Article ID Journal Published Year Pages File Type
507038 Computers & Geosciences 2013 8 Pages PDF
Abstract

The bubble-size distribution in 2.7 billion year old lava flows can be used as a proof of concept illustrating a new set of techniques for measuring volumes of geological materials with variable density contrasts using high-resolution X-ray computed tomography. Such studies have been limited in the past to high-contrast situations such as vesicles devoid of secondary fill. We present a new dynamic thresholding method for computationally separating amygdules from their basaltic matrix in X-ray images that is based on a technique used in seismology. The technique is sensitive to the gradient of the gray-scale value, rather than an absolute threshold value often applied to an entire set of X-ray images. Additionally, we present statistical methods for extrapolating the volumetric measurement mean and standard deviation of amygdules in the measured samples to the entire population in the flow. To do so, we create additional amygdule sample sets from the original sample set in the process of ‘bootstrap’ resampling, and use the Central Limit Theorem to calculate the mean and standard deviation of the amygdule population from these sample sets. This suite of methods allows the extension of bubble-size distribution studies typically done on modern flows to the ancient rock record and potentially has many other uses in geosciences where quantitative discrimination between materials with a range of densities is required.

► We present methods to measure the dimensions of geological materials with X-ray. ► We focus on materials with variable-density contrast compared to their surroundings. ► We measure the mean volume and standard deviation of amygdules in 2.7 billion year old lava flows. ► We identify the minerals composing the amygdules using thin-section microscopy. ► We present statistical methods that extend results to the population of amygdules.

Related Topics
Physical Sciences and Engineering Computer Science Computer Science Applications
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