Friday, 29 July 2011

TOP 10 AM papers: Riley et al. 1989, Hydrobiologia 176/177, 509-524

QEMSCAN mineral map of fluvial sediments collected from the 2010 Brisbane flood
Review of Riley et al. 1989, Hydrobiologia 176/177, 509-524

In terms of early adopters of automated mineralogy technology beyond mineral processing, due credit must be given to Riley, Creelman, Warner, Greenwood-Smith and Jackson from Australia. In their groundbreaking journal paper on "The potential in fluvial geomorphology of a new mineral identification technology (QEM*SEM)" they pioneer the application of automated SEM-EDS compositional mapping to the study of depositional environments and sediment sources. I only became aware of this paper after publishing a similar study on late Pleistocene flood deposits in the Flinders Ranges, South Australia as part of my PhD thesis, and would like to take this opportunity to give due credit to a study well ahead of it's time.

Riley et al. set out explaining why mineral analysis remains an underutilized diagnostic tool in fluvial geomorphology and sedimentology. Optical microscopy on fine fluvial sediments is cumbersome, statistically questionable and of limited value when it comes to discriminating source areas based on clay mineralogy.

The objective the authors face is to establish the maximum flood level the Nepean/Warragamba Rivers in New South Wales over the recent geological past. The deeply entrenched river terraces in the lower reaches are inappropriate indicators because they represent a different hydrological regime. In the absence of slack water deposits, veneers of alluvium mixed with colluvium by bioturbation remain the only record.

The task was to perform micron-scale textural analysis and discriminate non-fluvial from fluvial material as well as to quantify the sediment contributions from different tributaries in the catchment. Challenges in fingerprinting and provenancing fluvial sediments by mineralogical analysis include sorting and differential comminution during transport, post-depositional weathering, contamination by reworking and aeolian deposition, and mixing of sediments from different source rocks. All this requires a focus on minerals resistant to weathering, and sophisticated statistical analysis of the data.

The methodology section is exceptional. The paper provides detailed information on the QEM*SEM system configuration including a schematic diagram and a technical discussion of the data acquisition and processing in the appendix. The authors developed their own application-specific mineral identification protocol (SIP) and primary mineral lists, differentiating readily identifiable mineral species from broadly related silicate groups. This was at a time when no interactive Measurement Debug module for SIP development was available as in later versions of the iDiscover software package. Unfortunately, it was also before the powerful categorizer tools were developed that provide classification of particles by mineral association, size and shape. As a result, the authors had to make do with modal mineralogy data for 4 physical size fractions. Riley et al. clearly set an example by applying principle component analysis to produce and assess independent variables. The spatial relationship between minerals from different sampling locations was investigated by multiple cluster analysis.

The results are convincing and clearly differentiating fluvial from non-fluvial deposits by QEM*SEM mineralogical data. The conclusion was that the estimate of Probable Maximum Flood discharge for the downstream dam had to be revised. The conclusion after reading this paper is that sedimentologist can clearly benefit from revisiting this pioneering paper before applying the latest image analysis capabilities that compositional mapping solutions such as QEMSCAN provide.

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