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Extracting total grain size distribution data for ash fall deposits from densely populated volcanoes (Chaparraristique volcano, El Salvador)

Extracting total grain size distribution data for ash fall deposits from densely populated volcanoes (Chaparraristique volcano, El Salvador)
从人口稠密的火山(查帕拉里斯蒂克火山,萨尔瓦多)提取火山灰沉积物的总粒度分布数据
批准号:
NE/M005003/1
负责人:
Richard Brown
金额:
$4.43万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2014
资助国家:
英国
项目状态:
已结题
起止时间:
2014 至 --

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中文摘要
翻译
正如冰岛最近的火山爆发所表明的那样,火山灰云对人民、基础设施和航空构成直接危害。火山云如何在大气中扩散取决于火山爆发的特征(例如,喷发的体积和速率、粒度、喷发柱的高度)和大气条件(例如,风速和风向)。火山灰咨询中心(VAAC)使用计算机模型来预测火山爆发期间的灰云扩散,但为了使这些模型准确,它们需要从实时观测(卫星,空中和地面)获得的高质量数据集以及以前类似火山爆发的详细数据集(例如粒度分布)。这类数据集稀缺,是模型输出不确定性的重要来源。全球只有少数高质量的数据集,这些数据集偏向于大规模喷发和火山喷发成分。较小规模的喷发,发生频率较高,在区域和次区域范围内仍有相当大的影响,在这些数据集中的代表性很差。这是由于火山爆发的短暂性,减少了观察的机会,较薄的沉积物更快地被侵蚀。从最近的火山爆发中收集定量的现场数据的机会对科学来说是非常有价值的。 2013年12月29日,萨尔瓦多的查帕拉斯蒂克火山(又名圣米格尔火山)爆发,产生了约10公里高的羽状物,漂过全国,并将火山灰沉积在100公里以外。它是萨尔瓦多萨尔瓦多最活跃的火山之一,历史记录表明,这是300年来最强大的火山爆发。火山方圆3公里内的5000人被疏散。这座火山距离萨尔瓦多第三大城市圣米格尔12公里。尽管历史上有许多火山爆发,而且风险很高,但火山的定量数据很少,对其地质历史也知之甚少。由于热带气候,历史上喷发的火山灰层保存得很差。这项研究将获得关于2013年查帕雷蒂克火山爆发沉积物的独特定量数据集,以更好地了解火山爆发的原因,找出为什么它比过去的火山爆发更强大,并在沉积物被降雨清除之前完全覆盖。其中一些数据将用于绘制火山的概率危险图。这将有助于地方当局计划未来在一系列风力条件下从火山爆发类似的喷发。令人遗憾的是,喷发发生在旱季,火山灰沉积物在地面上保存得相当完好。迫切需要在4月底的雨季之前收集数据,这将剥离火山灰并增加火山泥流(雨水引发的火山灰浆)的风险。研究目标是:(1)获得综合的火山灰下落存款数据集;(2)绘制火山灰下落存款的厚度和粒度图;(3)计算重要的喷发参数(4)利用12月29日喷发羽流的目视和遥感观测数据对这些数据进行校准;(5)对火山灰进行结构、光学、形态和地球化学表征,以研究喷发过程。其中一些数据将用于建立火山灰扩散模型,并使用免费提供的火山灰运输和扩散分析模型(VATDM)对Chaparrastique进行概率危险评估。我们将把这些结果分发给萨尔瓦多的有关当局,并将粒度分布和折射率等关键数据分发给华盛顿VAAC和全球,以确保产生最大的影响。研究结果将有助于世界各地的机构了解类似火山爆发所造成的危害。
英文摘要
As recent eruptions in Iceland have shown, volcanic ash clouds present immediate hazards to people, infrastructure and aviation. How volcanic clouds are dispersed through the atmosphere is dependent on both the characteristics of the eruption (e.g., volume and rate of eruption, grain size, height of eruption column) and atmospheric conditions (e.g., wind speed and direction). Volcanic Ash Advisory Centres (VAAC) use computer models to forecast ash clouds dispersal during an eruption, but in order for these models to be accurate, they require high quality datasets gained from real-time observations (satellite, airborne and ground-based) and detailed datasets from previous similar eruptions (e.g. grainsize distribution). Such datasets are scarce and are a significant source of uncertainty in model outputs. There are only a handful of high quality datasets available globally and these are skewed towards large eruptions and silicic compositions. Smaller volume eruptions, which occur on a higher frequency and are still of considerable impact on a regional and sub-regional scale, are poorly represented in these datasets. This is due to the short-lived nature of the eruptions, which reduces opportunities for observation, and the thinner deposits are more quickly eroded. Opportunities to gather quantitative, field-derived data from a recent eruption is extremely valuable to science. On the 29th December 2013, Chaparrastique volcano (aka Volcán San Miguel) in El Salvador, erupted and produced a ~10 km-high plume that drifted across the country and deposited ash > 100 km away. It