Using Sieving and Unknown Sand Samples for a Sedimentation-Stratigraphy Class Project with Linkage to Introductory Courses

Article excerpt

ABSTRACT

Using sieving and sample "unknowns" for instructional grain-size analysis and interpretation of sands in undergraduate sedimentology courses has advantages over other techniques. Students (1) learn to calculate and use statistics; (2) visually observe differences in the grain-size fractions, thereby developing a sense of specific size ranges, weight percentages being plotted, and how grain composition and properties are a function of size; (3) are enthusiastic and observant as they search for clues of the origin of their sample, but discover that determining depositional environments using grain-size analysis is not the hoped for "fingerprinting" technique; and (4) enjoy learning the geographic origin and depositional environment of their sample. Plus, sieving equipment generally is less costly to acquire and maintain than "black box" techniques, and sieving is a commonly used procedure in industry. Using unknown sand samples results in some students making "incorrect" interpretations, which allows for illustrating that a scientist may have an excellent data set and a valid interpretation based on that data but, although the data are sound, the interpretation may be erroneous. Moreover, using a suite of unknowns allows students in a class to collectively be exposed to sands from a range of environments and geographic locales without the need for local sediment-rich environments. Building a set of unknowns is aided by recruiting students in introductory geology courses to collect and document samples during their school-break travels for future "unknowns," thereby linking courses and creating interest among nonmajors. When these students venture into the field to collect a sample and characterize the environment, they are enticed into thinking about sedimentary processes and possible anthropogenic effects, such as beach nourishment, while on break. © 2012 National Association of Geoscience Teachers. [DOI: 10.5408/11-279.1]

Key words: size analysis, sieving, sand, statistics, textural parameters, provenance

INTRODUCTION

Sediment size analysis rightfully remains an essential laboratory exercise in undergraduate sedimentology courses. Beyond using sediment grain-size and textural parameters to describe sediments and sedimentary rocks, and performing fundamental tasks such as facies analysis (e.g., Barnhart et al., 2002), such basic data are also used to evaluate the economic potential of sediments and sedimentary rocks. In addition, standardized grain-size analysis techniques provide students with valuable skills that are used in sedimentology for a variety of other tasks including heavy mineral studies (e.g., Sawakuchi et al., 2009) and tectonic provenance determination (e.g., Ingersoll and Estmond, 2007), as well as in a variety of other disciplines including engineering geology (e.g., King and Gavin, 2002), glaciology (e.g., Hubbard et al., 2004), magnetics (e.g., Hatfield et al., 2010), and volcanology (e.g., Wohletz et al., 1989; Saracchi et al., 2011). In addition to teaching students a useful skill, grain-size analysis exercises can help teach students the difference between obtaining a valid data set (their "results") that will stand the test of time, and their "interpretation" of that data set, which may be incorrect due to limitations of the data and the complexities of the natural world. This latter lesson is just as enlightening for the students as is obtaining the data set.

Using sediment-size distributions to help define depositional settings is going into its third century of application, although experts concede "empirical understanding and modeling of sedimentary morphodynamic process-response systems through their particle-size distributions has remained an elusive problem to this day" (Hartmann and Flemming, 2007, p. 333). However, in the process of utilizing grain-size distributions to address questions in loose boundary hydraulics and sedimentology, analytical techniques have evolved that have a much wider application, as noted above. …