Christina Widiwijayanti

Earth Observatory of Singapore

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B. Voight

Pennsylvania State University

Fidel Costa

Earth Observatory of Singapore

D. Hidayat

Politeknik STMI Jakarta

G. S. Mattioli

The University of Texas at Arlington

Derek Elsworth

Pennsylvania State University

A. B. Clarke

Arizona State University

E. Shalev

Duke University

P. E. Malin

Applied Spectral Imaging (United States)

R. S. J. Sparks

University of Bristol

A. T. Linde

Carnegie Institution for Science

Cooperative Institutions

Centre National de la Recherche Scientifique

Nanyang Technological University

Earth Observatory of Singapore

Montserrat Volcano Observatory

United States Geological Survey

Institut de physique du globe de Paris

Pennsylvania State University

Institut de Recherche pour le Développement

Université Paris Cité

Meteorological, Climatological, And Geophysical Agency

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Multiphase-flow numerical modeling of the 18 May 1980 lateral blast at Mount St. Helens, USA

Geology (2011)

Tomaso Esposti Ongaro Christina Widiwijayanti A. B. Clarke B. Voight Augusto Neri

Research Article| June 01, 2011 Multiphase-flow numerical modeling of the 18 May 1980 lateral blast at Mount St. Helens, USA T. Esposti Ongaro; T. Esposti Ongaro 1Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Pisa, Via della Faggiola 32, 56126 Pisa, Italy Search for other works by this author on: GSW Google Scholar C. Widiwijayanti; C. Widiwijayanti 2Earth Observatory of Singapore, Nanyang Technological University, 50 Nanyang Avenue N2-01a-14, Singapore 639798 Search for other works by this author on: GSW Google Scholar A.B. Clarke; A.B. Clarke 1Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Pisa, Via della Faggiola 32, 56126 Pisa, Italy3School of Earth and Space Exploration, Arizona State University, Tempe, Arizona 85287-1404, USA Search for other works by this author on: GSW Google Scholar B. Voight; B. Voight 4Department of Geosciences, Pennsylvania State University, University Park, Pennsylvania 16802, USA5U.S. Geological Survey, Cascades Volcano Observatory, 1300 SE Cardinal Court, Vancouver, Washington 98683, USA Search for other works by this author on: GSW Google Scholar A. Neri A. Neri 1Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Pisa, Via della Faggiola 32, 56126 Pisa, Italy Search for other works by this author on: GSW Google Scholar Geology (2011) 39 (6): 535–538. https://doi.org/10.1130/G31865.1 Article history received: 28 Oct 2010 rev-recd: 24 Jan 2011 accepted: 25 Jan 2011 first online: 09 Mar 2017 Cite View This Citation Add to Citation Manager Share Icon Share MailTo Twitter LinkedIn Tools Icon Tools Get Permissions Search Site Citation T. Esposti Ongaro, C. Widiwijayanti, A.B. Clarke, B. Voight, A. Neri; Multiphase-flow numerical modeling of the 18 May 1980 lateral blast at Mount St. Helens, USA. Geology 2011;; 39 (6): 535–538. doi: https://doi.org/10.1130/G31865.1 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyGeology Search Advanced Search Abstract Volcanic lateral blasts are among the most spectacular and devastating of natural phenomena, but their dynamics are still poorly understood. Here we investigate the best documented and most controversial blast at Mount St. Helens (Washington State, United States), on 18 May 1980. By means of three-dimensional multiphase numerical simulations we demonstrate that the blast front propagation, final runout, and damage can be explained by the emplacement of an unsteady, stratified pyroclastic density current, controlled by gravity and terrain morphology. Such an interpretation is quantitatively supported by large-scale observations at Mount St. Helens and will influence the definition and predictive mapping of hazards on blast-dangerous volcanoes worldwide. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.

Mount

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10.1130/g31865.1

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Citations (38)

Probabilistic analysis to correlate seismic data with lava extrusion phases at Merapi volcano (Indonesia)

Journal of Volcanology and Geothermal Research (2022)

T. Espinosa-Ortega Agus Budi-Santoso Sulistiyani Nang Thin Zar Win Christina Widiwijayanti

Volcanoes can produce a range of eruptive behavior even during a single eruption, changing quickly from effusive to explosive style, and the other way around. The changes in eruption phases (e.g. phreatic explosion, magmatic explosion, lava extrusion, etc.) can lead to different volcanic hazards and require timely assessment for the implementation of mitigation measures. Here we explore how to correlate a given eruption phase with changes in the monitoring data using statistical analysis and conditional probabilities. We calculate the success of detection of an eruption phase using a threshold of monitoring data, which includes the uncertainty on the eruption phase dates with a Monte Carlo simulation. We apply the method to dome forming eruptions of Mt. Merapi (Indonesia) and evaluate their time occurrence using an exceptionally long monitoring time series (from 1993 to 2012, over nineteen years) of Multiphase (Hybrid) Seismic Energy. We identify the seismic energy threshold that is associated with the lava extrusion phase with an accuracy of 90 ±2%, precision of 73 ± 2%, specificity of 96 ± 1%, and sensitivity of 56 ± 1%. We further test our method with the recent 2018 eruption (not used in the thresholds calculations) and we identify the lava extrusion with a precision of 67%, specificity of 70%, and sensitivity of 92%. We also seismically detected the 2018′s onset of the lava extrusion phase 14 days earlier than the visual observation. Given the link between dome-collapse pyroclastic flows and growth episodes of the lava dome at Merapi, our analysis also allows us to establish that 83% of the most energetic pyroclastic flows occur within the first 3 months after the onset of lava extrusion phase. Our method can be applicable to a range of time series of monitoring data (seismic, deformation, gas) and to other volcanoes that have a significant number of past events.