is one of El Salvador's most active volcanoes and historical records indicate that this was its most powerful eruption for 300 years. Five thousand people were evacuated within a 3 km radius of the volcano. The volcano lies 12 km from El Salvador's third largest city, San Miguel. Despite the many historical eruptions, and the high risk posed, few quantitative data exist for the volcano and little is known about its geologic history. Ash layers from historic eruptions are poorly preserved due to the tropical climate. This research will obtain a unique quantitative dataset on the deposits from the 2013 Chaparraristique eruption, to better understand the causes of the eruption, find out why it was more powerful than its past eruptions, and fully characterise the deposits before they are removed by rainfall. Some of the data will be used to create probabilistic hazard maps for the volcano. These will help the local authorities plan for similar eruptions from the volcano in the future under a range of wind conditions. Usefully, the eruption occurred during the dry season and the ash deposits remain reasonably well preserved on the ground. There is an urgent need collect data prior to the wet season at the end of April that will strip away the ash and increase the risk of lahars (rain-triggered ash slurries). The research objectives are: (1) obtain a comprehensive ash fall deposit dataset; (2) construct thickness and grainsize maps for the ash fall deposit; (3) calculate important eruption parameters (total grain size distribution, constrain dispersion, eruption volume, column height and mass discharge rates); (4) calibrate these with visual and remote observations of 29th December eruption plume; (5) carry out textural, optical, morphological and geochemical characterisations of the ash to investigate eruption processes. Some of these data will be used to model the ash dispersion and develop probabilistic hazard assessments for Chaparrastique using freely available analytical Volcanic Ash Transport and Dispersal Models (VATDM). We will disseminate these results to interested authorities in El Salvador, and critical data such as grainsize distribution and refractive index will be distributed to the Washington VAAC and globally to ensure maximum impact. The results will help agencies worldwide in understanding the hazards posed by similar eruptions.
期刊论文(2)
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科研奖励(0)
会议论文
Reconstruction of the 29th December 2013 eruption of San Miguel volcano, El Salvador, using video, photographs, and pyroclastic deposits
使用视频、照片和火山碎屑沉积物重建 2013 年 12 月 29 日萨尔瓦多圣米格尔火山喷发的情况
DOI: 10.30909/vol.05.02.271293
发表时间: 2022
期刊: Volcanica
影响因子: --
作者: [Brown R]
通讯作者: Brown R
DOI: 10.1016/j.chemgeo.2016.11.015
发表时间: 2017-01
期刊: Chemical Geology
影响因子: 3.9
作者: [P. Scarlato;S. Mollo;E. Bello;A. Quadt;Richard J. Brown;E. Gutiérrez;B. Martinez-Hackert;P. Papale]
通讯作者: P. Scarlato;S. Mollo;E. Bello;A. Quadt;Richard J. Brown;E. Gutiérrez;B. Martinez-Hackert;P. Papale
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    1939955
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    Standard Grant
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    $22.49万
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