A Quantitative Approach to the Analysis of Reactive Mineralogy and Surface Area

Abstract

The textural patterns of geologic materials both reflect and control reactive mineralogy and transport. This is true for a wide range of geochemical reactions. The chemistry of fluids controlled by dissolution during transport depends on the mineralogy to which those fluids have access, either directly on pore walls or fractures, or through diffusive or grain boundary transport in the bulk rock. Similarly, mineral growth during metamorphic, igneous, and diagenetic processes and the final mineral distribution depends on the accessibility of the necessary components to the growing phases and thus on their spatial distribution. To either derive petrologic information from a given rock sample, or to predict fluid chemistry during reactive transport, the geometric relationships between the pore structure and mineralogy of the sample must be quantified. This paper describes a method, based on two-point autocorrelation/crosscorrelation analysis, by which the statistical distribution of distances between the phases, including the pore space, in a given sample may be quantified as the percentage likelihood that a given phase lies at a given distance from any other phase (or the pore space or pore boundary) in that sample, and suggests how those results can be used to quantify reactive mineralogy. This approach is not data source dependent; any two-or three-dimensional image can be analyzed. The results are limited only by the quality of the imagery. Analysis of three examples, one from the Mt. Simon Sandstone in the Illinois Basin and two from the Nagaoka CO 2 sequestration site in Japan, demonstrates the method.

Coordinated neutron and X‐ray computed tomography of meteorites: Detection and distribution of hydrogen‐bearing materials

“…The anaglyph hints at some properties of the CC that could be recovered by NCXT, for instance, size distribution, roundness/sphericity in relation to size, and spatial distribution, which can be analyzed quantitatively to describe the texture (cf. Anovitz et al., 2022). While one expects CC fragments in a breccia like this to be randomly distributed, other objects in other meteorites might show nonrandom distributions.…”

“…These data both provide a guide to more specific sampling and direct determination of threedimensional structures prior to more quantitative textural analysis (cf. Anovitz et al, 2022).…”

Role of Mineralogy in Controlling Fracture Formation

“…Autocorrelation/Cross-Correlation. A two-point autocorrelation and cross-correlation analysis was used to evaluate and compare the spatial heterogeneities of mineral distributions using the approach from Anovitz et al 53 (cf. Anovitz and Cole 54 ).…”

“…As detailed in Anovitz et al, 53 to perform this calculation, a series of characteristic functions f(x) with values of only 1 and 0 must first be defined for each image. For this, 15 binary images were created representing quartz, carbonate, clay, pore, and other (encompassing all phases not previously categorized) pixels and every pair combination of the five categories.…”

In Situ Observations of Barium Sulfate Nucleation in Nanopores

“…This is in close agreement with SAXS measurements (Supporting Information, Figure S2 and Table S1). In addition, the pore−pore autocorrelation 50 was calculated for six TEM images (see the Supporting Information for details on the autocorrelation calculation, Figure S3). The surface area determined by nitrogen BET analysis was 174.86 m 2 /g, of which 166.38 m 2 /g reflects internal pore surface area.…”