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首页> 外文期刊>Journal of Volcanology and Geothermal Research >In situ terminal settling velocity measurements at Stromboli volcano: Input from physical characterization of ash
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In situ terminal settling velocity measurements at Stromboli volcano: Input from physical characterization of ash

机译：Stromboli火山的原位终端沉降速度测量：来自灰分物理特征的输入

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Ash particle terminal settling velocity is an important parameter to measure in order to constrain the internal dynamics and dispersion of volcanic ash plumes and clouds that emplace ash fall deposits from which source eruption conditions are often inferred. Whereas the total Particle Size Distribution (PSD) is the main parameter to constrain terminal velocities, many studies have empirically highlighted the need to consider shape descriptors such as the sphericity to refine ash settling velocity as a function of size. During radar remote sensing measurements of weak volcanic plumes erupted from Stromboli volcano in 2015, an optical disdrometer was used to measure the size and settling velocities of falling ash particles over time, while six ash fallout samples were collected at different distances from the vent. We focus on the implications of the physical parameters of ash for settling velocity measurements and modeling. Two-dimensional sizes and shapes are automatically characterized for a large number of ash particles using an optical morpho-grainsizer MORPHOLOGI G3. Manually sieved ash samples show sorted, relatively coarse PSDs spanning a few microns to 2000 mu m with modal values between 180 and 355 mu m. Although negligible in mass, a population of fine particles below 100 mu m form a distinct PSD with a mode around 5-20 mu m. All size distributions are offset compared to the indicated sieve limits. Accordingly, we use the diagonal of the upper mesh sizes as the upper sieve limit. Morphologically, particles show decreasing average form factors with increasing circle-equivalent diameter, the latter being equal to 0.92 times the average size between the length and intermediate axes of ash particles. Average particle densities measured by water pycnometry are 2755 +/- 50 kg m(-3) and increase slightly from 2645 to 2811 kg m(-3) with decreasing particle size. The measured settling velocities under laboratory conditions with no wind, 3.6 m s(-1), are in agreement with the field velocities expected for particles with sizes 460 mu m. The Ganser (1993) empirical model for particle settling velocity is the most consistent with our disdrometer settling velocity results. Converting disdrometer detected size into circle equivalent diameter shows similar PSDs between disdrometer measurements and G3 analyses. This validates volcanological applications of the disdrometer to monitor volcanic ash sizes and settling velocities in real-time with ideal field conditions. We discuss ideal conditions and the measurement limitations. In addition to providing sedimentation rates in-situ, calculated reflectivities can be compared with radar reflectivity measurements inside ash plumes to infer first-order ash plume concentrations. Detailed PSDs and shape parameters may be used to further refine radar-derived mass loading retrievals of the ash plumes. (C) 2019 Elsevier B.V. All rights reserved.
机译：为了限制火山灰羽流和云的内部动力学和散布，灰烬颗粒终末沉降速度是一个重要的测量参数，火山灰羽流和云层覆盖着灰烬沉积物，经常可以从中推断出喷发条件。尽管总粒度分布（PSD）是限制最终速度的主要参数，但许多研究从经验上强调了需要考虑形状描述符（例如球形度）以细化灰沉降速度随粒度变化的需要。在2015年对斯特龙博利火山喷发的弱火山羽进行雷达遥感测量期间，使用光学测速仪测量了随时间变化而下降的灰烬颗粒的大小和沉降速度，同时在距通风口不同距离处收集了六个灰烬沉降样品。我们关注灰分物理参数对沉降速度测量和建模的影响。使用光学形态粒度仪MORPHOLOGI G3，可以自动表征大量灰烬颗粒的二维尺寸和形状。手动筛分的灰分样本显示了经过分类的相对较粗的PSD，其跨度为几微米至2000微米，模态值介于180到355微米之间。尽管其质量可以忽略不计，但100微米以下的细颗粒群以5-20微米左右的模式形成了独特的PSD。与指定的筛分极限相比，所有尺寸分布均发生偏移。因此，我们将网格尺寸上限的对角线用作筛分上限。从形态上看，颗粒的平均形状系数随圆当量直径的增加而减小，后者等于灰分颗粒的长度和中间轴之间的平均尺寸的0.92倍。通过比重瓶比重法测得的平均颗粒密度为2755 +/- 50 kg m（-3），并且随着粒径的减小从2645略增至2811 kg m（-3）。在无风的实验室条件下测得的沉降速度<3.6 m s（-1），与尺寸<460μm的颗粒预期的实测速度一致。 Ganser（1993）的颗粒沉降速度经验模型与我们的测速仪沉降速度结果最一致。将测风头检测到的尺寸转换成圆当量直径后，在测风头测量和G3分析之间显示出相似的PSD。这验证了该测风仪在火山学方面的应用，可在理想的野外条件下实时监测火山灰的大小和沉降速度。我们讨论理想条件和测量限制。除了就地提供沉降速率外，还可以将计算出的反射率与灰羽内部的雷达反射率测量结果进行比较，以推断出一阶灰羽浓度。详细的PSD和形状参数可用于进一步优化灰羽的雷达衍生质量载荷检索。 （C）2019 Elsevier B.V.保留所有权利。

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