). This concept has been applied at a variety of volcanoes (e.g., Sheridan & Malin, 1983;Wadge & Isaacs, 1988;Höskuldsson & Cantagrel, 1994;Alberico et al, 2002;Sheridan et al, 2004) and also forms the basis for the FLOW2D and FLOW3D computer models (e.g., Kover & Sheridan, 1993;Martin del Pozzo et al, 1995;Sheridan & Macías, 1995;Hooper & Mattioli, 2001) which base shear resistance on basal friction (taken directly from H/L), viscosity, and turbulence.…”

Section: Mobility Metrics For Flow Modelingmentioning

confidence: 99%

Pooling strength amongst limited datasets using hierarchical Bayesian analysis, with application to pyroclastic density current mobility metrics

Ogburn

Berger²,

Calder³

et al. 2016

SIV

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In volcanology, the sparsity of datasets for individual volcanoes is an important problem, which, in many cases, compromises our ability to make robust judgments about future volcanic hazards. In this contribution we develop a method for using hierarchical Bayesian analysis of global datasets to combine information across different volcanoes and to thereby improve our knowledge at individual volcanoes. The method is applied to the assessment of mobility metrics for pyroclastic density currents in order to better constrain input parameters and their related uncertainties for forward modeling. Mitigation of risk associated with such flows depends upon accurate forecasting of possible inundation areas, often using empirical models that rely on mobility metrics measured from the deposits of past flows, or on the application of computational models, several of which take mobility metrics, either directly or indirectly, as input parameters. We use hierarchical Bayesian modeling to leverage the global record of mobility metrics from the FlowDat database, leading to considerable improvement in the assessment of flow mobility where the data for a particular volcano is sparse. We estimate the uncertainties involved and demonstrate how they are improved through this approach. The method has broad applicability across other areas of volcanology where relationships established from broader datasets can be used to better constrain more specific, sparser, datasets. Employing such methods allows us to use, rather than shy away from, limited datasets, and allows for transparency with regard to uncertainties, enabling more accountable decision-making.

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“…Hsü (1975), Sheridan (1979), andSheridan (1982) first used the energy-line or energy-cone concept (which is defined by H/L). This concept has been applied at a variety of volcanoes (e.g., Sheridan & Malin, 1983;Wadge & Isaacs, 1988;Höskuldsson & Cantagrel, 1994;Alberico et al, 2002;Sheridan et al, 2004) and also forms the basis for the FLOW2D and FLOW3D computer models (e.g., Kover & Sheridan, 1993;Martin del Pozzo et al, 1995;Sheridan & Macías, 1995;Hooper & Mattioli, 2001) which base shear resistance on basal friction (taken directly from H/L), viscosity, and turbulence.…”

Section: Mobility Metrics For Flow Modelingmentioning

confidence: 99%

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2016

SIV

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Pooling strength amongst limited datasets using hierarchical Bayesian analysis, with application to pyroclastic density current mobility metrics, Statistics in Volcanology 2.1 : 1 − 26. DOI: http://dx.doi.org/10.5038/2163-338X.2.1Authors retain copyright of their material under a Creative Commons Non-Commercial Attribution 3.0 License. Ogburn et al.Pooling strength among limited data using hierarchical Bayesian analysis 2 AbstractIn volcanology, the sparsity of datasets for individual volcanoes is an important problem, which, in many cases, compromises our ability to make robust judgments about future volcanic hazards. In this contribution we develop a method for using hierarchical Bayesian analysis of global datasets to combine information across different volcanoes and to thereby improve our knowledge at individual volcanoes. The method is applied to the assessment of mobility metrics for pyroclastic density currents in order to better constrain input parameters and their related uncertainties for forward modeling. Mitigation of risk associated with such flows depends upon accurate forecasting of possible inundation areas, often using empirical models that rely on mobility metrics measured from the deposits of past flows, or on the application of computational models, several of which take mobility metrics, either directly or indirectly, as input parameters. We use hierarchical Bayesian modeling to leverage the global record of mobility metrics from the FlowDat database, leading to considerable improvement in the assessment of flow mobility where the data for a particular volcano is sparse. We estimate the uncertainties involved and demonstrate how they are improved through this approach. The method has broad applicability across other areas of volcanology where relationships established from broader datasets can be used to better constrain more specific, sparser, datasets. Employing such methods allows us to use, rather than shy away from, limited datasets, and allows for transparency with regard to uncertainties, enabling more accountable decision-making.

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Cited by 17 publications

References 36 publications

Observation of infrasonic and gravity waves at Soufrière Hills Volcano, Montserrat

Observation of infrasonic and gravity waves at Soufrière Hills Volcano, Montserrat

Pooling strength amongst limited datasets using hierarchical Bayesian analysis, with application to pyroclastic density current mobility metrics

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