Stratified Sampling to Define Levels of Petrographic Variation in Coal Beds: Examples from Indonesia and New Zealand - Moore, Tim A. (Indonesian Journal on Geoscience, 2016)
DOI: 10.17014/ijog.3.1.29-51Stratified sampling of coal seams for petrographic analysis using block samples is a viable alternative to standard methods of channel sampling and particulate pellet mounts. Although petrographic analysis of particulate pellets is employed widely, it is both time consuming and does not allow variation within sampling units to be assessed - an important measure in any study whether it be for paleoenvironmental reconstruction or in obtaining estimates of industrial attributes. Also, samples taken as intact blocks provide additional information, such as texture and botanical affinity that cannot be gained using particulate pellets. Stratified sampling can be employed both on ‘fine’ and ‘coarse’ grained coal units. Fine-grained coals are defined as those coal intervals that do not contain vitrain bands greater than approximately 1 mm in thickness (as measured perpendicular to bedding). In fine-grained coal seams, a reasonable sized block sample (with a polished surface area of ~3 cm2) can be taken that encapsulates the macroscopic variability. However, for coarse-grained coals (vitrain bands >1 mm) a different system has to be employed in order to accurately account for the larger particles. Macroscopic point counting of vitrain bands can accurately account for those particles>1 mm within a coal interval. This point counting method is conducted using something as simple as string on a coal face with marked intervals greater than the largest particle expected to be encountered (although new technologies are being developed to capture this type of information digitally). Comparative analyses of particulate pellets and blocks on the same interval show less than 6% variation between the two sample types when blocks are recalculated to include macroscopic counts of vitrain. Therefore even in coarse-grained coals, stratified sampling can be used effectively and representatively.