Past industry experience has shown that when geoscientists start to think about sediment provenance, people’s first thoughts often go straight to traditional methods such as petrographic point counting and heavy mineral analyses.  However, it has long been known that the resistance of heavy minerals to weathering and transportation is highly variable and can therefore alter provenance signals.

“Perhaps the most general problem is that of heavy-mineral resistance to both mechanical and chemical processes…The co-operation of all sedimentary petrologists is needed in solving these major problems.” – Sindowski, 1949.

The complexity of heavy mineral analyses means that only a small minority of specialist labs still focus on the method whilst fully taking into account all possible source of bias (e.g. hydraulic sorting effects on grain size, and chemical and mechanical weathering). Automated mineralogy is becoming increasingly popular but still brings its own set of problems. Just as traditional point-counting relies on the experience of the operator, automated mineralogy is highly dependent on the dictionaries used to calibrate the software. However, the reproducibility and the number of samples that can be run means that more data (some might say more noise) can be generated and more samples can be analysed in-situ, removing potential mineral separation bias.

Current academic provenance studies tend to focus more on robust single-grain geochronological techniques, whole-rock radiogenic isotopes or thermochronology. U-Pb zircon geochronology in particular continues to gain popularity when it comes to detrital provenance studies (Spencer et al., 2016).


Figure 1. The increase in popularity of single grain U-Pb analyses (Spencer et al., 2016)

As the popularity of zircon studies continues to rise, an increasing number of studies are also highlighting how the diagenesis of heavy mineral assemblages under burial can severely alter provenance signatures (e.g. Morton and Hallsworth, 2007; Milliken, 2007; Garzanti et al., 2010; Ando et al., 2012; Garzanti et al., 2018). Unstable minerals are rapidly leached out down-section whilst moderately stable minerals increase their relative abundance, giving a skewed representation of the original heavy mineral assemblages associated with a given source area.

“Interpretation of provenance using heavy-mineral data from sandstones likely to have suffered burial diagenesis must carefully consider the possibility that some heavy-mineral species have been eliminated through dissolution.” – Morton and Hallsworth, 2007.


Figure 2. Heavy-mineral dissolution in the Nile Delta succession (Garzanti et al., 2018). This figure illustrates the chemical stability of unstable to stable heavy minerals in Miocene to Pleistocene Nile Delta sands. Unstable heavy minerals are absent in the oldest Nile Delta sample. In the past this has been interpreted as the absence of a particular hinterland (the Ethiopian Highlands), although this has now been shown not to be the case (Fielding et al., 2017).

Geoscientists in industry and academia alike are becoming more aware of this source of bias and are approaching the method with caution. Heavy mineral laboratories such as that at the University of Milano-Bicocca specialise in untangling the complexities of heavy mineral analyses whilst others incorporate the technique into studies using an integrated, multi-disciplinary approach.

Diagenesis aside, the method by which petrographic and heavy mineral data is arrived at has recently come under scrutiny. Dr István Dunkl from the University of Göttingen presented the findings of an Inter-laboratory Comparison for Heavy Mineral Analysis at this year’s Working Group on Sediment Generation (WGSG) in Dublin. The aim of the Heavy Mineral Round Robin was to find a common language when reporting point-counted heavy mineral data. This required each participant to point count two synthetic heavy mineral mixtures to compare the identification of heavy mineral species and quantify their proportions. Trained operator and automated mineralogy techniques were both used as a comparison and found varying results, with the automated methods proving to be much more accurate and reproducible. Several explanations were discussed as to why this could be:

1.      Counting statistics varied across all laboratories.

2.     Aliquot separation techniques. Numerous methods were reported when describing the preparation of the aliquot.

3.     Mineral identification vs operator experience. Mineral identification was inconsistent regardless of operator experience. In many cases, some operators did not detect all 8 mineral phases and occasionally added up to 5 or 6 phases which were not present in the sample at all.

The presentation was certainly an eye opener and I believe the intention is for the findings to be submitted to Sedimentary Geology this autumn. There was talk of a second phase of comparisons where it was suggested that the samples be pre-processed to reduce variation in results based on aliquot separation techniques.

This great study highlights issues not only within the method of heavy mineral point counting but also biases that may occur within other provenance techniques. It also reinforces the need for standardisation when it comes to recording heavy mineral point counts. Wouldn’t it be easier to compare like-for-like if counts were published as points as well as percentages? In the past I have attempted to amalgamate large provenance datasets and have found petrographic and heavy mineral counts to be the most difficult method to standardise (that and fission track!).

