优化形状分析,量化火山灰形态

GeoResJ Pub Date : 2015-12-01 DOI:10.1016/j.grj.2015.09.001
E.J. Liu, K.V. Cashman, A.C. Rust
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引用次数: 134

摘要

火山灰形态的精确测量对于提高我们对破碎过程的理解和预测颗粒行为的能力至关重要。在本研究中,我们提出了选择和应用与火山灰表征相关的形状参数的新方法。首先,我们比较了不同成像技术的形状测量,包括横断面(2-D)和投影面积图像,并讨论了它们各自的应用。然后,我们将重点放在可以从二维图像的形状分析中获得的特定信息上。使用聚类分析作为一种无偏方法来识别颗粒形态的关键控制,我们发现四个形状参数-固体度,凹凸度,轴比和形状因子-可以有效地解释大多数灰样品中的形态差异。重要的是,这些参数被缩放到0到1之间的值,因此对判别图的贡献是均匀的。特别是,凸度和固体度的共变可以根据特征气泡特性来区分不同的幼灰分成分。通过减少对自然样品的观察,简化灰的几何形状,我们量化了与气泡和颗粒的相对大小和形状变化相关的形态变化。利用这种关系,我们评估了尺寸依赖形状分析的潜在应用,以推断潜在的气泡尺寸分布,从而预测破碎前条件。最后,我们表明,包括所有可用粒度的颗粒形状分析不仅可以提供颗粒大小和形状的测量,还可以提供尺寸相关密度的信息。
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Optimising shape analysis to quantify volcanic ash morphology

Accurate measurements of volcanic ash morphology are critical to improving both our understanding of fragmentation processes and our ability to predict particle behaviour. In this study, we present new ways to choose and apply shape parameters relevant to volcanic ash characterisation. First, we compare shape measurements from different imaging techniques, including cross-sectional (2-D) and projected area images, and discuss their respective applications. We then focus on specific information that can be obtained from shape analysis of 2-D images. Using cluster analysis as an unbiased method to identify key controls on particle morphology, we find that four shape parameters – solidity, convexity, axial ratio, and form factor – can effectively account for the morphological variance within most ash samples. Importantly, these parameters are scaled to values between 0 and 1, and therefore contribute evenly to discrimination diagrams. In particular, co-variation in convexity and solidity can be used to distinguish different juvenile ash components based on characteristic bubble properties. By reducing observations of natural samples to simplified ash geometries, we quantify morphological changes associated with variations in the relative size and shape of bubbles and particles. Using this relationship, we assess the potential application of size-dependent shape analysis for inferring the underlying bubble size distribution, and thus the pre-fragmentation conditions. Finally, we show that particle shape analysis that includes the full range of available grain sizes can contribute not only measurements of particle size and shape, but also information on size-dependent densities.

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