Sedimentary rocks are globally important for hosting resources and as potential sites for carbon capture and storage in a low carbon future. The basins that host these deposits are important archives of past environments that allow geoscientists to gain insights into how major geological processes such as tectonics, climate and base level (sea level) variations can influence our environment. Due to the complex interaction of these processes, the resultant deposits vary significantly in time and space and have a multitude of different characteristics. Predictive depositional models are essential to the interpretation of ancient sedimentary successions as they enable us to identify common facies and depositional architectures that ultimately allow us to predict how major processes will affect the characteristics of the deposits in time and space.
In addition to being important archives to the past, deposits within sedimentary basins contain societally important resources such as ground water, petroleum and minerals (e.g. copper and uranium), and in certain cases, geothermal energy. Furthermore, a detailed understanding of these systems will allow us to target them as sites for carbon storage. For such deposits, it is essential to understand how the reservoirs (commonly sandstone-rich deposits) are distributed and connected in three-dimensions. In order to successfully exploit and extract these resources, predictive models are essential in reducing uncertainty in exploration efforts, which often operate with sparse datasets that are spatially limited. However, to date many traditional predictive models are qualitative in nature. It is essential that geoscientists move towards using quantitative approaches if we are to truly understand the variation, and controls, on depositional sequences.
Predictive models for individual fluvial systems within a basin are reasonably well-established from both modern and ancient examples, with well-documented downstream trends (e.g. Fig 1; Owen at al., 2015). Quantitative basin-scale predictive models however, are in their relative infancy in continental sedimentary successions (e.g. Owen at al., 2018), but they have successfully provided a predictive framework that can now be applied to other basins. These models use a ‘systems-based’ approach whereby palaeogeographic models of the basin are developed based on statistical information on key characteristics such as palaeocurrent trends, grain size, channel-body and storey thickness. These quantitative observations provide powerful information on downstream trends compared to more traditional lithostratigraphic approaches.
Research in this area has inevitably focused on continental sedimentary basins. However, there are numerous examples worldwide of modern and ancient basins where sediments compete for space with volcanic units (lavas and pyroclastic rocks) and reworked volcaniclastic units (Fig. 2) (Heimdal et al., 2018). These complex mixed successions are relatively poorly studied, with only some examples that have been mapped and a lithostratigraphic scheme developed (Passey and Bell 2007; Brown et al., 2009; Williamson and Bell, 2012), and some offshore seismic studies (e.g. Hardman et al., 2018). However, such basins are of increasing importance societally as they too contain resources, and are now targeted for exploration as extraction in more traditional basins is exhausted. Furthermore, volcanic areas are also potential sites of geothermal interest.
The architecture of the sedimentary and volcaniclastic units in these mixed basins is extremely complex due to active tectonics coupled with volcanism. Volcanic units, in particular lavas, can exert considerable influence on accommodation space and sediment distribution. For example, lavas can block and divert rivers and modify topography, which in turn influences downstream alluvial trends. By employing novel, quantitative systems-based approaches to these basins, we can identify spatial and temporal trends and develop predictive models of basin-scale processes, which is unprecedented in these settings. Furthermore, these data can then be applied to aid our understanding of the hazards posed by volcanism and the sedimentary response to catastrophic events in modern basins.
The main of this project is to provide a quantitative framework of a field example in order to build predictive quantitative models in understanding the behaviour of mixed volcanic and sedimentary systems and their resultant deposits.
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fig1_pj1: Example of a quantified systems scale predictive model of fluvial systems (Owen et al., 2015)
fig2_pj1: Absaroka volcanics and fluvial sedimentary rocks from the Bighorn Basin, Wyoming.
fig3_pj1: Mixed lava and sandstone sequence, Hreppar Formation, Iceland.
This project will use novel quantitative field-based studies to develop basin/system-scale models of volcano-sedimentary architecture. Data will be developed from local examples on the Isle of Mull, NW Scotland with the potential for further study in the in the Absaroka Volcanic Field (Wyoming, USA) (Fig. 2) or the Hreppar Formation of southern Iceland (Fig. 3). Over two field seasons extensive quantitative data will be collected using logs and architectural panels. These data will include measurements of key characteristics such as palaeocurrent trends, grain size, channel body thickness, and channel percentage. These methods will be applied to clastic units and lavas. Stratigraphical correlations will be determined where possible. These data will be statistically analysed to determine relationships between the different field criteria and to identify spatial and temporal trends. Field data will be supported by outcrop models collected using unpiloted aerial vehicle (UAV) to aid quantitative observations, but also to develop models of deposit architecture. Some petrographic and geochemical analyses will also be undertaken to aid correlations.
The project will involve a placement with the CASE partner, Siccar Point Energy in Aberdeen. The placement will allow comparison of field data with offshore seismic data, and help to refine exploration models.
Literature review; field data collection; outcrop model development; preliminary data analysis; petrographic and geochemical analyses; one month internship with Siccar Point Energy
Field data collection; outcrop model refinement; advanced data and statistical analysis; paper 1 on architecture of volcano-sedimentary systems
Two month internship with Siccar Point Energy; final data and statistical analysis; paper 2 on basin-scale predictive models; thesis writing
Thesis completion; further papers if relevant.
Depending on prior experience the student will receive training in:
1. The identification of a variety of lavas, volcaniclastic rocks (pyroclastic and reworked materials) and siliciclastic sedimentary rocks in the field, using a rigorous lithofacies approach.
2. Quantification of lithofacies architecture of depositional bodies through detailed logging and outcrop measurements.
3. Flying of a UAV and developing outcrop models.
4. Statistical analysis of quantitative data.
5. Optical microscopy and geochemical analysis, including sample preparation.
6. Seismic interpretation and processing.
7. Presentation and writing skills.
8. Expedition skills (working in extreme environments).
The student will be joining an innovative and multi-disciplinary geology group at the University of Glasgow. The School’s students and academic staff meet regularly for research seminars and discussions, and so the student will be involved in a research active environment. The student will join the volcanology and sedimentology research clusters at Glasgow, which form part of the â€œDynamic Earth and Planetary Evolutionâ€ and Global Landscapes and Climate Changeâ€ Research Themes in the School of Geographical and Earth Sciences.
Excellent employability skill training will be provided by IAPETUS2 and the University of Glasgow College of Science and Engineering Graduate School. The project will also be of interest to those considering careers in the resource, renewable energy, or hazards industries.
References & further reading
Brown et al., (2009): https://www.cambridge.org/core/journals/geological-magazine/article/sedimentary-and-volcanotectonic-processes-in-the-british-paleocene-igneous-province-a-review/BA64366B908EBB925384323AFDAFC125
Hardman et al. (2018): http://pg.lyellcollection.org/content/early/2018/01/08/petgeo2017-061
Heimdal et al., (2018): https://www.nature.com/articles/s41598-017-18629-8
Owen et al., (2015): https://pubs.geoscienceworld.org/sepm/jsedres/article/85/5/544/145482/?searchresult=1
Owen et al., (2018): https://onlinelibrary.wiley.com/doi/abs/10.1111/sed.12515
Passey and Bell (2007): https://link.springer.com/article/10.1007/s00445-007-0125-6
Williamson and Bell (2012): http://sjg.lyellcollection.org/content/48/1/1/tab-article-info
Applications: to apply for this PhD please use the url: https://www.gla.ac.uk/study/applyonline/?CAREER=PGR&PLAN_CODES=CF18-7316