Let’s not forget that many heavy mineral studies do work well when specific problems are addressed with a through understanding of sources of bias (e.g. Kilhams et al., 2013; Morton and Milne, 2012).  Heavy mineral analyses of the Clair Group (Morton and Milne, 2012) has been very successful and enables high resolution correlation between wells. This is likely due to factors such as no operator or laboratory variability, and a well understood reservoir, where heavy minerals are a proven discriminator.


Figure 3. High-resolution heavy mineral analyses in use down section in the Clair Group (Morton and Milne, 2012). Analyses focus on the more stable heavy mineral indices, which is key as you can see the point at which the unstable (Unst) heavy mineral concentrations drop off within Unit V .

There is no one ‘silver-bullet’ for provenance studies and the multi-disciplinary approach is key when it comes to accurately recording the evolution of a source-to-sink system.

“Detangling the various interacting factors controlling mineralogical and chemical compositional variability is a fundamental pre-requisite to improve decisively not only on our ability to unravel provenance, but also to understand much about climatic, hydraulic, and diagenetic processes.” Garzanti et al., 2010

For the purposes of this article I have focussed primarily on petrography and heavy mineral analyses. However, surely all other provenance techniques can also be subjected to this kind of bias and alteration? Perhaps the new LinkedIn “Source to Sink” Group could be used as a platform to discuss other sources of bias such as:

–       The controls of mineral distribution on radiogenic isotope concentrations (e.g. Garcon et al., 2014).

–       Introducing bias to U/Pb zircon studies. Hand picking, CL and target analyses vs the ‘blind-dating’ approach (e.g. Fielding et al., 2017 and Garzanti et al., 2018).

–       Hydraulic sorting and biasing effects on ALL provenance techniques (e.g. Malusa et al., 2016, Cascalho and Fradique, 2007, and Nesbitt et al., 1996).

 By Laura Fielding, Geoscience Director.

References

Andò, S., Garzanti, E., Padoan, M. and Limonta, M., 2012. Corrosion of heavy minerals during weathering and diagenesis: a catalog for optical analysis. Sedimentary Geology, 280, pp.165-178.

Cascalho, J. and Fradique, C., 2007. The sources and hydraulic sorting of heavy minerals on the northern Portuguese continental margin. Developments in Sedimentology, 58, pp.75-110.

Fielding, L., Najman, Y., Millar, I., Butterworth, P., Ando, S., Padoan, M., Barfod, D. and Kneller, B., 2017. A detrital record of the Nile River and its catchment. Journal of the Geological Society, 174(2), pp.301-317.

Garçon, M., Chauvel, C., France-Lanord, C., Limonta, M. and Garzanti, E., 2014. Which minerals control the Nd–Hf–Sr–Pb isotopic compositions of river sediments? Chemical Geology, 364, pp.42-55.

Garzanti, E., Andò, S., Limonta, M., Fielding, L. and Najman, Y., 2018. Diagenetic control on mineralogical suites in sand, silt, and mud (Cenozoic Nile Delta): Implications for provenance reconstructions. Earth-Science Reviews, 185, pp.122-139.

Kilhams, B., Morton, A., Borella, R., Wilkins, A. and Hurst, A., 2013. Understanding the provenance and reservoir quality of the Sele Formation sandstones of the UK Central Graben utilizing detrital garnet suites. Geological Society, London, Special Publications, 386, pp.SP386-16.

Milliken, K.L., 2007. Provenance and diagenesis of heavy minerals, Cenozoic units of the northwestern Gulf of Mexico sedimentary basin. Developments in Sedimentology, 58, pp.247-261.

Morton, A.C. and Hallsworth, C., 2007. Stability of detrital heavy minerals during burial diagenesis. Developments in Sedimentology, 58, pp.215-245.

Morton, A. and Milne, A., 2012. Heavy mineral stratigraphic analysis on the Clair Field, UK, west of Shetlands: a unique real-time solution for red-bed correlation while drilling. Petroleum Geoscience, 18, pp.115-128.

Nesbitt, H.W., Young, G.M., McLennan, S.M. and Keays, R.R., 1996. Effects of chemical weathering and sorting on the petrogenesis of siliciclastic sediments, with implications for provenance studies. The Journal of Geology, 104(5), pp.525-542.

Sindowski, F.K.H., 1949. Results and problems of heavy mineral analysis in Germany; a review of sedimentary-petrological papers, 1936-1948. Journal of Sedimentary Research, 19(1), pp.3-25.

Spencer, C.J., Kirkland, C.L. and Taylor, R.J., 2016. Strategies towards statistically robust interpretations of in situ U–Pb zircon geochronology. Geoscience Frontiers, 7(4), pp.581-589.