Lava dome

Effusive eruption

Phreatic

Volcanic hazards

Phreatic eruption

10.1016/j.jvolgeores.2022.107537

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Citations (3)

Volcanic ash classification through Machine Learning

Authorea (Authorea) (2023)

Damià Benet Fidel Costa Christina Widiwijayanti

Volcanic ash provides information that can help understanding the evolution of volcanic activity during the early stages of a crisis, and possible transitions towards different eruptive styles. Ash consists of particles from a range of origins in the volcanic system and its analysis can be indicative of the processes driving activity. However, classifying ash particles into different types is not straightforward. Diagnostic observations for particle classification are not standardized and vary across samples. Here we explore the use of machine learning (ML) to improve the classification accuracy and reproducibility. We use a curated database of ash particles (VolcAshDB) to optimize and train two ML-based models: an Extreme Gradient Boosting (XGBoost) that uses the measured physical attributes of the particles, from which predictions are interpreted by the SHAP method, and a Vision Transformer (ViT) that classifies binocular, multi-focused, particle images. We find that the XGBoost has an overall classification accuracy of 0.77 (macro F1-score), and specific features of color (hue_mean) and texture (correlation) are the most discriminant between particle types. Classification using the particle images and the ViT is more accurate (macro F1-score of 0.93), with performances across eruptive styles from 0.85 in dome explosion, to 0.95 for phreatic and subplinian events. Notwithstanding the success of the classification algorithms, the used training dataset is limited in number of particles, ranges of eruptive styles, and volcanoes. Thus, the algorithms should be tested further with additional samples, and it is likely that classification for a given volcano is more accurate than between volcanoes.

Phreatic

Volcanic ash

10.22541/essoar.169447359.93026994/v1

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Citations (2)

WOVOdat: A Worldwide Volcano Unrest Database

EGU General Assembly Conference Abstracts (2019)

Christina Widiwijayanti Fidel Costa Nang Thin Zar Win Erickson Fajiculay Christopher G. Newhall

Unrest

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Citations (0)

Lithospheric structure and geodynamic evolution of the northern Molucca Sea area constrained by 3-D gravity field inversion of a combined sea-surface and satellite gravity dataset

EAEJA (2003)

Christina Widiwijayanti Christel Tiberi M. Diament Christine Deplus В. О. Михайлов

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Pyroclastic Flow Generated Tsunami Waves Detected by CALIPSO Borehole Strainmeters at Soufriere Hills, Montserrat During Massive Dome Collapse: Numerical Simulations and Observations

AGUFM (2004)

E. J. van Boskirk B. Voight P. Watts Christina Widiwijayanti G. S. Mattioli

Dome (geology)

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VolcAshDB: a Volcanic Ash DataBase of classified particle images and features

Bulletin of Volcanology (2024)

Damià Benet Fidel Costa Christina Widiwijayanti John S. Pallister Gabriela Pedreros

Abstract Volcanic ash provides unique pieces of information that can help to understand the progress of volcanic activity at the early stages of unrest, and possible transitions towards different eruptive styles. Ash contains different types of particles that are indicative of eruptive styles and magma ascent processes. However, classifying ash particles into its main components is not straightforward. Diagnostic observations vary depending on the magma composition and the style of eruption, which leads to ambiguities in assigning a given particle to a given class. Moreover, there is no standardized methodology for particle classification, and thus different observers may infer different interpretations. To improve this situation, we created the web-based platform Volcanic Ash DataBase (VolcAshDB). The database contains > 6,300 multi-focused high-resolution images of ash particles as seen under the binocular microscope from a wide range of magma compositions and types of volcanic activity. For each particle image, we quantitatively extracted 33 features of shape, texture, and color, and petrographically classified each particle into one of the four main categories: free crystal, altered material, lithic, and juvenile. VolcAshDB ( https://volcash.wovodat.org ) is publicly available and enables users to browse, obtain visual summaries, and download the images with their corresponding labels. The classified images could be used for comparative studies and to train Machine Learning models to automatically classify particles and minimize observer biases.

Volcanic ash

Texture (cosmology)

Unrest

Particle (ecology)

10.1007/s00445-023-01695-4

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Citations (6)

WOVOdat – A Worlwide Volcano Unrest Database – aimed at improving eruption forecasts

Christina Widiwijayanti Fidel Costa Thin Zar Win Nang Erickson Fajiculay Chris Newhall

Unrest

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